Ahmad Zia Ul-Saufie (Universiti Teknologi MARA) – Air Pollution Modelling Through the Lens of Data Mining Techniques: Applications and Challenges

Air pollution is one of the most pressing environmental challenges, with significant implications for public health, policy, and sustainable development. Traditional statistical approaches have provided useful insights, but recent advances in data mining and machine learning offer powerful tools to improve prediction accuracy, interpretability, and decision support. This presentation will explore the application of data mining techniques, including tree-based models, ensemble learning, feature selection, and hybrid models for air pollution modelling, with a focus on PM10 concentrations during transboundary haze episodes in Malaysia. Case studies will demonstrate how these models contribute to early warning systems and policy interventions. This talk also discusses the challenges faced in this domain, including data quality, extreme haze event prediction, model generalizability, and integration with real-time monitoring systems. Finally, the talk aims to open discussion on collaborative opportunities to advance air pollution modelling, leveraging interdisciplinary expertise and international partnerships.