title: Assessment of source-specific health effects associated with an unknown number of major sources of multiple air pollutants: a unified Bayesian approach.
abstract: There has been increasing interest in assessing health effects associated with multiple air pollutants emitted by specific sources. A major difficulty with achieving this goal is that the pollution source profiles are unknown and source-specific exposures cannot be measured directly; rather, they need to be estimated by decomposing ambient measurements of multiple air pollutants. This estimation process, called multivariate receptor modeling, is challenging because of the unknown number of sources and unknown identifiability conditions (model uncertainty). The uncertainty in source-specific exposures (source contributions) as well as uncertainty in the number of major pollution sources and identifiability conditions have been largely ignored in previous studies. A multipollutant approach that can deal with model uncertainty in multivariate receptor models while simultaneously accounting for parameter uncertainty in estimated source-specific exposures in assessment of source-specific health effects is presented in this paper. The methods are applied to daily ambient air measurements of the chemical composition of fine particulate matter ([Formula: see text]), weather data, and counts of cardiovascular deaths from 1995 to 1997 for Phoenix, AZ, USA. Our approach for evaluating source-specific health effects yields not only estimates of source contributions along with their uncertainties and associated health effects estimates but also estimates of model uncertainty (posterior model probabilities) that have been ignored in previous studies. The results from our methods agreed in general with those from the previously conducted workshop/studies on the source apportionment of PM health effects in terms of number of major contributing sources, estimated source profiles, and contributions. However, some of the adverse source-specific health effects identified in the previous studies were not statistically significant in our analysis, which probably resulted because we incorporated parameter uncertainty in estimated source contributions that has been ignored in the previous studies into the estimation of health effects parameters.
authors.affiliation: Texas A&M Transportation Institute, College Station, TX 77843, USA e-park@tamu.edu.
authors.forename: Eun Sug
authors.initials: ES
authors.lastname: Park
authors.affiliation: Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY 13699, USA.
authors.forename: Philip K
authors.initials: PK
authors.lastname: Hopke
authors.affiliation: Department of Statistics, Ewha Women's University, Seoul 120-750, Korea.
authors.forename: Man-Suk
authors.initials: MS
authors.lastname: Oh
authors.affiliation: University of Texas School of Public Health, Houston, TX 77030, USA.
authors.forename: Elaine
authors.initials: E
authors.lastname: Symanski
authors.affiliation: Department of Epidemiology & Biostatistics, Texas A&M University, College Station, TX 77843, USA.
authors.forename: Daikwon
authors.initials: D
authors.lastname: Han
authors.affiliation: Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
authors.forename: Clifford H
authors.initials: CH
authors.lastname: Spiegelman
mesh_terms: D000393:Air Pollutants; D001499:Bayes Theorem; D002318:Cardiovascular Diseases / Q000401:mortality*; D006801:Humans; D015233:Models, Statistical; D035501:Uncertainty
chemical_list: D000393:Air Pollutants
keywords: Cardiovascular mortality; Model uncertainty; Multipollutant approach; Multivariate receptor models; PM health effects; Source apportionment