A Mechanistic Model of Annual Sulfate Concentrations in the United States

TL;DR

This paper introduces a mechanistic statistical model using a multivariate Ornstein-Uhlenbeck process to analyze sulfate concentrations in the US, linking emission sources, transport dynamics, and regulatory impacts.

Contribution

It develops a novel probabilistic framework for spatial sulfate data based on chemical transport dynamics, enabling inference of pollution processes and regulation effects.

Findings

Identifies key transport processes influencing sulfate levels.

Quantifies impact of FGD technologies on sulfate concentrations.

Provides a new statistical approach for spatial air quality data.

Abstract

We develop a mechanistic model to analyze the impact of sulfur dioxide emissions from coal-fired power plants on average sulfate concentrations in the central United States. A multivariate Ornstein-Uhlenbeck (OU) process is used to approximate the dynamics of the underlying space-time chemical transport process, and its distributional properties are leveraged to specify novel probability models for spatial data (i.e., spatially-referenced data with no temporal replication) that are viewed as either a snapshot or a time-averaged observation of the OU process. Air pollution transport dynamics determine the mean and covariance structure of our atmospheric sulfate model, allowing us to infer which process dynamics are driving observed air pollution concentrations. We use these inferred dynamics to assess the regulatory impact of flue-gas desulfurization (FGD) technologies on human exposure to sulfate aerosols.