Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran

Abdullah Kaviani Rad, Redmond R. Shamshiri, Armin Naghipour, Seraj-Odeen Razmi, Mohsen Shariati, Foroogh Golkar and Siva K. Balasundram
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Abdullah Kaviani Rad: Department of Soil Science, School of Agriculture, Shiraz University, Shiraz 71946-85111, Iran
Redmond R. Shamshiri: Leibniz Institute for Agricultural Engineering and Bioeconomy, 14469 Potsdam, Germany
Armin Naghipour: Clinical Research Development Center, Imam Reza Hospital, Kermanshah University of Medical Sciences, Kermanshah 67148-69914, Iran
Seraj-Odeen Razmi: Department of MBA, Faculty of Management, University of Tehran, Tehran 14179-35840, Iran
Mohsen Shariati: Department of Environmental Planning, Management, and Education, Factually of Environment, University of Tehran, Tehran 14179-35840, Iran
Foroogh Golkar: Department of Water Engineering & Oceanic and Atmospheric Research Center, College of Agriculture, Shiraz University, Shiraz 71946-85111, Iran
Siva K. Balasundram: Department of Agriculture Technology, Faculty of Agriculture, University Putra Malaysia, Serdang 43400, Selangor, Malaysia

Sustainability, 2022, vol. 14, issue 13, 1-25

Abstract: 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), NO 2 , SO 2 , O 3 , 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 (R 2 ), 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 (R 2 PM 2.5 = 0.36, R 2 PM 10 = 0.27, R 2 NO 2 = 0.46, R 2 SO 2 = 0.41, R 2 O 3 = 0.52, and R 2 CO = 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.

Keywords: air pollution; quality; meteorological factors; vegetation; interaction; modeling; machine learning; XGBoost; AQI; Iran (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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