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High resolution annual average air pollution concentration maps for the Netherlands

Long-term exposure to air pollution is considered a major public health concern and has been related to overall mortality and various diseases such as respiratory and cardiovascular disease. Due to the spatial variability of air pollution concentrations, assessment of individual exposure to air poll...

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Autores principales: Schmitz, Oliver, Beelen, Rob, Strak, Maciej, Hoek, Gerard, Soenario, Ivan, Brunekreef, Bert, Vaartjes, Ilonca, Dijst, Martin J., Grobbee, Diederick E., Karssenberg, Derek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6413687/
https://www.ncbi.nlm.nih.gov/pubmed/30860500
http://dx.doi.org/10.1038/sdata.2019.35
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author Schmitz, Oliver
Beelen, Rob
Strak, Maciej
Hoek, Gerard
Soenario, Ivan
Brunekreef, Bert
Vaartjes, Ilonca
Dijst, Martin J.
Grobbee, Diederick E.
Karssenberg, Derek
author_facet Schmitz, Oliver
Beelen, Rob
Strak, Maciej
Hoek, Gerard
Soenario, Ivan
Brunekreef, Bert
Vaartjes, Ilonca
Dijst, Martin J.
Grobbee, Diederick E.
Karssenberg, Derek
author_sort Schmitz, Oliver
collection PubMed
description Long-term exposure to air pollution is considered a major public health concern and has been related to overall mortality and various diseases such as respiratory and cardiovascular disease. Due to the spatial variability of air pollution concentrations, assessment of individual exposure to air pollution requires spatial datasets at high resolution. Combining detailed air pollution maps with personal mobility and activity patterns allows for an improved exposure assessment. We present high-resolution datasets for the Netherlands providing average ambient air pollution concentration values for the year 2009 for NO(2), NO(x), PM(2.5), PM(2.5absorbance) and PM(10.) The raster datasets on 5×5 m grid cover the entire Netherlands and were calculated using the land use regression models originating from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Additional datasets with nationwide and regional measurements were used to evaluate the generated concentration maps. The presented datasets allow for spatial aggregations on different scales, nationwide individual exposure assessment, and the integration of activity patterns in the exposure estimation of individuals.
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spelling pubmed-64136872019-03-13 High resolution annual average air pollution concentration maps for the Netherlands Schmitz, Oliver Beelen, Rob Strak, Maciej Hoek, Gerard Soenario, Ivan Brunekreef, Bert Vaartjes, Ilonca Dijst, Martin J. Grobbee, Diederick E. Karssenberg, Derek Sci Data Data Descriptor Long-term exposure to air pollution is considered a major public health concern and has been related to overall mortality and various diseases such as respiratory and cardiovascular disease. Due to the spatial variability of air pollution concentrations, assessment of individual exposure to air pollution requires spatial datasets at high resolution. Combining detailed air pollution maps with personal mobility and activity patterns allows for an improved exposure assessment. We present high-resolution datasets for the Netherlands providing average ambient air pollution concentration values for the year 2009 for NO(2), NO(x), PM(2.5), PM(2.5absorbance) and PM(10.) The raster datasets on 5×5 m grid cover the entire Netherlands and were calculated using the land use regression models originating from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Additional datasets with nationwide and regional measurements were used to evaluate the generated concentration maps. The presented datasets allow for spatial aggregations on different scales, nationwide individual exposure assessment, and the integration of activity patterns in the exposure estimation of individuals. Nature Publishing Group 2019-03-12 /pmc/articles/PMC6413687/ /pubmed/30860500 http://dx.doi.org/10.1038/sdata.2019.35 Text en Copyright © 2019, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.
spellingShingle Data Descriptor
Schmitz, Oliver
Beelen, Rob
Strak, Maciej
Hoek, Gerard
Soenario, Ivan
Brunekreef, Bert
Vaartjes, Ilonca
Dijst, Martin J.
Grobbee, Diederick E.
Karssenberg, Derek
High resolution annual average air pollution concentration maps for the Netherlands
title High resolution annual average air pollution concentration maps for the Netherlands
title_full High resolution annual average air pollution concentration maps for the Netherlands
title_fullStr High resolution annual average air pollution concentration maps for the Netherlands
title_full_unstemmed High resolution annual average air pollution concentration maps for the Netherlands
title_short High resolution annual average air pollution concentration maps for the Netherlands
title_sort high resolution annual average air pollution concentration maps for the netherlands
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6413687/
https://www.ncbi.nlm.nih.gov/pubmed/30860500
http://dx.doi.org/10.1038/sdata.2019.35
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