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Air pollution stands as the fourth leading cause of death globally. Whileextensive research has been conducted in this domain, most approaches rely onlarge datasets when it comes to prediction. This limits their applicability inlow-resource settings though more vulnerable. This study addresses this gap byproposing a novel machine learning approach for accurate air quality predictionusing two months of air quality data. By leveraging the World WeatherRepository, the meteorological, air pollutant, and Air Quality Index featuresfrom 197 capital cities were considered to predict air quality for the nextday. The evaluation of several machine learning models demonstrates theeffectiveness of the Random Forest algorithm in generating reliablepredictions, particularly when applied to classification rather thanregression, approach which enhances the model's generalizability by 42%,achieving a cross-validation score of 0.38 for regression and 0.89 forclassification. To instill confidence in the predictions, interpretable machinelearning was considered. Finally, a cost estimation comparing theimplementation of this solution in high-resource and low-resource settings ispresented including a tentative of technology licensing business model. Thisresearch highlights the potential for resource-limited countries toindependently predict air quality while awaiting larger datasets to furtherrefine their predictions.

Air Quality Forecasting Using Machine Learning: A Global perspective with Relevance to Low-Resource Settings