Spatiotemporal Air Quality Metrics Developed for Georgia for Use in Health Studies

摘要

A data fusion methodology has been developed from to improve exposure metrics used in epidemiologic cross-case studies from observational and chemical transport model datasets. In terms of the data fusion methodology, the observations provide reliable temporal trends at and near monitors, while the CMAQ modeled data provide spatially rich information that is less reliable temporally. Fusing the two data sets lead to the reduction of bias in original CMAQ species concentrations by use of the observational data. Data withholding results indicate the fusion methodology provides a robust way of estimating exposure metrics. The optimized dataset developed using two interpolation techniques yield the highest coefficients of determination. However, results are only as good as the original datasets, which in tern are dependent on monitoring networks and model inputs, such meteorology and emissions.