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SPATIAL PREDICTION OF AIR QUALITY DATA
Citation:
Holland, D M., W. M. Cox, R. Scheffe, A. J. Cimorelli, D. Nychka, AND P. K. Hopke. SPATIAL PREDICTION OF AIR QUALITY DATA. ENVIRONMENTAL MANAGER (August 2003):31-35, (2003).
Impact/Purpose:
Our main objective is to assess the exposure of selected ecosystems to specific atmospheric stressors. More precisely, we will analyze and interpret environmental quality (primarily atmospheric) data to document observable changes in environmental stressors that may be associated with legislatively-mandated emissions reductions.
Description:
Site-specific air quality monitoring data have been used extensively in both scientific and regulatory programs. As such, these data provide essential information to the public, environmental managers, and the atmospheric research community. Currently, air quality management practices use monitoring data as independent point measurements with an assumed area of representativeness (typically a county boundary). However, there is an increasing need to develop regional air quality management programs and associated regulatory policies using site measurements to make spatial statements at non-monitored locations. Based on deliberate statistical research over the past two decades and the advent of inexpensive, but powerful computing resources, there now exists well-accepted methodology for predicting pollutant values at unobserved locations across the entire spatial field of interest based on the data. This spatial information, coupled with prediction uncertainties, will enable air quality managers to construct better environmental management programs.