1 Clark University, Graduate School of Geography, Worcester, MA, USA.
2 Josbet Technical Nigeria Limited, Department of Electrical and Electronics Engineering, Port Harcourt, Rivers State, Nigeria.
3 Nnamdi Azikiwe University, Department of Environmental Management, Awka, Anambra state, Nigeria.
4 Federal University of Technology, Department of Biochemistry, Akure, Ondo State, Nigeria.
World Journal of Biology Pharmacy and Health Sciences, 2025, 23(01), 395–413
Article DOI: 10.30574/wjbphs.2025.23.1.0696
Received on 14 June 2025; revised on 20 July 2025; accepted on 22 July 2025
Per- and polyfluoroalkyl substances (PFAS) contamination in African aquatic systems represents a critical environmental and public health challenge that demands innovative monitoring approaches to overcome traditional analytical limitations. This comprehensive review examines the transformative potential of integrating satellite imagery with Artificial Intelligence (AI) technologies for PFAS contamination mapping across African water bodies, addressing the persistent gap between contamination prevalence and monitoring capacity. The synthesis of current research reveals that while PFAS contamination has been documented across multiple African countries including Ghana, Uganda, Burkina Faso, Ivory Coast, and South Africa, comprehensive monitoring remains severely constrained by the scarcity of mass spectrometry facilities, with only 49 out of 54 African countries lacking dedicated PFAS analytical capabilities. Our analysis demonstrates that satellite-based monitoring, enhanced by machine learning algorithms, offers unprecedented opportunities for large-scale, cost-effective surveillance that can reduce operational costs by 60-80% while providing continental-scale coverage with daily to weekly temporal resolution. The integration of remote sensing data with AI algorithms addresses critical environmental justice concerns by democratizing access to environmental monitoring capabilities and supporting evidence-based policy interventions in resource-constrained settings. This review provides a comprehensive framework for understanding PFAS contamination patterns, evaluating technological solutions, and implementing sustainable monitoring strategies that align with African development priorities and environmental protection needs. The findings underscore the urgent need for coordinated international cooperation, capacity building initiatives, and policy framework development to realize the full potential of these innovative monitoring approaches in protecting public health and environmental integrity across African aquatic systems.
PFAS; Satellite Imagery; Artificial Intelligence; Water Quality Monitoring; Africa; Environmental Justice; Remote Sensing; Public Health; Environmental Contamination; Machine Learning
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Grace Nwachukwu, Kalu Okigwe, Remigius Sunny Nwachukwu, Ethelbart, Chiamaka Immaculata and Ohwofasa Christianah. Application of satellite imagery and Artificial Intelligence (AI) for PFAS Contamination Mapping in African Aquatic Systems: Advancing Data-Driven Environmental and Public Health Risk Assessment. World Journal of Biology Pharmacy and Health Sciences, 2025, 23(01), 395-413. Article DOI: https://doi.org/10.30574/wjbphs.2025.23.1.0696.