aspects of this system. First, ambient ammonium (NH4) reacts preferentially with sulfate (H2SO4). Second, ammonium nitrate is only able to form if there is excess NH4 after reacting with sulfate. Finally, particulate nitrate formation is a decreasing function of temperature, so the ambient temperature at each receptor location is incorporated into the equilibrium calculations. To translate VOCs emissions into secondary organic particulates, APEEP uses the fractional aerosol yield coefficients estimated by Grosjean and Seinfeld (1989). These coefficients represent the yield of secondary organic aerosols corresponding to emissions of gaseous VOCs.

APEEP simulates O3 concentrations using an empirical model that translates ambient concentrations of VOC, CO, and NOx into ambient O3 concentrations. The model captures many of the factors contributing to ambient concentrations of O3, VOC, CO, and NOx. These factors include forests and agricultural land uses, which produce biogenic hydrocarbons, as well as the ambient air temperature and several geographic variables. For a complete depiction of the O3 modeling in APEEP, see Muller and Mendelsohn (2006). The inclusion of both linear and quadratic forms for NOx, CO, and VOC concentrations in the O3 models allows for the nonlinearity known to exist in O3 production chemistry (Seinfeld and Pandis 1998). Specifically, the quadratic forms capture titration. This approach is critical to accurately predict O3 levels in certain urban areas, where research has shown that additional emissions of NOx can result in reduced O3 concentrations (Tong et al. 2006).

The source-receptor matrices in APEEP are derived from the Climatological Regional Dispersion Model (CRDM) (Latimer (1996). The original CRDM matrices have been calibrated to produce estimates of pollution levels that are in good agreement with the predictions produced by CMAQ. The correlations between APEEP’s predicted surfaces and CMAQ’s are especially strong for annual mean PM2.5 levels and summer mean O3 levels; the correlation coefficients are 0.82 and 0.77, respectively.3 The matrices have been expanded in scope to encompass nearly 10,000 sources and source areas.

Following the estimation of ambient concentrations, exposures are computed by multiplying county-level populations times county-level pollution concentrations. In APEEP, populations include number of people (differentiated by age),4 crops produced, timber harvested, an inventory of anthropogenic materials, visibility resources, and recreation usage (for each


The correlations for PM2.5 are expressed over n = 3,110, reflecting the 3,110 counties in the contiguous 48 states. The correlations for tropospheric O3 are expressed over n = 24,880, reflecting eight hourly observations for the 3,110 counties in the coterminous U.S.


Population data are provided by CDC Wonder, which is a database of the Centers for Disease Control and Prevention of the U.S. Department of Health and Human Services.

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