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Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review

Author

Listed:
  • Shankar Subramaniam

    (Department of Mechatronics Engineering, Kongu Engineering College, Erode 638060, India)

  • Naveenkumar Raju

    (Department of Mechanical Engineering, Kongu Engineering College, Erode 638060, India)

  • Abbas Ganesan

    (Department of Mechatronics Engineering, Kongu Engineering College, Erode 638060, India)

  • Nithyaprakash Rajavel

    (Department of Mechatronics Engineering, Kongu Engineering College, Erode 638060, India)

  • Maheswari Chenniappan

    (Department of Mechatronics Engineering, Kongu Engineering College, Erode 638060, India)

  • Chander Prakash

    (School of Mechanical Engineering, Lovely Professional University, Phagwara 144411, India
    Division of Research and Development, Lovely Professional University, Phagwara 144411, India)

  • Alokesh Pramanik

    (School of Civil and Mechanical Engineering, Curtin University, Bentley, WA 6102, Australia)

  • Animesh Kumar Basak

    (Adelaide Microscopy, The University of Adelaide, Adelaide, SA 5005, Australia)

  • Saurav Dixit

    (Division of Research & Innovation, Uttaranchal University, Dehradun 248007, India
    World-Class Research Center for Advanced Digital Technologies, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia)

Abstract

Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM 2.5 , PM 10 , O 3 , CO, SO 2 , NO 2 , CO 2 ) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves. The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R 2 , RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.

Suggested Citation

  • Shankar Subramaniam & Naveenkumar Raju & Abbas Ganesan & Nithyaprakash Rajavel & Maheswari Chenniappan & Chander Prakash & Alokesh Pramanik & Animesh Kumar Basak & Saurav Dixit, 2022. "Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review," Sustainability, MDPI, vol. 14(16), pages 1-36, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:9951-:d:886046
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    References listed on IDEAS

    as
    1. Lu Bai & Jianzhou Wang & Xuejiao Ma & Haiyan Lu, 2018. "Air Pollution Forecasts: An Overview," IJERPH, MDPI, vol. 15(4), pages 1-44, April.
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    2. Dinggao Liu & Zhenpeng Tang & Yi Cai, 2022. "A Hybrid Model for China’s Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention," Sustainability, MDPI, vol. 14(23), pages 1-22, November.

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