Predictive modeling of air quality in the Tehran megacity via deep learning techniques
Abstract 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 PM2.5, from 2013...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84550-6 |
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| author | Abdullah Kaviani Rad Mohammad Javad Nematollahi Abbas Pak Mohammadreza Mahmoudi |
| author_facet | Abdullah Kaviani Rad Mohammad Javad Nematollahi Abbas Pak Mohammadreza Mahmoudi |
| author_sort | Abdullah Kaviani Rad |
| collection | DOAJ |
| description | Abstract 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 PM2.5, 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 PM2.5 (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 PM2.5 are most affected by temperature, humidity, PM2.5, 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. |
| format | Article |
| id | doaj-art-3e5d997d5494493d88e18d27721352a9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3e5d997d5494493d88e18d27721352a92025-01-12T12:24:43ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-024-84550-6Predictive modeling of air quality in the Tehran megacity via deep learning techniquesAbdullah Kaviani Rad0Mohammad Javad Nematollahi1Abbas Pak2Mohammadreza Mahmoudi3Department of Environmental Engineering and Natural Resources, College of Agriculture, Shiraz UniversityDepartment of Geology, Faculty of Sciences, Urmia UniversityDepartment of Computer Sciences, Shahrekord UniversityDepartment of Statistics, Faculty of Science, Fasa UniversityAbstract 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 PM2.5, 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 PM2.5 (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 PM2.5 are most affected by temperature, humidity, PM2.5, 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.https://doi.org/10.1038/s41598-024-84550-6Environmental modelingDeep learningMachine learningAir quality predictionAir pollution |
| spellingShingle | Abdullah Kaviani Rad Mohammad Javad Nematollahi Abbas Pak Mohammadreza Mahmoudi Predictive modeling of air quality in the Tehran megacity via deep learning techniques Scientific Reports Environmental modeling Deep learning Machine learning Air quality prediction Air pollution |
| title | Predictive modeling of air quality in the Tehran megacity via deep learning techniques |
| title_full | Predictive modeling of air quality in the Tehran megacity via deep learning techniques |
| title_fullStr | Predictive modeling of air quality in the Tehran megacity via deep learning techniques |
| title_full_unstemmed | Predictive modeling of air quality in the Tehran megacity via deep learning techniques |
| title_short | Predictive modeling of air quality in the Tehran megacity via deep learning techniques |
| title_sort | predictive modeling of air quality in the tehran megacity via deep learning techniques |
| topic | Environmental modeling Deep learning Machine learning Air quality prediction Air pollution |
| url | https://doi.org/10.1038/s41598-024-84550-6 |
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