Fine-scale damage estimates of particulate matter air pollution reveal opportunities for location-specific mitigation of emissions

Abstract

Fine particulate matter (PM_2.5) air pollution has been recognized as a major source of mortality in the United States for at least 25 years, yet much remains unknown about which sources are the most harmful, let alone how best to target policies to mitigate them. Such efforts can be improved by employing high-resolution geographically explicit methods for quantifying human health impacts of emissions of PM_2.5 and its precursors. Here, we provide a detailed examination of the health and economic impacts of PM_2.5 pollution in the United States by linking emission sources with resulting pollution concentrations. We estimate that anthropogenic PM_2.5 was responsible for 107,000 premature deaths in 2011, at a cost to society of $886 billion. Of these deaths, 57% were associated with pollution caused by energy consumption [e.g., transportation (28%) and electricity generation (14%)]; another 15% with pollution caused by agricultural activities. A small fraction of emissions, concentrated in or near densely populated areas, plays an outsized role in damaging human health with the most damaging 10% of total emissions accounting for 40% of total damages. We find that 33% of damages occur within 8 km of emission sources, but 25% occur more than 256 km away, emphasizing the importance of tracking both local and long-range impacts. Our paper highlights the importance of a fine-scale approach as marginal damages can vary by over an order of magnitude within a single county. Information presented here can assist mitigation efforts by identifying those sources with the greatest health effects.

Keywords: air pollution; environmental economics; marginal damages; particulate matter.

Fig. 1.

Marginal damages of emissions ($ t⁻¹; logarithmic scale) by emitted pollutant and emission location. Damages are generally higher for emissions upwind of population centers, but the relationship with population density varies by pollutant. The value displayed in a location represents the combined mortality impacts (in terms of dollar damages) to all downwind receptors from 1 t emitted at that location.

Fig. 2.

Within-urban and within-county variability in marginal damages ($ t⁻¹; logarithmic scale) of primary PM_2.5 emissions in the Los Angeles, CA and Seattle, WA regions. The black lines represent county boundaries.

Fig. 3.

Total damages and attributable premature mortality associated with anthropogenic emissions in 2011 ($ billions) by sector, pollutant, and emission height.

Fig. 4.

Cumulative damages by pollutant and distance of impacted populations from sources of anthropogenic emissions. The black dashed line at 32 km from the source represents the distance within which 50% of total damages occur.

Fig. 5.

Comparison of ISRM-predicted total PM_2.5 concentrations and observed concentrations at 840 monitor locations for year 2011. Evaluation metrics: MB, mean bias; ME, mean error; MFB, mean fractional bias; MFE, mean fractional error; best-fit slope (S), and R².