Modelling Particulate Matter (PM) Variations During Transboundary Haze Events Using a Modified Quantile Regression Approach.

Severe haze episodes in Southeast Asia, largely attributed to transboundary pollution from neighbouring countries, have led to substantial environmental degradation and adverse health effects. This study presents the development of a novel air quality forecasting model specifically developed to predict particulate matter with a diameter of less than 10 µm (PM) concentrations 1 to 3 days in advance during transboundary haze events in Malaysia. The innovation lies in the integration of quantile regression (QR) with advanced feature selection techniques-namely, Relief-based ranking, correlation-based selection and principal component analysis (PCA)-to form modified predictive models. These hybrid models, referred to as QR-Relief, QR-correlation and QR-PCA, demonstrated superior performance over traditional QR and multiple linear regression models across four urban locations: Klang, Melaka, Pasir Gudang and Petaling Jaya. Model accuracy was evaluated using selected performance metrics, including mean absolute error, normalized absolute error and root mean square error. The results indicate that reducing the dimensionality of input variables through analytical feature selection significantly improves predictive reliability. Furthermore, model validation using an independent dataset from 2019 confirmed their real-world applicability. This methodological advancement provides a robust analytical framework for developing early warning systems during haze events, offering valuable decision-support tools for environmental and public health management.