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Journal of Environmental Accounting and Management
Dmitry Kovalevsky (editor), Jiazhong Zhang(editor)
Dmitry Kovalevsky (editor)

Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Fischertwiete 1, 20095 Hamburg, Germany

Fax: +49 (0) 40 226338163 Email: dmitry.v.kovalevsky@gmail.com

Jiazhong Zhang (editor)

School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China

Fax: +86 29 82668723 Email: jzzhang@mail.xjtu.edu.cn


Random Forest for Toxicity of Chemical Emissions: Features Selection and Uncertainty Quantification

Journal of Environmental Accounting and Management 3(3) (2015) 229--241 | DOI:10.5890/JEAM.2015.09.003

Antonino Marvuglia$^{1}$, Michael Leuenberger$^{2}$, Mikhail Kanevski$^{2}$, Enrico Benetto$^{1}$

$^{1}$ Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, L-4422 Belvaux, Luxembourg

$^{2}$ University of Lausanne, Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics, Geopolis building CH-1015 Lausanne, Switzerland

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Abstract

Toxicity characterization of chemicals’ emissions is a complex task which proceeds via multimedia fate and exposure models attached to models of dose–response relationships. Several different environmental multimedia models exist, but in any case a vast amount of data on the properties of the chemical compounds being assessed is required. This paper deals with the selection of informative variables in the problem of deriving characterization factors for eco-toxicology and human toxicology of chemical compounds starting from molecular-based properties. The Random Forest algorithm has been applied to single out the most relevant variables when modelling one toxicity factor at the time. The set of variables retained varies according to the modeled output factor, but certain variables are almost always retained among the top three most important ones, regardless the output factor taken into consideration. The modelling performed in this paper is one of the first applications of nonlinear techniques to the database of organic substances made available by the multimedia fate and exposure model USEtox, largely used by the Life Cycle Assessment (LCA) community.

Acknowledgments

This work has been carried out in the framework of the project UNIC (Using Machine Learning for toxicological characterization of chemical emissions) under a research visiting grant provided by the Herbette Foundation, Lausanne, Switzerland.

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