Abstract
Background and Aims: Ambient monitors are often used to estimate air pollution exposure for epidemiological studies. This approach is efficient and economical but has limitations, including restricted coverage and resolution. Although three-dimensional air quality modeling has its own limitations, this emerging exposure approach has the potential to address some challenges of monitoring networks. We evaluated differences in exposure estimates obtained using monitoring data and simulation results based on the Community Multi-scale Air Quality (CMAQ) modeling system. Methods: Spatially-aggregated (county-level) exposure estimates for PM2.5 were calculated for the eastern two-thirds of the U.S. for years 2002-2006 from three sources: (1) 24hr ambient monitoring data; (2) 12 x 12 km gridded CMAQ simulation results; and (3) concentration estimates from a Bayesian space-time downscaler model “fusing” PM2.5 monitoring data with 12-km gridded CMAQ output (i.e., downscaler-derived exposures). Results: Exposure estimates generated from CMAQ and the downscaler provide greater spatial and temporal coverage compared to estimates from monitor data. The CMAQ and CMAQ-downscaler exposure estimates included 2818 counties and the monitoring approach produced estimates for 190 counties, which contain ~51% of the population within the CMAQ domain. Exposure estimates derived from CMAQ results underestimated PM2.5 levels (normalized mean bias [NMB]=-4.0%), while downscaler-derived exposures slightly overestimated observed PM2.5 (NMB=1.0%). Across time, correlation between monitor-derived exposure estimates and CMAQ- and downscaler-derived exposure estimates were 0.62 and 0.97, respectively. Conclusions: The downscaler approach had superior performance based on several metrics. Epidemiology may benefit from use of regional air quality models, with improved spatial and temporal coverage and ability to study populations far from monitors that may differ from those near monitors.
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