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

Shankar Subramaniam (), Naveenkumar Raju, Abbas Ganesan, Nithyaprakash Rajavel, Maheswari Chenniappan, Chander Prakash (), Alokesh Pramanik, Animesh Kumar Basak and Saurav Dixit ()
Additional contact information
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
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

Sustainability, 2022, vol. 14, issue 16, 1-36

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.

Keywords: air pollution; artificial intelligence; climate change; human health; machine learning (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 (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/16/9951/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/16/9951/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:16:p:9951-:d:886046

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().