Using CMAQ for Exposure Modeling and Characterizing the Subgrid Variability for Exposure Estimates

Abstract

Atmospheric processes and the associated transport and dispersion of atmospheric pollutants are known to be highly variable in time and space. Current air-quality models that characterize atmospheric chemistry effects, for example, the Community Multiscale Air Quality model (CMAQ), provide volume-averaged concentration values for each grid cell in the modeling domain given the stated conditions. Given the assumptions made and the limited set of processes included in any model?s implementation, there are many sources of ?unresolved? subgrid variability. This raises the question of the importance of the unresolved subgrid variations on exposure assessment results if such models were to be used to assess air toxics exposure. In this study, the Hazardous Air Pollutant Exposure Model (HAPEM) is applied to estimate benzene and formaldehyde inhalation exposure using ambient annually averaged concentrations predicted by CMAQ to investigate how within-grid variability can affect exposure estimates. An urban plume dispersion model was used to estimate the subgrid variability of annually averaged benzene concentration values within CMAQ grid cells for a modeling domain centered on Philadelphia, Pennsylvania. Significant (greater than a factor of 2) increases in maximum exposure impacts were seen in the exposure estimates in comparison with exposure estimates generated using CMAQ grid-averaged concentration values. These results consider only one source of subgrid variability, namely, the discrete location and distribution of emissions, but they do suggest the importance and value of developing improved characterizations of subgrid concentration variability for use in air toxics exposure assessments.