Surface Water Quality Research Articles

Hydrogeological study methods for induced bank filtration: practical examples and experiences from Finland

Bank filtration is often utilized at groundwater intake plants located in sand and gravel-dominated glaciofluvial formations bordered by a lake or a river. Bank filtration is favourable, since it can increase the amount of water that can be sustainably abstracted from an aquifer, and it may balance changes in the groundwater level. On the other hand, bank filtration may lower groundwater quality if the quality of infiltrating surface water is not sufficient. To provide up-to-date information for risk management and preparedness plans that are applicable to groundwater intake plants utilising bank filtration, several tools and methods were tested. A feasible test site was selected for piloting to better understand the processes that take place during bank filtration. Several traditional research methods were used to map the geological structure of the test aquifer. In addition, methods to assess seasonal changes in groundwater recharge patterns and groundwater quantity, as well as surface water and groundwater quality, were tested and piloted. The studies enabled the identification of infiltration zones from the lake to the aquifer, as well as the main groundwater flow paths within the aquifer. The conducted studies also provided practical experience on how to best monitor any future changes in natural surface water–groundwater interaction, as well as in induced bank filtration, expected due to climate change or increasing water demand.

Prediction of Urban Surface Water Quality Scenarios Using Water Quality Index (WQI), Multivariate Techniques, and Machine Learning (ML) Models in Water Resources, in Baitarani River Basin, Odisha: Potential Benefits and Associated Challenges

Prediction of Urban Surface Water Quality Scenarios Using Water Quality Index (WQI), Multivariate Techniques, and Machine Learning (ML) Models in Water Resources, in Baitarani River Basin, Odisha: Potential Benefits and Associated Challenges

Spatio-temporal monitoring of water quality in the Gharni reservoir (India) by multivariate statistical tools: a case study of a reservoir located in a rainfall deficit area.

The present study reported the effectiveness of multivariate statistical tools used to monitor spatio-temporal fluctuations in the water quality of Gharni reservoir. The Gharni reservoir is situated on the Gharni river sub-basin which is a tributary of the main river Manjara located in the Marathwada rainfall deficit area of Maharashtra, India. The water body is periodically sampled at five selected locations between August 2019 and January 2021 to assess water quality of 20 parameters. Different statistical techniques were employed to handle intricate data matrices, including cluster scanning/analysis (CA), factor examination (analysis)/principal component evaluation (analysis) (FA/PCA) for data reduction, and discriminant survey/analysis (DA) for data classification. This method of hierarchical CA categorized the five sampling stations into three groups and seasons into four groups/clusters based on resemblance in the recorded physico-chemical parameter readings. The FA/PCA successfully extracted a total of 14 factors, which accounted for 70% out of the total 20 measured variables. These factors were crucial in explaining 62% of the variability observed in the data. Furthermore, the analysis pointed out the specific components/factors responsible for the alteration in the quality of reservoir water. Additionally, the dominance of individual group was evaluated in relation to the comprehensive differentiability at five distinct sampled locations. The DA yielded 14 parameters with a 99% accuracy rate for assigning correct values. The varifactors (VF) developed in the factor analysis have shown that the variation in quality of surface water was interrelated to two groups. The first group included physico-chemical parameters like dissolved oxygen, temperature, and conductivity, whereas the second group covered nutrients like chlorophyll, phosphorus, and nitrogen and were deposited in the water by soil erosion during rainy season from agricultural land. This case study demonstrates the use of multivariate statistical tools as an excellent exploratory tool for analyzing and interpreting complex data sets. It highlights their effectiveness in assessing water quality and understanding its spatio-temporal variations, ultimately assisting in the management of reservoir water quality.

A dynamic meshing scheme for integrated hydrologic modeling to represent evolving landscapes.

A dynamic meshing scheme for integrated hydrologic modeling to represent evolving landscapes.

Study on salt deposition distribution and environmental effect of seawater cooling tower

A three-dimensional numerical simulation model was developed to examine the dispersion and migration of droplets in large, naturally ventilated seawater cooling towers, as well as the pattern of salt deposition influenced by nearby buildings and crosswinds. Following a comprehensive hydrogeological survey at the site, we analyzed the effects of salt deposition on the salinity of surface water, paddy fields, and soil. Additionally, a seed planting trial was conducted to observe the growth of wheat in 11 different soil salinity conditions. The findings revealed that salt deposition did not alter the classifications of surface water quality, paddy water functionality, or soil salinization. However, soil salinity significantly affected the germination rate, bud length, bud fresh weight, and root-to-shoot ratio of the wheat variety Cangmai 6002. Assuming no erosion or dilution from rainfall, the soil salinity remained under 0.2% for 40 years, not impacting wheat yield.

Assessment of surface water quality impacted by upstream artisanal mining in Prek Te area, Cambodia

Mining activities can significantly impact water quality by introducing various contaminants into water sources. The purpose of this study is to assess heavy metal concentrations and other elemental contaminants in water from the Prek Te area across two seasons and evaluate the potential health risks associated with drinking contaminated water. Surface water samples were collected twice in 2023: from March 5 – 11 (dry season) to October 20 – 26 (rainy season). A total of 21 surface water samples were collected from different locations along the Prek Te area. On-site measurements included pH, electrical conductivity, dissolved oxygen, and total dissolved solids, while laboratory analyses determined arsenic (As), chromium, nickel (Ni), copper, zinc, cadmium, and lead concentrations using inductively coupled plasma mass spectrometry. The results indicated that most parameters were within the World Health Organization guidelines and Cambodia Drinking Water Quality Standards, except for As and Ni. During the rainy season, the highest As concentration was 58.1 μg/L, followed by 22.0 μg/L and 10.8 μg/L. Health risk assessments revealed that the hazard index and hazard quotient for children exceeded 1, indicating potential non-carcinogenic health risks. In addition, children exhibited the highest lifetime cancer risk for As (>1.8 × 10−1), followed by women (>1.2 × 10−1) and men (>1 × 10−1). These findings suggest that children are more vulnerable to health risks associated with drinking contaminated water compared to adults. To ensure safe drinking water for communities along the Prek Te area, an urgent monitoring program and appropriate water treatment measures—particularly to remove As contamination—are recommended.

Integrated machine learning-based optimization framework for surface water quality index comparing coastal and non-coastal cases of Guangxi, China.

Integrated machine learning-based optimization framework for surface water quality index comparing coastal and non-coastal cases of Guangxi, China.

Assessing the Impacts of Soil Conservation Practices on Surface Runoff and Water Quality Using an Agricultural Experimental Setup, Generalised Additive Mixed Models, and Hydrologic Modelling

Assessing the Impacts of Soil Conservation Practices on Surface Runoff and Water Quality Using an Agricultural Experimental Setup, Generalised Additive Mixed Models, and Hydrologic Modelling

Interpretable data-driven chemometric approach for predicting non-optically active water quality parameters using ultraviolet-visible-near infrared absorption spectroscopy and physical-chemical measurements.

Interpretable data-driven chemometric approach for predicting non-optically active water quality parameters using ultraviolet-visible-near infrared absorption spectroscopy and physical-chemical measurements.

Applying the water quality indices, geographical information system, and advanced decision-making techniques to assess the suitability of surface water for drinking purposes in Brahmani River Basin (BRB), Odisha.

Surface water is used for a variety of purposes, including agriculture, drinking water, and other services. Therefore, its quality is crucial for irrigation, human welfare, and health. Thus, the main objective is to improve surface water quality assessment and geochemical analysis to evaluate anthropogenic activities' impact on surface water quality in the Brahmani Watershed, Odisha. In the present paper, emerging techniques such as CRITIC (Criteria Importance Through Inter-criteria Correlation), Additive Ratio Assessment (ARAS), Weighted Aggregated Sum-Product Assessment (WASPAS), SHAP (Shapley Additive Explanation), and Geographical Information System (GIS) were used to locate the origins of pollution in the surface water. The 5-year (2018-2023) database was created by analysing samples that varied geographically over seven sampling locations. The dataset was categorized according to its intended usage. The study employed Inverse Distance Weighting (IDW) tool, to forecast quantities and their geographical arrangement.The water temperature detected at several locations along the river revealed minor variations. The pH variations indicate that the surface water in the studied area is alkaline. Notably, the water's lowest temperature ever recorded was 25.72°C, at Q-(1). In addition, sufficient DO concentrations are monitored to ensure optimal water quality. The major parts of the study area were found to be majorly affected with high concentrations of PO43-, EC, Ca2+, Mg2+, and SO42-. To determine the degree of contamination, a basic standard reference is necessary to interpret the values, which range from the anthropogenic to the natural contribution.The statistical results reveal the dominant decreasing order amongst the cations, such as: Ca2+ > Mg2+ > Na+ > K+ and in anions, namely, SO42- > Cl- > NO3- > F- > PO43-, respectively. It displays seasonal variations in dissolved and specific phase metal fractions that are not statistically significant at any of the seven sites. Proceeding further,the water quality index showed that the four samples fall in the poor water quality class, whereas the rest, 3 samples, were of good water quality. The surface water is contaminated and negatively affected due to percolation of ions from landfill leachate as per the data of C-WQI. Based on ARAS and WASPAS, Q-(1) and Q-(2) were mainly not fit for consumption. Meanwhile, the SHAP-WQI showed an increase in the number of samples (71.43%) with unsuitable quality for drinking. This emphasizes on the importance of weathering, dissolution, terrigenous, leaching, ion exchange, lithological and evaporation as the primary processes. Human influences were the secondary factors. Overall, the findings indicate that the study area's surface water is safe to drink, with the exception of a few locations including, Q-(1), (2), (3), (4), and (7), in the river water. Integrating GIS using WQ methods gives a new knowledge on the spatial variation in surface water characteristics for designated use. When enforcing regulations and carrying out pollution control operations, this will help determine the precise sampling sites or the sections of the river that show significant degradation. Thus, the integrated model provides insightful data on surface watershed management for urban planners and decision-makers. In overall,these findings underscore the importance of coordinated efforts across administrative boundaries within the basin to reduce water governance costs, providing valuable insights for fostering the coordinated development of regional economies and environmental sustainability. As a result, future studies should be conducted in the area to precisely state the quality of water used for drinking and domestic purposes.

Hydrochemical analysis of surface water flow from an abandoned iron ore mine

Purpose. The purpose of this research is to investigate the potential formation of contaminants in abandoned mining areas due to the interaction between rocks, water, and air, as well as their impact on surface water quality around the mine area. Methods. Mineralogical analysis using an optical microscope and X-ray diffraction (XRD) on iron ore samples obtained from the stockpiles and chemical analysis of water obtained from the mining site and downstream river. Findings. Iron ore at the study site is dominated by iron oxide minerals such as magnetite, hematite, and goethite. Additionally, quartz, birnessite, pyrite, and chalcopyrite minerals were also found. The mineral content indicates the presence of two sulfide minerals that have the potential to form acid mine drainage, namely; pyrite (FeS2) and chalcopyrite (CuFeS2). The pH measurement results of the water flowing from the iron ore stockpile have a pH of 2.9, while the void and surrounding river vary from 6.4 to 8.2. Originality. Identifying iron ore minerals by combining polarized light microscopy and XRD can enhance the reliability of the observation results. Both methods showed the same results in identifying sulfide minerals, with chalcopyrite in the excavated iron ore stockpile and pyrite in the crushed iron ore stockpile. The formation of acid mine drainage at abandoned mine sites is only a local phenomenon, and after being diluted by other water flows, the water's pH returns to neutral. Practical implications. This research activity was conducted during the rainy season when overflow occurred, with water spilling from the mine pit lake to the surrounding areas. The results show that the formation of acid mine drainage and the high concentration of total Fe that occurred in one of the stockpiles did not affect the change in water quality around the mine, and there is potential for water in the mine void to be used as a water source for the surrounding community.

Regression-based machine learning models for nitrate and chloride prediction in surface water in a small agricultural sand plain sub-watershed in southwestern Ontario, Canada

Machine learning (ML) models have proven to be an efficient technique for better understanding and quantification of surface water quality, especially in agricultural watersheds where considerable anthropogenic activities occur. However, there is a lack of systematic investigations that can examine the application of different ML regression models in agricultural settings to predict the surface water quality using a group of input variables, including hydrological (e.g., surface flow), meteorological (e.g., precipitation), and field (e.g., crop cover) conditions. In this study, multiple ML regression models, including support vector machine (SVM) and regression trees (RT), were employed on a 2-year dataset collected from a sand plain agricultural sub-watershed in southwestern Ontario, Canada (i.e., Lower Whitemans Creek) to predict the nitrate and chloride concentrations in surface water at nine sampling sites within the sub-watershed. The prediction capabilities of these ML models were determined using a group of evaluation metrics including the coefficient of determination (R2) and root-mean squared error (RMSE). In general, the Gaussian Process Regression (GPR) model was the optimal algorithm to predict the nitrate and chloride concentrations in surface water (R2 was 0.99 and 0.98 respectively for training and testing). According to the results of a feature importance analysis, it was found that the field conditions (specifically the location of sampling site (main channel or tributary site) and crop cover) were the most crucial model input variables for accurate predictions of the output variables. This study underscores that ML regression models can be implemented to effectively quantify the water quality properties of surface water in agricultural watersheds using easily measurable parameters. These models can assist decision makers in advancing successful actions and steps towards protecting the available surface water resources.

Assessing the Water Quality of Rivers of a Small Town in Wangwenzhuang, Tianjin, China, Using the PCA–WQImin Method

The deterioration of the water environment in small towns in China due to urbanization has posed a significant challenge to their socioeconomic sustainability. Therefore, this study aims to employ an integrated assessment method based on the principal component analysis–minimal water quality index model (PCA–WQImin) to objectively evaluate the water quality and analyze the pollution characteristics of Wangwenzhuang in Tianjin, thereby providing a scientific basis and decision-making support for the sustainable development of small towns. This study’s findings reveal that the PCA–WQImin method identified four key water quality parameters essential for a comprehensive assessment of water quality across seasonal and spatial variations. The analysis revealed that the water quality in the investigated region remained at a “poor” level, with relatively consistent levels across the four catchments, ranked from poorest to best as the Xingchilong catchment area (29.61) < Jianxinpaigan catchment area (31.03) < Chilong catchment area (32.94) < Machangjian catchment area (39.61). This study revealed that the average WQImin values slightly increased in the wet season relative to those in the dry season. This research highlights the critical importance of effectively managing domestic rural sewage and aquaculture pollution to increase surface water quality in small towns. Pollution prevention policies and multiple sources of pollution evaluation will be considered the focus of future studies.

An enhanced multi-head attention-based LSTM model for forecasting the surface water quality index

ABSTRACT Forecasting the surface water quality is vital for environmental monitoring and ecological sustainability. Although existing statistical and machine learning methods have been applied deliberately to forecast water quality, they often do not utterly delineate its complex spatial and temporal dynamics promptly. This in turn limits ensuring the accuracy and reliability of predictions, which are indispensable for effective environmental management. In order to overcome these challenges, we develop a novel approach as a multi-head attention-based long short-term memory model, specifically designed to enhance predictive precision that can capture complex dependencies using water quality datasets more precisely for the very first time. The proposed model shows a significant improvement over existing machine learning and deep learning models, achieving around 5–8% more accuracy in water quality forecasting. These enhanced results suggest that the proposed approach is well-suited for large-scale environmental applications, offering a data-driven approach that the supports targeted intervention strategies appropriately and reliably. This work contributes finely to the advancement of automated water quality forecasting systems, aiding sustainable environmental management practices.

ҚАРАТАЛ, АҚСУ, ЛЕПСІ ӨЗЕНДЕРІНІҢ ГИДРОХИМИЯЛЫҚ РЕЖИМІНІҢ МОНИТОРИНГІ

This article presents the hydrochemical characteristics of Karatal, Ak-Su and Lepsy rivers, hydrological regime and results of hydrobiological research. It also presents the results of studies on the formation of water quality in these rivers at present. As a result, an assessment of the ecological status and water quality was carried out using information and analytical materials from RSE Kazhydromet and Annual data on surface water quality of Kazakhstan. Monitoring was conducted from 2015 to 2020 and included changes in water temperature, dissolved oxygen content, pH values, mineralization levels, as well as main inorganic constituents such as cations and anions. The influence of natural factors on chemical composition of these rivers was also considered.

Salinity levels, trends and drivers of surface water salinization across China's river basins.

Salinity levels, trends and drivers of surface water salinization across China's river basins.

Assessing the Effects of Bioenergy Cropping Scenarios on the Surface Water and Groundwater of an Intensively Agricultural Basin in Central Greece

Pinios river basin constitutes the most important agricultural production area in Greece but contributes to the degradation of the quality and quantity of surface water and groundwater bodies. Bioenergy crops implemented as part of the existing cropping systems could be a novel and efficient mitigation strategy against water degradation, contributing to the production of energy through renewable sources. This study uses the Soil and Water Assessment Tool (SWAT) to first develop a representative model of Pinios river basin and evaluate its current state with respect to water availability and nitrate water pollution. A low-input perennial bioenergy crop, switchgrass, is then simulated closely to the Greek conditions to investigate its potential effects on water in three implementation scenarios: the installation and growth of switchgrass in the entire irrigated cropland, exclusively in irrigated sloping (slopes > 1.5%) cropland, and exclusively in irrigated non-sloping cropland. The simulated results demonstrate that under all scenarios, the water quality improvements with respect to the nitrate loads entering surface water and groundwater bodies were significant, with their reduction being directly affected by the extent to which switchgrass replaced resource-demanding conventional crops. Specifically, the reduction in the annual nitrate loads in the surface water under these three scenarios varied from 7% to 18% at the river basin scale, while in certain cropland areas, the respective reduction even exceeded a level of 80%. The potential to improve the water status was also considerable, as the implementation of the bioenergy crop reduced the irrigation water used annually in the basin by 10% (64 Mm3) when switchgrass replaced the conventional crops only on the sloping land and by almost 30% (187 Mm3) when it replaced them throughout the irrigated land. At the same time, significant biomass production above 18 t/ha/y applied in all of the simulations. This study also highlights the contribution of the bioenergy crop to the rehabilitation of the groundwater levels across the basin, with the possibility of increasing them by >50% compared to the baseline, implying that the adoption of switchgrass could be a promising means against water scarcity.

Assessing Deep Learning Techniques for Remote Gauging and Water Quality Monitoring Using Webcam Images

River and stream gauging and water quality monitoring are essential for understanding and managing freshwater resources. The U.S. Geological Survey (USGS) has been implementing and expanding the coverage of webcams across the U.S. stream gauges. A publicly available website has been established, known as the Hydrological Imagery Visualization and Information System (HIVIS). Motivated by routine webcam monitoring and recent advances in image-based machine learning research, in this technical paper, we evaluate three convolutional neural network (CNN) models including two deep neural network models (CNN3, VGG16, and ResNet50) in predicting river gauging, turbidity, dissolved oxygen, and dissolved organic matter in the Missouri River. We select the Missouri River due to the logistical challenges in field data collection. Our objective is to evaluate the predictability of the selected CNN and deep CNN models in inferring water surface elevation and water quality parameters from webcam images. The results show that the images can provide robust prediction for gauge height, a reasonable prediction for dissolved oxygen, and unsatisfactory prediction for turbidity or dissolved organic matter. The results demonstrate the potential use and limitation of using webcam images to remotely sense water quantity and quality data.

Non-metal roofing materials as potential sources of pollutants- laboratory leaching studies on various roofing materials.

Stormwater runoff from urban surfaces contains pollutants that harm the water quality of surface or groundwater bodies. While runoff quality from metal roofs has already been widely studied, the contribution of non-metal roofing materials to stormwater pollution is still unclear. A leaching test was conducted to determine the substance emission from non-metal roofing materials. Analyzing parameters in the leachate of eight leaching rounds at predefined time intervals allowed for determining area release rates and leaching mechanisms. The test was conducted with commonly used materials applied on inclined roofs: clay tiles, concrete tiles, fiber cement, bitumen shingles, wood shingles, and plastic roofing tiles. The scope of the analysis is based on substances repeatedly detected in stormwater: anions, cations, heavy metals, biocides, phthalates, PCBs, PFAS, PAHs, nonylphenols, and Bisphenol A. Several roofing materials released relevant amounts of hazardous substances impacting water quality. Clay tiles are a source of vanadium, manganese, and arsenic. Treated wood shingles release ammonium, boron, copper, and Bisphenol A. Fiber cement and concrete roofing tiles are a source of biocides. Bitumen shingles released Bisphenol A. Further research is recommended regarding leaching in long-term field experiments to evaluate the impact of real weather conditions and on developing treatment facilities.

A study of surface water quality using organic pollution indices: Comparative characteristics and educational opportunities

ABSTRACT This study sets out to examine a variety of approaches to the assessment of organic pollution, and to consider the potential application of the results in the educational process of students specializing in the environment. Water samples from the Styr River were collected and analyzed for physicochemical parameters such as nitrogen, phosphorus, carbon components, and dissolved oxygen from 2018 to 2022. Four equations were used to calculate the Organic Pollution Index (OPI). Statistical analyses included Pearson correlation, multiple linear regression, principal component analysis (PCA), and network analysis (NA). Results indicated variations in OPI levels depending on the methodology, with pollution classifications ranging from ‘pure’ to ‘moderate’. Seasonal trends were observed, with higher pollution levels in summer and lower levels in winter. PCA and NA identified key variables contributing to pollution. Some methods showed stable OPI levels, while others exhibited periodic fluctuations. This study provides a comprehensive comparison of OPI calculation methods and integrates statistical techniques for river water quality assessment. It offers a valuable framework for environmental education, enhancing students' understanding of water quality assessment, data analysis, and statistical applications in environmental science.