Associations between long-term exposures to airborne PM2.5 components and mortality in Massachusetts: mixture analysis exploration
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Associations between long-term exposures to airborne PM2.5 components and mortality in Massachusetts : mixture analysis exploration. / Jin, Tingfan; Amini, Heresh; Kosheleva, Anna; Yazdi, Mahdieh Danesh; Wei, Yaguang; Castro, Edgar; Di, Qian; Shi, Liuhua; Schwartz, Joel.
I: Environmental Health, Bind 21, Nr. 1, 96, 2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Associations between long-term exposures to airborne PM2.5 components and mortality in Massachusetts
T2 - mixture analysis exploration
AU - Jin, Tingfan
AU - Amini, Heresh
AU - Kosheleva, Anna
AU - Yazdi, Mahdieh Danesh
AU - Wei, Yaguang
AU - Castro, Edgar
AU - Di, Qian
AU - Shi, Liuhua
AU - Schwartz, Joel
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Air pollution
KW - Particle components
KW - Weighted quantile sum regression
KW - 19 EUROPEAN COHORTS
KW - PARTICULATE MATTER
KW - AIR-POLLUTION
KW - CARDIOVASCULAR MORTALITY
KW - CHEMICAL-CONSTITUENTS
KW - EPITHELIAL-CELLS
KW - SOLUBLE METALS
KW - FINE PARTICLES
KW - TIME-SERIES
KW - ADMISSIONS
U2 - 10.1186/s12940-022-00907-2
DO - 10.1186/s12940-022-00907-2
M3 - Journal article
C2 - 36221093
VL - 21
JO - Environmental Health
JF - Environmental Health
SN - 1476-069X
IS - 1
M1 - 96
ER -
ID: 322940581