Predictive modeling of air quality in the Tehran megacity via deep learning techniques

Air pollution is a significant challenge in metropolitan areas, where increasing amounts of air pollutants threaten public health and environmental safety. The present study aims to forecast the concentrations of various air pollutants, including CO, O₃, NO₂, SO₂, PM₁₀, and PM_2.5, from 2013 to 2023 in the Tehran megacity, Iran, via deep learning (DL) models and evaluate their effectiveness over conventional machine learning (ML) methods. Key driving variables, including temperature, relative humidity, dew point, wind speed, and air pressure, were considered. R-squared (R²), root-mean-square error (RMSE), mean absolute error (MAE), and mean-square error (MSE) were used to assess and compare the models. This research demonstrated that DL models typically outperform ML models in forecasting air pollution. Gated recurrent units (GRUs), fully connected neural networks (FCNNs), and convolutional neural networks (CNNs) recorded R² and MSE values of 0.5971 and 42.11 for CO, 0.7873 and 171.40 for O₃, and 0.4954 and 25.17 for SO₂, respectively. Consequently, the FCNN and GRU presented remarkable performance in predicting NO₂ (R² = 0.6476 and MSE = 75.16), PM₁₀ (R² = 0.8712 and MSE = 45.11), and PM_2.5 (R² = 0.9276 and MSE = 58.12) concentrations. In terms of operational speed, the FCNN model exhibited the most efficiency, with a minimum and maximum runtime of 13 and 28 s, respectively. The feature importance analysis suggested that CO, O₃ and NO₂, SO₂ and PM₁₀, and PM_2.5 are most affected by temperature, humidity, PM_2.5, and PM₁₀, respectively. Thus, temperature and humidity were the primary factors affecting the variability in pollutant concentrations. The conclusions confirm that the DL models achieve significant accuracy and serve as essential instruments for managing air pollution, providing practical insights for decision-makers to adopt efficient air quality control strategies.