Cited by 193Air Quality Monitoring and ForecastingAir Quality and Health ImpactsCOVID-19 impact on air quality

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

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Subramaniam, Shankar, Raju, Naveenkumar, Ganesan, Abbas, Rajavel, Nithyaprakash, Chenniappan, Maheswari, Prakash, Chander, Pramanik, Alokesh, Basak, Animesh Kumar, Dixit, Saurav·2022·Sustainability

DOI 10.3390/su14169951

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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., PM2.5, PM10, O3, CO, SO2, NO2, CO2) 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 R2, RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.

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See all 193

Authors: Subramaniam, S., Raju, N., Ganesan, A., Rajavel, N., Chenniappan, M., Prakash, C., Pramanik, A., Basak, A. K., & Dixit, S.
Affiliations: Department of Mechatronics Engineering, Kongu Engineering College, Erode 638060, India
Department of Mechanical Engineering, Kongu Engineering College, Erode 638060, India
Lovely Professional University (IN)
Curtin University (AU)
The University of Adelaide (AU)
Peter the Great St. Petersburg Polytechnic University (RU)
Corresponding author: Prakash, Chander
Dixit, Saurav
ORCID: Subramaniam, Shankar
Raju, Naveenkumar
Ganesan, Abbas
Chenniappan, Maheswari
Prakash, Chander
Pramanik, Alokesh
Basak, Animesh Kumar
Dixit, Saurav
Venue: Sustainability
Publisher: MDPI AG
Published: 2022-08-11
Volume: 14
Issue: 16
Pages: 9951
ISSN: 2071-1050 (ISSN-L)
DOI: 10.3390/su14169951
OpenAlex: W4291670288
Cited by: 193
References: 152
Citations by year: 2026: 38 · 2025: 73 · 2024: 60 · 2023: 17 · 2022: 5
FWCI: 3.55
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Sustainability (publishedVersion, cc-by)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Ministry of Science and Higher Education of the Russian Federation (No. 075-15-2022-311, 5/8-4/30/ENV/2020-NCD-II)
Indian Council For Medical Research (ICMR) (No. 075-15-2022-311, 5/8-4/30/ENV/2020-NCD-II)
Topics: Physical Sciences › Environmental Science › Environmental Engineering › Air Quality Monitoring and Forecasting
Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Air Quality and Health Impacts
Physical Sciences › Environmental Science › Global and Planetary Change › COVID-19 impact on air quality
Keywords: air quality forecasting, artificial intelligence, machine learning, hybrid model, early warning systems, PM2.5, PM10, ozone, carbon monoxide, sulfur dioxide, nitrogen dioxide, carbon dioxide, chronic airway disease, heat waves, climate change prediction, pollution sources, human health effects

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

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