Pure and clean air is essential to make the ecosystem healthy. Air pollution is becoming a critical global concern for both the environment and human health. Presence of harmful pollutants such as PM2.5, PM10, CO2, NO2, SO2, and O3 continuously degrades air quality and influences climatic conditions. This study aims to present a comprehensive air quality monitoring between traditional and advanced ensemble-based machine learning models. To monitor air quality, data collected from major metropolitan cities of India from 2015 to 2023 (Three phases- Pre-COVID, during COVID-19, and post-COVID). After pre-processing the data, a baseline supervised machine learning method, Support Vector Machine (SVM), was applied for ease of implementation. Later, to train weak learner features, ensemble-based machine learning techniques include Gradient Boosting Machine (GBM) and Extreme Gradient Boosting Machine (XGBM), evaluated to get better prediction analysis. The systematic analysis is inspected using different performance parameters: R², Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error. The outcome indicates XGBM achieves superior predictive accuracy and robustness across most cities and time periods, and achieves better variability in spatial and temporal features in performance. The key findings highlight the importance of location-based specific modelling strategies and demonstrate the potential of ensemble learning models for reliable urban air quality monitoring.
Chauhan et al. (Thu,) studied this question.