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.

The spatial and temporal distribution of polycyclic aromatic hydrocarbons (PAHs) in three sediment cores from Brunei Bay, Southern South China Sea was investigated. The total concentrations of 16 priority PAHs (∑PAH₁₆) and their alkyl-substituted derivatives ranged from 10.4 to 376 ng g⁻¹ and 30.7 to 2263 ng g⁻¹, respectively. PAH biomarker diagnostic ratios and principal component analysis (PCA) combined with absolute principal component score (APCS) and multiple linear regression (MLR) were performed to apportion the source contribution. The results revealed mixed inputs of fuel combustion residues and uncombusted petrogenic products. The downcore PAH profile revealed that the highest peaks could be related to past human activities using biofuel and coal during the industrialization/agriculture revolution period. The 1,7/(2,6+1,7)-dimethylphenanthrene ratio also highlighted wood combustion during forest fire outbreaks, which appeared to coincide with the past climate events.

The distribution of aliphatic hydrocarbons in three sediment cores from Brunei Bay was investigated in order to understand their sources and the biogeochemical processes of these hydrocarbons. The total concentrations of C₁₅ to C₃₇n-alkanes ranged from 0.70 to 16.5 μg g⁻¹. Traces of hopanes with C₂₉–C₃₁ carbon homologs were detected in the study area. The carbon preference index (CPI₁₅–₃₇) ranged from 1.23 to 3.42 coupled with the natural n-alkane ratio (NAR₁₉–₃₂) ratios (1.52 to 5.34), and the presence of unresolved complex mixtures and hopanes, suggested slight contamination by anthropogenic hydrocarbons, presumably derived from activities along the coasts. The presence of C₂₇ trisnorhopene and diploptene, as well as their association with long-chain and short-chain n-alkanes, revealed a depositional environment of organic matter in the sediment cores.

Brunei Bay is one of the most important marine environments of East Malaysia (South China Sea), covering many productive ecosystems with activities including fisheries, tourism, and main shipping lanes for petroleum transfers. Evaluation of the sources and distributions of steroids in the surface sedimentary organic matter was carried out by gas chromatography–mass spectrometry (GC–MS). The concentrations of the total identified sterols (TIS) ranged between 0.81 and 12.69 μg g⁻¹ dry weight, and the total sterones were between 0.11 and 5.66 μg g⁻¹ dry weight. The coprostanol level was comparatively low (<0.10 μg g⁻¹), and the multi-biomarker proxies indicated that the region did not exhibit significant contamination from sewage effluents. Principal component analysis (PCA) revealed the coastal environment of the study area was dominated by allochthonous (mainly terrestrial) organic matter input.

The number of studies on microplastic accumulation in marine organisms has increased precipitously recently, though information is geographically-skewed and limited in terms of local effects. We characterized microplastic accumulation in oysters (Saccostrea cucullata) along a Bornean coastline, focusing on spatial variation. Comparisons were made between locally-polluted (Brunei Estuarine System, BES) and relatively pristine, open-shore (South China Sea, SCS) coastlines. Sixteen coloured microplastic types were characterized into three shapes (fragments, fibres, pellets). Fragments (74.9%), especially smaller polypropylene black fragments predominated in the samples (<50 μm, 31.7%). Site-specific levels of microplastic accumulation varied from 0.43 to 7.20 particles/g oyster tissue. BES and SCS sites differed qualitatively, indicating limited interaction. In the BES, accumulation was greatest near the predicted source (Bandar) and declined strongly seawards, implying current flow, environmental sequestration (local sinks) and seawater dilution effects. Such local-scale variation in microplastic loading in estuaries cautions against extrapolating from limited sampling.

Particulate matter (PM10) is the pollutant causing exceedances of ambient air quality thresholds, and the key indicator of air quality index in Brunei Darussalam for haze related episodes caused by the recurrent biomass fires in Southeast Asia. The present study aims at providing suitable forecasts for PM10 exceedances to aid in health advisory during haze episodes at the four administrative districts of the country. A framework based on random forests (RFs), genetic algorithm (GA) and back propagation neural networks (BPNN) computational intelligence techniques has been proposed in which the final prediction is made by the BPNN model. A hybrid combination of GA and RFs is initially applied to determine optimal set of inputs from the initial data sets of largely available meteorological, persistency of high pollution levels, short and long term variations of emissions rates parameters. The inputs selection procedure does not depend on the back propagation training algorithm. The numerical results presented in this paper show that the proposed model not only produced satisfactory forecasts but also consistently performed better via several statistical performance indicators when compared with the standard BPNN and GA optimisation based on back propagation training algorithm. The model also showed satisfactory threshold exceedances forecasts achieving for instance best true predicted rate of 0.800, false positive rate of 0.014, false alarm rate of 0.333 and success index of 0.786 at Brunei-Muara district monitoring station. Overall, the current study has profound implications on future studies to develop a real-time air quality forecasting system to support haze management.

The present study aims to define the possible sources that contribute to the level of Pb into the Brunei Bay, Borneo. The cluster analysis has classified the bay into the northern part with heavy and agriculture-related industries; the southern area with a moderate rural human settlement as well as the southwestern area with a more pristine environment and a low level of human settlement. The score plot of spatial discriminant analysis verified a significant influence of the river system toward the estuary, whereas the temporal discriminant analysis has discriminated the seasonal changes. In comparison to elsewhere, the stable Pb isotopic ratios in Brunei Bay showed a fingerprint similar to coal-related sources and of aerosol input. Briefly, even though Pb in the Brunei Bay ecosystem proved to be at a low level, the stable Pb isotopic ratios showed that human and industrial activities are slowly contributing Pb into the bay ecosystem.

Sewage pollution is one of major concerns of coastal and shoreline settlements in Southeast Asia, especially Brunei. The distribution and sources of LABs as sewage molecular markers were evaluated in surface sediments collected from Brunei Bay. The samples were extracted, fractionated and analyzed using gas chromatography- mass spectrometry (GC-MS). LABs concentrations ranged from 7.1 to 41.3ngg−1 dry weight (dw) in surficial sediments from Brunei Bay. The study results showed LABs concentrations variably due to the LABs intensity and anthropogenic influence along Brunei Bay in recent years. The ratio of Internal to External isomers (I/E ratio) of LABs in sediment samples from Brunei Bay ranged from 0.56 to 2.17 along Brunei Bay stations, indicating that the study areas were receiving primary and secondary effluents. This is the first study carried out to assess the distribution and sources of LABs in surface sediments from Brunei Bay, Brunei.

Trade openness continues to have the potential to influence many parts of today’s society, including religion, transportation, lifestyle, language, and international relations; however, its ability to impact environmental quality is the primary issue for environmental policy guidelines. In response to an increasing interest in finding the dynamic association between trade openness and environmental quality, the current study explores the trade openness- environmental quality nexus in the ten most open Organization of Islamic Cooperation (OIC) countries for the years 1991 to 2018. By taking CO₂ emissions and ecological footprint as environmental indicators, a novel methodology “quantile-on-quantile (QQ)” is used to indicate how different quantiles of trade openness asymmetrically affect the quantiles of environmental indicators by providing an adequate pattern to comprehend the overall dependence structure. A negative openness-CO₂ emissions association is dominant in seven out of ten selected OIC countries (i.e., Suriname, Malaysia, Jordan, UAE, Libya, Brunei, and Qatar). On the other hand, a positive impact of trade openness on ecological footprint is dominant in eight out of ten selected OIC countries (i.e., Oman, Jordan, UAE, Libya, Bahrain, Brunei, Qatar, and Kuwait). The outcomes indicate that the asymmetric strength of openness-induced environmental quality differs with countries at both upper and bottom quantiles of data distribution that need specific attention in contending trade and environment policies in OIC countries.

This study uses a consumer-based accounting approach to evaluate the CO₂ emission factors of 17 countries in Asia and the Pacific region by including all emissions in the supply chain from commodity location to final consumption location in country consumption patterns. In addition, the number of emissions connected with each country’s consumption of products and services in Asia and the Pacific region has received little attention. This study contributes to understanding the effects of countries’ consumption of products and services on carbon emission peaks and formulates efficient carbon mitigation plans for governments and decision-makers. Accelerating economic growth and industrialization have posed significant challenges to global carbon mitigation efforts and climate change responses. The Monte Carlo simulation technique was used to create a dynamic scenario simulation model to investigate possible future peaks in the carbon emissions of countries in Asia and the Pacific region while taking into account factor uncertainties. The results show that total consumption-based CO₂ emissions are remarkable in three Asian countries, including China (387,451.95 metric tons Mt CO₂), Japan (185,259.60 Mt CO₂), and India (100,720.46 Mt CO₂). In South Korea, Brunei, and Taiwan, annual consumption emissions are 1.77, 1.62, and 1.49 tons of CO₂ per person, respectively. In terms of final consumption, the household sector is the most noteworthy contributor to consumption-based emissions, accounting for 27–56%. The household sector probably peaks at 19.7 Gt CO₂ as per the dynamic scenario simulation. For the three other types of final demand, government expenditure will possibly reach a maximum of 44.0 Gt CO₂ in the next 30 years, while capital formation will probably reach its highest emission level at 149.5 Gt CO₂.