Temporally and spatially resolved air pollution in Georgia using fused ambient monitor data and chemical transport model results

Abstract

Health data geo-coded with residential coordinates are being used to investigate the relationship between ambient air quality and pediatric emergency department visits in the State of Georgia over the time period 2000–2010. Two types of ambient air quality data – observed concentrations from ambient monitors and predicted concentrations from a chemical transport model (CMAQ) – are being fused to provide spatially resolved daily metrics of five air pollutant gases (CO, NO2, NOx, SO2 and O3) and seven airborne particulate matter measures (PM10, PM2.5, and PM2.5 constituents SO42−, NO3−, NH4+, EC, OC). The observational data provide reliable temporal trends at and near monitors, but limited spatial information due to the sparse monitoring network; CMAQ data, on the other hand, provide rich spatial information but less reliable temporal information. Four data fusion techniques were applied to provide daily spatial fields of ambient air pollutant concentrations, with data withholding used to evaluate model performance. Two of the data fusion methods were combined to provide results that minimized bias and maximized correlation over time and space with withheld data. Results vary widely across pollutants. These results provide health researchers with complete temporal and spatial air pollutant fields, as well as with temporal and spatial error estimate fields that can be incorporated into health risk models. Future work will apply these methods to five cities for use in ongoing air pollution health studies and to examine strategies for incorporating land use regression variables for spatial downscaling of data fusion results.