An assessment of the spatio-temporal dynamics of Landsat-derived aerosol concentration in relation with land cover and road networks in the Lagos megacity
The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries...
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Springer Science and Business Media Deutschland GmbH
2023
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oai:scholars.utp.edu.my:343032023-01-26T01:39:31Z http://scholars.utp.edu.my/id/eprint/34303/ An assessment of the spatio-temporal dynamics of Landsat-derived aerosol concentration in relation with land cover and road networks in the Lagos megacity Ayodele, E. Okolie, C. Akinnusi, S. Mbu-Ogar, E. Alani, R. Daramola, O. Tella, A. The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries at four epochs (2002, 2013, 2015, and 2020) to evaluate the aerosol optical thickness (AOT) levels in relation to land cover and road networks in the Lagos megacity. A look-up table (LUT) was generated using Py6S, a python-based 6S module, to simulate the AOT using land surface reflectance and top of atmosphere reflectance. A comparative assessment of the method against in situ measurements of particulate matter (PM) at different locations shows a strong positive correlation between the imagery-derived AOT values and the PMs. The AOT concentration across the land cover and road networks showed an increasing trend from 2002 to 2020, which could be explained by urbanization in the megacity. The higher concentration of AOT along the major roads is attributed to the high air pollutants released from vehicles, including home/office generators and industries along the road corridors. The continuous rise in pollutant values requires urgent intervention and mitigation efforts. Remote sensing-based AOT monitoring is a possible solution. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. Springer Science and Business Media Deutschland GmbH 2023 Article NonPeerReviewed Ayodele, E. and Okolie, C. and Akinnusi, S. and Mbu-Ogar, E. and Alani, R. and Daramola, O. and Tella, A. (2023) An assessment of the spatio-temporal dynamics of Landsat-derived aerosol concentration in relation with land cover and road networks in the Lagos megacity. Environmental Science and Pollution Research. ISSN 09441344 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146388183&doi=10.1007%2fs11356-022-25042-w&partnerID=40&md5=3c5fe338f5c2e8d1681519ec9720a0d4 10.1007/s11356-022-25042-w 10.1007/s11356-022-25042-w 10.1007/s11356-022-25042-w |
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The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries at four epochs (2002, 2013, 2015, and 2020) to evaluate the aerosol optical thickness (AOT) levels in relation to land cover and road networks in the Lagos megacity. A look-up table (LUT) was generated using Py6S, a python-based 6S module, to simulate the AOT using land surface reflectance and top of atmosphere reflectance. A comparative assessment of the method against in situ measurements of particulate matter (PM) at different locations shows a strong positive correlation between the imagery-derived AOT values and the PMs. The AOT concentration across the land cover and road networks showed an increasing trend from 2002 to 2020, which could be explained by urbanization in the megacity. The higher concentration of AOT along the major roads is attributed to the high air pollutants released from vehicles, including home/office generators and industries along the road corridors. The continuous rise in pollutant values requires urgent intervention and mitigation efforts. Remote sensing-based AOT monitoring is a possible solution. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. |
format |
Article |
author |
Ayodele, E. Okolie, C. Akinnusi, S. Mbu-Ogar, E. Alani, R. Daramola, O. Tella, A. |
spellingShingle |
Ayodele, E. Okolie, C. Akinnusi, S. Mbu-Ogar, E. Alani, R. Daramola, O. Tella, A. An assessment of the spatio-temporal dynamics of Landsat-derived aerosol concentration in relation with land cover and road networks in the Lagos megacity |
author_facet |
Ayodele, E. Okolie, C. Akinnusi, S. Mbu-Ogar, E. Alani, R. Daramola, O. Tella, A. |
author_sort |
Ayodele, E. |
title |
An assessment of the spatio-temporal dynamics of Landsat-derived aerosol concentration in relation with land cover and road networks in the Lagos megacity |
title_short |
An assessment of the spatio-temporal dynamics of Landsat-derived aerosol concentration in relation with land cover and road networks in the Lagos megacity |
title_full |
An assessment of the spatio-temporal dynamics of Landsat-derived aerosol concentration in relation with land cover and road networks in the Lagos megacity |
title_fullStr |
An assessment of the spatio-temporal dynamics of Landsat-derived aerosol concentration in relation with land cover and road networks in the Lagos megacity |
title_full_unstemmed |
An assessment of the spatio-temporal dynamics of Landsat-derived aerosol concentration in relation with land cover and road networks in the Lagos megacity |
title_sort |
assessment of the spatio-temporal dynamics of landsat-derived aerosol concentration in relation with land cover and road networks in the lagos megacity |
publisher |
Springer Science and Business Media Deutschland GmbH |
publishDate |
2023 |
url |
http://scholars.utp.edu.my/id/eprint/34303/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146388183&doi=10.1007%2fs11356-022-25042-w&partnerID=40&md5=3c5fe338f5c2e8d1681519ec9720a0d4 |
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1756062141151969280 |
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13.211869 |