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Author Notes:

Pierre Masselot, 5-17 Tavistock Place, London, WC1H 9SH, United-Kingdom. Email: pierre.masselot@lshtm.ac.uk

We would like to thank the Atmospheric Composition Analysis Group for providing the composition dataset used in the present study. The authors are grateful to Editor Stephanie London and two anonymous referees for their valuable comments.

The authors declared that there is no conflict of interests.

Subjects:

Research Funding:

This work was supported by the Medical Research Council of UK (Grant ID: MR/M022625/1), the Natural Environment Research Council of UK (Grant ID: NE/R009384/1), and the European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655).

JJ was supported by the Academy of Finland (Grant ID: 310372) and EU HORIZON 2020 Project EMERGE (Grant ID: 874990).

NS is supported by the NIEHS-funded HERCULES Center (P30ES019776).

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Public, Environmental & Occupational Health
  • FINE PARTICULATE MATTER
  • AIR-POLLUTION
  • CHEMICAL-CONSTITUENTS
  • HOSPITAL ADMISSIONS
  • SHORT-TERM
  • CARDIOVASCULAR MORTALITY
  • R-PACKAGE
  • HEALTH
  • MODELS
  • EMISSIONS

Differential Mortality Risks Associated With PM2.5 Components A Multi-Country, Multi-City Study

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Journal Title:

EPIDEMIOLOGY

Volume:

Volume 33, Number 2

Publisher:

, Pages 167-175

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Background: The association between fine particulate matter (PM2.5) and mortality widely differs between as well as within countries. Differences in PM2.5 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 PM2.5 through time series regression analysis. We then pooled these estimates in a meta-regression model that included city-specific logratio-transformed proportions of seven PM2.5 components as well as meta-predictors derived from city-specific socio-economic and environmental indicators. Results: We found associations between RR and several PM2.5 components. Increasing the ammonium (NH4+) 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 (NO3-) 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 PM2.5 risk. Conclusions: These findings contribute to the identification of more hazardous emission sources. Further work is needed to understand the health impacts of PM2.5 components and sources given the overlapping sources and correlations among many components.

Copyright information:

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/).
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