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Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo network
Air pollution is a major global challenge associated with an increasing number of morbidity and mortality from lung cancer, cardiovascular and respiratory diseases, among others. However, there is scarcity of ground monitoring air quality data from Sub-Saharan Africa that can be used to quantify the...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9389188/ https://www.ncbi.nlm.nih.gov/pubmed/35990920 http://dx.doi.org/10.1016/j.dib.2022.108512 |
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author | Sserunjogi, Richard Ssematimba, Joel Okure, Deo Ogenrwot, Daniel Adong, Priscilla Muyama, Lillian Nsimbe, Noah Bbaale, Martin Bainomugisha, Engineer |
author_facet | Sserunjogi, Richard Ssematimba, Joel Okure, Deo Ogenrwot, Daniel Adong, Priscilla Muyama, Lillian Nsimbe, Noah Bbaale, Martin Bainomugisha, Engineer |
author_sort | Sserunjogi, Richard |
collection | PubMed |
description | Air pollution is a major global challenge associated with an increasing number of morbidity and mortality from lung cancer, cardiovascular and respiratory diseases, among others. However, there is scarcity of ground monitoring air quality data from Sub-Saharan Africa that can be used to quantify the level of pollution. This has resulted in limited targeted air pollution research and interventions e.g. health impacts, key drivers and sources, economic impacts, among others; ultimately hindering the establishment of effective management strategies. This paper presents a dataset of air quality observations collected from 68 spatially distributed monitoring stations across Uganda. The dataset includes hourly PM(2)(.)(5) and PM(10) data collected from low-cost air quality monitoring devices and one reference grade monitoring device over a period ranging from 2019 to 2020. This dataset contributes towards filling some of the data gaps witnessed over the years in ground level monitored ambient air quality in Sub-Saharan Africa and it can be useful to various policy makers and researchers. |
format | Online Article Text |
id | pubmed-9389188 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-93891882022-08-20 Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo network Sserunjogi, Richard Ssematimba, Joel Okure, Deo Ogenrwot, Daniel Adong, Priscilla Muyama, Lillian Nsimbe, Noah Bbaale, Martin Bainomugisha, Engineer Data Brief Data Article Air pollution is a major global challenge associated with an increasing number of morbidity and mortality from lung cancer, cardiovascular and respiratory diseases, among others. However, there is scarcity of ground monitoring air quality data from Sub-Saharan Africa that can be used to quantify the level of pollution. This has resulted in limited targeted air pollution research and interventions e.g. health impacts, key drivers and sources, economic impacts, among others; ultimately hindering the establishment of effective management strategies. This paper presents a dataset of air quality observations collected from 68 spatially distributed monitoring stations across Uganda. The dataset includes hourly PM(2)(.)(5) and PM(10) data collected from low-cost air quality monitoring devices and one reference grade monitoring device over a period ranging from 2019 to 2020. This dataset contributes towards filling some of the data gaps witnessed over the years in ground level monitored ambient air quality in Sub-Saharan Africa and it can be useful to various policy makers and researchers. Elsevier 2022-08-03 /pmc/articles/PMC9389188/ /pubmed/35990920 http://dx.doi.org/10.1016/j.dib.2022.108512 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Sserunjogi, Richard Ssematimba, Joel Okure, Deo Ogenrwot, Daniel Adong, Priscilla Muyama, Lillian Nsimbe, Noah Bbaale, Martin Bainomugisha, Engineer Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo network |
title | Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo network |
title_full | Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo network |
title_fullStr | Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo network |
title_full_unstemmed | Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo network |
title_short | Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo network |
title_sort | seeing the air in detail: hyperlocal air quality dataset collected from spatially distributed airqo network |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9389188/ https://www.ncbi.nlm.nih.gov/pubmed/35990920 http://dx.doi.org/10.1016/j.dib.2022.108512 |
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