Air 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. Model comparison statistics using coefficient of determination (R²), prediction accuracy (PA), index of agreement (IA), normalized absolute error (NAE) and root mean square error (RMSE) show that QR is slightly better than OLS with the performance of R² (0.60–0.73), PA (0.78–0.85), IA (0.86–0.92), NAE (0.15–0.17) and RMSE (9.52–22.15) for next-day predictions in BRT model.

空气污染目前正在成为一个重要的全球环境问题。马来西亚的空气污染源是移动的或固定的。机动车辆是移动来源之一。固定源源自城市发展，采石场和发电厂以及石化产品造成的排放。与其他污染参数相比，马来西亚半岛最明显的污染物是颗粒物（PM ₁₀），是空气污染指数（API）的最大贡献者。这项研究的目的是使用增强回归树（BRT）预测PM ₁₀来确定分位数回归（QR）和普通最小二乘法（OLS）之间的最佳损失函数集中在马来西亚巴生和哥打巴鲁的亚罗士打。使用确定系数（R ²），预测准确性（PA），一致性指数（IA），归一化绝对误差（NAE）和均方根误差（RMSE）进行的模型比较统计数据表明，QR的性能略优于OLS的- [R ²（0.60-0.73），PA（0.78-0.85），IA（0.86-0.92），NAE（0.15-0.17）和RMSE（9.52-22.15），用于BRT模型第二天预测。