Publication | Open Access
Differential Mortality Risks Associated With PM2.5 Components
83
Citations
39
References
2021
Year
Urban Air QualityAir QualityParticulate MatterSeveral Pm 2.5Environmental ExposurePm2.5 ComponentsEnvironmental HealthFine Particulate MatterPublic HealthPm 2.5Population ExposureCardiovascular EpidemiologyHealth Risk AssessmentEpidemiologyHealth EffectCardiovascular DiseaseGlobal HealthEnvironmental EpidemiologyBusinessAir PollutionPollution
Background: The association between fine particulate matter (PM 2.5 ) and mortality widely differs between as well as within countries. Differences in PM 2.5 composition can play a role in modifying the effect estimates, but there is little evidence about which components have higher impacts on mortality. Methods: We applied a 2-stage analysis on data collected from 210 locations in 16 countries. In the first stage, we estimated location-specific relative risks (RR) for mortality associated with daily total PM 2.5 through time series regression analysis. We then pooled these estimates in a meta-regression model that included city-specific logratio-transformed proportions of seven PM 2.5 components as well as meta-predictors derived from city-specific socio-economic and environmental indicators. Results: We found associations between RR and several PM 2.5 components. Increasing the ammonium (NH 4 + ) proportion from 1% to 22%, while keeping a relative average proportion of other components, increased the RR from 1.0063 (95% confidence interval [95% CI] = 1.0030, 1.0097) to 1.0102 (95% CI = 1.0070, 1.0135). Conversely, an increase in nitrate (NO 3 − ) from 1% to 71% resulted in a reduced RR, from 1.0100 (95% CI = 1.0067, 1.0133) to 1.0037 (95% CI = 0.9998, 1.0077). Differences in composition explained a substantial part of the heterogeneity in PM 2.5 risk. Conclusions: These findings contribute to the identification of more hazardous emission sources. Further work is needed to understand the health impacts of PM 2.5 components and sources given the overlapping sources and correlations among many components.
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