Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 19, issue 21
Articles | Volume 19, issue 21
https://doi.org/10.5194/amt-19-7231-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-7231-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 21
Research article
 | 
12 Nov 2026
Research article |  | 12 Nov 2026

Two decades of kilometer-scale daily PM2.5 from satellite observations and machine learning reveal geographically diverging exposure in Ghana

Abhishek Anand, Joe A. Amooli, Selina Amoah, Esi Nerquaye Tetteh, Nana Ama Browne Klutse, Clement Mensah Ackaah, Benjamin Essien, Kingsley Mawuli Amegah, and Daniel M. Westervelt

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Short summary
Air pollution remains poorly characterized across sub-Saharan Africa due to sparse monitoring infrastructure. We developed a multi-model machine learning framework to generate a 21-year, high-resolution PM2.5 dataset for Ghana, revealing pronounced spatial and seasonal variability, persistent geographic exposure disparities, and contrasting long-term trends. These findings provide new evidence to support air-quality management, health assessments, and environmental policy in data-sparse regions.
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