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During the past three decades, receptor models have been used to identify and apportion ambient concentrations to sources. A number of groups are employing these methods to provide input into air quality management planning. A workshop has explored the use of resolved source contributions in health effects models. Multiple groups have analyzed particulate composition data sets from Washington, DC and Phoenix, AZ. Similar source profiles were extracted from these data sets by the investigators using different factor analysis methods. There was good agreement among the major resolved source types. Crustal (soil), sulfate, oil, and salt were the sources that were most unambiguously identified (generally highest correlation across the sites). Traffic and vegetative burning showed considerable variability among the results with variability in the ability of the methods to partition the motor vehicle contributions between gasoline and diesel vehicles. However, if the total motor vehicle contributions are estimated, good correspondence was obtained among the results. The source impacts were especially similar across various analyses for the larger mass contributors (e.g., in Washington, secondary sulfate SE ¼ 7% and 11% for traffic; in Phoenix, secondary sulfate SE ¼ 17% and 7% for traffic). Especially important for time-series health effects assessment, the source-specific impacts were found to be highly correlated across analysis methods/researchers for the major components (e.g., mean analysis to analysis correlation, r40.9 for traffic and secondary sulfates in Phoenix and for traffic and secondary nitrates in Washington. The sulfate mean r value is 40.75 in Washington.). Overall, although these intercomparisons suggest areas where further research is needed (e.g., better division of traffic emissions between diesel and gasoline vehicles), they provide support the contention that PM 2.5 mass source apportionment results are consistent across users and methods, and that today's source apportionment methods are robust enough for application to PM 2.5 health effects assessments.

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The multivariate receptor model Unmix has been used to analyze a 3-yr PM 2.5 ambient aerosol data set collected in Phoenix, AZ, beginning in 1995. The analysis generated source profiles and overall average percentage source contribution estimates (SCEs) for five source categories: gasoline engines (33 Ϯ 4%), diesel engines (16 Ϯ 2%), secondary INTRODUCTIONElevated ambient aerosol concentrations and associated visibility degradation have been of scientific interest in the Phoenix, AZ, metropolitan area since at least the 1980s. 1,2 Understanding the source contributions to PM 2.5 is a necessary step toward the development of an effective air pollution abatement strategy. A 3-yr campaign of PM 2.5 air sampling beginning in 1995 has provided a database of sample chemical analyses that is an order of magnitude greater in size than the largest such Phoenix data resource before 1995. 3 One source apportionment analysis using this large database with the new receptor model Positive Matrix Factorization (PMF) has already been reported. 4 A recent health effects study using this database has evaluated associations between air pollutant species and mortality and also identified (but not quantified) sources of particulate matter using conventional factor analysis. 5 The objective of the present work is to perform an independent source apportionment analysis of the 1995-1998 database using a different receptor model, Unmix. 6,7 Like PMF, Unmix is a multivariate model with nonnegativity constraints, but the two models are otherwise built on completely different algorithms. The Phoenix database is well suited to the large data quantity requirements of multivariate analysis, and the existence of the prior analysis provides an opportunity for comparing the performance of two complex state-of-the-art receptor models. Unmix applications to aerosol data collected near a phosphorous production facility 8 and at a remote Vermont site 9 have recently been reported. The present study is the first demonstration of Unmix on a more typical urban aerosol data set and also provides more details on Unmix and its usage than have been

Although the association between exposure to ambient fine particulate matter with aerodynamic diameter < 2.5 μm (PM2.5) and human mortality is well established, the most responsible particle types/sources are not yet certain. In May 2003, the U.S. Environmental Protection Agency’s Particulate Matter Centers Program sponsored the Workshop on the Source Apportionment of PM Health Effects. The goal was to evaluate the consistency of the various source apportionment methods in assessing source contributions to daily PM2.5 mass–mortality associations. Seven research institutions, using varying methods, participated in the estimation of source apportionments of PM2.5 mass samples collected in Washington, DC, and Phoenix, Arizona, USA. Apportionments were evaluated for their respective associations with mortality using Poisson regressions, allowing a comparative assessment of the extent to which variations in the apportionments contributed to variability in the source-specific mortality results. The various research groups generally identified the same major source types, each with similar elemental makeups. Intergroup correlation analyses indicated that soil-, sulfate-, residual oil-, and salt-associated mass were most unambiguously identified by various methods, whereas vegetative burning and traffic were less consistent. Aggregate source-specific mortality relative risk (RR) estimate confidence intervals overlapped each other, but the sulfate-related PM2.5 component was most consistently significant across analyses in these cities. Analyses indicated that source types were a significant predictor of RR, whereas apportionment group differences were not. Variations in the source apportionments added only some 15% to the mortality regression uncertainties. These results provide supportive evidence that existing PM2.5 source apportionment methods can be used to derive reliable insights into the source components that contribute to PM2.5 health effects.