High-resolution space-time ozone modeling for assessing trends

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Description/Abstract

This article proposes a space–time model for daily 8-hour maximum ozone levels to provide input for regulatory activities: detection, evaluation, and analysis of spatial patterns and temporal trend in ozone summaries. The model is applied to the analysis of data from the state of Ohio that contains a mix of urban, suburban, and rural ozone monitoring sites. The proposed space–time model is autoregressive and incorporates the most important meteorological variables observed at a collection of ozone monitoring sites as well as at several weather stations where ozone levels have not been observed. This misalignment is handled through spatial modeling. In so doing we adopt a computationally convenient approach based on the successive daily increments in meteorological variables. The resulting hierarchical model is specified within a Bayesian framework and is fitted using Markov chain Monte Carlo techniques. Full inference with regard to model unknowns as well as for predictions in time and space, evaluation of annual summaries, and assessment of trends are presented.