Application of Model and Ambient Data Fusion Techniques to Predict Current and Future Year PM2.5 Concentrations in Unmonitored Areas

摘要

States with nonattainment areas for ozone and/or PM_(2.5) must attain the ambient air quality standards by their respective attainment dates. The majority of areas use photochemical models to estimate future year pollutant concentrations to show how they will meet and/or maintain the standards. The EPA photochemical modeling guidance (U.S. EPA 2007) recommends an ambient monitor based rela-tive attainment test. Since the test examines future year concentrations at monitor locations, it does not evaluate the likelihood of violations in unmonitored areas. To account for unmonitored areas, the modeling guidance also recommends an "unmonitored areas analysis" to examine future year concentrations of ozone and/or PM_(2.5) in locations without nearby monitors. For the unmonitored area analysis, the guidance recommends using data fusion techniques to combine spatially interpolated ambient data with gridded photochemical model output to produce a "fused" surface of spatial fields. These fused spatial fields can be produced for both current years and also combined with future year modeling output to produce future year fields. Because they utilize both ambient data and model outputs, the fused spatial fields should provide an improved concentration estimate in unmonitored areas. In this application, we attempt to examine the performance of fused spatial fields compared to a simple ambient data interpolation technique.