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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...

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Main Authors: Rad, Abdullah Kaviani, Shamshiri, Redmond R., Naghipour, Armin, Razmi, Seraj Odeen, Shariati, Mohsen, Golkar, Foroogh, Balasundram, Siva K.
Format: Article
Published: Multidisciplinary Digital Publishing Institute 2022
Online Access:http://psasir.upm.edu.my/id/eprint/102113/
https://www.mdpi.com/2071-1050/14/13/8027
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spelling my.upm.eprints.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
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
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.
format Article
author Rad, Abdullah Kaviani
Shamshiri, Redmond R.
Naghipour, Armin
Razmi, Seraj Odeen
Shariati, Mohsen
Golkar, Foroogh
Balasundram, Siva K.
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
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
title 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_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_sort machine learning for determining interactions between air pollutants and environmental parameters in three cities of iran
publisher Multidisciplinary Digital Publishing Institute
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/102113/
https://www.mdpi.com/2071-1050/14/13/8027
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score 13.211869

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