Τίτλος:
Development of Europe-Wide Models for Particle Elemental Composition Using Supervised Linear Regression and Random Forest
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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 μm (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. © 2020 American Chemical Society.
Συγγραφείς:
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.M.
Stafoggia, M.
Bellander, T.
Strak, M.
Wolf, K.
Vienneau, D.
Brunekreef, B.
Hoek, G.
Περιοδικό:
Environmental science and technology
Εκδότης:
American Chemical Society
Λέξεις-κλειδιά:
Decision trees; Land use, Chemical transport models; Elemental compositions; Epidemiological studies; Long term exposure; Model evaluation; Particulate Matter; Predictor variables; Standardized measurement, Random forests, copper; iron; nickel; potassium; silicon; sulfur; trace metal; vanadium; zinc; zinc, air quality; anthropogenic source; chemical composition; concentration (composition); particle size; regression analysis; supervised learning, air pollution; air sampling; altitude; Article; cross validation; energy dispersive X ray spectroscopy; Europe; health hazard; land use; linear regression analysis; long term exposure; particulate matter 2.5; particulate matter exposure; predictor variable; random forest; traffic; air pollutant; air pollution; environmental monitoring; particulate matter; statistical model, Satellites, Air Pollutants; Air Pollution; Environmental Monitoring; Europe; Linear Models; Particulate Matter; Zinc
DOI:
10.1021/acs.est.0c06595
