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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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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. |
format | Online Article Text |
id | pubmed-6957866 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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