The use of random forests in modelling short-term air pollution effects based on traffic and meteorological conditions: A case study in Wrocław.
Random forests, an advanced data mining method, are used here to model the regression relationships between concentrations of the pollutants NO 2 , NO x and PM 2.5 , and nine variables describing meteorological conditions, temporal conditions and traffic flow. The study was based on hourly values of...
| Publicado en: | Journal of Environmental Management Vol. 217; pp. 164 - 175 |
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| Formato: | Artículo |
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Academic Press Inc.
Jul2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=129335774&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 129335774 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03014797 EMJ jtl: Journal of Environmental Management issn: 03014797 maglogo: N pubinfo: dt: Jul2018 vid: 217 pid: 735 pub: Academic Press Inc. artinfo: ui: 129335774 10.1016/j.jenvman.2018.03.094 ppf: 164 ppct: 11 formats: tig: atl: The use of random forests in modelling short-term air pollution effects based on traffic and meteorological conditions: A case study in Wrocław. aug: au: Kamińska, Joanna A. affil: Department of Mathematics, Wroclaw University of Environmental and Life Sciences, ul. Grunwaldzka 53, 50-357 Wrocław, Poland su: Random forest algorithms Air pollutants Humidity Nitrogen oxides Air traffic sug: subj: Random forest algorithms Air pollutants Humidity Nitrogen oxides Air traffic keyword: Data subsets Meteorological conditions Random forest Traffic flow Urban air pollution Data subsets Meteorological conditions Random forest Traffic flow Urban air pollution ab: Random forests, an advanced data mining method, are used here to model the regression relationships between concentrations of the pollutants NO 2 , NO x and PM 2.5 , and nine variables describing meteorological conditions, temporal conditions and traffic flow. The study was based on hourly values of wind speed, wind direction, temperature, air pressure and relative humidity, temporal variables, and finally traffic flow, in the two years 2015 and 2016. An air quality measurement station was selected on a main road, located a short distance (40 m) from a large intersection equipped with a traffic flow measurement system. Nine different time subsets were defined, based among other things on the climatic conditions in Wrocław. An analysis was made of the fit of models created for those subsets, and of the importance of the predictors. Both the fit and the importance of particular predictors were found to be dependent on season. The best fit was obtained for models created for the six-month warm season (April–September) and for the summer season (June–August). The most important explanatory variable in the models of concentrations of nitrogen oxides was traffic flow, while in the case of PM 2.5 the most important were meteorological conditions, in particular temperature, wind speed and wind direction. Temporal variables (except for month in the case of PM 2.5 ) were found to have no significant effect on the concentrations of the studied pollutants. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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