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Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France

Monitoring of air pollution is an important task in public health. Availability of data is often hindered by the paucity of the ground monitoring station network. We present here a new spatio-temporal dataset collected and processed from the Sentinel 5P remote sensing platform. As an example applica...

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Autores principales: Omrani, Hichem, Omrani, Bilel, Parmentier, Benoit, Helbich, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6957866/
https://www.ncbi.nlm.nih.gov/pubmed/31956677
http://dx.doi.org/10.1016/j.dib.2019.105089
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author Omrani, Hichem
Omrani, Bilel
Parmentier, Benoit
Helbich, Marco
author_facet Omrani, Hichem
Omrani, Bilel
Parmentier, Benoit
Helbich, Marco
author_sort Omrani, Hichem
collection PubMed
description Monitoring of air pollution is an important task in public health. Availability of data is often hindered by the paucity of the ground monitoring station network. We present here a new spatio-temporal dataset collected and processed from the Sentinel 5P remote sensing platform. As an example application, we applied the full workflow to process measurements of nitrogen dioxide (NO(2)) collected over the territory of mainland France from May 2018 to June 2019. The data stack generated is daily measurements at a 4 × 7 km spatial resolution. The supplementary Python code package used to collect and process the data is made publicly available. The dataset provided in this article is of value for policy-makers and health assessment.
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spelling pubmed-69578662020-01-17 Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France Omrani, Hichem Omrani, Bilel Parmentier, Benoit Helbich, Marco Data Brief Computer Science Monitoring of air pollution is an important task in public health. Availability of data is often hindered by the paucity of the ground monitoring station network. We present here a new spatio-temporal dataset collected and processed from the Sentinel 5P remote sensing platform. As an example application, we applied the full workflow to process measurements of nitrogen dioxide (NO(2)) collected over the territory of mainland France from May 2018 to June 2019. The data stack generated is daily measurements at a 4 × 7 km spatial resolution. The supplementary Python code package used to collect and process the data is made publicly available. The dataset provided in this article is of value for policy-makers and health assessment. Elsevier 2020-01-07 /pmc/articles/PMC6957866/ /pubmed/31956677 http://dx.doi.org/10.1016/j.dib.2019.105089 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Computer Science
Omrani, Hichem
Omrani, Bilel
Parmentier, Benoit
Helbich, Marco
Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France
title Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France
title_full Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France
title_fullStr Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France
title_full_unstemmed Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France
title_short Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France
title_sort spatio-temporal data on the air pollutant nitrogen dioxide derived from sentinel satellite for france
topic Computer Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6957866/
https://www.ncbi.nlm.nih.gov/pubmed/31956677
http://dx.doi.org/10.1016/j.dib.2019.105089
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