al. (2002) as the integrated incremental intake of a pollutant summed over all exposed individuals and occurring over a given exposure time, released from a specified source or source class, per unit of pollutant emitted. Since that time, numerous studies have estimated intake fractions for various source categories (such as power plants, mobile sources, residential wood burning, indoor cleaning products, and aircraft) and pollutants (such as particulate matter and toxic air pollutants). Most important, use of the intake fraction approach has increasingly become a tool for model performance evaluation and model comparisons.

For source-receptor estimates from power plants, work by Nishioka et al. (2002) provided a model evaluation opportunity. To assess the health effects of increased pollution, Nishioka et al. (2002) modeled state-by-state exposures to fine particulate matter (PM2.5) originating from power-plant combustion and used intake fraction as an intermediate output. The committee was able to compare its power-plant intake fraction obtained from APEEP with theirs and got consistent results. Moreover, Nishioka et al. (2002) multiplied their population-weighted exposures derived from intake fractions by exposure-response functions for premature mortality and selected morbidity outcomes, providing the committee with further opportunity to evaluate APEEP results.

In the transportation impact modeling, there were two studies that provide key evaluation opportunities. In an effort to better characterize the relationship between mobile-source emissions and subsequent PM2.5 exposure, Greco et al. (2007) characterized PM2.5 exposure magnitude and geographic distribution using the intake fraction. They modeled total U.S. population exposure to emissions of primary PM2.5 as well as particle precursors SO2 and NOx from each of 3,080 counties in the United States. Their mean PM2.5 intake fraction was 1.6 per million with a range of 0.12 to 25 per million compared with 1.0 per million with a range of 0.04 to 33 per million obtained from APEEP. Greco et al. (2007) concluded that long-range dispersion models with coarse geographic resolution are appropriate for risk assessments of secondary PM2.5 or primary PM2.5 emitted from mobile sources in rural areas but that more-resolved dispersion models are warranted for primary PM2.5 in urban areas because of the substantial contribution of near-source populations. One of the advantages of APEEP is better spatial resolution in urban counties, but it may still lack the necessary level of spatial detail, giving rise to some uncertainty about results.

Marshall et al. (2005) used three alternative methods to estimate intake fractions for vehicle emissions in U.S. urban areas. Their best estimate of the urban intake fraction for diesel particles was 4 per million, results that are consistent with the urban-county results in APEEP. However, the need for future efforts to provide exposure resolution below the county scale remains a priority.

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