Machine learning-based modeling of ground level ozone formation in Bangalore and New Delhi cities in India

India is highly vulnerable to atmospheric pollution, especially in city areas, due to the exponential growth in urbanization and industrialization. The increasing level of pollutants in the atmosphere worsened the ambient air quality. The Air Quality Index (AQI) has been increasing at an alarming rate in major cities of India. This dire situation has helped us shed more light on the modelling of ground-level ozone formation over major Indian cities like Delhi and Bangalore. Ground-level ozone is a secondary pollutant formed from various precursors like NOx and VOCs in the presence of sunlight. Pollutant forecasting will be helpful in providing valuable information for selecting the optimal air pollution control strategies. Artificial Neural Networks (ANN) performed better than linear models and Support Vector Machines (SVM) regression models for the modelling of tropospheric ozone formation in both cities. The R² values for linear models were 0.757 and 0.8116, and for SVM regression models, R² values were 0.79 and 0.86, respectively, for Delhi and Bangalore. Conversely, the ANN models for Delhi and Bangalore yielded R² values of 0.91 and 0.95, respectively. This clearly depicts the ANN model’s efficacy compared to linear and SVM models for modelling tropospheric ozone formation. Sensitivity analysis has revealed the importance of meteorological parameters for ozone formation in Delhi and Bangalore. Also, the importance of meteorological parameters varied for both cities. Solar radiation has the highest impact in Delhi, while in Bangalore, it is relative humidity.