Sustainable energy towards air pollution and climate change mitigation.

This is an evidence from a high-income economy in Southeast Asia and a support for scientific planning of the energy sector in ensuring air pollution and climate change mitigation. A comparative analysis of the energy options for electricity generation in the nation was made considering availability, cost and greenhouse gases emission - CO2, N2O and CH4, using a two-stage method comprising multi-objective optimization and TOPSIS. The renewable (RE) and non-renewable energy (NRE) options available were assessed through the lifecycle approach to determine the lifecycle greenhouse gas emission (LCGHG) and levelized cost of energy (LCOE) per MWh of electricity. Considering historical electricity consumption, annual GDP and population growth from 1965, energy consumption for the year 2035 was forecasted using support vector machine regressor in Weka. Future plans in energy diversification pathways were examined through various scenario multi-objective optimizations with a constraint on resource availability and energy target using genetic algorithm in MATLAB. The outputs were ranked using TOPSIS method. Results showed that greenhouse gases emission could be reduced by 10.3 percent compared to business as usual scenario while the energy mix could attain 10 percent renewable energy in the grid at a relatively lower generation cost.

Influence of Southeast Asian Haze episodes on high PM10 concentrations across Brunei Darussalam.

Particulate matter (PM10) is the key indicator of air quality index in Brunei Darussalam and the principal pollutant for haze related episodes in Southeast Asia. This study examined the temporal and spatial distribution of PM10 base on a long-term monitoring data (2009-2014) in order to identify the emission sources and favorable meteorological conditions for high PM10 concentrations across the country. PM10 concentrations measured at the various locations differ significantly but the general temporal characteristics show clear patterns of seasonal variations across the country with the highest concentrations recorded during the southwest monsoon. The high PM10 values defined in the study were not evenly distributed over the years but occurred mostly within the southwest monsoon months of June to September. Further investigations with bivariate polar concentrations plots and k-means clustering demonstrated the significant influence of Southeast Asian regional biomass fires on the high PM10 concentrations recorded across the country. The results of the polar plots and cluster analyses were further confirmed by the evaluations with Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backward air masses trajectories analysis and the Moderate Resolution Imaging Spectroradiometer (MODIS) fire records. Among the meteorological variables considered, temperature, rainfall and relative humidity were the most important meteorological variables that influence the concentration throughout the year. High PM10 values are associated with high temperatures and low amounts of rainfall and relative humidity. In addition, wind speed and direction also play significant role in the recorded high PM10 concentrations and were mainly responsible for its seasonality during the study period.