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Abstract:
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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 mining activities. Our study develops a mechanistic spatial model for heavy metal concentrations in Cape Krusenstern National Monument (CAKR), Alaska, USA. Moss tissue samples collected in CAKR have elevated heavy metal concentrations from pollution that is spread during the transportation of ore on a mining haul road through CAKR. To better understand the mechanisms governing the spread of these pollutants, 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 analysis provides an example of how using spatio-temporal processes in statistical models for spatial data can improve understanding of mechanisms governing the spread of pollution.
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