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Comparisons of simple and complex methods for quantifying exposure to individual point source air pollution emissions
Name
nihms-1568078.pdf
Description
Accepted version
Size
1.59 MB
Format
Adobe PDF
Checksum (MD5)
49855d032bf29f79fff5236075f8ed18
Author(s)
Henneman, Lucas RF
Dedoussi, Irene C
Casey, Joan A
Choirat, Christine
Barrett, Steven RH
Zigler, Corwin M
Date Issued
2020
Journal
Journal of Exposure Science and Environmental Epidemiology
Publisher
Springer Science and Business Media LLC
Version
Author's final manuscript
Abstract
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc. Expanded use of reduced complexity approaches in epidemiology and environmental justice investigations motivates detailed evaluation of these modeling approaches. Chemical transport models (CTMs) remain the most complete representation of atmospheric processes but are limited in applications that require large numbers of runs, such as those that evaluate individual impacts from large numbers of sources. This limitation motivates comparisons between modern CTM-derived techniques and intentionally simpler alternatives. We model population-weighted PM2.5 source impacts from each of greater than 1100 coal power plants operating in the United States in 2006 and 2011 using three approaches: (1) adjoint PM2.5 sensitivities calculated by the GEOS-Chem CTM; (2) a wind field-based Lagrangian model called HyADS; and (3) a simple calculation based on emissions and inverse source-receptor distance. Annual individual power plants’ nationwide population-weighted PM2.5 source impacts calculated by HyADS and the inverse distance approach have normalized mean errors between 20 and 28% and root mean square error ranges between 0.0003 and 0.0005 µg m−3 compared with adjoint sensitivities. Reduced complexity approaches are most similar to the GEOS-Chem adjoint sensitivities nearby and downwind of sources, with degrading performance farther from and upwind of sources particularly when wind fields are not accounted for.
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
10.1038/S41370-020-0219-1
