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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Abdullah Kaviani Rad & Redmond R. Shamshiri & Armin Naghipour & Seraj-Odeen Razmi & Mohsen Shariati & Foroogh Golkar & Siva K. Balasundram, 2022. "Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran," Sustainability, MDPI, vol. 14(13), pages 1-25, June.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8027-:d:852958
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    References listed on IDEAS

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    1. Narita, Daiju & Oanh, Nguyen Thi Kim & Sato, Keiichi & Huo, Mingqun & Permadi, Didin Agustian & Chi, Nguyen Nhat Ha & Ratanajaratroj, Tanatat & Pawarmart, Ittipol, 2019. "Pollution Characteristics and Policy Actions on Fine Particulate Matter in a Growing Asian Economy: The Case of Bangkok Metropolitan Region," Open Access Publications from Kiel Institute for the World Economy 231375, Kiel Institute for the World Economy (IfW Kiel).
    2. Daniel A. Vallero, 2016. "Air Pollution Monitoring Changes to Accompany the Transition from a Control to a Systems Focus," Sustainability, MDPI, vol. 8(12), pages 1-9, November.
    3. Zhi Qiao & Feng Wu & Xinliang Xu & Jin Yang & Luo Liu, 2019. "Mechanism of Spatiotemporal Air Quality Response to Meteorological Parameters: A National-Scale Analysis in China," Sustainability, MDPI, vol. 11(14), pages 1-16, July.
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    Cited by:

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