Statistical assessment of spatio-temporal pollutant trends and meteorological transport models

Up to now, modeling and computational difficulties have impeded efforts toward a formal statistical assessment of large-scale, spatio-temporal pollutant trends and the predictive performance of meteorological pollutant transport and deposition models. Until such statistical assessments can be made however, environmental regulators may not always be able to defend regulatory decisions, and modelers may not always be able to give convincing evidence of a proposed model's predictive validity. This article gives a two-stage statistical method that allows such hypothesis testing and model assessment using data observed irregularly through space and time. Stage 1 is to estimate the global covariance matrix of the random disturbances of a pollutant deposition process and, using this global covariance matrix, stage 2 is to conduct a Monte-Carlo hypothesis test of either a pollutant trend hypothesis or goodness-of-fit of a meteorological pollutant transport and deposition model. This statistical method is described and demonstrated using conterminous U.S. wet sulfate deposition data.