Air pollution particulate matter (PM2.5) prediction in South African cities using machine learning techniques.

Background: Air pollution contributes to the most severe environmental and health problems due to industrial emissions and atmosphere contamination, produced by climate and traffic factors, fossil fuel combustion, and industrial characteristics. Because this is a global issue, several nations have established control of air pollution stations in various cities to monitor pollutants like Nitrogen Dioxide (NO2), Ozone (O3), Sulfur Dioxide (SO2), Carbon Monoxide (CO), Particulate Matter (PM2.5, PM10), to notify inhabitants when pollution levels surpass the quality threshold. With the rise in air pollution, it is necessary to construct models to capture data on air pollutant concentrations. Compared to other parts of the world, Africa has a scarcity of reliable air quality sensors for monitoring and predicting Particulate Matter (PM2.5). This demonstrates the possibility of extending research in air pollution control. Methods: Machine learning techniques were utilized in this study to identify air pollution in terms of time, cost, and efficiency so that different scenarios and systems may select the optimal way for their needs. To assess and forecast the behavior of Particulate Matter (PM2.5), this study presented a Machine Learning approach that includes Cat Boost Regressor, Extreme Gradient Boosting Regressor, Random Forest Classifier, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Decision Tree. Results: Cat Boost Regressor and Extreme Gradient Boosting Regressor were implemented to predict the latest PM2.5 concentrations for South African Cities with recording stations using past dated recordings, then the best performing model between the two is used to predict PM2.5 concentrations for South African Cities with no recording stations and also to predict future PM2.5 concentrations for South African Cities. K-Nearest Neighbor, Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest Classifier were implemented to create a system predicting the Air Quality Index (AQI) Status. Conclusion: This study investigated various machine learning techniques for air pollution to analyze and predict air pollution behavior regarding air quality and air pollutants, detecting which areas are most affected in South African cities.