Full metadata record
DC FieldValueLanguage
dc.contributor.authorVIZCAINO MARIA PILARen_GB
dc.contributor.authorLAVALLE CARLOen_GB
dc.date.accessioned2018-05-19T00:03:38Z-
dc.date.available2018-05-17en_GB
dc.date.available2018-05-19T00:03:38Z-
dc.date.created2018-05-16en_GB
dc.date.issued2018en_GB
dc.date.submitted2018-04-24en_GB
dc.identifier.citationENVIRONMENTAL POLLUTION vol. 240 p. 140-154en_GB
dc.identifier.issn0269-7491en_GB
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0269749117348674?via%3Dihuben_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC111760-
dc.description.abstractA new Land Use Regression model was built to develop pan-European 100 m resolution maps of NO2 concentrations. The model was built using NO2 concentrations from routine monitoring stations available in the Airbase database as dependent variable. Predictor variables included land use, road traffic proxies, population density, climatic and topographical variables, and distance to sea. In order to capture international and inter regional disparities not accounted for with the mentioned predictor variables, additional proxies of NO2 concentrations, like levels of activity intensity and NOx emissions for specific sectors, were also included. The model was built using Random Forest techniques. Model performance was relatively good given the EU-wide scale (R2 = 0.53). Output predictions of annual average concentrations of NO2 were in line with other existing models in terms of spatial distribution and values of concentration. The model was validated for year 2015, comparing model predictions derived from updated values of independent variables, with concentrations in monitoring stations for that year. The algorithm was then used to model future concentrations up to the year 2030, considering different emission scenarios as well as changes in land use, population distribution and economic factors assuming the most likely socio-economic trends. Levels of exposure were derived from maps of concentration. The model proved to be a useful tool for the ex-ante evaluation of specific air pollution mitigation measures, and more broadly, for impact assessment of EU policies on territorial development.en_GB
dc.description.sponsorshipJRC.B.3-Territorial Developmenten_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherELSEVIER SCI LTDen_GB
dc.relation.ispartofseriesJRC111760en_GB
dc.titleDevelopment of European NO2 Land Use Regression Model for Present and Future Exposure Assessment: Implications for Policy Analysisen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.1016/j.envpol.2018.03.075en_GB
JRC Directorate:Growth and Innovation

Files in This Item:
There are no files associated with this item.


Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.