PM source apportionment and health effects: 1. Intercomparison of source apportionment results

Abstract

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.

Ambient particle source apportionment and daily hospital admissions among children and elderly in Copenhagen

“…We expect that the crustal material and the sea salt mostly belong to the coarse PM 10 fraction, whereas the other sources mostly contribute to the fine (PM 2.5 ) fraction. This division in fine fraction sources and coarse fraction sources suggests that the potential of the source apportionment for health effects assessment would be similar, with alternative source apportionment methods for PM 2.5 mentioned by Hopke et al (2006), and thus robust for health effects studies implementation. On the other hand, considering that PM is a mix of components dependent on the local sources and conditions that influence its toxicological potency, results from this study may not necessarily be consistent with results achieved elsewhere.…”

“…A recent study on intercomparison of nine different PM 2.5 mass source apportionment methods found that these were consistent across methods and robust enough for application to health effects assessment (Hopke et al, 2006). The study compared multivariate factoranalysis-based methods for the source apportionment of PM 2.5 , which is based on well-defined source profiles for most of the sources.…”

Multi-site PM2.5 and PM2.5-10 aerosol source apportionment in Rio de Janeiro, Brazil

“…On the other hand, black carbon and secondary aerosol, related to anthropogenic sources as fuel burning, represents 45-58% of the PM 2.5 fraction, taking into account the association between the fine mode aerosol and respiratory diseases, a reduction of this contribution should have a substantial impact on the population health. 27,28 …”

The relationship between daily cardiovascular mortality and daily ambient concentrations of particulate pollutants (sulfur, arsenic, selenium, and mercury) and daily source contributions from coal power plants and smelters (individually, combined, and with interaction) in Phoenix, AZ, 1995–1998: A multipollutant approach to acute, time-series air pollution epidemiology: I

“…Several types of source apportionment models (SAMs) have used the Phoenix pollutant database (Mar et al, 2000;Hopke et al, 2006;Mar et al, 2006). We next use factor analysis (FA), positive matrix factorization (PMF), and principal component analysis (PCA).…”