Using CALPUFF to evaluate the impacts of power plant emissions in Illinois: model sensitivity and implications

Air pollution emissions from older fossil-fueled power plants are often much greater than emissions from newer facilities, in part because older plants are exempt from modern emission standards required of new plants under the Clean Air Act. To quantify potential health benefits of emission reductions, there is a need to apply atmospheric dispersion models that can estimate the incremental contributions of power plants to ambient concentrations with reasonable accuracy over long distances. We apply the CALPUFF atmospheric dispersion model with meteorological data derived from NOAA's Rapid Update Cycle model to a set of nine power plants in Illinois to evaluate primary and secondary particulate matter impacts across a grid in the Midwest. In total, the population-weighted annual average concentration increments associated with current emissions are estimated to be 0.04 mug m(-3) of primary fine particulate matter (PM2.5), 0.13 mug m(-3) of secondary sulfate particles, and 0.10 mug m(-3) of secondary nitrate particles (maximum impacts of 0.3, 0.2, and 0.2 mug m(-3), respectively). The aggregate impact estimates are moderately insensitive to parametric assumptions about chemical mechanism, wet/dry deposition, background ammonia concentrations, and size of the receptor region, with the largest uncertainties related to nitrate particles and long-range transport issues. Additional uncertainties may be associated with inherent limitations of CALPUFF, but it appears likely that the degree of uncertainty in atmospheric modeling will not dominate the total uncertainty associated with health impact or benefit estimation. Although the annual average concentration increments from a limited number of sources are relatively small, the large population affected by long-range transport and the number of power plant sources around the US imply potentially significant public health impacts using standard epidemiological assumptions. Our analysis demonstrates an approach that is applicable in any setting where source controls are being evaluated from a public health or benefit-cost perspective, (C) 2002 Elsevier Science Ltd. All rights reserved.