Machine learning for determining interactions between air pollutants and environmental parameters in three cities of Iran
Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environment...
| Main Authors: | , , , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/102113/ |
| _version_ | 1848863717837504512 |
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| author | Rad, Abdullah Kaviani Shamshiri, Redmond R. Naghipour, Armin Razmi, Seraj Odeen Shariati, Mohsen Golkar, Foroogh Balasundram, Siva K. |
| author_facet | Rad, Abdullah Kaviani Shamshiri, Redmond R. Naghipour, Armin Razmi, Seraj Odeen Shariati, Mohsen Golkar, Foroogh Balasundram, Siva K. |
| author_sort | Rad, Abdullah Kaviani |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data. |
| first_indexed | 2025-11-15T13:37:22Z |
| format | Article |
| id | upm-102113 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:37:22Z |
| publishDate | 2022 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1021132023-06-16T20:19:17Z http://psasir.upm.edu.my/id/eprint/102113/ Machine learning for determining interactions between air pollutants and environmental parameters in three cities of Iran Rad, Abdullah Kaviani Shamshiri, Redmond R. Naghipour, Armin Razmi, Seraj Odeen Shariati, Mohsen Golkar, Foroogh Balasundram, Siva K. Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data. Multidisciplinary Digital Publishing Institute 2022-06-30 Article PeerReviewed Rad, Abdullah Kaviani and Shamshiri, Redmond R. and Naghipour, Armin and Razmi, Seraj Odeen and Shariati, Mohsen and Golkar, Foroogh and Balasundram, Siva K. (2022) Machine learning for determining interactions between air pollutants and environmental parameters in three cities of Iran. Sustainability, 14 (13). art. no. 8027. pp. 1-25. ISSN 2071-1050 https://www.mdpi.com/2071-1050/14/13/8027 10.3390/su14138027 |
| spellingShingle | Rad, Abdullah Kaviani Shamshiri, Redmond R. Naghipour, Armin Razmi, Seraj Odeen Shariati, Mohsen Golkar, Foroogh Balasundram, Siva K. Machine learning for determining interactions between air pollutants and environmental parameters in three cities of Iran |
| title | Machine learning for determining interactions between air pollutants and environmental parameters in three cities of Iran |
| title_full | Machine learning for determining interactions between air pollutants and environmental parameters in three cities of Iran |
| title_fullStr | Machine learning for determining interactions between air pollutants and environmental parameters in three cities of Iran |
| title_full_unstemmed | Machine learning for determining interactions between air pollutants and environmental parameters in three cities of Iran |
| title_short | Machine learning for determining interactions between air pollutants and environmental parameters in three cities of Iran |
| title_sort | machine learning for determining interactions between air pollutants and environmental parameters in three cities of iran |
| url | http://psasir.upm.edu.my/id/eprint/102113/ http://psasir.upm.edu.my/id/eprint/102113/ http://psasir.upm.edu.my/id/eprint/102113/ |