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A Machine Learning Based Approach for Air Quality Index (AQI) Prediction
Summary
Researchers built a highly accurate computer model (using AI techniques like machine learning) that predicts air quality, including new concerns like microplastic pollution, nearly in real time. This matters because the tool doesn't just forecast pollution levels—it could eventually give people personalized health warnings, like when to wear a mask or stay indoors, helping communities better protect themselves from the health risks of dirty air before it becomes a problem.
The main sources of air pollution are such pollutants as PM2.5, PM10, NO2, and CO, whereas the present models of AQI forecasting are characterized by such limitations as the inaccuracy of the forecast, slowness of its processing, and failure to adapt to the changing pattern of pollution. This study seeks to design a very precise model of predicting AQI with the help of machine learning algorithms, in particular, Random Forest, XGBoost, LSTM, and SVR, so that it could provide real-time predictions, consider the more recent contaminants such as microplastics, and offer practical health advice. The strategy includes collecting historical data and real-time data together at governmental surveillance points, as well as the processing of the data: work with missing values, normalization of measurements, and the extraction of significant characteristics. The parameter tuning of the model optimization uses GridSearchCV with the results as RMSE (4.20), MAE (2.20) and R2 (0.99). The most notable ones are the ability to combine several algorithms using the ensemble technique, time-delayed variables, temporal analysis, and the possibility to combine real-time IoT sensor data with news to find correlations between the events. The user-friendly platform will provide customized health information according to risk factors to each user, including advice on protective equipment, and will support a variety of languages and tailored health-related messages, which will help protect the community health by highlighting the risks of poor air quality and ensure optimal health results.