Machine learning-based assessment of aerosol optical depth over Ghana, West Africa using MODIS satellite data

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. Given the region’s dense vegetation, this phenomenon can be attributed to biogenic emissions. 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.Author summary: Air pollution from fine particulate matter is a growing public health concern in Ghana, yet ground-based monitoring is limited. In this study, we used satellite data and machine learning techniques to estimate Aerosol Optical Depth (AOD), a key proxy for air pollution, over Ghana from 2003 to 2019. We applied multiple linear regression (MLR) and artificial neural networks (ANN) to predict AOD using meteorological and energy balance variables. To improve performance, we developed a hybrid ANN(MLR) model that structurally mimics linear regression within a neural network framework. This model consistently outperformed traditional MLR and ANN methods, especially in densely populated cities like Accra and Takoradi. Our analysis revealed persistently high aerosol levels in southwestern Ghana, likely driven by biogenic emissions, illegal mining, and industrial activity. Despite challenges such as data gaps and missing observations, our models were able to provide reliable spatial and temporal estimates of aerosol patterns. This work offers a valuable tool for air quality assessment in regions lacking routine monitoring and demonstrates how satellite observations combined with machine learning can bridge critical environmental data gaps in West Africa.