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Chen, J.* ; Rodopoulou, S.* ; de Hoogh, K.* ; Strak, M.* ; Andersen, Z.J.* ; Atkinson, R.* ; Bauwelinck, M.* ; Bellander, T.* ; Brandt, J.* ; Cesaroni, G.* ; Concin, H.* ; Fecht, D.* ; Forastiere, F.* ; Gulliver, J.* ; Hertel, O.* ; Hoffmann, B.* ; Hvidtfeldt, U.A.* ; Janssen, N.A.H.* ; Jöckel, K.H.* ; Jørgensen, J.* ; Katsouyanni, K.* ; Ketzel, M.* ; Klompmaker, J.O.* ; Lager, A.* ; Leander, K.* ; Liu, S.* ; Ljungman, P.* ; MacDonald, C.J.* ; Magnusson, P.K.E.* ; Mehta, A.* ; Nagel, G.* ; Oftedal, B.* ; Pershagen, G.* ; Peters, A. ; Raaschou-Nielsen, O.* ; Renzi, M.* ; Rizzuto, D.* ; Samoli, E.* ; van der Schouw, Y.T.* ; Schramm, S.* ; Schwarze, P.* ; Sigsgaard, T.* ; Sørensen, M.* ; Stafoggia, M.* ; Tjønneland, A.* ; Vienneau, D.* ; Weinmayr, G.* ; Wolf, K. ; Brunekreef, B.* ; Hoek, G.*

Long-term exposure to fine particle elemental components and natural and cause-specific mortality – a pooled analysis of eight European cohorts within the ELAPSE project.

Environ. Health Perspect. 129:47009 (2021)
Verlagsversion Postprint DOI
BACKGROUND: Inconsistent associations between long-term exposure to particles with an aerodynamic diameter ≤ 2.5   μ m [fine particulate matter ( PM 2.5 )] components and mortality have been reported, partly related to challenges in exposure assessment. OBJECTIVES: We investigated the associations between long-term exposure to PM 2.5 elemental components and mortality in a large pooled European cohort; to compare health effects of PM 2.5 components estimated with two exposure modeling approaches, namely, supervised linear regression (SLR) and random forest (RF) algorithms. METHODS: We pooled data from eight European cohorts with 323,782 participants, average age 49 y at baseline (1985-2005). Residential exposure to 2010 annual average concentration of eight PM 2.5 components [copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)] was estimated with Europe-wide SLR and RF models at a 100 × 100   m scale. We applied Cox proportional hazards models to investigate the associations between components and natural and cause-specific mortality. In addition, two-pollutant analyses were conducted by adjusting each component for PM 2.5 mass and nitrogen dioxide ( NO 2 ) separately. RESULTS: We observed 46,640 natural-cause deaths with 6,317,235 person-years and an average follow-up of 19.5 y. All SLR-modeled components were statistically significantly associated with natural-cause mortality in single-pollutant models with hazard ratios (HRs) from 1.05 to 1.27. Similar HRs were observed for RF-modeled Cu, Fe, K, S, V, and Zn with wider confidence intervals (CIs). HRs for SLR-modeled Ni, S, Si, V, and Zn remained above unity and (almost) significant after adjustment for both PM 2.5 and NO 2 . HRs only remained (almost) significant for RF-modeled K and V in two-pollutant models. The HRs for V were 1.03 (95% CI: 1.02, 1.05) and 1.06 (95% CI: 1.02, 1.10) for SLR- and RF-modeled exposures, respectively, per 2   ng / m 3 , adjusting for PM 2.5 mass. Associations with cause-specific mortality were less consistent in two-pollutant models. CONCLUSION: Long-term exposure to V in PM 2.5 was most consistently associated with increased mortality. Associations for the other components were weaker for exposure modeled with RF than SLR in two-pollutant models. https://doi.org/10.1289/EHP8368.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Use Regression-models; Particulate Matter; Risk; Profile; Escape; Pm2.5; Men
ISSN (print) / ISBN 0091-6765
e-ISSN 1552-9924
Quellenangaben Band: 129, Heft: 4, Seiten: , Artikelnummer: 47009 Supplement: ,
Verlag Research Triangle Park
Verlagsort NC [u.a.]
Begutachtungsstatus Peer reviewed
Förderungen China Scholarship Council
German Federal Ministry of Health and Social Security
State of Bavaria
German Research Center for Environmental Health - German Federal Ministry of Education, Science, Research, and Technology
Helmholtz Zentrum Mtinchen
Swedish Research Council
U.S. Environmental Protection Agency (EPA)