China's seaborne foreign oil supply through the Malacca Strait is facing security challenges due to territorial disputes, pirate attacks, and geopolitics. To overcome these challenges, China plans to import oil through one of the corridors of the Belt and Road Initiative (BRI)-the China-Pakistan Economic Corridor (CPEC). This study estimated and compared ship emissions and their externalities associated with seaborne oil supply from the top five oil suppliers to China through the existing shipping route via the Malacca Strait and proposed route via CEPC. Ship activity-based methodology is applied to estimate the emissions of air pollutants (CO2, NOx, SO2, PM10, and CO) during cruising, maneuvering, and hoteling periods. The results show that the total ship emissions of China's seaborne oil supply can be significantly reduced from 6.2 million tons to 2.1 million tons via the CPEC route. While external cost can be reduced up to 65.9% via the CPEC route.
Microplastic pollution has become a major global environmental issue, negatively impacting terrestrial and aquatic ecosystems as well as human health. Tackling this complex problem necessitates a multidisciplinary approach and collaboration among diverse stakeholders. Within this context, the Quintuple Helix framework, which highlights the involvement of academia, government, industry, civil society, and the environment, provides a comprehensive and inclusive perspective for formulating effective policies to manage atmospheric microplastics. This paper discusses each helix's roles, challenges, and opportunities and proposes strategies for collaboration and knowledge exchange among them. Furthermore, the paper highlights the importance of interdisciplinary research, innovative technologies, public awareness campaigns, regulatory frameworks, and corporate responsibility in achieving sustainable and resilient microplastic management policies. The Quintuple Helix approach can mitigate microplastics, safeguard ecosystems, and preserve planetary health by fostering collaboration and coordination among diverse stakeholders.
There are many factors that influence PM(10) concentration in the atmosphere. This paper will look at the PM(10) concentration in relation with the wet season (north east monsoon) and dry season (south west monsoon) in Seberang Perai, Malaysia from the year 2000 to 2004. It is expected that PM(10) will reach the peak during south west monsoon as the weather during this season becomes dry and this study has proved that the highest PM(10) concentrations in 2000 to 2004 were recorded in this monsoon. Two probability distributions using Weibull and lognormal were used to model the PM(10) concentration. The best model used for prediction was selected based on performance indicators. Lognormal distribution represents the data better than Weibull distribution model for 2000, 2001, and 2002. However, for 2003 and 2004, Weibull distribution represents better than the lognormal distribution. The proposed distributions were successfully used for estimation of exceedences and predicting the return periods of the sequence year.
Due to the increase of the human population and the rapid industrial growth in the past few decades, air quality monitoring is essential to assess the pollutant levels of an area. However, monitoring air quality in a high-density area like Sunway City, Selangor, Malaysia is challenging due to the limitation of the local monitoring network. To establish a comprehensive data for air pollution in Sunway City, a mobile monitoring campaign was employed around the city area with a duration of approximately 6 months, from September 2018 to March 2019. Measurements of air pollutants such as carbon dioxide (CO2) and nitrogen dioxide (NO2) were performed by using mobile air pollution sensors facilitated with a GPS device. In order to acquire a more in-depth understanding on traffic-related air pollution, the measurement period was divided into two different time blocks, which were morning hours (8 a.m.-12 p.m.) and afternoon hours (3 p.m.-7 p.m.). The data set was analysed by splitting Sunway City into different zones and routes to differentiate the conditions of each region. Meteorological variables such as ambient temperature, relative humidity, and wind speed were studied in line with the pollutant concentrations. The air quality in Sunway City was then compared with various air quality standards such as Malaysian Air Quality Standards and World Health Organisation (WHO) guidelines to understand the risk of exposure to air pollution by the residence in Sunway City.
This study investigated the potential relationship between dengue cases and air quality - as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were -800.66, -796.22, and -790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio- temporally based on neighbouring zones, do not have a significant effect on dengue cases.
Plant (vegetable) oil has been evaluated as a substitute for mineral oil-based lubricants because of its natural and environmentally friendly characteristics. Availability of vegetable oil makes it a renewable source of bio-oils. Additionally, vegetable oil-based lubricants have shown potential for reducing hydrocarbon and carbon dioxide (CO2) emissions when utilized in internal combustion (IC) engines and industrial operations. In this study, sunflower oil was investigated to study its lubricant characteristics under different loads using the four-ball tribometer and the exhaust emissions were tested using a four-stroke, single-cylinder diesel engine. All experimental works conformed to American Society for Testing and Materials standard (ASTM D4172-B). Under low loads, sunflower oil showed adequate tribological characteristics (antifriction and antiwear) compared with petroleum oil samples. The results also demonstrated that the sunflower oil-based lubricant was more effective in reducing the emission levels of carbon monoxide (CO), CO2, and hydrocarbons under different test conditions. Therefore, sunflower oil has the potential to be used as lubricant of mating components.Implications: An experimental investigation of the characteristics of nonedible sunflower oil tribological behaviors and potential as a renewable source for biofluids alternative to the petroleum oils was carried out. The level of emissions of a four-stroke, single-cylinder diesel engine using sunflower oil as a biolubricant was evaluated.
Modeling and evaluating the behavior of particulate matter (PM10) is an important step in obtaining valuable information that can serve as a basis for environmental risk management, planning, and controlling the adverse effects of air pollution. This study proposes the use of a Markov chain model as an alternative approach for deriving relevant insights and understanding of PM10 data. Using first- and higher-order Markov chains, we analyzed daily PM10 index data for the city of Klang, Malaysia and found the Markov chain model to fit the PM10 data well. Based on the fitted model, we comprehensively describe the stochastic behaviors in the PM10 index based on the properties of the Markov chain, including its states classification, ergodic properties, long-term behaviors, and mean return times. Overall, this study concludes that the Markov chain model provides a good alternative technique for obtaining valuable information from different perspectives for the analysis of PM10 data.
Seasonal haze episodes and the associated inimical health impacts have become a regular crisis among the ASEAN countries. Even though many emerging experimental and epidemiological studies have documented the plausible health effects of the predominating toxic pollutants of haze, the consistency among the reported findings by these studies is poorly understood. By addressing such gap, this review aimed to critically highlight the evidence of physical and psychological health impacts of haze from the available literature in ASEAN countries. Systematic literature survey from six electronic databases across the environmental and medical disciplines was performed, and 20 peer-reviewed studies out of 384 retrieved articles were selected. The evidence pertaining to the health impacts of haze based on field survey, laboratory tests, modelling and time-series analysis were extracted for expert judgement. In specific, no generalization can be made on the reported physical symptoms as no specific symptoms recorded in all the reviewed studies except for throat discomfort. Consistent evidence was found for the increase in respiratory morbidity, especially for asthma, whilst the children and the elderly are deemed to be the vulnerable groups of the haze-induced respiratory ailments. A consensual conclusion on the association between the cardiovascular morbidity and haze is unfeasible as the available studies are scanty and geographically limited albeit of some reported increased cases. A number of modelling and simulation studies demonstrated elevating respiratory mortality rates due to seasonal haze exposures over the years. Besides, evidence on cancer risk is inconsistent where industrial and vehicular emissions are also expected to play more notable roles than mere haze exposure. There are insufficient regional studies to examine the association between the mental health and haze. Limited toxicological studies in ASEAN countries often impede a comprehensive understanding of the biological mechanism of haze-induced toxic pollutants on human physiology. Therefore, the lack of consistent evidence among the reported haze-induced health effects as highlighted in this review calls for more intensive longitudinal and toxicological studies with greater statistical power to disseminate more reliable and congruent findings to empower the institutional health planning among the ASEAN countries.
Dhaka and its neighboring areas suffer from severe air pollution, especially during dry season (November-April). We investigated temporal and directional variations in particulate matter (PM) concentrations in Dhaka, Gazipur, and Narayanganj from October 2012 to March 2015 to understand different aspects of PM concentrations and possible sources of high pollution in this region. Ninety-six-hour backward trajectories for the whole dry season were also computed to investigate incursion of long-range pollution into this area. We found yearly PM10 concentrations in this area about three times and yearly PM2.5 concentrations about six times greater than the national standards of Bangladesh. Dhaka and its vicinity experienced several air pollution episodes in dry season when PM2.5 concentrations were 8-13 times greater than the World Health Organization (WHO) guideline value. Higher pollution and great contribution of PM2.5 most of the time were associated with the north-westerly wind. Winter (November to January) was found as the most polluted season in this area, when average PM10 concentrations in Dhaka, Gazipur, and Narayanganj were 257.1, 240.3, and 327.4 μg m(-3), respectively. Pollution levels during wet season (May-October) were, although found legitimate as per the national standards of Bangladesh, exceeded WHO guideline value in 50 % of the days of that season. Trans-boundary source identifications using concentration-weighted trajectory method revealed that the sources in the eastern Indian region bordering Bangladesh, in the north-eastern Indian region bordering Nepal and in Nepal and its neighboring areas had high probability of contributing to the PM pollutions at Gazipur station.
This research focuses on examining the potential impact of charcoal briquettes and lumps on human health due to the emissions they release, and verifying their quality standards. Quality assessment was conducted using a device capable of measuring toxic gases to identify contaminants from various sources such as biomass, synthetic resins, coal, metals, and mineral matter. Toxicity assessments were carried out on five types of briquettes and two varieties of lump charcoal. All charcoal samples were subjected to elemental analysis (SEM/EDAX), including the examination of Ca, Al, Cr, V, Cu, Fe, S, Sr, Si, Ba, Pb, P, Mn, Rb, K, Ti, and Zn. The results showed that burning lump charcoal had toxicity indexes ranging from 2.5 to 5, primarily due to NOx emissions. Briquettes, on the other hand, exhibited higher toxicity indices between 3.5 and 6.0, with CO2 being the main contributor to toxicity. The average 24-h CO content of all charcoal samples exceeded the World Health Organization's 24-h Air Quality Guideline of 6.34 ppm, with a measurement of 37 ppm. The data indicates that most of the products tested did not meet the prevailing quality standard (EN 1860-2:2005 (E) in Appliances, solid fuels and firelighters for barbecuing-Part 2: Barbecue charcoal and barbecue charcoal briquettes-Requirements and test method, 2005), which specifies a maximum of 1% contaminants, with some products containing as much as 21% impurities. The SEM analysis revealed irregularly shaped grains with an uneven distribution of particles, and the average particle size distribution is quite broad at 5 μm. Malaysia Charcoal had the highest calorific value at 32.80 MJ/Kg, with the value being influenced by the fixed carbon content-higher carbon content resulting in a higher calorific value.
Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that well-performing ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no one-size-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review analyzed the shortcomings, best practices, and prospects of ANNs for MSW-related trend predictions, supporting the realization of practical applications of ANNs to enhance waste management practices and reduce carbon emissions.
With the rapid economic development of Xinjiang Uygur Autonomous Region (Xinjiang), energy consumption became the primary source of carbon emissions. The growth trend in energy consumption and coal-dominated energy structure are unlikely to change significantly in the short term, meaning that carbon emissions are expected to continue rising. To clarify the changes in energy-related carbon emissions in Xinjiang over the past 15 years, this paper integrates DMSP/OLS and NPP/VIIRS data to generate long-term nighttime light remote sensing data from 2005 to 2020. The data is used to analyze the distribution characteristics of carbon emissions, spatial autocorrelation, frequency of changes, and the standard deviation ellipse. The results show that: (1) From 2005 to 2020, the total carbon emissions in Xinjiang continued to grow, with noticeable urban additions although the growth rate fluctuated. In spatial distribution, non-carbon emission areas were mainly located in the northwest; low-carbon emission areas mostly small and medium-sized towns; and high-carbon emission areas were concentrated around the provincial capital and urban agglomerations. (2) There were significant regional differences in carbon emissions, with clear spatial clustering of energy consumption. The clustering stabilized, showing distinct "high-high" and "low-low" patterns. (3) Carbon emissions in central urban areas remained stable, while higher frequencies of change were seen in the peripheral areas of provincial capitals and key cities. The center of carbon emissions shifted towards southeast but later showed a trend of moving northwest. (4) Temporal and spatial variations in carbon emissions were closely linked to energy consumption intensity, population size, and economic growth. These findings provided a basis for formulating differentiated carbon emission targets and strategies, optimizing energy structures, and promoting industrial transformation to achieve low-carbon economic development in Xinjiang.
Waste management can be regarded as a cross-cutting environmental 'mega-issue'. Sound waste management practices support the provision of basic needs for general health, such as clean air, clean water and safe supply of food. In addition, climate change mitigation efforts can be achieved through reduction of greenhouse gas emissions from waste management operations, such as landfills. Landfills generate landfill gas, especially methane, as a result of anaerobic degradation of the degradable components of municipal solid waste. Evaluating the mode of generation and collection of landfill gas has posted a challenge over time. Scientifically, landfill gas generation rates are presently estimated using numerical models. In this study the Intergovernmental Panel on Climate Change's Waste Model is used to estimate the methane generated from a Malaysian sanitary landfill. Key parameters of the model, which are the decay rate and degradable organic carbon, are analysed in two different approaches; the bulk waste approach and waste composition approach. The model is later validated using error function analysis and optimum decay rate, and degradable organic carbon for both approaches were also obtained. The best fitting values for the bulk waste approach are a decay rate of 0.08 y(-1) and degradable organic carbon value of 0.12; and for the waste composition approach the decay rate was found to be 0.09 y(-1) and degradable organic carbon value of 0.08. From this validation exercise, the estimated error was reduced by 81% and 69% for the bulk waste and waste composition approach, respectively. In conclusion, this type of modelling could constitute a sensible starting point for landfills to introduce careful planning for efficient gas recovery in individual landfills.
Rural background stations provide insight into seasonal variations in pollutant concentrations and allow for comparisons to be made with stations closer to anthropogenic emissions. In Malaysia, the designated background station is located in Jerantut, Pahang. A fifteen-year data set focusing on ten major air pollutants and four meteorological variables from this station were analysed. Diurnal, monthly and yearly pollutant concentrations were derived from hourly continuous monitoring data. Statistical methods employed included principal component regression (PCR) and sensitivity analysis. Although only one of the yearly concentrations of the pollutants studied exceeded national and World Health Organisation (WHO) guideline standards, namely PM10, seven of the pollutants (NO, NO2, NOx, O3, PM10, THC and CH4) showed a positive upward trend over the 15-year period. High concentrations of PM10 were recorded during severe haze episodes in this region. Whilst, monthly concentrations of most air pollutants, such as: PM10, O3, NOx, NO2, CO and NmHC were recorded at higher concentrations between June and September, during the southwest monsoon. Such results correspond with the mid-range transport of pollutants from more urbanised and industrial areas. Diurnal patterns, rationed between major air pollutants and sensitivity analysis, indicate the influence of local traffic emissions on air quality at the Jerantut background station. Although the pollutant concentrations have not shown a rapid increase, an alternative background station will need to be assigned within the next decade if development projects in the surrounding area are not halted.
A short-term investigation on the chemical composition of rainwater was carried out at five selected sampling stations in Kuantan district, Pahang, Malaysia. Sampling of rainwater was conducted by event basis between September and November 2011. Rainwater samples were collected using polyethylene containers and the parameters measured were cations (sodium, potassium, ammonium, calcium and magnesium) and anions (chlorides, nitrates and sulphates). The average pH value for rainwater samples was 6.0 ± 0.57 in which most of the sampling sites exhibited pH values >5.6. Calcium and sulphate were the most abundant cation and anion, respectively, whilst the concentrations of other major ions varied according to sampling location.
In this project, several surrogate surfaces designed to directly measure Hg dry deposition were investigated. Static water surrogate surfaces (SWSS) containing deionized (DI), acidified water, or salt solutions, and a knife-edge surrogate surface (KSS) using quartz fiber filters (QFF), KCl-coated QFF and gold-coated QFF were evaluated as a means to directly measure mercury (Hg) dry deposition. The SWSS was hypothesized to collect deposited elemental mercury (Hg⁰), reactive gaseous/oxidized mercury (RGM), and mercury associated with particulate matter (Hg(p)) while the QFF, KCl-coated QFF, and gold-coated QFF on the KSS were hypothesized to collect Hg(p), RGM+Hg(p), and Hg⁰+RGM+Hg(p), respectively. The Hg flux measured by the DI water was significantly smaller than that captured by the acidified water, probably because Hg⁰ was oxidized to Hg²+ which stabilized the deposited Hg and decreased mass transfer resistance. Acidified BrCl, which efficiently oxidizes Hg⁰, captured significantly more Hg than other solutions. However, of all collection media, gold-coated QFFs captured 6 to 100 times greater Hg mass than the other surfaces, probably because there is no surface resistance for Hg⁰ deposition to gold surfaces. In addition, the Hg⁰ concentration is usually 100-1000 times higher than RGM and Hg(p). For all other media, co-located samples were not significantly different, and the combination of daytime plus nighttime results were comparable to 24-h samples, implying that Hg⁰, RGM and Hg(p) were not released after they deposited nor did the surfaces reach equilibrium with the atmosphere. Based on measured Hg ambient air concentrations and fluxes, dry deposition velocities of RGM and Hg⁰ to DI water and other surfaces were 5.6±5.4 and 0.005-0.68 cm s⁻¹ in this study, respectively. These results suggest surrogate surfaces can be used to measure Hg dry deposition; however, extrapolating the results to natural surface can be challenging.
The decomposition of municipal solid waste (MSW) in landfills under anaerobic conditions produces landfill gas (LFG) containing approximately 50-60% methane (CH(4)) and 30-40% carbon dioxide (CO(2)) by volume. CH(4) has a global warming potential 21 times greater than CO(2); thus, it poses a serious environmental problem. As landfills are the main method for waste disposal in Malaysia, the major aim of this study was to estimate the total CH(4) emissions from landfills in all Malaysian regions and states for the year 2009 using the IPCC, 1996 first-order decay (FOD) model focusing on clean development mechanism (CDM) project applications to initiate emission reductions. Furthermore, the authors attempted to assess, in quantitative terms, the amount of CH(4) that would be emitted from landfills in the period from 1981-2024 using the IPCC 2006 FOD model. The total CH(4) emission using the IPCC 1996 model was estimated to be 318.8 Gg in 2009. The Northern region had the highest CH(4) emission inventory, with 128.8 Gg, whereas the Borneo region had the lowest, with 24.2 Gg. It was estimated that Pulau Penang state produced the highest CH(4) emission, 77.6 Gg, followed by the remaining states with emission values ranging from 38.5 to 1.5 Gg. Based on the IPCC 1996 FOD model, the total Malaysian CH( 4) emission was forecast to be 397.7 Gg by 2020. The IPCC 2006 FOD model estimated a 201 Gg CH(4) emission in 2009, and estimates ranged from 98 Gg in 1981 to 263 Gg in 2024.
Polycyclic aromatic hydrocarbons (PAHs) are present in both gaseous and particulate phases. These compounds are considered to be atmospheric contaminants and are human carcinogens. Many studies have monitored atmospheric particulate and gaseous phases of PAH in Asia over the past 5 years. This work compares and discusses different sample collection, pretreatment and analytical methods. The main PAH sources are traffic exhausts (AcPy, FL, Flu, PA, Pyr, CHR, BeP) and industrial emissions (BaP, BaA, PER, BeP, COR, CYC). PAH concentrations are highest in areas of traffic, followed by the urban sites, and lowest in rural sites. Meteorological conditions, such as temperature, wind speed and humidity, strongly affect PAH concentrations at all sampling sites. This work elucidates the characteristics, sources and distribution, and the healthy impacts of atmospheric PAH species in Asia.
The concentrations and distributions of particle bound polycyclic aromatic hydrocarbons (PAHs) collected over a 10 month period in ambient environment, at street levels as well as during a hazy episode are reported. Ambient and street level distributions of PAHs were similar and their occurrence was attributed to vehicular emissions. However, in haze particles, a different pattern of PAHs was observed, characterized by relatively low levels of benzo[a]pyrene (BaP) and high levels of benzofluoranthenes (BFs). The BaP equivalency results showed that the potential health risk associated with haze smoke particles was 4 times higher than that of street level particles whereas the lowest health risk was associated with ambient atmospheric particles.
PM10 airborne particles and soot deposit collected after a fire incident at a chemical store were analyzed in order to determine the concentrations of polycyclic aromatic hydrocarbons (PAHs). The samples were extracted with 1:1 hexane-dichloromethane by ultrasonic agitation. The extracts were then subjected to gas chromatography-mass spectrometric (GC-MS) analysis. The total PAHs concentrations in airborne particles and soot deposit were found to be 3.27 +/- 1.55 ng/m3 and 12.81 +/- 24.37 microg/g, respectively. Based on the molecular distributions of PAHs and the interpretation of their diagnostic ratios such as PHEN/(PHEN + ANTH), FLT/(FLT + PYR) and BeP/(BeP + BaP), PAHs in both airborne particles and soot deposit may be inferred to be from the same source. The difference in the value of IP/(IP + BgP) for these samples indicated that benzo[g, h, i] perylene and coronene tend to be attached to finer particles and reside in the air for longer periods. Comparison between the molecular distributions of PAHs and their diagnostic ratios observed in the current study with those reported for urban atmospheric and roadside soil particles revealed that they are of different sources.