Evaluation of observation-fused regional air quality model results for population air pollution exposure estimation

In this study, Community Multiscale Air Quality (CMAQ) model was applied to predict ambient gaseous and particulate concentrations during 2001 to 2010 in 15 hospital referral regions (HRRs) using a 36-km horizontal resolution domain. An inverse distance weighting based method was applied to produce exposure estimates based on observation-fused regional pollutant concentration fields using the differences between observations and predictions at grid cells where air quality monitors were located. Although the raw CMAQ model is capable of producing satisfying results for O₃and PM_2.5based on EPA guidelines, using the observation data fusing technique to correct CMAQ predictions leads to significant improvement of model performance for all gaseous and particulate pollutants. Regional average concentrations were calculated using five different methods: 1) inverse distance weighting of observation data alone, 2) raw CMAQ results, 3) observation-fused CMAQ results, 4) population-averaged raw CMAQ results and 5) population-averaged fused CMAQ results. It shows that while O₃(as well as NO_x) monitoring networks in the HRRs are dense enough to provide consistent regional average exposure estimation based on monitoring data alone, PM_2.5observation sites (as well as monitors for CO, SO₂, PM₁₀and PM_2.5components) are usually sparse and the difference between the average concentrations estimated by the inverse distance interpolated observations, raw CMAQ and fused CMAQ results can be significantly different. Population-weighted average should be used to account for spatial variation in pollutant concentration and population density. Using raw CMAQ results or observations alone might lead to significant biases in health outcome analyses.