Meteorological variability and predictive forecasting of atmospheric particulate pollution.

Due to increasingly documented health effects associated with airborne particulate matter (PM), challenges in forecasting and concern about their impact on climate change, extensive research has been conducted to improve understanding of their variability and accurately forecasting them. This study shows that atmospheric PM concentrations in Brunei-Muara district are influenced by meteorological conditions and they contribute to the warming of the Earth's atmosphere. PM predictive forecasting models based on time and meteorological parameters are successfully developed, validated and tested for prediction by multiple linear regression (MLR), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN).

Energy consumption, CO emissions and electricity costs of lighting for commercial buildings in Southeast Asia.

Building energy intensity (BEI) has been used to assess a building's overall energy performance. However, the energy performance, CO footprint and electricity costs due to lighting in buildings are currently required to assist relevant authorities to develop, revise and implement energy-efficient lighting policies that are effective and acceptable for the country. This work presents an estimation approach for lighting in commercial buildings in Southeast Asia and its decarbonisation pathway for benchmarking.

Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter (PM and PM) Concentrations.

Despite extensive research on air pollution estimation/prediction, inter-country models for estimating air pollutant concentrations in Southeast Asia have not yet been fully developed and validated owing to the lack of air quality (AQ), emission inventory and meteorological data from different countries in the region. The purpose of this study is to develop and evaluate two machine learning (ML)-based models (i.e.