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Chen, J.* ; de Hoogh, K.* ; Gulliver, J.* ; Hoffmann, B.* ; Hertel, O.* ; Ketzel, M.* ; Weinmayr, G.* ; Bauwelinck, M.* ; van Donkelaar, A.* ; Hvidtfeldt, U.A.* ; Atkinson, R.* ; Janssen, N.A.H.* ; Martin, R.V.* ; Samoli, E.* ; Andersen, Z.J.* ; Oftedal, B.* ; Stafoggia, M.* ; Strak, M.* ; Wolf, K. ; Vienneau, D.* ; Brunekreef, B.* ; Hoek, G.*

Development of Europe-wide models for particle elemental composition using supervised linear regression and random forest.

Environ. Sci. Technol. 54, 15698-15709 (2020)

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We developed Europe-wide models of long-term exposure to eight elements (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) in particulate matter with diameter <2.5 mu m (PM2.5) using standardized measurements for one-year periods between October 2008 and April 2011 in 19 study areas across Europe, with supervised linear regression (SLR) and random forest (RF) algorithms. Potential predictor variables were obtained from satellites, chemical transport models, land-use, traffic, and industrial point source databases to represent different sources. Overall model performance across Europe was moderate to good for all elements with hold-out-validation R-squared ranging from 0.41 to 0.90. RF consistently outperformed SLR. Models explained within-area variation much less than the overall variation, with similar performance for RF and SLR. Maps proved a useful additional model evaluation tool. Models differed substantially between elements regarding major predictor variables, broadly reflecting known sources. Agreement between the two algorithm predictions was generally high at the overall European level and varied substantially at the national level. Applying the two models in epidemiological studies could lead to different associations with health. If both between- and within-area exposure variability are exploited, RF may be preferred. If only within-area variability is used, both methods should be interpreted equally.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Land-use Regression; Long-term Exposure; Particulate Matter; Intraurban Variation; Source Apportionment; Spatial Variation; Pm2.5 Absorbency; Areas; Components; Mortality

ISSN (print) / ISBN 0013-936X

e-ISSN 1520-5851

Journal Environmental Science & Technology

Quellenangaben Volume: 54, Issue: 24, Pages: 15698-15709

Publisher ACS

Publishing Place Washington, DC

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Epidemiology II (EPI2)

Grants China Scholarship Council
United States Environmental Protection Agency (EPA)