Ben MAR!

mmmmmmmmummmmmmmmmmmmmmmm COMMUNITY EDITION

Environmental Benefits Mapping and Analysis Program -

Community Edition

User's Manual

April 2017


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Contents

Table of Contents

CHAPTER 1 Welcome to BenMAP-CE	1-1

1.1	Overview of BenMAP-CE & Benefits Assessment	1-2

1.2	How to Use this Manual	1-7

1.3	Computer Requirements 	1-8

1.4	Installing BenMAP-CE	1-9

1.5	Uninstalling BenMAP-CE	1-10

1.6	Contacts for Comments, Questions & Bug Reporting	1-10

1.7	Sources for More Information	1-11

1.8	Frequently Asked Questions (General)	1-12

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Chapter 1 - Welcome to BenMAP-CE

CHAPTER 1

Welcome to
BenMAP-CE

In this chapter, find...

¦	An overview of the tool.

¦	Instructions for installing the tool.

¦	Contacts, sources for more information, and
answers to frequently asked general questions.

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The environmental Benefits Mapping and Analysis Program—Community Edition
(BenMAP-CE) is a powerful, yet easy-to-use program that helps you estimate the
number and economic value of health impacts resulting from changes in air pollution
concentrations. The open-source BenMAP-CE tool replaces the proprietary version of
the program (BenMAP) that the U.S. Environmental Protection Agency (U.S. EPA) first
developed in 2003 to analyze national-scale air quality regulations. These analyses
include health benefits assessments for the National Ambient Air Quality Standards
(NAAQs) for Particulate Matter (2006, 2012) and Ozone (2008, 2010) as well as the
Locomotive Marine Engine Rule (2008).

U.S. EPA and its partners designed BenMAP-CE to serve the analytical needs of a range
of users, including scientists, policy analysts, and decision makers. Most users apply the
BenMAP-CE tool to answer one of two types of questions:

1.	What are the human health and economic benefits associated with a policy
improving air quality?

2.	What is the human health burden attributable to total air pollution levels?

While the BenMAP-CE development team designed the program to be accessible to
novice users, the tool includes a number of features that will appeal to advanced
analysts as well. For example, analysts can add their own health impact and valuation
functions, map results, and perform a suite of sensitivity analyses. Beginning users can
take advantage of U.S. EPA's pre-programmed settings and reports in the core program.

1.1 Overview of BenMAP-CE & Benefits Assessment

The BenMAP-CE program estimates the human health impacts and economic value of
air quality changes. That is — BenMAP-CE relates air quality changes to human health
benefits. Such analyses are a critical component of air quality policy assessments. As
such, a variety of Federal, State and Local air pollution officials have used BenMAP-CE
to inform air quality management decisions.1

BenMAP-CE estimates benefits from improvements in human health, such as reductions
in the risk of premature death, heart attacks, 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-CE. After estimating the reductions in the
incidence of adverse health effects, BenMAP-CE calculates the monetary benefits
associated with those reductions.

How does BenMAP-CE estimate human health effects?

First, BenMAP-CE determines the change in ambient air pollution using user-specified
air quality data. Because BenMAP-CE does not model air quality changes, these data

1 For a list of peer-reviewed articles that used the BenMAP and BenMAP-CE tools, see: www.epa.gov/benmap

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must be input into BenMAP-CE as modeling data or generated from air pollution
monitoring data (some monitoring data is pre-loaded in BenMAP-CE). Next, BenMAP-CE
applies the relationship between the pollution and certain health effects (also known as
health endpoints). This relationship is often referred to as the health impact function or
the concentration-response (C-R) function, which is derived from epidemiology studies
as shown in Figure 1-1. BenMAP-CE applies that relationship to the exposed population
to calculate health impacts.

Epidemiology study

Incidence
(log scale)

Ln(y) = Ln(B) + B
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Chapter 1 - Welcome to BenMAP-CE

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 the risk of an adverse health effect due to a one unit
change in ambient air pollution. Epidemiological studies are 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.

¦	Health Baseline Incidence. The health incidence rate is an estimate of the
average number of people who 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.2

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

BenMAP-CE 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 (see Figure 1-2):

Economic Value = Health Effect * Value of Health Effect

2 The World Health Organization is a good source for international health data, see: http: //www.who.int.

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• •
• •

i* • •	• • •

• • •• ••<
• • •• ••
• • • • •
• • • • « •

• •

• • • •
• • • <

$5,000/admission

100 • $5,000 =
$500,000

An air quality policy
reduces the number of
hospital admissions by
100

The economic value of
each avoided admission
is $5,000 in the year
2010

The economic value is
the number of cases
multiplied by the value
of each admission

Figure 1-2. Estimating the Economic Value of Human Health Effects

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 a
group of people are willing to pay to slightly reduce the risk of premature death in the
population. For other health effects, the medical costs of the illness may be the only
valuation data available. The BenMAP-CE database includes several different functions
for VSL and valuation functions for other health effects for you to choose, or you can
rely on U.S. EPA's preloaded selections.

Figure 1-3 summarizes the basic steps in BenMAP-CE. 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-CE does not have air quality modeling capabilities, and
instead relies on externally created air quality modeling and monitoring data.

What else can BenMAP-CE do?

BenMAP-CE incorporates a geographic information system (GIS), allowing users to
create, utilize, visualize, and export maps of air pollution, population, incidence rates,
incidence rate changes, economic valuations, and other types of data.

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;aselineAirQualii

Post-Policy Scenario1
Air Quality

Change in air quality

(difference between baseline and
control air pollution conditions)

Incidence &/or
Prevalence Rates

Health Impact
Functions

Y n.o-BAPM\

BenMAP Analysis: Inputs & Outputs

Air Quality Surface

Health Impact Configuration

Change in population-level
exposure to air pollution

Change in health effect
incidence

(deaths and disease cases)

Aggregation, Pooling & Valuation

Valuation
Functions

.4 • AUGoodsIndex

Monetary value (benefits) of
health effects incidence change

Audit Trail Report

Results in tabular formats,
maps, audit trails

Figure 1-3. BenMAP-CE Flow Diagram

Analysts can use BenMAP-CE to:

¦	Create maps illustrating the population/community level ambient pollution
levels;

¦	Compare benefits associated with various regulatory programs;

¦	Characterize the distribution of health impacts among population sub-groups;

¦	Estimate health impacts and economic values of existing air pollution
concentrations;

¦	Estimate the health benefits of alternative ambient air quality standards; and

¦	Perform sensitivity analyses of health or valuation functions, or of other inputs.

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1.2 How to Use this Manual

Chapters 2 through 9 of this manual provide step-by-step instructions on how to use
BenMAP-CE. New users should start with Chapters 2 and 3, which are both very short.
These chapters provide a basic overview of the tool and how it works, and they explain
some potentially confusing terminology.3 Use the rest of the manual to answer any
specific questions you may have, or to walk you step-by-step through the various
components. Chapter 4 discusses how to enter data into BenMAP-CE, Chapters 5
through 7 cover each of the main steps in the Core Program, and Chapters 8 and 9 cover
mapping report options, and additional tools.

Each chapter is introduced by a short section that 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 detail for 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 chapters that provide
instructions on navigating the tool, the following conventions are observed: tree menu
items, buttons, tabs 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-CE.

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-CE

Appendix M: Function Editor

References

3 Another good reference is the BenMAP-CE Quick Start Guide, see: http: //www.epa.gov/benmap.

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1.3 Computer Requirements

The computer hardware requirements for BenMAP-CE are typically modest, though this
will vary depending on the complexity of the analysis. BenMAP-CE requires a Windows
platform and can be used on machines running Windows 7, Windows 8 or Windows 10.
In particular, BenMAP-CE requires a computer with:

¦	Windows 7 or Windows 8 (64-bit operating system recommended)

¦	Adobe Acrobat Reader

¦	Microsoft Excel or other spreadsheet program (in order to read exported .xlsx
files)4

¦	Microsoft .NET Framework 4 (or 4.5)5

¦	4 Gigabytes of RAM or greater

¦	Intel or compatible processor, Core i5 (or better)

¦	At least 10 GB free disk space is recommended for the installation of the
BenMAP-CE database and ancillary files.

BenMAP-CE works best in a 64-bit Windows environment. With a 32-bit installation
there are limits on the memory available to the software application; it can utilize no
more than 2 GB of RAM. This will impact performance when processing large
spatial datasets or numerous health impact/valuation functions.6 A solid state drive
(SSD) has also shown improved performance over hard disk drives (HDD).

4	OpenOffice and LibreOffice are two open-source options for spreadsheet tools.

5	If.NET is not pre-installed, BenMAP-CE will provide a message advising you to install .NET. A standalone
installer is available on the Microsoft website (URL: http: //www.microsoft.com/en-

us/download/details.aspx?id=177181 Install the runtime version and associated files (e.g.,
dotnetfx40_full_x86_x64.exe).

6	To determine whether your computer is running a 32-bit or 64-bit version of Windows, refer to this article
from Microsoft: https://support.microsoft.com/en-us/help/15056/windows-7-32-64-bit-faq

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1.4 Installing BenMAP-CE

The installation of BenMAP-CE is very simple. Double click 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.

BenMAP-CE

LsJ s

Welcome to the BenMAP-CE Setup Wizard



The installer will guide you through the steps required to install BenMAP-CE on your computer.

WARNING: This computer program is protected by copyright law and international treaties.
Unauthorized duplication or distribution of this program, or any portion of it, may result in severe civil
or criminal penalties, and will be prosecuted to the maximum extent possible under the law.

Cancel	< Back	Next >

jj-j) BenMAP-CE	| en |' S

Installation Complete

BenMAP-CE has been successfully installed.
Click "Close" to exit.

Please use Windows Update to check for any critical updates to the .NET Framework.

Cancel	< Back	Close

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Upgrading to a new version of BenMAP-CE

Periodically, new versions of BenMAP-CE will be made available and posted to the
BenMAP-CE website: http://www.epa.gov/benmap/. If you are upgrading, first
uninstall the outdated version of the software on your computer. Then extract the files
from the installer package (.zip file) and run the executable ("setup.exeA new
installer package can be relatively large, about 600 megabytes, because the database is
embedded in the installer. However, once downloaded, the installation process is
generally very fast.

Installation instructions are typically provided with each software release. Refer to
these supplemental instructions for important additional information.

1.5	Uninstalling BenMAP-CE

To uninstall BenMAP-CE, go to Control Panel, Programs and Features and remove
BenMAP-CE. Note that uninstalling BenMAP-CE does not also remove any results files
that you have created with BenMAP-CE.

1.6	Contacts for Comments, Questions & Bug Reporting

For comments and questions, please contact Neal Fann at the U.S. EPA.

Address: C539-07, U.S. EPA Mailroom, Research Triangle Park, NC 27711
Email: fann.neal@epa.gov
Telephone: 919-541-0209

Alternatively, you can send a message at the BenMAP-CE website:
https://www.epa.gov/benmap/forms/contact-us-about-benmap. or by simply emailing
benmap@epa.gov.

To report programming bugs or suggest additions to the software in BenMAP-CE:

¦	Select the Help menu in the main window;

¦	Open the Provide Feedback form;

¦	Complete the form and submit the report.

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i—M

BenMAP-CE is open-source software. If you encounter an error or would like to request a feature, please consider submitting a report by filling out this
form. We apologize for any issues you may have encountered and value your feedback as it will help us continue to improve BenMAP-CE.

Operating System Microsoft Windows NT 6.1.7601 Service Pack 1

BenMAP-CE Version 1.3.5.12 (April 18, 2017)

Your name (optional)

Your email address (optional)

Country where you are located
(optional)

What type of report would you like to submit?

® Software Error
© Requested Feature

How severe is the error you experienced?

Minor-this issue has little or no impact on my ability to use BenMAP-CE
# Major - this issue significantly hinders my ability to use BenMAP-CE
Blocking - this issue prevents me from using BenMAP-CE

What component of BenMAP-CE does the error affect?
| Graphic User Interface: suggested changes	

The audit trail report may help us to debug or understand your report better.
f^l Include BenMAP-CE generated audit trail report.

Please describe what you were doing when you encountered the error. Can you tell us how to reproduce the error? (5000 character limit)

To submit this report to us, click the Submit button. Thank you for your time.

1.7 Sources for More Information

For supplemental information on BenMAP-CE, such as articles and presentations,
manuals, and training materials go to:

¦	U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards (OAQPS), BenMAP-CE website. Available at:

http://www.epa.gov/benmap/

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

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

https://www.epa.gov/clean-air-act-overview/benefits-and-costs-clean-air-act

¦	U.S. EPA (2006). Final Regulatory 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:

https://www3.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf

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¦	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:

https://nepis.epa.gov/Exe/ZyPDF.cgi/P10024CN.PDF?Dockey=P10024CN.PDF

¦	U.S. EPA (2010). Final Regulatory Impact Analysis (RIA) for the S02 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/fso2rial00602ch5.pdf

¦	U.S. EPA (2010). Final Regulatory Impact Analysis (RIA) for the N02 National
Ambient Air Quality Standards (NAAQS). Office of Air Quality Planning and
Standards. June. See: Chapter 4. Available at:

https://www3.epa.gov/ttn/ecas/docs/ria/naaqs-no2 ria final 2010-01.pdf

¦	U.S. EPA (2010). Guidelines for Preparing Economic Analyses. Office of the
Administrator, National Center for Environmental Economics. EPA 240-R-10-
001. December. Available at:

https://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0568-50.pdf/$file/EE-
0568-50.pdf

¦	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/ttnecasl/regdata/RIAs/matsriafinal.pdf

¦	U.S. EPA (2012). Regulatory Impact Analysis for the Final Revisions to the
National Ambient Air Quality Standards for Particulate Matter. See: Chapter 5.
Available at: http://www.epa.gov/ttnecasl/regdata/RIAs/finalria.pdf

1.8 Frequently Asked Questions (General)

Is BenMAP-CE free? Is there a Terms of Use agreement? Are there any restrictions on
using BenMAP-CE?

BenMAP-CE is free. There is no Terms of Use agreement and there are no restrictions on
using BenMAP-CE. Feel free to share it with others.

How do I know which version of BenMAP-CE I am using? How do I know if I have the
most current version of BenMAP-CE? How do I get the most current version?

You can identify the version of BenMAP-CE you are using by going to the Help menu and
choosing About. Here you will see the version number and contact information. To

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determine whether you have the most recent version of BenMAP-CE, you can check the
BenMAP-CE website fhttp://www.epa.gov/benmap/1 which will have the latest
version that is publicly available. Alternatively, you can use the contact information to
inquire about any upcoming versions of the model.

About BenMAP-CE 1.3



W*'

Environmental Benefits Mapping and Analysis Program - Community Edition (BenMAP-CE)
Version: 1.3.5.12 (April IS, 2017)

U.S. EPA, 2015. Environmental Benefits Mapping and Analysis Program - Community Edition
(Version 1.3), 2015. Research Triangle Park, NC. Available on the Internet at

.

BenMAP-CE is free and open source software. Requests for the source code, as well as
comments and questions, should be sent to mai11c:benmapepa, gov.

Release Notes

OK

Why don't my files created with an older version of BeniVIAP work with BenMAP-CE?

Files created with an older version of BenMAP will not, in most cases, work with
BenMAP-CE because of changes to the program. For example, later versions of
BenMAP-CE have the capability to handle population data differentiated by ethnicity.
For this reason, after completing an analysis with BenMAP-CE, it is always good to
archive the BenMAP-CE installer along with the files used in your analysis, so that you
will always be able to reproduce your work in the future.

Why are my pop-up windows too small? Why are buttons missing?

If the BenMAP-CE pop-up windows do not show the entire content (display seems cut
off or buttons are missing), please check the display properties for your computer.
Locate your Control Panel, then your Display settings, and choose the screen
resolution associated with 96 DPI (display pixels per inch). Here is what the screen
looks like for Windows 7 Professional operating system.

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Q9-1

Control Panel ~

File Edit View Tools Help

Control Panel Home

Adjust resolution
Adjust brightness
(jjjp Calibrate color

Change display settings
Connect to a projector
Adjust ClearType text
Set custom text size (DPP

All Control Panel Items ~ Display

^ Search Control Panel

r^irsirai
p\

Make it easier to read what's on your screen

You can change the size of text and other items on your screen by choosing one of these options. To
temporarily enlarge just part of the screen, use the Magnifier tool.

% Smaller -100%

© Medium -125% (default)
© Larger - 150%

See also
Personalization
Devices and Printers

Custom DPI Setting

For a custom DPI setting, select a percentage from the lis
drag the ruler with your mouse.

Scale to this percentage of normal size: 100%

9 point Segoe UI at 96 pixels per inch.

3 Use Windows XP style DPI scaling

Apply

Why do I get different results than someone else?

There are many possible reasons why your results might differ from someone else's
results. One good place to start is the Audit Trail Reporting option. With the Audit Trail
you can examine the assumptions and selections that you have made to generate your
results and compare your selections with those made in another analysis.

What do I need to be aware of if I use BenMAP-CE 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-CE. See Chapter 4 (Section 4.1.1) to read about 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-CE. Chapter 4 also provides
information on loading data into BenMAP-CE.

Does BenMAP-CE estimate effects of air pollution that are not related to human health
(i.e., ecological effects)?

No. BenMAP-CE 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-CE is designed to combine different types of geographically variable data. To

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do so, you would need to develop and load data and impact functions appropriate to
estimating ecological effects of interest.

How can I get training for BenMAP-CE?

A variety of training resources are available, including self-paced exercises, online
interactive modules and instructor-led training are available at:
https://www.epa.gov/benmap/benmap-ce-training-materials

Where can I find the source code for BenMAP-CE?

BenMAP-CE is an open source program and the development team welcomes
contributions and scrutiny from the user community. If you are interested in receiving a
current copy of the source code, please contact U.S. EPA at benmap@epa.gov.

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Contents

Table of Contents

CHAPTER 2 Terminology	2-1

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Chapter 2 - Terminology

CHAPTER
Terminology

In this chapter...

¦ Find definitions for common terms used in the BenMAP-CE
tool and in this manual.

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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
your preferences regarding how to aggregate your results, whether and how to pool
your results, and any economic valuation functions you have applied. For example, an
APV file might aggregate your estimated change in incidence to the U.S. county level, it
might pool across multiple hospital admission health impact functions and it could
include an economic valuation function. APV configurations are stored in files with an
.apvx file extension. The results derived from an APV configuration have an .apvrx file
extension. APV files are by default stored in the APV folder, and APV results files are by
default stored in the CFGR folder.

Air Quality Surface. An air quality surface contains modeled or monitored air pollution
data in a series of cells; these cells may be a regular shape (like a 12km by 12km grid)
or an irregular shape (like a county or census tract). These surfaces are also referred to
as air quality grids. BenMAP-CE uses one air quality grid to represent the baseline
scenario and a second grid to represent the control scenario. These baseline and control
grids must share the same geographic structure. The program calculates the difference
between baseline and control grids as an input to the health impact function. Air Quality
Grids are stored in files with an .aqgx file extension.

Air Quality Metric. The metric expresses the time period over which air quality values
are modeled or observed and whether that modeled or observed air quality value is an
average, maximum or minimum. For example, the metric DailyMean represents the
average concentration for the sampling day. This could be taken directly from a single
24-hour observation or from an average of hourly (or more frequent) observations. In
addition to the time period, some metrics also specify the method used for averaging or
aggregation. For example, a typical ozone metric D8HourMax represents the highest of
the 8-hour moving averages during the day.

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 (like CMAQ1 and
CAMx2) are used to estimate the air pollutant concentration at specific 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

1	Community Multi-scale Air Quality (CMAQ) Model is available at:

http: //www.epa.gov/amad/Research/RIA/cmaq.html or https: //www.cmascenter.org/cmaq/.

2	Comprehensive Air Quality Model with Extensions (CAMx) is available at: http: //www.camx.com/.

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area where factors such as meteorology, topography, and emissions from both local and
remote sources could be important. BenMAP-CE does not contain an air quality model.

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-CE creates.

Background Concentration. The concentration of a pollutant, generally in the absence
of human sources.

Background Incidence. The incidence of a given adverse effect due to all causes
including air pollution. Also called baseline incidence rates.

Baseline Scenario. The air quality levels prior to whatever policy change you are
evaluating. The baseline is sometimes referred to as "Business as Usual." The baseline
scenario is usually considered to be the reference scenario against which to compare a
potential "control scenario", in which air quality levels are changed from the baseline levels.

Beta. The coefficient for the health impact function. The value of beta (IS) typically
represents the percent change in a given adverse health impact per unit of pollution.

Closest Monitor. The procedure by which data from the closest monitor is used to
represent air pollutant levels in a population grid cell. BenMAP-CE can also scale the
data from the closest monitor with air pollution modeling data. BenMAP-CE includes two
types of scaling - "temporal" and "spatial". See "Scaling" for additional information.

Community Multi-scale Air Quality (CMAQ) Model. An open-source photochemical
grid air quality model that the U.S. EPA and others rely upon to predict levels and changes
in pollutant concentrations.

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 .cfgx file
extension. CFGX files are by default stored in the CFG folder. The results derived from a
Configuration have a .cfgrx file extension. CFGR files are by default stored in the CFGR
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.

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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 control scenario generally represents air quality levels after a
new policy has been implemented.

Core BenMAP-CE. The fully-featured benefits analysis program that accepts user-
defined air quality data, quantifies health impacts, aggregates, values and pools results.

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
economic 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.

Currency Year. The value of the currency based on the year specified. Valuation
estimates should use a consistent currency year to account for inflation. 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.

Deltas. The difference between two data points. As used in BenMAP-CE, mapping the air
quality deltas shows the change in air pollution between the baseline air quality grid
and the control air quality grid.

Discount Rate. In a cost-benefit analysis, 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 economic benefit
would 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. An endpoint group represents a broad class of adverse health effects,
such as premature mortality or hospital admissions. BenMAP-CE only allows pooling of
adverse health effects to occur within a given endpoint group, as it generally does not make
sense to sum the number of cases of disparate health effects, such as premature mortality
and hospital admissions.

Epidemiology. The study of factors affecting the health and illness of populations.
Epidemiological studies cannot prove that a specific risk factor actually causes the
disease being studied but can only show that a risk factor is associated (correlated)
with a higher incidence of disease in the population exposed to that risk factor.

FIPS Code. Federal Information Processing Standard codes. Each state in the United
States is assigned a 2-digit code. For example, "01" refers to Alaska, "37" refers to North
Carolina, and "56" refers to Wyoming.

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Fixed Effect Pooling. Fixed effect pooling is one method to combine two or more
distributions of health impact or economic value estimates into a single new
distribution. Fixed effect 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 studies that
exhibit lower variance and less weight to the input distributions with higher variance.
See Random Effects Pooling below for additional information regarding pooling
techniques.

Fixed Radius. An option to interpolate air quality data points 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.

Global Burden of Disease. The World Health Organization global burden of disease
(GBD) study measures burden of disease using the disability-adjusted-life-year (DALY).
This time-based measure combines years of life lost due to premature mortality and
years of life lost due to time lived in states of less than full health. The DALY metric was
developed in the original GBD 1990 study to assess the burden of disease consistently
across diseases, risk factors and regions.3

Grid Cell. One of the many geographic, or spatial, components within a Grid Definition.
These can be regularly or irregularly shaped.

Grid Definition. A BenMAP-CE 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 GIS 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 grids (e.g., rectangles). All grid definitions must have a unique (i.e.,
non-repeating) column and row index.

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.

3 For more information on the GBD, see: http://www.who.int/topics/global_burden_of_disease.

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Incidence. The total number of adverse health effects in a geographic region in a given
unit time. In BenMAP-CE, this is the total number of adverse health effects avoided due
to a change in air pollution levels.

Incidence Rate. The background rate of a health effect per person in a given
geographic region. The average number of adverse health effects per person per unit of
time, typically a day or a year. The incidence rate must be expressed at the same time
scale as the remainder of the health impact function. For example, a health impact
function quantifying day-to-day changes in premature death must specify a daily death
rate.

Income Growth Adjustment. Adjusting certain valuation functions to reflect increases
in real income over time. 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-CE 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. Longitude identifies the east to west
location of a point on the Earth.

Layer. In GIS, a layer represents a logical separation of mapped data usually
representing a theme, such as political boundaries, roads, ozone data, number of
mortalities avoided, etc.

Layer Statistics. The summary statistics that correspond to the active layer in
BenMAP-CE. For example, "mean", "standard deviation" or "max" of PM2.5 air quality
grid.

Metadata. Data that serves to provide context or additional information about other
data. BenMAP-CE stores a minimum set of standardized metadata fields for imported
data files (e.g., file name, file date, reference, import date, and description). For certain
data types, additional metadata are recorded. For example, GIS metadata will include
information about datum, geographic coordinate system, resolution, and units.

Micrograms per Cubic Meter (ng/m3). The unit of measure for particulate matter in
the NAAQS. This unit represents the mass of PM and other particle pollutants found in a
cubic meter of air.

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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
observed concentrations.

Monetize. In the context of human health benefits assessment, this is the practice of
expressing society's preferences for avoiding certain health effects as an economic
value (e.g., in U.S. dollars). In BenMAP-CE 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 from
an air quality monitor. "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 from an
air quality model.

Monitoring. Actual measurements of air pollution concentrations. The U.S. EPA has
monitoring data, as well as other information related to monitoring available through
its Air Quality System (AQS): https://www.epa.gov/aqs.

Monte Carlo Simulation. A technique used in BenMAP-CE to quantify the confidence
intervals around mean incidence and economic value estimate by randomly sampling
an uncertainty distribution around the effect coefficients or willingness to pay
estimates.

Morbidity. A measure of being diseased or afflicted by an illness (generally 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.

Odds Ratio. A quantitative measure reported in epidemiology studies of the
relationship between exposure to air pollution and a health outcome. Odds Ratios must
be converted to beta coefficients to be used in BenMAP-CE

Ordinality. In relation to air quality 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 four, 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

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than three daily averages in exceedance of the standard, then the rollback technique
will be applied to that monitor.

Ozone (O3). BenMAP-CE focuses on ground level or "bad" ozone, which is not emitted
directly into the air, but is created by chemical reactions between oxides of nitrogen
(NOx) and volatile organic compounds (VOCs) in the presence of sunlight. Emissions
from industrial facilities and electric utilities, motor vehicle exhaust, gasoline vapors,
and chemical solvents are some of the major sources of NOx and VOC. Breathing ozone
can trigger a variety of health problems, particularly for children, the elderly, and
people of all ages who have lung diseases such as asthma. Ground level ozone can also
have harmful effects on sensitive vegetation and ecosystems.

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 PM10-2.5 (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-CE are reported in units of ppb.

POC (Parameter Occurrence Code). An identifier used by U.S. EPA to distinguish
between multiple monitors at the same site that are measuring the same parameter. For
criteria pollutants, multiple monitors may be collocated to check precision. For
combining data at the site level, the POC identifies the primary monitor (most frequent
sampling). (POC appears in BenMAP-CE's advanced filtering options for monitor data.)

Pooling. The combining of different sets of data. BenMAP-CE has several pooling
methods, including fixed effects, fixed/random effects, and subjective weighting.
Appendix K discusses the pooling approaches available in BenMAP-CE.

Point Mode. When defining the configuration, you may choose to either estimate
adverse health effects in point mode or using percentiles. The point mode simply means
that BenMAP-CE 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 micro-environments, such as the freeway, outdoors and indoors. BenMAP-CE
only represents population exposure.

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Population-weighted Air Quality. Modeled or monitored ambient concentrations that
have been weighted according to the number of people exposed.

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.

Random Effect Pooling. Random effect pooling is one method to combine two or more
distributions of health impact or economic value estimates into a single new
distribution. This approach 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 in an
epidemiology study. More specifically, it is the ratio of the risk of illness with a higher
pollution level 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-
CE rolls back monitor data in three ways. 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.

Setup. A BenMAP-CE 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 GIS file, and has a .shp extension. These files
are accompanied by companion files with .shx and .rf£»/extensions, and can be used to
create Shapefile Grid Definitions. See

http://www.esri.com/library/whitepapers/pdfs/shapefile.pdf for more information.

Sum Dependent Pooling. Summing two or more incidence or valuation results,
assuming the underlying functions are correlated. For example, summing the incidence
of respiratory hospital admissions for two different age groups quantified using C-R
functions from the same study.

Sum Independent Pooling. Summing two or more incidence or valuation results,
assuming the underlying functions are independent (uncorrected). For example,

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summing the incidence of respiratory hospital admissions quantified using C-R
functions from different studies using different methods.

Threshold. BenMAP-CE's advanced settings for health impact functions allows you to
specify an air quality threshold; this is an air quality level below which benefits are not
calculated. For example, if the threshold is 5 |ig/m3, then only areas with PM2.5
concentrations equal to or greater than 5 |ig/m3 will be included in estimating health
incidence results. Specifying a threshold does not affect the shape of the C-R function
used to quantify impacts.

Unit Value. A unit value is the estimated mean economic value of avoiding a single case
of a particular health effect.

User-defined Weights Pooling. User-defined 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-CE normalizes the weights so that they do.

Valuation Function. Valuation functions are used by BenMAP-CE 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).

Variable Datasets. Health Impact functions and valuation functions may sometimes
refer to variables other than those for which BenMAP-CE automatically calculates
values. For example, some valuation functions reference the median income within
each area of analysis. To facilitate this, BenMAP-CE allows you to load datasets of
variables for use in functions, which may be used globally or may vary geographically
(meaning they are associated with a particular Grid Definition).

VNA (Voronoi Neighbor Averaging). An algorithm used by BenMAP-CE to interpolate
air quality monitoring data to an unmonitored location. BenMAP-CE 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 or service,
such as a reduction in the risk of illness. In general, economists tend to view an
individual's WTP for an 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.

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Contents

Table of Contents

CHAPTER 3_0verview of BenMAP-CE Features	3-1

3.1	Core Program	3-3

3.1.1	Create Air Quality Surfaces	3-4

3.1.2	Estimate Health Impacts	3-6

3.1.3	Aggregate, Pool, and Value	3-7

3.2	Menus	3-9

3.2.1	Tools Menu	3-10

3.2.2	Help Menu	3-11

3.3	Outputs	3-12

3.3.1	Results	3-12

3.3.2	Maps	3-13

3.3.3	Audit Trail Report	3-13

3.3.4	Dataset Validation Reports	3-14

3.3.5	File Types	3-14

3.4	Frequently Asked Questions	3-15

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Chapter 3 - Overview ofBenMAP-CE Features

CHAPTER

Overview of
BenMAP-CE
Features

In this chapter...

¦	Get an overview of the features available with the Core
Program.

¦	Learn about additional BenMAP-CE modules.

¦	Learn about the Tools and Help menu options.

¦	Find descriptions of the various outputs including types of
files, results, maps and reports available from BenMAP-CE.

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Upon starting BenMAP-CE for the first time, you will see the following Welcome screen.

BenMAP-CE 1.3

Welcome

The User Interface

Create Air Quality Grids

Calculate Health Impacts

Aggregate, Pool and Value

BenMAP-CE uses a series of "stoplights" that change colors as you
complete each stage of the program:

Operation not yet started.

Operation completed and requires no further attention.
Operation completed, but you may need to re-run this step.
You can find a quick-start guide to get you up and running here.

I~l Don't show this welcome screen again.

OK

The Welcome screen gives a brief description of the user interface and highlights the
"stoplight" metaphor used in BenMAP-CE to indicate the status of analytical steps
performed using the tree menu on the left side of the main window. Clicking the links
on the left side of the Welcome screen (e.g., Create Air Quality Grids) will provide
information about each feature. You will also find a link to the BenMAP-CE website for
downloading the most current BenMAP-CE software and other reference information.
If you do not wish to see the Welcome screen at program start-up, check the option
'Don't show this window screen again' in the lower left-hand corner of the screen. To re-
enable the Welcome screen, go to Tools menu on the main BenMAP-CE window, select
Options, and check the option for Show Start Window. Press OK on the Welcome
screen to close this window and display the BenMAP-CE main window.

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Tools and Help
Menus

Setup

Option

Expand Symbol •
[+] or Collapse
Symbol [-]

Indicator —
("Stoplight" colors)

¦J BenMAP-CE 13,

1

File * United States - Modify Data:

a

o AirQuaiiTv&jrfaces
! Pollutant
; ~ H source of Aii Quaiiw Data
Baseline
Control

i-i 0 tstimateheaitn impacts
| Population Dataset
1 -H heaitn impact Functions
--Aggregate, Pool & Value

! £ Aggregation
^ ' 1 Pooiine Method
i.-.^ri Valuation Method

Tree Menu Item

Current Setup: United Statei

is ts Tools * Help	^	^

Ith Imped Results le Foo-ea Incidercs Results |	Valuator Results



Pool Incidence
Results Tab

Step 3

Health Impact
Results Table
(Columns and Rows)

dpoint Group

End point

Pollutant

Author Start Age



Audit Trail Report

End Age DatasetNa... Race

After results aregpnerated h»rp. doublp-rikk thpselpctpd study !r display map/data/chart hctow
[ctrl- or shift-cick to select multiple studies and then ciicfc "Show result* to display data for multiple studies..

Create mop,doto and chart (double-click the selected studr) Aggregation for tow dote. |

© deaisaaiaMCieltoimuliloieswolei	j seiectsesult Fialdc |j sum Hsuits

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• GIS Map | f*>. Dato | Chart | ^ Audit Tf



BenMAP

COMMUNITY EDITION

Doubla-click AQ data file or health study result
to display the map/table(data)/chart here

The tree menu on the left-hand window pane lists the analytical steps used in the Core
Program. The tree menu items allow you to perform a highly customized health impact
analysis. The Tools menu at the top of the screen is for less frequently used functions,
such as importing and exporting data and special modules described in later sections.

The first section in this Chapter describes the Core Program features. The second
section describes the additional functions found in the Tools and Help menus. The next
section covers BenMAP-CE output options. Lastly, we answer some frequently asked
questions. Note that this chapter provides an overview of functionality, not keystroke
by keystroke instructions. Those may be found in Chapters 4 through 9.

3.1 Core Program

Beginning at the main BenMAP-CE window, you can choose the Setup you want to use
for an analysis by selecting it from the Setup drop-down list. You are then ready to
begin using the features available through the tree menu.

File -

~ 0 Ai
ft



United States * Modify Datasets J Tools » Help -

Health Impact Results |t)F

United States

China
Detroit

Endpoint Group

Endpo

Aftprrp^nlts arpepn

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The tree menu takes you through the steps of an analysis. The first step, Air Quality
Surfaces, allows you to select the pollutant of interest, and then specify the baseline
and control air quality surfaces. The second step, Estimate Health Impacts, lets you
choose the population dataset for a particular analysis, and then specify the health
impact functions to estimate the incidence of adverse health effects. The last step,
Aggregate, Pool & Value, gives you different options for combining the health effects
estimates and choosing economic valuation functions.

3.1.1 Create Air Quality Surfaces

BenMAP-CE 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-CE combines population data with air
quality surfaces, which it generates using some combination of air quality modeling
and/or monitoring data. In BenMAP-CE, air quality surfaces can be described as air
quality grids (the structure) that have been populated with air pollution values (the
data). The following is a brief description of each step. For detailed instructions, see
Chapter 4: Loading Data and Chapter 5: Creating Air Quality Surfaces.

Pollutant

The Pollutants section of a setup specifies the pollutants that BenMAP-CE will analyze
and defines the air quality metrics to be used by BenMAP-CE. You are not importing air
pollution data, but rather naming your pollutants and defining the measures or metrics
BenMAP-CE will use when performing an analysis for each pollutant.

Grid Definition

Air quality surfaces contain air pollution exposure estimates for a particular Grid
Definition, as defined in the Modify Datasets 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 *.aqgx extension that
BenMAP-CE uses to designate these file types.

¦	Model Data. Model Data grid creation simply takes raw model data and converts
it into a file that BenMAP-CE recognizes as an air quality grid. This type of grid
definition allows you to directly specify the air pollution values for each grid cell
in a Grid Definition.

¦	Monitor Data. Monitor Data grid creation uses air pollution monitoring data to
estimate air pollution levels for each grid cell in the selected Grid Definition. This

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may be done using one of three interpolation procedures - Closest Monitor,
Voronoi Neighborhood Averaging (VNA), or Fixed Radius. With closest monitor,
BenMAP-CE simply uses the data of the monitor closest to each grid cell's
centroid. With VNA, BenMAP-CE 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. With fixed
radius, BenMAP-CE constrains the VNA interpolation to a user-specified distance
around each monitor.

¦	Monitor Rollback. Monitor Rollback grid creation allows you to reduce, or roll
back, monitor data using three methods: Percentage Rollback, Incremental
Rollback, or 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 Data grid creation)
or combined with modeling data. This approach is described in more detail in
Chapter 5: Creating Air Quality Grids, as well as in the Appendix A: Monitor
Rollback Algorithms.

¦	Open *.aqgx file. Open *.aqgx file allows you to load a previously created or
saved air quality surface.

J Choose a Grid Creation Method

Grid Type:

® Model Data

© Monitor Data
© Monitor Rollback
© open '.aqgxfile

CMAQ 12km Nation

ve As( .aqg:

Save New Form at Cancel	Next

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3.1,2 Estimate Health Impacts

The Estimate Health Impacts section allows you to calculate the change in the
incidence of adverse health effects associated with changes in air quality. There are
three steps in the process. The following is a brief description of each step. For detailed
instructions, see Chapter 6: Estimating Incidence.

¦ Step 1. Specify the Population Dataset and Population Year.

Population Dataset



Population Dataset: United States Census - County

Popu I ation Yea r: 2010

Map

OK

¦ Step 2. Choose the Health Impact Functions that will he used to estimate the
incidence of adverse health effects.

Health Impact Functions

r^Tal^l

Selected Health Impact Functions
Filter Dataset

EPA Standard Health Functions

Filter Endpoint Group

py] Groups

Dataset Endpoint Group
Acute Bronchitis [1 item]
EPA Sta... Acute Bronchitis

Pollutant Author Start Age End Age Beta Distribution

Seasonal Metric

_~

Acute Bronchitis PM2.5

D24HourMe... QuarterlyMean

Acute Myocardial Infarction [5 items]

EPA Sta... Acute Myocardial Infar... Acute Myocardial... PM2.5

EPA Sta... Acute Myocardial Infar... Acute Myocardial...	PM2.5

EPA Sta... Acute Myocardiallnfar... Acute Myocardial...	PM2.5

EPASta... Acute Myocardial Infar... Acute Myocardial...	PM2.5

Zanobe...	0

Zanobe...	0

Sullivan...	0
Peters e.

18

Normal
Normal
Normal
Normal

D24HourMe...
D24HourMe...
D24HourMe...
D24HourMe...

Selected Health Impact Functions

Endpoint Group Dataset Name Endpoint Pollutant

Author Start Age End Age Race

Gender Incidence Dataset

( Selected items to be shown here

Delete Selected

Advanced | J Cancel | | Save As (*.cfgx) | J Run

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Step 3. BenMAP-CE performs a full Monte-Carlo analysis to quantify the confidence
intervals around mean incidence and economic value estimates by randomly
sampling an uncertainty distribution around the effect coefficients or willingness to
pay estimates. In general, the computation time increases as you specify additional
percentiles to report from the Monte-Carlo generated distribution. If you want to
replicate the Monte-Carlo distribution from another analysis, then you may also
specify the Random Seed.) Specify the Air Quality 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.

^ Advanced Settings

s 1^1

Run In Point Mode





Percentiles:

[20

'1

Random Seed:

1



Air quality threshold:

0



BenMAP-CE can store configuration choices in a user-named file with a .cfgx extension,
and can store incidence change estimates in a user-named file with a .cfgrx extension.

3.1.3 Aggregate, Pool, and Value

The Aggregate, Pool, & Value feature on the BenMAP-CE tree menu allows you to
aggregate and pool previously calculated incidence estimates and place an economic
value on these pooled and aggregated incidence estimates. You can also aggregate the
economic values, and finally pool the aggregated economic values. There are several
steps in this process. The following is a brief description of each step. For detailed
instructions, see Chapter 7: Aggregating, Pooling, and Valuing.

¦ Step 1. Choose the desired aggregation levels.

' : Aggregation

B L

Incidence Aggregation: County

Valuation Aggregation:

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¦ Step 2. Choose the desired pooling and aggregation options for the incidence
results,

@ Select and Pool Incidence Estimates	-OX

Available Incidence Results

Filter Dataset	Filter Endpoint Group	Filter		

v] |	-wl	~Groups View: i Details v |	Select study fields 			

Endpoint

Endpoint Group

Dataset Name

Start Age

End Age

Author

~ Mortality, All.

Mortality

EPA Standard Hea

. 30

99

Krewski...

~ Mortality All.

Mortality

EPA Standard Hea

. 30

99

Krewski-.

~ Mortality, All.

Mortality

EPA Standard Hea

. 30

99

Krewski...

~ Mortality, All.

Mortality

EPA Standard Hea

. 30

99

Krewski-.

Remove Study

Remove All Studie:

Select Pooling Methods

Mortality j

Studies. By Endpi
B ij. Mortality

target Grid Type: CMAQ 12*m I



Start Age End Age

Rando... 116 U.S.... 30
Rando... 116U.S.... 30

TSP. 03, S04, S02	ASIAN

TSP, 03, S04, S02	BLACK

TSP. 03. S04, S02	NATAMER

TSP. 03, S04, S02	WHITE

(1-<1/EX...	PM2.5

(1-(1/EX...	PM2.5

(l-d/EX...	PM2.5

(l-fl/EX...	PM2.5

D24Hou... Q
D24Hou... 0
024Hou... Quarte

ERASta...
ERA Sta...
ERASta...

Condensed View Advanced

i? Select and Pool Incidence Estimates

Available Incidence Results
Filter Dataset

Filter Endpoint Group
~ [ Acute Respiratory Symp t |

n Groups View: | Details " | f Select study fields J j Check HIF Changes j

Endpoint	Endpoint Group	Dataset Name Start Age End Age Author

~ Minor Restrict... Acute Respiratory Sym... EPA Standard Hea... 18	64	Ostroa...

PI Minor Restrict... Acute Respiratory Sym... EPA Standard Hea... 18	64	Ostroa...

Add All Studies

Remove All Studies

Select Pooling Methods

Pooling Window Name: Acute Respiratory Symf

Show Tile Pooling Window Number: 3

Target Grid Type: State

Acute Bronchitis Acute Respiratoiy Symptoms |Acute Myocardial Infarction

Studies, By Endpoint Pooling Method

Endpoi... Author

Qualifier Location

Start Age

End Age Year

Other Pollutan... Race

Ethnicity Gen<

B-U Acute Respiratoiy Sympto... |Sum Dependent

""[MinorR... Ostroa...



18

64





1—Ostro

MinorR... Ostroa...

Nation...

18

64 1989

PM2.5



L^Ostro

Minor R... Ostro a...

Nation-

18

64 1989





« 1	1	J *

| Condensed View~j | Advanced | [ Cancel j j Next j

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¦ Step 3. Choose the economic valuation functions to apply to the pooled and
aggregated incidence results.

^ Select Valuation Methods, Pooling and Aggregation

Valuation Methods

EndPoint	Endpoint Group	Start A...	End...	Function	Datasi

Mortality,...	Mortality	0	99	A*B*AII...	ERASta

Mortality,...	Mortality	0	99	A*AIIGo... EPA Sta

Mortality,...	Mortality	0	99	A*AIIGo... EPA Sta

Mortality,...	Mortality	0	99	A*AIIGo... EPA Sta

Show Selections
Mortality

Studies, By Endpoint
El-Mortality

*	Krewski

*	Krewski
" Krewski

*	Krewski

Click and select the valuation methods on the left panel,

and then drag them to the right panel under the desired Endpoint

Pooling Method

None

None

None

None

None

Variable Dataset: EPA Standard Variables v Delete Selected

Resolve Pooling Conflict

Advanced Cancel Save As (*.apvx) Run As (*.apvrx)

BenMAP-CE can store APV configuration choices in a user-named file with an .apvx
extension, and can store APV configuration results in a user-named file with an .apvrx
extension. As needed, you can access both files for later use.

3.2 Menus

There are five menu options found at the top of the main window: File, Setup, Modify
Datasets, Tools and Help.

¦	File. This menu provides options for selecting saved project files (.projx) to open,
starting a new project file, saving your work in a project file, and exiting the
program.

¦	Setup. BenMAP-CE comes pre-loaded with datasets for the United States and
China setups. The selected setup will be displayed in the menu bar. To learn
more about modifying setups, see Chapter 4: Loading Data.

¦	Modify Datasets. BenMAP-CE stores the information needed to run analyses for
a particular geographic area, such as a city, region, or nation in a single dataset
called a setup. Many users will never need to modify the preloaded setups.

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However, the Modify Datasets menu provides tools to add, modify (load
additional datasets), or delete these setups if needed. This is discussed in detail
in Chapter 4: Loading Data.

¦	Tools. This menu provides access to data import and export functionality in
addition to a number of other features. An overview is provided below.

¦	Help. This menu provides access to a Quick Start Guide (available on EPA's
website), information About BenMAP-CE, and a form to Provide Feedback about
software errors or requested features. An overview is provided below.

3.2.1 Tools Menu

The Tools menu has several options: Air Quality Surface Aggregation, Database Export,
Database Import, Online Database Export, Online Database Import, Export Air Quality
Surface, GBD Rollback, Neighbor File Creator, PopSim and Options menu. A brief
description is given below, but further information can be found in Chapter 9: Tools
Menu.

¦	Air Quality Surface Aggregation. Create a new air quality surface for a specified
grid definition (e.g., County) from an existing air quality surface created with a
different (and generally finer) grid definition (e.g., 12km CMAQ).

¦	Database Export. Export all or part of BenMAP-CE's internal database to a database
file or multiple csv or shapefiles which can later be used on another computer or
by another user. Manually loading data into BenMAP-CE 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 created using the Database Export tool.

¦	Online Database Export. Allows users to post/share data for BenMAP-CE via a
cloud-based system.

¦	Online Database Import. Allows users to download data shared by others for
BenMAP-CE via a cloud-based system.

¦	Export Air Quality Surface. Generate a text file (.csv) with all of the data in air quality
surface, including summary statistics such as mean, median, minimum, and maximum.

¦	GBD Rollback Tool. The GBD Rollback tool allows you to select a country, region,
or group of countries and see the impact of lowering PM2.5 emissions based on

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the data from the 2010 GBD study1. The outputs include the baseline and policy
case PM2.5 concentrations as well as the population-weighted air quality change.

¦	Neighbor File Creator. Create a text file (. txt) identifying "neighbor" monitors and
associated interpolation weights for each grid cell in an air quality grid.

¦	PopSim. A dynamic population simulation that incorporates the cumulative
effects of air pollution on different age groups over time.

¦	Options. Select options for start-up and exit screens, validation logs and default set-
ups.

¦	Compute Grid Crosswalks. Remove all crosswalks for selected setups and re-create
them. This tool is for creating or repairing crosswalks which were broken due to
database error or force quitting of the application.

3.2.2 Help Menu

The Help menu has a few options to choose from: Quick Start Guide link, Information About
BenMAP-CE, and a form to Provide Feedback.

¦	Quick Start Guide. A link will open a webpage to the U.S. EPA site for BenMAP-CE
information. On the webpage, the Quick Start Guide can be found under Training
Materials. Click on the link for The BenMAP-CE Quick Start Guide, to begin
download of the guide. Files that the Quick Start Guide refers to can be found in
the same bullet, but under the BenMAP-CE Quick Start Data Files (zip) link. The
Quick Start Guide will take you through the basic operations of the BenMAP-CE
imports and decision-making.

¦	About. Opens a window that displays information about the program (e.g.,
software version, contact information, and a suggested citation). You can click on
Release Notes to read about software modifications and any known issues.

¦	Provide Feedback. This feature allows you to submit any problems that you may
encounter while running BenMAP-CE or requested features to the BenMAP-CE
development team. There are fields to provide your contact information
(optional) and information about the error or requested feature. Your feedback
will be logged into an issue tracking system for U.S. EPA to evaluate.

1 Exposure Assessment for Estimation of the Global Burden of Disease Attributable to Outdoor Air Pollution.
Michael Brauer, Markus Amann, Rick T. Burnett, Aaron Cohen, Frank Dentener, Majid Ezzati, Sarah B.
Henderson, Michal Krzyzanowski, Randall V. Martin, Rita Van Dingenen, Aaron van Donkelaar, and George
D. Thurston. Environmental Science & Technology 2012 46 (2), 652-660

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BenMAP-CE is open-source software. If you encounter an error or would like to request a feature, please consider submitting a report by fit ling out this
form. We apologize for any issues you may have encountered and value your feedback as it will help us continue to improve BenMAP-CE.

Operating System Microsoft Windows NT 6.1.7601 Service Pack 1

BenMAP-CE Version L3.5.12 (April 18, 2017)

Your name (optional)

Your email address (optional)

Country where you are located
(optional)

What type of report wouId you like to submit?

Software Error
Requested Feature

How severe is the error you experienced?

Minor-this issue has little or no impact on my ability to use BenMAP-CE
® Major - this issue significantly hinders my ability to use BenMAP-CE
Blocking - this issue prevents me from using BenMAP-CE

What component of BenMAP-CE does the error affect?
Graphic User Interface: suggested changes

The audit trail report may help us to debug or understand your report better.
[71 Include BenMAP-CE generated audit trail report.

Please describe what you were doing when you encountered the error. Can you tell us how to reproduce the error? (5000 character limit)

To submit this report to us, click the Submit button. Thank you for your time.

3.3 Outputs
3.3.1 Results

If you are interested in viewing or exporting the results of an analysis, these reports can
be accessed by clicking on the appropriate Results tabs from the upper portion of the
main BenMAP-CE window. The number next to the tab description indicates the
associated "step" of the BenMAP-CE analysis (from the tree menu).

©Health Impact Results	Pooled Incidence Results | @ Pooled Valuation Results | Audit Trail Report

Endpoint Group

Endpoint

Pollutant

Author

Start Age

End Age

Dataset Na... Race

Acute Bronchitis

Acute Bronchitis

PM2.5

Docker...

S

12

EPA Standar...

i | id

1









>

'a Create map,data and chart (double-click the selected study)
O Create d ata (ta b I e) for m u Iti pi e stu d i es

Aggregation for raw data:

SelectResultFields

Show Results

Select the appropriate tab for the type of results you wish to create:

¦ Health Impact Results uses a Configuration Results file (with the .cfgrx

extension) to create a map, data table and bar chart for incidence results based

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on the selected health impact studies. "Raw" incidence estimates are those that
have not been aggregated, pooled or valued. See Chapter 6: Estimating Incidence,
for detailed instructions for creating health impact results.

¦	Pooled Incidence Results uses an Aggregation, Pooling, and Valuation Results
file (with the .apvrx extension) to create maps, data tables and bar charts for
incidence, aggregated incidence and pooled incidence results. See Chapter 7:
Aggregation, Pooling, and Valuation, for detailed instructions for creating pooled
incidence results.

¦	Pooled Valuation Results also uses an Aggregation, Pooling, and Valuation
Results file (with the .apvrx extension) to create maps, data tables and bar charts
for valuation, aggregated valuation and pooled valuation. See Chapter 7:
Aggregate, Pool, and Value, for detailed instructions for creating pooled
incidence results.

The results data are viewable on the Data tab in the lower right frame of the
BenMAP-CE main window (see below). You can also view the results in a GIS Map
(described in the following section), or simple Chart format. All results can be exported
as comma-separated value files (.csv), which can be read into spreadsheet and database
programs.

r*": GIS Map

|"0" Data ] Q chart [W Audit Trail Report

Column

Row

D24HourMean

QuarterlyMean

1

29

11.95

11.95

1

31

10.69

10.69

1

37

11.66

11.66

1

39

10.22

10.22

1

41

10.91

10.91

1

45

10.90

10.90

1

49

12.01

12.01

1

53

10.12

10.12

1

57

11.12

11.12

3.3.2	Maps

GIS Maps can be viewed in the lower right frame of the BenMAP-CE main window. Once
an air quality surface is displayed, you can choose which layers to view by selecting or
deselecting items in the GIS table of contents. Spatial layers (except regional
administrative layers) are semi-transparent so that overlapping layers are viewable.
You can export a formatted graphics file (e.g., .png format) with the map legend and
title. For detailed instructions on GIS maps, see Chapter 8: GIS/Mapping.

3.3.3	Audit Trail Report

The Audit Trail Report provides a summary of the options selected in the various
parts of the analysis. You may generate an audit trail with any of the file types used in
BenMAP-CE: Air Quality Grids (with the .aqgx extension), Configurations (with the .cfgx

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extension), Configuration Results (with the .cfgrx extension), Aggregation, Pooling, and
Valuation Configurations (with the .apvx extension), and Aggregation, Pooling, and
Valuation Configuration Results (with the .apvrx 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 Trail Report.

r* : GIS Map Data i Chart

: Audit Trail Report

Name

BenMAP-CE 1.3.4
Estimate Health Impacts

—	Create Datetirne:2016-08-08 09:02:51

—	I s R u n I n Po i ntMode:Fal se
Latin Hypercube Points20

Po p u I ati o n D ata s et: M exPo p- M exi co-lOkrn-ai r£ji d
—Year:2010
—ThresholdO
Baseline And Control GroupO
(^Pollutant

I—Narne:PM2.5
Uobservati on Type:Daily
UseasonO January 01-March 31
Useasonl:April 01-June30
Useason2:July01-September30
Useason3:0ctober01-Decernber31
UMetri cO:D24HourMean
1— Seasonal MetridZ^QuarterlyMean

f Basel i ne : Ai r Qual ity Surfaces
	I—Create Datetime:2016-08-08 08:56:40	

Filter:

Note that each successive step in an analysis contains a summary of its inputs and
attributes, and those of each previous step in the analysis. For example, in the above
report the attributes of the Health Impact Function file used to generate the APV Results
are present in the Estimate Flealth Impacts node. Similarly, the metadata for both the
baseline and control air quality grids are present under the Estimate Health Impacts
node. For more information on audit trails, see Chapter 8: GIS/Mapping.

3.3.4	Dataset Validation Reports

You may load data to BenMAP-CE to tailor the analysis to your specific needs (click
Modify Datasets from the main menu). Loading data requires specific formatting.
BenMAP-CE offers a validation option to confirm that the proper headings and data
types are present in the selected file. The validation routines also check that values are
within reasonable ranges for certain types of data. If the file does not meet the
validation requirements, error and/or warning messages will be reported. For more
information about loading and validating data, see Chapter 4: Loading Data.

3.3.5	File Types

Export...

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BenMAP-CE has a number of file types that you can use to store the settings used in a
BenMAP-CE analysis, the results of an analysis, as well as maps and reports. Table 3-1
presents the names of the different file types, their functions, and their default folder
locations.

Table 3-1. File Types Generated by BenMAP-CE

File
Extension

Description

Default Folder
Location1

*.aqgx

Air quality grid.

Result\AQG

*.apvx

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.

Result\APV

*.apvrx

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.

Result\APVRX

*.bdbx

BenMAP-CE Database Export tool creates files which can
contain individual datasets or entire setups. These are
saved in a specific format for importing to BenMAP-CE.

User-specified

*.cfgx

Configuration specifying the health impact functions and
other options used to generate incidence estimates.

Result\CFG

*.cfgrx

Configuration results, containing incidence results at the
grid cell level.

Result\CFGR

*.csv

Reports (Results Tables) and PopSim tool results are
exported as *.csv files, which may be viewed in a text editor,
or in programs such as Excel.

Result\CFGRX or

Result\APVRX,

PopSim

*.rtf

Validation results from data imports

ValidationResults

*.shp

Shape files generated by BenMAP-CE's geographic
information maps system. These files can be viewed within
BenMAP-CE or within shape file viewers, such as ArcView.

AppData\...
\Shapefiles2

*.xlsx

GBD Rollback Tool results are exported as .xlsx files, which
may be viewed in a spreadsheet tool such as Excel (there is
also a .csv option).

GBD

1	Most files generated by BenMAP-CE are stored within the User's directoiy under C:\Users\\Documents\My BenMAP-CE Files\.

2	Shape files (*.shp) are stored at

C:\Users\\AppData\Local\BenMAP-CE\Data\Shapefiles\\.

3.4 Frequently Asked Questions

When creating reports from *.cfgrx and *.apvrx files, why do some of the variables
that I have checked appear as blanks?

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When results are pooled, some of the identifying information for individual health
impact functions gets lost. For example, when pooling 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-CE 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 Section 3.3.1 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 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 and then view where particular
Column and Row variables occur in the map.

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Contents

Table of Contents

CHAPTER 4	4-3

Loading Data	4-3

4.1 Add, Modify, and Delete a Setup	4-4

4.1.1	Grid Definitions	4-7

4.1.1.1	Regular Grid	4-10

4.1.1.2	Shapefile Grid	4-12

4.1.2	Pollutants	4-13

4.1.2.1	Hourly Metrics	4-18

4.1.2.2	Manage Seasons for Individual Pollutant Metrics	4-22

4.1.2.3	Define Seasons for all Pollutant Metrics	4-24

4.1.3	Monitor Datasets	4-25

4.1.3.1	Add Monitor Datasets	4-25

4.1.3.2	Format for Monitor Data	4-29

4.1.4	Incidence and Prevalence Rates Data	4-30

4.1.4.1	Add Incidence/Prevalence Rates	4-30

4.1.4.2	Format for Incidence/Prevalence Data	4-34

4.1.5	Population Data	4-35

4.1.5.1	Add Population Data	4-36

4.1.5.2	Format for Population Data	4-41

4.1.6	Health Impact Functions	4-42

4.1.6.1	Add Health Impact Functions	4-43

4.1.6.2	Format for Health Impact Functions	4-47

4.1.7	Variable Data	4-51

4.1.7.1	Add Variable Data	4-51

4.1.7.2	Format for Variable Data	4-55

4.1.8	Inflation Data	4-56

4.1.8.1. Add Inflation Data	4-56

4.1.8.2 Format for Inflation Data	4-58

4.1.9	Valuation Data	4-58

4.1.9.1	Add Valuation Data	4-59

4.1.9.2	Format for Valuation Data	4-63

4.1.10	Income Growth Data	4-64

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4.1.10.1	Add Income Growth Data	4-64

4.1.10.2	Developing Income Growth Adjustment Factors	4-65

4.1.10.3	Format for Income Growth Adjustment Data	4-68

4.2	Export and Import Setups	4-69

4.3	Frequently Asked Questions	4-70

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Chapter 4 - Loading Data

CHAPTER
Loading Data

In this chapter...

¦	Learn how to create a new setup for your project.

¦	Learn more about the file structure for data inputs.

¦	Learn how to export and import a setup.

BenMAP-CE can store the information needed to run analyses for a particular
geographic area, such as a city, region, or nation, in a single dataset. This dataset is
called a "Setup" and consists of 10 categories of data:

Grid definitions
Pollutants
Monitor data

Incidence and prevalence rates
Population data

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Chapter 4 - Loading Data

¦	Health impact functions

¦	Variable data (socioeconomic variables)

¦	Inflation rates

¦	Valuation functions

¦	Income growth 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 a setup. 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.

Many users will never need to modify the setup. If you are performing an analysis
with the pre-loaded United States, China, or Detroit setups, you may find that
BenMAP-CE contains all of the data you need to perform your analysis, and no
additional modifications are necessary.

We will also discuss how to load (import) data into an existing setup. There are a
number of steps involved in formatting and loading these data, so it is important to
carefully review the steps in this chapter. For most dataset types, BenMAP-CE provides
a validation tool to help you check your data file format (column names, required
columns, and data types) before import. Validation reports are provided which
describe any errors or warnings with the associated row number and column name.1

4.1 Add, Modify, and Delete a Setup

To add a new setup, modify an existing setup, or delete a setup, choose Modify
Datasets from the menu bar. This will bring up the Modify Datasets window. The
United States setup, which comes preinstalled with BenMAP-CE, includes a variety of
datasets and looks like this:

1 Validation reports are saved to C:\Users\\Documents\My BenMAP-CE Files\ValidationResults.

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y:,J Modify Datasets

E \\m2m\

Available Setups United States

Add

Delete

Grid Definitions

Pollutants

Monitor Datasets

CMAQ 12km Nation



CMAQ 12km Nation - Clipped

CMAQ 36km



County



Nation



State





Manage

Ozone



PM2.5





Manage

EPA Standard Monitors 03
EPA Standard Monitors PM2.5

Manage

Incidence/Prevalence Rates

Population Datasets

Health Impact Functions

Variable Datasets

Inflation Datasets

Valuation Functions

EPA Standard Inflators

Manage

OK

Add a Setup. To add a setup (e.g., to add a new country that was not pre-loaded with
BenMAP-CE), click the Add button. The New Setup window will appear where you can
type a name for the new setup.

T—•		—	

s||p New Setup	| o | 0 |j

New Setup Name:







Cancel



OK







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After naming the new setup, you can define the elements that comprise a setup.
Table 4-1 lists the 10 dataset types within BenMAP-CE and indicates which types of
data are needed to perform certain analyses.

Table 4-1. BenMAP-CE Data Elements

Dataset Type

Required to
Estimate Health
Impacts

Required to Quantify
Economic Values

Grid Definitions

V

V

Pollutants

V

V

Monitor Datasets (or

V

V

Modeled Data")





Incidence/Prevalence Rates

V

V

Population Datasets

V

V

Health Impact Functions



V

Variable Datasets





Inflation Datasets



V

Valuation Functions



V

Income Growth



V

Adjustments





Some of the elements of a setup are fundamental and should be entered before the
others, namely, Grid Definitions and Pollutants. The Incidence/Prevalence Rates,
Population, and Variable Datasets depend on the Grid Definitions, and the Monitor
and Health Impact Functions datasets depend on the Pollutants that you have
defined. Therefore, it is best to start by defining your Grid Definitions and Pollutants,
and then define the other elements of the setup.

Modify a Setup. To modify a setup, click Modify Datasets on the menu bar. Choose the
setup for modification from the drop-down list of Available Setups (the default value is
United States). Then, click on the Manage button under one of the ten components
comprising a setup. The sections below provide more information for each of these
components.

Delete a Setup. To delete a setup, click Modify Datasets on the menu bar. Choose the
setup for deletion from the drop-down list of Available Setups. Click the Delete button.
You will then be asked to confirm your decision.

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4.1.1 Grid Definitions

A BenMAP-CE Grid Definition specifies geographic units (i.e., grid cells) that serve two
purposes: (1) the program assigns air quality, population, and baseline incidence rates
to these grids when calculating impacts; and (2) you can use these grids to report
results in the GIS. You can define a grid in one of two ways: by loading a Shapefile (a
particular type of GIS file) or by specifying a regularly shaped grid pattern. These are
referred to as Shapefile Grid Definitions and Regular Grid Definitions, respectively.
A Regular Grid Definition is used when you want to specify a grid that is regularly
shaped (e.g., 12 x 12 km squares). A Shapefile Grid Definition can be used to create
either grids that are regularly shaped or grids that match an irregular shape, like a
political boundary. All Shapefile Grid Definitions must contain an attribute table with
a unique (i.e., non-repeating) column and row index.

At least one Grid Definition should be created to outline the area of interest for the
BenMAP-CE analysis (a city boundary, for example). Additional grid definitions can also
be created for subdivisions 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, an analysis for the United States might use one or more of 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.

¦	State - this Shapefile Grid Definition contains state borders, for use in
generating reports and maps with results aggregated to the state level.

¦	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. To start adding or
modifying grid definitions, click Modify Datasets on the menu bar. Click on the
Manage button below the Grid Definitions box. The Manage Grid Definitions
window will appear.

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(¦!iji Manage Grid Definitions

Available Grid Definitions

CMAQ 12km Nation

CMAQ. 12km Nation - Clipped

CMAQ 36km

County

Nation

State

Grid Type:

Shapefile
Default Grid Type:

CMAQ 12km Nation

GI5 Projection

This projection will be used when performing area- or distance-based calculations on all shapefile grid
definitions in this setup.

We recommend using one of the Albers projections in the list below. For U.S. setups, we suggest
USAContiguousAlbersEqualAreaConicUSGS. To see a list of all available projections, check the
"Show AH" box.

Show All ~

NorthAmerica - USAContiguousAlbersEquaiAreaConicUSGS

Delete

Add

Edit

View Metadata

OK

Click on the Add button to display the Grid Definition window. Provide a name for the
Grid Definition in the Grid ID field and specify the Grid Type: Shapefile Grid or Regular
Grid.

When you add a new Setup, you will be asked to select a GIS projection. The projection
you specify will be used when performing area- or distance-based calculations on all
shapefile grid definitions in this setup. We recommend selecting a regional setting that
best represents the geographic area you are evaluating from one of the Albers
projections provided in the list box. (For U.S. setups, we suggest
USAContiguousAlbersEqualAreaConicUSGS.)

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Manage Grid Definitions

Available Grid Definitions

Grid Type:

Shapefile
Default Grid Type:

GIS Projection

This projection will be used when performing area- or distance-based calculations on all shapefile grid
definitions in this setup.

We recommend using one of the Allbers projections in the list below. For U.S. setups, we suggest
USAContiguousAlbersEqualAreaConicUSGS. To see a list of all available projections, check the
"Show AM" box.

Show All ~

Africa - AfricaAlbersEqualAreaConic

Asia - AsiaNorthAlbersEqualAreaConic

Asia - AsiaSouthAlbersEqualAreaConic

Europe ¦ EuropeAlbersEqualAreaConic

NorthAmerica - AlaskaAlbersEqualAreaConic

NorthAmerica - CanadaAlbersEqualAreaConic

NorthAmerica - HawaiiAlbersEqualAreaConic

NorthAmerica - NorthAmericaAlbersEqualAreaConic

NorthAmerica - USAContiguousAlbersEqualAreaConicUSGS

SouthAmerica - SouthAmericaAlbersEqualAreaConic

If you have reason to choose another type of projection (e.g., in consultation with a
geospatial analyst), you may check the "Show All" box and select from an expanded list
of choices.

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§l§ Grid Definition

Grid Definition
Grid ID: US Counties

(E)

Grid Type. I Shapefile Grid

Load Shapefile:

>h a pefi I es\Cou nty_e pa 2_WGS1984.sh p

Current Shapefile Name: County_epa2_WGS1984
Columns:	56

Rows:	840

View Metadata

|—I Create crosswalk between this grid definition and ,
— all other grid definitions in this setup.

Selecting this option will substantially increase the
time required to import the shapefile.

Cancel

OK

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 selecting Regular Grid from the Grid Type drop-
down menu. Type the name of the grid definition in the Grid ID box, and then define
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 you specify 16 columns and 2
columns per degree longitude, then the grid will span 8 degrees of longitude. And if you

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specify 25 rows and 4 rows per degree latitude, then the grid will span 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 will
run 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 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.

Grid Definition

i = i @ ms .r

Grid Definition

Grid ID: CMAQ 12km Nation

















1 ° l



















































































i

























































































"1





































































































Minimum Longituc

e

Minimum Latitude



















































































—



















































































































4®































































































































View Metadata Preview





























































r Create crosswalk between this grid definition and
all other grid definitions in this setup.

Selecting this option will substantially increase the
time required to import the shapefile.













































































































































Cancel

OK













To calculate health impacts and economic benefits, BenMAP-CE uses air quality,
population, and demographic data at different spatial scales. To do this, the program
calculates a crosswalk (percentage file) that relates data at one spatial scale to another
(e.g., 12km CMAQ grid to county). This step is performed only once per crosswalk and
the results are saved to the database for subsequent calculations.

If you wish to pre-calculate the crosswalk (percentage overlap) between this grid
definition and all other grid definitions in this setup, check the option box. This will
take longer to load the data now, but can save you time later when you are ready to

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calculate health impacts. If you do not create the crosswalks at the grid definition stage,
BenMAP-CE will create crosswalks as needed during the configuration or aggregation,
pooling, and valuing stages.

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, or Add to define a new grid definition. View
Metadata is not applicable for Regular Grid Definitions. Click OK to return to the
Modify Datasets window.

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

https://www.esri.com/libraiy/whitepapers/pdfs/shapefile.pdf. When a shapefile is
being used to create a new grid, BenMAP-CE will: (1) check to see if the file is projected
to GCS NAD 83; and (2) if necessary, re-project the file (with notification) from the
native projection to GCS NAD 83. 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 Shapefile Grid from the Grid Type dropdown menu, 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 input box. After locating the file, click Open. This
will choose the file, and bring you back to the Grid Definition window. To view the
shapefile, click Preview. You can add metadata using the View Metadata button.
Adding metadata allows you to supply a file Reference (e.g., person, organization,
publication, or model that produced or supplied the values in the electronic file) and
Description (or any relevant notes about the use or limitations of the data). Other
minimal file attributes are pre-populated automatically during import.

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\ Grid Definition

Til

Grid Definition

Grid ID: NewGridDefinition

Grid Type, jshapefiie Grid



Load Shapefile:

CurrentShapefile Name: Detroit_Counties
Columns:	26

Rows:	165

View Metadata

~

Create crosswalk between this grid definition and ,
all other grid definitions in this setup.

Selecting this option will substantially increase the
time required to import the shapefile.

When you are satisfied that the shapefile looks correct, click OK. This will save the
shapefile and bring you back to Manage Grid Definitions window.2 Note that the Grid
Type box displays the type of grid for each of your grid definitions. The View Metadata
button will allow you to see and edit any comments that were previously entered.

Click OK when you are finished loading grid definitions. The Modify Datasets screen
will now list the Grid Definitions that you have just created. At any time, you may click
the Manage button to add, modify, or delete grid definitions.

WARNING! If you delete a Grid Definition, you will permanently delete any gridded
data that is dependent on it, such as any Incidence/Prevalence, Population, and
Variable Datasets that use this particular Grid Definition. As we discuss each of these
other setup elements below, we will describe how this might happen.

4.1,2 Pollutants

The Pollutants section of a setup specifies the pollutants that BenMAP-CE will analyze
and defines the air quality metrics to be used by BenMAP-CE. You are not importing air
pollution data, but rather naming your pollutants and defining the measures or metrics

2 Shapefiles are saved to C:\Users\\AppData\Local\BenMAP-CE\Data\Shapefiles\\.

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BenMAP-CE will use when performing an 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-CE analysis.

A key concept for pollutants is the Metric. Air quality metric describes the period of the
day over which the pollutant observations are averaged. For example, a metric of
D24HourMean is a daily average of hourly measurements. A metric of D8HourMax is the
average of the 8-hour period during the day when pollutant levels are the highest (see
Table 4-2 below). 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-CE is hierarchical - a pollutant can have
multiple Metrics, each of which has multiple Statistics (these are automatically
calculated by BenMAP-CE) and which can have multiple Seasonal Metrics. Similarly,
Seasonal Metrics have multiple Statistics. Furthermore, air pollution data can be
provided to BenMAP-CE at any of these levels, in addition to the daily and hourly
observation level, as described in more detail in Section 4.3.

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Table 4-2. Example Calculation of D8HourMax

Hourly Period1

Hourly average 03
concentration2 (ppm)

Moving 8-hour
average3

D8HourMax4

07:00

0.000

...



08:00

0.005

...



09:00

0.010

...



10:00

0.015

...



11:00

0.020

...



12:00

0.025

...



13:00

0.030

...



14:00

0.035

...



15:00

0.040

0.020



16:00

0.045

0.025



17:00

0.050

0.030



18:00

0.055

0.035



19:00

0.060

0.040



20:00

0.055

0.044



21:00

0.050

0.047



22:00

0.045

0.048



23:00

0.040

0.049

<- 0.049

00:00

0.035

0.048



01:00

0.030

0.047



02:00

0.025

0.044



03:00

0.020

0.040



04:00

0.015

0.035



05:00

0.010

0.030



06:00

0.000

0.024



1	Days for measuring ozone start and end at 7:00 AM local standard time.

2	Hourly average is the average of individual measurements taken during the
hour.

3	Moving 8-hour average is the average of the hour and the proceeding 7
hours.

4	D8HourMax is the maximum of the moving 8-hour averages.

Reference: Federal Register 79 FR 75233, Dec. 17, 2004.
https://www.federalregister.gov/articles/2014/12/17/2014-
28674/national-ambient-air-quality-standards-for-ozone#h-193

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Add a Pollutant

Air pollution data in BenMAP-CE is of two types: (1) point source monitoring data and
(2) Grid Definition-based modeling data. For both types, the data must be associated
with a particular pollutant. Table 4-3 describes these variables used to define a
pollutant in BenMAP-CE.

Table 4-3. BenMAP-CE Pollutant Definitions

Pollutant Field Name

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 PM10 and PM25 have daily observations.

Metrics

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
mean of all twenty four hourly observations, etc.

Seasonal Metrics

Seasonal values calculated from metric values. In the United
States, for example, quarterly means are calculated for PM2 5
from daily means.

To add pollutant definitions to BenMAP-CE, click Modify Datasets on the menu bar.
Then click on the Manage button below the Pollutants group. 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.

Manage Pollutants

Available Pollutants

Pollutant Metrics

Delete

Add

Edit

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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 define the characteristics of this pollutant - the Observation
Type and Metrics.

Main

Pollutant ID:

PM2.5

Observation Type:

Daily

Metrics

Define Seasons for all
Pollutant Metrics

Detail
Metric Name:

Hourly Metric Generation:

D24HourMean

Fixed Window

Fixed Window

Start Hour:
End Hour:
Statistic:

0





23





Mean

-

Manage Seasons for Individual Pollutant Metrics

QuarterlyMean

Edit

Cancel

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.

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 in the Pollutant
Definition window. 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 4-4. These are provided just as an example, you may use
any names that you like. However, keep in mind that the names that you use for your

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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-CE'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.

Table 4-4. Examples of Metric Names

Name

Description

DIHourMax

Highest hourly value from 12:00 A.M. through 11:59 P.M.

D8HourMax

Highest eight-hour average calculated between 12:00 A.M. and 11:59 P.M.

D24HourMean

Average of hours from 12:00 A.M. through 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 4-4 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 Detail section of the Pollutant Definition window lets you define the metrics that
you want to use. There are three options that you may choose using the Hourly Metric
Generation drop-down list: Fixed Window, Moving Window, and Custom.

The Fixed Window option 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.

¦	DIHourMax: 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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¦J Pollutant Definition



Main

Pollutant ID:

Ozone

Observation Type:

Hourlv

Metrics
DlHourMax

D24HourMean

DSHourMax
DSHourMean

Define Seasons for all
Pollutant Metrics

Detail
Metric Name:

Hourly Metric Generation:

D24HourMean

Fixed Window

Fixed Window

Fixed Window

Moving Window
_ Custom

a

Statistic:

Mean

Manage Seasons for Individual Pollutant Metrics

The Moving Window option 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-CE will use the
statistics generated for each window.

For example, consider the highest eight-hour mean (DSHourMax) over the course of a
day. You would have the following settings: Window Size = 8. Window Statistic =
Mean. Daily Statistic = Max. BenMAP-CE 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-CE would then choose the eight-hour mean that
has the highest value (see Table 4-2 above for an example).

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Pollutant Definition



Main

Pollutant ID:

Ozone



Observation Type:



| Hourly



D24HourMean

DSHourMax

DSHourMean

Define Seasons for all
Pollutant Metrics

Detail
Metric Name:

Hourly Metric Generation:

DlHourMax

Moving Window

Moving Window

Window Size:

Window Statistic: | Mean
Daily Statistic:

Max

Mean
Median

Min
Sum

3

Manage Seasons for Individual Pollutant Metrics

Edit

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The Custom tab lets you define Metrics using a mathematical function that you specify.
These functions can include measures such as the sum of the number of hours of ozone
exposure above 60 ppb. The possibilities are quite diverse, as evidenced by the range of
functions and variables available for use as shown in Table 4-5. However, the syntax for
using these functions is somewhat involved, so we have reserved discussion of this for
the Appendix M: Function Editor.

Pollutant Definition

Main

Pollutant ID:

PM2.5



Observation Type:



Hourly

-

Metrics

Define Seasons for all
Pollutant Metrics

Detail
Metric Name:

Hourly Metric Generation:

D24HourMean

Custom

Custom

Available Functions:

Available Variables:

ABS(x)



Observations!]

EXP(x)



OailyObservationO

IPOWER(x,y)



SoredObservationO

LN(x)



Day

POWER(x,y)



Mean

SQR(x)



Median

SQRT(x)



Min





Max





Sum

NoObservation

Function:

Manage Seasons for Individual Pollutant Metrics

Cancel

Edit

OK

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Table 4-5. Available Functions and Variables for Custom Metrics

Name

Description

Functions

ABS(x)

Returns the absolute value ofx

EXP(x)

Returns e the power x, where e is the base of the natural
logarithm.

IPOWER(x,y)

Returns x to the powery [y an integer value).

LN(x)

Returns the natural logarithm ofx

POWER(x,y)

Returns x to the powery [y a floating point value).

SQR(x)

Returns the square of x.

SQRT(x)

Returns the positive square root ofx.

Variables

Observations [i]

All hourly observations for the year (index begins at zero,
typically ranging to 8,760).

DailyObservations [i]

All hourly observations for the day (indexed zero to twenty-
three).

SortedObservations[i]

All hourly observations for the day, sorted from low to high
(indexed zero to twenty-three).

Day

Index of the day whose metric value is being generated (index
begins at zero).

Mean

Mean of the daily observations.

Median

Median of the daily observations

Min

Minimum of the daily observations.

Max

Maximum of the daily observations.

Sum

Sum of the daily observations.

NoObservation

Flag value indicating a missing observation (-345)

4.1.2.2 Manage Seasons for Individual Pollutant Metrics

Manage Seasons for Individual Pollutant Metrics allow you to aggregate daily Metric
values over a portion of the year that you define. This has a number of uses. For example, if
pollutant values vary greatly by season of the year, 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 for individual pollutant metrics, first select the metric of
interest (e.g., D24HourMean), then click on the Edit button below the Manage Seasons
for Individual Pollutant Metrics box. The Manage Seasons for Individual Pollutant
Metrics window will appear.

To add a Seasonal Metric, click on the Add button below the Seasonal Metrics box. A
default name Seasonal Metric 0 will appear in the box. Since the default name is not very

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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
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 Seasonal Metric
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. To change the date, click on the month or day,
and use the arrows to change the month or day accordingly.

Manage Seasons for Individual Pollutant Metrics	| ~ | Is] ImShH

Metric ID: D24HourMean

Seasonal Metrics

Seasonal Metric Name: QuarterlyMean
Seasonal Metric Seasons	Selected Season Details

Start Date:

Season 1
Season 2
Season 3

Delete

Add

October 01 |^j

End Date:

December 31 |^j

Custom Function

Statistic:

OK

Next, you need to choose the Statistic tab or the Custom Function 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 Appendix M: Function Editor 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 will return you to the Manage Pollutants
window.

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q:;J Manage Pollutants

Available Pollutants

Pollutant Metrics

Ozone

D24HourMean
Quartery Mean

Delete

Add

Edit

OK

4.1.2.3 Define Seasons for all Pollutant Metrics

The Define Seasons for all Pollutant Metrics button on the Pollutant Definition

window allows you to associate Seasons with a Pollutant. These seasons differ
somewhat from the Seasonal Metrics defined for individual pollutant metrics
(discussed above). They are used to define:

¦	The portion of the year for which benefits are calculated for a Pollutant. You can
think of Seasons as defining this period of the year "globally" for the pollutant,
as it affects the portion of the year over which both Metrics and Seasonal
Metrics are calculated. For example, in the United States ozone benefits are
often only calculated for the ozone season, from May 1 through September 30.

¦	The portion(s) of the year for which missing pollutant concentrations are filled
in by BenMAP-CE. That is, in order to calculate benefits, BenMAP-CE 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.

To define Seasons for a Pollutant, click the Define Seasons for all Pollutant Metrics

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 monitoring period; and the appropriate Number of Bins, which
define the number of bins used in scaling monitor values. The advanced options for
PM2.5 look like the following:

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¦' | Define Seasons

Seasons

Season 2
Season 3
Season 4

Delete

Add

Selected Season Detail
Start Date:

End Date:

Start Hour:

End Hour:

Number of Bins:

January 01 j . j

March 31



23

OK

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 Pollutant within the Available Pollutants box and click the Edit button.
When you are done, click OK to return to the Modify Datasets window.

After defining all of the pollutants that you want, click OK. This will return you to the
Modify Datasets window.

4.1.3 Monitor Datasets

The Monitor Datasets section of the Modify Datasets window allows you to add air
pollution monitoring data to your setup. Air pollution monitoring data may be used to
estimate ambient pollution levels in each grid cell defined by a Grid Definition.
BenMAP-CE 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-CE is of two types: (1) point source monitoring
data and (2) Grid Definition-based modeling data. Both types of data must be
associated with a particular pollutant that you have already defined. Only the point
source monitoring data is stored in the setup database. The modeling data are loaded
into BenMAP-CE as you need them for a particular analysis.

4.1.3.1 Add Monitor Datasets

To start, click on the Manage button below the Monitor Datasets box. The Manage
Monitor Datasets window will appear. From this window you may Add monitoring

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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 view the metadata of a particular monitor
dataset, select an Available Dataset and click on a row from the Dataset Contents,
then click the View Metadata button. This allows you to view further information
about references or descriptions of the file.

Manage Monitor Datasets

Available Datasets

Dataset Contents (Number of Monitors by Pollutant by Year)

Delete Add	Edit

Pollutant

Monitor Count

[This list is empty.

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 Pollutant3 drop-down menu, and then type the 4-digit
Year of the data in the Year box.

NOTE: The Dataset that you define can have one or more pollutants and multiple years
of data (e.g., representing a particular monitoring network). However the data must be
imported one pollutant and one year at a time.

3 The pollutants in the Pollutant drop-down menu have been defined under the Pollutants box on the
Modify Datasets window.

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§p Monitor Dataset Definition



MonitorDataSetO

Dataset Name: 	

Dataset Contents (Number of Monitor by Pollutant by Year):

Pollutant

Year

Monitor Count

This list is empty.

Pollutant: PM2.5

Year: 1999

Output Sample File

Load Data From File

OK

Monitor data must be formatted in a database file, with monitor definition information
and monitor values in a single line.

After specifying the Pollutant and Year, click on the Load Data From File button to
bring up a window from which you can Browse the BenMAP-CE Data directory to find
the desired data file. Click Open, to choose the file.

Users are advised to click the Validate button before loading the monitor dataset.
BenMAP-CE's validation tool will review the file format (column names, required
columns, and data types) and provide a report with any errors or warnings.4 The user
also has the option of adding metadata to the file to save references and comments
about that specific file. To add metadata, click the View Metadata button. After
passing validation (and adding metadata if desired) click the OK button to bring you
back to the Monitor Dataset Definition window.

4 Validation reports are saved to C:\Users\\Documents\My BenMAP-CE Files\ValidationResults.

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Repeat this procedure to load all of your monitoring data. If you try to load the data for
the same pollutant/year combination, BenMAP-CE will warn you of the duplication.

, Manage Monitor Datasets

Available Datasets

EPA Standard Monitors 03

EPA Standard Monitors PM2.5

Copy

Dataset Contents (Number of Monitors bv Pollutant bv Year)

Pollutant

Year

Monitor Count

PM2.5

2000

1311

PM2.5

2001

1339

PM2.5

2002

1328

PM2.5

2003

1316

PM2.5

2004

1226

PM2.5

2005

1260

PM2.5

2006

1197

PM2.5

2007

1144

PM2.5

2008

1179





t Metadata

OK

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. To view the metadata for a
particular entry in the dataset, choose an Available Dataset and row in the Dataset
Contents and click the View Metadata button.

To delete existing datasets, select the dataset in the Available Datasets list and click
the Delete button. To edit an existing dataset, select the dataset in the Available
Datasets list and click the Edit button. (Note: Certain pre-loaded datasets cannot be
edited. Instead if you select one of these, you will have the option to Copy the locked
dataset and then you can edit the copied dataset.)

When you have finished loading your monitor data, click OK in the Manage Monitor
Datasets window. This will take you back to the Modify Datasets window, which will
show the name of the Dataset(s) that you just entered.

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4.1.3.2 Format for Monitor Data

Monitor data is required to be formatted in a single database file, with monitor
definition information and monitor values in a single line. Tables 4-6a and 4-6b list the
variables in the monitor dataset and provide 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-CE.

Table 4-6a. Required 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.

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 values 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 annual statistics] or
must be one of: None, Mean, Median, Max, Min, Sum.
(e.g., mean of the 1-hour maximum for the year]

Values

Text

Yes

If Metric is blank, values are supplied as a comma-
delimited string of values for the year [e.g., 365 or 366
(leap year] values for daily data, 8760 or 8784 (leap
year] values for hourly data]. If Metric is defined, but
Seasonal Metric and Statistic are blank, 365 or 366
metric values. If Seasonal Metric is defined, but
Statistic is blank, n seasonal metric values. If Statistic is
defined, one annual statistic 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 4-6b. Required Format, Sample Air Monitoring Data File

Monitor Name

Monitor Description

Latitude Longitude

Metric

Seasonal Metric

Statistic

Values

260050003881011

'RESIDENTIAL, SUBURBAN

42.7678

-86.1486

D24HourMean

Quart erlyMe an

Mean

11.67

260170014881011

•RESIDENTIAL, URBAN AND CENTER CITY'

43.5714

-83.8907

D24HourMean

QuaiterlyMean

Mean

11.62

260210014881011

COMMERCIAL, RURAL'

42.1978

-86.3097

D24HourMean

QuarterlyMean

Mean

11.58

260490021881011

'RESIDENTIAL, URBAN AND CENTER CITY'

43.0472

-83.6702

D24HourMean

QuarterlyMean

Mean

11.56

260650012881011

'RESIDENTIAL, URBAN AND CENTER CITY'

42.7386

-84.5346

D24HourMean

QuarterlyMean

Mean

11.49

260770008881011

'COMMERCIAL, URBAN AND CENTER CITY1

42.2781

-85.5419

D24HourMean

QuarterlyMean

Mean

11.44

260810007881011

'INDUSTRIAL.. URBAN AND CENTER CITY1

42.9561

-85.6791

D24HourMean

QuarterlyMean

Mean

11.4

260810020881011

'INDUSTRLAL, URBAN AND CENTER Cmr*

42.9842

-85.6713

D24HourMean

QuarterlyMean

Mean

11.36

260990009881011

'COMMERCIAL, SUBURBAN'

42.7314

-82.7935

D24HourMean

QuarterlyMean

Mean

11.43

261010922881011

'RESIDENTIAL, URBAN .AND CENTER CITY'

44.3070

-86.2426

D24HourMean

QuarterlyMean



11.43,11.07,10.98,10.90

261130001881011

'FOREST, RURAL'

44.3106

-84.8919

D24HourMean

QuarterlyMean



11.44,14.23,11.20,9.30

261150005881011

•AGRICULTURAL, RURAL'

41.7639

-83.4719

D24HourMean

QuarterlyMean



11.39,13.71,10.04,11.24

261210040881011

'COMMERCIAL, URBAN AND CENTER CITY

43.2331

-86.2386

D24HourMean

Quart erlyMean



11.28,9.2.3,15.03,11.10

261250001881011

'RESIDENTIAL, SUBURBAN

42.4631

-83.1832

D24HourMean

QuarterlyMean



11.27,11.12,13.15,12.03

261390005881011

'RESIDENTIAL, SUBURBAN

42,8945

-85.8527

D24HourMean

QuarterlyMean



11.27,10.05,10.9,12.13

261470005881011

'RESIDENTIAL, SUBURBAN

42.9533

-82.4562

D24HourMean

QuarterlyMean



11.26,12.13,15.00,10.01

261610008881011

'COMMERCIAL, URBAN AND CENTER CITY'

42.2406

-83.5996

D24HourMean

QuarterlyMean



11.24,14.05,11.03,10.10

261630001881011

•COMMERCIAL, SUBURBAN

42.2286

-83.2082

D24HourMean

QuarterlyMean



11.23,9.23,13.04,10.09

261630015881011

'COMMERCIAL, URBAN AND CENTER CITY'

42.3028

-83.1065

D24HourMean

QuarterlyMean



11.23,11.24,10.45,10.04

261630016881011

'RESIDENTIAL, URBAN .AND CENTER CITY'

42.3578

-83.0960

D24HourMean

QuarterlyMean



11.2,10.73,11.23,10.45

261630019881011

'RESIDENTIAL, SUBURBAN

42.4308

-83.0001

D24HourMean





.,.,13.01,.,.,17.19,.,.,3.89,.,.,13.86,.,.,5.9

261630025881011

'COMMERCLAL, SUBURBAN'

42.4231

-83.4263

D24HourMean





.,.,13.48,.,.,8.73,., .,6.079, .,.,14.534, .,.,3.

261630033881011

'INDUSTRIAL, SUBURBAN'

42.3067

-83.1488

D24HourMean





.,.,15.675,.,.,12.72,.,.,6.46,.,.,16.24,.,.,6.

261630036881011

'COMMERCLAL, SUBURBAN'

42.1873

-83.1539

D24HourMean





.,.,16.52,.,.,15.01,.,.,5.41,.,.,15.39,.,.,5.0

261630038881011

'RESIDENTIAL, URBAN AND CENTER CITY

42.3350

-83.1096

D24HourMean





-,-=20.52,.,.,21.85,.,.,5.88,.,., 19.28,.,.,5.1

261630039881011

'RESIDENTIAL, URBAN AND CENTER CITY"

42.3233

-83.0685

D24HourMean





.,.^0.14,.,.,7.59,.,.,8.17,.,.,13.11,.,.,7.59

4.1.4 Incidence and Prevalence Rates 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 rates (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 of the total population.

NOTE: For both incidence and prevalence rates, BenMAP-CE allows the user to have
rates that vary by race, ethnicity, gender, and age group. BenMAP-CE can support
multiple sets of incidence and prevalence rates, if the rates differ by year or by grid
definition.

4.1.4.1 Add Incidence/Prevalence Rates

To start adding incidence and prevalence data files, click on the Manage button below
the Incidence/Prevalence Rates box. The Manage Incidence Datasets window will
appear.

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Manage Incidence Datasets

0

Available Datasets

Dataset Incidence Rates

Endpoint Group Endpoint

Type

Race

Ethnicity Gender Start Age End Age

Add

Edit

Filter Endpoint Group

Filter Endpoint

Filter

IH Group

View Metadata

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 window. Give a name to the dataset that you are creating by typing a name
in Dataset Name box.

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^ Incidence Dataset Definition

@ r

Dataset Name: IncidenceDataSetO

Column Row

Grid Definition:

Dataset Incidence Rates
Filter Endpoint Group

Filter Endpoint

Filter

1H Group

Endpoint Group Endpoint Type

Ethnicity Gender Start Age End Age

This list is empty.

This list is empty.

Output Sample File

Load From File	Delete

of0 I ~ HI,

NOTE: If you have multiple incidence or prevalence datasets that vary, for example, by
year and grid definition, then use the name to provide a reference to the year and grid
definition.

In the Grid Definition drop-down list choose the item that matches the grid definition
used to develop the incidence/prevalence dataset. Note: The incidence and prevalence
rate data must use the same column/row information as the matching grid definition.
Click the Load From File button. Then click on the Browse button, to browse for the
dataset file. (The format for the dataset is detailed in the next sub-section.)

Load Incidence/Prevalence Database



Grid Definition:

County

Database:

fiew Metadata

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After locating the file, click Open. Click the Validate button before loading the data.
BenMAP-CE's validation tool will review the file format (column names, required
columns, and data types) and provide a report with any errors or warnings. You also
have the option of adding metadata to the dataset. This is done by clicking the View
Metadata button and adding any references or descriptions that you see fit. Click OK
on the Load Incidence/Prevalence Database window to load the selected file. The
Incidence Dataset Definition window will appear, displaying the rates in the data file
that you just loaded.

-'.J: Incidence Dataset Definition

Dataset Name:

Grid Definition:

Mortality Incidence (2000)

County

Dataset Incidence Rates
Filter Endpoint Group

Filter Endpoint

~ Group

Endpoint Group

Endpoint

Type Race

Ethnicity Gender Start Age

End Age

-

Mortality

Mortality A...

Incidence

0

0



Mortality

Mortality A...

Incidence

1

17



Mortality

Mortality A...

Incidence

18

24



Mortality

Mortality A...

Incidence

25

34



Mortality

Mortality A...

Incidence

35

44

Mortality

Mortality A...

Incidence

45

54

Mortality

Mortality, A...

Incidence

55

64

Mortality

Mortality, A...

Incidence

65

74

Mortality

Mortality A...

Incidence

75

84

Mortality

Mortality A...

Incidence

85

99

Mortality

Mortality C...

Incidence

0

0

Mortality

Mortality C...

incidence

1

17

Mortality

Mortality C...

Incidence

18

24

Mortality

Mortality C...

Incidence

25

34

Mortality

Mortality C...

Incidence

35

44

Mortality

Mortality, C...

Incidence

45

54

Mortality

Mortalitv C

Inridpnrp

55

FA







rrr





4 1	





	1 ~

Output Sample File

Load From File

Column Row

Value

1 1

:,::35233s

1 3

0.00397209

1 5

0.00352938

1 7

0.00352938

1 9

0.00352938

1 11

0.00352938

1 13

0.00352938

1 15

0.00352938

1 17

0.00352938

1 19

0.00352938

1 21

0.00352938

1 23

0.00352938

1 25

0.00352938

1 27

0.00352938

1 29

0.00352938

1 31

0.00352938

1 33

0.00352938

1 35

0.00352938

1 37

0.00352938

1 39

0.00352938

1 41

0.00352938

1 43

0.00352938

1 45

0.00352938

1 47

0.00352938

1 49

0.00352938

Mil

A25 |>j

If the data look correct, click OK. This will return you to the Manage Incidence
Datasets window. To view any metadata that was added, select an Available Dataset
and a Rate, and click the View Metadata button. The user can view or edit the
metadata of the imported files.

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J Manage Incidence Datasets

@ |		-p

Available Datasets

Mortality Incidence (2000)

Mortality Incidence (2005)
Mortality Incidence (2010)
Mortality Incidence (2015)
Mortality Incidence (2020)
Mortality Incidence (2025)
Mortality Incidence (2030)
Mortality Incidence (2035)
Mortality Incidence (2040)
Mortality Incidence (2045)
Mortality Incidence (2050)
Other Incidence (2000)
Other Incidence (2007)
Prevalence (2008)

Add

Edit

Dataset Incidence Rates

Filter Endpoint Group

Filter Endpoint

Filter

View Metadata

Endpoint Group

Endpoint

Type

Race

Ethnicity Gender Start Age

End Age

-

Mortality

Mortality, A..

F



0

0



Mortality

Mortality, A..

F



1

17

-

Mortality

Mortality, A..

F



18

24

U

Mortality

Mortality, A..

F



25

34



Mortality

Mortality, A..

F



35

44



Mortality

Mortality, A..

F



45

54



Mortality

Mortality, A..

F



55

64



Mortality

Mortality, A..

F



65

74



Mortality

Mortality, A..

F



75

84



Mortality

Mortality, A..

F



85

99



Mortality

Mortality, C..

F



0

0



Mortality

Mortality, C..

F



1

17



Mortality

Mortality, C..

F



18

24



Mortality

Mortality, C..

F



25

34



Mortality

Mortality,

F



35

44

-

4 1





rrr



1 ~



« 1











H Group

OK

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 Rates box in the
Modify Datasets window will show the datasets that you have entered.

4.1.4.2 Format for Incidence/Prevalence Data

Table 4-7a presents the variables that can be used in incidence and prevalence datasets,
and Table 4-7b presents a sample dataset that follows this format.

Table 4-7a. Health Incidence and Prevalence Dataset Variables

Field Name

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"
(from one or more Population
Configurations).

Ethnicity

Text

No

Should either be blank (signifying All
Ethnicities) or reference a defined Ethnicity,
such as "Hispanic" (from one or more
Population Configurations).

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Field Name

Type

Required

Notes

Gender

Text

No

Should either be blank (signifying All
Genders) or reference a defined Gender
(from one 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" include infants through the first 12
months of life and all one-year old infants.

End Age

Integer

Yes

Column

Integer

Yes

The Column and the Row link the
incidence/prevalence data with cells from a
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-CE assumes that the value is an
incidence rate.

Table 4-7b. Sample Health Incidence Dataset

Endpoint Group

Endpoint

Year

Race

Gender Ethnicity7

Start Age

End Age

Column

Row

Type

Value

Hospital Admissions, Respiratory

HA Asthma

2002





0

99

1

2

Incidence

2.62E-06

Hospital Admissions, Respiratory

HA, Asthma

2002





0

99

1

3

Incidence

7.73E-06

Hospital Admissions, Respiratory'

HA, Asthma

2002





0

99

1

4

Incidence

1.68E-06

Hospital Admissions, Respiratory

HA Asthma

2002





0

99

1

5

Incidence

5.45E-06

Hospital Admissions, Respiratory

HA Asthma

2002





0

99

1

6

Incidence

1.02E-06

Hospital Admissions, Respiratory

HA Asthma

2002





0

99

1

7

Incidence

2.72E-06

Hospital Admissions, Respiratory7

HA Asthma

2002





0

99

1

8

Incidence

5.05E-06

Hospital Admissions, Respiratory7

HA Asthma

2002





0

99

1

9

Incidence

3.03E-06

Hospital Admissions, Respiratory7

HA, Asthma

2002





0

99

1

10

Incidence

5.S9E-06

Hospital Admissions, Respiratory7

HA Asthma

2002





0

99

1

11

Incidence

3.11E-06

Hospital Admissions, Respiratory7

HA Asthma

2002





0

99

1

12

Incidence

3.30E-06

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-CE allows you to
specify race, ethnicity, gender, and age of the population, as well as the year of the
population estimate.

Population data loaded into BenMAP-CE 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

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example, 'OTOl' 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-CE should then
contain population values for all combinations of race, ethnicity, gender, and age range.
The population values may be 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), you can have the option of using different sets of
population data for different analyses.

BenMAP-CE 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.

4.1.5.1 Add Population Data

To add population data to BenMAP-CE, click on the Manage button below the Population
Datasets box in the Modify Datasets window. The Manage Population Datasets window
will appear.

Manage Population Datasets

Available Datasets

Dataset Detail
Grid Definition:

Population Configuration:
Values

Delete

Add

Race Ethnicity Gender Age Ra... Column

Row Value





[This list is empty.



View Metadata

Click on the Add button to display the Load Population Dataset window. Name the
dataset using the Population Dataset Name box.

The Grid Definition drop-down list provides the list of existing grid definitions.
Choose a grid definition that matches your population dataset.

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The Population Configuration section allows you to define the variables that are in the
population data file to be loaded into BenMAP-CE. 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. Clicking the Add
button will open a Population Configuration Definition window where you can enter
the fields that appear in the file that will be later uploaded (discussed in more detail
below).

The Browse button to the right of the Database box allows you to find the data file that
you want to load into BenMAP-CE. Once you click Open and load the file, the Validate
and View Metadata buttons become active. You can click the Validate button before
the file is loaded into the Manage Population Datasets form. BenMAP-CE's validation
tool will review the file format (column names, required columns, and data types) and
provide a report with any errors or warnings. You can also add metadata, which
includes references and descriptions, by clicking the View Metadata button.

Load Population Dataset

Population DataSet Name: PopulationDataSetO
Grid Definition:

County

Population Configuration:

I | Use Woods and Poole Population Projections

Delete



View





Database:

[H Use Population Growth Weights

If you wish to run an analysis based on an air quality grid not already defined in
BenMAP-CE, you may need to import a new population file matched to that grid
definition. The PopGrid program allocates the 2010 block-level U.S. Census population
to a user-defined grid, creating a population file ready for importation to BenMAP-CE.5

5 The PopGrid program may be downloaded from EPA's website. See:
http://www2.epa.gov/benmap/benmap-communitv-edition.

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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. See Appendix J for a more detailed discussion of
population growth weights in the United States setup.

Defining a Population Configuration

If you are performing an analysis outside of the U.S. and are loading your own
population data, you will need to first create a new Population Configuration. The
Population Configuration defines the age range (Start Age and End Age), Race,
Ethnicity, and Gender variables in your population
database.

Check that your population variables align with the
population configuration already defined for your setup
(the spellings must match exactly). If your data fails to
load correctly, you will 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.

It is critical that the age, race,
ethnicity, and gender variables
defined in the population
configuration match your
population input data exactly,
otherwise BenMAP-CE will fail to
load the population data.

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Population Configuration Definition

Population Configuration Name:
Race

Available Races

Popu lationConfigurationO

ASIAN

BLACK

NATAMER

WHITE

ALL

Other

Gender

Available Genders

Ethnicity

Available Ethnicity

NON-HISPANIC

HISPANIC

ALL

Age Ranges

o

o

New	Remove

o

New	Remove

AgeRange

Start Age

End Age

In the Population Configuration Name box, replace 'PopulationConfigurationO' 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 selected category from the Races, Genders, or Ethnicity list
boxes, then highlight the category that you want to deselect and click the Remove
button. (It is not possible to delete categories from the Available Races, Available
Genders, or Available Ethnicity list boxes.)

The next step is to create the age ranges that match the age ranges in your population
file. To 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-CE
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: OtoO, lto4, 5to9,

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10tol4,15tol9,20to24,25to29,30to34,35to39, 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
input file.

' ij AgeRange Definition	1 ' I





Age Range ID:

|AgeRange





Low Age:

0

High Age:

0

OK

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 will
remove the last age range 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-CE.

¦ 1 ED |

Age Ranges

1 AgeRange

Start Age

End Age

OTOQ

0

0



1T04

1

4



5TO9

5

9



10TO14

10

14



15T019

15

19



20TO24

20

24



25T029

25

29



30TO34

30

34



35T039

35

39



40T044

40

44



45T049

45

49



50T054

50

54



55T059

55

59



&0TO64

60

64



65T069

65

69



70TO74

70

74



75T079

75

79



80T084

80

84



85UP

85

99









Population Configuration Definition

Population Configuration Name:
Race

Available Races

United States Census

Races

ASIAN

BLACK

NATAMER

WHITE

ALL

Other
New



Gender

Available Genders

C=>

Ethnicity

Available Ethnicity

NON-HISPANIC

HISPANIC

ALL

<=£>

ASIAN
BLACK
NATAMER
WHITE

FEMALE
MALE

NON-HISPANIC
HISPANIC

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Click OK on the Population Configuration Definition window to return to the Load
Population Dataset window.

Click OK on the Load Population Dataset window to return to the Manage
Population Datasets window. To view or edit any metadata that was previously
added, click the View Metadata button. Click OK on the Manage Population Datasets
window. In the Population Datasets box of the Modify Datasets window you should
see an entry for the population dataset that you j ust loaded.

%}jji Manage Population Datasets	[ 1=1 | [h]

Available Datasets

Dataset Detail
Grid Definition:

Population Configuration:
Values

United States Census

Race

Ethnicity

Gender

Age Ra...

Column

Row

Value

WHITE

HISPANIC

MALE

55T059

3

22

1.0000

WHITE

NON-HI

MALE

5T09

3

22

1.0171

BLACK

NON-HI...

MALE

5T09

3

22

0.0048

NATAMER

NON-HI„

MALE

5T09

3

22

0.0084

ASIAN

NON-HI...

MALE

5T09

3

22

0.0103

WHITE

NON-HI.„

MALE

25T029

3

22

1.0000

BLACK

NON-HL..

MALE

25T029

3

22

0.0156

NATAMER

NON-HI...

MALE

25T029

3

22

0.0156

WHITE

NON-HI...

MALE

35T039

3

22

1.0067

BLACK

NON-HI...

MALE

35T039

3

22

0.0016

~

View Metadata

4.1.5.2 Format for Population Data

Table 4-8 presents the variables that can be used in population datasets. Note that the
names you define for age ranges do not need to follow the same pattern used in this
manual; the age ranges should be based on what seems most appropriate for you.
However, it is critical that the age, race, ethnicity, and gender variables in your
population input data exactly match those defined for the population configuration,
otherwise BenMAP-CE will fail to load the population data

Table 4-8. Population Dataset 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 from a census), as well as
population forecasts.

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Variable

Type

Required

Notes

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.

Ethnicity

Text

Yes

References a defined ethnicity in the
associated Population Configuration. If no
ethnicity is specified in the data, "ALL"
should be listed throughout the entire
column.

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 respiratory- related hospital admissions, among other categories.
(BenMAP-CE 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 variables
are useful for this.) A number of health impact functions have been developed based on
epidemiological studies in the United States and Europe. However, researchers have
conducted an increasing number of epidemiological studies 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

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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. 6

4.1,6.1 Add Health Impact Functions

To add health impact functions to BenMAP-CE, click on the Manage button below the
Health Impact Functions box in the Modify Datasets window. The Manage Health
Impact Functions Datasets window will appear.

Manage Health Impact Function Datasets

Available Datasets

Health Impact Functions In DataSet

Endpoint Group

Endpoint

Seasonal Metric Metric Statistic

Add rows to the above table to see them here

Filter Endpoint Group

Filter Pollutants

(T] Group

i Metadata

OK

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. (See Appendix C for more information about the pre-loaded health
impact functions.) The section on the right under the Health Impact Functions in
Dataset lets you view the functions in a selected dataset.

6 U.S. EPA-default configuration and pooling setup files for ozone and PM2.5 health impact assessments are
available on the BenMAP-CE website. See: http: //www.epa.gov/benmap/benmap-communitv-edition.

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To add a new dataset, click the Add button. The Health Impact Function Dataset
Definition window will appear. Type the name that you want to use for the dataset in
the Health Impact Function Dataset Name box.

You may then enter functions into this dataset through an externally created database
by clicking the Load From File button. Alternatively, you may Add, Delete, and Edit
individual functions within BenMAP-CE.

To add a database, click the Load From File button. In the Load Health Impact
Dataset window, click the Browse button and then find and select the health impact
function database that you want to load into your setup. Click Open. If vali dation is
required, then you will have to click the Validation button before the file can be
imported. BenMAP-CE's validation tool will review the file format (column names,
required columns, and data types) and provide a report with any errors or warnings.
You can also add metadata (references and descriptions) to the file that is about to be
imported by clicking the View Metadata button. Click the OK button on the Load
Health Impact Dataset window to load the dataset.

The Health Impact Function Dataset Definition window will reappear, and you can

then view the health impact functions that you have loaded into your dataset.

4J? Health Impact Function Dataset Definition

Health Impact Function DataSet Name: Detroit Health Impact Function
Filter Endpoint Group	Filter Pollutants

| J Group

Endpoint Group	Endpoint

Mortality	Mortality All C...

Hospital Admissio...	HA, All Respira...

Hospital Admissio...	HA, All Respira...

Hospital Admissio...	HA, All Respira...

Hospital Admissio...	HA, All Respira...

Pollutant

PM2.5

PM2.5

PM2.5

PM2.5

PM2.5

Metric

D24Hour..

D24Hour..

D24Hour..

D24Hour..

D24Hour..

Season Metric	Metric Statistic

None	None

None	None

None	None

None	None

None	None

Author	Year

Zanobetti...	2009

Kimetal.	2012

Kloog et al.	2012

Grineski...	2011

Levy et al.	2012

Output Sample File	Load From File

Add	Delete	Edit

Cancel	OK

By clicking and holding the cursor on a column header, you may move it to provide the
most useful display. For example, by clicking and holding on the Pollutant column
header and then dragging it to the far left of the window, you can sort all of the health

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impact functions by Pollutant. (Note rearranging the columns is only for display and
has no effect on the underlying health impact functions in the database.)

Clicking OK brings you back to the Manage Health Impact Function Datasets
window. The new datasetyou just loaded will be displayed in the list of Available
Datasets and the associated functions will be displayed in the Health Impact
Functions In Dataset grid to the right. If you have more than one data set, you can
select the dataset by clicking on it.

Manage Health Impact Function Datasets

Available Datasets

Detroit Health Impact Function

Health Impact Functions In DataSet

Endpoint Group
Mortality

Hospital Adrnissio...
Hospital Adrnissio...

Endpoint
Mortality, All C...
HA, All Respira...
HA, All Respira...

Hospital Adrnissio... HA, All Respira...
Hospital Adrnissio... HA, All Respira...

Pollutant	Metric	Seasonal Metric Metric Statistic	A

PM2.5	D24Hour...	None	Zi

PM2.5	D24Hour...	None	Ki

PM2.5	D24Hour...	None	Ki

PM2.5	D24Hour...	None	G

PM2.5	D24Hour...	None	Le

<

Filter Endpoint Group

Filter Pollutants

PI Group

To edit an existing function, first click to select the dataset in the list of Available
Datasets. Next, select a particular function in the data grid under Health Impact
Functions in Dataset. Then, click the Edit button. The Health Impact Function
Dataset 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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Health Impact Function Definition

l

End point Group:

Acute Bronchitis

Endpoint:

Acute Bronchitis

Pollutant:



PM2.5

-1

Metric:



| D24HourMean

-



Annual Statistic:



| None

-



Seasonal Metric:



1







Race:

Author:

1





Ethnicity:

Year of Publication:

1



0

Gender:

Qualifier:

I





Location Name:



-ocation:

i





Start Age:



End Age:





°a

Co-Pollutants Specified in Regression Model:



Reference:

Beta Distribution:
I None

Beta Parameterl:

Beta:

Beta Parameter2:

0	0

Constant Description: Constant Value:

A:
B:

C:

Incidence Dataset:

Variable DataSet:

Prevalence DataSet:











j Function 1

Baseline Incidence Function







Commonly Used Functional Forms:

Operators:

Available Variables:

Population Variables(optional):

(l-( 1/EXP( Beta * DELTAQ))) * I ncid ence* POP
(l-(l/EXP(Beta"DELTAQ)))*lncidence*POP*A
(l-EXP(-Beta-DELTAQ))*lncidence-POP
(l-(l/((l-lncidence)*EXP(Beta*DeltaQ)+lncidence
(!-(!/((1-A) - EXP( Beta * De ltaQ)+A)))*A*POP* Preva i

ABS(x)

EXP(x)

LOG(x)

POW£x,y)

SQR(x)

ACOS(x)

ASIN(x)

~

Beta

DELTAQ

POP

Incidence
Prevalence
00
Q1

Cancel

OK

From the Health Impact 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 Health Impact Function Definition window will appear and you can then create
new health impact functions. (See Appendix M: Function Editor for additional
information about the syntax for developing functions with this editor.)

After defining the new health impact function, click OK. This will take you back to the
Health Impact Function Dataset Definition window. When you are finished with any
editing or adding of health impact functions, click OK. From the Manage Health
Impact Function Datasets window, you can also select an Available Dataset and Data
row and view the Metadata. To view the Metadata associated with the data file, click
the View Metadata button to view and edit existing references and descriptions. Click
OK on the Manage Health Impact Function Datasets window when you are satisfied
with all your inputs. The Modify Datasets window will appear. Here in the Health
Impact Functions box you should see an entry for any health impact function datasets
that you have loaded.

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4.1.6.2 Format for Health Impact Functions

Table 4-9 presents the variables that can be used in health impact function datasets.

Table 4-9. Health Impact Function Dataset Variables

Variable

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.

Pollutant

Text

Yes

Should reference an already defined Pollutant

Metric

Text

Yes

Should reference an already defined Metric for
the Pollutant

Annual
Statistic

Text

No

Should either be blank (signifying no annual
metric value) or be one of: None, Mean, Median,
Min, Max, Sum.

Seasonal
Metric

Text

No

Should either be blank (signifying no Seasonal
Metric value) or reference an already defined
Seasonal Metric for the Metric.

Race



No

Should either be blank (signifying All Races) or
reference a defined Race.

Ethnicity



No

Should either be blank (signifying All
Ethnicities) or reference a defined Ethnicity.

Gender



No

Should either be blank (signifying All Genders)
or reference a defined Gender.

Start Age

Integer

Yes

Specifies the low and high ages, inclusive. For
example, Start Age of '0' and End Age of'I'
includes infants through the first 12 months of
life and all one-year old infants.

End Age

Integer

Yes

Author

Text

No

The author(s) of the study from which the
function is derived.

Year of
Publication

Integer

Yes

The year of publication 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.

Location
Name



No

Type of study area. For the 'United States'
setup, choose between 'State', 'County', and
'MSA (metropolitan area)'.

Location

Text

No

The specific location of the study.

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Variable

Type

Required

Notes

Co-Pollutants
Specified in
Regression
Model

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-CE when running an analysis to
estimate air pollution-related health impacts.
For example, the log-linear form is as follows:
EXP(Beta*DELTAQ))) *Incidence*POP'.

Baseline

Incidence

Function

Text

Yes

The functional form, interpreted (executed) by
BenMAP-CE to estimate health impacts due to
all causes. This typically has the form:
'Incidence*POP'.

Beta

Distribution

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.

Beta

Numeric
(double)

No

Mean value of the Beta distribution.

Beta

Parameter 1

Numeric
(double)

No

Parameter 1 of the Beta distribution (meaning
depends on the distribution - for Normal
distributions this represents the standard
deviation).

Beta

Parameter 2

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.

Name B

Text

No

Description of variable B.

B

Numeric
(double)

No

A constant value which can be referenced by the
Function.

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 from which incidence data
will be derived. The user may choose from
multiple datasets. (Initially this field may be left
blank.) See Section 4.1.4.

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Variable

Type

Required

Notes

Prevalence
Dataset

Text

No

Specifies the dataset from which prevalence data
will be derived. The user may choose from
multiple datasets. (Initially this field may be left
blank.) See Section 4.1.4.

Variable
Dataset

Text

No

Specifies the dataset from which "variable" data
(e.g., income data) will be derived. The user may
choose from multiple datasets. (Initially this field
may be left blank.) See Section 4.1.7.

While the mean value of the Beta distribution is always available in BenMAP-CE, the
values associated with Beta Parameter 1 and Beta Parameter 2 may change depending
on the type of distribution. Table 4-10 provides a list of the variables associated with
each type of beta distribution in BenMAP-CE.

Table 4-10. Beta Distribution Types and Variables

Distribution

Formula

Beta
Parameter 1

Beta
Parameter 2

Notes

Normal

ya-Jljr

Standard
deviation
(sigma)

N/A

The Normal distribution has two
parameters - the mean, mu, and the
standard deviation, sigma.

Triangular

2 (x — a)
(b — a) (c — a)

for a < x < c

2{b — x)
(b — a) (b — c)

for c < x < b

Minimum
value (a)

Maximum
value (b)

The Triangular distribution has three
parameters - the minimum value (a),
the maximum value (b), and the most
likely value (c). BenMAP-CE uses the
mean value, the minimum, and the
maximum to calculation the most
likely value.

Poisson

£e~x jx*.

Lambda

N/A

The Poisson distribution has a single
parameter, lambda.

Binomial

* ,p'(\-pr'

(m — x)!

n

P

The Binomial distribution has two
parameters, n and p.

LogNormal



Standard
deviation
(sigma) of the
corresponding
Normal
distribution

N/A

The LogNormal distribution has two
parameters - the mean of the
corresponding Normal distribution,
mu, and the standard deviation of the
corresponding Normal distribution,
sigma.

Uniform

l/(B - A) for A < x < B

A

B

The Uniform distribution has two
parameters, A and B, which define
the interval on which the distribution
is defined.

Exponential

J.

u

Mu

N/A

The Exponential distribution has one
parameter, mu.

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Distribution

Formula

Beta
Parameter 1

Beta
Parameter 2

Notes

Geometric

p(l - pY for 0 < p < 1

P

N/A

The Geometric distribution has one
parameter, p.

Weibull



alpha

beta

The Weibull distribution has two
parameters, alpha and beta.

Gamma

1

mo)

a

b

The Gamma distribution has two
parameters, a and b.

Logistic

e
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Chapter 4 - Loading Data

Variable

Type

Notes

Prevalence

Numeric

Number of people who already have a given
adverse health condition (i.e., chronic illness).

QO

Numeric

Control pollutant concentration

Qi

Numeric

Baseline pollutant concentration

A

Numeric

User-defined constant

B

Numeric

User-defined constant

C

Numeric

User-defined constant

The "Population Variables (optional)" box provides a list of setup variables which
have been defined under the Variable Datasets (see Section 4.1.7).

4.1.7 Variable Data

Health Impact Functions and Valuation Functions may sometimes refer to
socioeconomic variables for which BenMAP-CE does not automatically calculate values.
For example, some valuation functions reference the median income within each area of
analysis. Other functions apply to populations living below the poverty line in a given
country. To facilitate this type of analysis, BenMAP-CE allows you to load datasets of
socioeconomic Variables, which apply either globally or to a specific geographic area
(i.e., they are associated with a Grid Definition).

4.1.7.1 Add Variable Data

To add Dataset Variables to BenMAP-CE (such as income and other miscellaneous
variables that might be needed in the analysis), click on the Manage button below the
Variables Datasets box in the Modify Datasets window. The Manage Setup Variable
Datasets window will appear.

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J Manage Setup Variable Datasets
Available Datasets

Gel]' s Ma-|

Dataset Variables

CollegeDegree

CollegeDegree_asianp

Col I egeDegree_bl ack

CollegeDegree_hisp

CollegeDegree_natam

CollegeDegree_nhisp

Col I egeDegree_w h ite

HSDiploma

HSDiploma_asianp

HSDiploma_black

HSDiploma_hisp

HSDiploma_natam

HSDiploma_nhisp

H5Diploma_white

NoHSDiploma

NoHSDiploma_asianp

NoH5Diploma_black

NoHSDiploma_hisp

NoHSDiploma_natam

NoHSDiploma_nhisp

NoHSDiplom a_w h ite

i/iew Metadata

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 a Variable dataset 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 File button for any of your predefined Grid
Definitions.

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ry* Setup Variable Datasets Definition

© SS

Dataset Name:

VariableDataSetO
Grid Definition:

Dataset Variables:

Delete

Loaded Value:

Column

Row

Value

Click 'Load Fr om File' to load
a variable database



Output Sample File

Load From File



II

OK

To start, type the name that you want to use for the Variable Dataset in the Dataset
Name box. (This is a name that is internal to BenMAP-CE and used just for
identification.)

To add an externally created Variable Dataset, click the Load From File button. This
will bring up the Load Variable Database window. Here you need to choose the grid
definition from the Grid Definition drop-down list that matches the level of
aggregation in your variable data file. Remember that the Variable Dataset you import
must use the same column/row index as the Grid Definition. Next, you may use the
Browse button to find and select the desired Database (i.e., input file) and click Open.
You can click the Validate button to ensure the file is properly formatted before
importing. BenMAP-CE's validation tool will review the file format (column names,
required columns, and data types) and provide a report with any errors or warnings.
You can also add metadata to the imported data (reference and description of the data
file) by clicking the View Metadata button.

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Cv J Load Variable Database

Grid Definition:

CMAQ 12km Nation

i.° i-a,r

Browse

Cancel

OK

After choosing (and validating) the input file, click OK. This takes you back to the Setup
Variable Datasets Definition window. This window displays the variables in the
dataset and their values.

Setup Variable Datasets Definition

Dataset Name:

Example Variable Dataset
Grid Definition:

0 U3m\

CMAQ 12km Nation

Dataset Variables:

Loaded Value:



Output Sample File

Load From File

Column

Row

Value



56

45

0.11173065



56

43

0.11173065



56

41

0.11173065

_

56

39

0.11173065



56

37

0.11173065



56

35

0.11173065



56

33

0.11173065



56

31

0.11173065



56

29

0.11173065



56

27

0.11173065



56

25

0.06816218



56

23

0.11173065



56

21

0.13128258



56

19

0.11173065



56

17

0.11173065



56

15

0.11173065



56

13

0.11173065



56

11

0.11173065



56

9

0.11173065



« i

rrr

k.



l ~



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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¦;.j Manage Setup Variable Data sets

Available Datasets

EPA Standard Variables

Delete

Add

Edit

Dataset Variables

CollegeDegree

CollegeDegree_asianp

CollegeDegree_black

CollegeDegree_hisp

Col I egeDegre e_n ata m

Col I egeDegre e_n h isp

CollegeDegree_white

HSDiploma

HSDiploma_asianp

HSDiploma_black

HSDiploma_hisp

HSDiploma_natam

HSDiploma_nhisp

HSDiploma_white

NoHSDiploma

NoHSDiploma_asianp

NoHSDiploma_black

NoHSDiploma_hisp

NoHSDiploma_natam

NoHSDiploma_nhisp

NoHSDiploma_white

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 Modify Datasets window, where the entry for the
variable dataset that you just entered should be visible under the Variable Datasets
box.

4.1.7.2 Format for Variable Data

Table 4-12a presents the variables that can be used in variable datasets, and Table 4-
12b presents a sample of what a dataset might look like. Note that if you are loading
your own variable data, you can choose your own variable names.

Table 4-12a. Variable Dataset 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

Medianjncome

Numeric
(double)

Yes

Example: Median income value.



Numeric
(double)

No

Additional variables may be specified.

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Table 4-12b. Sample Variable Dataset

COL

ROW

medianjncome

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 economic values generated by Valuation Functions to
account for inflation and generate economic benefits using currency for years other
than the year initially specified by your valuation data. To do this, you can load
Inflation Datasets into BenMAP-CE, 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 currency year, and this currency
year has to be kept in mind when developing the inflation datasets. That is, whichever
currency year is used for your valuation functions, then the inflation values for that year
should be set to 1. For example, in the United States setup, the valuation functions have
a dollar year of 2015, so the inflation datasets have a value of 1 for the year 2015.
(Values for years earlier than 2015 are less than 1, and values for years after 2015 are
greater than 1, because inflation has increased from one year to the next.) The United
States setup in BenMAP-CE 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 work loss days, the United States setup uses a function like
the following: Daily Wag e*Wagelndex, where the DailyWage is specified in year 2015
dollars. In the Inflation Dataset, the Wagelndex scales this DailyWage value up or
down depending on the currency Year you have chosen. If the currency Year is 2015,
then the Wagelndex has a value of 1 and no change is made to the DailyWage. If the
currency Year is specified to, say, 2020, then the Wagelndex will have a value greater
than 1 because of the inflation that has occurred between 2015 and 2020.

4.1.8.1. Add Inflation Data

To add inflation data to BenMAP-CE, click on the Manage button below the Inflation
Datasets box in the Modify Datasets window. The Manage Inflation Datasets

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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 under Inflation Detail
presents the inflation factors in a selected dataset.

Click on the Add button. In the Load Inflation Dataset window, type in the name of the
dataset in the Inflation Dataset Name box, and then click on the Browse button to the
right of the Database box to choose the dataset that you want to import and click Open.
You can click the Validate button to ensure the file is properly formatted before
importing. BenMAP-CE's validation tool will review the file format (column names,
required columns, and data types) and provide a report with any errors or warnings. In
addition, you can add metadata to the file, to include references and descriptions, by
clicking the View Metadata button. Click OK. The Manage Inflation Datasets window
will appear. Here you may view the data that you just l oaded.

Manage Inflation Datasets

Available Datasets

0 I RH

Inflation Detail

EPA Standard Inflators

Output Sample File

Delete

Add

Year

All Goods Index

Medical Cost Index

Wage Index

-

1992

0.814750015735626

0.728910982608795

0.76674097776413



1993

0.839141011238098

0.772239029407501

0.788833975791931



1994

0.860626995563507

0.809049010276794

0.811612010002136



1995

0.885016977787018

0.845475018024445

0.835246026515961



1996

0.911149978637695

0.875

0.862819015979767



1997

0.93205600976944

0.89954000711441

0.893303990364075



1998

0.946573972702026

0.928297996520996

0.92909699678421



1999

0.967480003833771

0.960889995098114

0.96078097820282



2000

1

1

1



2001

1.02845501899719

1.04601204395294

1.03733503818512



2002

1.0447154045105

1.09509205818176

1.07158756256104



2003

1.06852495670319

1.13918709754944

1.10241484642029



2004

1.09698021411896

1.18903374671936

1.13050174713135

_

2005

1.13414633274078

1.23926377296448

1.15019690990448



2006

1.17073166370392

1.2891104221344

1.19098019599915



2007

1.20407664775848

1.34606599807739

1.23155522346497



2008

1.25030779838562

1.39595472812653

1.26891934871674



2009

1.24585950374603

1.44023394584656

1.28847706317902



2010

1.26629495620728

1.48940181732178

1.30920243263245

_





4

4













	

1 f



View Metadata

OK

At this point you may add more data by clicking Add, or you may view and edit the
Metadata for a specific dataset. This is done by selecting an Available Dataset and an
entry in the Inflation Detail box and then clicking the View Metadata button. If you
are satisfied with all import data, you can complete this step by clicking OK. Clicking OK
takes you to the Modify Datasets window, where you should see an entry for the
inflation dataset that you just loaded under the Inflation Datasets box.

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4.1.8.2 Format for Inflation Data

Table 4-13a presents the variables that can be used in Inflation Datasets, and Table
4-13b 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 of specifying 'AllGoodsIndex' you could have a variable called 'General Index' —
this is fine as long as you make sure that your valuation functions properly reference
these inflation variables.

Table 4-13a. Inflation Dataset 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

Example

All goods inflation index value.

MedicalCostlndex

Integer

No

Example

Medical cost inflation index value.

Wagelndex

Integer

No

Example

Wage inflation index value.



Integer

No

Additional indices can be specified.

Table 4-13b. Sample Inflation Dataset

YEAR

AllGoodsIndex

MedicalCostlndex

Wagelndex

2005

0.75

0.62

0.71

2006

0.79

0.67

0.74

2007

0.81

0.72

0.76

2008

0.83

0.77

0.78

2009

0.86

0.8

0.81

2010

0.88

0.84

0.83

2011

0.91

0.87

0.86

2012

0.93

0.89

0.89

2013

0.94

0.92

0.92

2014

0.96

0.96

0.96

2015

1

1

1

2016

1.02

1.04

1.03

4.1.9 Valuation Data

BenMAP-CE allows the valuation estimates to vary by Endpoint Group, Endpoint, and
Age (note that multiple estimates may be provided for a particular combination).
BenMAP-CE allows the valuation function to be quite detailed, and allows an uncertain
parameter (A) with a user-specified distribution. You 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-CE has two fields to more clearly identify the valuation function (i.e., Qualifier
and Reference).

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When reviewing the economic literature to develop a valuation database or to simply
add 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 Chapter 7:
Aggregating, Pooling, and Valuing.

4.1.9.1 Add Valuation Data

To add valuation functions to BenMAP-CE, click on the Manage button below the
Valuation Datasets box in the Modify Datasets window. The Manage Valuation
Function Datasets window will appear.

.. y Manage Valuation Function Datasets

Available Datasets

EPA Standard Valuation Functions

Valuation Function In Dataset

Filter Endpoint Group

Filter Endpoint



Endpoint Group

Endpoint

Qualifier Reference

Start Age

End Age

ra

Acute Bronchitis

Acute Bronchitis

WTP: 1 day illn	

0

*

~

Acute Bronchitis

Acute Bronchitis

WTP: 6 day illn...

0

17

J

Acute Bronchitis

Acute Bronchitis

WTP: 28sympto...

0

17



Acute Myocardial l„

Acute Myocardi...

COI: 5yrs med, ...

0

24



Acute Myocardial 1..

Acute Myocardi...

COI: 5 yrs med,...

25

44



Acute Myocardial l._

Acute Myocardi...

COI: 5 yrs med,

45

54



Acute Myocardial I..

Acute Myocardi...

COI: 5 yrs med,...

55

64



Acute Myocardial 1..

Acute Myocardi...

COI: 5 yrs med, ...

65

99



Acute Myocardial 1..

Acute Myocardi...

COI: 5 yrs med,...

25

44



Acute Myocardial 1..

Acute Myocardi...

COI: 5 yrs med, „

45

54



Acute Myocardial l„

Acute Myocardi...

COI: 5 yrs med,...

55

64



Acute Myocardial 1..

Acute Myocardi...

COI: 5 yrs med,...

65

99



Acute Myocardial 1..

Acute Myocardi...

COI: 5 yrs med,

0

24



Acute Myocardial 1..

Acute Myocardi...

COI: 5 yrs med,...

25

44



Acute Myocardial 1..

Acute Myocardi...

COI: 5 yrs med,...

45

54



Acute Myocardial L.

Acute Myocardi...

COI: 5 yrs med,...

55

64



Acute Myocardial l._

Acute Myocardi...

COI: 5 yrs med,...

65

99



„__ ,,	,

* ^ n J-

„A1. r	,

n





* Fir







~

O Group

View Metadata

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 Function In Dataset lets you view the
valuation factors in a selected dataset.

If the dataset is large, there are filters available to view a subset of the list by selecting a
value from the Filter Endpoint Group and/or Filter Endpoint drop-down lists. Or, you
can type a value in the Filter box to search for a particular word or phrase. You can
also group data by Endpoint Group by selecting the Group option.

To add a dataset, click on Add. This will display the Valuation Function Dataset
Definition window.

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'..J Valuation Function Dataset Definition

Valuation Function Dataset Name: Va I u ati on Function DataSetO
Filter Endpoint Group	Filter Endpoint

[H Group

Endpoint Group

Endpoint Qualifier Reference Start Age End Age

Function









( Add rows to the above table to see them here J









< i	

J

y

Output Sample File	Load From File	Delete	Add

Edit

You may load valuation data with an externally-created data file, or you may add
individual valuation functions from within BenMAP-CE. To import valuation functions,
click on the Load From File button. This will bring you to the Load Valuation
Function Dataset window.

Load Valuation Function Dataset

Valuation Function Dataset Name:

Valuation Function DataSetO
Database:

Validate

~ 0 I -&H

Browse

Cancel

OK

In the Load Valuation Function Dataset window provide a name for the valuation
function dataset. Use the Browse button to choose the valuation database and click
Open. Once again, you can click the Validate button before the file is imported. In
addition, you can create metadata, which includes references and descriptions of the
file, by clicking on the View Metadata button. Click OK on the Load Valuation

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Function Dataset window. 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.

The columns within each of the list boxes can be rearranged in order to provide the
most useful display. (Note that rearranging the columns is only for display and has no
effect on the underlying Valuation Function dataset.) You can also change how you
view the list by specifying a text string to Filter the records (e.g., Asthma), or create
groupings of records by their Endpoint Group (click the Group option box).

f Valuation Function Dataset Definition



Valuation Function Dataset Name: Detroit Valuation Functions
Filter Endpoint Group	Filter Endpoint

j | Group

Endpoint Group Endpoint	Qualifier

Mortality	Mortality, All C... VSL, based on 2...

Hospital Admissio... HA, AM Respira... COI: med costs ...

Start Age
0
0

End Age
99
99

Function
A*B

A*B'((C)/(52-5))

| OutputSample File | | Load From File | Delete	Add	Edit

In the Valuation Function Dataset Definition window, you can also edit the functions
already existing in your dataset by highlighting a particular Valuation Function and
then clicking the Edit button.

If the dataset is large, there are filters available to view a subset of the list by selecting a
value from the Filter Endpoint Group and/or Filter Endpoint drop-down lists. When
you are finished, click OK.

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Valuation Function Definition

IbJ I

Endpoint Group:	Endpoint:

| Hospital Admissions, Respirat ~ j | HA, Aii Respiratory



A Description:

mean hospital charge, 2000$

Start Age:

a

End Age:

A Distribution:

A Parameterl:

COI: med costs + v.*age loss
Reference:

A Parameter2:

Edit Function
Available Functions:

Available Compiled Functions: All Available Functions:

B Constant Description:
mean length of stay (LOS)

B Constant Value:

C Constant Description:
Median national income

C Constant Value:

41525

D Constant Description:

D Constant Value:

Available Variables: Available Setup Variables:

ABS(x)

EXP(x)

LOG(x)

POW(x,y)

SQR(x)

ACOS(x)

ASlN(x)

ATAN(x)

§

AIIGoodslndex
A*AIIGoodslndex
A'MedicalCostlndex
A*B*AIIGoodslndex
A*B*C*AIIGoodslndex
A*MedicalCostlndex+B*Wa
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Chapter 4 - Loading Data

4.1.9.2 Format for Valuation Data

Table 4-14 presents the variables that can be used in Valuation Datasets.

Table 4-14. Valuation Dataset Variables

Field Name

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.

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 first 12 months of life and all one-year old
infants.

End Age

Integer

No

Point Estimate

Numeric
(double)

Yes

Central estimate of the unit value.

Function

Text

Yes

The functional form, interpreted (executed) by BenMAP-
CE when running an analysis.

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.

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4.1.10	Income Growth Data

According to economic theory, willingness-to-pay (WTP) to avoid air-pollution related
morbidity effects and premature mortality should grow as real income increases.
BenMAP-CE 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.

Note that the WTP estimates in the default valuation functions in the United States setup
are assumed to be based on 1990 income, so the income growth adjustments are all
relative 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.

4.1.10.1 Add Income Growth Data

To add income growth data to BenMAP-CE, click on the Manage button below the
Income Growth Adjustments box in the Modify Datasets window. The Income
Growth Adjustment Dataset Manager window will appear.

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Income Growth Adjustment Dataset Manager

Available Datasets

Income Growth Detail

Output Sample File

Delete

Add

Endpoint Groups

Add rows to the above table to see
them here

OK

In this window you may Add and Delete datasets. The section on the left under
Available Datasets lists the Income Growth Adjustments Datasets that are currently
in the setup database. The section on the right under Income Growth Detail 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 Dataset
Name box, and then click on the Browse button to the right of the Database box to
choose the dataset that you want to import and click Open. To confirm the file has
proper formatting, click the Validation button before importing the file. In addition,
you can edit the metadata for the imported file, to include references and descriptions,
by clicking the View Metadata button.

Click OK. The Income Growth Adjustment Dataset Manager window will appear.
Here you may view the data that you just loaded. From this window, you can also view
or edit metadata by selecting an Available Dataset and Income Growth Detail and

then clicking the View Metadata button.

4.1.10.2 Developing Income Growth Adjustment Factors

When reviewing the economic literature to develop income growth adjustment factors,
it is important to have an economist assist. For an overview of valuation, see the
Overview of Valuation section in Chapter 7: Aggregating, Pooling, and Valuing.

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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-CE 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-CE
includes income elasticity estimates specific to the type of health endpoint associated
with the WTP estimate. BenMAP-CE contains elasticity estimates for three types of
health effects: minor, severe and premature mortality. Minor health effects are those of
short 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 the default United States setup in BenMAP-CE.

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 4-15
summarizes the income elasticity estimates found in BenMAP-CE 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 4-15. 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. The
Congressional Budget Office's (2016) ten year projections of US Gross Domestic Product
(GDP) are used to estimate changes in future income. Historical GDP data came from the
U.S. Bureau of Commerce's Bureau of Economic Analysis. GDP values were adjusted for
inflation as needed using the Implicit Price Deflator annual index, published by the
Economic Research Division of the Federal Reserve Bank of St. Louis. We divided the
projected change in GDP by the Woods & Poole (2015) 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 4-15 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:

11	AWTP

~WTF (wrp2 ~ WTPJ x (/2 + /x)

£ A/ (/2 - /x) x (WTP2 + WTP±)

I

2)	eI2WTP2 + eI2WTP1 - sI1WTP2 - eI1WTP1

= i2wtp2 + I1WTP2 - I2WTP1 - I1WTP1

3)	WTP2 X ( s/2 — e/i - I2 - h) = WTP1 X ( e/i - e/2 - h - 12)
J WTP2 = WTP1 X —		-=	-i	p

~ sh ~ h ~ h

Table 4-16 below summarizes the income-based WTP adjustments used within
BenMAP-CE for minor health endpoints, severe health endpoints, and premature
mortality.

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Table 4-16, Income-Based WTP Adjustments by Health Effect and Year

Minor Health Endpoirit

Severe Health Endpolnt

Mortality

Year

Low

Mid

Upper

Low

Mid

Upper

Low

Mid

Upper

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

1

0.99943614
1.00027895
1.00083518
1.001928926
1.00252676
1.003553152
1.004830718
1.006105304
1.007476926
1.008633733
1.008626103
1.008962274
1.009722471
1.010831594
1.011770725
1.012430668
1.01275146
1.012262344
1.01079917
1.011459589
1.011786222
1.012354255
1.012712717
1.013344169
1.014022827
1.014274955
1.014827728
1.015322924
1.015639186
1.015908599
1.016283393
1.016681671
1.017086029
1.017486334
1.017879009
1.018263578

1

0.997887194
1.0010463
1.003135324
1.007252812
1.009508491
1.013389945
1.018236518
1.023088813
1.028329492
1.032765627
1.032736301
1.034028053
1.036953092
1.041232586
1.044866562
1.047425628
1.048671246
1.04677248
1.041107416
1.043661356
1.044926405
1.047128916
1.048520923
1.050976157
1.053619623
1.054602981
1.056761265
1.058697701
1.059935808
1.060991406
1.062461257
1.064024925
1.065614223
1.067189336
1.068736196
1.070252538

1

0.995778859
1.002093554
1.006280541
1.014558434
1.019107819
1.026960373
1.036808729
1.046717048
1.057473063
1.066622734
1.066562176
1.069233894
1.075297475
1.084201217
1.091792464
1.097154975
1.099770427
1.095784903
1.083940148
1.089271665
1.091917515
1.096532583
1.099454761
1.104618788
1.110193372
1.11227107
1.116838455
1.120945096
1.123574972
1.125819683
1.128949761
1.132284641
1.135679722
1.139050007
1.142365217
1.145620227

1

0.99648118
1.00174439
1.005231023
1.012117267
1.01589787
1.022416711
1.030580044
1.038779855
1.047665954
1.055212379
1.05516243
1.057363987
1.062356591
1.069678664
1.075912952
1.080312252
1.08245647
1.079188704
1.069464207
1.073843718
1.076015592
1.079801798
1.082197666
1.086428881
1.090992332
1.092692018
1.096426606
1.099781871
1.101929545
1.103761792
1.106315613
1.109035254
1.11180234
1.114547729
1.117246628
1.119895339

1

0.993674994
1.003141999
1.009435654
1.021917462
1.028799176
1.040715098
1.055725813
1.070903659
1.087466359
1.101626635
1.101532698
1.105680108
1.115113139
1.129017591
1.140922546
1.149360299
1.153483868
1.147202373
1.12860918
1.136964202
1.141119123
1.148379683
1.15298593
1.161142945
1.169972539
1.173269868
1.180531025
1.187074065
1.191271544
1.194858909
1.199867845
1.205213666
1.210665345
1.216086745
1.221428633
1.226682782

1

0.991575539
1.004191637
1.01260066
1029330373
1.038584113
1054657698
1.074997663
1.095668674
1118347049
1.137836933
1137707233
1143433094
1.156486392
1175803781
1.192415953
1204231024
1.210017681
1.201206207
1.175235033
1.186884999
1192690849
1.202856183
1.209318399
1.220786929
1233237505
1.237896562
1.248175263
1.257459402
1.263426423
1.268532872
1.275673389
1.283308029
1.291108131
1.298879623
1.306551456
1.314110994

1

0.998872638
1.000557899
1.001670957
1.003861666
1.005059958
1.00711906
1.00968492
1.012248039
1.01500988
1.017342448
1.01732707
1.018005252
1.019539833
1.021781206
1.023680925
1.025016785
1.025666475
1.024675965
1.021715641
1.023051262
1.023712158
1.024862051
1.025588155
1.026867747
1.02824378
1.028755188
1.029876947
1.030882478
1031524897
1.032072306
1.032834172
1.03364408
1.034466624
1.035281181
1.036080599
1.036863804

1

0.994375765
1.002792478
1.008382797
1019458413
1.025558352
1.036109447
1.049381137
1062778115
1.077371955
1.089827657
1.089745045
1.093389511
110167253
111386621
1.12429142
1.131672144
1.135276675
1.12978518
1.113508224
1.120826483
1.124463439
1.130814672
1134841442
1.141967177
1.149673343
1.152549148
1.158878565
1.164577603
1.168231487
1.171352863
1.175709248
1.180356026
1.185091853
1.189798594
1194433689
1.198989868

1

0.985998511
1.006995797
1.021089673
1.049366593
1.065144777
1.092786551
1.128201008
1.164700031
1.205345869
1.24079144
1.240554214
1.251058578
1.275163889
1.31124568
1.342671752
1.36525023
1.376378059
1.359451532
1.310176253
1.332167268
1.343194842
1.362613082
1.375030875
1.397211194
1.421498775
1.430643797
1.45092845
1.469381213
1.48130703
1.491554141
1.505947828
1.521420956
1.53731966
1.553251266
1.56906867
1.584743023

4.1.10.3 Format for Income Growth Adjustment Data

Table 4-17a presents the variables that can be used in Income Growth Adjustment
Datasets and Table 4-17b presents a sample of what a dataset might look like.

Table 4-17a. Income Growth Adjustment Dataset 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

Numeric
(double)

Yes

Mean income growth adjustment factor.

EndPointGroup

Text

Yes

Endpoint group (e.g., Chronic Asthma).

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Table 4-17b. Sample Income Growth Adjustment Dataset

Year

Mean

EndpointGroup

1990

1

Acute Bronchitis

1991

0.99788719

Acute Bronchitis

1992

1.0010463

Acute Bronchitis

1993

1.00313532

Acute Bronchitis

1994

1.00725281

Acute Bronchitis

1995

1.00950849

Acute Bronchitis

1996

1.01338995

Acute Bronchitis

1997

1.01823652

Acute Bronchitis

1998

1.02308881

Acute Bronchitis

1999

1.02832949

Acute Bronchitis

2000

1.03276563

Acute Bronchitis

2001

1.0327363

Acute Bronchitis

2002

1.03402805

Acute Bronchitis

2003

1.03695309

Acute Bronchitis

2004

1.04123259

Acute Bronchitis

2005

1.04486656

Acute Bronchitis

2006

1.04742563

Acute Bronchitis

2007

1.04867125

Acute Bronchitis

2008

1.04677248

Acute Bronchitis

2009

1.04110742

Acute Bronchitis

2010

1.04366136

Acute Bronchitis

2011

1.0449264

Acute Bronchitis

2012

1.04712892

Acute Bronchitis

2013

1.04852092

Acute Bronchitis

2014

1.05097616

Acute Bronchitis

2015

1.05361962

Acute Bronchitis

2016

1.05460298

Acute Bronchitis

2017

1.05676126

Acute Bronchitis

2018

1.0586977

Acute Bronchitis

2019

1.05993581

Acute Bronchitis

2020

1.06099141

Acute Bronchitis

2021

1.06246126

Acute Bronchitis

2022

1.06402493

Acute Bronchitis

2023

1.06561422

Acute Bronchitis

2024

1.06718934

Acute Bronchitis

2025

1.0687362

Acute Bronchitis

2026

1.07025254

Acute Bronchitis

4.2 Export and Import Setups

BenMAP-CE allows you to export and import entire databases (all Available Setups),
individual setups (e.g., United Statest, China), and parts of individual setups (e.g. all Grid
Definitions, or individual Health Impact Function datasets). This functionality can be
used to archive data, share data with other BenMAP-CE 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

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other computers. See Chapter 9: Tools Menu for more information about the Database
Export and Database Import tools.

4.3 Frequently Asked Questions

I've loaded new baseline incidence rates, but BenMAP-CE won't let me select it in
the configuration stage.

When formatting these data for importing to BenMAP-CE, take special care to ensure
that you have specified the health endpoints correctly. The baseline incidence rate must
be associated with a specific health endpoint and endpoint group in BenMAP-CE. Be
sure that you have recorded the endpoint group and endpoint exactly as it is recorded
in BenMAP-CE. For example, if the baseline incidence rate is for asthma-related hospital
admissions, be sure you have recorded the endpoint group as 'Hospital Admissions,
Respiratory' and the endpoint as 'HA, Asthma'.

I've loaded a new grid and new population data into BenMAP-CE 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
need to 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,
mentioned in Section 4.1.5 and described in Appendix J on Population Data for the 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, 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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Contents

Table of Contents

CHAPTER 5_Creating Air Quality Surfaces	5-1

5.1	Model Data	5-3

5.2	Monitor Data	5-5

5.2.1	Closest Monitor for Monitor Data	5-6

5.2.2	Voronoi Neighborhood Averaging (VNA) for Monitor Data	5-7

5.2.3	Fixed Radius for Monitor Data	5-10

5.2.4	Custom Monitor Filtering for EPA Standard Monitors	5-10

5.3	Monitor Rollback	5-14

5.3.1	Example: A Single Rollback in One Region	5-15

5.3.2	Example: Combining Three Rollback Approaches in Different Regions	5-19

5.4	Open *.aqgx File	5-22

5.5	Frequently Asked Questions	5-23

i


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Chapter 5 - Creating Air Quality Surfaces

C

Creating Air Quality

Surfaces

In this chapter...

¦	Define air quality grids

¦	Create air quality surfaces using different methods.

¦	Learn how to structure input files.

¦	Learn how to interpolate monitoring data with Closest
Monitor, Voronoi Neighborhood Averaging, or Fixed Radius.

¦	Learn about the Monitor Rollback feature.

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Chapter 5 - Creating Air Quality Surfaces

BenMAP-CE is not an air quality model and it cannot generate air quality data
independently Instead it relies on the air quality inputs given to it. To estimate
population exposure to air pollution, BenMAP-CE uses air quality surfaces that it
generates from input air quality data (modeling or monitoring data). Essentially, the air
quality surfaces can be described as air quality grids (the structure) that have been
populated with air pollution values (the data).

BenMAP-CE creates air quality surfaces to estimate the average exposure to ambient air
pollution of people living in a "grid." These grids are either regularly shaped areas like
those used by air quality models, or irregular shapes, like provinces, local government
areas, cities, or nations. BenMAP-CE does not estimate personal exposure. Instead, the
program calculates average pollutant concentration to which people are exposed in
each grid cell. BenMAP-CE then uses these average values to calculate health impact
functions.

To create air quality surfaces, BenMAP-CE uses a number of inputs, including modeling
data or monitoring data. You may enter your own modeling and monitoring data,
provided that the data are in a format recognized by BenMAP-CE.

To start the grid creation process, locate Air Quality Surfaces
on the BenMAP-CE tree menu. Under this header, double-click
Pollutant. On the selection window, click to select a pollutant
from the left site and click "Add" button to add it to the right
side. You may also click, hold and drag the pollutant of choice
from the left side to the right side. Click OK.

To remove a selected pollutant, click the Remove button or
double click the pollutant name to remove it from the left site.

Next, double-click Baseline on the BenMAP-CE tree menu to
open the Grid Creation Method window.

BenMAP-CE will then ask which Grid Type (previously loaded
shapefile) to use and which of the following types of air quality data you wish to use:

¦	Model Data. 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 Data. Choose this option if you wish to use point source monitoring data
(measured observations).

¦	Monitor Rollback. Choose this option if you want to reduce monitor levels by a
specified amount.

¦	Open *.aqgx file. Choose this option to import a file that has already been
created.

File - United States - Modify Datas

E© Air Quality Surfaces
1-0 Pollutant

B-© Source of Air Quality Data
© Baseline
© Control
$¦& Estimate Health Impacts
0 Population Dataset
© Health Impact Functions
a© Aggregate, Pool St Value
0 Aggregation
0 Pooling Method
0 Valuation Method

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Chapter 5 - Creating Air Quality Surfaces

Choose a Grid Creation Method

Grid Type:	| County

<§>) Model Data

© Monitor Data
Q Monitor Rollback
O open ".aqgxfile

Save NewFormat Cancel Next

Select your Grid Type and then click Next. BenMAP-CE will direct you through the
necessary steps for each option (described below).

5.1 Model Data

After choosing the Model Data option, use the Generic Model Database tab to load
grid-definition-based modeling data (e.g., CMAQ or CAMx),1 Note: The New Format
Database is designed to support a new format model (under development).

Model Data

I 1=1 I 0

Grid Type:

CMAQ 12km Nation

Pollutant: Ozone

Generic Model Database j New Format Database

Model Database:

The file layout is: Column, Row, Metric, Seasonal
Metric, Annual Metric, Values; Values is a string of
comma delimited model values.

Validate







Cancel



OK







1 Community Multi-scale Air Quality (CMAQ) Model is available at:

http://www.epa.gov/amad/Research/RIA/cmaq.html or https://www.cmascenter.org/cmaq/.

Comprehensive Air Quality Model with Extensions (CAMx) is available at: http://www.camx.com/.

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Chapter 5 - Creating Air Quality Surfaces

The Model Database specifies the location of the air quality model results 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 at
https://www.epa.gov/scram.)

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
Grid Definition.

Row

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
maximum values for the months of June through August).

Annual Metric

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, \fin. Sum (e.g., mean of the 1-hour maximum values for
the year)

Values

Comma
Separated
Values
(Text)2

Yes

If Metric is blank, values are supplied as a comma-delimited
string of values for the year [e.g., 365 or 366 (leap year) values
for daily data, 8760 or 8784 (leap year) values for hourly data].
If Metric is defined, but Seasonal Metric and Annual Metric are
blank, 365 or 366 metric values. If Seasonal Metric is defined,
but Statistic is blank, n seasonal metric values. If Annual
Metric is defined, one annual statistic value for either the
Metric (if Seasonal Metric is blank) or the Seasonal Metric.
Missing values are signified with a period ('.')•

2 The list of comma-separated values must be surrounded by quotation marks, otherwise only the first value
in the list will be used.

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Chapter 5 - Creating Air Quality Surfaces

Table 5-2. Sample Air Modeling Data File

Column

Row

Metric

Seasonal Metric

Annual Metric

Values

24

101







,.,5.90,.,.,11,.,.

24

102







19.80,.,.,3.5,.,.,14.70,	4.9C

24

103







11.5,.,.,1.5,.,.,10.20,.,.,2.5,.,.,2. C

24

104







12.60	4.60	7.30	9.2C

24

105

Daily Average





,.,5.90,.,.,11,.,.

24

106

Daily Aver age





19.80,.,.,3.5,.,.,14.70,.,.,.,.,.,4.9C

24

107

Daily Average





11.5,.,.,1.5,.,.,10.20,.,.,2.5,.,.,2. C

24

108

Daily Aver age





12.60,.,.,4.60,.,.,7.30,.,.,.,.,.,9.2C

24

109

DailyAverage

QuarterlyAverage



11.82,13.24,18.79,14.25

24

110

Daily Average

QuarterlyAverage



12.31,13.89,18.27,21.19

25

101

DailyAverage

QuarterlyAverage



12.33,15.68,18.49,15.96

25

102

DailyAverage

QuarterlyAverage



13.52,13.69,19.04,22.43

25

103

DailyAverage



Mean

14.52

25

104

DailyAverage



Mean

16.41

25

105

DailyAverage



Mean

15.62

25

106

DailyAverage



Mean

17.17

25

107

DailyAverage

QuarterlyAverage

Mean

14.29

25

108

DailyAverage QuarterlyAverage

Mean

18.97

25

109

DailyAverage

QuarterlyAverage

Mean

20.14

25

110

DailyAverage

QuarterlyAverage

Mean

16.46

5.2 Monitor Data

Using the Monitor Data grid creation option, you create an air quality grid directly from
air pollution monitoring data. At the top left of the Monitor Data window, you will see
the previously selected grid definition in the Grid Type field, and the previously
selected pollutant in the Pollutant field. Below the Pollutant field of the Monitor Data
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-CE uses the interpolation methods to generate representative air
quality metric values for each grid cell from monitor data for all of these cases.

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@ Monitor Data

Grid Type: County

Pollutant: PM2.5

Monitor Dataset: Library
Text File

Interpolation Method
© Closest Monitor
@ Voronoi Neighborhood Averaging
© Fixed Radius (km)

Library

Monitor Dataset

EPA Standard Monitors PM2.5

Monitor Library Year:

2000

Map

BenMAP-CE 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
Neighborhood 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 (user-specified) radius
measured from the center of the grid cell. Each method is described below. For more
detail, also see Appendix B on Air Pollution Exposure Estimation Algorithms.

On the right side of the Monitor Data window, you can specify a source for your
monitor data. Using the Library tab, you may select from data that you have already
loaded into BenMAP-CE. Choose the Monitor Dataset and Monitor Library Year from
the drop-down lists.

If you want to load your own monitor data, choose Text File from the Monitor Dataset
drop-down list. You can then browse to locate the data file you want to load. See
Chapter 4 for more information about formatting monitor datasets.

At the bottom of the Monitor Data window, there is a Map button. The Map button
opens the Monitor Map window, allowing you to preview the map that you are about
to load.

5.2.1 Closest Monitor for Monitor Data

If you choose the Closest Monitor option, BenMAP-CE 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).

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@ Advanced Options

Maximum Neighbor Distance (in km):

¦iIII'MJIiiI'IhI'MM

Maximum Relative Neighbor Distance:

No Maximum Relative Distance-

Get Closest if None within Radius

Weighting Approach

® Inverse Distance

O Inverse Distance Squared

Custom Monitor Filtering

Cancel

OK

The Maximum Neighbor Distance specifies the maximum distance (measured in
kilometers) that a monitor may be from the center of a grid cell. 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 monitor's distance from the center of the
grid cell).

Note: The Maximum Relative Neighbor Distance and the Weighting Approach

options are irrelevant (and are therefore disabled) when using the Closest Monitor
method, since BenMAP-CE is only choosing a single monitor to assign to any given grid
cell.

5.2.2 Voronoi Neighborhood Averaging (VNA) for Monitor Data

If you choose the Voronoi Neighborhood Averaging option, BenMAP-CE 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-CE calculates an inverse-distance
weighted average of these neighboring monitors. In this section, we provide some
examples of the different ways that BenMAP-CE calculates the average of the
neighboring monitors. See Appendix B 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 the center of 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 monitor's distance to the
center of the grid cell).

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¦	Maximum Relative Neighbor Distance specifies the maximum ratio for the
distance of each included monitor to the distance of the closest monitor. The
default setting is infinite.

¦	Weighting Approach specifies whether BenMAP-CE should use inverse-
distance weighting for the monitors, or inverse-distance-squared weighting of
the monitors. The default setting is inverse-distance weighting.

The following examples illustrate how varying these options affects the final average
concentration estimate.

Example 1: Monitor Data VNA method
Default options

Consider the following example at a hypothetical rural grid cell, where there are
relatively few monitors, and where the distance from a monitor to the center of a grid
cell can be fairly large. With VNA, BenMAP-CE 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 km from the grid cell, with annual PM2.5 levels of 8,13,12,18, and 15
|ig/m3, respectively. BenMAP-CE would calculate an inverse-distance weighted average
of the monitor values as follows:

— *8 + — *13 + ^— • 12 h—— • 18 H——*15
PM25 average = ^^	100	200	400	= 10.68

2.5	s	11111

25 50 100 200 400

Example 2: Monitor Data 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 km, and left unchanged the default options (infinite value) for
Maximum Relative Neighbor Distance. BenMAP-CE would only consider the first two
monitors, and would calculate an inverse-distance weighted average of the monitor
values as follows:

— *8 + — *13
PM25 average=25	=9.67

25 50

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Example 3: Monitor Data VNA method

Maximum Relative Neighbor Distance = 10

Alternatively, if you left the Maximum Neighbor Distance at the default (infinite), but
have set the Maximum Relative Neighbor Distance to 10, then BenMAP-CE 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-CE would drop the monitor with a ratio of 16. BenMAP-CE
would then calculate an inverse- distance weighted average of the monitor values as
follows:

— *8 + — «13 + — *12 + — *18
PM2 5 average = ^	^0—_—1^	_J200	= 10.53

25 + 50 + 100 + 200

Example 4: Monitor Data VNA method

Inverse-distance squared neighbor scaling

In addition, you can specify 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-CE would then calculate an
inverse-distance-squared weighted average of the monitor values as follows:

1 ~ 1 *13 + —1—*12 + —1—*18 +	1	*15

625 2,500 10,000 40,000 160,000

PM0, average=									= 9.26

11111

625 2,500 10,000 40,000 160,000

Example 5: Monitor Data VNA method

Maximum Neighbor Distance = 75
Maximum Relative Neighbor Distance = 10
Inverse-distance squared Weighting Approach

Finally, you could specify changes to all three options: a Maximum Neighbor Distance
of 75 km, a Maximum Relative Neighbor Distance of 10, and a Weighting Approach

of Inverse Distance Squared weighting. BenMAP-CE would then calculate the following
average:

*8h		—*13

625 2>500

25 m,emge = i | i = 9 00

625 2,500

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5.2.3	Fixed Radius for Monitor Data

If you choose the Fixed Radius (km) option, BenMAP-CE averages all of the monitor
values within a fixed radius (measured in kilometers) that you specify. The way that the
monitor values 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-CE 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 select Get Closest if
None within Radius, then for those grid cells without any monitors within the fixed
radius, BenMAP-CE will choose the nearest monitor (regardless of distance) and apply
that value as the "average".

5.2.4	Custom Monitor Filtering for EPA Standard Monitors

Custom Monitoring Filtering options apply only to the EPA Standard Monitors library
in the United States setup: these are the only monitoring values that include all of the
variables that BenMAP-CE needs in order to filter the data properly. This tool allows
you to filter, map, and export your monitor data. You can reach the Custom Monitor
Filtering tool by first choosing your pollutant, data source (e.g., monitor library) and
year on the Monitor Data window.

^ Monitor Data

Grid Type: County
Pollutant:

PM2.5

Interpolation Method
0 Closest Monitor
C<§) Voronoi Neighborhood Averaging
© Fixed Radius (km)

Monitor Dataset: Library

Library

Monitor Dataset

EPA Standard Monitors PM2.5

Monitor Library Year:

2000



Map



Next

Click the Advanced button. This will take you to the Advanced Options window.

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oyi Advanced Options

Maximum Neighbor Distance (in km):

Weighting Approach
@ Inverse Distance



Maximum Relative Neighbor Distance:
- No Maximum Relative Distance -

(_> Inverse Distance Squared





|~~1 Get Closest if None within Radius

Custom Monitor Filtering





Cancel

OK

Click the Custom Monitor Filtering button. This will take you to the Filter Monitors

window.

^ Filter Monitors



If you wish to include individual monitors by ID,
enter the IDs here, separated by commas. If you
do not enter any monitors here, all monitors
which meet the rest of the selection criteria will
be included:

2. If you wish to exclude individual monitors by ID,
enter the IDs here, separated by commas:

3. If you wish to restrict monitors to certain states
or areas, enter comma separated state
abbreviations, and minimum and/or maximum
latitudes and longitudes:

States:

Minimum Longitude: -130

Maximum Longitude: -65

Minimum Latitude: 20

Maximum Latitude: 55

Export

Map

4. Select maximum POC code to include and the POC
preference:

Maximum POC:

POC Preference Order: 1,2,3,4

5. Select the Methods you wish to include:

6. Select the parameters specific to the pollutant:

Number of Valid Observations
Required Per Quarter:

Data Types To Use
(i®) Local © Standard

Preferred Type

(§) Local	Standard

11

0 Both

Output Type
# Local

Standard

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Note that the first five options are essentially the same for each pollutant, and the sixth
option depends on the pollutant The example above shows what the form looks like with
PM2.5 as the selected 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-CE will include all
monitors that meet the rest of the selection criteria.

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-CE 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
include the continental U.S. Here again, if this option is left blank then BenMAP-CE will
include all monitors that meet the rest of the selection criteria.

4.	Parameter Occurrence Code (POC)3. Sometimes, multiple monitors are collocated
at the same site measuring the same parameter (e.g., to check precision). 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.4 The Method codes listed indicate U.S. EPA-defined methods for collecting
and analyzing samples; these codes depend on the pollutant. In the case of PM2.5, only
federal-reference methods (FRM) are chosen by default - specifically numbers 116
through 120. In the case of Ozone (O3), all methods are checked by default.

6.	Parameters Specific to the Pollutant. The default options vary by pollutant. Below,
we have described the options that appear with PM2.5 and ozone.

¦ PM2.5 Monitor Filter: The Number of Valid Observations Required per Quarter

specifies the number of days of data needed. The default is to require 11
observations per quarter. The Data Types to Use options specify whether to use
data at Local conditions (parameter code 88101), Standard conditions (parameter
code 81104), or Both.5 The default for PM2.5 is to use data at Local conditions. When
data at standard and local conditions are both available at the same monitor

3	For information about data codes used in U.S. EPA's Air Quality System, refer to the AQS Data Coding
Manual-, https://www.epa.gov/sites/production/files/2015-09/documents/aqs_data_coding_manual_0.pdf

4	For more information, refer to U.S. EPA's List of Designated Reference and Equivalent Methods-.
https://www3.epa.gov/ttnamtil/files/ambient/criteria/AMTIC%20List%20Dec%202016-2.pdf

5	Particulate concentrations are expressed in either local conditions (volume is at temperature and pressure
of the ambient sample) or at standard conditions (where the volume has been converted to standard
conditions, typically 20° C at 760 mm Hg).

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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 condition data are used. The default is to
use the Local output type, so Standard condition data will be converted to Local.

¦ Ozone Monitor Filter: The ozone specific options differ from PMzsm large part
because ozone is monitored hourly in the United States. The Number of Valid
Hours specifies the number of hours needed for a particular day of monitoring to be
considered "valid." BenMAP-CE counts the number of non-missing hourly values
from the Start Hour through the End Hour and compares this number with that
specifi ed 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. The example below
shows what the form looks like with Ozone as the selected Pollutant.

('CJ Filter Monitors



L If you wish to include individual monitors by ID,
enter the IDs here, separated by commas. If you
do not enter any monitors here, all monitors
which meet the rest of the selection criteria will
be included:

2. If you wish to exclude individual monitors by ID,
enter the IDs here., separated by commas:

If you wish to restrict monitors to certain states
or areas, enter comma separated state
abbreviations, and minimum and/or maximum
latitudes and longitudes:

States:

Minimum Longitude: -130

Maximum Longitude: -65

Minimum Latitude: 20

Maximum Latitude: 55

Export

Map

4. Select maximum POC code to include and the POC
preference:

Maximum POC:

POC Preference Order: 1,2,3,4

5. Select the Methods you wish to include:

~ 003

E7] 047

[V] 091

mon

H 053

g] 103

0 014

O 055

@ 112

g] 019

g] 078



[7] 020

(2] 087



« |

|



6. Select the parameters specific to the pollutant:

Start Hour:	8

End Hour:

Number of Valid Hours: 9

19

Start Date:	Mav 01 [^j

End Date:	September 30 |4j

Precent of Va I i d Days: 50

Cancel

OK

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You can view a map of your data with the specified filter options by clicking the Map
button on the bottom left side of the Filter Monitors window. This provides a brief
preview of what will be shown once the data are loaded.

You can also export your data by clicking the Export button, which is found in the
bottom left side of the Filter Monitors window. A Save As window will appear,
allowing you to save the data as a CSV file.

5.3 Monitor Rollback

The Monitor Rollback option allows you to quickly test what the benefits would be from
reducing historical monitor levels. BenMAP-CE has three methods to reduce, or "roll
back," monitor data: Percentage Rollback, Incremental Rollback, or Rollback to a
Standard. Each of these methods is depicted below. Note that with each of these
methods you can use the same two interpolation algorithms (Closest Monitor or VNA) as
you can use with Monitor Data.

Percentage Rollback reduces all monitor observations by the same percentage.

Mg/rn3

I

33% (percentage)

DAY

Incremental Rollback reduces all observations by the same increment.

[jg/rn3

j— 10 [jg/m3
(incremental)

DAY

Rollback to a Standard lets you choose a standard, and then reduces any monitor
observations exceeding the standard to the level of the standard.

fjg/m3

60 |jg/m3 daily standard
(to standardj	

DAY

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5.3.1 Example: A Single Rollback in One Region

To apply a monitor rollback, first click the Create Air Quality Grids button. On the Air
Quality Grid Creation Method window, choose Monitor Rollback. Click Next.

IP Monitor Rollback: (1) Select Monitors

Pollutant:

PM2.5

Rollback Grid Type: State

Cancel

Library

Text File



Monitor Dataset:



EPA Standard Monitors PM2.5 t



Monitor Library Year:



2006

















Advanced

Next

There are three steps to the Monitor Rollback method.

1. Select Monitors. Choose the Rollback Grid Type from the drop-down list. This
allows you to determine 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).

If you use data from an existing dataset, then choose the Library tab, and from the
drop-down list choose the Monitor Dataset and the Monitor Library Year. If you

want to use your own data, then choose the Text File tab. The file should have the
monitor data format specified in Chapter 4: Loading Data.

When you have finished making your choices, click Next.

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(Fj Export After Rollback

1.	Start by clicking Add Region and setting up a rollback method

2.	Select a region in the left pane} and select areas on the map

3.	Click the "X" button next to each region name to remove the region

Rollback Regions
| Add Region 1

m e i3 k

y? Monitor Rollback Settings:{2) Select Rollback Regions and Settings	1 ° I a LaJ

2. 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 roll back.

Click the Add Region button to display the three rollback options: Percentage Rollback,
Incremental Rollback, and Rollback to a Standard.

^ Select Region Rollback Type

Rollback Type

o Percentage Rollback

0 Incremental Rollback

Rollback to a Standard

Cancel

OK

Choosing the Rollback Type and click OK. Then, specify the amount of the rollback and
the region to which you want to apply it. You can click on the map to select and deselect
the states (or other defined areas depending on your rollback grid type) to add to the
region.

At the top of the map are five GIS toolbar icons, typically seen in mapping programs.
The first four tools allow you to zoom in and zoom out, and to focus on the particular

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groups of grid cells that interest you. The fifth tool allows you to select grid cells
graphically, by clicking or dragging a box around them.

In this example, we specified a 10 percent reduction, a background of 0 ppb, and
applied it to all monitors in the state of California (by clicking on the particular state in
the CIS window).

Monitor Rollback Settings:{2) Select Rollback Regions and Settings

' I B \m£2m

Rollback Regions

¦

Region 1
Rollback Parameters

Percent: 10|
Background: 0-00

|H Region 2
Rollback Parameters

Background: O.OO

Add Region

0

0

% 
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Chapter 5 - Creating Air Quality Surfaces

3. Additional Grid Settings.

The third stage is similar to the Monitor Data grid creation method. As with monitor
data, you need to specify the Interpolation Method (Closest Monitor, VNA, or Fixed
Radius) and the Grid Type.

You may select a Scaling Method (None or Spatial Only). The concept of scaling is to use
modeled data to improve interpolation of the actual monitor observations. If you
choose spatial scaling the modeled data are used to provide information in those areas
where monitoring data are unavailable. Specify the Grid Type and then the location of
your modeled data using the Adjustment box.

Monitor Rollback Settings: (3) Additional Grid Settings i a I —

Select Interpolation Method	Select Scaling Method

© Closest Monitor	© None
© Voronoi Neighborhood Averaging

© Fixed Radius (km)	0 Spatial Only

Grid Type: County
Adjustment: |

|?1 Make Baseline Grid (in addition to Control Grid).

Back	Advanced

Go

By checking the option for Make Baseline Grid (in addition to Control Grid) you tell
BenMAP-CE to 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 will serve as both baseline and control
scenarios and are automatically selected in the "Air Quality Surfaces" stage of the
analysis.

Note that there is an Advanced button that lets you select the Maximum Neighbor
Distance (in km), Maximum Relative Neighbor Distance, and Weighting Approach.

The specific availability of advanced features depends on the interpolation method that
you choose. The Advanced Options window is described in more detail in Section
5.2.4. You can view a map of the inputs to the rollback grids that you are creating as
well as the grids themselves by accessing the Monitor Map. To do this, click on the
Advanced button, then the Custom Monitor Filtering button, and then the Map
button (on the Filter Monitors window).

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5.3,2 Example: Combining Three Rollback Approaches in Different Regions

BenMAP-CE allows you to have different rollback approaches in different regions. In
this example, we'll use the United States setup to combine the three rollback types:
Percentage Rollback, Incremental Rollback, and Rollback to a Standard.

Start by clicking on the Pollutant button in the left-hand pane of the main screen, and
choosing Ozone. Next, click Baseline under Source of Air Quality Data. Select Monitor
Rollback as the Grid Creation Method. On the Monitor Rollback: (1) Select Monitors
window, select the Rollback Grid Type [State), Monitor Dataset (EPA Standard
Monitors 03) and Monitor Library Year (2000), and click Next.

On the Monitor Rollback Settings: (2) Select Rollback Regions and Settings

window, click the Add Region button and select the Percentage Rollback method. Click
OK. Enter 10 for the Percent. In the previous example, we included only one state in
the rollback region. In this example we want to create three regions. Click on the three
western-most states to add them to the first region. The states you have added to the
region will fill in, as in the picture below.

| | Export After Rollback

1.	Start by clicking Add Region and setting up a rollback method

2.	Select a region in the left panel and select areas on the map

3.	Click the "X" button next to each region name to remove the region

: r : r

Next

Monitor Rollback Settings:{2) Select Rollback Regions and Settings

ID|a teaJ

Rollback Regions

® |Wl Region 1	[x]

Rollback Parameters

Increment: 10.00
Background: 0.00
Add Region

©, O© k

To add states with a second type of rollback, click on the Add Region button, choose the
rollback type, and then click on the states to include in this second region, which
BenMAP-CE denotes as Region 2. In this example, we have chosen an Incremental
Rollback with an Increment of 5 and a Background of 0, and applied it to the 14 next
western-most states.

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|||j Monitor Rollback Settings:{2) Select Rollback Regions and Settings

Id § 23

Rollback Regions

|J Region 1
Rollback Parameters

Increment: 10.00
Background: 0.00

(<§)	Region 2

Rollback Parameters

Increment: 5.00
Background: 0-00

Add Region

0

0



Deselect All

1.	Start by clicking Add Region and setting up a rollback method

2.	Select a region in the left panel and select areas on the map

3.	Click the "X" button next to each region name to remove the region

[T~i Export After Rollback

Back	Next

The map now depicts two rollback regions. We can toggle back and forth between each
region by clicking on the button on the legend on the left side of the map. Any states
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 states. Note that once states 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.

If you want to add or remove states from a defined region, make sure you select the
appropriate region by clicking on the button to the left of that region before clicking on
the map to select or deselect the state(s).

To add a third rollback type covering the rest of the states, click again on the Add
Region button, and then choose the rollback type. However, instead of individually
choosing the states, simply click the Select All button. This will select all of the states
that are not yet included in a region, and these remaining states 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.

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The Attainment Test parameters are Daily Metric, Seasonal Metric, Annual Statistic,

Ordinality, and Standard, (Note: You will need to use the scroll bar to view more
detail on the leftside of the screen.)

In this step BenMAP-CE calculates the metric to be used to determine whether a
monitor's values must be rolled back and, if so, how much (e.g., if Metric is D8HourMax,
BenMAP-CE calculates the 8-hour daily maximum for each day at each monitor).

A monitor is considered in attainment if the nth 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 DSHourMax, Ordinality is 4, and Standard is
85, a monitor will be considered in attainment if the fourth highest value of the eight-
hour daily maximum is at or below 85 ppb.

y? Monitor Rollback Settings:{2) Select Rollback Regions and Settings



Rollback Regions

(<$	Region 3

Attainment Test
Daily Metric:

0

4

D24HourMean

Seasonal Metric:

Annual Statistic:

Ordinality

1

Highest value of
D24HourMean<=0.00

Rollback Methods
Interday Rollback Method:

Percentage

Background:

| Select All j j Deselect All

1.	Start by clicking Add Region and setting up a rollback method

2.	Select a region in the left panel and select areas on the map

3.	Click the "X" button next to each region name to remove the region

IH Export After Rollback

Back	Next

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 Annual Statistic). For example,
if you want the annual mean ozone level to stay below 60 ppb, then you would choose
the daily 24 hour mean (D24HourMean) from the drop-down list below Daily Metric,
choose Mean from the drop-down list below Annual Statistic, and set the Standard to
60. (Note that in this case Ordinality cannot be chosen because there is only a single
annual value.)

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The Rollback Methods parameters determine the rollback procedures used to
simulate out-of-attainment monitors coming into attainment:

¦	Interday Rollback Method (with associated Background level) - These are
used to generate target values for the metric specified by the Attainment Test.
Method types include Percentage, Incremental, and Peak Shaving.

¦	Intraday Rollback Method (with associated Background level) - These are
used to adjust hourly observations to meet the target metric values generated in
the previous step. Method types include Percentage and Incremental.

The methods involved for each can be somewhat complicated, so we have included a
section in Appendix A: Monitor Rollback Algorithms which goes through several
examples.

5.4 Open *.aqgx File

The final option for uploading an Air Quality Grid is to select the Open *.aqgx File
option from the Choose a Grid Creation Method window.

IP Choose a Grid Creation Method

G rid Type:	CMAQ 12k m N ati on

© Model Data

© Monitor Data
© Monitor Rollback
@ open ".aqgx file

E3

Save As(*.aqgx)

we Nev

Cancel

Next

Choosing this option will activate the Open File Browser button located directly below
this option. Click the Open File Browser button. This will cause an Open window to
appear, allowing you to search for an Air Quality Grid (*.aqgx file) that has already
been created. Select a file and click Open. The file path and name should appear in the
box beside the Open File Browser button. Click Next to close the window and begin to
create the map layer.

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5.5 Frequently Asked Questions

How can I generate a map of an air quality grid and export it?

When viewing any of the displayed maps n the GIS Map tab (lower right frame of the
main window), click on the GIS toolbar icon for Save Shapefile (looks like a 3.5-inch
diskette). Follow the prompts to provide a name and location for the file. BenMAP-CE
will export a set of files (.dbf, .prj, .shp, .shx) associated with the shapefile that you can
use with any GIS viewer. To export the map as an image, click the Export map image
icon (immediately below the Save Shapefile icon). This will use built-in DotSpatial GIS
tools to allow you to save the map as a formatted image (.png) file.

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-CE lets you choose from among those
approaches.) The Background level associated with the Interday Rollback Method
specifies the bound, below which, BenMAP-CE will not make adjustments to daily levels.

Rollback Methods
Interday Rollback Method:
Peak Shaving	t

Background:

0.00

Intraday Rollback Method:
Percentage	t

Background:

0.00

The Intraday Rollback Method option is only relevant for hourly air pollution data,
like ozone measurements. This option specifies the approach (e.g., Percentage) used by
BenMAP-CE to reduce hourly air pollution levels to reach the target metric values. 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 Method specifies the bound, below which, BenMAP-CE will not
make adjustments to hourly levels.

The Interday and Intraday options are complicated. Appendix A on Monitor Rollback
Algorithms explains these options in more detail and gives some numerical examples.

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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 quality grids made with Model Data and Monitor Data 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 currently only estimate impacts one pollutant at a time; however, BenMAP-CE
allows you to aggregate the results of more than one pollutant. This is discussed in
Chapter 7: Aggregating, Pooling, and Valuing.

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-CE will run faster and
generate smaller air quality grids. This is an advanced step, so do not do it if you are
unsure.) The type of computer you use can also affect processing speeds. Refer to
Chapter 1, Section 1.3 for recommended hardware specifications.

How do I access data in an Air Quality Surface?

You can access the data in an air quality grid by going to the Tools menu and choosing
the Export Air Quality Surface option. (The Tools menu is available on the toolbar of
the main BenMAP-CE window.) Locate the air quality grid from which you want to
export air quality data and then give a name to your exported file. BenMAP-CE will
generate a text file that you can then examine. This is discussed in detail in Chapter 3,
Section 3.2.1 (Tools).

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¦ How do I perform a rollback to simulate attainment with an annual and
daily PM2.5 standard?

Unfortunately, BenMAP-CE will roll back to either an annual standard or a daily
standard—but not both. If this feature is of interest to you, please contact the
BenMAP-CE developers at benmap@epa.gov.

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Contents

Table of Contents

CHAPTER 6_Estimating Incidence	6-1

6.1	Introduction to Estimating Health Incidence	6-3

6.2	Creating a Health Impact Configuration	6-4

6.2.1	Configuration Settings (Pollutants and Air Quality Grids)	6-4

6.2.2	Select Health Impact Functions	6-6

6.2.3	Advanced Configuration Settings	6-10

6.3	Run Health Impact Configuration	6-12

6.4	View and Export Incidence Results	6-12

6.5	Questions Regarding Health Impact Configurations	6-15

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Chapter 6 - Estimating Incidence

Estimating Incidence

In this chapter...

¦	Get an overview of how BenMAP-CE estimates the incidence
of health outcomes.

¦	Learn to create a health impact configuration.

¦	Learn about baseline and control scenarios.

¦	Learn the difference between Point Mode and the Monte Carlo
analysis options.

¦	Learn how to run, save, and re-open a configuration.

¦	Learn how to view and export Incidence Results.

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To estimate changes in the incidence of adverse health effects from air pollution, you
will need to create and run a BenMAP-CE configuration file (*.cfgx). A configuration is a
reusable file that specifies the air quality grids, health impact functions, population
data, and other parameters necessary for an analysis. It is a record of the choices you
make in estimating the change in adverse health effects between a baseline and control
scenario. The choices include the following:

¦	The pollutant and air quality grids for the baseline and control scenarios;

¦	The year for the analysis;

¦	The population dataset for the analysis;

¦	The health impact functions to be used in estimating adverse health effects; and

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 (Monte
Carlo-generated percentiles).BenMAP-CE gives you flexibility in creating, editing, and
saving configuration files. You can open an already existing configuration and proceed
directly to estimate incidence. Or, you can create a new file, and then estimate
incidence. In addition, you may save any edits made to existing configuration files. After
calculating the change in adverse health effects, BenMAP-CE saves the results in a
"configuration results" file with a .cfgrx extension. The results obtained from running a
configuration are sometimes referred to as "raw" results because they represent the
estimated change in incidence for each air quality grid cell in a given scenario; they
have not been aggregated, pooled, or valued (see Chapter 7).

To load a previously saved configuration, click Estimate Health Impacts, which is
beside Step 2 in the BenMAP-CE tree menu (on the left side of the main window). An
Open Existing Configuration window will appear, as shown below:

^ Open Existing Configuration

Open Existing Configuration File (*.cfgx)



Cancel

OK

Or Open Existing Configuration Result File (•.cfgrx) :

E3

Cancel

To open an existing Configuration file, click the top file browser button. To open an
existing Configuration Result file, click the bottom file browser button. After selecting
either of these options, search and select a file to load in the Open window. The file

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Chapter 6 - Estimating Incidence

path and name should appear in the box next to the file browser button. Once you have
selected the desired option, and are satisfied with the file selection, click OK,

6.1 Introduction to Estimating Health Incidence

Health impact functions relate a change in the concentrati on 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 in an
epidemiology study. For example, the pollutant concentration being measured may be
particulate matter (PM2.5), and the population response may be daily premature deaths.
For the purposes of estimating benefits, we are not interested in the health impact
function itself, but rather the relationship between the change in concentration of the
pollutant, and the corresponding change in the population-health response. We may
want to know, for example, if the concentration of PM2.5 is reduced by 10|ig/m3, how
many premature deaths will be avoided?

To estimate changes in health incidence, the first step is to calculate the change in
pollution concentrations for a particular policy scenario, such as an air quality
improvement produced by a set of emissions controls. The concentration change in a
pollutant is the increment between the baseline scenario and the control 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 epidemiological studies 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) due to a decrease in pollution. In BenMAP-CE,
the selected health impact functions are stored in configurations, which can be re-used
over and over again.

Health
impact

Steps to Calculating Health Impacts

A Y =Yo (l-e BAPM) *Pop

Pollutant change	Population	Baseline incidence

Effect
estimate

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6.2 Create a Health Impact Configuration

There are three major steps to creating a new configuration. First, select a Pollutant
and specify the Source of Air Quality Data for the Baseline and the Control layers
using the BenMAP-CE tree menu on the left side of the main window. Next, choose the
Population Dataset and Health Impact Functions that you want to use in your
analysis. Finally, using the Advanced button on the Health Impact Functions window,
specify whether BenMAP-CE will run in Point Mode or perform a Monte-Carlo analysis
(default setting). (See Section 6.2.3 Advanced Configuration Settings for additional
detail.)

Note: As you move through the analysis steps, the BenMAP-CE tree menu will update
its "stoplight" colors to reflect progress. Yellow indicates an operation has not yet
started. Green indicates that an operation has been successfully completed. Red
indicates that an operation completed, but you may need to re-run this step.

6.2.1 Air Quality Surfaces - Pollutants and Air Quality Grids

Using the BenMAP-CE tree menu, specify the pollutant(s) of interest. Click the
Pollutant tree menu item to open the Pollutant Definition window. Select the
pollutant(s) of interest by clicking on an item in the Pollutants box (left side) and then
clicking the Add button to move the pollutant(s) to the Selected Pollutant box. To
deselect a pollutant and remove it from the Selected Pollutant box, click on the item in
the Selected Pollutant box and click the Remove button.

^^Pollutant Definition

Pollutants:

n Pollutant Details

Selected Pollutant:

Add >

< Remove





Cancel



OK

If you want to view or modify any pollutant details, click the box next to Pollutant
Details and the window will expand to display details and options for the highlighted
Pollutant (left box). Note that the details are based on the defined Pollutant
Definitions (see Chapter 4: Loading Data).

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Chapter 6 - Estimating Incidence

Pollutant Definition

Pollutants:

Pollutant Details





Cancel



OK

Pollutant: PM2.5

Observation Type: Daily

Metric: D24HourMean ~ Seasonal Metric: QuarterlyMean ~

Once your pollutant is selected, click OK to close the form. The stoplight for Pollutant
will change from yellow to green when the operation is successfully completed.

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Next, select air quality grids for the Baseline file and Control file. You may choose
existing air quality grids by double-clicking the Source of Air Quality Data from the
tree menu. The Open Existing AQ Data window will be displayed. Select air quality
grids, designated with an '.aqgx' extension, for Baseline and Control. To create a new
air quality grid, follow the steps outlined in Chapter 5: Creating Air Quality Grids.

@ Open Existing AQ Data

Grid Type: [county

Pollutant: PM2.5

Open existing AQ data (.csy .aqgx, etc.):
Baseline:

Open

Control:

Open

The Baseline file contains the air quality metrics for the scenario assumed to occur
without any change in policy. The Control file specifies the air quality metrics assuming
that some type of policy or change has been implemented. The air quality grids should
be for 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 Control file. Conversely, it would not be possible to use
County grid-type in the Baseline and a Tract grid-type in the Control file.

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 (see
the information in Chapter 3 on monitor and model data formats for more information
on how this can occur) only those Health Impact Functions associated with those
Metrics will be available.

Once the Baseline and Control files are selected, click OK to close the form. The
stoplights for each of these under Source of Air Quality Data will change from yellow
to green when the operation is successfully completed.

6.2.2 Estimate Health Impacts - Population and Health Impact Functions

The second step in creating a health impact configuration is to select the Population
Dataset, Population Year and Health Impact Functions. If you want to open an
existing configuration, double-click the Estimate Health Impacts tree menu item and
select the file to open. Follow instructions from Section 3.1.2 Estimate Health
Impacts.

To continue creating a new configuration, double-click Population Dataset in the tree
menu. Here, you can choose the Population Dataset and Population Year that will be

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used in the analysis. The values in the drop-down list for Population Year depend on
the range of values in your Population Dataset. (See Chapter 4: Loading Data.)

^ Population Dataset

Population Dataset: United States Census - County

Population Year:

2010

Map

Cancel

OK

Clicking the Map option in the bottom left hand corner will open a separate Population
Map window. fWarning: this procedure can require a significant amount of time to
complete as it creates the population grid and loads the population growth weights).
This provides a map of your selected population. If you want to view a different
population subgroup based on race, gender, ethnicity, or age range, choose from the
available drop-down list (this runs fairly quickly as it uses the same population grid
already loaded). The map will refresh automatically.

To change the Population Dataset or Population Year, click on their respective drop-
down list and click Draw to update the map and display new results. Warning: This
requires BenMAP-CE to recalculate the grid.

Click the close (V) button at the top of the Population Map window to close it.

Clicking OK on the Population Dataset window will close the window and change the
stoplight color to green in the tree menu.

Next, double-click Health Impact Functions on the tree menu to display the Health
Impact Functions window.

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Chapter 6 - Estimating Incidence

i Health Impact Functions



Selected Health Impact Functions
Filter Dataset

Filter Endpoint Group

EPA Standard Health Functions

R7I Groups

Dataset

Endpoint Group

Endpoint

Pollutant

Author

Start Age

End Age

Beta Distribution

Metric

Seasonal Metric

*

Acute Bronchitis [1 item]



































EPA Sta...

Acute Bronchitis

Acute Bronchitis

PM2.5

Docker...

8

12

Normal

D24HourM...

QuarterlyMean





















EPA Sta...

Acute Myocardial Infa...

Acute Myocardia...

PM2.5

Zanob...

0

99

Normal

D24HourM...





EPA Sta._

Acute Myocardial Infa...

Acute Myocardia...

PM2.5

Zanob...

0

99

Normal

D24HourM...





EPA Sta...

Acute Myocardial Infa...

Acute Myocardia...

PM2.5

Sulliva...

0

99

Normal

D24HourM...





EPA Sta...

Acute Myocardial Infa...

Acute Myocardia...

PM2.5

Peters...

18

99

Normal

D24HourM...





PDA Ct-a

A/-i Ifo M,inrarr4i = ! In#=



DMT c

Dnna a

n

DO

M^rrv> = l

m/IWrwirM





< L

rrr















~



Selected Health Impact Functions (0)





















Endpoint Group Dataset Name Endpoint Pollutant

Author

Start Age End Age Race

Ethnicity

Gender Incidence Dataset

Prevc

Delete Selected

J 1 Cancel |_

Save As (*.cfgx)	Run

The Health Impact Functions window is split into two display frames. The upper
frame presents the Available Health Impact Functions, which you may select, and the
lower frame shows the Selected Health Impact Functions that you have already
selected. Both frames have a tree structure and the ability to change the order of the
fields for easy viewing of the functions. Above the Available Health Impact Functions
frame, there are a series of buttons that give you the option to select different datasets
and filter options for each one.

To add studies to your configuration, simply click to select the health impact functions
of interest in the top frame and drag them down to the lower frame. You can do this for
blocks of health impact functions by selecting the Groups option, clicking on the h eader
of an endpoint group, and dragging the entire endpoint group into the lower frame.
And, you can use the options to Filter Dataset, Filter Endpoint Group, and Filter (by
keyword) to filter the list according to your preference. Once you are satisfied with the
filter, click the Add Selected button to apply the selection to the list of selected
functions.

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Chapter 6 - Estimating Incidence

Health Impact Functions

©

Selected Health Impact Functions
Filter Dataset

Filter Endpoint Group

EPA Standard Health Functions

[57] Groups | Add Selected

Dataset

Endpoint Group

Endpoint

Pollutant

Author

Start Age

End Age

Beta Distribution

Metric

Seasonal Metric

-

Acute Bronchitis [1 item]
EPASta... Acute Bronchitis



















Acute Bronchitis

PM2.5

Docker...

8

12

Normal

D24HourM...

QuarteriyMean



Acute Myocardial Infarction [5 items]

































EPASta...

Acute Myocardial Infa...

Acute Myocardia...

PM2.5

Zanob...

0

99

Normal

D24HourM...





EPA Sta...
EPA Sta...

Acute Myocardial Infa...
Acute Myocardial Infa...

Acute Myocardia...
Acute Myocardia...

PM2.5
PM2.5

Zanob...
Sulliva...

0
0

99
99

Normal
Normal

D24HourM...
D24HourM...





'

w

i













~





Selected Health Impact Functions (6)

Endpoint Group

Dataset Name

Endpoint

Pollutant

Author

Start Age

End Age Race

Ethnicity

Gender Incidence Dataset Prevc

Acute Bronchitis

EPA Standard ...

Acute B...

PM2.5

Docker...

8

12



Other Incidence (2000)

Acute Myocardi...

EPA Standard ...

Acute M...

PM2.5

Peters ...

18

99



Other Incidence (2007)

Acute Myocardi...

EPA Standard ...

Acute M...

PM2.5

Pope e...

0

99



Other Incidence (2007)



Acute Myocardi...

EPA Standard ...

Acute M...

PM2.5

Sulliva...

0

99



Other Incidence (2007)



Acute Myocardi...

EPA Standard ...

Acute M...

PM23

Zanob...

0

99



Other Incidence (2007)



Acute Myocardi„.

EPA Standard ...

Acute M...

PM23

Zanob...

0

99



Other Incidence (2007)



















4 1

in



—









~

Delete Selected

Save As (*.cfgx)

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 (or click the Delete Selected button on the form).

BenMAP-CE displays information for selected studies in two broad categories:

Function Identification (column headings in black text) and Function Parameters
(column headings in pink text). 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.

Clicking on a column header will sort the health impact functions according to that
variable. For example, clicking the Start Age column will sort the functions by youngest
to oldest Start Age. You can add the same study to the selection multiple times, and
then make edits, in order to be able to calculate the impact of changes in these variables
of the function. For example, you can perform an analysis using multiple versions of the
same function with different age ranges. To edit the default Start Age and End Age, just
highlight the appropriate cell and type in the desired age values. Keep in mind that
these ages 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 to apply a single age year
(e.g., only children who are eleven years old), then type the same year (e.g., '113 in both

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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. Use the drop-down lists in the Incidence Dataset,
Prevalence Dataset, and Variable Dataset fields.

^ Health Impact Functions

1 @ U&J'

Selected Health Impact Functions

Filter Dataset



Filter Endpoint Group

Filter



i . ]

lA" -1



[7] Groups [ Add Selected

















Dataset

Endpoint Group

Endpoint

Pollutant

Author

Start Age

End Age

Beta Distribution

Metric

Seasonal Metric

EPASta...
EPA Sta...
EPA Sta...
EPA Sta...

Asthma Exacerbation
Asthma Exacerbation
Asthma Exacerbation
Asthma Exacerbation

Asthma Exacerb...
Asthma Exacerb...
Asthma Exacerb...
Asthma Exacerb...

PM2.5
PM2.5
PM2.5
PM2.5

Ostro e...
Ostro e...
Mar et...
Mar et...

6
6
6
6

18
18
18
18

Normal
Normal
Normal
Normal

D24HourM...
D24HourM...
D24HourM...
D24HourM...



1 EPASta...

Asthma Exacerbation

Asthma Exacerb...

PM2.5

Ostro e...

6

18

Normal

D24HourM...





EPASta... Chronic Bronchitis

Chronic Bronchitis

PM2.5

Abbey

27

99

Normal

D24HourM...

QuarteriyMean

Selected Health Impact Functions (3)

Endpoint Group

Dataset Name

Endpoint

Pollutant

Author

Start Age

End Age Race Ethnicity

Gender Incidence Dataset

Prevt

Acute Myocardi...

EPA Standard ....

Acute M...

PM2.5

Zanob...

0

99

Other Incidence (2007)



Acute Respirat-

EPA Standard ...

Minor R...

Ozone

Ostro a...

18

64





Asthma Exacer...

ERA Standard

Asthma-

PM2.5

Ostro e...

6

18



Prev<

< 1

in













y

Delete Selected

Save As (*.cfgx)

6.2.3 Advanced Configuration Settings

The last step in creating the health impact configuration is to specify whether
BenMAP-CE will calculate incidence using Point Mode or the Monte Carlo method
(percentiles) and whether you want to designate an air quality threshold. Click the
Advanced button at the bottom of the Health Impact Functions window to make
these selections.

The Point Mode and Monte Carlo 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. If you select the option for Run in Point Mode, BenMAP-CE uses the
mean values of the inputs to the health impact functions, and generates a single "point
estimate" of the change in adverse health effects.

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With the Monte Carlo (default) option, you can generate a number of estimates that
mirror the variability in the inputs to the health impact functions. The Monte Carlo
option allows you to generate specific percentiles along the estimated incidence
distribution. For example, if you specify 20 Percentiles (default value), then BenMAP-
CE will generate incidence estimates of the 2.5th percentile, 7.5th percentile, and so on,
up through the 97.5th percentile. The number of points suggested in the drop down
menu for Percentiles varies between 10 and 100. The greater the number of chosen
points, the greater the amount of time BenMAP-CE will need to process the results. The
relationship between the number of points and time to process is essentially linear, so a
doubling of the number of points would double the processing time.

If you choose to Run in Point Mode, the field for Percentile points for Monte Carlo
sampling is disabled and will be ignored (treated as zero). However, with the default
Monte Carlo option, the program will still report a point estimate. As discussed in
Chapter 7 on Aggregation, Pooling, and Valuation, by choosing the Point Mode, you limit
your ability to pool the results. You cannot conduct fixed effect/random effects pooling
or any other procedure that depends on knowing the distribution, or the range of
variability of the incidence estimates.

The Air Quality Threshold indicates the minimum air quality value that BenMAP-CE
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 estimate. The
Air Quality Threshold option allows you to explore the impact of any given threshold
on the incidence estimate. This can also be useful for scenarios where you might want
to know the incidence associated with changes in air quality occurring only above a
standard.

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If you are estimating impacts for specific population subgroups (e.g., stratification by
gender, age, race, or ethnicity), BenMAP-CE provides two options for calculating
baseline incidence:

1)	You can use incidence rates averaged over gender, race, and ethnicity (default).
(This option must be selected for the rates supplied with BenMAP-CE, as they are
not stratified by gender/race/ethnicity).

2)	You can use incidence rates that match the gender, race, and ethnicity selected with
your health impact function. (You must import stratified rates for use with this
option). If you select this option and have not imported rates that match each
subgroup, the groups without incidence rates will not be included in the
calculations and will have point estimates of zero.

After making selections for calculating impact functions, BenMAP-CE allows you to save
the configuration for future use. Click the Save As (*.cfgx) button and specify a file with
a .cfgx extension.

6.3 Run Health Impact Configuration

To execute the calculation of incidence for the health impact functions in the
configuration, click the Run button on the bottom right-hand corner of the Health
Impact Functions window. BenMAP-CE will require that you specify a file in which to
save the results, with a .cfgrx extension.

Run and Save

Run and save the CFG results file (*.cfgrx)?

Yes

No

6.4 View and Export Health Impact Results

The Health Impact Results report gives you the opportunity to examine the incidence
results of each health impact function applied at the grid-cell level, or temporarily
aggregate them to, say, the state or national level. The configuration results files (.cfgrx)
contain "raw" health impact estimates that you have not yet aggregated, pooled, or valued.

To begin, click the Health Impact Results tab in the upper portion of the main window. To
display incidence results for a single study (i.e., health impact function), double-click on the
study of interest (or select the study and click the Show Results button). Your results will be
generated and displayed in a results table (on the Data tab). If you choose a single
study, you will also have options to view results on the GIS Map tab and in a simple bar

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chart (view in Chart tab.) To change the selected study, double-click on a different
study choice or select the study and click the Show Results button to refresh the
display.

The Health Impact Results tab provides a simple tool to aggregate the raw incidence
results. You may select an aggregation level from the Aggregation for raw data drop-
down list. If you do so, the Data table will refresh to display the incidence results
aggregated to the selected level.

If you check the option to Create data (table) for multiple studies, results will only be
available in the Data tab. For example, you might want to select three different studies
and view aggregated results at the national level. As you modify your choices, the
display will be updated accordingly.

On the Data tab, clicking the Select Result Fields button opens a Configuration
Results Report window that allows you to choose the columns that will appear in the
results table.

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¦	Grid Fields permit the inclusion of Column and Row fields, which can be helpful
in identifying the grid-cell of a particular line in the report. For example, when
results have been temporarily aggregated to the national level.

¦	Health Impact Function Fields permit the inclusion of various fields which
describe or define a function (e.g., Endpoint Group, Endpoint, Pollutant, Metric,
Author, Year, Start Age, End Age, Gender, Race, Ethnicity, Beta, Beta Distribution).
These fields can be helpful in identifying the health impact function associated
with a particular line in the report.

¦	Result Fields permit the inclusion of fields associated with results of this
analysis (e.g., Point Estimate, Population, Delta, Mean, Percentiles).

' © Configuration Results Report	1 1=1 I ^

Column Selection

Grid Fields:	Health impact Function Fields:	Result Fields:

Field Name



Field Name

*

Field Name

*

|7l Column



Dataset



|7| Point Estimate



[VI Row



Endpoint Group



|7l Population







[71 Endpoint



[jg Delta

—





IH Pollutant



171 Mean







IH Metric



[7] Baseline







[r] Seasonal Metric



[7] Percent of Baseline







IH Metric Statistic



[7] Standard Deviation







[7] Author



|7l Variance







~ Year

-

IH Population Weighted Delta

-

IH Only show user-assigned percentiles	[ OK

At the bottom of the Data tab, there is also an option to specify the number of digits that
appear after the decimal point (click the up or down arrows to edit the number beside
the Digits After Decimal Point field, or type a number directly in the box).

Table 6-1 provides a summary of the optional fields that have not been previously
described in this section.

Table 6-1. Selected Variables in the Reports Based on the APVR file

Variable

Description

Column

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 U.S. county results, this is the county
FIPS code. For U.S. state and national results, this is always 1.

Dataset

Specifies the dataset from which a health impact function was
chosen.

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Variable

Description

Population

Population provides the number of persons used in the health
impact function calculation.

Delta

The difference between the baseline and control scenarios for the
metric used in the health impact function. Calculated by subtracting
the metric value in the control scenario from the metric value in the
baseline scenario.

Point Estimate

The point estimate for the result from the health impact function.
The point estimate is generally based on the mean estimate of the
"Beta" from the health impact function.

Mean

Mean of the points in the Monte Carlo-generated distribution 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 from the baseline to the
control scenario. Calculated by dividing the Point Estimate by the
Baseline.

Standard Deviation

Standard deviation calculated based on the points in the Monte
Carlo-generated percentiles for this result

Variance

Variance calculated based on the points in the Monte Carlo-
generated percentiles for this result.

Percentiles

The number of percentiles depends on the number of points in
Monte Carlo-generated percentiles for this result

Once all of the options have been selected for your report, you can export the Health
Impact Results data. First select the Data tab then click the Export button. This will
bring up a window allowing you to name the file you want to save. Note that by default
BenMAP-CE will export the file to the CFGR folder. Carefully name the file that you are
generating so that you will recognize it in the future!

6.5 Frequently Asked Questions

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 another BenMAP-CE user, who is familiar with the epidemiological literature and
develop your own set of health impact functions. Reviewing the epidemiological
literature can be time consuming, though in some situations, this might be the best
option. For example, it would be worthwhile to develop health impact functions to
estimate the impacts of carbon monoxide exposure, for which BenMAP-CE does not
have pre-installed functions.

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Another option is to use the ozone and PM2.5 configurations used by EPA. These are
available on the BenMAP-CE website (http://www2.epa.gov/benmap/benmap-
community-edition). These functions are derived from the epidemiology literature
described in the appendices to this user manual. 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 Datasets window
available from the BenMAP-CE main menu. See the health impact function section in
Chapter 4: Loading Data for details on how to do this.

How do I learn more about the population data in BenMAP-CE?

Appendix J describes the population data for the United States setup 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.

How do I determine what the Column and Row refer to in the result table?

The Column and Row are variables designed to uniquely identify each grid cell in the
grid definition. In the case of the U.S. County grid definition, the Column refers to the
state FIPS code 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 where particular Column and Row variables occur in the map.

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Contents

Table of Contents

CHAPTER 7 Aggregating, Pooling, and Valuing	7-1

7.1	Introduction to Valuation, Discounting, and Pooling	7-2

7.1.1	Overview of Economic Valuation	7-2

7.1.1.1	Monetizing Benefits	7-3

7.1.1.2	Valuing Reductions in Premature Mortality	7-4

7.1.2	Overview of Discounting	7-5

7.1.3	Overview of Pooling	7-9

Why would you want to pool results?	7-10

7.2	Create Aggregation, Pooling, and Valuation (APV) Configuration	7-16

7.2.1	Selecting Aggregation Levels	7-16

7.2.2	Pooling Incidence Results	7-17

7.2.3	Valuing Pooled Incidence Results	7-26

7.2.4	APV Configuration Advanced Settings	7-30

7.2.4.1	Aggregation and Pooling	7-31

7.2.4.2	Currency Year and Income Growth	7-32

7.3	Open & Modify Existing APV Configuration	7-34

7.4	Run APV Configuration	7-35

7.5	View and Export Pooled Incidence and Valuation Results	7-35

7.5.1	Pooled Incidence Results	7-35

7.5.2	Pooled Valuation Results	7-35

7.6	Frequently Asked Questions	7-36

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7

Aggregating, Pooling,

and Valuing

In this chapter...

¦	Get an overview of valuation, discounting, and pooling.

¦	Configure an Aggregation, Pooling, and Valuation (APV) file.

¦	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.

¦	View and export APV results.

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This section presents an introduction to valuation, discounting, and pooling. Most
BenMAP-CE users find this portion of the program the most complex to understand and
use. You may find yourself referring to this chapter frequently.

Once you have created a configuration results file with incidence results based on your
two air quality grids (refer to Chapter 6), you can use the Aggregate, Pool, and Value
feature to combine the incidence results and place an economic value on the combined
results. You have two options.

¦	Create a New Configuration to Aggregate, Pool, and Value results. You can

create a new type of configuration, termed an Aggregation, Pooling, and
Valuation (APV) Configuration. This allows you to (1) specify the geographic
level at which you want to report your results, (2) specify how you might want
to combine or "pool" the incidence results, and (3) specify how to assign an
economic value to the health incidence results. These selections can be saved in
an APV Configuration file (.apvx) and used to calculate results, which are stored
in an APV Results file (.apvrx).

¦	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

Valuation generally refers to placing a monetary value on estimated health incidence. In
the example below, we discuss U.S. dollar values and provide a brief introduction to
discounting, which has to do with placing less weight on things occurring in the future
than on things occurring today. Finally, we discuss pooling, which has to do with
combining comparable results.

7.1.1 Overview of Economic Valuation

Improvements in ambient air quality generally lower the risk of developing an adverse
health effect by a fairly small amount across 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, or
the observed Cost of Illness (COI) for an effect, and is typically referred to as valuation.
BenMAP-CE uses valuation functions to estimate the monetized benefits of reducing air
pollution.

These benefits (reductions in risk) may vary across the population (and could be zero
for some individuals). Likewise, the WTP for a given benefit is likely to vary from one
individual to another. In theory, the total social value associated with the decrease in
risk of a given health problem resulting from a given reduction in pollution

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concentrations is generally taken to be the sum of everyone's WTP for the benefits they
receive.

7.1.1.1 Monetizing Benefits

Epidemiological studies 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 economic values come from contingent valuation studies, in which
study participants are queried about their WTP to avoid a specific adverse health effect.
When estimates of WTP are not available, it can be approximated by other measures,
most notably COI 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 COI to society, including the total value of the
medical resources 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 medical resources 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 economic value of avoiding the illness, perhaps substantially.

The contingent valuation method (and conjoint analysis) attempts to elicit from people
what they would be willing to pay (WTP) to avoid the illness. Because of the distortion
in the market for medical goods and services, whereby individuals generally do not pay

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the full value of the medical care, this method too is likely to understate the total
economic value of avoiding the illness.

Although the COI and WTP are the two most common methods, other methods have
been used in certain circumstances. The method the benefit analyst chooses to value a
particular health endpoint will depend in part on what data are available. The unit
values currently available for use in BenMAP-CE 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.

7.1.1.2 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 an air quality improvement 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-CE currently offers a variety of options reflecting the uncertainty surrounding
the unit value for premature mortality. See the Appendix I for more detail on the
valuation functions available in BenMAP-CE.

Monetary estimates of changes in premature mortality 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. VSL is the aggregate dollar amount
that a large group of people would be willing to pay for a reduction in their individual
risks of dying in a year, such that we would expect one fewer death among the group
during that year on average. 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 individual fatality risks
resulting from environmental regulation are typically very small, the VSL approach is
usually acceptable for these types of benefit analyses.

The U.S. EPA National Center for Environmental Economics provides answers to
frequently asked questions regarding the economic value of mortality risk on its
website: https://www.epa.gov/environmental-economics/mortality-risk-valuation.
You may wish to consult this site as you have questions regarding how U.S. EPA derives
VSL and applies it in an environmental benefits analysis.

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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 social rate 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 / (l+r)1

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 three key
reasons:

1.	Today's society values benefits that occur today more highly than benefits that will
occur 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 in a future year need to be
comparable to the cost estimates for that same year (which are also discounted). 3.

Discounting can be used to compare the future streams of benefits and costs.
The core BenMAP-CE program 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.1 Discounting can be used to compare the
future benefits with benefits occurring during the analysis year.

1 The PopSim tool estimates the change in population mortality risk over a multi-year period, but it is not yet
possible to estimate the economic value of these impacts in BenMAP-CE.

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Under which scenarios would I need to discount benefits?

Health benefits may occur three different ways after the analysis year specified in
BenMAP-CE.

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-CE. 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 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 environmental regulations 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

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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. To comply with OMB and EPA's recommendations, EPA currently uses
discount rates of 3% and 7% for benefit analyses. For more details, see EPA [1999;
2000] listed in Chapter 1, Section 1.7 (Sources for More Information).

Which health endpoints accrue medical expenses or lost earnings for multiple
years, and how do I discount them?

BenMAP-CE 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).

¦	Chronic bronchitis is assumed to last from the initial onset of the illness
throughout the rest of the individual's life. BenMAP-CE currently includes one
WTP function as well as two COI functions representing the two discount rates
for chronic bronchitis.

¦	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-CE.

AMIs are assumed to accrue costs over five years. Although WTP functions for
AMIs are not available, BenMAP-CE currently includes several COI 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.

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.

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, 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-CE that falls into this category is PM2.5-related premature

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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 exposure-
response model." International Journal 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 Regulatory Impact
Analysis for more detail on assumed lag structures for PM2.5-related premature
mortality (http://www.epa.gov/ttn/ecas/regdata/RIAs/finalria.pdf). Some example lag
structures from the PM RIA are shown in Figures 7-1 and 7-2 below. Currently,
BenMAP-CE does not have the capability to do this type of discounting so you must
discount outside of BenMAP-CE.

Note: Discounting is not necessary for ozone-related premature mortality because it
occurs within the analysis year.

Assumed Lag Structures for PM2 5 Premature Mortality

Year Following Reduction in PM25

Figure 7-1. Graphical representation of assumed lag structures
analyzed in EPA's PM RIA as sensitivity analyses

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100%

Assumed Lag Structures for PM2.5 Premature Mortality (Cumulative)

Year Following Reduction in PM2.5

Figure 7-2. Graphical representation of cumulative assumed lag
structures analyzed in EPA's PM RIA as sensitivity analyses

-Alternate Segmented Lag
-Exponential decay model
-5-year distributed lag

0% '

1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th 11th 12th 13th 14th 15th 16th 17th 18th 19th 20th
year year year year year year year year year year year year year year year year year year year year

7.1.3 Overview of Pooling

For many of the health endpoints (e.g., respiratory hospital admissions), BenMAP-CE
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 meta-analysis. For a number of reasons,
it is often impractical or impossible to combine the original data sets. Combining the
results of studies provides a second-best way to synthesize information. This is referred
to as pooling.

BenMAP-CE allows users to pool the estimated incidence changes predicted by several
studies for the same pollutant-health endpoint group combination (e.g., PM2.5-related
cardiovascular hospital admissions). It also allows the pooling of the corresponding
study-specific estimates of monetary benefits.

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Why would you want to pool results?

There are two good reasons to pool across study results, one practical and one
methodological. Pooling allows you to

¦	Combine or aggregate multiple study estimates into a single estimate. This
combined estimate is easier to report.

¦	Certain types of pooling—including random effects techniques—account for
heterogeneity in the risk estimates reported in the epidemiological literature
used to construct the health impact functions you used to calculate incidence.

However, as we discuss below, pooling may not be such a good option if

¦	You don't know a great deal about the studies used to quantify health impacts;
you'll need to know a lot about epidemiological studies used to construct the
health impact functions in order to pool properly.

¦	You think it is important to convey the variability across incidence estimates for
a given health endpoint.

BenMAP-CE allows you to pool in five different ways:

¦	Addition

¦	Subtraction

¦	User-assigned weights

¦	Random Effects

¦	Fixed Effect.

The examples that follow demonstrate each of these methods (Fixed and Random
Effects are combined in one example.) In each of the examples below, the distribution of
estimated health impacts are represented by a normally distributed probability density
function (PDF) shaped as a bell curve. In each PDF, the mean health impact estimate is
represented by a dashed line. While we illustrate these examples using a normally
distributed PDF for ease of presentation, BenMAP-CE can accommodate several other
types of distributions (e.g. Weibull, Triangular).

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Chapter 7 - Aggregating, Pooling, and Valuing

Example 1: Pooling by Addition

You might want to use the Pooling by Addition option if you would like to aggregate two
outcomes that are non-overlapping. In the example below, we have estimated ischemic
heart disease hospital admissions and dysrhythmia hospital admissions. You'll see that
each endpoint is associated with unique, and non-overlapping, International
Classification of Disease 9th edition (ICD-9) code (Slee 1978). Therefore, it's ok to add
the two estimates, because doing so would not double-count impacts.

Hospital visits for ischemic heart disease (ICD-9:410-414)

Addition allows us to
combine non-
overlapping estimates of
a common health
endpoint

Mean

The sum of ischemic
heart disease and
dysrhythmia is provides
a better overall
characterization of the
effects of air pollution
on cardiovascular
outcomes than either
endpoint alone.

Hospital visits for ischemic heart disease and dysrhythmia

Mean

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Example 2: Pooling by Subtraction

In this example, suppose that you have estimated the incidence of total cardiovascular
hospital admissions using one health impact function. Suppose also that you estimated the
incidence of total cardiovascular hospital admissions, less stroke. In this instance, you could
subtract the second incidence estimate from the first incidence estimate to yield the
number of stroke hospital admissions.

Hospital visits all cardiovascular outcomes (ICD-9:390-459)

o
o

 (N

c
Q)

U
C

Subtraction allows us to
"net out" the incidence
of a health endpoint
from two or more
studies

Mean

Hospital visits for all cardiovascular outcomes except stroke (ICD-9 390-440)

c

a)

T3
O
C

o
o

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Chapter 7 - Aggregating, Pooling, and Valuing

Example 3: Pooling with User-Assigned Weights

In this case, you might have estimated the change in incidence using two different health
impact functions for the same health endpoint and would like to combine them using
weights that you specify.

Peng et al. 2009 Multi-City Study of Cardio Hospital Admissions

Some studies examine a
common health

endpoint and share a
similar methodology,
but may differ slightly in
the populations
examined

Mean

Bell et al. 2008 Multi-City Study of Cardio Hospital Admissions

75% weight

Users may wish to
combine these study
estimates together
using equal weights

Mean

Pooled estimate of Peng & Bell

The pooled value
reflects a weighted
average of the two
studies

Mean

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Example 4: Pooling Using the Fixed Effect and Random Effects

The Fixed Effect and Random Effects pooling techniques are among the most complicated
and are best applied only when you understand clearly the assumptions inherent in the
method and its suitability to the incidence estimates. The example below describes the
procedure for performing this technique.

RRi	RR^ RR3 RR4

Adapted from: Mosteller and Coiditz (1996);Charles Poole EPID 731

For the risks of a given
health outcome there is
a true but unknown
distribution

The individual studies in
the literature report
individual risk estimates
from that distribution

Random effects pooling
accounts for
heterogeneity in the
individual risk estimates
to generate a single
mean risk estimate

The Random-Effects
model assigns each
study a weight based on
two factors:

1.	The spread of
estimates reported
by each study (i.e.
the variance)

2.	How much that
spread of estimates
differs from spread
reported by the
other studies

Adapted from: Mosteller and Coiditz (l996);Charles Poole EPID 73 I

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Pooling: Random-Effects

RRp.

The Random-Effects
model assigns each
study a weight based on
two factors:

I. The spread of
estimates reported
by that study (i.e.
the variance)
1. How much that
spread of estimates
differs from the
other studies

Finally, the
Random-Effects
model calculates a
weighted average
of the studies

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 Monte Carlo 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.

Table 7-1. Pooling Approaches for Incidence and Valuation Results

Pooling
Approach

Description of Pooling Approach

Availability

Point
Mode

Monte
Carlo

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
Monte Carlo mode, BenMAP-CE chooses the first point
from each result in the pooling and does a simple sum
to 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 from the
distribution for each result, and the sum of 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 percentile points.

No

Yes

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Pooling
Approach

Description of Pooling Approach

Availability

Point
Mode

Monte
Carlo

Subtraction
(Dependent)

Results are subtracted assuming they are perfectly
correlated. All subsequent results are subtracted from
the 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 Monte Carlo mode, BenMAP-CE
chooses the first point from each 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 from the
distribution for the first result, and then random
points are chosen from the distribution for each
subsequent result and subtracted from the 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 percentile points.

No

Yes

User defined
Weights

Weights are specified by the user. In Point Mode, the
new result is generated by a simple weighted sum of
the input results. In Monte Carlo 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-CE will normalize them internally.
Also note that BenMAP-CE initializes all the weights to
1 /n, where n is the number of results being pooled.

Yes

Yes

7.2 Create Aggregation, Pooling, and Valuation (APV) Configuration

Once you have run a configuration result file (see Chapter 6), you can begin creating
your APV configuration. You will start with selecting the aggregation levels for
incidence and valuation results, then move on to pooling and valuation. These
processes are described in detail below.

7.2.1 Selecting Aggregation Levels

Double-clicking Aggregation from the BenMAP-CE tree menu item opens the window
which lets you choose the level of aggregation for the incidence and valuation results.

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Chapter 7 - Aggregating, Pooling, and Valuing

@ Aggregation

l-| ||N*W|

Incidence Aggregation:
Valuation Aggregation:



County ~ ]



CMAQ 12km Nation ~ ]







Cancel OK

7.2.2 Pooling Incidence Results

To begin pooling incidence results, double-click Pooling Method from the BenMAP-CE
tree menu. In the top half of the Select and Pool Incidence Estimates window, you
will find a list of Available Incidence Results. The results are represented by the
health impact functions from which they were created.

'f Select and Pool Incidence Estimates
Available Incidence Results



Filter Dataset

Filter Endpoint Group

1

L -1





fH Groups View: [ Details ~ ] j Select study fields j Check HIF Changes

Endpoint

[rl Acute Bronchi...
[p] Asthma Exace...
1H Asthma Exace...
[r] Asthma Exace...
|T] Emergency Ro...
[r] Emergency Ro...
IH Asthma Exace...
IT] Asthma Exace...

Endpoint Group
Acute Bronchitis
Asthma Exacerbation
Asthma Exacerbation
Asthma Exacerbation
Emergency Room Visit-
Emergency Room Visit._
Asthma Exacerbation
Asthma Exacerbation

Dataset Name
EPA Standard Hea..
EPA Standard Hea..
EPA Standard Hea..
EPA Standard Hea..
EPA Standard Hea..
EPA Standard Hea..
EPA Standard Hea..
EPA Standard Hea..

Start Age End Age

Author
Dockery...
Mar et al.
Ostro et...
Ostro et...
Norris e...
Slaught...
Mar et al.
Ostro et...

Add All Studies

Remove All Studies

Select Pooling Methods

Pooling Window Name: PoolingWindow2	| Add | | Delete j j Show Tile ] Pooling Window Number: 1	Target Grid Type:jcMAQ 12km Nation

PoolingWindow2

Studies, By Endpoint

Pooling Method

Endpoi... Author Qualifier Location Start Age End Age Year Other Pollutan... Race Ethnicity Gem

[ Please place incidence results here. ]

Condensed View

There are several steps to pooling your incidence results:

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Step 1. Add incidence results to the pooling window

Using the available incidence results, you can drag individual incidence results to the
pooling window, or select the result(s) using the checkboxes next to each study, and
click Add Study to move them to the pooling window. You do not have to add all of your
incidence results to the pooling window, but note that only those results showing in the
pooling window will be included in the pooled incidence or valuation results. As with
health impact functions, in Chapter 6, there is a filter method above the Available
Incidences Results for your convenience.

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.).

v Select and Pool Incidence Estimates
ill Available Incidence Results
Filter Dataset

1111 n

Filter Endpoint Group

FH Groups View: | Details ~ | | Select study fields j j Check HIP Changes^

Endpoint

Endpoint Group

Dataset Name Start Age

End Age Author

|Tj Acute Bronchi..

Acute Bronchitis

EPA Standard Hea... 8

12 Dockery...

|r] Asthma Exace..

Asthma Exacerbation

EPA Standard Hea... 6

18 Mar et al.

|H Asthma Exace..

Asthma Exacerbation

ERA Standard Hea... 6

18 Ostro et...

|r| Asthma Exace..

Asthma Exacerbation

EPA Standard Hea... 6

18 Ostro et...

|r| Emergency Ro..

Emergency Room Visits, Respiratory

EPA Standard Hea... 0

17 Norris e...

[n] Emergency Ro..

Emergency Room Visits, Respiratory

EPA Standard Hea... 0

99 Slaught...

0 Asthma Exace..

Asthma Exacerbation

EPA Standard Hea... 6

18 Maretal.

fr| Asthma Exace..

Asthma Exacerbation

EPA Standard Hea... 6

18 Ostro et...



Add All Studies

Remove All Studies

Select Pooling Methods

Pooling Window Name: PoolingWindow2
PoolingWindow2

Show Tile Pooling Window Number: 1

Target Grid Type: CMAQ 12km Nation

Studies, By Endpoint	Pooling Method

B-fti Emergency Room Visits, R... |None
(-j^Norris
LfH Slaughter

Asth m a Exa ce rb ati on |None
U^Mar
L--H Ostro

Endpoi... Author

^Emerge... Norris e...	0

Emerge... Norris e...	Seattle,... 0

Emerge... Slaught...	Spokan... 0

7]Asthma... Maret...	6
Asthma... Maretal. Uses in... Spokan... 6
Asthma... Ostro et... Ostro st... LosAng... 6

Qualifier Location Start Age End Age Year

Other Pollutan... Race

Ethnicity Gem

18
18

1999
2005

2004
2001

Condensed View

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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Chapter 7 - Aggregating, Pooling, and Valuing

Select and Pool Incidence Estimates



Available Incidence Results
Filter Dataset

Filter Endpoint Group

IH Groups View: [ Details ~ ] j Select study fields~~j | Qieck HIF Changes~

Endpoint

Endpoint Group

Dataset Name Start Age

End Age Author

in Acute Bronchitis

Acute Bronchitis

EPA Standard Hea... 8

12 Dockery...

[rl Asthma Exacerbation, Shortness of Breath

Asthma Exacerbation

EPA Standard Hea... 6

18 Maretal.

H Asthma Exacerbation, Shortness of Breath

Asthma Exacerbation

EPA Standard Hea... 6

18 Ostro et...

0 Asthma Exacerbation, Wheeze

Asthma Exacerbation

EPA Standard Hea... 6

18 Ostro et..

fH Emergency Room Visits, Asthma

Emergency Room Visits, Respiratory

EPA Standard Hea... 0

17 Norris e...

[P] Emergency Room Visits, Asthma

Emergency Room Visits, Respiratory

EPA Standard Hea... 0

99 Slaught...

[r| Asthma Exacerbation, Cough

Asthma Exacerbation

EPA Standard Hea... 6

18 Maretal.

[r] Asthma Exacerbation, Cough

Asthma Exacerbation

EPA Standard Hea... 6

18 Ostro et...

Add All Studies

Remove All Studies

Select Pooling Methods

Pooling Window Name: PoolingWindowl
PoolingWindonl |

Show Tile Pooling Window Number: 1

Target Grid Type: CMAQ 12km Nation

Studies, By Endpoint

B-fti Asthma Exacerbation

R- ij Asthma Exacerbation,... |None
l-^Mar
L|—1 Ostro
-J"H Ostro
-O Ostro

Pooling Method
| None

Endpoint

Author Qualifier Location Start Age End Age Year

"1 Asth ma Exacerbation, Shortness of Breath Asthma Ex...	Ostro et...

~] Asthma Exacerbation, Cough	Mar et...

Asthma Exacerbation, Cough	Maretal.	Uses in...	Spokan...

Asthma Exacerbation, Cough	Ostro et...	Ostro st...	Los Ang...

Asthma Exacerbation, Shortness of Breath	Ostro et...	Ostro st...	Los Ang...

Asthma Exacerbation, Wheeze	Ostro et...	Ostro st...	Los Ang...

J

Condensed View

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 all four
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 the
Pooling Method is always the first column after the Studies, By Endpoint 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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Chapter 7 - Aggregating, Pooling, and Valuing

j Select and Poo) Incidence Estimates



Available Incidence Results
Filter Dataset

Filter End point Group

[Pi Groups View: | Details ~] | Select study fields | | Check H1F Changes

Endpoint

Endpoint Group

Dataset Name Start Age

End Age

Author

PI Acute Bronchitis

Acute Bronchitis

EPA Standard Hea... 8

12

Dockery...

[r| Asthma Exacerbation, Shortness of Breath

Asthma Exacerbation

EPA Standard Hea... 6

18

Mar etal.

F1 Asthma Exacerbation, Shortness of Breath

Asthma Exacerbation

EPA Standard Hea... 6

18

Ostro et...

p| Asthma Exacerbation, Wheeze

Asthma Exacerbation

EPA Standard Hea... 6

18

Ostro et..

in Emergency Room Visits, Asthma

Emergency Room Visits, Respiratory

EPA Standard Hea... 0

17

Norris e...

0 Emergency Room Visits, Asthma

Emergency Room Visits, Respiratory

EPA Standard Hea... 0

99

Slaught...

|r| Asthma Exacerbation, Cough

Asthma Exacerbation

EPA Standard Hea... 6

18

Mar et al.

[r] Asthma Exacerbation, Cough

Asthma Exacerbation

EPA Standard Hea... 6

18

Ostro et...



Add Ail Studies

Remove All Studies

Select Pooling Methods

Pooling Window Name: PoolingWindowl
PoolingWindowl

Show Tile Pooling Window Number: 1

Target Grid Type:: CMAQ 12km Nation

Studies, By Endpoint
s-lii Asthma Exacerbation
l-flMar
B-k Ostro etal.

Ostro
Ostro
-P"| Ostro

Pooling Method

Author

Endpoint

Qualifier Location Start Age End Age

None

j^Mar et al. Ostro et al.	Asthma Exacerbation, Cough Asthma Exacerbation, Sh...

Mar et al.	Asthma Exacerbation, Cough	Uses in...

Ostro et al.	Asthma Exacerbation, Cough Asthma Exacerbation, Sh...

Ostro et al.	Asthma Exacerbation, Wheeze	Ostro st...

Ostro etal.	Asthma Exacerbation, Shortness of Breath	Ostro st...

Los Ang...
Los Ang...

Ostro et al.

Asthma Exacerbation, Cough

Ostro st... Los Ang...

Condensed View

This example uses exactly the same incidence results as the previous example, but with
the Author column (variable) immediately after the Pooling Method column.

In the Tile View (accessed by selecting Tile View from the View Menu), you can sort
studies by endpoint tiles by selecting the appropriate criterion in the Sort By drop-
down menu.

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Chapter 7 - Aggregating, Pooling, and Valuing

Select and Pool Incidence Estimates

Available Incidence Results
Filter Dataset	Filter Endpoint Group	Filter

Sort By

t | iTl Groups View: [Tile

Select study fields Check HIF Changes

Add Study Remove Study

Add All Studies

Remove All Studies

Select Pooling Methods

Pooling Window Name: PoolingWindowl	[ Add ] j Delete ] [ Show Tile ] Pooling Window Number: 1

PoolingWindowl

Target Grid Type: CMAQ 12km Nation

Studies, By Endpoint

Asthma Exacerbation
Ostro etal.
-C Ostro
-^1 Ostro
L~n Ostro
—^Mar

Pooling Method

Author
Mar et...
Ostro et...
Ostro et...
Ostro et...
Ostro et...

Endpoi...
Asthma ..
Asthma ..
Asthma ..
Asthma ..
Asthma ..

Qualifier Location Start Age End Age Year

Other Pollutan... Race Ethnicity Gem

Mar etal. Asthma.

Ostro st...	Los Ang...

Ostro st...	Los Ang...

Ostro st...	Los Ang...

Uses in...	Spokan...

2001
2001
2001
2004

Condensed View

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-CE helps you to
identify these spots by inserting a value of None in the Pooling Method column at each
spot where pooling is possible.

When a row that represents a group of incidence results is selected, an arrow appears
at the right of the Pooling Method column, indicating the availability of a drop-down
menu where you can select the desired Pooling Method. If you select User Defined
Weights from the Pooling Method menu, a Weight column will appear to the right of
the Pooling Method menu. All studies are assigned equal weight by default unless you
update them. The sum of all User Defined Weights must equal 1.

If a Pooling Method is selected, you can view the calculated weights before executing
the pooling by pressing the Preview button. In the case of the Random or Fixed
Effects pooling method, the Preview button will reveal whether random or fixed
effects are being applied.

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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 pool results. 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. The pooling windows are displayed in a tabbed format.

To open a new pooling window, simply click on the Add button next to the Pooling
Window Name. 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 clicking on the window you wish to
delete and clicking the Delete button.

JS Select and Pool Incidence Estimates
Available Incidence Results
Filter Dataset



Filter Endpoint Group

[H Groups View: [ Details ~ [ | Select study fields~j

Endpoint

Endpoint Group

Dataset Name

Start Age

End Age

Author

|F] Acute Bronchi..

Acute Bronchitis

EPA Standard Hea...

8

12

Dockery...

|r ] Asthma Exace..

Asthma Exacerbation

EPA Standard Hea...

6

18

Mar et al.

n Asthma Exace..

Asthma Exacerbation

Ef% Standard Hea...

6

18

Ostro et...

|r| Asthma Exace..

Asthma Exacerbation

EPA Standard Hea...

6

18

Ostro et...

0 Emergency Ro..

Emergency Room Visit-

EPA Standard Hea...

0

17

Norris e...

IH Emergency Ro..

Emergency Room Visit...

EPA Standard Hea...

0

99

SI aught-

Remove Study

Add All Studies

Remove All Studies

Select Pooling Methods

Pooling Window Name: Acute Bronchitis	j Add j j Delete | | Show Tile j Pooling Window Number: 3	Target Grid Type:|CMAQ 12km Nation

Acute Bronchitis j Asthma Exacerbation Emergency Room Visits, Respiratoiy |

Studies, By Endpoint

Pooling Method

Endpoi..

Author Qualifier Location Start Age End Age

Year

Other Pollutan... Race

Ethnicity Gem

B-fo: Asthma Exacerbation

UHI Ostro

Lj—| Ostro

|None

| Asthma .
Asthma.
Asthma .

Ostro et... 6 18
Ostro et... Ostro st... LosAng... 6 18
Ostro et... Ostro st... LosAng... 6 18

2001
2001





<| in 1 ~

Condensed View

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Example: Simple Sorting & Pooling

If you add a single incidence result to the pooling window, you will see just one line, and
therefore no opportunities to pool. This is shown in the example below:

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.

Select Pooling Methods

Pooling Window Name: Emergency Room Visits J Add j j Delete [ | Show Tile | Pooling Window Number: 3	Target Grid Type:|CMAQ 12km Nation

Acute Bronchitis | Asthma Exacerbation Emergency Room Visits, Respiratory

Studies, By Endpoint Pooling Method

Endpoi..

Author

Qualifier Location

Start Age End Age

Year

Other Pollutan... Race

Ethnicity Gem

El-li Emergency Room Visits, R... (None

| Emerge...

Slaught...



0 99







|~n Slaughter
LfHNorris

Emerge...
Emerge...

Slaught...
Norris e...

Spokan...
Seattle,...

0 99
0 17

2005
1999

N02, S02





fir



















1







Condensed View Advanced	Cancel	Next

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 'Asthma Exacerbation, Shortness of Breath'
incidence results.

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If you now add two more incidence results to the window whose health impact
functions have the same Endpoint Group but different Endpoints, you will see the
following:

Pooling Window Name: PoolingWindow2	j Add j | Delete | | Show Tile | Pooling Window Number: 1	Target Grid Type: jCMAQ 12km Nation

PoolingWindov/2

Studies, By Endpoint

Pooling Method



Endpoint

Author

Qualifier Location

Start Age End Age

Year

Oth t

B-ty Asthma Exacerbation

None

Asthma Exacerbation, Wheeze Asthma Exacerbati..

Ostro et...



6 18







i-i|c Asthma Exacerbation,...

None

Asthma Exacerbation, Shortness of Breath

Mar et...



6 18







UOMar





Asthma Exacerbation, Shortness of Breath

Mar et al.

Uses in... Spokan...

6 18

2004





Ln Ostro





Asthma Exacerbation, Shortness of Breath

Ostro et...

Ostro st... Los Ang...

6 18

2001





-f™] Ostro





Asthma Exacerbation, Wheeze

Ostro et...

Ostro st... Los Ang...

6 18

2001





-^Mar





Asthma Exacerbation, Cough

Mar et al.

Uses in... Spokan...

6 18

2004



<





irr













Condensed View

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 Studies, By Endpoint corresponding to Endpoint 'Asthma
Exacerbation, Shortness of Breath' you would end up with three results (one pooled and
two unpooled) instead of four individual incidence results.

Pooling Window Name: PoolingWindow2	Add	Delete Show Tile Pooling Window Number: 1	Target Grid Type: CMAQ 12km Nation

PoolingWindow2

Studies, By Endpoint

Pooling Method



Endpoint

Author

Qualifier

Location

Start Age

nd Age Year

Oths

B-5t Asthma Exacerbation

|None



Asthma Exacerbation, Wheeze Asthma Exacerbati..

Ostro et...





6







-1A Asthma Exacerbation,..

jiFixed Effects

i *

Asthma Exacerbation, Shortness of Breath

Maret...





6







UOMar





Asthma Exacerbation, Shortness of Breath

Mar et al.

Uses in...

Spokan...

6

2004





1—Q Ostro





Asthma Exacerbation, Shortness of Breath

Ostro et...

Ostro st...

Los Ang...

6

2001





Ostro





Asthma Exacerbation, Wheeze

Ostro et...

Ostro st...

Los Ang...

6

2001



4 1

L-^Mar



irr

Asthma Exacerbation, Cough

Maretal.

Uses in...

Spokan...

6

2004

>

Condensed View

If you choose to pool at the Studies, By Endpoint for the Endpoint Group 'Asthma
Exacerbation' (where the Pooling Method field says 'None' in the above imagej, you
will end up with a single result representing all four of the original incidence results.

Pooling Window Name: PoolingWindow2	| Add J j Delete | j Show Tile | Pooling Window Number: 1	Target Grid Type:jcMAQ 12km Nation

PoolingWindov/2

Studies, By Endpoint

fl, Asthma Exacerbation

B-Asthma Exacerbation,..

kOMar
L-Pl Ostro
-f™| Ostro
-^Mar

Pooling Method

User Defined Weights

|None

Weight Endpoint

Author Qualifier Location Start Age End Age Year

Asthma Exacerbation, Wheeze Asthma Exacerbati... Ostro et...

Asthma Exacerbation, Shortness of Breath
Asthma Exacerbation, Shortness of Breath
Asthma Exacerbation, Shortness of Breath
Asthma Exacerbation, Wheeze
Asthma Exacerbation, Cough

Mar et...

Maretal.	Uses in...

Ostro et...	Ostro st...

Ostro et...	Ostro st...

Spokan...
Los Ang...
Los Ang...

Maretal. Uses in... Spokan... 6

2004
2001
2001
2004

Condensed View

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Chapter 7 - Aggregating, Pooling, and Valuing

If you pool at both spots:

¦	First, the 'Asthma Exacerbation, Shortness of Breath' results are pooled to a give a
single 'Asthma Exacerbation, Shortness of Breath' result.

¦	Next, the three separate Endpoint results are pooled to give a single 'Asthma
Exacerbation' result.

Pooling Window Name: PoolingWindow2

Show Tile Pooling Window Number: 1

Target Grid Type: CMAQ 12km Nation

Poolin?V/indow2

Studies, By Endpoint

Asthma Exacerbation
Asthma Exacerbation,...
[-~Mar
L-HOstro
-nOstro
|~~ Mar

Pooling Method

Weight Endpoint

Author Qualifier Location Start Age

User Defined Weights

Asthma Exacerbation, Wheeze Asthma Exacerbati... Ostro et...

]Asthma Exacerbation, Shortness of Breath
Asthma Exacerbation, Shortness of Breath
Asthma Exacerbation, Shortness of Breath
Asthma Exacerbation, Wheeze
Asthma Exacerbation, Cough

Maret._	6

Maretal.	Uses in...	Spokan...	6

Ostro et...	Ostro st...	Los Ang...	6

Ostro et...	Ostro st...	Los Ang...	6

Maretal.	Uses in...	Spokan...	6

2004
2001
2001
2004

~

Condensed View

These same principles apply no matter how many incidence results are being pooled,
and regardless of which pooling methods are selected.

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.

For example, you might want to pool all results of health impact functions by a
particular Author, rather than pooling all results 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. In order to change the sorting levels of the
health impact functions, move the first category on which you wish to sort to the first
position after the Pooling Method column. In this example, you would drag the Author
column ahead of Endpoint column. As you can see, after this change the pooling options
are very different.

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Chapter 7 - Aggregating, Pooling, and Valuing

Sorted by Author:

Select Pooling Methods

Pooling Window Name: PooiingWindow2
PoolingWindovvl PoolingWindow2 |

Show Tile Pooling Window Number: 2

Target Grid Type: CMAQ 12km Nation

Studies, ByEndpoint

Asthma Exacerbation
t3-lA Ostroetal.
UH1 Ostro
Lf| Ostro
J Maretal.
HHMar
L^|Mar

Pooling Method

Fixed Effects

|None

Author Endpoint

Ostro et... Asthma Exacerbation, Wheeze Asthma Exacerbati...
Ostro et... Asthma Exacerbation, Wheeze Asthma Exacerbati...
Ostro et... Asthma Exacerbation, Wheeze
Ostro et... Asthma Exacerbation, Shortness of Breath
^Mar et a I. Asthma Exacerbation, Shortness of Breath Asthm...
Mar et a I. Asthma Exacerbation, Shortness of Breath
Mar et al. Asthma Exacerbation, Cough

Qualifier Location Start Age End Age Year

Ostro st...
Ostro st...

Uses in...
Uses in...

Los Ang...
Los Ang...

Spokan...
Spokan...

2001
2001

2004
2004

Condensed View

Sorted by Endpoint:

Select Pooling Methods

Pooling Window Name: PoolingWindowl

PoolingWindov/1

|poolingVVindcvv2

Show Tile Pooling Window Number: 2

Target Grid Type: CMAQ. 12km Nation

Studies, ByEndpoint
B-i Asthma Exacerbation
H- „ Asthma Exacerbation,..
U^Mar
I—n Ostro
-HOstro

Pooling Method

Endpoint

Asthma Exacerbation, Wheeze Asthma Exacerbati...
Asthma Exacerbation, Shortness of Breath
Asthma Exacerbation, Shortness of Breath
Asthma Exacerbation, Shortness of Breath
Asthma Exacerbation, Wheeze
Asthma Exacerbation, Cough

Author	Qualifier	Location	Start Age End Age Year

Ostro et...	6	18

Maret...	6	18

Maretal.	Uses in...	Spokan...	6	18	2004

Ostro et...	Ostro st...	Los Ang... 6	18	2001

Ostro et...	Ostro st...	Los Ang... 6	18	2001

Maretal.	Uses in...	Spokan...	6	18	2004

Condensed View

If you use two different pooling windows, each sorted as shown above, you can create
results pooled by Author, and results pooled by Endpoint.

7.2.3 Valuing Pooled Incidence Results

After you have specified your incidence pooling options, click on the Next button and
select valuations and valuation pooling options from the Select Valuation Methods,
Pooling and Aggregation window. This window should look quite similar to the Select
and Pool Incidence Estimates window, with tree views on the left side representing
the valuation methods available, and various pooling windows on the right side
representing the selected valuations and pooling options.

For each pooling window you created on the Select and Pool Incidence Estimates
window, there is a corresponding pooling window in the Select Valuation Methods,
Pooling, and Aggregation window. You will notice that the number of incidence
estimates available in the valuation pooling window will reflect any pooling choices you
made in the Incidence Pooling and Aggregation window. For example, if in the Select
and Pool Incidence Estimates window you pooled 5 incidence estimates into a single
incidence estimate, you will see a single incidence estimate in the Valuation Methods
Pooling and Aggregation window.

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Chapter 7 - Aggregating, Pooling, and Valuing

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 Select and Pool Incidence Estimates 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:

O? Select Valuation Methods, Pooling and Aggregation

Valuation Methods

EndPoint

Endpoint Group

Start A...

End

Function

Dai

*

Asthma E.

. Asthma Exacerb..

18

99

A*B*AII.„

EPA



Asthma E.

. Asthma Exacerb..

0

17

A*B*AII..,.

EPA



Asthma E.

. Asthma Exacerb..

0

17

A*B*AIL_

EPA



Asthma E.

. Asthma Exacerb..

18



A-AIIGo...

EPA



Asthma E.

. Asthma Exacerb..

0

17

A*AIIGo...

EPA



Asthma E.

. Asthma Exacerb..

18



A-B-AII...

EPA



Asthma E.

. Asthma Exacerb..

0

17

A-B-AII...

EPA



Asthma E.

. Asthma Exacerb..

0

17

A*B-AII...

EPA



Asthma E.

. Asthma Exacerb..

18



A-AIIGo...

EPA



Asthma E.

. Asthma Exacerb..

0

17

A*AIIGo...

EPA



Asthma E.

. Asthma Exacerb..

18



A-B*AII...

EPA



Asthma E.

. Asthma Exacerb..

0

17

A*B*AII...

EPA



Asthma E.

. Asthma Exacerb..

0

17

A*B*AII...

EPA

Asthma E.

. Asthma Exacerb..

18



A*AIIGo...

EPA

Asthma E.

. Asthma Exacerb..

0

17

A-AIIGo...

EPA

Asthma E.

. Asthma Exacerb..

18



A-B-AII...

EPA

Asthma E.

. Asthma Exacerb..

0

17

A-B-AII...

EPA

Asthma E.

. Asthma Exacerb..

0

17

A-B-AII...

EPA

Asthma E.

. Asthma Exacerb..

18

99

A'AHGo...

EPA

Asthma E.

. Asthma Exacerb..

0

17

A-AIIGo...

EPA

~

< i

i







~

Click and select the valuation methods on the left panel,

and then drag them to the right panel under the desired Endpoint

Resolve Pooling Conflic

Show Selections

PooiingWindowO	;|PoolingWindowl

Tree node

B- j. Asthma Exacerbation
Ostroetal.
~~ Ostro

-	Ostro

-	Ostro
0-"~" Mar



Variable Dataset:

Pooling Method

None

None

None

None

None

None

Al
Mc
Os
Os
Os
05
Mc

Delete Selected

Advanced Cancel Save As (-.apvx) Run As (-.apvrx)

There are several steps to take in the Select Valuation Methods, Pooling, and
Aggregation window:

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 on the left-side of the window 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 from the table
and drag-and-drop it onto the appropriate incidence result in the pooling window. Note
that BenMAP-CE will only allow you to drop Valuation Methods onto incidence results
which have the same Endpoint Group value. For example, BenMAP-CE will not allow
you to drop a 'Mortality' valuation on a 'Hospital Admissions' incidence result. Note also

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Chapter 7 - Aggregating, Pooling, and Valuing

that you can only drag-and-drop individual Valuation Methods, not entire groups of
them. For explanations of the various valuation methods, see Appendix I.

If you have added any of your own valuation methods, as described in the Valuation
Data section of Chapter 4: Loading Data, you can drag-and-drop them in the same way
as the EPA Standard Valuation Functions shown in these examples.

Select Valuation Methods, Pooling and Aggregation

13 J

Valuation Methods

EndPoint

Endpoint Group

Start A...

End

Function

Dai



Asthma E.

. Asthma Exacerb...

18

99

A*B-A!I...

EPA



Asthma E..

.. Asthma Exacerb...

0

17

A*B*AII...

EPA



Asthma E.

.. Asthma Exacerb...

0

17

A'B'AII...

EPA



Asthma E.

.. Asthma Exacerb...

18

99

A*"AIIGo...

EPA



Asthma E.

. Asthma Exacerb...

0

17

A*AHGo...

EPA



Asthma E.

. Asthma Exacerb...

18

99

A*B#AII„.

EPA



Asthma E..

. Asthma Exacerb...

0

17

A*B*AII...

EPA



Asthma E..

. Asthma Exacerb...

0

17

A*B#AII...

EPA



Asthma E.

. Asthma Exacerb...

18

99

A'AIIGo...

EPA



Asthma E.

. Asthma Exacerb...

0

17

A'AIIGo...

EPA



Asthma E.

. Asthma Exacerb...

18

99

A*B*AII...

EPA



Asthma E.

. Asthma Exacerb...

0

17

A"B*AII„.

EPA

	

Asthma E..

. Asthma Exacerb...

0

17

A-B-AII...

EPA



Asthma E..

. Asthma Exacerb...

18

99

A'AIIGo...

EPA



Asthma E..

. Asthma Exacerb...

0

17

A-AIIGo...

EPA



Asthma E..

. Asthma Exacerb...

18

99

A*B*AII„.

EPA



Asthma E..

. Asthma Exacerb...

0

17

A*B*AII...

EPA



Asthma E.

. Asthma Exacerb...

0

17

A*B*AII.„

EPA



Asthma E..

. Asthma Exacerb...

18

99

A'AIIGo...

EPA



Asthma E..

. Asthma Exacerb...

0

17

A-AIIGo...

EPA

~

< i

i







~

Click and select the valuation methods on the left panel,

and then drag them to the right panel under the desired Endpoint

Resolve Pooling Conflict

Show Selections

PoolingWindoviO 'poolingWindwJL |

Tree node

0- 1 Asthma Exacerbation
G-D-sJ, Ostroetal.

Ostro
Ostro

l—i WTP: bad astj-irn..
"" Ostro
S-™~" Mar

Pooling Method

At,

None

Mc

None

Os

None

Os

None

Os



Os

None

Os'

None

Mc

Variable Dataset:

Delete Selected

Advanced Cancel Save As (".apvx) Run As (*.apvrx)

When BenMAP-CE runs the APV Configuration, it will generate a valuation result for
each Valuation Method you select by running the method's Function on the selected
incidence results. You do not need to select a Valuation Method for every incidence
result—incidence results without any Valuation Methods will simply be ignored when
valuation results are generated, aggregated, and pooled.

Because valuation functions include an uncertainty distribution around them,
generating valuation results is fairly complicated. The procedure depends on whether
the incidence results being used were generated in Point Mode or with the default
Monte Carlo method. See Chapter 6: Incidence Estimation (Section 6.2.3) for details on
these advanced configuration settings.

In Point Mode, BenMAP-CE simply runs the valuation functions once using the point
estimate of the incidence result and the mean of the valuation function as inputs.

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With the Monte Carlo feature, on the other hand, BenMAP-CE 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-CE multiplies each combination of values from the incidence result
with each of the hundred valuation points, and puts the results into a holding container.
(For example, if the incidence result has 10 percentile 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 percentile 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 Select and Pool Incidence
Estimates 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.

Show Selections

PoolingWindfMO PoolingWindowl

i

I—It

Tree node	Pooling Method

El-Asthma Exacerbation None
EjD- J,. Asthma Exacerbation,... None
Mar

WTP: bad asthrn
•!L WTP: 1 sympto..

B-"- Ostro
S-~~ Ostro
Mar

Author

Ostro et a I. Mar-
Mar et al. Ostro...
Mar et al.

Mar et al.

Mar et al.

Ostro et al.

Ostro et al.
Mar et al.

Qualifier Start Age
6
6
6
6
6
6
6
6

Uses in...
WTP: ba...
WTP: 1 s...
Ostro St...
Ostro st...
Uses in...

Step 4. Select Variable Dataset

In order to proceed to the next step, you must select a Variable Dataset from the drop-
down menu beneath the pooling window. 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.

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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.

j Select Valuation Methods, Pooling and Aggregation

Valuation Methods

EndPoint

Endpomt Group Start A...

End ...

FuiKtion

Datas

*

Asthma £..

Asthma Exacerb.-. IB

99

A*B*AII...

EPA Sti



Asthma E...

Asthma Exacerb... 0

17

A*B*AII.„

EPA St;



Asthma E..

Asthma Exacerb... 0

17

A-B'AII...

EPASti



Asthma E_

Asthma Exacerb._ 18



A-AllGo...

EPA St?



Asthma E_.

Asthma Exacerb... 0

17

A-AIIGo...

EPA SO



Asthma E..

Asthma Exacerb.. 18



A"8"AII...

EPA St,

E '

Asthma €..

Asthma Exacerb. 0

17

A-8-AII...

EPAStJ



Asthma E..

Asthma Exacerb,. 0

17

A*B*AII...

ERAStJ



Asthma E-

Asthma Exacerb..- IB



A*AIIG0...

EPA SO



Asthma E_.

Asthma Exacerb... 0

17

A*AIIGO~.

EPAStJ



Asthma E-

Asthma Exacerb... 18



A*B*AII..-

EPA So



Asthma E_

Asthma Exacerb... 0

17

A*B*AII...

EPA SO



Asthma E_.

Asthma Exacerb... 0

17

A-B-AII...

EPA So

Asthma E..

Asthma Exacerb.. 18



A"AIIGo...

EPA SO

Asthma E.

Asthma Exacerb... 0

17

A-AIIGo...

EPA SO

Asthma E...

Asthma Exacerb.. 18



A-B"AII...

EPA SO

Asthma E

Asthma Exacerb. 0

17

A"B*AII...

ERA SO

Asthma E~

Asthma Exacerb... 0

17

A*B*AII..

EPA SO

Asthma E-

Asthma Exacerb... IB

99

A-AIIGo...

EPA SO

Asthma £..

Asihma Exacerb... 0

17

A'AIIGo...

EPASti



Click and setea (tie valuation methods on the (eft panel.

and then drag them to the right panel under the desired Endpoint

Show Selections

p»oli»eWiiuto
-------
Chapter 7 - Aggregating, Pooling, and Valuing

Advanced Pooling Settings:

£2

Aggregation and Pooling

Default Advanced Pooling Method:

Random Seed:

Sort Incidence Results

Default Monte Carlo Iterations:

Cancel

OK

7.2.4.1 Aggregation and Pooling

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-CE lets users assign
"subjective" weights and it assigns 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 list:

Round weights to two digits. BenMAP-CE 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 percentile 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 percentile points.

Round weights to three digits. BenMAP-CE rounds each weight to three digits (e.g.
0.732), and then multiplies these weights by 1,000 to get three-digit integers. Each
distribution (set of percentile points) is then put into a holding container for an
integral number of times, according to its integral weight. This holding container is

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Chapter 7 - Aggregating, Pooling, and Valuing

then sorted low to high and binned down to the appropriate number of percentile
points.

Use exact weights for Monte Carlo. BenMAP-CE uses exact weights and a Monte Carlo
simulation. During each iteration of the procedure, a particular result is selected
with a probability equal to its weight. Once a result is selected, one of its percentile
points is chosen at random and put into a holding container. This is done some
number of times (see Monte Carlo Iterations, below), and the holding container is
then sorted low to high and binned down to the appropriate number of percentile
points.

Default Monte Carlo Iterations

This drop-down list is only enabled when Use exact weights for Monte Carlo is
selected as the Default 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 Advanced Pooling
Settings window (see Step 1, above).

Random Seed

The Advanced Pooling Settings window allows the specification of a Random
Seed. Many of the pooling methods require the generation of sequences of random
numbers, e.g. choosing a random percentile 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
determined from an APV Configuration Result file (.apvrx) Audit Trail Report.

Sort Incidence Results

The Sort Incidence Results should generally be always checked. This setting ensures
that the incidence percentile-point results are sorted low to high.

7.2.4.2 Currency Year and Income Growth

The Currency Year and Income Growth window, accessed via the Advanced

button on the Valuation Method window allows you to specify an Inflation
Dataset and a Currency Year, which in combination allow you to change the
currency year to account for inflation. The Income Growth Adjustment panel

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Chapter 7 - Aggregating, Pooling, and Valuing

allows you to adjust the valuation estimates to account for the growth in income
over time for WTP functions.

I Advanced Valuation Settings'



Currency Year and Income Growth

Inflation Adjustment
Inflation Dataset:

EPA Standard Inflators

Currency Year:

2010

Income Growth Adjustment

Income Growth Dataset: | EPA Standard Income Growth
Income Growth Year:

2010

Endpoint Groups:

Acute Bronchitis

Acute Respiratory Symptoms

Asthma Exacerbation

Chronic Asthma

Chronic Bronchitis

Lower Respiratory Symptoms

Mortality

Upper Respiratory Symptoms

Select the income growth dataset, then the year and the
endpoint groups to which you want to apply the adjustment.
You may select more than one endpoint group.

Cancel

OK

Inflation Adjustment

The Inflation Adjustment needs to be carefully considered in relation to the
valuation dataset that you are using. (This is discussed in detail in the section on
loading inflation data in Chapter 4.) The default valuation database in the United
States 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.

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As with the Inflation Adjustment, the Income Growth Adjustment has a close
connection to the valuation estimates. For example, the valuation estimates in the
United States setup 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 in Chapter 4.)

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 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 Open & Modify Existing APV Configuration

If you have an existing APV configuration (*.apvx) file or APV result file (*.apvrx),
you can open, and edit it. Double click Aggregate, Pool & Value from the main tree
menu to load the APV Configuration or APV Result file.

' Open Existing APV/APVR File	1 <=• I l^^l]

Open Existing APV Configuration File (*.apvx) :

E3

Or Open Existing APV Result File (*.apvrx):

E3

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 Select and Pool
Incidence Estimates 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 helpful for generating new APV
Configuration Results from several different Configuration Results files which were
generated from different baseline/control scenarios, but with the same set of health
impact functions.

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Chapter 7 - Aggregating, Pooling, and Valuing

7.4	Run APV Configuration

After having specified the various aggregation, pooling, and valuation options, you
can save your APV Configuration for future use. The file that you save has an ".apvx"
extension. The configuration that you have specified for APV is similar to the
configuration that you developed for choosing health impact functions. (That
configuration has a ".cfgx" extension.) Both files allow you to save choices that you
have made, and re-run them at a later time.

To save your APV configuration with your valuation pooling choices, click the Save
As (*.apvx) button, and name your configuration file. We suggest that you save this
in the Configurations folder. When ready to generate APV Configuration results,
click the Run As (*.apvrx) button. BenMAP-CE then requires that you specify a file
in which to save the results, with an ".apvrx" extension.

7.5	View and Export Pooled Incidence and Valuation Results

Using the results from the APV Results file (".apvrx" extension), you can create, view
and export reports that reflect the choices you made about how to aggregate, pool and
value your results.

7.5.1	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 individual
results.

To generate pooled incidence results, click the Pooled Incidence tab in the upper
portion of the main window. Double-click to select the study results you would like to
view. The selected results should show up in the Data tab below, if not, click the Show
Results button. Notice thatyou cannot re-aggregate the results in this stage, because
you have already defined how to aggregate the results. You may also view the results on
a map using the GIS Map tab and on a bar chart using the Chart tab.

7.5.2	Pooled Valuation Results

The Pooled Valuation Results report presents valuation results aggregated and
pooled to the level you specified using 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 individual results.

Click the Pooled Valuation Results tab to begin viewing these results. Similar to the
Pooled Incidence Results, you can double-click the study of interest and view the results

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table in the Data tab. Similarly, you may view the results on a map using the GIS Map
tab and on a bar chart using the Chart tab.

7.6 Frequently Asked Questions

I am at the BenMAP-CE 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 drop-
down 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
drop- down menu available in the upper left-hand corner of the main BenMAP-CE
window. See the valuation function section in the chapter on Loading Data for details on
how to do this.

How do I know what year dollars (currency year) were used?

You can find the answer in the Audit trail for 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.
Currency years are always historical because we do not forecast inflation.

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Contents

Table of Contents

CHAPTER 8 GIS/Mapping	8-1

8.1	Overview of Mapping	8-2

8.2	Results Panel	8-2

8.3	GIS Mapping Panel	8-4

8.3.1	GIS Map Tab	8-4

8.3.1.1	GIS Table of Contents	8-4

8.3.1.2	GIS Toolbar	8-8

8.3.2	Data Tab	8-11

8.3.3	Chart Tab	8-13

8.3.4	Audit Trail Report	8-13

8.4	Frequently Asked Questions	8-15

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Chapter 8 - GIS/Mapping

CHAPTER
GIS/Mapping

In this chapter...

¦	Learn about BenMAP-CE's mapping functions.

¦	Map different variables and modify the map display.

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Chapter 8 - GIS/Mapping

The BenMAP-CE Geographic Information System (GIS) will display maps of air quality,
health and economic data. These maps can help answer a number of questions:

¦	Quality assurance: Do your air quality changes seem to be distributed correctly?
Are your air quality changes and health impacts occurring in approximately the
same location?

¦	Presentations: In what states or provinces are most of the benefits/disbenefits of
your policy scenario concentrated?

¦	Analysis: Which air quality grid cells contain the highest ozone values?

The main GIS Map tool will be available once you have successfully completed the first
stage of the BenMAP-CE analysis (Air Quality Surfaces in the main BenMAP-CE tree
menu).

8.1	Overview of Mapping

The GIS will map three categories of data:

1.	Air quality (.aqgx). Air quality grid maps represent summary air quality metrics
(e.g., daily average, daily maximum, or other metric where available) within each
grid cell. Air Quality Grids can be added to the GIS Map by following the steps in
Chapter 5: Creating Air Quality Grids.

2.	Incidence (.cfgrx). A configuration results file contains the results of your
analysis reported at each air quality grid cell. These results have not been
aggregated, pooled or valued. For more information on choosing configuration
settings, see Chapter 6: Estimating Incidence.

3.	Aggregated, pooled and valued results (.apvrx). These are results that have been
aggregated to a coarser spatial scale, see Chapter 7: Aggregation, Pooling, and
Valuation.

When mapping APV Configuration results, you can generate six different types of maps:
(1) Incidence, (2) Aggregated Incidence, (3) Pooled Incidence, (4) Valuation, (5)
Aggregated Valuation, and (6) Pooled Valuation. For more information on how to load
these files into BenMAP-CE, see Chapters 6 and 7 (Estimating Incidence, and
Aggregation, Pooling, and Valuation).

8.2	Results Panel

Once you have gone through all the steps outlined in the previous chapters to import
data and files, you are ready to begin exploring the visual results. Each of your selected
endpoint groups will be displayed in the results panel (upper right portion of the main
BenMAP-CE window).

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Chapter 8 - GIS/Mapping



File ' United States ' Modify Dai

Ei @ Air Quality Surfaces
h § Pollutant

EI-® Source of Air Quality Data (PM2
- W Baseline

j j5j PM25_12km_base.csv
El- (j) Control

--[Si PM25_12km_control.csv
s--l&l Air quality delta (baseline
B © Estimate Health Impacts
® Population Dataset
: ® Health Impact Functions
El © Aggregate, Pool & Value
I -® Aggregation

) Valuation Method





Health Impact Results Pooled Incidence Results i © Pooled Valuation Results l Audit Trail Reoort ]

Endpoint Group

Endpoint

Pollutant

Author

Start Age

End Age

Dataset Na... Race

Acute Bronchitis

Acute Bronchitis

PM2.5

Docker-

8

12

EPA Standar...

Mortality

Mortality, All Ca...

PM2.5

Wood r...

0

0

EPA Standar...

Mortality

Mortality, Ische...

PM2.5

Krewsk...

30

99

EPA Standar...

< | in | ~

(») Create map,data and chart (double-click the selected study) Aggregation for raw data: Q
© Create data (table) for multiple studies

Show results

Column headers

GIS Map ) Data [• ) Chart J ¦;Audit Trail Report

El ^ Map Layers
El SI Region Admin La
El O Pollutants
B W\ PM2.5
B E3 QuarterlyMean
EI 13|Baselin^
¦<= 3.71
3.71 - 7.41
| 7.41-11.1
| 11.1-14.8
| 14.8-18.5
H >18.5
B ® Control
<= 4.31
4.31 - 8.62
8.62 -12.9
~ 12.9-17.2
| 17.2-21.5
| >21.5
H El Delta
<= 0.580

United States Setup: PM2.5, Delta

Current Setup: United States

There are four tabs within the results panel, Health Impact Results, Pooled Incidence
Results, Pooled Valuation Results, and Audit Trail Report. To create a results map
layer for an endpoint group, select an entry, or entries, from the list and drag it down
onto the GIS Mapping panel. BenMAP-CE will create the map and place an entry into
the GIS table of contents below the newly created results group. The layer will most
likely be listed under the author's name as subgroup of the main results group. This
process of dragging-and-dropping can be done with as many entries as you like, on any
of the first three tabs.

Audit Trail Reports facilitate transparency and reproducibility by reporting a summary of
your assumptions underlying each step of the analysis. This is described in more detail
below.

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Chapter 8 - GIS/Mapping

8.3 GIS Mapping Panel

The GIS Mapping panel is the centerpiece of this tool. Here is where you can view, edit,
add, and remove layers from the table of contents and GIS map viewer.



f BenMAP-CE 1.0.13

File - United States t Modify Datasets Tools *• Help *¦
Health impact Results

Eridpoint Group

Mortality
Mortality

End point

Acute Bronchitis PM2J>

B O Air Quality Surfaces
Pollutant
® Source of Air Quality Data (PM2
& Baseline

L-|B| PM25_12km_base.csv
Control

j--lKl PM25_12km_control.csv
•1SI Air quality delta (baseline - 0 Create data (table) for multiple studies
G ^ Estimate Health Impacts

- i

& Pooled Incidence Results I©

Pooled Valuation Results | AuditTrail Reoort |

Author Start Age End Age DatasetNa... Race

Mortality, All Ca...
Mortality, Ische...

=]

PM2.5
PM2.5

Woodr... 0
Krewsk... 30

EPA Standar...
EPA Standar...

o Create map.data and chart (double-click the selected study)

Aggregation for raw data:

Column headers

QP Population Dataset
(j) Health Impact Functions
^ Aggregate, Pool & Value
# Aggregation
4D Pooling Method
fc) Valuation Method

Current Setup: United States

' GIS Map j i. Data j (.jChart | AuditTrail Report j

o
a
o

8?

M
hi
C
+

m

¦	1.62-2.15
| 2.15-2.69
| >2.69

B lZ] Results
B 13 Health Impacts
B (13 Dockery
B Incidence
H <=10.5
10.5 - 21.0
21.0 - 31.5
31.5 - 42.0
| 42.0-52.5

¦	>52.5



United States Setup: Health Impacts- Dockery

8.3.1 GIS Map Tab

After creating an air quality surface, the GIS Map tab is used to view the air quality data
(double-click on an available air quality surface to display it). Here you will find a GIS
table of contents, toolbar, and interactive map.

8.3.1.1 GIS Table of Contents

The GIS table of contents is where you will find all your loaded map layers. The layers
are sorted in groups, with subgroups below them. The general setup will include
Region Admin Layers group, Pollutants group, and Results group. You can select or
deselect as many layers or groups as you like, for viewing on the map to the right.

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Q BenMAP-CE 1.0.13

File ' United States •



Modify Datasets Tools " Help ~
O Health Impact Results

Endpoint Group
Acute Bronchitis
Mortality
Mortality
- ¦¦¦

Endpoint	Pollutant	Author

Acute Bronchitis	PM2.5	Docker-

Mortality, All Ca...	PM2.5	Woodr...

Mortality, Ische...	PM2.5	Krewsk...

Ef'"U Air Quality Surfaces
Pollutant

B-® Source of Air Quality Data (PM2
FEI-fc) Baseline

1--ISI PM25_12km_base.csv
Control

!. PM25_12km control.csv ® Create map,data and chart (double-click the selected study)
•¦-•151 Air quality delta (baseline - © Create data (table) for multiple studies
B-© Estimate Health Impacts
Population Dataset
Health Impact Functions
a- Aggregate, Pool & Value
I- fc) Aggregation
; & Pooling Method
40 Valuation Method

Pooled Incidence Results | © Pooled Valuation Results | AuditTrail Reoort ]

Start Age End Age

Dataset Na... Race
EFWStandar...
EPAStandar...
EPAStandar...

Aggregation for raw data:

Show results

Column headers



¦r_ G -Map I Data : Chart Audit iran Report

B ^ Map Layers
B 0 Region Admin Layers
B 0 Pol lutants
B W\ PM2.5
B 0 QuarterlyMean
B 0 jBaselin^

<= 3.71
3.71-7.41
7.41 -11.1
| 11.1-14.8
| 14.8-18.5
| >18.5
B 0 Control
<= 4.31
4.31-8.62
8.62-12.9
n 12.9-17.2
| 17.2-21.5
| >21.5
B 0 Delta
<= 0.580

United States Setup: Health Impacts- Dockery

Current Setup: United States

Region Admin Layers: This is where the administrative grids can be found for
country, state, and county boundaries. For preloaded data, regional
administrative layers have been defined. For example, in the United States setup,
the State layer will be automatically selected. You can change the selection to
country or county layers by expanding the group.

Pollutants: This is where all the available pollutant data (from the tree menu)
will be visible. Under this main group, there will be a group for each pollutant
that was selected (e.g., PMzs). Below the individual pollutant, there will be
metrics that were defined earlier during import and loading (e.g., Quarterly
Mean, D24HourMean). Below each metric, there will be entries for each Air
Quality Grid that was loaded (e.g., Baseline, Control, and DeltaJ.

Results: This is where the layers for Health Impact Results, Pooled Incidence
Results, and Pooled Validation Results will be listed. Under the results group,
there will be a subgroup labeled for each set of results. Usually, these entries
will be labeled by the study author's name.

Color Ramps

The color ramps are standard for the imported files. The default color ramp for the
Baseline and Control Air Quality Grids goes from light green to dark blue. The
default color ramp for the Delta Air Quality Grid goes from light yellow to dark red.

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Chapter 8 - GIS/Mapping

Finally, the default color ramp for the Results group entries will be different from
each other and any other color ramps that are already in the table of contents.

These color ramps can be changed by the user, if desired. There are two possible
ways to change the colors:1

1) To change the whole color scheme: Right click on a layer and select the

Properties option. This will open a Layer Properties window, where you can
change the color ramps and other properties.

Layer Properties

1 9 Marf

S^boiogy jl Detailed Property

0 Custom 0 Unique Values
EZ] Use Color Range
HSL RGB j	

o Quantities Field:

[ J Use Gradients

Aigle

SSI SHE*]



nail])

1 Symbol

Values

Legend Text

Count



0-3.15

<=3.15

23008



3.15-6.31

3.15-6.31

48153

¦

6.31 -9.46

6.31 - 9.46

24019



3.46 -12.6

9.46-12.6

2104

¦

12.6-15.8

12.6-15.8

96

¦

>15.8

>15.8

36



Statistics Graph

Normalization Field

Interval Method: Equal Interval

Interval Snap: Rounding
Rounding Digits: Q |^j|

Exdude Members
Num Breaks: S |

Count



Min



Max



Sum



Mean



Median



Std



Apply

2) To change one index within a color ramp: Click on the color box or number range
that you wish to change. This opens the Polygon Symbolizer Properties
window.

1 BenMAP-CE uses DotSpatial to incorporate, analyze, and map spatial data. The editing tools for layer
properties are those included in the DotSpatial libraries and have not been customized for BenMAP-CE. For
more information, see: http://dotspatial.codeplex.com/.

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Chapter 8 - GIS/Mapping

Polygon Symbolizer Properties

Symbolizer Characteristics

Scale Mode: | Simple
Smoothing

Preview:

Units: Pixel

Type: [Simple

Fill Color:

Qpacr^jj

Outline

m Use Outline

Width: 0

Outline Color:

Edit...

i6pacr~~l

Add To Custom Patterns

Cancel

Apply

Add/Remove a Group

To add a group to the GIS table of contents, right-click on a group, and click Create
New Group from the pop-up menu. This will add the new group below the group
that was right-clicked on.

To remove a group from the GIS table of contents, right-click on the group you wish
to remove, and then click Remove Group from the pop-up menu. This will remove
the entire group from the table of contents.

Remove a Layer

To remove a spatial layer from the GIS table of contents, right-click on the layer you
wish to remove, and then choose Remove Layer from the pop-up menu. This will
remove the layer from the table of contents.

Note: Adding a layer will be discussed in the GIS Toolbar section (Section 8.3.1.2).

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Chapter 8 - GIS/Mapping

8.3.1.2 GIS Toolbar

There are a number of standard buttons used in most map viewing programs which
you can use to navigate and customize the map view. To see the name of each
button in the toolbar (to the left of the GIS table of contents), simply hold the cursor
over it.

<0
&

Increase zoom. Allows you to zoom in.

Decrease zoom. Allows you to zoom out.

4>>

Drag mode. Allows you to manually move the map by clicking and dragging.

yi

Zoom to full extent. Allows you to view the whole map that you are viewing.

Click to display info for the cell in popup window. Allows you to display
information (all the variable values) for individual cells or points by clicking on
them.

Show Table of Contents. Allows you to show/hide the GIS table of contents to
view a larger map.

Save Shapefile. Opens a Save As window to all you to save the map (layer) as a
*.shp file.

a
c

a

Export Map Image. Opens a DotSpatial Print Layout window where you can
edit/save the data map layer.

Change Projection to *projection type*. Allows you to change/toggle the type
of map projections between the following types: GCS/NAD 83 (standard), Albers.

Add Layer. Allows you to add a new layer to the table of contents and map.
Typically the new layer is added near the top of the table of contents.

Attribute Table. Opens an Attribute Table Editor Window where the user can
view, edit, or filter the data for a specific layer.



Select Features. Allows you to select features from the map. Press and hold Ctrl
to select multiple features at one time.

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Chapter 8 - GIS/Mapping

Select By Location. Allows you to select features from a layer based on its spatial
relationship with another layer.

Clear Selection. Clear selections from all layers.

Creating a Query

You can create a query on any map layer that shows up in the GIS table of contents.
After importing a file (such as Baseline or Control) you can begin a query by
selecting a layer. The selected layer will become highlighted with a light blue bubble
around it. Next, click the Attribute Table button in the GIS toolbar. This will open
the Attribute Table Editor window, shown below:



[^j Attribute Table Editor | ~ || E) ||^3—





Edit View Selection Tools



COL ROW D24HourMean QuartedyMean FIDO



*

~

1

29

11.95

11.95

0



1

31

10.69

10.69

1

1

37

11.66

11.66

2

1

39

10.22

10.22

3

1

41

10.91

10.91

4

1

45

10.9

10.9

5

1

49

12.01

1201

6

1

53

10.12

1012

7

1

57

11.12

11.12

8

1

61

1022

1022

9

1

67

11.37

11.37

10

1

75

11.18

11 18

11

1

77

10.57

10.57

12

1

79

10.91

10.91

13

1

1

11.49

11.49

14

1

3

9.38

9.88

15











C:\Users',jmriAppData\Local\BenMAP-CE\Tmp\6G18BBaseline.5hp 7 of 3109 selected.

Close



From the window above, click Selection from the top menu bar. This will open a
drop-down menu where you can select the Query option.

*5



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Chapter 8 - GIS/Mapping



AttributeTable Editor

Edit View

H c; i j

Selection Tools



Query F6

COL

Select All
Select None
Invert Selection
Export Selected Features

QuarteriyMean

1



11.95

1



10.69

1



11.66

1 39 10.22

10.22

This will open Expression Editor window (shown below):

Expression Editor
Field Names:

COL
ROW

D24HourMean
QuarteriyMean
FIDO

Is Null

Like

~ E

| And | [ Or | [ Not

Is Not Null

Get
Unique
Values

SELECT * FROM [^tributes] WHERE

H
0
E
B

Unique Values

00

Minimum:	0

Maximum: 100

OK

Cancel

Apply

The Expression Editor is used to query your data. First, select a Field Name to
query, by double-clicking on a desired entry. The Field Name should show up in
brackets in the bottom text box. Next, select an operator from the panel of buttons.
The selected operator symbol should show up in the bottom text box next to the
field name. Finally, to complete the first query entry, click in the bottom text box
(after the operator symbol) and enter a value that you would like to compare
against (e.g., [D24HourMean] >= 15). More attributes can be added to the query by
clicking the And, Or, or Not buttons. Once you are satisfied with the query
statement, click OK on the Expression Editor window. It may take a few minutes to
find all the results. The Attributes Table Editor should appear, with the requested
selections highlighted. The map on the GIS portion of the main BenMAP-CE home
screen should show the query results.

To save these results, click the Selection button from the top menu bar of the
Attribute Table Editor window. From the dropdown menu, select the Export
Selected Features entry. This will open a Save As window, where you can save the
query into a shapefile (shp). Click the Close button on the Attribute Table Editor.

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Chapter 8 - GIS/Mapping

Attribute Table Editor









Edit View

Selection | Tools









y j> m J



Query

F6







COL



Select All



Quartertylvlean



1



Select None





11.95



1



Invert Selection



10.69



1

Export Selected Features



11.66



1

39

10.22

1022

To add the query as a new layer, click the '+' button located in the GIS toolbar
(located next to the GIS table of contents). An Open window will be displayed,
where you can select your recently saved shapefile. Select the file and click Open.
The new layer should appear near the top of the GIS table of contents. The layer can
be dragged-and-dropped into any map group you would like within the GIS table of
contents.

8.3.2 Data Tab

The Data tab allows you to view all the data that is being presented in the map.

i) BenMAP-CE 13

1-1^'

File " United States ' Modify Datasets 2^ Tools *¦ Help *¦
© Health Impact Results

I Pooled Incidence Results 1 Q Pooled Valuation Results 1 Audit Trail Report 1

Endpoint Group
Acute Bronchitis

Endpoint
Acute Bronchitis

Pollutant
PM2.5

Author
Docker...

Start Age End Age

3 0 Air Quality Surfaces

r (0 Pollutant
I El- ® Source of Air Quality Data (PM2
Q'40 Baseline

1 ;Sb, Mexico_baseline_PM25.t
Control

; k Mexico control 25 0 pe( ® Create map.data and chart (double-click the selected study) Aggregation for raw data: |
151 Air quality delta (baseline - © Create data (table) for multiple studies
Q Estimate Health Impacts
Population Dataset
W Health Impact Functions
B © Aggregate. Pool & Value
I" H Aggregation
: Pooling Method
Valuation Method

Dataset Na...
EPA Standar...

Select Result Fields

Show Results

Current Setup: United States

GIS Map | (3 ^ata | Chart Audit Trail Report

Column

Row

Endpoint

Author

Start Age

End Age

Point Estimate

Population

Delta

Me *

630

883

Acute Bron...

Dockery..

8

12

0.0000

3,735.5774

2.4700

0.0

630

884

Acute Bron...

Dockery...

8

12

0.0000

5,465.5361

2.8200

0.0

631

883

Acute Bron...

Dockery...

8

12

0.0000

5,911.2837

1.9900

0.0 _

631

884

Acute Bron...

Dockery...

8

12

0.0000

5,904.9287

3.0300

o.o"

632

880

Acute Bron...

Dockery...

8

12

0.0000

701.2690

1.8400

0.0

632

881

Acute Bron...

Dockery...

8

12

0.0000

4,973.4951

1.6800

0.0

632

882

Acute Bron...

Dockery...

8

12

0.0000

5,882.3779

1.4800

o.o-

632

883

Acute Bron...

Dockery...

8

12

0.0000

5,911.2837

1.6100

0.0

632

884

Acute Bron...

Dockery..

8

12

0.0000

5,904.9287

2.2700

0.0

632

885

Acute Bron...

Dockery...

8

12

0.0000

3,357.0786

2.3700

0.0

633

879

Acute Bron...

Dockery...

8

12

0.0000

30.7565

1.8400

0.0

633

880

Acute Bron...

Dockery...

8

12

0.0000

454.1035

1.5800

0.0

633

881

Acute Bron...

Dockery...

8

12

0.0000

4,996.5103

1.3700

0.0

633

882

Acute Bron...

Dockery...

8

12

0.0000

5,905.7363

1.3200

0.0

633

883

Acute Bron...

Dockery...

8

12

0.0000

5,404.2476

1.4600

0.0

633

884

Acute Bron...

Dockery..

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3,494.9126

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51.1900

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14 4 1

Digits After Decimal Point: |4

At the bottom of this tab, there are a few options:

¦	The left-most set of buttons allows you to toggle between pages of data.

¦	The middle option allows you to change the number of digits that appear after
the decimal point.

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Chapter 8 - GIS/Mapping

¦ The far right entry allows you to export the data table. Clicking the Export
button opens a Save As window, allowing you to save the data as a .csv file.

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Chapter 8 - GIS/Mapping

8.3,3 Chart Tab

The Chart tab allows you to select certain regions of data within the main layer to
compare localized results more easily.

File " United States " Modify Datasets ATo°ls " Help -

Health Impact Results	tioled Incidence Result: 10	) Audit Trail ReDort |

Endpoint Group
Mortality

Endpoint
Mortality, All Ca...

Pollutant
PM2J

B @ Air Quality Surfaces
!¦ (0 Pollutant

0-^ Source of Air Quality Data (PM2
Baseline
L-|E»1 Base Data
El ^ Control

0ontro| Qata	@ Create map,data and chart (double-click the selected study)

Air quality delta (baseline - © Create data (table) for multiple studies
&¦© Estimate Health Impacts
# Population Dataset

S -^ Aggregate, Pool & Value
H Aggregation
¦¦¦($ Pooling Method
0 Valuation Method

Author Start Age
Krewsk„. 30

Dataset Na... Race
ERA Standar...

Aggregation for raw data:

Select Result Fields

Show Results

Current Setup: United States

i'" GIS Map | (0: Data^ '¦ _ Chart |: J: Audit Trail Report

Bar Graph

Region Name	Col Ro\

g] Maine	23

[y] Massach...	25

W\ Michigan	26

[7] Montana	30

Nevada	32

[PI New Jers...	34

~	New York	36

~	North Ca...	37
Q Ohio	39
IH Pennsylv...	42
Irl Rhode Is...	44

Incidence

Select X Axis

STATE_NAME

Region

You have the option of selecting the regions that you would like to compare using the
list to the left of the chart by checking and unchecking certain regions. The chart
automatically updates with each new selection.2

8.3.4 Audit Trail Report

Audit Trail Reports facilitate transparency and reproducibility by reporting a summary of
your assumptions underlying each of five types of files generated by BenMAP-CE: Air
Quality Grids (with the xiqgx extension), Incidence Configurations (with the .cfgx extension),
Configuration Results (with the .cfgrx extension), Aggregation, Pooling, and Valuation
Configurations (with the .apvx extension), and Aggregation, Pooling, and Valuation Results
(with the .apvrx extension).

2 The data charts were originally developed using ZedGraph software, which is no longer supported. The
BenMAP-CE development team is considering options to update and improve this feature.

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Chapter 8 - GIS/Mapping

(:j BenMAP-CE 1.3

SHar

File " United States *¦ Modify Datasets i' Tools *¦ Help *¦

© Health ImDact Results | ^ Pooled Incidence Results | Fooled Valuation Results AuditTrail Report

B-O Ai r Qu a I ity Su rf aces
i Pollutant

&Source of Air Quality Data (PM2
Baseline

Mexico_baseline_PM25.(
&¦(D Control

ISSil Mexico_control_25.0_per
LS « r quality delta (baseline -
Estimate Health Impacts
-(j) Population Dataset
-(j) Health Impact Functions
© Aggregate, Pool & Value
{-¦0 Aggregation
I--0 Pooling Method
¦¦¦¦¦£$ Valuation Method

(§) Current AuditTrail Report

0	AuditTrail Reports (Created from *.aqgxfiles, *.cfgx files, ".cfgrx files, *.apvxfiles and *.apvrx files)

1	1 ® I 0K I

l

j

f) GIS Map I 0 Data ("i Chart j © AuditTrail Report j

Name

- BenMAP-CE 1.3.4
0- E sti m ate H e a I th I mparts

Create Datetime:2016-08-08 09:02:51
I sRunl nPoi ntMode:False
— Latin Hypercube Points20
Po p u I ati o n D ata s et: M exPo p- M exi co-lOkm-ai rgri d
Year:2010
ThresholdO
l^l-Baseline And Control GroupO
[^-Pollutant

I—Narne:PM2.5
~ObservationType:Daily
—SeasonO January 01-March 31
—Seasonl:April 01-June30
—Season2:July01-September30
- Season3:0ctober01-December31
—MetridO:D24HourMean
Seasonal Metric0:Quarter1vMean
l^-Baseline: Air Quality Surfaces

I—Create Datetime:2016-08-08 08:56:40	

Export-

Current Setup: United States

Note that each successive step in an analysis contains a summary of its inputs and
attributes, and those of each previous step in the analysis. For example, in the above
report the attributes of the Health Impact Function file used to generate the APV Results
are present in the Estimate Health Impacts node. Similarly, the metadata for both the
baseline and control air quality grids are present under the Estimate Health Impacts
node.

The process of creating an Audit Trail is described below:

¦	Click the AuditTrail Report tab in the results window. Select Current Audi t
Trail Report (this is the default setting). Click OK

¦	Carefully review the report, ensuring that the air quality grids, population data,
health incidence data, health impact functions and economic value estimates
appear as you expected.

¦	Click the Export button to save the audit trail report. The default location for
saving audit trail export {.txt, .ctlx, or .xml) files will be the location you chose
earlier for saving your shape files.

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Chapter 8 - GIS/Mapping

Audit Trail Reports have three export options: .txt, .ctlx, and .xml. These file types can
all be viewed using a standard text editor. The .txt and .xml files will contain the same
information displayed in the Audit Trail Report window (however, the .xml file
contains tags to retain the tree structure). If you are familiar with the command line
feature, you may use the audit trail report to produce a control [.ctlx) file using an
existing analysis, rather than creating one from scratch. The control file documents
variables and configurations (file paths) associated with an analysis. For more
information about the command line tool see Appendix L.

8.4 Frequently Asked Questions

Can I reorder the data layers?

Yes, data layers may be dragged within the GIS table of contents to reorder them within
a group.

How do I export shapefiles?

When viewing any of the displayed maps n the GIS Map tab (lower right frame of the
main window), click on the GIS toolbar icon for Save Shapefile (looks like a 3.5-inch
diskette). Follow the prompts to provide a name and location for the file. BenMAP-CE
will export a set of files (.dbf .prj, .shp, shx) associated with the shapefile that you can
use with any GIS viewer.

How do I save a GIS map as an image?

To export the map as an image, click the Export map image icon (immediately below the
Save Shapefile icon). This will use built-in DotSpatial GIS tools to allow you to save the
map as a formatted image (.png) file. Alternately, use the Print Screen (PrtScn) button
on your keyboard to create an image (saved in memory) which you can then paste into
a graphics editor or document.

Can I display my map using a projection other than GCS-NAD 83 or Albers?

Yes, click the GIS toolbar icon for "change projection to..."; this feature will allow you
to alternate between GCS-NAD83 and Albers projections. For more options, right-click
on the Map Layers feature in the GIS table of contents and select Projection from the
pop-up menu. This will display information about the current projection. Click the
Change Projection button to view and apply other available projections.

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Contents

Table of Contents

CHAPTER 9 Tools Menu	9-1

9.1	Air Quality Surface Aggregation	9-3

9.2	Database Export	9-5

9.3	Database Import	9-9

9.4	Online Database Export	9-10

9.5	Online Database Import	9-11

9.6	Export Air Quality Surface	9-12

9.7	GBD Rollback	9-13

9.7.1	Create New Scenario	9-15

9.7.2	Select Regions or Countries	9-15

9.7.3	Choose Rollback Settings	9-15

9.7.4	Execute Scenarios and Save Results	9-17

9.8	Monitor Data Conversion	9-19

9.9	Neighbor File Creator	9-20

9.10	PopSim	9-22

9.11	Options	9-24

9.12	Compute Grid Crosswalk	9-24

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Chapter 9 - Tools Menu

CHAPTER
Tools Menu

In this chapter...

¦ Learn about the options in the Tools menu.

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Chapter 9 - Tools Menu

The Tools menu, available on the main BenMAP-CE screen, provides access to six
special add-on tools and an Options menu. Below we summarize the purpose of each
tool. Note that other sections of the manual have already covered several of these items,
so we merely list them here and point you to the appropriate section.

© BenMAP-CE 1.3

Air Quality Surfaces

File - United States - Modify Datasets
3 ©|	

;••••© Pollutant

3-© Source of Air Quality Data
© Baseline
© Control
30 Estimate Health Impacts
© Population Dataset
© Health Impact Functions
a© Aggregate, Pool & Value
!"¦•© Aggregation
I--© Pooling Method
L © Valuation Method

Jv Tools - Help -

Aggregate Air Quality Surface
Database Export
Database Import
Online Database Export
k Online Database Import
Export Air Quality Surface
GBD Rollback
Monitor Data Conversion
Neighbor File Creator
PopSim
Options

Compute Grid Crosswalks

10

thes
m die

tudy)
rr

¦	Aggregate Air Quality Surface. Use this tool to change an air quality grid based
on one grid definition to a coarser grid definition, using a simple spatially
weighted average approach. For example—you could aggregate your air quality
surface from 12km x 12km grids to U.S. Counties.

¦	Database Export. Export your entire database (every item in your setup) or
parts of your setup (e.g. all GridDefinitions, or individual Health Impact Function
Datasets) to a specified file location.

¦	Database Import. Import an entire setup or parts of an individual setup from a
specified file location.

¦	Online Database Export. Export your BenMAP-CE dataset(s) and upload to a
cloud-based data archive to share with the BenMAP-CE community.

¦	Online Database Import. Import dataset(s) provided by the BenMAP-CE
community from a shared cloud-based data archive.

¦	Export Air Quality Surface. Generate a data file that contains all of the air
quality values saved within an air quality surface (.aqgx) file.

¦	GBD Rollback. This application estimates the air pollution-attributable health
burden, and the benefits of improved air quality, in each country using data from
the Global Burden of Disease (GBD) study.

¦	Monitor Data Conversion. Convert monitor data files into BenMAP-CE format.

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Chapter 9 - Tools Menu

¦	Neighbor File Creator. Identify the monitors and weights used in the
interpolation process when creating air quality grids.

¦	PopSim. Simulate the cumulative effects of air pollution on different age groups
over time (U.S. data only).

¦	Options. View and edit the general options for BenMAP-CE.

¦	Compute Grid Crosswalks. Remove or re-create crosswalks between grid
definitions.

9.1 Air Quality Surface Aggregation

Using the Aggregate Air Quality Surface tool, you can change an air quality grid based
on one grid definition to another (coarser) grid definition, using a simple spatially
weighted average approach.

To start, choose Aggregate Air Quality Surface from the Tools drop-down menu. This
will bring up the Aggregate Air Quality Surface window. Click the Browse button to
find the air quality grid (.aqgx file) that you want to change and then use the
Aggregation Surface drop-down list to select the new grid definition that you want to
use. For example, you might want to aggregate a 12km model grid to the county-level.
Click OK when done.

§0 Aggregate Air Quality Surface



Air Quality Surface:

Aggregation Surface:

Cancel

OK

This will bring up the Save Aggregated Air Quality Grid window, where you specify
the name of the newly aggregated air quality surface (.aqgx file) you are creating and its
location. After the file is created, BenMAP-CE will return you to the main BenMAP-CE
screen. You can then use the new file just as you would any other air quality surface.

Below is an example of an air quality surface for PM2.5 created by the CMAQ model
using a 12km grid. (The following images are cropped from the main BenMAP-CE
window to focus on the GIS panel.)

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Chapter 9 - Tools Menu

@ GIS Map [O Data | ( Chart m Audit Trail Report

£
B

is

H
B
C
+

SB

B ^ Map Layers
0 0 Region Admin Layers
B 0 Pollutants
B 0 PM2.5
B El QuarterlyMean
B 0 Baseline
<= 7.77
7.77 -10.6
10.6 -13.5
13.5 -16.4
16.4-19.3
| >19.3
B 0 D24HourMean
B 0 Baseline
<= 8.30
8.30 -11.7
11.7-15.1
15.1-18.4
18.4-21.8
¦ >21.8

United States Setup: PM2.5, Baseline

Here is an example of the above 12km air quality surface aggregated to the county level.

ijv; G IS Map ^ Data | jjSji Chart | Audit Trail Report

©
B

Be

M

0
C
+

J

B Map Layers
B 0 Region Admin Layers
B 0 Pollutants
B 0 PM2.5
B 0 QuarterlyMean
B 0 Baseline
<= 7.85
7.85-10.1
10.1-12.4
12.4 -14.7
14.7-17.0
| >17.0
B 0 D24HourMean
B 0 Baseline
<= 7.84
7.84-10.1
10.1-12.4
12.4-14.7
14.7-17.0
¦ >17.0

United States Setup: PM2.5, Baseline

And, here is an example of the 12km air quality surface aggregated to the state level.

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Chapter 9 - Tools Menu

C"- ¦ GISMap Q: Data i _ Chart Audit Trail Report

©
©

4»

o

h

a

c
+

B [?] Region Ad rn in Layers
B El Pollutants
B O PM2.5
B [Z] QuarterlyMean
B 13 Baseline
<= 8.37

8.37	-10.1
10.1 -11.8
11.8 -13.5
13.5 -15.2

| >15.2
B 0 D24HourMean
B [3 Baseline
<= 8.38

8.38	-10.1
10.1 -11.8
11.8 -13.5

| 13.5-15.2
¦ >15.2

United States Setup: PM2.5, Baseline

Observe that there is more variation in the county-level file than the state-level file. This
is expected, because BenMAP-CE is just using a simple spatially weighted average of the
data.

9.2 Database Export

BenMAP-CE allows you to export and import entire databases (all Available Setups),
individual setups (e.g., United States, China), and parts of individual setups (e.g. all Grid
Definitions, or individual Health Impact Function datasets). This functionality can be
used to archive data, share data with other BenMAP-CE users, move databases between
computers, as well as to view data in other applications like Excel or ArcMap. 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.

To export part or all of an existing setup, go to the Tools menu, and choose the Database
Export option.

This will bring up the Database Export window. Initially, all of the setups are listed in a
tree menu, which is initially in a collapsed view. To expand any of the menu items, click
on the '+' sign to the left of menu item. This will expand the tree menu to show
additional listings for the expanded item. To collapse the tree menu, simply click on the
sign.

Choose a dataset to export by selecting it from the tree menu. The default Type of
Export is BenMAP CE database. Press OK. In the screenshot below, we have chosen to
export EPA Standard Monitors 03 to a BenMap-CE database file.

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Chapter 9 - Tools Menu

ty* Database Export

a

Select Type of Export:

® BenMAP CE Database Other File Format (e.g. shp, csv)
Select Object to Export:

0-Available Setups
(j - United States

ED- Grid Definitions
EjD- Pollutant
IE) Monitor Datasets

EPA Standard Monitors Ozone

EPA Standard Monitors PM2.5
Incidence/Prevalence Datasets
0- Population Datasets
0- Health Impact Functions
0- Variable Datasets
dD- Inflation Datasets
[j-Valuation Functions
(j)- Income Growth Adjustments
0- China
GD- Detroit

This will bring up the Save As window. From here you may name the export file and
choose the save location.

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Chapter 9 - Tools Menu

Save As

/^| <<: My Documents ~ My BenMAP-CE Files ~ Result ~

| Search Result

~P\

Organize '

New folder

8== -

Favorites
SI Desktop
£ Downloads
is Recent Places

^ Libraries
-J Documents
Jfr Music
Q Pictures
H Videos

]*¦ Computer
cdrive (C:)

File name:

Documents library

Result

Name

Date modified

Type

APV

7/25/2014 2:32 PM

File folder

APVR

7/25/2014 2:32 PM

File folder

AQG

7/25/2014 1:34 PM

File folder

CFG

6/9/2014 3:10 PM

File folder

. CFGR

7/25/2014 2:34 PM

File folder

Project

6/11/201410:26 AM

File folder

< r

Save as type: BenMAPCEDatabase(*.bdbx)

Hide Folders

Arrange by: Folder '

Size

Save

Cancel

NOTE: Exported BenMAP-CE database files have a .bdbx extension, and are a binary
format not suitable for viewing in external applications.

To view exported data in external application, choose "Other file Format". The tool will
export Grid definitions in shapefile (*.shp) format and the rest of the data in CSV (*.csv)
format. If the dataset contains multiple tables, it will be exported into multiple CSV files
In the screen below, we have chosen to export EPA Standard Variables to a CSV file.

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Chapter 9 - Tools Menu

Database Export

Select Type of Export:

0 BenMAP C'E Database >9: Other File Format (e.g. shp, csv)
Select Object to Export:

Ei-Available Setups
0- United States

(jj-Grid Definitions
(jl- Pollutant
ij] Monitor Datasets
El - Incidence/Prevalence Datasets
E3 - Population Datasets
B- Health Impact Functions

EPA Standard Health Functions

Expert PM25 Functions
AMI - Age-Dependent Survival Rates
GBD Integrated Exposure Response
B- Variable Datasets
j EPA Standard Variables
GE)- Inflation Datasets
El-Valuation Functions
(3 - Income Growth Adjustments
China
Detroit

Cancel

OK

In the Save As window, the File name has been pre-populated with dataset name. You
may leave it as is or change to something you want.



tliji Save As



« My Documents ~ My BenMAP-CE Files ~ Result ~

Search Result

Organize

New folder

Favorites

Name

Date modified

Type



Ji APV

9/26/2016 12:55 PM

File folder

.,-,j Libraries

APVR

9/26/2016 2:10 PM

File folder

M Documents

AQG

3/15/2017 10:37 AM

File folder

Music

j, CFG

5/10/2016 4:45 PM

File folder

B Pictures

CFGR

3/15/2017 10:39 AM

File folder

0 Videos

Project

3/30/2017 12:26 PM

File folder

;*¦» Computer
flfi Network

File name:

^ Hide Folders

Save

|5E
Size

|EPA Variables,

~

f /-ri ,ri /*

| rue { .csvj

T|

Cancel

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Chapter 9 - Tools Menu

In this case, each variable of EPA Standard Variables dataset is saved as one CSV file
with its variable name appended to the end of the dataset name.



«

My BenMAP-CE Files ~ Result ~ w



Search Result p

Organize »
K Favorites

I Libraries

Computer

Network

| Open ~ Share with

Name

jU APV
APVR
A AQG
jii CFG
CFGR
Project
gjj EPA Standard
EPA Standard
EPA Standard
^ EPA Standard
l§j] EPA Standard
EPA Standard
l§i] EPA Standard
^ EPA Standard
^1 EPA Standard
^ EPA Standard
^ EPA Standard
y EPA Standard
fiy EPA Standard

Print

E-mail

Burn

Variables_avgHHsize,csv

Va riables_CollegeDegree.csv

Va riables_CollegeDegree_asianp,csv

Variables_CollegeDegree_black.csv

Variables_CollegeDegree_hisp.csv

Variables_CollegeDegree_natam.csv

Variables_CollegeDegree_nhisp.csv

Va ria b I es_Co 11 eg eD eg ree_wh ite. csv

VariablesJHSDiploma.csv

Variables_HSDiploma_asianp.csv

Va riables_HSDiploma_black.csv

Variables_HSDiploma_hisp.c5v

Va riables_HSDiploma_natam,csv

1=

IS

EPA Standard Variables_poor65up_black.,.,

Microsoft Excel Comma Separated Values File

Date modified: 3/30/2017 12:26 PM
Size: 75.6 KB

SI #

Date modifir

9/26/2016 15
9/26/2016 2:
3/15/20171C
5/10/20164
3/15/20171C
3/30/2017 1
3/30/2017 1
3/30/201712
3/30/20171
3/30/2017 1
3/30/2017 1
3/30/2017 1
3/30/2017 1
3/30/2017 1
3/30/2017 1
3/30/2017 1
3/30/20171.
3/30/201712
3/30/2017 11

~

NOTE: When exporting datasets in Other File Format, only one dataset can be selected
at one time.

9.3 Database Import

Import entire setups or parts of individual setups. This option is described in the
Import Setups section of Chapter 4: Loading Data.

To import part or all of an existing setup, go to the Tools menu, and choose the
Database Import option. This will bring up the Database Import window.

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Chapter 9 - Tools Menu

ry* Database Import

U



r-3^











Database Object File:















Target Setup:









1





-

)











,







11



| Cancel

OK

1









j

The Database Object File identifies the file that you want to import. Click on the
Browse icon to locate the file. This will display the Open window.

Find and select the .bdbx file that you want to import, and then click Open. This will
return you to the Database Import window. Click OK to finish the import process.

If the import file contains a subset of a setup, such as a collection (e.g., a set of Grid
Definitions) or an individual dataset (e.g., a single grid definition from among many
available), select the Setup into which it should be imported from the Target Setup
drop-down list. Click OK to finish.

NOTE: Duplicates of datasets (typically identified by their names, e.g., 'Detroit
Population') will overwrite existing data in a setup. New datasets (i.e., non-duplicated)
will be added to the setup.

9.4 Online Database Export

The Online Database Export feature was designed to facilitate sharing of BenMAP-CE
datasets among the user community. The data is stored in an online database. Sharing and
use of this data is at the discretion of the user community.

To share data in the online repository, select Online Database Export from the BenMAP-CE
Tools menu. In the EPRI Online Database Export form, provide your name (required
field), organization, and a description of the data you are sharing. Then select the object
you wish to export from the tree menu. Similar to the database export feature (see Section
9.2), you can select an entire setup, dataset type, or specific data element. Once you make
your selection and click "OK", you will be prompted to confirm the upload.

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Chapter 9 - Tools Menu

$£$ EPRI Online Database Export

Your Name: Joe Smith

Organization ABC Company
Description/Comments:

Example dataset

Select Object to Export:

El-Available Setups

>

United States





fj]" Grid Definitions





(j - Pollutant





Monitor Datasets





El- Incidence/Prevalence Datasets





Population Datasets





Health Impact Functions

E





H EPA Standard Health Functions^^^^^^^^H







Expert PM25 Functions







PM NAAQS Final 2012 Additional Functions







AMI - Age-Dependent Survival Rates







• GBD Integrated Exposure Response





SB- Variable Datasets





EE- Inflation Datasets





SJ" Valuation Functions





[¥)•¦ Inrnmp Growth Ariii



9.5 Online Database Import

The Online Database Import feature was designed to provide user access to an online
repository of BenMAP-CE datasets shared by the user community. To import data from the
online repository, select Online Database Import from the BenMAP-CE Tools menu. Select
the data you wish to import by clicking on the record selector on the left side of the data
grid. The record will be highlighted. Next choose your Target Setup from the dropdown
list. Then, click the "OK" button. The progress bar near the bottom of the screen will
update to show progress of the import. When complete, the system will notify you that the
"The database file was imported successfully."

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Chapter 9 - Tools Menu

i EPR1 Online Database Import

ia |p " J



Uploaded By

Uploaded

BenMAP
Version

Organization

Description

Size

Downloa<



Shawn Holl.„

11/18/2015 1:24 PM



Ramboll Environ

test upload

726

0

~

NealFann

9/4/2015 1:35 PM



EPA

Test China subset

385



1







Ml "r | ~ 1

Target Setup:

-

United States



China



Detroit



Cancel

OK

9.6 Export Air Quality Surface

The Export Air Quality Surface tool generates a data file (.csv) reporting all of the data
contained in the air quality grid. After choosing Export Air Quality Surface from the
Tools menu, the Export Air Quality Surface window will appear. Click the Browse
button to choose the air quality grid that you want to examine.

Export Air Quality Surface

Air Quality Surface:

Browse

Click OK after you have selected your file. Use the Save As window to choose the
directory 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 use the same file name as
your air quality grid, or something very similar. (If you use the same name, you can
always distinguish the two files by the extension. An air quality grid has an .aqgx
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
12km PM2.5 air quality surface, you can see in the first grid cell (Column = 1, Row = 246)
that the mean of the D24HourMean values is 9.15 and the mean of the QuarterlyMean
values is 9.2.

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Col

Row

D24HourMean

QuarterlyMean

1

246

9.15

9.2

2

246

9.16

9.2

3

246

9.16

9.21

4

246

9.17

9.21

5

246

9.17

9.22

6

246

9.54

9.65

Note that the exported files may be very large (tens to hundreds of megabytes in file
size and with row counts exceeding typical spreadsheet applications). With large files,
you might need to use a database program to work with the files. Alternatively, these
files can also be read by simple text editors.

9.7 GBD Rollback

The World Health Organization global burden of disease (GBD) study measures burden
of disease using the disability-adjusted-life-year (DALY). This time-based measure
combines years of life lost due to premature mortality and years of life lost due to time
lived in states of less than full health. The DALY metric was developed in the original
GBD 1990 study to assess the burden of disease consistently across diseases, risk
factors and regions.1

The GBD Rollback tool uses data from the 2013 GBD study to allow users to estimate
the human health burden of PM2.5 levels in each country as well as the benefits of
reducing these air pollution levels. Users can "roll back," or adjust ambient PM2.5 levels
in one or more countries or regions and calculate the total burden, or avoided deaths, in
that region. This feature is analogous to the monitor roll-back tool already available in
core BenMAP-CE (discussed in Chapter 5); that tool allows users to adjust downward
(or upward) air quality monitoring data in specified locations according to various
algorithms (including proportional, quadratic and incremental rollbacks). The tool uses
a grid with 0.1 degree resolution (approximately 10km) grid cells and includes:

•	2013 PM2.5 pollution concentrations2. Negative concentrations were adjusted
to zero.

•	2015 global population3 data stratified by age and gender. Elder populations
were combined into an "80UP" age group to align with the incidence dataset.

1	For more information on the GBD, see: http: //www.who.int/topics/global burden of disease.

2	Ambient Air Pollution Exposure Estimation for the Global Burden of Disease 2013. Michael Brauer, Greg
Freedman, Joseph Frostad, Aaron van Donkelaar, Randall V. Martin, Frank Dentener, Rita van Dingenen, Kara
Estep, Heresh Amini, Joshua S. Apte, Kalpana Balakrishnan, Lars Barregard, David Broday, Valery Feigin,
Santu Ghosh, Philip K. Hopke, Luke D. Knibbs, Yoshihiro Kokubo, Yang Liu, Stefan Ma, Lidia Morawska, Jose
Luis Texcalac Sangrador, Gavin Shaddick, H. Ross Anderson, Theo Vos, Mohammad H. Forouzanfar, Richard T.
Burnett, and Aaron Cohen. Environmental Science & Technology 2016 50 (1), 79-88.

DOI: 10.1021/acs.est.5b03709

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• 2013 mortality incidence4 for four health endpoints (COPD, cerebrovascular
disease, ischemic heart disease, and lung cancer) stratified by age and gender.
Neonatal ("0 to 0") and "1 to 4" age groups were combined into a "0 to 4" age
group to align with population data.

To begin, select GBD Rollback from the Tools menu. The GBD Rollback Tool window
will be displayed.	

J GBD Rollback Tool

Create a new scenario

Scenario Name:

Map

a © o

Describe
Scenario:

Select Region ->

Scenarios

Edit Scenario

Delete Scenario

Execute Scenarios CAUsers^mlXDocumentsVMy BenMAP-CE Files\GBD

Browse..

Format: XLSX ~

Scenario
Name

Color

Total
Countries

Total

Population

Type Of
Rollback

Execute?

3	Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded
Population of the World, Version 4 [GPWv4]: Administrative Unit Center Points with Population Estimates.
Palisades, NY: NASA Socioeconomic Data and Applications Center [SEDAC],
http://dx.doi.org/10.7927/H4F47M2C.

4	Global Burden of Disease Study 2013. Global Burden of Disease Study 2013 (GBD 2013) Incidence,
Prevalence, and Years Lived with Disability 1990-2013. Seattle, United States: Institute for Health Metrics and
Evaluation [IHME], 2015. http://ghdx.healthdata.org/record/global-burden-disease-study-2013-gbd-2013-
incidence-prevalence-and-years-lived-disability

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9.7.1	Create New Scenario

To create a new scenario, first provide a Scenario Name (required) and description for
the scenario (optional). Then, click the Select Region button.

9.7.2	Select Regions or Countries

The Region Selection box provides options to view Regions (as defined in the GBD
study) with the associated countries listed beneath, or to view Countries. Multiple
selections are possible with either selection method and you can toggle between the
two.

If you view Regions, you can select an entire region (all countries within this region) by
checking the box next to the desired region. To view the individual countries for a
region, click on the '+' sign to the left of the region. This will expand the tree menu. To
collapse the tree menu, simply click on the sign.

If you do not know the name of the region which contains the country you want to
select, click the option to view Countries. You can search the alphabetically-sorted list
to find countries.

As you make selections, the map will highlight them in a bright blue color. Note that the
map is not interactive (i.e., you cannot click on the map to make selections). The
toolbar located above the map allows you to zoom in and out, pan, view full extent, and
identify countries.

Region Selection

(0) Regions © Countries

El S Andean Latin America
B-fyl Australasia
171 Australia

Rollback Settings

Rollback Type. I Percentage Rollback

($]¦[~ Caribbean
~ n Central Asia
P I Amienia
••[H Azerbaijan
• P I Georgia
•H I Kazakhstan
•¦in Kyrgyzstan
-|i~l Mongolia
Q Tajikistan
Q Turkmenistan
•H I Uzbekistan
B -in Central Europe
^¦•~Central Latin America
S- d Central sub-Saharan /tfrica
(j-Q East Asia
a n Eastern Europe

1	.u

Options

Percentage {%): 5

Function:

Krewski

<- Scenario Name

Rollback Settings ->

<- Region Selection | Save Scenario

9.7.3 Choose Rollback Settings

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After selecting the countries or regions to analyze, click the Rollback Settings button.
The Rollback Settings pane allows you to choose the Rollback Type (Percentage
Rollback, Incremental Rollback, or Rollback to a Standard] and configuration options.5
(An illustrative example for the selected Rollback Type will be displayed below the
Options box.) The tool allows for a negative rollback, indicating an increase in pollution
concentration.

For Percentage and Incremental Rollback types, you must enter a percent value or
whole number by which to reduce the pollutant concentration in all grid cells in the
selected countries. If you select Rollback to a Standard, you may select from a list of
national pollutant standards enforced by various countries in different years6.

The Function dropdown is designed to allow for selection between functions. The tool
currently provides only one default function labeled Krewski1.

5	The tool uses a background PM2.5 concentration = 5.8 |ig/m3 (lowest measured level in epidemiological
literature).

6	Air Quality Standards / Guidelines / Objectives For Different Countries (updated April 2014). Table
developed by Scott Voorhees, US EPA.

7	Krewski, D., M. Jerrett, R.T. Burnett, R. Ma, E. Hughes, et al. (2009). Extended Follow-Up and Spatial Analysis
of the American Cancer Society Study Linking Particulate Air Pollution and Mortality. Health Effects Institute,
Report Number 140. Cambridge, May. http://pubs.healtheffects.org/getfile.php?u=47

Standard Group

WHO
US Primary
US Secondary
EU
Japan
China Class I
China Class II
Hong Kong
India
Singapore

Concentration Limit (ng/m3) Exposure Duration

10
12
15
25
15
15
35
35
40
12
25
35
35
35
75
75
60
37.5

Annual

Annual

Annual

Annual

Annual

Annual

Annual

Annual

Annual

Annual

24-hour

24-hour

24-hour

24-hour

24-hour

24-hour

24-hour

24-hour

WHO
US Primary
Japan
China Class I
China Class II
Hong Kong
India
Singapore

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Click the Save Scenario button to save your rollback configuration. The map will
update the color of the selected countries so that each saved scenario is uniquely
different.

The table at the bottom of the window will update to display the attributes for the saved
scenario (Scenario Name, Color, Total Countries, Total Population, Type of Rollback),
along with an Execute? option for you to indicate whether the tool should execute or
ignore the scenario. If you double-click in the Total Countries or Total Population fields,
the program will display a table of the individual countries and populations included in
the scenario.

GBD Rollback Tool

Create a new scenario

Scenario Name:

I <=> H l-^l

Map

Describe
Scenario:

Select Region ->

Scenarios

Edit Scenario

Delete Scenario

Execute Scenarios | C:\UsereNjml\Documents\My BenMAP-CE RlesXGBD

Browse... Format: XLSX ~

Scenario
Name

Color

Total
Countries

Total

Population

Type Of j- , n
Rollback Execule?

.Australasia



2

23.425.326

5% Rollback

D

East Asia



4

1.373.218.012

1Opg/m3 Rollback

~

South Asia



6

1.560,024.987

Rollback to WHO
(annual) (1 Opg/m3}
Standard



Close

9.7.4 Execute Scenarios and Save Results

Click the Execute Scenarios button to generate results for saved scenarios. If a
selected country does not have sufficient data (i.e., population, pollution concentration,
or incidence rates) to perform the analysis, it will be bypassed and a message (including
the country name) will be provided.

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If you are adding to a list of saved scenarios and do not want to re-run the previously
executed ones, uncheck Execute? in the summary table so that only the new scenario is
checked.

Upon execution, the GBD Rollback tool will export results in (.xlsx format) to a default
file location.8 You can change the file location by clicking the Browse button. You can
also change the export file format to CSV by clicking on the Format drop-down list. The
results file is named using the Scenario Name followed by the time stamp of scenario
execution.

¦	The XLSX option provides a formatted summary table, detailed results, charts, and
metadata about the supporting datasets used in this analysis.

¦	The CSV option will only create two files: one containing the summary data and one
with detailed results.

If you want to edit or delete saved scenarios, select one record at a time from the
scenario table (use the record selector on the left side of the grid) and click the Edit
Scenario or Delete Scenario button. If a scenario is edited and re-executed, the
timestamp in the filename will help the user identify the new version.

Once you close the GBD Rollback tool, the scenarios are cleared from memory;
scenarios are not saved in the BenMAP-CE database. Information about the scenario
configurations are saved in the results file to help you document the analysis and re-
create it if necessary.

Date

3/25/2015

Scenario Name

South Asia

Scenario Description



GBD Year

2010

Pollutant

PM 2.5

Background Concentration

0 Mg/m'



Rollback to WHO (annual)

Rollback Type

(lOpg/m1) Standard

Regions and Countries

South Asia



Afghanistan



Bangladesh



Bhutan



India



Nepal



Pakistan

IE

D

E

F

G

H

1

J K L

M N 0

P































2010 Air Quality Levels

Policy scenario



Population
Affected

Avoided Deaths
(Total)

95% CI

% of Baseline
Mortality

Deaths per
100,000

Avoided Deaths
(% Population)

Min

Median

Max

Min

Median

Max

Air Quality Change
(Population
Weighted)

1.600.000.000

1.200.000

820.000- 1.500.000

9.857536876

76.35

0.07634642

-4.42

22.23

77.60

-4.42

10.00

10.00

18.09746753

% Avoided Deaths By Country

Afghani:

Avoided Deaths (Total): 1,200,000

Detailed Results Avoided Deaths By Country Deaths Per 100,000 Metadata

~lit

The GBD results file [.xlsx format) contains 5 worksheet tabs, described as follows:

8 The files are stored by default at the following location: C:\Users\\Documents\My BenMAP-
CE Files\GBD.

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¦	Summary: Gives a basic background on the scenario chosen, including name,
description, pollutants, rollback type, and countries. It also provides a quick
overview of total and affected population results.

¦	Detailed Results: Gives in-depth breakdown of population and results for each
selected country. Important fields on this tab include Population Affected, Avoided
Deaths (Total), and Avoided Deaths (%Population).

¦	Avoided Deaths by Country: Displays the number of avoided deaths for the region
and by country in a bar graph. This graph was generated from the data on the
Detailed Results tab.

¦	Deaths Per 100,000: Displays the number of deaths for each country (with a
population multiplier of 100,000) in a bar graph. This graph was generated from
the data on the Detailed Results tab.

¦	Metadata: Provides supplementary information about the underlying data and
functions used by the GBD Rollback tool.

9.8 Monitor Data Conversion

The Monitor Data Conversion Tool provides the user with a mechanism to transform
their data from commonly available formats (e.g., one monitoring result per row) to
BenMAP-ready format.

j Monitor Data Conversion Tool

uO

This tool converts daily monitor data to a BenMAP-ready format.

Source Data

Browse-

Converted Data

Convert

OK

To view an example, click the help (?) button near the top of the form.

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Chapter 9 - Tools Menu

Monitor Data Conversion Tool - Formatting

Source File-Columns and Sample Data Format

Note: Monitor Description field may be left blank

Monitor Name,Monitor Description,Latitude,Longitude,Metric,Seasonal Metric,Statistic,Date,Value
123456789101112,"' M ethodCod e=123',' La n dUse=RE5l DENTIAL',' LocationSetting=SUBU RBAN',' ProbeLocati or
123456789101112,"'MethodCode=123VLandUse=RESIDENTlAL','LocationSetting=SUBURBAN',lProbeLocatior
123456789101112,,"MethodCode=123','LandUse=RES!DENT!AL','LocationSetting=SUBURBAN','ProbeLocatior

rrr

Converted File-Columns and Sample Data Format

Monitor Name,Monitor Description,Latitude,Longitude,Metric,Seasonal Metric,Statistic,Values
123456789101112,,"MethodCode=123','LandUse=RESIDENTlAL','LocationSetting=SUBURBAN','ProbeLocatior

OK

To use this tool, first click "Browse,.." to locate your source data file. Then click
"Browse..." to specify the file name and location for the converted file. Click the "Convert"
button to begin the conversion process. Once the file is converted, click "OK" to close the
form.

9.9 Neighbor File Creator

The Neighbor File Creator tool generates a file containing gridded monitor data
created by BenMAP-CE for Monitor Data or Monitor Rollback air quality surfaces.

To start, choose Neighbor File Creator from the Tools drop-down menu. This will bring
up the Create Neighbors File window. Click on the Browse button, and find the air
quality surface you want to analyze.

Create Neighbors File

Air Quality Surface:

Cancel

OK

After locating the file, click the OK button. The file path for the selected file will be
displayed in the Air Quality Surface box. Click OK. A Save As window will open.
Provide a file name for the Neighbors file (.csv) you want to create and click Save. An
example Neighbors File is shown below.

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Col

Row

MonitorName

Weight

Distance

1

246

410030013881011

0.215167

627.5258

1

246

530410006881011

0.253047

533.5881

1

246

530090009881011

0.286508

471.2713

2

246

410030013881011

0.213318

620.813

2

246

530410006881011

0.252881

523.688

2

246

530090009881011

0.287978

459.8632

3

246

410030013881011

0.211387

614.2613

3

246

530410006881011

0.252682

513.8765

3

246

530090009881011

0.289525

448.4836

The first two columns specify the Column and Row variables for each grid cell. In the
example above, you will see that Column = 1 and Row = 246 are repeated three times,
indicating that three different monitors were used to estimate air quality at this grid
cell. The MonitorName column provides the monitor identifier. The Weight column
specifies the weight used in the air quality calculation (e.g., Voronoi Neighbor
Averaging). And the Distance column gives the distance (in kilometers) from the
monitor location to the center of the grid cell.

Note that if an air quality grid was created using the Closest Monitor option (see Chapter
5 under the Monitor Data section), then only a single monitor is used for any given grid
cell. As a result, the neighbor file will contain the same 5 fields, but the Weight column
will contain a value of "I" all the way down. In addition, there will only be one entry for
each grid cell.

Col

Row

MonitorName

Weight

Distance

1

246

410030013881011

1

627.5258

2

246

410030013881011

1

620.813

3

246

410030013881011

1

614.2613

4

246

410030013881011

1

607.8755

5

246

410030013881011

1

601.661

6

246

410671003881011

1

548.5376

7

246

410671003881011

1

541.038

8

246

410671003881011

1

533.702

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9.10 PopSim

The Population Simulation (PopSim)9 model was designed to estimate two outputs related
to premature mortality attributed to the CAAA: life-years lost, and changes in life
expectancy. The population simulation approach provides some advantages over the
BenMAP-CE model in terms of simulation of the dynamic effects of mortality across a
population through time, but also has several significant disadvantages relative to
BenMAP-CE in terms of the spatial resolution of pollutant exposure estimates. As a result,
the population simulation approach operates as a supplement to the BenMAP-based
primary estimates for selected measures of the impact of reducing risks of premature
mortality.

The core BenMAP-CE program 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. Conversely, the PopSim tool estimates the change in
population mortality risk over a multi-year period, but it is not yet possible to estimate
the economic value of these impacts in BenMAP-CE.

PopSim is designed to track the effect of alternative assumptions about the mortality effects of
fine particulate matter (PM2.5) air pollution in the U.S. population over time. The tool
incorporates detailed life table data for historical years, by age, gender, and cause of death,
obtained from the Census Bureau and the Centers for Disease Control (CDC). It also
incorporates Census mortality and population projections for future years, again by age and
gender, using the projected death and birth rates that underlie the Census Bureau's published
population projections.

The PopSim model allows users to:

•	Simulate population in the U.S. by single year cohorts of age and gender for years
between 1980 and 2050 under alternative assumptions about the degree of hazard
posed by air pollution relative to baseline historical and projected Census mortality
rates;

•	Estimate changes in life years relative to baseline Census mortality rates;

•	Apply air pollution hazards differentially by cause of death; and

•	Analyze the effect of alternative cessation lag structures on the timing of total
mortality and on total life years in the U.S. population, based on differential
application by cause of death or other specifications of cessation lag.

The dynamic life-table approach used in this model can theoretically provide improved
estimates of the mortality impacts of air pollution in future years over the more common

9 The PopSim tool in BenMAP-CE is based upon an Access-based model originally developed for EPA by
Industrial Economics, Inc. This Population Simulation model is described in Chapter 5 of EPA's "Second
Prospective Study - 1990 to 2020 - Benefits and Costs of the Clean Air Act. U.S. Environmental Protection
Agency, Office of Air and Radiation, April 2011."

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static approach because it explicitly accounts for the year-to-year cascade of impacts on
mortality and population following an air pollution change.

To use the PopSim tool, first select PopSim from the BenMAP-CE Tools menu.

PopSim	1 ~ | [°1 UfrJ

introduction

Years

PopSim

Response

PopSim is a dynamic population simulation

PM changes

that incorporates the cumulative effects of

Ages affected

air pollution on different age groups over

Lagtype

time.

Other



Run

Note that this version only includes U.S. data.



Reference: Second Prospective Study -1990 to 2020 - Benefits and



Costs of the Clean Air Act, U.S. Environmental Protection Agency,



Office of Air and Radiation, April 2011





Next>

Click through each of the menu items (e.g., Years, Response, PM changes, Ages affected,

Lag type, and Other) and review the default settings, or adjust the values as needed. For
more information on the underlying datasets or model specifications, please refer to the
"Population Simulation Model for Air Pollution Hazards. Version 3.0. User Manual and
Documentation" (IEc, September 2015).

When you are finished with the model settings, click the Run model button on the Run
screen. A progress bar will be displayed as the calculations are performed. When the
model run has completed, a dialog will be displayed. Click OK to acknowledge the message.
Then click the Output button. Specify a location to save the model results. By default, the
tool will save to the "My Documents\My BenMAP-CEFiles\PopSim" folder. The progress bar
will once again display as the files are saved to your destination. When complete, a dialog
will indicate "File Saved". Click OK to acknowledge the message. You may close the PopSim
tool, or return to the beginning to initiate a new model run. The tool will remember the
settings from the previous run only.

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9.11 Options

You can customize some of the generic options for BenMAP-CE using the Options
window available from the Tools menu.



10 Options 1 = 1 l-S-r



[yl Show Start Window



[yl Show Exit Window



Require Validation for Data Imports



Delete Validation logs after 30 days



| Delete Validation Error Logs Now |

Default setup:

| United States ~ |

1 0K 1





¦	Show Start Window: If unchecked, the Welcome window will not appear during
subsequent start-ups. You can update your choice at any time.

¦	Show Exit Window: If unchecked, the window confirming you would like to exit
will not appear during subsequent shut-downs. You can update your choice at any
time.

¦	Require Validation for Data Imports: If checked, you will be required to validate
their input files prior to importing datasets. If unchecked, validation will be an
available option but it will not be required.

¦	Delete Validation logs after _ days: You can specify the number of days BenMAP-
CE will retain the validation logs (the default value is 30 days). If the number of
days is left blank, BenMAP-CE will not automatically delete any validation logs.10

¦	Delete Validation Error Logs Now: Select this button if you want to immediately
clear all validation logs. Note: There is no confirmation option here - once the button
is clicked, all logs will be deleted.

¦	Default setup: Select the preferred setup to appear by default in the main
BenMAP-CE window.

9.12 Compute Grid Crosswalk

The Compute Grid Crosswalk tool allows users to manually remove crosswalks and re-
generate selected crosswalks. A crosswalk is a file used to relate data, such as air

quality, population and demographic data, at one spatial scale to another. Crosswalks

10 Validation logs are saved at C:\Users\\Documents\My BenMAP-CE Files\ValidationResults.

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are created during when importing a grid definition or calculating health impacts and
economic benefits. BenMap-CE does not remove or overwrite a crosswalk automatically
if it already exists. In cases where a crosswalk was broken due to database error or
force quitting of the application, users can use this tool to remove the crosswalk from
the database. Users may then let BenMap-CE automatically re-generate the crosswalk
during processing or re-create it in the tool to save time later.

To re-create crosswalks, go to the Tools menu, and choose the Compute Grid
Crosswalks.

This will bring up the Crosswalk Calculator window. You will see all grid definitions
associated to the active setup are listed in both windows. To switch to a different setup,
choose another setup from Available Setups.

Crosswalks are used by Ben MAP to relate grids of different sizes and resolutions. Generally', crosswalks are created automatically when needed.
This form allows you to manually' remove all crosswalks and regenerate selected crosswalks.

Available Setups United States

~ear Existing Crosswalks

CMAQ 3Skm

County

State

Nation

CMAQ 12km Nation - Clipped
CMAQ 12km Nation

Compute Crosswalk

Cancel Operation

CMAQ 36km
County

kUJ	

Nation

CMAQ 12km Nation - Clipped
CMAQ 12km Nation

Close

To remove all crosswalks among the grid definitions in the windows, click Clear
Existing Crosswalks. A message box will pop up asking you to confirm this operation.
Click OK to confirm. In the screenshot below, we are trying to remove all existing
crosswalks for Detroit setup from the database. NOTE: crosswalks for default setups
(e.g. China and United States) are locked and cannot be removed.

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Chapter 9 - Tools Menu

BenMAP - Crosswalk Calculator

£2 I

Crosswalks are used by BenMAP to relate grids of different sizes and resolutions. Generally, crosswalks are created automatically when needed.
This form allows you to manually remove all crosswalks and regenerate selected crosswalks.

Available Setups Detroit

~ear Existing Crosswalks

Detroit ZIP Codes

Detroit 1km
Detroit Counties
County

Detroit ZIP Codes

Detroit 1km
Detroit Counties

Confirm Crosswalk Deletion

S3

i This action will delete all current crosswalk definitions in the selected
setup requiring them to be rebuilt when they are requested.

OK

Cancel

Close

To create or overwrite a crosswalk between two grid definitions, highlight one grid
definition from the left side, highlight the other grid definition from the right side, and
click Compute Crosswalk button. If the crosswalk already exists, you will be asked to
confirm replacement by clicking OK in the message box. In the screenshot below, we
selected Detroit ZIP Codes from the left window and Detroit Counties from the right
window to create crosswalks between the grids for both directions.

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BenMAP - Crosswalk Calculator

£2 I

Crosswalks are used by BenMAP to relate grids of different sizes and resolutions. Generally, crosswalks are created automatically when needed.
This form allows you to manually remove all crosswalks and regenerate selected crosswalks.

Available Setups Detroit

~ear Existing Crosswalks

Detroit ZIP Codes

Detroit 1km
Detroit Counties
County

Detroit ZIP Codes
Detroit 1km

D-=trnit rni infies

Replace crosswalk

|-^i—|

,^j| The requested crosswalk already exists in the database.
Do you want to replace it?

OK

Cancel

Close

The calculation may process slowly depending on the size and resolution of your grid
cells. If you have to interrupt the process, simply click Cancel Operation. It is
recommended to highlight the grid definition with higher resolution from the left
window and the one with lower resolution from the right window. This will speed up
the calculation.

BenMAP-CE User's Manual

9-27

April 2017


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