{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T19:02:58Z","timestamp":1783537378523,"version":"3.55.0"},"reference-count":18,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Air plays a vital role in human life, and poor air quality can lead to respiratory infections. Given the significant impact of air quality on people\u2019s health, monitoring and assessing air quality is crucial. With advancements in machine learning (ML) and artificial intelligence (AI), we now have extensive tools to measure the Air Quality Index (AQI). Air quality is influenced by various pollutants, including carbon monoxide (CO), nitrogen dioxide (NO\n                    <jats:sub>2<\/jats:sub>\n                    ), ozone (O\n                    <jats:sub>3<\/jats:sub>\n                    ), and sulfur dioxide (SO\n                    <jats:sub>2<\/jats:sub>\n                    ), which are prevalent in highly polluted areas and contribute to a wide range of illnesses. Particulate matter, such as PM2.5 (particles with an aerodynamic diameter of less than 2.5 \u00b5m) and PM10, poses additional health risks. To address these concerns, this study focuses on predicting AQI values for major cities in Pakistan, specifically Karachi and Peshawar, using four prominent ML algorithms: Random Forest (RF), Gradient Boosting (GB), Linear Regression (LR), and Ridge Regression (RR). The results indicate that the models effectively predicted AQI using evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R\n                    <jats:sup>2<\/jats:sup>\n                    ). This research\u2019s novelty lies in using the latest AQI datasets for Karachi and Peshawar and applying standard scaling for AQI normalization. Additionally, the study compares evaluation metric results across different cities, highlighting the importance of using standard scalers to achieve optimal model performance. This research underscores the value of advanced ML techniques for accurate AQI prediction and analysis.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0349858","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T17:40:57Z","timestamp":1783532457000},"page":"e0349858","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Air quality index prediction using machine learning regression models: A comparative analysis"],"prefix":"10.1371","volume":"21","author":[{"given":"Fiaz","family":"Majeed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sana","family":"Saleha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laraib","family":"Abbas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Syed Ali","family":"Ghalib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2440-6596","authenticated-orcid":true,"given":"Muhammad","family":"Hamid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Saleem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Aman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3576-8365","authenticated-orcid":true,"given":"Arshad","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,8]]},"reference":[{"issue":"19","key":"pone.0349858.ref001","doi-asserted-by":"crossref","first-page":"4069","DOI":"10.3390\/app9194069","article-title":"Air quality index and air pollutant concentration prediction based on machine learning algorithms","volume":"9","author":"H Liu","year":"2019","journal-title":"Appl Sci"},{"issue":"1","key":"pone.0349858.ref002","doi-asserted-by":"crossref","DOI":"10.1186\/s40537-021-00548-1","article-title":"Air-pollution prediction in smart city, deep learning approach","volume":"8","author":"A Bekkar","year":"2021","journal-title":"J Big Data"},{"issue":"1","key":"pone.0349858.ref003","doi-asserted-by":"crossref","first-page":"5","DOI":"10.3390\/bdcc2010005","article-title":"A machine learning approach for air quality prediction: model regularization and optimization","volume":"2","author":"D Zhu","year":"2018","journal-title":"BDCC"},{"key":"pone.0349858.ref004","doi-asserted-by":"crossref","first-page":"100093","DOI":"10.1016\/j.dche.2023.100093","article-title":"Air Quality Index prediction using machine learning for Ahmedabad city","volume":"7","author":"NN Maltare","year":"2023","journal-title":"Digit Chem Eng"},{"key":"pone.0349858.ref005","doi-asserted-by":"crossref","unstructured":"Madan T, Sagar S, Virmani D. Air quality prediction using machine learning algorithms \u2013A Review. In: Proceedings - IEEE 2020 2nd International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2020, Institute of Electrical and Electronics Engineers Inc. 2020. p. 140\u20135.","DOI":"10.1109\/ICACCCN51052.2020.9362912"},{"issue":"1","key":"pone.0349858.ref006","doi-asserted-by":"crossref","first-page":"8","DOI":"10.18178\/ijesd.2018.9.1.1066","article-title":"Air quality prediction: big data and machine learning approaches","volume":"9","author":"GK Kang","year":"2018","journal-title":"IJESD"},{"key":"pone.0349858.ref007","article-title":"Smart air pollution monitoring system with smog prediction model using machine learning","author":"SA Siddiqui","year":"2023","journal-title":"IJACSA"},{"issue":"1","key":"pone.0349858.ref008","doi-asserted-by":"crossref","DOI":"10.3390\/atmos13010046","article-title":"A comparison of machine learning methods to forecast tropospheric ozone levels in Delhi","volume":"13","author":"EK Juarez","year":"2022","journal-title":"Atmosphere"},{"key":"pone.0349858.ref009","doi-asserted-by":"crossref","first-page":"35710","DOI":"10.1109\/ACCESS.2023.3265725","article-title":"Long short-term memory bayesian neural network for air pollution forecast","volume":"11","author":"HAD Nguyen","year":"2023","journal-title":"IEEE Access"},{"issue":"8","key":"pone.0349858.ref010","doi-asserted-by":"crossref","first-page":"141","DOI":"10.3390\/environments10080141","article-title":"Machine learning techniques to predict the air quality using meteorological data in two urban areas in Sri Lanka","volume":"10","author":"L Mampitiya","year":"2023","journal-title":"Environments"},{"key":"pone.0349858.ref011","article-title":"Air quality prediction by machine learning models: a predictive study on the Indian coastal city of Visakhapatnam","volume":"338","author":"G Ravindran","year":"2023","journal-title":"Chemosphere"},{"key":"pone.0349858.ref012","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2023\/4916267","article-title":"Prediction of air quality index using machine learning techniques: a comparative analysis","volume":"2023","author":"NS Gupta","year":"2023","journal-title":"J Environ Public Health"},{"issue":"5","key":"pone.0349858.ref013","doi-asserted-by":"crossref","first-page":"5333","DOI":"10.1007\/s13762-022-04241-5","article-title":"Air pollution prediction with machine learning: a case study of Indian cities","volume":"20","author":"K Kumar","year":"2023","journal-title":"Int J Environ Sci Technol (Tehran)"},{"issue":"1","key":"pone.0349858.ref014","doi-asserted-by":"crossref","first-page":"6795","DOI":"10.1038\/s41598-024-54807-1","article-title":"Optimized machine learning model for air quality index prediction in major cities in India","volume":"14","author":"SK Natarajan","year":"2024","journal-title":"Sci Rep"},{"key":"pone.0349858.ref015","doi-asserted-by":"crossref","first-page":"123","DOI":"10.6000\/1927-5129.2017.13.21","article-title":"Health Monitoring considering air quality index prediction using neuro fuzzy inference model: a case study of Lahore, Pakistan","volume":"13","author":"M Saima","year":"2017","journal-title":"J Basic Appl Sci"},{"issue":"7","key":"pone.0349858.ref016","article-title":"Estimation of ground PM2.5 concentrations in Pakistan using convolutional neural network and multi-pollutant satellite images","volume":"14","author":"M Ahmed","year":"2022","journal-title":"Remote Sens (Basel)"},{"key":"pone.0349858.ref017","unstructured":"Mishra A, Jalaluddin M, Mahamuni CV. 1st IEEE International Conference on Communication Systems and Network Technologies Air Quality Analysis and Smog Detection in Smart Cities for Safer Transport using Machine Learning (ML) Regression Models. doi: 10.1109\/csnt.2022.37"},{"issue":"6","key":"pone.0349858.ref018","article-title":"Application of ANN, XGBoost, and other ML methods to forecast air quality in Macau","volume":"15","author":"TMT Lei","year":"2023","journal-title":"Sustainability"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0349858","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T17:41:04Z","timestamp":1783532464000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0349858"}},"subtitle":[],"editor":[{"given":"Assoc. 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