Analysis of PM_2.5 Using the Environmental Benefits Mapping and Analysis Program (BenMAP)∗

∗The opinions expressed in this article are the authors' and do not necessarily represent those of the U.S. Environmental Protection Agency.

Abstract

As epidemiological work from around the world continues to tie PM_2.5 to serious adverse health effects, including premature mortality, the U.S. Environmental Protection Agency (U.S. EPA) has developed a number of policies to reduce air pollution, including PM_2.5. To assist in the benefit-cost analyses of these air pollution control policies, the U.S. EPA has developed the Environmental Benefits Mapping and Analysis Program (BenMAP). BenMAP is meant to (1) provide a flexible tool for systematically analyzing impacts of changes in environmental quality in a timely fashion, (2) ensure that stakeholders can understand the assumptions underlying the analysis, and (3) adequately address uncertainty and variability. BenMAP uses a “damage-function” approach to estimate the health benefits of a change in air quality. The major components of the damage-function approach are population estimates, population exposure, adverse health effects, and economic costs. To demonstrate BenMAP's ability to analyze PM_2.5 pollution control policy scenarios, we assess two sample applications: (1) benefits of a national-level air quality control program, and (2) benefits of attaining two annual PM_2.5 standards in California (annual average standards of 15 μg/m³ and 12 μg/m³). In the former, we estimate a scenario where control of PM_2.5 emissions results in $100 billion of benefits annually. In the analysis of alternative standards, we estimate that attaining the more stringent standard (12 μg/m³) would result in approximately 2000 fewer premature deaths each year than the 15 μg/m³ achieves. BenMAP has a number of features to help clarify the analysis process. It allows the user to record in a configuration all of the choices made during an analysis. Configurations are especially useful for recreating already existing policy analyses. Also, BenMAP has a number of reporting options, including a set of mapping tools that allows users to visually inspect their inputs and results.

Notes

∗The opinions expressed in this article are the authors' and do not necessarily represent those of the U.S. Environmental Protection Agency.

∗BenMAP can use output from a variety of models, including Regulatory Model System for Aerosols and Deposition (REMSAD), the Comprehensive Air Quality Model with Extensions (CAMx), the Urban Airshed Monitoring—Variable grid model (UAM-V), and the Community Multi-Scale Air Quality model (CMAQ). The following links provide more information on each model: REMSAD: http://remsad.saintl.com/; CAMx: http://www.camx.com; UAM-V: http://uamv.saintl.com/; and CMAQ: http://www.epa.gov/asmdnerl/models3/cmaq.html.

∗The appendices in the BenMAP user's manual describe the population data, forecasting approach, and other aspects of the model in detail. The manual is available at http://www.epa.gov/ttn/ecas/models/modeldoc.pdf

∗Each scaling approach is documented in detail in Appendix C of the BenMAP user's manual, available at http://www.epa.gov/ttn/ecas/models/modeldoc.pdf

∗The sources of prevalence and incidence data included in BenMAP are documented in Appendix E of the user's manual (http://www.epa.gov/ttn/ecas/models/modeldoc.pdf), all PM-related concentration response functions packaged with BenMAP are documented in Appendix F, and Appendix I describes in detail the uncertainty and pooling options available to the user.

^†The Latin hypercube method is used to enhance computer processing efficiency. It is a sampling method that divides a probability distribution into intervals of equal probability, with an assumption value for each interval assigned according to the interval's probability distribution. Compared with conventional Monte Carlo sampling, the Latin hypercube approach is more precise over a fewer number of trials because the distribution is sampled in a more even, consistent manner.

^‡Appendix I of the BenMAP user's manual provides the documentation and algorithms used for all weighting methods available to the user, including fixed-effects and random-effects weighting.

^¶Appendix H of the BenMAP user's manual documents the source and derivation of all health effect unit values included in the software (http://www.epa.gov/ttn/ecas/models/modeldoc.pdf).

∗The quantified PM-related health effects presented here are a sample of those that BenMAP can quantify. Others include acute bronchitis, lower and upper respiratory illness, asthma exacerbations, respiratory symptoms, and infant mortality. Note that there are many other PM-related health effects that BenMAP is unable to quantify, but are known to be related to PM exposures, such as low birth weight, changes in pulmonary function, chronic respiratory diseases other than chronic bronchitis, etc.

Greenbaum, D. 2002. Letter to colleagues dated May 30, 2002 [available at healtheffects.org]. Letter from L. D. Grant, Ph.D. to Dr. P. Hopke re: external review of EPA's Air Quality Criteria for Particulate Matter, with copy of 05/30/02 letter from Health Effects Institute re: re-analysis of National Morbidity, Mortality and Air Pollution Study data attached. Docket No. A-2000–01. Document No. IV-A-145.