Wan Nur ShaziayaniAhmad Zia Ul-SaufieHasfazilah AhmatDhiya Al-Jumeily2024-05-102024-05-102021Shaziayani, W. N., Ul-Saufie, A. Z., Ahmat, H., & Al-Jumeily, D. (2021). Coupling of quantile regression into boosted regression trees (BRT) technique in forecasting emission model of PM10 concentration. Air Quality, Atmosphere & Health, 14(10), 1647-1663.https://repoemc.ukm.my/handle/123456789/541Air pollution is currently becoming a significant global environmental issue. The sources of air pollution in Malaysia are mobile or stationary. Motor vehicles are one of the mobile sources. Stationary sources originated from emissions caused by urban development, quarrying and power plants and petrochemical. The most noticeable contaminant in the Peninsular of Malaysia is the particulate matter (PM10), the highest contributor of Air Pollution Index (API) compared to other pollution parameters. The aim of this study is to determine the best loss function between quantile regression (QR) and ordinary least squares (OLS) using boosted regression tree (BRT) for the prediction of PM10 concentration in Alor Setar, Klang and Kota Bharu, Malaysia.enParticulate matter (PM10)Quantile regressionOrdinary least squares (OLS)Boosted regression treeJournal