Current Water Research Articles

A Review of Sanitation Options and Programming in Ghana, West Africa

This review examines Ghana’s progress in sanitation. These efforts include the containment, transport, treatment, and disposal or reuse of human waste. This research specifically examined the current sanitation processes and programming efforts within Ghana, West Africa. Some reports indicate that Ghana is the second dirtiest country in Africa and the seventh dirtiest country in the world. A review of the current water, sanitation, and hygiene (WASH) options within the country indicates that significant efforts need to be undertaken. There are multiple toilet options and current practices within the country for the disposal of excrement. Unfortunately, the municipal sewer systems and wastewater treatment facilities within the country are either broken down or severely inadequate compared to their surrounding population. Current WASH programming, extension efforts, and key factors affecting the uptake of sanitation investment are reviewed. The price point where consumers would utilize sanitation products and services was analyzed, as well as the cost-to-benefit ratio of specific technologies for community members. This review demonstrated that initiatives should be undertaken to provide more sanitation professionals, renovate and expand urban municipal sewerage systems, and provide basic access to rural community areas. It was recommended that future programming, extension, and policy efforts be intentional and include relevant stakeholders and community members to provide a greater opportunity for establishing successful and substantial efforts.

Dynamic Water Microskin Induced by Photothermally Responsive Interpenetrating Hydrogel Networks for High‐Performance Light‐Tracking Water Evaporation

AbstractSolar‐driven interfacial water evaporation is attracting increasing attention as a promising environmentally‐friendly solution to freshwater scarcity. It has been proven that a thin water layer on photothermal materials can prevent solar energy from invalidly heating the excess water to enhance the evaporation rate. However, the current water layers are usually formed on inelastic materials via confined capillarity, which are static and uncontrollable. Herein, we propose a flexible hydrogel‐based photothermal conversion material with thermal responsiveness by facile frontal polymerization, which can generate a unique dynamic water microskin (DWMS) during the solar evaporation process. The copolymerized hydrogel, introduced by the second polymeric poly(vinyl alcohol) network and thermally responsive poly(N‐isopropylacrylamide) (PNIPAM), exhibits reinforced mechanical strength and photothermally triggers reversible shrinking/swelling cycles that enable a thin water layer (≈32 µm) to balance the feedwater supply and photothermic energy input dynamically. As a result, a stable superior vaporization rate of 8.7 kg m−2 h−1 is achieved based on a cylindrical hydrogel with 6 cm height under 1 sun. Moreover, the simultaneous responsive bending allows efficient omnidirectional solar evaporation by light‐tracking to ensure maximum perpendicular solar absorption, which provides an alternative strategy for durable high‐efficiency solar evaporators for effective thermal management and solar utilization.

Assessing the Impact of Climate Change on Methane Emissions from Rice Production Systems in Southern India

The impact of climate change on methane (CH4) emissions from rice production systems in the Coimbatore region (Tamil Nadu, India) was studied by leveraging field experiments across two main treatments and four sub-treatments in a split-plot design. Utilizing the closed-chamber method for gas collection and gas chromatography analysis, this study identified significant differences in CH4 emissions between conventional cultivation methods and the system of rice intensification (henceforth SRI). Over two growing seasons, conventional cultivation methods reported higher CH4 emissions (range: from 36.9 to 59.3 kg CH4 ha−1 season−1) compared with SRI (range: from 2.2 to 12.8 kg CH4 ha−1 season−1). Experimental data were subsequently used to guide parametrization and validation of the DeNitrification–DeComposition (DNDC) model. The validation of the model showed good agreement between the measured and modeled data, as denoted by the statistical tests performed, which included CRM (0.09), D-index (0.99), RMSE (7.16), EF (0.96), and R2 (0.92). The validated model was then used to develop future CH4 emissions projections under various shared socio-economic pathways (henceforth SSPs) for the mid- (2021–2050) and late (2051–2080) century. The analysis revealed a potential increase in CH4 emissions for the simulated scenarios, which was dependent on specific soil and irrigation management practices. Conventional cultivation produced the highest CH4 emissions, but it was shown that they could be reduced if the current practice was replaced by minimal flooding or through irrigation with alternating wetting and drying cycles. Emissions were predicted to rise until SSP 370, with a marginal increase in SSP 585 thereafter. The findings of this work underscored an urgency to develop climate-smart location-specific mitigation strategies focused on simultaneously improving current water and nutrient management practices. The use of methanotrophs to reduce CH4 production from rice systems should be considered in future work. This research also highlighted the critical interaction that exists between agricultural practices and climate change, and emphasized the need to implement adaptive crop management strategies that can sustain productivity and mitigate the environmental impacts of rice-based systems in southern India.

SPFDNet: Water Extraction Method Based on Spatial Partition and Feature Decoupling

Extracting water information from remote-sensing images is of great research significance for applications such as water resource protection and flood monitoring. Current water extraction methods aggregated richer multi-level features to enhance the output results. In fact, there is a difference in the requirements for the water body and the water boundary. Indiscriminate multi-feature fusion can lead to perturbation and competition of information between these two types of features during the optimization. Consequently, models cannot accurately locate the internal vacancies within the water body with the external boundary. Therefore, this paper proposes a water feature extraction network with spatial partitioning and feature decoupling. To ensure that the water features are extracted with deep semantic features and stable spatial information before decoupling, we first design a chunked multi-scale feature aggregation module (CMFAM) to construct a context path for obtaining deep semantic information. Then, an information interaction module (IIM) is designed to exchange information between two spatial paths with two fixed resolution intervals and the two paths through. During decoding, a feature decoupling module (FDM) is developed to utilize internal flow prediction to acquire the main body features, and erasing techniques are employed to obtain boundary features. Therefore, the deep features of the water body and the detailed boundary information are supplemented, strengthening the decoupled body and boundary features. Furthermore, the integrated expansion recoupling module (IERM) module is designed for the recoupling stage. The IERM expands the water body and boundary features using expansion and adaptively compensates the transition region between the water body and boundary through information guidance. Finally, multi-level constraints are combined to realize the supervision of the decoupled features. Thus, the water body and boundaries can be extracted more accurately. A comparative validation analysis is conducted on the public datasets, including the gaofen image dataset (GID) and the gaofen2020 challenge dataset (GF2020). By comparing with seven SOTAs, the results show that the proposed method achieves the best results, with IOUs of 91.22 and 78.93, especially in the localization of water bodies and boundaries. By applying the proposed method in different scenarios, the results show the stable capability of the proposed method for extracting water with various shapes and areas.

Evaluating Institutional Success: Regional Water Agreements with Tribal Nations

Abstract Since the early 1900s, U.S. national, tribal, and state institutions have been addressing conflicts over water entitlements of Native American tribes. The resulting regional water agreements encompass ensuring tribal access to water, defining entitlements for reliable future access, and providing environmental and financial benefits. Identifying approaches to evaluating institutional success in these regional water agreements is useful for effectively addressing current and future water disputes. This article discusses characteristics of eight water agreements that have occurred in the Southwestern United States, applying five economic criteria to evaluate success. The evaluation suggests that newer agreements perform better on the economic criteria, implying that learning about effective agreements is occurring over time. As threats to water quality and quantity intensify worldwide, understanding what contributes to successful regional water agreements becomes increasingly urgent.

An influence of the Antarctic bottom water currents on the formation of the upper sediment cover in the Vema Fracture Zone, Central Atlantic

An influence of the Antarctic bottom water currents on the formation of the upper sediment cover in the Vema Fracture Zone, Central Atlantic

Low-rank rotation matrices-based DOA estimation with a moored circular vector sensor array

Abstract Water currents affect circular vector sensor arrays (CVSAs) suspended from a moored platform, causing them to rotate underwater. This rotation alters the direction of sources within the array coordinate system over time. Traditional methods that rely on numerous snapshots often yield inaccurate results, particularly for faint sources. To improve the accuracy, we introduce a direction-finding technique for CVSAs that employs low-rank rotation matrices. The low-rank rotation matrices are constructed using the heading information of the CVSAs and the subregion array manifold vector matrices to achieve spatial focusing. When these matrices are applied to the measurement data, the resulting covariance matrix displays subspace characteristics similar to that of a stationary CVSA. Our performance analysis revealed that low-rank rotation matrices offer higher focusing gains than conventional rotation matrices. The method proposed in this study effectively improves the direction estimation performance for weak targets and extends the practical applicability of measurement techniques for rotating platforms. Both simulations and experiments confirm that our approach outperforms the modified traditional beamforming and other spatial focusing techniques in terms of resolution and precision. Notably, when the signal-to-noise ratio (SNR) is below -4 dB, the resolution for distinguishing between the two sources increases by more than 50%.

Smart Irrigation System Using Soil Moisture Prediction with Deep CNN for Various Soil Types

Soil moisture sensing plays a crucial role in agriculture as it directly impacts plant growth and can significantly enhance crop productivity. With the advent of technology, agriculture applications have undergone a revolution, enabling more advanced and efficient practices. One such advancement is the use of soil moisture sensors, which provide valuable information about the current water level of the soil, including whether it is dry, wet, or excessively saturated. These sensors have become indispensable tools for farmers and growers, empowering them to make informed decisions regarding irrigation schedules, water management strategies, and overall crop health. By accurately assessing soil moisture levels, farmers can optimize water usage, prevent water stress or overwatering, and promote healthier plant development, ultimately leading to improved yields and sustainability in agriculture. The objective of the proposed study is to investigate the effective soil moisture sensors by considering three sensors and an automated system for watering the soil for agriculture. A comparative analysis is performed for different commercial off-the-shelf soil moisture sensors in cost, accuracy, durability, and corrosion resistance. Secondly, this study further gives soil moisture reading as data input to the Convolutional Neural Network (CNN) to classify whether water is required or not for the soil at a particular temperature which would help to conserve water and develop agriculture. An automated system which can help the farmer for irrigation and cultivation of the soil obtained accuracy as 95.23%.

Peculiarities of the Species Composition, Abundance and Biotope Distribution of Mollusks Fam. Sphaeriidae in Rivers with a Many Lakes in the Catchment Area (by the Example of Water Courses of the Onega Lake Basin)

Using the example of the rivers of the Onega Lake basin, the species composition of mollusks of the Sphaeriidae family, their abundance, biomass, and contribution to benthic communities in rivers with many lakes in the catchment areas were analyzed. Patterns of spatial distribution of species across biotopes and gradients of environmental variables were traced. 26 species of mollusks of this family were identified, among them European-Siberian species predominate. Most of the species of mollusks of the family Sphaeriidae found by us have wide ecological ranges and are found in water bodies of various types. It was found that the contribution of Sphaeriidae to the species richness and functioning of aquatic communities in rivers in the conditions of branched lake-river systems (Lake Onega basin) exceeds the values usual for rivers with a small number of lakes in the catchment area. Using the ANOSIM and PERMANOVA methods, it was proved that the species composition of rocky rapids and riffles, sandy rifts and soft bottom soils is different. Using the IndVal test, seven species were shown to have a statistically significant association with one of these biotopes, and four of them, with rocky bottoms of river rapids and riffles. Using the NMDS method, it was shown that the greatest changes in the species composition of representatives of Sphaeriidae are observed when environmental conditions change from small streams with slow current and brown water to large rivers with fast current and clear water.

Ecological and Anatomical Characteristics of riverbank Plants of Water Currents and Waterbodies of the Lower Amur Region

The results of an ecological and anatomical study of plants in the coastal shallows of rivers and lakes o the Lower Amur region are presented in order to identify their resistance and adaptability to the conditions of existence. 7 stenotopic species were studied. It was revealed that the vegetative organs of plants are characterized by a combination of typical hydromorphic and specific adaptive features. Adaptation of species to sandy-silty shallow habitats is carried out due to histological transformations of the main tissues. The narrow specialization of species does not affect the typical structure of plant organs and does not lead to a simplification of their internal structure. The studied species are more characterized by signs of terrestrial micromorphology (sclerification, suberinization and cutinization of tissues) rather than hydrophytic. It is assumed that a specific complex of shallow flora was formed mainly from terrestrial species.

Nanobubble Formation and Coverage during High Current Density Alkaline Water Electrolysis.

Gas bubbles are a necessary byproduct of water electrolysis whereby hydrogen and oxygen are produced from water. These attached gases reduce the electrode's active area, which necessitates a deep understanding of the bubble life cycle starting from nanobubbles. Synchronized with the electrochemistry, the time evolution of the surface nanobubble size and coverage is resolved using grazing incidence small-angle X-ray scattering (GISAXS) and correlated with optical microscopy and theoretical calculations to show that a significant portion of the surface is covered in nanobubbles after larger micron-sized bubbles are observed. These nanobubbles increase in number and decrease in size, toward 2 nm diameter, with the charge passed. The trend in size and number is consistent with an increase in supersaturation, which reduces the nascent bubble size. Altogether, this study suggests a significant portion of the surface contains nanobubbles and that strategies to reduce the dissolved hydrogen would be effective at reducing the nanobubble surface coverage.

A Biomimetic Adhesive Disc for Robotic Adhesion Sliding Inspired by the Net-Winged Midge Larva.

Net-winged midge larvae (Blephariceridae) are known for their remarkable ability to adhere to and crawl on the slippery surfaces of rocks in fast-flowing and turbulent alpine streams, waterfalls, and rivers. This remarkable performance can be attributed to the larvae's powerful ventral suckers. In this article, we first develop a theoretical model of the piston-driven sucker that considers the lubricated state of the contact area. We then implement a piston-driven robotic sucker featuring a V-shaped notch to explore the adhesion-sliding mechanism. Each biomimetic larval sucker has the unique feature of an anterior-facing V-shaped notch on its soft disc rim; it slides along the shear direction while the entire disc surface maintains powerful adhesion on the benthic substrate, just like the biological counterpart. We found that this biomimetic sucker can reversibly transit between "high friction" (4.26 ± 0.34 kPa) and "low friction" (0.41 ± 0.02 kPa) states due to the piston movement, resulting in a frictional enhancement of up to 93.9%. We also elucidate the frictional anisotropy (forward/backward force ratio: 0.81) caused by the V-shaped notch. To demonstrate the robotic application of this adhesion-sliding mechanism, we designed an underwater crawling robot Adhesion Sliding Robot-1 (ASR-1) equipped with two biomimetic ventral suckers. This robot can successfully crawl on a variety of substrates such as curved surfaces, sidewalls, and overhangs and against turbulent water currents with a flow speed of 2.4 m/s. In addition, we implemented a fixed-wing aircraft Adhesion Sliding Robot-2 (ASR-2) featuring midge larva-inspired suckers, enabling transit from rapid water surface gliding to adhesion sliding in an aquatic environment. This adhesion-sliding mechanism inspired by net-winged midge larvae may pave the way for future robots with long-term observation, monitoring, and tracking capabilities in a wide variety of aerial and aquatic environments.

Improving sustainability of rice-canola rotation through water and nitrogen management in a humid region

Intensive cropping systems face significant environmental and economic challenges due to current water and nutrient management practices. This study addresses the limited research on optimizing these practices in rice-canola rotations, focusing on enhancing sustainability through improved water and nitrogen (N) management. Utilizing the DSSAT crop growth model, we evaluated three irrigation scenarios for rice- I1 (100 % irrigation requirement: IR), I2 (80 % IR), and I3 (60 % IR)-alongside three N application levels: N1 (30 % below RAN: regional average N), N2 (RAN), and N3 (30 % above RAN). Key sustainability indicators, including water productivity (WP), nitrogen use efficiency (NUE), and economic productivity, were analyzed. The DSSAT model demonstrated strong predictive accuracy, with yield differences of only 236 kg ha⁻¹ for rice and 121 kg ha⁻¹ for canola between observed and simulated values. A 40 % reduction in water use increased rice yield to a maximum of 5654 kg ha⁻¹ under I3N2 conditions. Additionally, this reduction in water consumption resulted in a 27 % decrease in production costs, significantly enhancing WP, net benefits, and gross benefits of rice by 46.7 %, 43.1 %, and 47.9 %, respectively. Conversely, a 30 % increase in N application for canola correlated with a 9 % yield increase, underscoring the importance of tailored N strategies. Furthermore, N3 raised WP, NUE, net benefits, and gross benefits of canola by 6.7 %, 1.4 %, 9.1 %, and 9.2 %, respectively, compared with current N application level. Overall, this study illustrates the potential for substantial water savings in rice cultivation while recommending stable or reduced N application levels. These management strategies enhance farm performance, lower production costs, and contribute to the sustainability of rice-canola cropping systems, promoting both agricultural productivity and environmental stewardship.

Performance of artificial intelligence model (LSTM model) for estimating and predicting water quality index for irrigation purposes in order to improve agricultural production.

The primary goal of this study is to predict the current and future water quality index for irrigation (WQII) of the western Mitidja alluvial aquifer in northern Algeria. The modified WQII was used to evaluate groundwater suitability for irrigation through geographic information system (GIS) techniques. Additionally, a long short-term memory (LSTM) model was employed to calculate the WQII and map future groundwater quality, considering factors like overexploitation, anthropogenic pollution, and climate change. Two scenarios were analyzed for the year 2030. Results from applying the modified WQII model to 2020 data showed that about 83% of the study area has medium to high groundwater suitability for irrigation. The LSTM model exhibited strong predictive accuracy with determination coefficients (R2) of 0.992 and 0.987, and root mean square error (RMSE) values of 0.061 and 0.084 for the training and testing phases, respectively. For the first 2030 scenario, the area with low and medium groundwater suitability is expected to increase by 4% and 7% compared to the 2020 map. Conversely, under the second scenario, groundwater quality is predicted to improve, with a decrease of 14% and 11% in the low and medium suitability areas. The combination of the modified WQII and LSTM model proves to be an effective tool for estimating and predicting water quality indices in similar regions globally, offering valuable insights for water resource management and decision-making processes.

Inversion of Crop Water Content Using Multispectral Data and Machine Learning Algorithms in the North China Plain

(1) Background: Accurate inversion of crop water content is key to making an intelligent irrigation decision. However, little effort has been devoted to accurately estimating the crop water content of winter wheat in the North China Plain. (2) Method: The crop water content of winter wheat was measured at jointing, flowering and grain-filling stages, respectively. UAV-based multispectral remote sensing images were used to calculate thirteen vegetation indices, including SAVI, EVI, R-M, NDRE, OSAVI, GOSAVI, REOSAVI, GBNDVI, NDVI, RVI, DVI, GNDVI, and TVI. Five machine learning (ML) algorithms (i.e., MLR, RF, PLSR, ElasticNet, and ridge regression) were adopted to estimate the crop water content of winter wheat at the three growth stages. The benchmark datasets, which include CWC as well as vegetation indices calculated based on spectral indices, were adopted to validate the performance of the ML models. (3) Results: The correlation coefficients ranged from 0.64 to 0.82 at different growth stages. The optimal vegetation indices were GNDVI for the jointing stage, NDRE for the flowering and the grain-filling stage, respectively. Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R2) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. At the same time, subjectivity and bias may exist in the data collection, which may have a certain impact on the accuracy of the results. Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations.

A survey of water quality in areas of Albanian ports

This study report data on physical-chemical parameters and on concentrations of organic pollutants in marine water samples of Albanian ports. Nutrients, PH, temperature, conductivity, TSS, DO, BOD, COD, chlorines, sulfates, magnesium and calcium ions were the indicators used for evaluation of water quality. Also, organic pollutants (organochlorine pesticides, PCBs, PAH and benzene) were determined in seawater samples. Albania is facing by Ionian and Adriatic seas in a coastline of more than 300 km. Several ports are in function in Albanian coastline. Ship transport and many activities (automobile transport, mechanical activities, deposits, etc.) are the main factors of water pollution in port areas. Water samples from seven ports (the main Albanian ports) starting from Saranda port in the South to Shengjini port in the North, were analyzed in this study. Sampling was realized in May 2023. Based on physical-chemical indicators the water in the port of Saranda was the cleanest water, while Porto-Romano, Petrolifera and the fishing port in Zvernec was the most polluted one. Organic pollutants were detected in all water samples. Presence of the degradation products of pesticides may be related with previous uses of them. Presence of PCBs can be because of atmospheric deposition (volatile congeners), water currents and mechanical businesses (heavy congeners). PAHs and Benzene were detected on more than 70% of the analyzed samples. Intense ship and automobile transport in the port areas, mechanical businesses discharge and hydrocarbon processing i(Petrolifera and Porto-Romano) can be main factor for their presence. Water currents and new arrivals from the rivers can influence the levels and profile of pollutants. The found levels of organic pollutants in water samples of Albanian ports were higher than reported levels in previous studies for the Adriatic Sea (Albania coastline).

Increasing the available water diversion volume of water source project through flood resource utilization: A case study of the middle route of the South-to-North water diversion project in China

Water supply operating rules are critical for guaranteeing the safety of the water supply in the source project and receiving areas and overcoming the uneven distribution of water resources among different regions. Previous studies have mainly focused on optimizing the water supply operating rules to balance multiple benefits under the current engineering design parameters. In this study, a framework is proposed in which the water supply operating rules for the resource reservoir are adapted through flood resource utilization. The correlations among seasonal floods are first analyzed by considering the influence of water diversion projects. The seasonal flood-limited water levels (FLWLs) are then re-designed in terms of a flood damage assessment index under the deduced most-probable seasonal floods. Finally, the water supply operating curves of the resource reservoir are optimized by adopting the recommended seasonal FLWLs. The middle route of the South-to-North Water Diversion Project in China is taken as a case study. The results show that the recommended seasonal FLWLs are 160.2 m in summer and 164.2 m in autumn, and that the annual average available water diversion volume can be increased by 0.52 billion m3 without increasing the flood risk. Compared with the current water supply operating rules, the optimal operating curves make the annual distribution of the available water diversion volume more uniform in different typical-year scenarios and reduce the surplus reservoir water. These findings are helpful for exploring the potential of water resource utilization.

Use of an Ecological Compensation Model in Water Resource Development: A Case Study from Shaanxi Province, China

This study aims to analyze the current situation of water resource management in Shaanxi Province, study the basic principles of ecological compensation, evaluate the impact of water conservation projects on the ecological environment by establishing a model, and propose a sustainable water resource management model. Hanzhong City has certain typicality and representativeness within Shaanxi Province, and the problems it faces in water resource management and ecological environment may represent the entire province or similar regions. At the same time, Hanzhong City has rich data and research foundations. Therefore, by conducting a detailed analysis of the current water resource status in the Hanzhong City area of Shaanxi Province, the problems in current water resource management are revealed, and the basic principles of ecological compensation are intensely studied. The original ecological compensation plan in Shaanxi Province has been summarized. Guided by the concept of sustainable development, an ecological compensation model is established using algorithms, and the model is applied to sustainable water resource management. Establish a model for water conservation and water resource management through data collection, preprocessing, and cleaning, and apply it to practical cases in Hanzhong City. Through simulation and analysis of Hanzhong City, the new water resource management model effectively mitigates the adverse effects of water conservation projects on the ecological environment while improving water resource utilization efficiency. The changes in various environmental parameters indicate that the new plan has improved the ecological environment. Through the application of the model, the ecological compensation plan formulated has achieved sustainable protection of the ecological environment while promoting economic development. This study proposes a sustainable water resource management model through a comprehensive study of water resource management and ecological compensation in Shaanxi Province and verifies it in practical cases, demonstrating that the model has not only good applicability but also has significant effects in promoting economic growth and ecological environment protection.

Key Issues and Challenges in Establishing Regionally Differentiated Water Quality Standard in China

The goal of environmental management in China is transitioning from pollution control to the improvement of ecological quality. The establishment of regional differential water quality standards can better adapt to the ecological characteristics and development needs of different regions. Given the shortcomings of current water quality standards and the needs of technological development, this study analyzed the causes and influencing factors of regional differences in water quality standards and summarized China's regional variations in areas such as the characteristics of receiving water bodies, social attributes, climate conditions, physicochemical properties of water, and aquatic biotic populations. It also examined the impact of regional characteristics on the assessment of biological toxicity. Based on these analyses, the study identified key scientific questions that may arise in the development of regional differential water quality standards in China and outlined future critical research directions in this field. These directions include developing theories and methods for determining the effluent dilution coefficient, establishing methods for identifying regional key water quality indicators and assessing their impact on water quality standards, determining regional characteristic species and their influence on water quality standards, and formulating theories and methods for categorizing different regions across the country. The aim of the study was to provide a reference for the scientific establishment of water quality standards in China.

Multivariate analysis of water quality in the Dhaleshwari River, Bangladesh: identifying pollution sources and environmental implications

ABSTRACT This study aims to evaluate the current water quality status of the Dhaleshwari River, considering seasonal variations, and identify potential pollution sources using physicochemical parameters, multivariate statistical analysis, and the water quality index (WQI) method. Sampling was conducted at six locations along the Dhaleshwari River from January 2021 to April 2022, encompassing both dry and wet seasons. Eleven physicochemical parameters were tested following standard procedures. The calculated WQI values demonstrated that the overall water quality of the Dhaleshwari River is very poor (264.7 ± 64.5). Seasonal variation in water quality was significant, with the worst quality (305.7 ± 54.3) observed during the dry season; however, water quality during the wet season was also unsatisfactory (223.6 ± 46.3). Geospatial analysis shows the distribution of water quality in the study locations. Principal components analysis was performed for source appointment, which indicated that direct wastewater discharge from surrounding industries, particularly tanneries, as well as municipal wastewater, were the major pollution sources. In conclusion, this study sheds light on the deteriorating water quality of the Dhaleshwari River due to industrialization and human activities. The findings underscore the urgent need for interventions to mitigate pollution sources and improve the overall health of the river ecosystem.