A Mechanistic Model of Annual Sulfate Concentrations in the United States

Abstract

Understanding how individual pollution sources contribute to ambient sulfate pollution is critical for assessing past and future air quality regulations. Since attribution to specific sources is typically not encoded in spatial air pollution data, we develop a mechanistic model which we use to estimate, with uncertainty, the contribution of ambient sulfate concentrations attributable specifically to sulfur dioxide (SO₂) emissions from individual coal-fired power plants in the central United States. We propose a multivariate Ornstein–Uhlenbeck (OU) process approximation to the dynamics of the underlying space-time chemical transport process, and its distributional properties are leveraged to specify novel probability models for spatial data that are viewed as either a snapshot or time-averaged observation of the OU process. Using US EPA SO₂ emissions data from 193 power plants and state-of-the-art estimates of ground-level annual mean sulfate concentrations, we estimate that in 2011—a time of active power plant regulatory action—existing flue-gas desulfurization (FGD) technologies at 66 power plants reduced population-weighted exposure to ambient sulfate by 1.97 μg/m³ (95% CI: 1.80–2.15). Furthermore, we anticipate future regulatory benefits by estimating that installing FGD technologies at the five largest SO₂-emitting facilities would reduce human exposure to ambient sulfate by an additional 0.45 μg/m³ (95% CI: 0.33–0.54). Supplementary materials for this article are available online.

Keywords:

• Air pollution
• Ornstein-Uhlenbeck process
• SDEs
• Space-time processes
• Spatial statistics

Supplementary Materials

The supplementary materials include a sensitivity analysis of the discretization grid size, details on the numerical approximation of the differential operator, and a description of the three alternative models discussed in Section 4.1, as well as R code which replicates the results of this analysis.

Acknowledgments

We thank the editor and two anonymous reviewers for their helpful comments.

Additional information

Funding

This work was partially supported by research funding from NSF DMS-2015273, NIH R01ES026217, and EPA 83587201. This publication’s contents are solely the responsibility of the grantee and do not necessarily represent the official views of the Environmental Protection Agency. Further, the Environmental Protection Agency does not endorse the purchase of any commercial products or services mentioned in this publication.