PuSH - Publication Server of Helmholtz Zentrum München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
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)
Postprint Research data DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
7.864
0.000
6
14
Tags
Annotations
Special Publikation
Hide on homepage

Edit extra information
Edit own tags
Private
Edit own annotation
Private
Hide on publication lists
on hompage
Mark as special
publikation
Publication type Article: Journal article
Document type Scientific Article
Keywords Land-use Regression; Long-term Exposure; Particulate Matter; Intraurban Variation; Source Apportionment; Spatial Variation; Pm2.5 Absorbency; Areas; Components; Mortality
Language english
Publication Year 2020
HGF-reported in Year 2020
ISSN (print) / ISBN 0013-936X
e-ISSN 1520-5851
Journal Environmental Science & Technology
Quellenangaben Volume: 54, Issue: 24, Pages: 15698-15709 Article Number: , Supplement: ,
Publisher ACS
Publishing Place Washington, DC
Reviewing status Peer reviewed
Institute(s) Institute of Epidemiology (EPI)
POF-Topic(s) 30202 - Environmental Health
Research field(s) Genetics and Epidemiology
PSP Element(s) G-504000-001
Grants China Scholarship Council
United States Environmental Protection Agency (EPA)
DOI 10.1021/acs.est.0c06595
WOS ID WOS:000600100400015
PubMed ID 3237771
Erfassungsdatum 2021-02-03
[➜Report correction]