High resolution annual average air pollution concentration maps for the Netherlands
Raster-based air pollution concentration maps for the Netherlands. The dataset consists of air pollution concentration maps for six pollutants (NO2, NO2background, NOx, PM2.5, PM2.5absorbance, PM10), covering the land mass of the Netherlands at 5m spatial resolution. The maps were calculated using the Land Use Regression models from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Several Python scripts used for data preparation and the model scripts creating the datasets are included. Use the free 7-Zip to uncompress. Uncompressed size: 78 GiB. A description of concep... Mehr ...
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Dokumenttyp: | other |
Erscheinungsdatum: | 2018 |
Verlag/Hrsg.: |
Zenodo
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Schlagwörter: | Land Use Regression models / Health / NO2 / NOx / PM10 / PM2.5 / ESCAPE |
Sprache: | unknown |
Permalink: | https://search.fid-benelux.de/Record/base-28804629 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://doi.org/10.5281/zenodo.1408592 |
Raster-based air pollution concentration maps for the Netherlands. The dataset consists of air pollution concentration maps for six pollutants (NO2, NO2background, NOx, PM2.5, PM2.5absorbance, PM10), covering the land mass of the Netherlands at 5m spatial resolution. The maps were calculated using the Land Use Regression models from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Several Python scripts used for data preparation and the model scripts creating the datasets are included. Use the free 7-Zip to uncompress. Uncompressed size: 78 GiB. A description of concepts, datasets and scripts is given in the manuscript "High resolution annual average air pollution concentration maps for the Netherlands" by Oliver Schmitz, Rob Beelen, Maciej Strak, Gerard Hoek, Ivan Soenario, Bert Brunekreef, Ilonca Vaartjes, Martin J. Dijst, Diederick E. Grobbee, and Derek Karssenberg. Scientific Data 6:190035 (2019). https://doi.org/10.1038/sdata.2019.35 The datasets are licensed under a Creative Commons license (CC-BY 4.0). The Python scripts are licensed under the MIT License. Contact: o.schmitz@uu.nl