Tingfan Jin1, Heresh Amini2, Anna Kosheleva3, Mahdieh Danesh Yazdi4, Yaguang Wei3, Edgar Castro3, Qian Di5, Liuhua Shi6, Joel Schwartz3. 1. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA. tingfanjin@hsph.harvard.edu. 2. Department of Public Health, University of Copenhagen, Copenhagen, Denmark. 3. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 4. Department of Family, Population, & Preventive Medicine, Program in Public Health, Stony Brook University, New York, NY, USA. 5. Vanke School of Public Health, Tsinghua University, Beijing, China. 6. Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Abstract
BACKGROUND: Numerous studies have documented PM2.5's links with adverse health outcomes. Comparatively fewer studies have evaluated specific PM2.5 components. The lack of exposure measurements and high correlation among different PM2.5 components are two limitations. METHODS: We applied a novel exposure prediction model to obtain annual Census tract-level concentrations of 15 PM2.5 components (Zn, V, Si, Pb, Ni, K, Fe, Cu, Ca, Br, SO42-, NO3-, NH4+, OC, EC) in Massachusetts from 2000 to 2015, to which we matched geocoded deaths. All non-accidental mortality, cardiovascular mortality, and respiratory mortality were examined for the population aged 18 or over. Weighted quantile sum (WQS) regression models were used to examine the cumulative associations between PM2.5 components mixture and outcomes and each component's contributions to the cumulative associations. We have fit WQS models on 15 PM2.5 components and a priori identified source groups (heavy fuel oil combustion, biomass burning, crustal matter, non-tailpipe traffic source, tailpipe traffic source, secondary particles from power plants, secondary particles from agriculture, unclear source) for the 15 PM2.5 components. Total PM2.5 mass analysis and single component associations were also conducted through quasi-Poisson regression models. RESULTS: Positive cumulative associations between the components mixture and all three outcomes were observed from the WQS models. Components with large contribution to the cumulative associations included K, OC, and Fe. Biomass burning, traffic emissions, and secondary particles from power plants were identified as important source contributing to the cumulative associations. Mortality rate ratios for cardiovascular mortality were of greater magnitude than all non-accidental mortality and respiratory mortality, which is also observed in cumulative associations estimated from WQS, total PM2.5 mass analysis, and single component associations. CONCLUSION: We have found positive associations between the mixture of 15 PM2.5 components and all non-accidental mortality, cardiovascular mortality, and respiratory mortality. Among these components, Fe, K, and OC have been identified as having important contribution to the cumulative associations. The WQS results also suggests potential source effects from biomass burning, traffic emissions, and secondary particles from power plants.
BACKGROUND: Numerous studies have documented PM2.5's links with adverse health outcomes. Comparatively fewer studies have evaluated specific PM2.5 components. The lack of exposure measurements and high correlation among different PM2.5 components are two limitations. METHODS: We applied a novel exposure prediction model to obtain annual Census tract-level concentrations of 15 PM2.5 components (Zn, V, Si, Pb, Ni, K, Fe, Cu, Ca, Br, SO42-, NO3-, NH4+, OC, EC) in Massachusetts from 2000 to 2015, to which we matched geocoded deaths. All non-accidental mortality, cardiovascular mortality, and respiratory mortality were examined for the population aged 18 or over. Weighted quantile sum (WQS) regression models were used to examine the cumulative associations between PM2.5 components mixture and outcomes and each component's contributions to the cumulative associations. We have fit WQS models on 15 PM2.5 components and a priori identified source groups (heavy fuel oil combustion, biomass burning, crustal matter, non-tailpipe traffic source, tailpipe traffic source, secondary particles from power plants, secondary particles from agriculture, unclear source) for the 15 PM2.5 components. Total PM2.5 mass analysis and single component associations were also conducted through quasi-Poisson regression models. RESULTS: Positive cumulative associations between the components mixture and all three outcomes were observed from the WQS models. Components with large contribution to the cumulative associations included K, OC, and Fe. Biomass burning, traffic emissions, and secondary particles from power plants were identified as important source contributing to the cumulative associations. Mortality rate ratios for cardiovascular mortality were of greater magnitude than all non-accidental mortality and respiratory mortality, which is also observed in cumulative associations estimated from WQS, total PM2.5 mass analysis, and single component associations. CONCLUSION: We have found positive associations between the mixture of 15 PM2.5 components and all non-accidental mortality, cardiovascular mortality, and respiratory mortality. Among these components, Fe, K, and OC have been identified as having important contribution to the cumulative associations. The WQS results also suggests potential source effects from biomass burning, traffic emissions, and secondary particles from power plants.
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