Mechanistic spatial models for heavy metal pollution

Wilson J. Wright, Peter N. Neitlich, Alyssa E. Shiel and Mevin B. Hooten

Environmetrics, 2022, vol. 33, issue 8

Abstract: Mining operations can contribute substantial amounts of pollution in the form of atmospheric dust. Statistical models predicting the spread of pollutants from these sources are useful for evaluating the environmental impacts of mines. Our study develops a mechanistic spatial model for heavy metal concentrations in Cape Krusenstern National Monument (CAKR), Alaska, USA. We characterize the spatial structure in our statistical model using a spatio‐temporal process for atmospheric dispersion. Mathematically, this is modeled using an advection‐diffusion partial differential equation that incorporates information about pollutant sources, diffusion, duration of spread, and advection (i.e., prevailing winds). Our approach improves upon previous statistical methods by including a temporally varying advection component and linking indirect concentration measurements to the spatio‐temporal dynamics in the model. We estimated concentrations of three heavy metals jointly using a Bayesian hierarchical model to account for the similarity in processes across chemicals. Our mechanistic statistical model is beneficial because it can predict chemical concentrations for scenarios where mining activities change. Additionally, our analysis provides an example of how using spatio‐temporal processes in statistical models for spatial data can incorporate understanding of mechanisms governing the spread of pollution and provide inferences for parameters associated with these processes.

Date: 2022
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