{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:53:18Z","timestamp":1792788798318,"version":"4.5.0"},"reference-count":51,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T00:00:00Z","timestamp":1736467200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Direcci\u00f3n de Investigaci\u00f3n Cient\u00edfica y Tecnol\u00f3gica","award":["062317EP_Ayudante"],"award-info":[{"award-number":["062317EP_Ayudante"]}]},{"DOI":"10.13039\/501100024022","name":"UNACH","doi-asserted-by":"crossref","award":["OPRIM"],"award-info":[{"award-number":["OPRIM"]}],"id":[{"id":"10.13039\/501100024022","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Vice Presidency of Research","award":["Article Processing Charges"],"award-info":[{"award-number":["Article Processing Charges"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>\n                    In this comprehensive analysis of Chile\u2019s air quality dynamics spanning 2016 to 2021, the utilization of data from the National Air Quality Information System (SINCA) and its network of monitoring stations was undertaken. Quintero, Puchuncav\u00ed, and Coyhaique were the focal points of this study, with the primary objective being the construction of predictive models for sulfur dioxide (SO\n                    <jats:sub>2<\/jats:sub>\n                    ), fine particulate matter (PM\n                    <jats:sub>2.5<\/jats:sub>\n                    ), and coarse particulate matter (PM\n                    <jats:sub>10<\/jats:sub>\n                    ). A hybrid forecasting strategy was employed, integrating Autoregressive Integrated Moving Average (ARIMA) models with Artificial Neural Networks (ANN), incorporating external covariates such as wind speed and direction to enhance prediction accuracy. Vital monitoring stations, including Quintero, Ventanas, Coyhaique I, and Coyhaique II, played a pivotal role in data collection and model development. Emphasis on industrial and residential zones highlighted the significance of discerning pollutant origins and the influence of wind direction on concentration measurements. Geographical and climatic factors, notably in Coyhaique, revealed a seasonal stagnation effect due to topography and low winter temperatures, contributing to heightened pollution levels. Model performance underwent meticulous evaluation, utilizing metrics such as the Akaike Information Criterion (AIC), Ljung-Box statistical tests, and diverse statistical indicators. The hybrid ARIMA-ANN models demonstrated strong predictive capabilities, boasting an R\n                    <jats:sup>2<\/jats:sup>\n                    exceeding 0.90. The outcomes underscored the imperative for tailored strategies in air quality management, recognizing the intricate interplay of environmental factors. Additionally, the adaptability and precision of neural network models were highlighted, showcasing the potential of advanced technologies in refining air quality forecasts. The findings reveal that geographical and climatic factors, especially in Coyhaique, contribute to elevated pollution levels due to seasonal stagnation and low winter temperatures. These results underscore the need for tailored air quality management strategies and highlight the potential of advanced modeling techniques to improve future air quality forecasts and deepen the understanding of environmental challenges in Chile.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0314278","type":"journal-article","created":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T15:11:59Z","timestamp":1736521919000},"page":"e0314278","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["Enhancing air quality predictions in Chile: Integrating ARIMA and Artificial Neural Network models for Quintero and Coyhaique cities"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5835-298X","authenticated-orcid":true,"given":"Fidel","family":"Vallejo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diana","family":"Y\u00e1nez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patricia","family":"Vi\u00f1\u00e1n-Guerrero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis A.","family":"D\u00edaz-Robles","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0333-7845","authenticated-orcid":true,"given":"Marcelo","family":"Oyaneder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicol\u00e1s","family":"Reinoso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luna","family":"Billartello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6362-9100","authenticated-orcid":true,"given":"Andrea","family":"Espinoza-P\u00e9rez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorena","family":"Espinoza-P\u00e9rez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5133-2812","authenticated-orcid":true,"given":"Ernesto","family":"Pino-Cort\u00e9s","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,1,10]]},"reference":[{"issue":"1","key":"pone.0314278.ref001","doi-asserted-by":"crossref","first-page":"199","DOI":"10.46488\/NEPT.2023.v22i01.018","article-title":"Assessment and Prediction of Air Quality Level Using ARIMA Model: A Case Study of Surat City, Gujarat State, India","volume":"22","author":"H. 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