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.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 6 print issues and online access
$259.00 per year
only $43.17 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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.
References
Wei Y, Wang Y, Di Q, Choirat C, Wang Y, Koutrakis P, et al. Short term exposure to fine particulate matter and hospital admission risks and costs in the Medicare population: time stratified, case crossover study. BMJ. 2019;367:l6258.
Di Q, Wang Y, Zanobetti A, Wang Y, Koutrakis P, Choirat C, et al. Air pollution and mortality in the medicare population. N Engl J Med. 2017;376:2513–22.
Fann N, Baker KR, Fulcher CM. Characterizing the PM2.5-related health benefits of emission reductions for 17 industrial, area and mobile emission sectors across the U.S. Environ Int. 2012;49:141–51.
Fann N, Lamson AD, Anenberg SC, Wesson K, Risley D, Hubbell BJ. Estimating the national public health burden associated with exposure to ambient PM2.5 and ozone. Risk Anal. 2012;32:81–95.
Fann N, Coffman E, Timin B, Kelly JT. The estimated change in the level and distribution of PM2.5-attributable health impacts in the United States: 2005–2014. Environ Res. 2018;167:506–14.
Krall JR, Mulholland JA, Russell AG, Balachandran S, Winquist A, Tolbert PE, et al. Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four U.S. cities. Environ Health Perspect 2016. https://doi.org/10.1289/EHP271.
Abrams JY, Klein M, Henneman LRF, Sarnat SE, Chang HH, Strickland MJ, et al. Impact of air pollution control policies on cardiorespiratory emergency department visits, Atlanta, GA, 1999–2013. Environ Int. 2019;126:627–34.
Casey JA, Karasek D, Ogburn EL, Goin DE, Dang K, Braveman PA, et al. Coal and oil power plant retirements in California associated with reduced preterm birth among populations nearby. Am J Epidemiol. 2018. https://doi.org/10.1093/aje/kwy110/4996680.
Heo J, Adams PJ, Gao HO. Public health costs accounting of inorganic PM 2.5 pollution in metropolitan areas of the United States using a risk-based source-receptor model. Environ Int. 2017;106:119–26.
Levy JI, Baxter LK, Schwartz J. Uncertainty and variability in health-related damages from coal-fired power plants in the United States. Risk Anal. 2009;29:1000–14.
Mikati I, Benson AF, Luben TJ, Sacks JD, Richmond-Bryant J. Disparities in distribution of particulate matter emission sources by race and poverty status. Am J Public Health. 2018;108:480–5.
Strasert B, Teh SC, Cohan DS. Air quality and health benefits from potential coal power plant closures in Texas. J Air Waste Manag Assoc. 2019;69:333–50. https://doi.org/10.1080/10962247.2018.1537984.
Tessum CW, Apte JS, Goodkind AL, Muller NZ, Mullins KA, Paolella DA, et al. Inequity in consumption of goods and services adds to racial–ethnic disparities in air pollution exposure. Proc Natl Acad Sci. 2019;116:201818859.
Baker KR, Amend M, Penn S, Bankert J, Simon H, Chan E, et al. A database for evaluating the InMAP, APEEP, and EASIUR reduced complexity air-quality modeling tools. Data Br. 2020;28:104886.
U.S. EPA. Air Markets Program Data. 2016. https://ampd.epa.gov/ampd/.
Henneman LRF, Choirat C, Zigler CM. Decreases in negative health outcomes associated with coal emissions reductions between 2005 and 2012 in the United States. Epidemiology. 2019;30:477–85.
Henze DK, Hakami A, Seinfeld JH. Development of the adjoint of GEOS-Chem. Atmos Chem Phys 2007. https://doi.org/10.5194/acp-7-2413-2007.
Dedoussi IC, Eastham SD, Monier E, Barrett SRH. Premature mortality related to United States cross-state air pollution. Nature. 2020;578:261–5.
U.S. EPA. National Emissions Inventory. 2016. https://www.epa.gov/air-emissions-inventories/national-emissions-inventory.
Li Y, Henze DK, Jack D, Kinney PL. The influence of air quality model resolution on health impact assessment for fine particulate matter and its components. Air Qual Atmos Heal. 2016;9:51–68.
Thompson TM, Saari RK, Selin NE. Air quality resolution for health impact assessment: influence of regional characteristics. Atmos Chem Phys. 2014;14:969–78.
Punger EM, West JJ. The effect of grid resolution on estimates of the burden of ozone and fine particulate matter on premature mortality in the USA. Air Qual Atmos Heal. 2013;6:563–73.
U.S. EPA. SMOKE v3.5.1 User’s Manual.
Recht H. censusapi: Retrieve Data from the Census APIs. 2019. https://cran.r-project.org/package=censusapi.
Draxler RR, Hess GD. An overview of the HYSPLIT_4 modelling system for trajectories, dispersion, and deposition. Aust Meteorol Mag. 1998;47:295–308.
Stein AF, Draxler RR, Rolph GD, Stunder BJB, Cohen MD, Ngan F. Noaa’s hysplit atmospheric transport and dispersion modeling system. Bull Am Meteorol Soc. 2015;96:2059–77.
Barrie LA, Yi Y, Leaitch WR, Lohmann U, Kasibhatla P, Roelofs GJ, et al. A comparison of large-scale atmospheric sulphate aerosol models (COSAM): overview and highlights. Tellus, Ser B Chem Phys Meteorol. 2001;53:615–45.
Henneman LRF, Choirat C, Ivey CE, Cummiskey K, Zigler CM. Characterizing population exposure to coal emissions sources in the United States using the HyADS model. Atmos Environ. 2019;203:271–80.
Ivey CE, Holmes HA, Hu YT, Mulholland JA, Russell AG. Development of PM2.5 source impact spatial fields using a hybrid source apportionment air quality model. Geosci Model Dev. 2015;8:2153–65.
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, et al. The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc. 1996;77:437–71.
Emery C, Liu Z, Russell A, Talat Odman M, Yarwood G, Kumar N. Recommendations on statistics and benchmarks to assess photochemical model performance. J Air Waste Manag Assoc. 2016;67:582–98.
Dedoussi IC, Allroggen F, Flanagan R, Hansen T, Taylor B, Barrett SRH, et al. The co-pollutant cost of carbon emissions: an analysis of the US electric power generation sector. Environ Res Lett. 2019;14:094003.
Dedoussi IC, Barrett SRH. Air pollution and early deaths in the United States. Part II: attribution of PM2.5 exposure to emissions species, time, location and sector. Atmos Environ. 2014;99:610–7.
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.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41370-020-0219-1
Keywords
This article is cited by
-
A Directionally Varying Change Points Model for Quantifying the Impact of a Point Source
Journal of Agricultural, Biological and Environmental Statistics (2022)
-
Improved asthma outcomes observed in the vicinity of coal power plant retirement, retrofit and conversion to natural gas
Nature Energy (2020)