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.
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).
The high concentration of nitrogen dioxide (NO2) is to blame for West Java's poor Air Quality Index (AQI). So, this study aims to determine the influence of industrial activity as reflected by the value of its imports and exports, wind speed, and ozone (O3) on the high concentration of tropospheric NO2. The method used is the econometric Vector Error Correction Model (VECM) approach to capture the existence of a short-term and long-term relationship between tropospheric NO2 and its predictor variables. The data used in this study is in the form of monthly time series data for the 2018-2022 period sourced from satellite images (Sentinel-5P and ECMWF Climate Reanalysis) and publications of the Central Bureau of Statistics (BPS-Statistics Indonesia). The results explained that, in the short-term, tropospheric NO2 and O3 influence each other as they would in a photochemical reaction. In the long-term, exports from the industrial sector and wind speed have a significant effect on the concentration of tropospheric NO2. The short-term effect occurs directly in the first month after the shock, while the long-term effect occurs in the second month after the shock. Wind gusts originating from industrial areas cause air conditions to be even more alarming because tropospheric NO2 pollutants spread throughout the region in West Java. Based on the coefficient correlation result, the high number of pneumonia cases is one of the impacts caused by air pollution.
Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region.
This study investigates hydrocarbon pollution in the Ahoada community of the Niger Delta region of Nigeria. The study uses a geographic information system (GIS) for mapping oil spill hotspots in the region. The resistivity method was used to delineate the extent of hydrocarbon pollution to a depth of 19.7 m in the Ahoada area of the region. Three categories of soil samples, impacted soil (IMS), remediated soil (RS), and control soil (CS), were collected and analyzed for the presence of BTEX, PAH, TPH, TOC, and TOG. The concentrations of the samples from the IMS and RS were compared to that of the CS to determine the extent of pollution. The GIS mapping shows that the most polluted areas in the Niger Delta Region are Rivers, Bayelsa, and Delta states. Results of the geophysical images revealed contaminants' presence to depths beyond 20 m at some locations in the study area. The highest depth of contaminant travel was at Ukperede. Soil samples' analysis showed that the range of concentrations of TPH in IMS at Oshie was 17.27-58.36 mg/kg; RS was 11.73-50.78 mg/kg which were higher than the concentrations of 0.68 mg/kg in the CS. PAHs are more prevalent in Ukperede, ranging from 54.56 to 77.54 mg/kg. BTEX concentrations ranged from 0.02 to 0.38 mg/kg for IMP and 0.01-2.7 mg/kg for RS against a CS value of 0.01 mg/kg. The study revealed that there are characteristically high resistivity values in the samples which were corroborated by the findings from the resistivity survey. TOC was found to be higher in the IMS and RS than in the CS, demonstrating that a significant quantity of the hydrocarbon has undergone appreciable decomposition.
As an inland dryland lake basin, the rivers and lakes within the Lake Bosten basin provide scarce but valuable water resources for a fragile environment and play a vital role in the development and sustainability of the local societies. Based on the Google Earth Engine (GEE) platform, combined with the geographic information system (GIS) and remote sensing (RS) technology, we used the index WI2019 to extract and analyze the water body area changes of the Bosten Lake basin from 2000 to 2021 when the threshold value is -0.25 and the slope mask is 8°. The driving factors of water body area changes were also analyzed using the partial least squares-structural equation model (PLS-SEM). The result shows that in the last 20 years, the area of water bodies in the Bosten Lake basin generally fluctuated during the dry, wet, and permanent seasons, with a decreasing trend from 2000 to 2015 and an increasing trend between 2015 and 2019 followed by a steadily decreasing trend afterward. The main driver of the change in wet season water bodies in the Bosten Lake basin is the climatic factors, with anthropogenic factors having a greater influence on the water body area of dry season and permanent season than that of wet season. Our study achieved an accurate and convenient extraction of water body area and drivers, providing up-to-date information to fully understand the spatial and temporal variation of surface water body area and its drivers in the basin, which can be used to effectively manage water resources.
This study analyzed the spatial-temporal change pattern and underlying factors in production-living-ecological space (PLES) of Nanchong City, China, over the past 20 years using historical land use data (2000, 2010, 2020). A land use transfer matrix was calculated from the historical land use maps, and spatial analysis was conducted to analyze changes in the land use dynamics degree, standard deviation ellipse, and center of gravity. The results showed that there was a rapid spatial evolution of the PLES in Nanchong from 2000 to 2010, followed by a stabilization in the second decade. The transfer of ecological-production space occurred mainly in the Jialing and Yilong River basins, while the reduction of production space and the increase of living space were most prominent in the intersection of three districts (Shunqing, Jialing, and Gaoping districts). The return of production-ecological space was observed in the south and northeast of Yingshan, and there was little notable transfer of other types. The distribution of production space in Nanchong evolved in a north-south to east-west trend, with the center of gravity moving from Yilong to Peng'an County. The living space and production space expanded in a north-south direction, and the center of gravity position was in Nanbu, indicating a more balanced growth or decrease in the last 20 years. The changes in the spatial-temporal pattern of PLES in Nanchong were attributed to the intertwined factors of national policies, economic development, population growth, and the natural environment. This study introduced a novel approach towards rational planning of land resources in Nanchong, which may facilitate more sustainable urban planning and development.
Acid mine drainage (AMD) is recognized as a major environmental challenge in the Western United States, particularly in Colorado, leading to extreme subsurface contamination issue. Given Colorado's arid climate and dependence on groundwater, an accurate assessment of AMD-induced contamination is deemed crucial. While in past, machine learning (ML)-based inversion algorithms were used to reconstruct ground electrical properties (GEP) such as relative dielectric permittivity (RDP) from ground penetrating radar (GPR) data for contamination assessment, their inherent non-linear nature can introduce significant uncertainty and non-uniqueness into the reconstructed models. This is a challenge that traditional ML methods are not explicitly designed to address. In this study, a probabilistic hybrid technique has been introduced that combines the DeepLabv3+ architecture-based deep convolutional neural network (DCNN) with an ensemble prediction-based Monte Carlo (MC) dropout method. Different MC dropout rates (1%, 5%, and 10%) were initially evaluated using 1D and 2D synthetic GPR data for accurate and reliable RDP model prediction. The optimal rate was chosen based on minimal prediction uncertainty and the closest alignment of the mean or median model with the true RDP model. Notably, with the optimal MC dropout rate, prediction accuracy of over 95% for the 1D and 2D cases was achieved. Motivated by these results, the hybrid technique was applied to field GPR data collected over an AMD-impacted wetland near Silverton, Colorado. The field results underscored the hybrid technique's ability to predict an accurate subsurface RDP distribution for estimating the spatial extent of AMD-induced contamination. Notably, this technique not only provides a precise assessment of subsurface contamination but also ensures consistent interpretations of subsurface condition by different environmentalists examining the same GPR data. In conclusion, the hybrid technique presents a promising avenue for future environmental studies in regions affected by AMD or other contaminants that alter the natural distribution of GEP.
In recent years, ozone pollution in China has been shown to increase in frequency and persistence despite the concentrations of fine particulate matter (PM2.5) decreasing steadily. Open crop straw burning (OCSB) activities are extensive in China and emit large amounts of trace gases during a short period that could lead to elevated ozone concentrations. This study addresses the impacts of OCSB emissions on ground-level ozone concentration and the associated health impact in China. Total VOCs and NOx emissions from OCSB in 2018 were 798.8 Gg and 80.6 Gg, respectively, with high emissions in Northeast China (31.7%) and North China (23.7%). Based on simulations conducted for 2018, OCSB emissions are estimated to contribute up to 0.95 µg/m3 increase in annual averaged maximum daily 8-hour (MDA8) ozone and up to 1.35 µg/m3 for the ozone season average. The significant impact of OCSB emissions on ozone is mainly characterized by localized and episodic (e.g., daily) changes in ozone concentration, up to 20 µg/m3 in North China and Yangtze River Delta region and even more in Northeast China during the burning season. With the implementation of straw burning bans, VOCs and NOx emissions from OCSB dropped substantially by 46.9%, particularly over YRD (76%) and North China (60%). Consequently, reduced OCSB emissions result in an overall decrease in annual averaged MDA8 ozone, and reductions in monthly MDA8 ozone could be over 10 µg/m3 in North China. The number of avoided premature death due to reduced OCSB emissions (considering both PM2.5 and ozone) is estimated to be 6120 (95% Confidence Interval: 5320-6800), with most health benefits gained over east and central China. Our results illustrate the effectiveness of straw burning bans in reducing ozone concentrations at annual and national scales and the substantial ozone impacts from OCSB events at localized and episodic scales.
Rapid development and industrialization in Southeast (SE) Asia has led to environmental pollution, potentially exposing the general population to environmental contaminants. Human biomonitoring (HBM), measurement of chemical and/or their metabolites in human tissues and fluids, is an important tool for assessing cumulative exposure to complex mixtures of chemicals and for monitoring chemical exposures in the general population. While there are national HBM programs in several developed countries, there are no such national programs in most of the SE Asian countries. However, in recent years there has been progress in the field of HBM in many of the SE Asian countries. In this review, we present recent HBM studies in five selected SE Asian countries: Bangladesh, Indonesia, Malaysia, Myanmar and Thailand. While there is extensive HBM research in several SE Asian countries, such as Thailand, in other countries HBM studies are limited and focus on traditional environmental pollutants (such as lead, arsenic and mercury). Further development of this field in SE Asia would be benefited by establishment of laboratory capacity, improving quality control and assurance, collaboration with international experts and consortiums, and sharing of protocols and training both for pre-analytical and analytical phases. This review highlights the impressive progress in HBM research in selected SE Asian countries and provides recommendations for development of this field.
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.
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.
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.
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.