The DOI matches its Crossref record; 153 of the 153 references it cites were found in a registry or an index.
Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review
Open accessAbstract
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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“Similarly, these techniques have been used to assess the health impacts of environmental exposures, such as air pollution, by estimating daily pollutant concentrations and providing high-resolution exposure assessments for epidemiological studies [21].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [21]
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Olawade, D. B., Wada, O. Z., Ige, A. O., Egbewole, B. I., Olojo, A. S., & Oladapo, B. I. (2024) · Hygiene and Environmental Health Advances · doi:10.1016/j.heha.2024.100114 · Cited by 418
Breathing in danger: Understanding the multifaceted impact of air pollution on health impacts
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Metadata
- 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
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2022-08-11
- Volume
- 14
- Issue
- 16
- Pages
- 9951
- ISSN
- 2071-1050 (ISSN-L)
- 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
- 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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