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Leveraging data science to uncover solutions to air pollution-related adverse health outcomes in Africa: policy advice and recommendations
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DOI：10.1016/j.envdev.2026.101517.png)
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Abstract
En 中文
• Limited tools are accessible to predict how climate change affects air quality and health • Data science enables spatio-temporal analyses using innovative methods like machine learning and geospatial forecasting • Predictive models support targeted, evidence-based public health interventions • Data science helps close research gaps and improve climate-health resilience in Africa • Local capacity, regional collaboration, and investment in data systems is needed in Africa
Keywords:
air quality
data science
environmental health
respiratory health impacts
mitigation
interventions
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