Comprehensive Water Quality Research Articles

Source-specific nitrate intake and all-cause mortality in the Danish Diet, Cancer, and Health Study

IntroductionNitrate and nitrite are naturally occurring in both plant- and animal-sourced foods, are used as additives in the processing of meat, and are found in water. There is growing evidence that they exhibit a spectrum of health effects, depending on the dietary source. The aim of the study was to examine source-dependent associations between dietary intakes of nitrate/nitrite and both all-cause and cause-specific mortality.MethodsIn 52,247 participants of the Danish Diet, Cancer and Health Study, associations between source-dependent nitrate and nitrite intakes––calculated using comprehensive food composition and national drinking water quality monitoring databases––and all-cause, cardiovascular disease (CVD)-related, and cancer-related mortality over 27 years were examined using restricted cubic splines within Cox proportional hazards models adjusting for demographic, lifestyle, and dietary confounders. Analyses were stratified by factors hypothesised to influence the formation of carcinogenic N-nitroso compounds (namely, smoking and dietary intakes of vitamin C, vitamin E, folate, and polyphenols).ResultsPlant-sourced nitrate intake was inversely associated with all-cause mortality [HRQ5vsQ1: 0.83 (0.80, 0.87)] while higher risks of all-cause mortality were seen for higher intakes of naturally occurring animal-sourced nitrate [1.09 (1.04, 1.14)], additive permitted meat-sourced nitrate [1.19 (1.14, 1.25)], and tap water-sourced nitrate [1.19 (1.14, 1.25)]. Similar source-dependent associations were seen for nitrite and for CVD-related and cancer-related mortality except that naturally occurring animal-sourced nitrate and tap water-sourced nitrate were not associated with cancer-related mortality and additive permitted meat-sourced nitrate was not associated with CVD-related mortality. No clear patterns emerged in stratified analyses.ConclusionNitrate/nitrite from plant sources are inversely associated while those from naturally occurring animal-sources, additive-permitted meat sources, and tap water-sources are positively associated with mortality.

Groundwater chemical evolution characteristics and human health risk assessment in Shicheng County, Jiangxi Province.

Groundwater plays a pivotal role in the water resources of Shicheng County; however, the issue of excessive fluoride content in groundwater and its associated health risks often goes unnoticed. Groundwater assumes a crucial role in the hydrological dynamics of Shicheng County; nevertheless, the matter concerning elevated levels of fluoride within groundwater and its accompanying health hazards frequently evades attention. The hydrogeochemical analysis, obscure comprehensive water quality assessment based on cloud model, and probabilistic human health risk assessment using Monte Carlo simulation were conducted on 34 collected water samples. The findings indicate that the predominant groundwater hydrochemical types are SO4·Cl-Na and HCO3-Na. The processes of rock weathering and cation exchange play crucial roles in influencing water chemistry. Groundwater samples generally exhibit elevated concentrations of F-, surpassing the drinking water standard, primarily attributed to mineral dissolution. The concentrations of F- in more than 52.94% and 23.53% of the groundwater samples exceeded the acceptable non-carcinogenic risk limits for children and adults, respectively. Considering the inherent uncertainty in model parameters, it is anticipated that both children and adults will have a probability exceeding 49.36% and 30.50%, respectively, of being exposed to elevated levels of F ions in groundwater. The utilization of stochastic simulations, in contrast to deterministic methods, enables a more precise depiction of health risks. The outcomes derived from this investigation possess the potential to assist policymakers in formulating strategies aimed at ensuring the provision of secure domestic water supplies.

Detection of <I>Giardia</I> cyst and <I>Taenia</I> eggs in river water samples collected from Aleto River in Eleme Local Government Area, Rivers State, Nigeria

This study aimed at detecting Giardia cyst and Taenia eggs in river water samples collected from Aleto River in Eleme Local Government Area, River State, Nigeria, to check for potability and purity of water.Water samples of river water were collected from two points (downstream and midstream) of Aleto river. Labelled clean sample bottles with tightly-fitted lids were used to collect water samples from the river and stored in coolers with ice packs to maintain their temperature and transportation to the laboratory. Collected water samples were transferred into sterile vial tubes and centrifuged at 2000 rpm for five minutes, after which the supernatant fluids were discarded, while the sediments smeared using spatula on labelled slides stained with specific dyes, using the wet mounts, trichome stain technique, and modified Ziehl–Neelsen techniques, and viewed under the light microscope to identify the presence of parasites.This study showed the presence of ten trophozoites of Giardia lamblia and ten eggs of Taenia species isolated from the river water samples collected. These findings highlight the potential health risks associated with waterborne parasites and emphasizes the importance of effective water quality management to safeguard public health. Furthermore, the observation of parasites underscores the need for comprehensive water quality assessment and monitoring. This study highlights the contamination of the river with parasitic organisms and urgently needs effective water management strategies to mitigate the risks to public health. Vital interventions encompass enhancing sanitation practices, curbing fecal contamination, and augmenting water quality surveillance mechanisms.

Hydrochemical characterization and comprehensive water quality assessment of groundwater within the main stream area of Yishu River.

To gain a comprehensive understanding of the hydrochemical characteristics, controlling factors, and water quality of groundwater in the main stream area of Yishu River (MSYR), a study was conducted using water quality data collected during both the dry and wet seasons. Through statistical analysis, hydrochemical methods, fuzzy comprehensive evaluation, and health risk evaluation modeling, the water chemical characteristics of the main stream area of Yishu River were studied, and the water quality of the area was comprehensively evaluated. The findings indicate that HCO3- and Ca2+ are the predominant anions and cations in the MSYR during the dry and wet seasons, respectively. Moreover, anion concentration in groundwater follows HCO3- > SO42- > NO3- > Cl-, while cations are ranked as Ca2+ > Na+ > Mg2+ > K+. Overall, the groundwater manifests as weakly alkaline and is predominantly classified as hard-fresh water. During the wet season, there is greater groundwater leaching and filtration, with rock and soil materials more readily transferred to groundwater, and the concentrations of main chemical components in groundwater are higher than those during the dry season, and the hydrochemical types are primarily characterized as HCO3-Ca·Mg and SO4·Cl-Ca·Mg types. These results also suggest that the chemical composition of the groundwater in the MSYR is influenced mainly by water-rock interaction. The primary ions originate from the dissolution of silicate rock and carbonate rock minerals, while cation exchange plays a critical role in the hydrogeochemical process. Groundwater in the MSYR is classified mostly as class II water, indicating that it is generally of good quality. However, areas with high levels of class IV and V water are present locally, and NO3- concentration is a crucial factor affecting groundwater quality. In the wet season, more groundwater and stronger mobility lead to greater mobility of NO3- and wider diffusion. Therefore, the risk evaluation model shows that nitrate health risk index is higher in the wet season than it is in the dry season, with children being more vulnerable to health risks than adults. To study groundwater in this area, its hydrochemical characteristics, water quality, and health risk assessment are of great practical significance for ensuring water safety for residents and stable development of social economy.

Temporal and spatial water quality impacts of point-source versus catchment-derived nitrogen loads in an urbanised estuary

The Brisbane River estuary is an anthropogenically-impacted waterway in southeast Queensland, Australia. The estuary is over 80 km long and flows through an urbanised region. It receives over 500 t per year of total nitrogen (N) from direct point-source discharges in addition to sporadic flood loads of N from an agriculturally impacted upper catchment. Comprehensive water quality monitoring data for the estuary have been collected from at least 2001. This monitoring data includes ambient nutrient concentrations in the estuary, nutrient concentration and volume of the catchment inflows, and nutrient concentration and volume of point source discharges. This long-term data from a range of sources was used to determine temporal and spatial variations in concentrations, forms, stores and loads of N along the estuary for the period 2001 to 2022. Results showed that, during low-flow periods, the store of N in the mid-upper estuary (33–81 km upstream) is significantly determined by point-source discharges to this reach, and therefore the store of N can be modelled. Model parameters are the daily point source loads, a point source load decay factor, and a background constant store. In the lower estuary (0–33 km upstream) N store can be accurately determined based on dilution with seawater, with point sources not having significant influence on total N in the reach. Total N from large flood events was found to largely pass through the estuary without detectable removal processes, delivering catchment derived N directly to coastal waters. This work informs potential application of nutrient offsets in the estuary, guiding where and when offset options will be effective to mitigate the water quality impacts of point-source nutrients.

Integrated assessment of groundwater contamination: A multi-marker approach for comprehensive water quality monitoring

Abstract This study investigated the presence of adenoviral markers (Library-Independent microbial source tracking tool) in the groundwater of Ludhiana, Punjab, India, a hitherto unexplored area. While deep aquifers post-chlorination were adenovirus free, shallow aquifers near farm wastewater pits exhibited human adenovirus and bovine adenovirus. Coliform-negative samples also harboured pathogens, highlighting the limitations of conventional indicators. Surface water displayed higher viral contamination, potentially impacting groundwater. The use of farm wastewater for irrigation and open pit disposal emerged as key contributors, advocating for sustainable wastewater management. Physiochemical and microbial analyses revealed variations across sites, emphasising regional and temporal variations. The weighted arithmetic water quality index ranged from good to very poor, with deep aquifers showing better quality than shallow ones. A novel approach incorporating graphical representations of adenovirus estimations alongside water quality index provided a more comprehensive understanding. Intriguingly, the study revealed the presence of coliforms irrespective of water quality grade, questioning its reliability as a sole indicator. Correlations between specific water quality grades and adenovirus types suggested targeted control measures. The lack of significant correlations between viral markers and conventional parameters in groundwater compared to surface water studies highlighted the unique dynamics of groundwater contamination.

Development of AI-based hybrid soft computing models for prediction of critical river water quality indicators.

Prediction of river water quality indicators (RWQIs) using artificial intelligence (AI)-based hybrid soft computing modeling techniques could provide essential predictions required for efficient river health planning and management. The study described the development of a novel AI-based relative weighted ensemble (AIRWE) hybrid model for predicting critical RWQIs, i.e., biochemical oxygen demand (BOD) and total coliform (TC). The study involved comprehensive water quality (WQ) monitoring from 30 locations along the Damodar River to establish the baseline data and delineate the WQ. The representative input features showing a strong association with BOD and TC were identified using Spearman's rank-coupled orthogonal linear transformation (SOT). The relative weighted ensemble (RWE) method was applied to determine the relative weights for base learners in the AIRWE model. The statistical analysis of the developed model revealed that it was most efficient and accurate for predicting BOD (R2, 0.97; RMSE, 0.06; MAE, 0.04) and TC (R2, 0.98; RMSE, 0.06; MAE, 0.05) over the traditional techniques. The tstat (BOD 0.02 and TC 0.47) was lesser than tcrit (1.672), confirming its unbiased predictions. The SOT technique removed the data noise and multicollinearity, whereas RWE curtailed the individual model's limitations and predicted more reliable results. The model resulted 97% accuracy with high precision (96%) in classifying the river water quality for various end uses. The study describes a novel approach for researchers, scientists, and decision-makers for modeling and predicting various environmental attributes.

Deep hierarchical cluster analysis for assessing the water quality indicators for sustainable groundwater

Groundwater quality assessment is a paramount aspect of ensuring the availability of safe drinking water. The conventional reliance on individual parameter thresholds often overlooks intricate interdependencies within the dataset. To overcome this, deep learning algorithms are employed to automatically extract meaningful features from multidimensional data. This innovative approach aims to capture complex relationships between water quality parameters that may be missed by traditional methods. The research identifies a significant gap in current groundwater quality assessment methodologies, emphasizing the need for a more data-driven approach. This research addresses the limitations of traditional methods by introducing a pioneering approach that integrates deep learning and hierarchical cluster analysis to identify comprehensive water quality indicators. The study collects data from diverse monitoring wells, encompassing chemical, physical, and biological parameters. By applying deep cluster analysis to the feature-extracted data, latent patterns and relationships among water quality parameters are unveiled. This clustering method reveals hidden structures within the dataset, leading to the identification of water quality indicators that consider both individual parameters and their interactions. The proposed method in this research offers an understanding of groundwater quality dynamics, contributing to the advancement of water resource management strategies. The results of the deep cluster analysis provide a more comprehensive and accurate representation of groundwater quality, enabling better-informed decision-making for sustainable water resource utilization.

Spatio-temporal dynamics of ecological, bacteriological, and overall water quality of the Damodar River, India.

Assessing river water quality is crucial for human and ecological needs because of the deterioration of the river and escalated water pollution under the threats of anthropogenic activities. In order to assess river water quality, the Damodar River water was evaluated from the perspectives of spatio-temporal dynamics of ecological (organic pollution index or OPI and eutrophication index or EI), bacteriological (coliform count and comprehensive bathing water quality index or CBWQI), and overall water quality assessments (water quality index or WQI and comprehensive pollution index or CPI). The OPI reveals that 44.66% of water samples have heavy organic pollution; however, EI depicts that almost all water samples of Damodar River have severe eutrophication, especially in the pre- and post-monsoon seasons. Moreover, the fecal coliform count and CBWQI indicate the unsuitability of river water for bathing. The overall WQI portrays that 21.56%, 33.59%, and 22.47% of water samples have heavy pollution in pre-monsoon, monsoon, and post-monsoon, respectively. Moreover, 73.39% of water samples have low CPI indicating slight comprehensive pollution. This study also reveals that the pollution level in the Damodar River downstream of the Durgapur barrage is high among the other stations. The major reasons behind the severe contamination of Damodar River water are urban-industrial and agricultural effluents mixing into the river that lead to higher concentrations of BOD, DO, fecal coliform, COD, fluoride TSS, and turbidity in the river water. Thus, this study carries appreciated information on policy recommendations for the different stakeholders of the Damodar River basin including regional planners, agri-engineers, and ecological river engineers for sustainable river management.

The Role of Stream Restoration in Mitigating Sediment and Phosphorous Loads in Urbanizing Watersheds

Stream corridor erosion can majorly contribute to the overall sediment and phosphorus load in urbanizing watersheds. However, the relative contribution of stream bed and bank erosion, compared with upland watershed sources and the potential for stream restoration to mitigate total contaminant loads, is poorly understood. In this study, a new method was developed, using the percent impervious cover (PIC) indicator of urbanization to evaluate the relative contribution of the stream corridor versus upland watershed contributions to observed total sediment and phosphorus loads in the receiving watercourse. This method was used to develop a cost-optimized mitigation plan, including implementing low-impact development (LID) stormwater infrastructure for urban areas and agricultural best management practices (BMPs) for rural areas in the watershed and stream restoration for the degraded stream reaches. A new cost–benefit analysis methodology is developed and used to assess the relative benefits of the mitigation measures for the case study of the Tannery Creek sub-watershed of the East Holland River in Ontario, Canada. The novel contributions of this study include the development of three relationships to estimate sediment and associated phosphorus loading based on contributing catchment area and land use, as well as a method to optimize the costs and benefits of planned mitigation measures. The results support stream restoration as an essential and cost-effective part of a comprehensive water quality watershed management plan to help maintain healthy streams in urbanized watersheds.

Coupling Monte Carlo simulation with CRITIC-enhanced water quality assessment for the Weishui Reservoir

Abstract Traditional methods for water quality assessment often overlook the uncertainty of water quality data during the sample collection process, leading to limitations in their application. Therefore, this study combines the comprehensive water quality index (CWQI) method and the improved CWQI method based on CRITIC with the Monte Carlo method to evaluate the water quality in the Weishui Reservoir watershed. The results indicate that (1) there is a noticeable difference in water quality between the Shaxiping and Dayanzui sampling points. The water quality at the Shaxiping sampling point is excellent, with a water quality classification of Class I. In contrast, the water quality at the Dayanzui sampling point is comparatively poorer, with an average water quality classification of Class III. (2) Sensitivity analysis shows that TN, NH4+-N, and TP are more sensitive than other indicators, suggesting that they are the primary factors influencing the evaluation results. (3) Compared to the traditional CWQI method, combining the CRITIC-based improved CWQI method with the Monte Carlo method is more scientifically rigorous. It considers the variety of evaluation indicators, allocates weights rationally, and provides evaluation results that align better with seasonal variations, resulting in higher discriminative power.

Anthropogenic activities enhance mercury methylation in sediments of a multifunctional lake: Evidence from dissolved organic matter and mercury-methylating microorganisms

Multifunctional lakes are highly susceptible to anthropogenic influences, potentially introducing exogenous pollutants or nutrients into aquatic sediments. This, in turn, affects the mercury (Hg) methylation in the sediments. This study was conducted in the Changshou Lake, a representative multifunctional lake in southwestern China, with a specific focus on investigating the Hg variations, the potential of Hg methylation, and the influential factors affecting the methylation process within sediments across different functional areas. The results revealed significant variations in total Hg concentrations between the ecological culture area (area I), the ecological tourism area (area II), and the wetland protection area (area III), suggesting the possibility of exogenous Hg introduction associated with human activities. Furthermore, sediments from areas I and II displayed a greater potential for Hg methylation. This was ascribed to the enhanced diversity and relative abundance of Hg-methylating microorganisms, especially Geobacteraceae, induced by elevated levels of dissolved organic carbon in these two areas from human activities like historical cage culture. This study provides evidence that anthropogenic activities enhance the process of Hg methylation in the sediments of multifunctional lakes, highlighting the necessity of implementing comprehensive scientific water quality management practices to mitigate the negative impacts of human influences on these unique ecosystems.

Three-dimensional augmentation for hyperspectral image data of water quality: An Integrated approach using machine learning and numerical models

This research introduces a comprehensive methodology to enhance hyperspectral image data (HSD) utility, specifically focusing on the three-dimensional (3-D) augmentation of Chlorophyll-a (Chl-a). This study comprises three significant steps: (1) the augmentation of limited field water quality data in terms of time interval and number of variables using neural network models, (2) the generation of 3-D data using numerical models, and (3) the extension of the hyperspectral image data into 3-D data using machine learning models. In the first phase, Multilayer Perceptron (MLP) models were developed to train water quality interactions and successfully generated high-frequency water quality data by adjusting biased measurements and predicting detailed water quality variables. In the second phase, high-frequency data generated by MLP models were applied to develop two numerical models. These numerical models successfully generated 3-D data, thereby demonstrating the effectiveness of integrating numerical modeling with neural networks. In the final phase, ten machine learning models were trained to generate 3-D Chl-a data from HSD. Notably, the Gaussian Process Regression model exhibited superior performance, effectively estimating 3-D Chl-a data with robust accuracy, as evidenced by an R-square value of 0.99. The findings align with theories of algal bloom dynamics, further validating the effectiveness of the approach. This study demonstrated the successful integrated development for HSD extension using machine learning models, numerical models, and original HSD, highlighting the potential of such integrated methodologies in advancing water quality monitoring and estimation. Notably, the approach leverages readily accessible data, allowing for the swift generation of results and bypassing time-consuming data collection processes. This research marks a significant step towards more robust, comprehensive water quality monitoring and prediction, thereby facilitating better management of aquatic ecosystems.

Assessment of water quality and associated human health risk of a tropical freshwater body in Edo State, Nigeria

The quality of freshwater sources is a key factor in sustainable development. This study investigated the water quality suitability of Ossiomo River for human consumption and the implications for public health. Water samples were collected and analysed from October 2021 to March 2022. Results indicated that the water physico-chemical parameters from sampled locations were within their respective National Environmental Standards and Regulation Enforcement Agency (NESRA) permissible limits except for pH (< 6.5), dissolved oxygen (< 6.0mg L−1), cadmium (> 0.005m /L−1), manganese (> 0.05mg L−1) and total hydrocarbon content (>10mg L−1). Comprehensive pollution index (0.4–1.0) and water quality index (251.57–765.37) values indicate that the water body was slightly polluted and unsuitable for human consumption (WQI > 50). Health risk assessment showed that children and adults were predisposed to non-carcinogenic risk (HIoral > 1) and carcinogenic risk (ICR: 1.21E-02 and 2.84E-03) from heavy metals through oral exposure. Natural run-off and anthropogenic activities were identified sources of heavy metals and hydrocarbons within the catchment. The study has provided data for water resource quality monitoring and raised concerns as to the need for immediate action to safeguard the public health of communities surrounding the Ossiomo River, through the provision of alternative sources of potable water.

Assessing Hydrological Dynamics during the Monsoon Season in the Hooghly River, West Bengal, India: An Evaluation of Comprehensive Pollution Index and Water Quality index

This study meticulously evaluates the water quality parameters of the Hooghly River during the period spanning 2022 to 2023, specifically during high tide, with a particular focus on the locales of Kamarhati, Panihati, and Khardah. A comprehensive analysis encompassed both physico-chemical and biological parameters. The application of Pollution indices, notably the Water Quality Index (WQI) and Comprehensive Pollution Index (CPI), elucidates the water quality status at the selected sampling locations. Elevated E. coli levels (Kamarhati: 10000, Panihati: 15000, Khardah: 20000), turbidity, and total suspended solids exceeded WHO and BIS standards, categorizing the river as slightly polluted (CPI: Kamarhati-0.67, Panihati-0.68, Khardah-0.59), rendering the water unsuitable for consumption (WQI &gt; 100). Low dissolved oxygen levels (1.9-2.4 mg/l) and significant microbial contamination (coliform: 10000-20000 MPN) underline the urgent need for targeted interventions. While pH, temperature, and DO levels suggested a stable aquatic environment, elevated TDS, TSS, and E. coli concentrations signify potential pollution sources, necessitating immediate water treatment. Moderate levels of BOD and COD indicate a manageable organic pollutant load, and nitrate and nitrite within permissible limits are positive.

Modelagem integrada de qualidade e quantidade para gestão de recursos hídricos: estudo de caso na Bacia do Alto Paranapanema

ABSTRACT This study introduces an integrated model for water resources planning and management that accounts for both the quantity and quality of water in watersheds and reservoirs. Therefore, it provides a comprehensive approach for better understanding and assessing water systems. The model facilitates analyses of the spatial and temporal dynamics of hydrological processes, pollutant transport, and the behavior of rivers and reservoirs. The study evaluated pollutant load transport in the Upper Paranapanema Basin by applying the Soil Moisture Accounting Procedure (SMAP) model, Muskingum method, Streeter-Phelps buildup/washoff model, and zero-dimensional (0D) models to estimate inflows to the Jurumirim reservoir. A Python-based public library was developed to model all hydrological processes. The model calibration yielded a NSE, KGE, PBIAS and RMSE result of 0.746, 0.778, 6.5% and 73.3 m 3 s − 1, respectively. Indicating a robust simulation that attests to the versatility and reliability of the proposed approach. The methodology outlined in this research enables comprehensive water quality simulation at basin scale, thereby serving as a tool for understanding water quantity and quality dynamics and support decision-making regarding water resource planning and management.

Long-term trend prediction of surface water quality of two main river basins of China using Machine Learning Method

Rivers play a crucial role as sources of freshwater worldwide. The Yangtze and Yellow rivers, often regarded as the lifelines of China, significantly contribute to the nation's economy through their abundant water resources. Maintaining the quality of water in these river basins is essential for both human health and a thriving ecological system. This study intended to assess the historical water quality of China's two mother river basins including the Yangtze River, and the Yellow River. This assessment analyzes the historical dissolved oxygen, Ammonium nitrogen, and permanganate, and calculates comprehensive water quality identification (CWQII) values to find the trend and predict future values using a Machine learning model. To evaluate the stationarity of historical data, different tests and measures have been taken such as ADF and KSPP to test stationarity. Autocorrelation and partial autocorrelation also have been performed to understand the nature of the data. According to the SARIMA model, the Mean Absolute Percentage Error (MAPE) for the CWQII was 9.1% for the Yangtze and 13.3% for the Yellow River. In contrast, the LSTM model yielded MAPE values of 7% and 14%, respectively. For Dissolved Oxygen (DO) prediction, the SARIMA model showed MAPEs of 8.9% for the Yangtze and 9.87% for the Yellow River, while the LSTM model resulted in 8% and 16%, respectively. The current and future forecasting of CWQII values suggest that, based on model forecasts, the water quality in the Yellow River basin may be poorer compared to the Yangtze River basin This study might help the government, researchers, and other stakeholders to take proper steps to develop water pollution control strategy.

Cluster Analysis to Identify Priority Areas for Water Quality Management in Water Supply Systems

Zoning methodologies, such as district metered areas (DMA), are commonly employed to robustly maintain water pipe network systems in both normal and abnormal situations. However, the management and evaluation typically associated with existing DMAs primarily focus on ensuring stable water volume and pressure. Consequently, these methods do not adequately address the maintenance of water quality elements within the water supply system, such as adequately managing residual chlorine and reducing water quality complaints. This study introduced a zoning tailored explicitly for managing water quality-oriented elements, facilitating stable water quality management, and enhancing responses to water quality incidents in large-scale domestic water supply networks. A method was proposed to establish priorities for each zone, using various geographic information system (GIS)-based water quality-related structured data (such as water quality measurement data and pipe data) and unstructured data (such as water quality complaints). Comprehensive water quality management was achieved by applying machine learning techniques based on clustering analysis to derive evaluation factors. The proposed methodology was implemented in Metropolitan City A in Korea, leading to the derivation and analysis of evaluation results. This data-centric water supply network priority management area designation methodology, as presented in this study, is anticipated to serve as a valuable decision-making tool for enhancing the accuracy and reliability of water supply network operation and the overall management of water supply operators.

A Coherent Approach of Indexing Methods to Assess the Water Quality of River Bodies in Arrah, Bihar, India

Different river bodies are diverse sustaining systems that provide freshwater to rapidly growing needs at the domestic, agricultural and industrial levels of the people of India. The river bodies need periodical examination for conservation and rejuvenation because they are currently under stress due to pollution and climate change. Arrah is a district of Bihar, India and occupies an area of 2395 km2 for agriculture as the predominant activity. It is an attempt to assess the water quality of River Ganga, Sone and Gangi in this area by combining the overall pollution index (OPI), comprehensive pollution index (CPI) and water quality index (WQI) from November 2021 to October 2023 from 648 water samples and nine sampling sites to establish a relationship between any change in water quality and their sources. We assessed eleven water quality parameters following standard methods for winter, summer and monsoon seasons. Observations showed that dissolved oxygen, total alkalinity, fluoride and rarely biochemical oxygen demand exceeded Bureau of Indian Standards (BIS 2012) prescribed limits and indicated deterioration in the water quality of these river bodies. Two-way ANOVA suggested that the water quality parameters of these river bodies showed significant differences (p&lt;0.05). We used Numerical equations to transform the concentration values into pollution indices. Values of OPI (1.70 to 2.40) and CPI (0.65 to 0.90) indicate acceptable/ slightly polluted/moderately polluted water in these rivers. Values of WQI of these river bodies ranged from 182.0-192.1, 220.1-242.2 and 190.7-238.9 during winter, summer and monsoon seasons, indicating poor/very-poor water quality. The observation shows that the causes of the declining water quality in these water bodies are anthropogenic activity, agricultural waste runoff and entry of untreated sewage. To reduce the time-consuming and expensive water quality monitoring and assessment programmes for these river bodies, the water quality of these water bodies felt the need to adopt proper management policies and conservation efforts.

Nitrogen and Phosphorus Pollution Discharge and Water Quality Evaluation in a Small Basin of the Upper Reaches of Lijiang River

The Lijiang River Basin is a humid, subtropical, karst landform in China and is listed as a World Heritage Site. However, with the rapid development of urbanization and tourism activities in recent years, it faces increasingly severe non-point source pollution. To understand the temporal and spatial variations in nitrogen and phosphorus pollution discharge and the changes in river water quality, the Jingui Small Basin, in the upper reaches of the Lijiang River, was chosen as a representative system. Changes in nitrogen and phosphorus concentrations were continuously monitored in the main river channel and the river water quality was evaluated using the comprehensive water quality identification index method. The results indicated that there were obvious seasonal changes in nitrogen and phosphorus discharge loads in the basin. Both nitrogen and phosphorus discharge loads were higher in the crop-growing season than in the non-growing season. No significant difference in nitrogen and phosphorus discharge load between different scales was found, and the scale was not the key factor affecting the nitrogen and phosphorus discharge load of Jingui River. As the river flowed from the initial water source to the outlet of the basin, water quality was characterized by the spatial pattern of the upper reaches &gt; the middle reaches &gt; the lower reaches. Except for the water quality at the outlet of the basin in November and December, which reached Class V, the comprehensive water quality of each sub-basin reached the target water quality of the water function zoning from May to December. The elucidation of the nitrogen and phosphorus pollution discharge patterns in the Jingui River and the changes in water quality provide a reference for the control and management of agricultural non-point source pollution in the Lijiang River Basin.