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...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-10-01
|
| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340922007065 |
| _version_ | 1798000937204187136 |
|---|---|
| author | Richard Sserunjogi Joel Ssematimba Deo Okure Daniel Ogenrwot Priscilla Adong Lillian Muyama Noah Nsimbe Martin Bbaale Engineer Bainomugisha |
| author_facet | Richard Sserunjogi Joel Ssematimba Deo Okure Daniel Ogenrwot Priscilla Adong Lillian Muyama Noah Nsimbe Martin Bbaale Engineer Bainomugisha |
| author_sort | Richard Sserunjogi |
| collection | DOAJ |
| 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 PM2.5 and PM10 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. |
| first_indexed | 2024-04-11T11:28:10Z |
| format | Article |
| id | doaj.art-67102e51849b4e389c4bc6e16e52ab93 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-04-11T11:28:10Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-67102e51849b4e389c4bc6e16e52ab932022-12-22T04:26:14ZengElsevierData in Brief2352-34092022-10-0144108512Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo networkRichard Sserunjogi0Joel Ssematimba1Deo Okure2Daniel Ogenrwot3Priscilla Adong4Lillian Muyama5Noah Nsimbe6Martin Bbaale7Engineer Bainomugisha8Corresponding author.; AirQo, Department of Computer Science, Makerere University, Kampala, UgandaAirQo, Department of Computer Science, Makerere University, Kampala, UgandaAirQo, Department of Computer Science, Makerere University, Kampala, UgandaAirQo, Department of Computer Science, Makerere University, Kampala, UgandaAirQo, Department of Computer Science, Makerere University, Kampala, UgandaAirQo, Department of Computer Science, Makerere University, Kampala, UgandaAirQo, Department of Computer Science, Makerere University, Kampala, UgandaAirQo, Department of Computer Science, Makerere University, Kampala, UgandaAirQo, Department of Computer Science, Makerere University, Kampala, UgandaAir 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 PM2.5 and PM10 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.http://www.sciencedirect.com/science/article/pii/S2352340922007065Air quality datasetSub-Saharan AfricaAir pollutionPM2.5PM10Particulate matter |
| spellingShingle | Richard Sserunjogi Joel Ssematimba Deo Okure Daniel Ogenrwot Priscilla Adong Lillian Muyama Noah Nsimbe Martin Bbaale Engineer Bainomugisha Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo network Data in Brief Air quality dataset Sub-Saharan Africa Air pollution PM2.5 PM10 Particulate matter |
| 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 | Air quality dataset Sub-Saharan Africa Air pollution PM2.5 PM10 Particulate matter |
| url | http://www.sciencedirect.com/science/article/pii/S2352340922007065 |
| work_keys_str_mv | AT richardsserunjogi seeingtheairindetailhyperlocalairqualitydatasetcollectedfromspatiallydistributedairqonetwork AT joelssematimba seeingtheairindetailhyperlocalairqualitydatasetcollectedfromspatiallydistributedairqonetwork AT deookure seeingtheairindetailhyperlocalairqualitydatasetcollectedfromspatiallydistributedairqonetwork AT danielogenrwot seeingtheairindetailhyperlocalairqualitydatasetcollectedfromspatiallydistributedairqonetwork AT priscillaadong seeingtheairindetailhyperlocalairqualitydatasetcollectedfromspatiallydistributedairqonetwork AT lillianmuyama seeingtheairindetailhyperlocalairqualitydatasetcollectedfromspatiallydistributedairqonetwork AT noahnsimbe seeingtheairindetailhyperlocalairqualitydatasetcollectedfromspatiallydistributedairqonetwork AT martinbbaale seeingtheairindetailhyperlocalairqualitydatasetcollectedfromspatiallydistributedairqonetwork AT engineerbainomugisha seeingtheairindetailhyperlocalairqualitydatasetcollectedfromspatiallydistributedairqonetwork |