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Comparisons of simple and complex methods for quantifying exposure to individual point source air pollution emissions

Abstract

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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Fig. 1: Schematic of the three approaches for calculating PM2.5 source impacts.
Fig. 2: 50 top units in 2006 and 2011 by annual average population-weighted PM2.5 source impacts on the entire United States using the Average GEOS-Chem Adjoint results.
Fig. 3: Top: linear correlation (Pearson R), Normalized Mean Bias (−100% < NMB < +∞) and root mean square error (RMSE) evaluations of \({\mathbf{PWSI}}_{{\boldsymbol{P}},\, {\boldsymbol{j}}}^{\mathbf{HyADS}}\) and \({\mathbf{PWSI}}_{{\boldsymbol{P}},\, {\boldsymbol{j}}}^{{\mathbf{IDWE}}}\) source impacts evaluated against \({\mathbf{PWSI}}_{{\boldsymbol{P}},\, {\boldsymbol{j}}}^{{\mathbf{Adjoint}}}\) in individual states and entire United States (US).
Fig. 4: Normalized mean bias (−100% < NMB < +∞) of \({\mathbf{PWSI}}_{{\boldsymbol{P}},\, {\boldsymbol{j}}}^{{\mathbf{IDWE}}}\) evaluated against \({\mathbf{PWSI}}_{{\boldsymbol{P}},\, {\boldsymbol{j}}}^{{\mathbf{HyADS}}}\).

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Data availability

Annual and monthly datasets of unit-level population-weighted PM2.5 source impacts are available at https://github.com/lhenneman/simple_and_complex_AQ.

Code availability

We provide R code to reproduce the analyses and plots at https://github.com/lhenneman/simple_and_complex_AQ.

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Acknowledgements

This work was supported by research funding from NIHR01ES026217, NIHK99ES027023, EPA 83587201, and HEI 4953. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the USEPA. Furthermore, USEPA does not endorse the purchase of any commercial products or services mentioned in the publication.

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Correspondence to Lucas R. F. Henneman.

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Henneman, L.R.F., Dedoussi, I.C., Casey, J.A. et al. Comparisons of simple and complex methods for quantifying exposure to individual point source air pollution emissions. J Expo Sci Environ Epidemiol 31, 654–663 (2021). https://doi.org/10.1038/s41370-020-0219-1

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