{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T07:48:16Z","timestamp":1792568896610,"version":"4.5.0"},"reference-count":180,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>Air quality is crucial for public health and the environment, which makes it important to both monitor and forecast the level of pollution. Polluted air, containing harmful substances such as particulate matter, nitrogen oxides, or ozone, can lead to serious respiratory and circulatory diseases, especially in people at risk. Air quality forecasting allows for early warning of smog episodes and taking actions to reduce pollutant emissions. In this article, we review air pollutant concentration forecasting methods, analyzing both classical statistical approaches and modern techniques based on artificial intelligence, including deep models, neural networks, and machine learning, as well as advanced sensing technologies. This work aims to present the current state of research and identify the most promising directions of development in air quality modeling, which can contribute to more effective health and environmental protection. According to the reviewed literature, deep learning\u2013based models, particularly hybrid and attention-driven architectures, emerge as the most promising approaches, while persistent challenges such as data quality, interpretability, and integration of heterogeneous sensing systems define the open issues for future research.<\/jats:p>","DOI":"10.3390\/s25196044","type":"journal-article","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T09:37:21Z","timestamp":1759311441000},"page":"6044","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Comprehensive Review of Data-Driven Techniques for Air Pollution Concentration Forecasting"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4488-3488","authenticated-orcid":false,"given":"Jaroslaw","family":"Bernacki","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering Systems, Wroc\u0142aw University of Science and Technology, Wyb. Wyspia\u0144skiego 27, 50-370 Wroc\u0142aw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9592-262X","authenticated-orcid":false,"given":"Rafa\u0142","family":"Scherer","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Cz\u0229stochowa University of Technology, al. Armii Krajowej 36, 42-200 Cz\u0229stochowa, Poland"},{"name":"Faculty of Computer Science, AGH University of Krak\u00f3w, 30-059 Krak\u00f3w, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"key":"ref_1","first-page":"95","article-title":"Effects of particulate matter on allergic respiratory diseases","volume":"4","author":"Wu","year":"2018","journal-title":"Chronic Dis. Transl. Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mak, H.W.L., and Ng, D.C.Y. (2021). 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