Examining PM_2.5 concentrations and exposure using multiple models

Epidemiologic studies have found associations between fine particulate matter (PM_2.5) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM_2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM_2.5 concentrations in 2011, ii) potential changes in PM_2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM_2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM_2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 microg m^-3) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM_2.5 concentrations were estimated to decrease by about 1 microg m^-3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 microg m^-3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM_2.5 concentrations less than 10 microg m^-3 in 2011 and PM_2.5 improvements of about 2 microg m^-3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average.