Fine particle sources and cardiorespiratory morbidity: an application of chemical mass balance and factor analytical source-apportionment methods.

BACKGROUND: Interest 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. OBJECTIVE: The key objective of this analysis was to compare epidemiologic findings generated using both factor analysis and mass balance source-apportionment methods. METHODS: We 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. RESULTS: There were significant, positive associations between same-day PM(2.5) (PM with aero-dynamic diameter

An integrated framework for multipollutant air quality management and its application in Georgia.

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.

Effects of instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, Georgia.

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO2, CO, NOx, and O3) and fine particulate matter ([PM2.5] PM2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7-40% of the temporal variation in the daily pollutant measures and was largest for the PM2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO2, CO, NOx, and EC). Population-weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O3 to 70% of the temporal variation for SO2 and EC. Wind

Optimization-based source apportionment of PM2.5 incorporating gas-to-particle ratios.

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.