  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.
