Microplastics have been considered as contaminants of emerging concern due to ubiquity in the environment; however, the occurrence of microplastics in river estuaries is scarcely investigated. The Klang River estuary is an important ecosystem that receives various contaminants from urbanised, highly populated areas and the busiest maritime centre in Selangor, Malaysia. This study investigates the abundance and characteristics of microplastics in surface water of the Klang River estuary. The abundance of microplastics ranged from 0.5 to 4.5 particles L-1 with a mean abundance of 2.47 particles L-1. There is no correlation between the abundance of microplastics and physicochemical properties, while there is a strong correlation between salinity and conductivity. The microplastics were characterised with a stereomicroscope and attenuated total reflection-Fourier transform infrared spectroscopy to analyse size, shape, colour, and polymer composition. The microplastics in the surface water were predominantly in the 300-1000 μm size class, followed by > 1000 μm and
Miri city has a dynamic coastal environment, mainly influenced by intensive sedimentation from the Baram River and excessive trace metal loading by the Miri River, which are significant environmental concerns. As the mobility, bioavailability, and toxicity of the trace metals in the sediments are largely controlled by their particulate speciation, the modified BCR sequential extraction protocol was applied to determine the particulate speciation of trace metals in the coastal sediments of Miri, to unravel the seasonal geochemical processes responsible for known observations, and to identify possible sources of these trace metals. The granulometric analysis results showed that littoral currents aided by the monsoonal winds have influenced the grain size distribution of the sediments, enabling us to divide the study area into north-east and south-west segments where the geochemical composition are distinct. The Cu (>84%) and Zn (82%) concentrations are predominantly associated with the exchangeable fraction, which is readily bioavailable. Pb and Cd are dominant in non-residual fractions and other metals viz., Fe, Mn, Co, Ni, and Cr are dominant in the residual fraction. Using Pearson's correlation and factor analysis, the major mechanisms controlling the chemistry of the sediments are identified as association of Cu and Zn with fine fraction sediments, sulphide oxidation in the SW segment of the study area, atmospheric fallout of Pb and Cd in the river basins, precipitation of dissolved Fe and Mn supplied from the rivers and remobilization of Mn from the coastal sediments. Based on various pollution indices, it is inferred that the coastal sediments of NW Borneo are contaminated with Cu and Zn, and are largely bioavailable, which can be a threat to the local aquatic organisms, coral reefs, and coastal mangroves.
This article takes the Guangdong Province of China as the research object and uses the difference-in-difference model to evaluate the impact of smart city construction on the quality of public occupational health and intercity differences. The obtained results show that smart city construction significantly improves the quality of public occupational health, and it is still valid after a series of robustness tests. The effect of this policy is stronger in cities that belong to the Pearl River Delta region or sub-provincial level cities. This study indicates that the central government should improve the pilot evaluation system and the performance appraisal mechanism of smart cities from the perspective of top-level design during the process of promoting smart city construction, which aims to correctly guide local governments to promote the construction of smart cities. To achieve the full improvement effect of smart city construction on the quality of public occupational health, local governments should implement smart city strategies in a purposeful and planned way according to the actual situation of the development of the jurisdiction.
Mediterranean climate river systems are among the most threatened ecosystems worldwide, due to a long history of anthropogenic impacts and alien invasive species introductions. Many of such rivers naturally exhibit a non-perennial flow regime, with distinct seasonal, inter-annual and spatial heterogeneity. The present study seeks to detect diatom community patterns and to understand the processes that cause these structures in an Austral Mediterranean river system among different months and river sections. In general, most environmental variables showed an increasing trend downstream for both months, with the exception of pH, dissolved oxygen, PO₄3- and substrate embeddedness, which decreased downstream. A total of 110 diatom species between the two study months (October - 106 taxa; January - 78 taxa) were identified, dominated by 30 species with at least >2% abundance. Diatom community structure differed significantly across river zones, while no significant differences were observed between the study months. A boosted regression trees model showed that B (43.3%), Cu (20.8%), Fe (3.4%) and water depth (3.2%) were the most significant variables structuring diatoms. Diatom species communities reflected environmental variables (i.e., sediment and water chemistry) in this Mediterranean climate river system, as sediment metals such as B, Cu and Fe were found to be important in structuring diatom communities. Biotic influences from fish communities had little effect on diversity, but shifted diatom community structure. Therefore, the current study highlights how river systems have complex interactions that play an important role in determining diatom species composition.
The study sought to investigate the chronology of events and timing of embryogenesis, as well as breeding performances of three strains of Heterobranchus longifilis from Nigeria. Fish samples were collected from Benue River in Makurdi, Niger River in Onitsha, and Rima River in Sokoto for this study. Induced spawning of the strains was carried out so that egg development could be tracked from fertilization to hatching using a simple microscope. The microphotographs obtained showed that the embryogenesis of the strains followed a similar pattern to those of other members of the family Clariidae, however with changes occurring in the specific timing of the sequences of events (i.e. interstrain and interspecies differences). When the different strains were compared, the study noted similarities (P > 0.05) in the overall breeding performance (except for fertilization rate), survival at different stages of development, timing of embryogenesis, and larvae characteristics. The outcomes of this study, therefore, provide baseline information on what genetic improvement of the species through strain crossing can be attempted in future studies.
Global climate change has led to an increase in both the frequency and magnitude of extreme events around the world, the risk of which is especially imminent in tropical regions. Developing hydrological models with better capabilities to simulate streamflow, especially peak flow, is urgently needed to facilitate water resource planning and management as well as climate change mitigation efforts in the tropics. In view of the need, this paper explores the feasibility of improving streamflow simulation performance in the tropical Kelantan River Basin (KRB) of Peninsular Malaysia through coupling a conceptual process-based hydrological model - Soil and Water Assessment Tool (SWAT) with a deep learning model - Bidirectional Long Short-Term Memory (Bi-LSTM) in two ways. All SWAT parameters were set as their default values in one hybrid model (SWAT-D-LSTM), whereas three most sensitive SWAT parameters were calibrated in the other hybrid model (SWAT-T-LSTM). Comparison of daily streamflow simulation results have shown that SWAT-T-LSTM consistently performs better than SWAT-D-LSTM as well as the stand-alone SWAT and Bi-LSTM model throughout the simulation period. Particularly, SWAT-T-LSTM performs considerably better than the other three models in simulating daily peak flow. Based on the latest projection results of five GCMs from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) under three emission scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), the best-performed SWAT-T-LSTM was run to assess the potential impacts of climate change on streamflow in the KRB. Ensemble assessment results have concluded that both average and extreme streamflow is much likely to increase considerably in the already wet northeast monsoon season from November to January, which has surely raised the alarm for more frequent flood occurrence in the KRB.
Due to urbanization, solid waste pollution is an increasing concern for rivers, possibly threatening human health, ecological integrity, and ecosystem services. Riverine management in urban landscapes requires best management practices since the river is a vital component in urban ecological civilization, and it is very imperative to synchronize the connection between urban development and river protection. Thus, the implementation of proper and innovative measures is vital to control garbage pollution in the rivers. A robot that cleans the waste autonomously can be a good solution to manage river pollution efficiently. Identifying and obtaining precise positions of garbage are the most crucial parts of the visual system for a cleaning robot. Computer vision has paved a way for computers to understand and interpret the surrounding objects. The development of an accurate computer vision system is a vital step toward a robotic platform since this is the front-end observation system before consequent manipulation and grasping systems. The scope of this work is to acquire visual information about floating garbage on the river, which is vital in building a robotic platform for river cleaning robots. In this paper, an automated detection system based on the improved You Only Look Once (YOLO) model is developed to detect floating garbage under various conditions, such as fluctuating illumination, complex background, and occlusion. The proposed object detection model has been shown to promote rapid convergence which improves the training time duration. In addition, the proposed object detection model has been shown to improve detection accuracy by strengthening the non-linear feature extraction process. The results showed that the proposed model achieved a mean average precision (mAP) value of 89%. Hence, the proposed model is considered feasible for identifying five classes of garbage, such as plastic bottles, aluminum cans, plastic bags, styrofoam, and plastic containers.
The study investigates the latent pollution sources and most significant parameters that cause spatial variation and develops the best input for water quality modelling using principal component analysis (PCA) and artificial neural network (ANN). The dataset, 22 water quality parameters were obtained from Department of Environment Malaysia (DOE). The PCA generated six significant principal component scores (PCs) which explained 65.40 % of the total variance. Parameters for water quality variation are mainlyrelated to mineral components, anthropogenic activities, and natural processes. However, in ANN three input combination models (ANN A, B, and C) were developed to identify the best model that can predict water quality index (WQI) with very high precision. ANN A model appears to have the best prediction capacity with a coefficient of determination (R2) = 0.9999 and root mean square error (RMSE) = 0.0537. These results proved that the PCA and ANN methods can be applied as tools for decision-making and problem-solving for better managing of river quality.
Land exploitation for mining sector may leave a series of environmental impacts on our ecosystem if not appropriately managed. Therefore, the present study attempts to evaluate the various environmental aspects due to abandoned metal mining including former iron ore, bauxite, and tin mining lands in view of its hydrogeochemical behavior. Mine-impacted waters and sediments were ascertained from former mining ponds, mine tailings, and impacted streams for interpretation of aqueous and sediment geochemistry, major and trace elements, hydrochemical facies, chemical weathering rate and CO2 consumption, and water quality classification. Results indicated that the environmental impact of the long-abandoned iron ore mine was still evident with some high concentration of metals and acidic pH. Higher concentrations of Fe and Mn in water were noticeable in some areas while other trace elements (Pb, Zn, As, Cd, Cr, and Cu) were found below the recommended guideline values. Sediment quality reflected the trend of water quality variables mainly associated with metal(loid) elements, resulting in potential ecological risk, classified as having low to moderate risk. There were variations in terms of hydrochemical facies of the waters suggesting the influence of minerals in water. The chemical weathering rate suggests that contribution of carbonate mineral weathering was more important (up to 60%) than silicate weathering. The resulting CO2 consumption by mineral weathering was estimated to be in the range of 1.7-9.8 × 107 mol/year (former bauxite and tin mining areas can act as temporary sinks for CO2). Water quality classifications according to several chemical indices (Kelly's ratio, sodium absorption ratio, soluble sodium percentage, residual sodium carbonate, magnesium absorption ratio, and permeability index) were also discussed in regards to mine water reuse for irrigation purpose. The findings suggest that a holistic approach that integrates all important hydrogeochemical aspects is essential for a thorough evaluation of the implication of medium- to long-term mining exploitation on its surrounding ecosystems. This would be beneficial in light of restoration potential of degraded mining land so as for future mitigation strategies in the mining sector.
Non-point source (NPS) pollution has become a vital contaminant source affecting the water environment because of its wide distribution, hydrodynamic complexity, and difficulty in prevention and control. In this study, the identification and evaluation of NPS pollution risk based on landscape pattern were carried out in the Hanjiang River basin above Ankang hydrological section, Shaanxi province, China. Landscape distribution information was obtained through land use data, analyzing the contribution of "source-sink" landscape to NPS pollution through the location-weighted landscape contrast index. Using the NPS pollution risk index to identify and evaluate the regional NPS pollution risk considering the slope, cost distance, soil erosion, and precipitation erosion affect migration of pollutants. The results showed that (i) the pollution risk was generally high in the whole watershed, and the sub-watersheds dominated by "source" landscapes account for 74.61% of the whole basin; (ii) the high-risk areas were distributed in the central, eastern, and western regions of the river basin; the extremely high-risk areas accounted for 12.7% of the whole watershed; and the southern and northern regions were dominated by forestland and grassland with little pollution risk; (iii) "source" landscapes were mostly distributed in areas close to the river course, which had a great impact on environment, and the landscape pattern units near the water body needed to be further adjusted to reduce the influence of NPS pollution.
Short-term streamflow prediction is essential for managing flood early warning and water resources systems. Although numerical models are widely used for this purpose, they require various types of data and experience to operate the model and often tedious calibration processes. Under the digital revolution, the application of data-driven approaches to predict streamflow has increased in recent decades. In this work, multiple linear regression (MLR) and random forest (RF) models with three different input combinations are developed and assessed for multi-step ahead short-term streamflow predictions, using 14 years of hydrological datasets from the Kulim River catchment, Malaysia. Introducing more precedent streamflow events as predictor improves the performance of these data-driven models, especially in predicting peak streamflow during the high-flow event. The RF model (Nash-Sutcliffe efficiency (NSE): 0.599-0.962) outperforms the MLR model (NSE: 0.584-0.963) in terms of overall prediction accuracy. However, with the increasing lead-time length, the models' overall prediction accuracy on the arrival time and magnitude of peak streamflow decrease. These findings demonstrate the potential of decision tree-based models, such as RF, for short-term streamflow prediction and offer insights into enhancing the accuracy of these data-driven models.
This paper aims to select the most appropriate rain-based meteorological drought index for detecting drought characteristics and identifying tropical drought events in the Johor River Basin (JRB). Based on a multi-step approach, the study evaluated seven drought indices, including the Rainfall Anomaly Index (RAI), Standardized Precipitation Index (SPI), China-Z Index (CZI), Modified China-Z Index (MCZI), Percent of Normal (PN), Deciles Index (DI), and Z-Score Index (ZSI), based on the CHIRPS rainfall gridded-based datasets from 1981 to 2020. Results showed that CZI, MCZI, SPI, and ZSI outperformed the other indices based on their correlation and linearity (R2 = 0.96-0.99) along with their ranking based on the Compromise Programming Index (CPI). The historical drought evaluation revealed that MCZI, SPI, and ZSI performed similarly in detecting drought events, but SPI was more effective in detecting spatial coverage and the occurrence of 'very dry' and 'extremely dry' drought events. Based on SPI, the study found that the downstream area, north-easternmost area, and eastern boundary of the basin were more prone to higher frequency and longer duration droughts. Furthermore, the study found that prolonged droughts are characterized by episodic drought events, which occur with one to three months of 'relief period' before another drought event occurs. The study revealed that most drought events that coincide with El Niño, positive Indian Ocean Dipole (IOD), and negative Madden-Julian Oscillation (MJO) events, or a combination of these events, may worsen drought conditions. The application of CHIRPS datasets enables better spatiotemporal mapping and prediction of drought for JRB, and the output is pertinent for improving water management strategies and adaptation measures. Understanding spatiotemporal drought conditions is crucial to ensuring sustainable development and policies through better regulation of human activities. The framework of this research can be applied to other river basins in Malaysia and other parts of Southeast Asia.
The study explores the spatio-temporal variation of water quality parameters in the Hooghly estuary, which is considered an ecologically-stressed shallow estuary and a major distributary for the Ganges River. The estimated parameters are chlorophyll-a, total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM). The Sentinel-3 OLCI remote sensing imageries were analyzed for the duration of October 2018 to February 2019. We observed that the water quality of the Hooghly estuaries is comparatively low-oxygenated, mesotrophic, and phosphate-limited. Ongoing channel dredging for maintaining shipping channel depth keeps the TSM in the estuary at an elevated level, with the highest amount of TSM observed during March of 2019 (41.59g m-3) at station A, upstream point. Since the pre-monsoon season, TSM data shows a decreasing trend towards the mouth of the estuary. Chl-a concentration is higher during pre-monsoon than monsoon and post-monsoon periods, with the highest value observed in April at 1.09 mg m-3 in station D during the pre-monsoon period. The CDOM concentration was high in the middle section (January-February) and gradually decreased towards the estuary's head and mouth. The highest CDOM was found in February at locations C and D during the pre-monsoon period. Every station shows a significant correlation among CDOM, TSM, and Chl-a measured parameters. Based on our satellite data analysis, it is recommended that SNAP C2RCC be regionally used for TSM, Chl-a, and CDOM for water quality product retrieval and in various algorithms for the Hooghly estuary monitoring.
The current world economy needs to undergo a green transformation. Green total factor productivity provides the basis for judging whether a country or region can attain long-term sustainable development. However, there is little research into the factors that influence green total factor productivity and this has become an obstacle in the transition to a greener economy. On filtering relevant articles and interviews data collected from 2009 to 2019, open decoding, spindle decoding, and selective decoding are carried out to classify research conducted into green total factor productivity. From this analysis, cutting-edge research and knowledge gaps in green total factor productivity are identified. Also, an influencing factor model of green total factor productivity is built. Findings suggest that technical, economic, and government are the three main research streams involved in this transformation process. In particular, technology plays a decisive role, economy plays a guaranteeing role, and government plays a regulatory role. Moreover, the impact of these factors cannot be isolated, as each influence and mediate the other two. Results from this study will help further popularize green total factor productivity and provide a new starting point for reducing energy consumption and environmental pollution.
Deforestation and forest degradation are among the leading global concerns, as they could reduce the carbon sink and sequestration potential of the forest. The impoundment of Kenyir River, Hulu Terengganu, Malaysia, in 1985 due to the development of hydropower station has created a large area of water bodies following clearance of forested land. This study assessed the loss of forest carbon due to these activities within the period of 37 years, between 1972 and 2019. The study area consisted of Kenyir Lake catchment area, which consisted mainly of forests and the great Kenyir Lake. Remote sensing datasets have been used in this analysis. Satellite images from Landsat 1-5 MSS and Landsat 8 OLI/TRIS that were acquired between the years 1972 and 2019 were used to classify land uses in the entire landscape of Kenyir Lake catchment. Support vector machine (SVM) was adapted to generate the land-use classification map in the study area. The results show that the total study area includes 278,179 ha and forest covers dominated the area for before and after the impoundment of Kenyir Lake. The assessed loss of carbon between the years 1972 and 2019 was around 8.6 million Mg C with an annual rate of 0.36%. The main single cause attributing to the forest loss was due to clearing of forest for hydro-electric dam construction. However, the remaining forests surrounding the study area are still able to sequester carbon at a considerable rate and thus balance the carbon dynamics within the landscapes. The results highlight that carbon sequestration scenario in Kenyir Lake catchment area shows the potential of the carbon sink in the study area are acceptable with only 17% reduction of sequestration ability. The landscape of the study area is considered as highly vegetated area despite changes due to dam construction.
This study reports the concentrations of trace metals in core sediments profile from the coastal and four rivers estuary in the Kuching Division of Sarawak, Malaysia, and the controlling mechanisms influencing their availability in sediments of the studied area. The bonding of trace metals with non-mobile fractions was confirmed with the sequential extraction. Inductively coupled plasma-optical emission spectroscopy (ICP-OES) was used to measure the concentrations of the trace metals. Granulometric analyses were performed using normalized sieve apertures to determine the textural characteristics of the sediments. Enrichment factor was used to evaluate the level of metal enrichment. Heavy metals concentrations in sediment samples varied in the range: Pb (8.9-188.9 mg/kg d.w.), Zn (19.4-431.8 mg/kg d.w.), Cd (0.014-0.061 mg/kg d.w.), Ni (6.6-33.4 mg/kg d.w.), Mn (2.4-16.8 mg/kg d.w.), Cu (9.4-133.3 mg/kg d.w.), Ba (1.3-9.9 mg/kg d.w.), As (0.4-7.9 mg/kg d.w.), Co (0.9-5.1 mg/kg d.w.), Cr (1.4-7.8 mg/kg d.w.), Mg (68.8-499.3 mg/kg d.w.), Ca (11.3-64.9 mg/kg d.w.), Al (24.7-141.7 mg/kg d.w.), Na (8.8-29.4 mg/kg d.w.), and Fe (12,011-35,124.6 mg/kg d.w.). The estimated results of the enrichment factor suggested enrichments of Pb, Zn, and Cu in all the core sediment samples and depths at all sites. The other trace metals showed no enrichments in almost all the sampled stations. Continuous accumulation of Pb, Zn, and Cu metals over a period can be detrimental to living organisms and the ecology. The results obtained from the statistical analyses suggested that the deposition of trace metals in the studied sites is due to anthropogenic inputs from the adjacent land-based sources.
The occurrence of floods has the potential to escalate the transmission of infectious diseases. To enhance our comprehension of the health impacts of flooding and facilitate effective planning for mitigation strategies, it is necessary to explore the flood risk management. The variability present in hydrological records is an important and neglecting non-stationary patterns in flood data can lead to significant biases in estimating flood quantiles. Consequently, adopting a non-stationary flood frequency analysis appears to be a suitable approach to challenge the assumption of independent and identically distributed observations in the sample. This research employed the generalized extreme value (GEV) distribution to examine annual maximum flood series. To estimate non-stationary models in the flood data, several statistical tests, including the TL-moment method was utilized on the data from ten stream-flow stations in Johor, Malaysia, which revealed that two stations, namely Kahang and Lenggor, exhibited non-stationary behaviour in their annual maximum streamflow. Two non-stationary models efficiently described the data series from these two specific stations, the control of which could reduce outbreak of infectious diseases when used for controlling the development measures of the hydraulic structures. Thus, the application of these models may help prevent biased prediction of flood occurrences leading to lower number of cases infected by disease.
River outgassing has proven to be an integral part of the carbon cycle. In Southeast Asia, river outgassing quantities are uncertain due to lack of measured data. Here we investigate six rivers in Indonesia and Malaysia, during five expeditions. CO2 fluxes from Southeast Asian rivers amount to 66.9 ± 15.7 Tg C per year, of which Indonesia releases 53.9 ± 12.4 Tg C per year. Malaysian rivers emit 6.2 ± 1.6 Tg C per year. These moderate values show that Southeast Asia is not the river outgassing hotspot as would be expected from the carbon-enriched peat soils. This is due to the relatively short residence time of dissolved organic carbon (DOC) in the river, as the peatlands, being the primary source of DOC, are located near the coast. Limitation of bacterial production, due to low pH, oxygen depletion or the refractory nature of DOC, potentially also contributes to moderate CO2 fluxes as this decelerates decomposition.
The concentrations were ranged from 1.35 ± 0.16 to 2.22 ± 0.34 µg/g (dry weight) and 2.65 ± 0.34 to 4.36 ± 0.53 µg/g (dry weight) for Cd and Pb, respectively, in blood cockle Anadara granosa from four sites of Sabang River, namely, Kampung Sambir, Kampung Tambirat, Beliong Temple, and Kampung Tanjung Apong, which are located at Asajaya, Sarawak, Malaysia. All values exceeded safety limits set by Malaysian Food Regulation (1985). It may be the cause of serious human health problems after long term consumption. Thus, consumer should have consciousness about such type of seafood from mentioned sites and need further investigation.
The mitogenome of the Australian freshwater blackfish, Gadopsis marmoratus was recovered coverage by genome skimming using the MiSeq sequencer (GenBank Accession Number: NC_024436). The blackfish mitogenome has 16,407 base pairs made up of 13 protein-coding genes, 2 ribosomal subunit genes, 22 transfer RNAs, and a 819 bp non-coding AT-rich region. This is the 5th mitogenome sequence to be reported for the family Percichthyidae.