{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T18:19:50Z","timestamp":1777832390883,"version":"3.51.4"},"reference-count":200,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T00:00:00Z","timestamp":1765411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"projects Universidad Estatal Peninsula Santa Elena, Ecuador, provided to researcher T.G."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Air quality prediction is a critical challenge amid rising environmental and health risks from pollution. This study conducts a systematic literature review (SLR) to compare traditional statistical models and machine learning (ML) techniques applied to air quality forecasting. Following the PRISMA 2020 protocol, 412 peer-reviewed articles (2016\u20132025) were analyzed using thematic filters and bibliometric tools. Results show a marked shift toward ML methods, particularly in Asia (73.2%), with limited representation from Latin America and Africa. Statistical models focused mainly on MLR (88.6%) and ARIMA (11.4%), while ML approaches (n = 574) included Random Forest, LSTM, and SVM. Only 12% of studies conducted direct comparisons. A total of 1177 predictor variables and 307 performance metrics were systematized, highlighting PM2.5, NO2, and RMSE. Hybrid models like CNN-LSTM show strong potential but face challenges in implementation and interpretability. This review proposes a consolidated framework to guide future research toward more explainable, adaptive, and context-aware predictive models.<\/jats:p>","DOI":"10.3390\/a18120783","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T15:43:43Z","timestamp":1765467823000},"page":"783","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Statistical and Machine Learning Models for Air Quality: A Systematic Review of Methods and Challenges"],"prefix":"10.3390","volume":"18","author":[{"given":"Luzneyda Ballesteros","family":"Peinado","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Universidad Nacional Lomas de Zamora, Lomas de Zamora B1132, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9602-0692","authenticated-orcid":false,"given":"Teresa","family":"Guarda","sequence":"additional","affiliation":[{"name":"Faculty of Systems and Telecommunications, Universidad Estatal Peninsula Santa Elena, Santa Elena 240104, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0152-6712","authenticated-orcid":false,"given":"Germ\u00e1n","family":"Herrera-Vidal","sequence":"additional","affiliation":[{"name":"Industrial Engineering Program, Ciptec Research Group, Fundaci\u00f3n Universitaria Tecnol\u00f3gico Comfenalco, Cartagena 130001, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8455-1197","authenticated-orcid":false,"given":"Claudia","family":"Minnaard","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Universidad Nacional Lomas de Zamora, Lomas de Zamora B1132, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4360-6128","authenticated-orcid":false,"given":"Jairo R.","family":"Coronado-Hern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Department of Productivity and Innovation, Universidad de la Costa, Barranquilla 080001, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/s41598-024-84342-y","article-title":"Application of the Lasso regularisation technique in mitigating overfitting in air quality prediction models","volume":"15","author":"Pak","year":"2025","journal-title":"Sci. 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