![]() Gaseous pollutants in particulate matter epidemiology: confounders or surrogates? Environ Health Perspect 2001: 109: 1054–1061. Sarnat J.A., Schwartz J., Catalona P.J., and Suh H. Associations between source-resolved particulate matter and cardiorespiratory emergency department visits. Sarnat J.A., Marmur A., Klein M., Kim E., Russell A.G., Mulholland J.A., Hopke P.K., Sarnat S.E., Peel J.L., and Tolbert P.E. Ambient air pollution and respiratory emergency department visits. Peel J., Tolbert P.E., Klein M., Metzger K., Flanders W.D., Todd K., Mulholland J.A., Ryan P.B., and Frumkin H. Ambient air pollution and cardiovascular emergency department visits. Metzger K.B., Tolbert P.E., Klein M., Peel J.L., Flanders W.D., Todd K., Mulholland J.A., Ryan P.B., and Frumkin H. Optimization-based source apportionment of PM2.5 incorporating gas-to-particle ratios. Marmur A., Unal A., Mulholland J.A., and Russell A.G. Source apportionment of PM2.5 in the southeastern US using receptor and emissions-based models: conceptual differences and implications for time-series health studies. Marmur A., Park S-K., Mulholland J.A., Tolbert P.E., and Russell A.G. Source identification of Atlanta aerosol by positive matrix factorization. ![]() Improving source identification of Atlanta aerosol using temperature resolved carbon fractions in positive matrix factorization. Comparison of chamber and face-mask 6.6-h exposures to ozone on pulmonary function and symptoms responses. Caveats and considerations in interpreting the multipollutant model results are discussed.Īdams W.C. In multipollutant models, PM 10 and ozone persisted as predictors, with ozone the stronger predictor. For respiratory visits, associations were observed with ozone, PM 10, CO, and NO 2 in single-pollutant models. In multipollutant models, CO was the strongest predictor. For cardiovascular visits, associations were observed with CO, NO 2, and PM 2.5 elemental carbon and organic carbon. In the present analysis, we report results for two outcome groups: a respiratory outcomes group and a cardiovascular outcomes group. Poisson generalized linear models were used to examine outcome counts in relation to 3-day moving average concentrations of pollutants of a priori interest (ozone, nitrogen dioxide, carbon monoxide, sulfur dioxide, oxygenated hydrocarbons, PM 10, coarse PM, PM 2.5, and the following components of PM 2.5: elemental carbon, organic carbon, sulfate, and water-soluble transition metals). Relative to our earlier publications, reporting results through 2000, the period for which the speciated data are available is now tripled (6 years in length). Emergency department visits from 41 of 42 hospitals serving the 20-county Atlanta metropolitan area for the period 1993–2004 ( n=10,206,389 visits) were studied in relation to ambient pollutant levels, including speciated particle measurements from an intensive monitoring campaign at a downtown station starting in 1998. In this paper, these issues will be illustrated in the context of an ongoing study of emergency visits in Atlanta. ![]() In situations where two or more pollutant variables may be acting as surrogates for the etiologic agent(s), multipollutant models can help identify the best surrogate, but the risk estimates may be influenced by inclusion of a second variable that is not itself an independent risk factor for the outcome in question. Their appropriate interpretation depends on the relationships among the pollutant measurements and the outcomes in question. In the presence of differing levels of measurement error across pollutants under consideration, however, they can be biased and as misleading as single-pollutant models. Multipollutant models are frequently used to differentiate roles of multiple pollutants in epidemiologic studies of ambient air pollution.
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