title: 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=7% and 11% for traffic; in Phoenix, secondary sulfate SE=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, r>0.9 for traffic and secondary sulfates in Phoenix and for traffic and secondary nitrates in Washington. The sulfate mean r value is >0.75 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.
authors.affiliation: Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY 13699-5708, USA. hopkepk@clarkson.edu
authors.forename: Philip K
authors.initials: PK
authors.lastname: Hopke
authors.forename: Kazuhiko
authors.initials: K
authors.lastname: Ito
authors.forename: Therese
authors.initials: T
authors.lastname: Mar
authors.forename: William F
authors.initials: WF
authors.lastname: Christensen
authors.forename: Delbert J
authors.initials: DJ
authors.lastname: Eatough
authors.forename: Ronald C
authors.initials: RC
authors.lastname: Henry
authors.forename: Eugene
authors.initials: E
authors.lastname: Kim
authors.forename: Francine
authors.initials: F
authors.lastname: Laden
authors.forename: Ramona
authors.initials: R
authors.lastname: Lall
authors.forename: Timothy V
authors.initials: TV
authors.lastname: Larson
authors.forename: Hao
authors.initials: H
authors.lastname: Liu
authors.forename: Lucas
authors.initials: L
authors.lastname: Neas
authors.forename: Joseph
authors.initials: J
authors.lastname: Pinto
authors.forename: Matthias
authors.initials: M
authors.lastname: Stölzel
authors.forename: Helen
authors.initials: H
authors.lastname: Suh
authors.forename: Pentti
authors.initials: P
authors.lastname: Paatero
authors.forename: George D
authors.initials: GD
authors.lastname: Thurston
mesh_terms: D000393:Air Pollutants / Q000633:toxicity*; D006801:Humans; D008962:Models, Theoretical; D010316:Particle Size
chemical_list: D000393:Air Pollutants