Machine-Learning Approaches for Assessing Aerosol Optical Depth (AOD) in Ghana, West Africa

In the field of environmental health, assessing air pollution exposure has historically posed challenges, primarily due to sparse ground observation networks. To overcome this limitation, satellite remote sensing of aerosols provides a valuable tool for monitoring air quality and estimating particulate matter concentration (PM) at the surface. In this study, we employ two predictive models to estimate Aerosol Optical Depth (AOD) levels over Ghana and selected localities from January 2003 to December 2019. Our investigation focuses on evaluating the capabilities of multiple linear regression (MLR) and artificial neural network (ANN) models in predicting AOD levels. Additionally, we introduce a novel approach to constructing the MLR model by leveraging the ANN architecture. These models utilize meteorological variables as input, to facilitate accurate predictions. Despite Ghana's alarming air pollution health ranking and its substantial role in mortality, routine monitoring remains sparse. This research contributes a comprehensive sixteen-year assessment (2003-2019) of AOD at a 3 km resolution, obtained from MODIS Aqua and Terra satellites. The findings indicate that the southwestern part of the country displays elevated aerosol levels compared to other major cities. This phenomenon can be attributed to biogenic emissions, given the region's dense vegetation. Additionally, many small cities within this area are recognized as hotspots for surface mining operations, potentially contributing to increased local dust loadings in the atmosphere. Notably, the MLR model, implemented using the ANN model structure, outperformed the other utilized models. This endeavor aims to unravel the spatiotemporal distribution patterns of aerosols across Ghana, and its major urban hubs.