Surface sediments along the Southern Terengganu coast (≤7 km from the coast) were analyzed for polycyclic aromatic hydrocarbons (PAHs). The concentrations of 16 USEPA priority polycyclic aromatic hydrocarbons (ΣPAH16) ranged from 2.59 to 155 ng g-1 and their respective alkylated ranged between 8.80 and 24.90 ng g-1. Traces of acephenanthrylene, benzo[c]phenanthrene, thiophenic PAH, and benzonaphthofuran were identified. PAH diagnostic ratios and cross-plots revealed that these sedimentary PAH compounds are derived mainly from pyrogenic sources, primarily from biomass burning and petroleum combustion residues with minor petrogenic input. The high correlations between pyrogenic PAHs to total PAHs (r >0.73, p <0.5), and the Bap/Bep ratio to total PAHs (r = 0.88, p <0.5), suggest that atmospheric deposition and urban runoff are the main deposition pathways. The concentrations of the PAHs in the southern South China Sea fall in the moderate contamination range of 100-1000 ng g-1.
Microplastic (MP) pollution is a major global issue that poses serious threats to aquatic organisms. Although research on MP pollution has been extensive, the relationship between MPs and water quality parameters in estuarine water systems is unclear. This work studied the spatiotemporal distribution and characteristics of MPs in the Karnaphuli River estuary, Bangladesh. MP abundance was calculated by towing with a plankton net (300 μm mesh size) at three river gradients (up-, mid- and downstream) and the association between physicochemical parameters of water (temperature, pH, salinity, electrical conductivity, total dissolved solids, and dissolved oxygen) and MP distribution patterns was also investigated. Mean MP abundance in water was higher during the wet season (April) (4.33 ± 2.45 items per m3) compared to the dry season (September) (3.65 ± 2.54 items per m3). In descending order, the highest MP abundance was observed downstream (6.60 items per m3) > midstream (3.15 items per m3) > upstream (2.22 items per m3). pH during the wet season (April) and temperature during the dry season (September) were key physicochemical parameters that correlated with river MP abundance (r = -0.74 and 0.74 respectively). Indicating that if the Karnaphuli River water has low pH or high temperature, there is likely to be high MPs present in the water. Most MP particles were film-shaped, white in color, and 1-5 mm in size. Of the six polymers detected, polypropylene (PP), polystyrene (PS), polyethylene terephthalate (PET), and cellulose were predominant, comprising roughly 17-19% each. These results can be used to model MP transport in the freshwater ecosystem of the Karnaphuli River estuary in Bangladesh to help develop future mitigation strategies.
Even though Pacific - Indian Ocean exchange [Indonesian Throughflow (ITF)] has been measured for the last three decades, the measurements of microplastic in the region is very limited. This study was the initial investigation of the vertical distribution of microplastic in the deep-sea areas across the ITF Pathway. Niskin water samples were utilized to obtain the samples from a water column in a range of 5 to 2450 m. A total of 924 microplastic particles with an average abundance of 1.062 ± 0.646. n/L were found in the water column. Our findings indicate that water temperature and water density are the most significant factors correlated to the microplastic concentration. This study will be the first report discussing the distribution of microplastics in the deep-sea water column that could be highly significant in determining the fate and transport of microplastic within Indonesian waters that exits into the Indian Ocean.
Benthic foraminifera, single-celled marine organisms, are known for their wide distribution, high abundance and species diversity, test (i.e., shell) preservation in the sedimentary (e.g., historical) record, and sensitivity to environmental changes. Because of these characteristics, they have been widely used as bioindicators in environmental monitoring and, more recently, as Biological Quality Elements (BQEs) in the Ecological Quality Status (EcoQS) evaluation. The global scientific literature on benthic foraminifera as bioindicators was gathered from the Scopus database (overall 966 papers from 1973 to 2022) and explored with scientometric software. The outcomes highlight that the investigation of benthic foraminiferal response to pollutants started over 50 years ago. Indeed, not only the number of published documents has recently peaked (i.e., 2021 and 2022) but there has been also a growth in the percentages of papers falling within the Decision Sciences category that deals with the application of foraminiferal indices for the EcoQS assessment.
Global warming caused by carbon emissions has become a significant concern for countries worldwide. This study thoroughly examines the spatiotemporal patterns and spatial spillover effects of carbon emissions in China. This research employs kernel density estimation, Moran's index, and the standard deviation ellipse model to analyse the spatiotemporal evolution of carbon emissions in China while utilizing the spatial Durbin model to explore the spatial spillover effects of the digital economy on carbon emissions. The subsequent findings are derived from the following: (1) China's carbon emissions are characterized by substantial spatial and temporal agglomeration. Low carbon emissions are in the eastern littoral regions, while high carbon emissions are concentrated in the inland areas, such as the northwest. The local Moran index suggests that high-high and low-low clustering patterns characterize China's carbon emissions. (2) The spatial trends and evolutionary characteristics of carbon emissions in China are readily apparent. During the sample period, the carbon emission level in the east and west was significantly higher than in the central region, and the gap between the areas was progressively narrowing. The results of the standard deviation ellipse indicate that China's carbon emissions are undergoing a substantial discrete phenomenon in their spatial distribution. (3) Digital economies reduce carbon emissions, have regional spillover effects, and reveal geographical variance across eastern, central, and western regions. This study offers quantitative evidence for integrated nationwide and regional emission reduction and carbon mitigation strategies, as well as for region-specific emission reduction programs.
Seagrass is a valuable marine ecosystem engineer. However, seagrass population is declining worldwide. The lack of seagrass research in Malaysia raises questions about the status of seagrasses in the country. The seagrasses in Lawas, which is part of the coral-mangrove-seagrass complex, have never been studied in detail. In this study, we examine whether monthly changes of seagrass population in Lawas occurred. Data on estimates of seagrass percentage cover and water physicochemical parameters (pH, turbidity, salinity, temperature, and dissolved oxygen) were measured at 84 sampling stations established within the study area from June 2009 to May 2010. Meteorological data such as total rainfall, air temperature, and Southern Oscillation Index were also investigated. Our results showed that (i) the monthly changes of seagrass percentage cover are significant, (ii) the changes correlated significantly with turbidity measurements, and (iii) weather changes affected the seagrass populations. Our study indicates seagrass percentage increased during the El-Nino period. These results suggest that natural disturbances such as weather changes affect seagrass populations. Evaluation of land usage and measurements of other water physicochemical parameters (such as heavy metal, pesticides, and nutrients) should be considered to assess the health of seagrass ecosystem at the study area.
Forests and agricultural lands are the main resources on the earth's surface and important indicators of regional ecological environments. In this paper, Landsat images from 1990 and 2017 were used to extract information on forests in Malaysia based on a remote-sensing classification method. The spatial-temporal changes of forests and agricultural lands in Malaysia between 1990 and 2017 were analyzed. The results showed that the natural forests in Malaysia decreased by 441 Mha, a reduction of 21%. The natural forests were mainly converted into plantations in Peninsular Malaysia and plantations and secondary forests in East Malaysia. The area of agricultural lands in Malaysia increased by 55.7%, in which paddy fields increased by 1.1% and plantations increased by 98.2%. Paddy fields in Peninsular Malaysia are mainly distributed in the north-central coast and the Kelantan Delta. The agricultural land in East Malaysia is dominated by plantations, which are mainly distributed in coastal areas. The predictable areas of possible expansion for paddy fields in Peninsular Malaysia's Kelantan (45.2%) and Kedah (16.8%) areas in the future are large, and in addition, the plantations in Sarawak (44.7%) and Sabah (29.6%) of East Malaysia have large areas for expansion. The contradiction between agricultural development and protecting the ecological environment is increasingly prominent. The demand for agriculture is expected to increase further and result in greater pressures on tropical forests. Governments also need to encourage farmers to carry out existing land development, land recultivation, or cooperative development to improve agricultural efficiency and reduce the damage to natural forests.
Setiu Wetland is rapidly developing into an aquaculture and agriculture hub, causing concern about its water quality condition. To address this issue, it is imperative to acquire knowledge of the spatial and temporal distributions of pollutants. Consequently, this study applied combinations of hydrodynamic and particle tracking models to identify the transport behaviour of pollutants and calculate the residence time in Setiu Lagoon. The particle tracking results indicated that the residence time in Setiu Lagoon was highly influenced by the release location, where particles released closer to the river mouth exhibited shorter residence times than those released further upstream. Despite this fact, the pulse of river discharges successfully reduced the residence time in the order of two to twelve times shorter. Under different tidal phases, the residence time during the neap tide was longer regardless of heavy rainfalls, implying the domination of tidal flow in the water renewal within the lagoon.
Stormwater runoff is a major concern in urban areas which is mostly the result of vast urbanization. To reduce urban stormwater runoff and improve water quality, low impact development (LID) is used in urban areas. Therefore, it is vital to find the optimal combination of LID controls to achieve maximum reduction in both stormwater runoff and pollutants with optimal cost. In this study, a simulation-optimization model was developed by linking the EPA Storm Water Management Model (SWMM) to the Multi-Objective Particle Swarm Optimization (MOPSO) using MATLAB. The coupled model could carry out multi-objective optimization (MOO) and find potential solutions to the optimization objectives using the SWMM simulation model outputs. The SWMM model was developed using data from the BUNUS catchment in Kuala Lumpur, Malaysia. The total suspended solids (TSS) and total nitrogen (TN) were selected as pollutants to be used in the simulation model. Vegetated swale and rain garden were selected as LID controls for the study area. The LID controls were assigned to the model using the catchment characteristics. The target objectives were to minimize peak stormwater runoff, TSS, and TN with the minimum number of LID controls applications. The LID combination scenarios were also tested in SWMM to identify the best LID types and combination to achieve maximum reduction in both peak runoff and pollutants. This study found that the peak runoff, TSS, and TN were reduced by 13%, 38%, and 24%, respectively. The optimal number of LID controls that could be used at the BUNUS catchment area was also found to be 25.
The agriculture sector responsible for global food and nutrition security has an urgent need to examine climatic trends so that adaptations can be exercised in advance. Freely available dataset from satellite sources can greatly ease rainfall analysis, especially for smallholder farmers who typically operate under limited resources. Tests to determine their accuracy, however, are so far not deployed in tropical Southeast Asia. We compared in situ observations with dataset from the Global Satellite Mapping of Precipitation (GSMaP) and the Prediction of Worldwide Energy Resources (POWER) in two sites located 180 km apart in the tropical Malay Peninsula for 30 days. We found that in situ precipitation values were markedly overestimated by GSMaP (34.9-67.5%) and POWER (180.5-289.2%), and the possible reasons are discussed. Nonetheless, we conclude that GSMaP remains the best hope for smallholder farmers and its dataset can still be used under the precaution of error margins determined by the practical method described herein.
Decision-makers require useful tools, such as indicators, to help them make environmentally sound decisions leading to effective management of hazardous wastes. Four hazardous waste indicators are being tested for such a purpose by several countries within the Sustainable Development Indicator Programme of the United Nations Commission for Sustainable Development. However, these indicators only address the 'down-stream' end-of-pipe industrial situation. More creative thinking is clearly needed to develop a wider range of indicators that not only reflects all aspects of industrial production that generates hazardous waste but considers socio-economic implications of the waste as well. Sets of useful and innovative indicators are proposed that could be applied to the emerging paradigm shift away from conventional end-of-pipe management actions and towards preventive strategies that are being increasingly adopted by industry often in association with local and national governments. A methodological and conceptual framework for the development of a core-set of hazardous waste indicators has been developed. Some of the indicator sets outlined quantify preventive waste management strategies (including indicators for cleaner production, hazardous waste reduction/minimization and life cycle analysis), whilst other sets address proactive strategies (including changes in production and consumption patterns, eco-efficiency, eco-intensity and resource productivity). Indicators for quantifying transport of hazardous wastes are also described. It was concluded that a number of the indicators proposed could now be usefully implemented as management tools using existing industrial and economic data. As cleaner production technologies and waste minimization approaches are more widely deployed, and industry integrates environmental concerns at all levels of decision-making, it is expected that the necessary data for construction of the remaining indicators will soon become available.
In the current context of rapid development and urbanization, land use and land cover (LULC) types have undergone unprecedented changes, globally and nationally, leading to significant effects on the surrounding ecological environment quality (EEQ). The urban agglomeration in North Slope of Tianshan (UANST) is in the core area of the Silk Road Economic Belt of China. This area has experienced rapid development and urbanization with equally rapid LULC changes which affect the EEQ. Hence, this study quantified and assessed the spatial-temporal changes of LULC on the UANST from 2001 to 2018 based on remote sensing analysis. Combining five remote sensing ecological factors (WET, NDVI, IBI, TVDI, LST) that met the pressure-state-response(PSR) framework, the spatial-temporal distribution characteristics of EEQ were evaluated by synthesizing a new Remote Sensing Ecological Index (RSEI), with the interaction between land use change and EEQ subsequently analyzed. The results showed that LULC change dominated EEQ change on the UANST: (1) From 2001 to 2018, the temporal and spatial pattern of the landscape on the UANST has undergone tremendous changes. The main types of LULC in the UANST are Barren land and Grassland. (2) During the study period, RSEI values in the study area were all lower than 0.5 and were at the [good] levels, reaching 0.31, 0.213, 0.362, and 0346, respectively. In terms of time and space, the overall EEQ on the UANST experienced three stages of decline-rise-decrease. (3) The estimated changes in RSEI were highly related to the changes of LULC. During the period 2001 to 2018, the RSEI value of cropland showed a trend of gradual increase. However, the rest of the LULC type's RSEI values behave differently at different times. As the UANST is the core area of Xinjiang's urbanization and economic development, understanding and balancing the relationship between LULC and EEQ in the context of urbanization is of practical application in the planning and realization of sustainable ecological, environmental, urban, and social development in the UANST.
Urban areas are quickly established, and the overwhelming population pressure is triggering heat stress in the metropolitan cities. Climate change impact is the key aspect for maintaining the urban areas and building proper urban planning because spreading of the urban area destroyed the vegetated land and increased heat variation. Remote sensing-based on Landsat images are used for investigating the vegetation circumstances, thermal variation, urban expansion, and surface urban heat island or SUHI in the three megacities of Iraq like Baghdad, Erbil, and Basrah. Four satellite imageries are used aimed at land use and land cover (LULC) study from 1990 to 2020, which indicate the land transformation of those three major cities in Iraq. The average annually temperature is increased during 30 years like Baghdad (0.16 °C), Basrah (0.44 °C), and Erbil (0.32 °C). The built-up area is increased 147.1 km2 (Erbil), 217.86 km2 (Baghdad), and 294.43 km2 (Erbil), which indicated the SUHI affects the entire area of the three cities. The bare land is increased in Baghdad city, which indicated the local climatic condition and affected the livelihood. Basrah City is affected by anthropogenic activities and most areas of Basrah were converted into built-up land in the last 30 years. In Erbil, agricultural land (295.81 km2) is increased. The SUHI study results indicated the climate change effect in those three cities in Iraq. This study's results are more useful for planning, management, and sustainable development of urban areas.
This study employs an artificial neural network optimization algorithm, enhanced with a Genetic Algorithm-Back Propagation (GA-BP) network, to assess the service quality of urban water bodies and green spaces, aiming to promote healthy urban environments. From an initial set of 95 variables, 29 key variables were selected, including 17 input variables, such as water and green space area, population size, and urbanization rate, six hidden layer neurons, such as patch number, patch density, and average patch size, and one output variable for the comprehensive value of blue-green landscape quality. The results indicate that the GA-BP network achieves an average relative error of 0.94772%, which is superior to the 1.5988% of the traditional BP network. Moreover, it boasts a prediction accuracy of 90% for the comprehensive value of landscape quality from 2015 to 2022, significantly outperforming the BP network's approximate 70% accuracy. This method enhances the accuracy of landscape quality assessment but also aids in identifying crucial factors influencing quality. It provides scientific and objective guidance for future urban landscape structure and layout, contributing to high-quality urban development and the creation of exemplary living areas.
This study investigated the impact of soil type, pH, and geographical locations on the accumulation of arsenic (As), lead (Pb), and cadmium (Cd) in rice grains cultivated in Ghana. One hundred rice farms for the sampling of rice grains and soil were selected from two regions in Ghana-Volta and Oti. The concentrations of As, Pb, and Cd were analyzed using ICP-OES. Speciation modeling and multivariate statistics were employed to ascertain the relations among measured parameters. The results showed significant variations in soil-As, Pb, and Cd levels across different soil types and pH ranges, with the highest soil-As and Cd found in alkaline vertisols. For soil-As and Cd, the vertisols with a pH more than 7.0 exhibited the highest mean concentration of As (2.51 ± 0.932 mgkg-1) and Cd (1.00 ± 0.244 mgkg-1) whereas for soil-Pb, the luvisols of soil types with a pH less than 6.0 exhibited the highest mean concentration of Pb (4.91 ± 1.540 mgkg-1). Grain As, Pb, and Cd also varied across soil types and pH levels. In regards to grain-As, the vertisols soil type, with a pH less than 6.0, shows the highest mean concentration of grain As, at 0.238 ± 0.107 mgkg-1. Furthermore, vertisols soil types with a pH level less than 6.0 showed the highest mean concentration of grain Cd, averaging at 0.231 ± 0.068 mgkg-1 while luvisols, with a pH less than 6.0, exhibited the highest mean concentration of grain Pb at 0.713 ± 0.099 mgkg-1. Speciation modeling indicated increased bioavailability of grains Cd2+ and Pb2+ ions in acidic conditions. A significant interaction was found between soil-Cd and pH, affecting grain-As uptake. The average concentrations of soil As, Pb, and Cd aligned with international standards. Generally, the carcinogenic metals detected in grain samples collected from the Volta region are higher than that of the Oti region but the differences are insignificant, and this may be attributed to geographical differences and anthropogenic activities. About 51% of the study area showed a hazard risk associated with grain metal levels, although, no carcinogenic risks were recognized. This study highlights the complex soil-plant interactions governing metal bioaccumulation and emphasizes the need for tailored strategies to minimize metal transfer into grains.
Polycyclic Aromatic Hydrocarbons (PAHs) profoundly impact public and environmental health. Gaining a comprehensive understanding of their intricate functions, exposure pathways, and potential health implications is imperative to implement remedial strategies and legislation effectively. This review seeks to explore PAH mobility, direct exposure pathways, and cutting-edge bioremediation technologies essential for combating the pervasive contamination of environments by PAHs, thereby expanding our foundational knowledge. PAHs, characterised by their toxicity and possession of two or more aromatic rings, exhibit diverse configurations. Their lipophilicity and remarkable persistence contribute to their widespread prevalence as hazardous environmental contaminants and byproducts. Primary sources of PAHs include contaminated food, water, and soil, which enter the human body through inhalation, ingestion, and dermal exposure. While short-term consequences encompass eye irritation, nausea, and vomiting, long-term exposure poses risks of kidney and liver damage, difficulty breathing, and asthma-like symptoms. Notably, cities with elevated PAH levels may witness exacerbation of bronchial asthma and chronic obstructive pulmonary disease (COPD). Bioremediation techniques utilising microorganisms emerge as a promising avenue to mitigate PAH-related health risks by facilitating the breakdown of these compounds in polluted environments. Furthermore, this review delves into the global concern of antimicrobial resistance associated with PAHs, highlighting its implications. The environmental effects and applications of genetically altered microbes in addressing this challenge warrant further exploration, emphasising the dynamic nature of ongoing research in this field.
There are three primary objectives of this work; first: to establish a gas concentration map; second: to estimate the point of emission of the gas; and third: to generate a path from any location to the point of emission for UAVs or UGVs. A mountable array of MOX sensors was developed so that the angles and distances among the sensors, alongside sensors data, were utilized to identify the influx of gas plumes. Gas dispersion experiments under indoor conditions were conducted to train machine learning algorithms to collect data at numerous locations and angles. Taguchi's orthogonal arrays for experiment design were used to identify the gas dispersion locations. For the second objective, the data collected after pre-processing was used to train an off-policy, model-free reinforcement learning agent with a Q-learning policy. After finishing the training from the training data set, Q-learning produces a table called the Q-table. The Q-table contains state-action pairs that generate an autonomous path from any point to the source from the testing dataset. The entire process is carried out in an obstacle-free environment, and the whole scheme is designed to be conducted in three modes: search, track, and localize. The hyperparameter combinations of the RL agent were evaluated through trial-and-error technique and it was found that ε = 0.9, γ = 0.9 and α = 0.9 was the fastest path generating combination that took 1258.88 seconds for training and 6.2 milliseconds for path generation. Out of 31 unseen scenarios, the trained RL agent generated successful paths for all the 31 scenarios, however, the UAV was able to reach successfully on the gas source in 23 scenarios, producing a success rate of 74.19%. The results paved the way for using reinforcement learning techniques to be used as autonomous path generation of unmanned systems alongside the need to explore and improve the accuracy of the reported results as future works.
Oil palm agriculture has caused extensive land cover and land use changes that have adversely affected tropical landscapes and ecosystems. However, monitoring and assessment of oil palm plantation areas to support sustainable management is costly and labour-intensive. This study used an unmanned aerial vehicles (UAV) to map smallholder farms and applied multi-criteria analysis to data generated from orthomosaics, to provide a set of sustainability indicators for the farms. Images were acquired from a UAV, with structure from motion (SfM) photogrammetry then used to produce orthomosaics and digital elevation models of the farm areas. Some of the inherent problems using high spatial resolution imagery for land cover classification were overcome by using texture analysis and geographic object-based image analysis (OBIA). Six spatially explicit environmental metrics were developed using multi-criteria analysis and used to generate sustainability indicator layers from the UAV data. The SfM and OBIA approach provided an accurate, high-resolution (~5 cm) image-based reconstruction of smallholder farm landscapes, with an overall classification accuracy of 89%. The multi-criteria analysis highlighted areas with lower sustainability values, which should be considered targets for adoption of sustainable management practices. The results of this work suggest that UAVs are a cost-effective tool for sustainability assessments of oil palm plantations, but there remains the need to plan surveys and image processing workflows carefully. Future work can build on our proposed approach, including the use of additional and/or alternative indicators developed through consultation with the oil palm industry stakeholders, to support certification schemes such as the Roundtable on Sustainable Palm Oil (RSPO).
Microplastic contamination is an emerging concern in marine ecosystems, with limited knowledge on its impact on coral reefs, particularly in Malaysia. Surface waters were collected from several coral reef regions in Peninsular Malaysia by towing a plankton net behind the boat. Microplastics were detected at all sites, with a mean abundance of 0.344 ± 0.457 MP/m3. Perhentian Islands (0.683 ± 0.647 MP/m3) had significantly higher microplastic levels than Tioman Island (0.108 ± 0.063 MP/m3), likely due to oceanographic differences. Over half of the microplastics (55.7 %) were small microplastics (<1 mm), with the 0.05-0.5 mm size class being most abundant (29.2 %). Fragments and fibres dominated, and black, blue, and green were the prevalent colours. Polyethylene (PE), rayon (RY), chlorinated polyethylene (CPE), and polypropylene (PP) were the most common polymers. This study reveals the abundance and characteristics of microplastics, provides important data for further research on microplastics in coral reef ecosystem.
The sea surface microlayer (SML), particularly in monsoon-influenced regions, remains largely unexplored. This study aims to determine the concentrations, enrichment, and factors controlling the enrichment processes of surface-active substances (SASs), which include surfactants, dissolved monosaccharides (MCHOs), polysaccharides (PCHOs), total dissolved carbohydrates (TDCHOs), and transparent exopolymer particles (TEPs) around the coastal area of Malaysian Peninsula. The SML samples and underlying water (ULW) from a depth of 1 m were collected during the southwest (August and September 2023) and northeast (November 2023) monsoons. Surfactants, TEPs, and dissolved carbohydrates were measured spectrometrically using methylene blue, the Alcian blue assay, and 2,4,6-Tri(2-pyridyl)-s-triazine (TPTZ), respectively. The results showed that stations influenced by anthropogenic activities were generally enriched with surfactants (Enrichment factor, EF = 1.40 ± 0.91) and carbohydrate species (TDCHOs = 1.38 ± 0.28, MCHOs = 1.54 ± 0.57, PCHOs = 1.85 ± 1.43). However, TEP enrichment was not observed in our study (EF = 0.68 ± 0.24). The SASs in the SML were correlated with their underlying concentrations, implying that transport from underlying water could be a major source of substances in the SML. High carbohydrate concentrations and enrichment were found during the northeast monsoon, implying that rain and runoff water affect concentrations in the SML. Besides, the enrichment of SASs persists at moderate wind speeds and is depleted at high wind speeds.