BackgroundInterest in the health effects of particulate matter (PM) has focused on identifying sources of PM, including biomass burning, power plants, and gasoline and diesel emissions that may be associated with adverse health risks. Few epidemiologic studies, however, have included source-apportionment estimates in their examinations of PM health effects. We analyzed a time-series of chemically speciated PM measurements in Atlanta, Georgia, and conducted an epidemiologic analysis using data from three distinct source-apportionment methods.ObjectiveThe key objective of this analysis was to compare epidemiologic findings generated using both factor analysis and mass balance source-apportionment methods.MethodsWe analyzed data collected between November 1998 and December 2002 using positive-matrix factorization (PMF), modified chemical mass balance (CMB-LGO), and a tracer approach. Emergency department (ED) visits for a combined cardiovascular (CVD) and respiratory disease (RD) group were assessed as end points. We estimated the risk ratio (RR) associated with same day PM concentrations using Poisson generalized linear models.ResultsThere were significant, positive associations between same-day PM2.5 (PM with aero-dynamic diameter ≤ 2.5 μm) concentrations attributed to mobile sources (RR range, 1.018–1.025) and biomass combustion, primarily prescribed forest burning and residential wood combustion, (RR range, 1.024–1.033) source categories and CVD-related ED visits. Associations between the source categories and RD visits were not significant for all models except sulfate-rich secondary PM2.5 (RR range, 1.012–1.020). Generally, the epidemiologic results were robust to the selection of source-apportionment method, with strong agreement between the RR estimates from the PMF and CMB-LGO models, as well as with results from models using single-species tracers as surrogates of the source-apportioned PM2.5 values.ConclusionsDespite differences among the source-apportionment methods, these findings suggest that modeled source-apportioned data can produce robust estimates of acute health risk. In Atlanta, there were consistent associations across methods between PM2.5 from mobile sources and biomass burning with both cardiovascular and respiratory ED visits, and between sulfate-rich secondary PM2.5 with respiratory visits.

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A modified approach to PM2.5 source apportionment is developed, using source indicative SO2/PM2.5, CO/PM2.5, and NOx/PM2.5 ratios as constraints, in addition to the commonly used particulate-phase source profiles. Additional information from using gas-to-particle ratios assists in reducing collinearity between source profiles, a problem that often limits the source-identification capabilities and accuracy of traditional receptor models. This is especially true in the absence of speciated organic carbon measurements. In the approach presented here, the solution is based on a global optimization mechanism, minimizing the weighted error between apportioned and ambient levels of PM2.5 components, while introducing constraints on calculated source contributions that ensure that the ambient gas-phase pollutants (SO2, CO, and NOy) are reasonable. This technique was applied to a 25-month dataset of daily PM2.5 measurements (total mass and composition) at the Atlanta Jefferson Street SEARCH site. Results indicate that this technique was able to split the contributions of mobile sources (gasoline and diesel vehicles) more accurately than particulate-phase source apportionment methods. Furthermore, this technique was able to better quantify the direct contribution (primary PM2.5) of coal-fired power plants to ambient PM2.5 levels.

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Air protection agencies in the United States increasingly confront non-attainment of air quality standards for multiple pollutants sharing interrelated emission origins. Traditional approaches to attainment planning face important limitations that are magnified in the multipollutant context. Recognizing those limitations, the Georgia Environmental Protection Division has adopted an integrated framework to address ozone, fine particulate matter, and regional haze in the state. Rather than applying atmospheric modeling merely as a final check of an overall strategy, photochemical sensitivity analysis is conducted upfront to compare the effectiveness of controlling various precursor emission species and source regions. Emerging software enables the modeling of health benefits and associated economic valuations resulting from air pollution control. Photochemical sensitivity and health benefits analyses, applied together with traditional cost and feasibility assessments, provide a more comprehensive characterization of the implications of various control options. The fuller characterization both informs the selection of control options and facilitates the communication of impacts to affected stakeholders and the public. Although the integrated framework represents a clear improvement over previous attainment-planning efforts, key remaining shortcomings are also discussed.