Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania

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dc.authoridEl Mghouchi, Youness/0000-0003-2164-2056
dc.authoridUdristioiu, Mihaela Tinca/0000-0002-5811-5930
dc.contributor.authorEl Mghouchi, Youness
dc.contributor.authorUdristioiu, Mihaela Tinca
dc.contributor.authorYildizhan, Hasan
dc.date.accessioned2025-01-06T17:36:11Z
dc.date.available2025-01-06T17:36:11Z
dc.date.issued2024
dc.description.abstractInadequate air quality has adverse impacts on human well-being and contributes to the progression of climate change, leading to fluctuations in temperature. Therefore, gaining a localized comprehension of the interplay between climate variations and air pollution holds great significance in alleviating the health repercussions of air pollution. This study uses a holistic approach to make air quality predictions and multivariate modelling. It investigates the associations between meteorological factors, encompassing temperature, relative humidity, air pressure, and three particulate matter concentrations (PM10, PM2.5, and PM1), and the correlation between PM concentrations and noise levels, volatile organic compounds, and carbon dioxide emissions. Five hybrid machine learning models were employed to predict PM concentrations and then the Air Quality Index (AQI). Twelve PM sensors evenly distributed in Craiova City, Romania, provided the dataset for five months (22 September 2021-17 February 2022). The sensors transmitted data each minute. The prediction accuracy of the models was evaluated and the results revealed that, in general, the coefficient of determination (R2) values exceeded 0.96 (interval of confidence is 0.95) and, in most instances, approached 0.99. Relative humidity emerged as the least influential variable on PM concentrations, while the most accurate predictions were achieved by combining pressure with temperature. PM10 (less than 10 mu m in diameter) concentrations exhibited a notable correlation with PM2.5 (less than 2.5 mu m in diameter) concentrations and a moderate correlation with PM1 (less than 1 mu m in diameter). Nevertheless, other findings indicated that PM concentrations were not strongly related to NOISE, CO2, and VOC, and these last variables should be combined with another meteorological variable to enhance the prediction accuracy. Ultimately, this study established novel relationships for predicting PM concentrations and AQI based on the most effective combinations of predictor variables identified.
dc.identifier.doi10.3390/s24051532
dc.identifier.issn1424-8220
dc.identifier.issue5
dc.identifier.pmid38475068
dc.identifier.scopus2-s2.0-85187479329
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/s24051532
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1792
dc.identifier.volume24
dc.identifier.wosWOS:001182990700001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSensors
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectair pollution
dc.subjecthybrid machine learning
dc.subjectlow-cost sensors
dc.subjectPM sensor
dc.subjecturban monitoring
dc.titleMultivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania
dc.typeArticle

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