Assessment of aerosol burden over Ghana

Fosu-Amankwah, Kwabena; Bessardon, Geoffrey; Quansah, Emmanuel; Amekudzi, Leonard K.; Brooks, B. J.; Damoah, R.

doi:10.1016/j.sciaf.2021.e00971

Cited by 2 publications

(3 citation statements)

References 80 publications

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“…This recurring observation aligns with prior studies undertaken in the same geographical vicinity (Aklesso et al, 2018;Fosu-Amankwah et al, 2021) and can be ascribed to various factors. The presence of dense vegetation in the southwestern region likely contributes to this aerosol distribution pattern, possibly linked to increased biogenic emissions.…”

Section: Discussionsupporting

confidence: 90%

“…MODIS stands out, utilizing Aqua and Terra satellites. Its widespread adoption is owed to its impressive characteristics: a wide swath width covering 2330 kilometers and near-global coverage every 1 to 2 days (Fosu-Amankwah et al, 2021). MODIS aerosol retrievals rely on three main algorithms: Dark Target (DT), Deep Blue (DB), and the combined Dark Target and Deep Blue (DTB) algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Agriculture, particularly among young adults, plays a vital role in the economy (Ghana Statistical Service, 2014), with activities like biomass burning contributing to aerosol levels. Additionally, urban areas in Ghana are the major hub for economic activities and substantial emissions from industries, transportation, construction, petroleum extraction, and mining, further impacting aerosol concentrations (Fosu-Amankwah et al, 2021). This study aims to explore these aerosol dynamics in Ghana's evolving environmental landscape, offering valuable insights for scientific research.…”

Section: Introductionmentioning

confidence: 99%

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

Gilbert,

Aryee,

Adjei

2024

Preprint

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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.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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