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date: 03 August 2020

Abstract and Keywords

This article discusses an approach that combines monitoring data and computer model outputs for environmental exposure assessment. It describes the application of Bayesian data fusion methods using spatial Gaussian process models in studies of weekly wet deposition data for 2001 from 120 sites monitored by the US National Atmospheric Deposition Program (NADP) in the eastern United States. The article first provides an overview of environmental computer models, with a focus on the CMAQ (Community Multi-Scale Air Quality) Eta forecast model, before considering some algorithmic and pseudo-statistical approaches in weather prediction. It then reviews current state of the art fusion methods for environmental data analysis and introduces a non-dynamic downscaling approach. The static version of the dynamic spatial model is used to analyse the NADP weekly wet deposition data.

Keywords: environmental exposure assessment, fusion methods, spatial Gaussian process models, wet deposition data, environmental computer models, CMAQ (Community Multi-Scale Air Quality) Eta forecast model, weather prediction, environmental data analysis, downscaling approach, dynamic spatial model

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