Water plays a significant role in sustaining the lives of humans and other living organisms. Groundwater quality analysis has become inevitable, because of increased contamination of water resources and global warming. This study used machine learning (ML) models to predict the water quality index (WQI) and water quality classification (WQC). Forty groundwater samples were collected near the Ranipet industrial corridor, and the hydrogeochemistry and heavy metal contamination were analyzed. WQC prediction employed random forest (RF), gradient boosting (GB), decision tree (DT), and K-nearest neighbor (KNN) models, and WQI prediction used extreme gradient boosting (XGBoost), support vector regressor (SVR), RF, and multi-layer perceptron (MLP) models. The grid search method is used to evaluate the ML model by F1 score, accuracy, recall, precision, and Matthews correlation coefficient (MCC) for WQC and the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and median absolute percentage error (MAPE) for WQI. The WQI results indicate that the groundwater quality of the study area is very poor and unsuitable for drinking or irrigation purposes. The performance metrics of the RF model excelled in predicting both WQC (accuracy = 97%) and WQI (R2 = 91.0%), outperforming other models and emphasizing ML\'s superiority in groundwater quality assessment. The findings suggest that ML models perform well and yield better accuracy than conventional techniques used in groundwater quality assessment studies.

Water quality, increasingly recognized for its significant impact on health, is garnering heightened attention. Previous studies were limited by the number of water quality indicators and the duration of analysis. This study assessed the drinking water quality and its associated health risk in suburban areas of Wuhan, a city in central China, from 2016 to 2021. We collected 368 finished water samples and 1090 tap water samples and tested these for 37 different indicators. The water quality was evaluated using the water quality index, with trends over time analyzed via the Mann-Kendall test. Furthermore, an artificial neural network model was employed for future water quality prediction. Our findings indicated that the water quality in rural Wuhan was generally good and had an improvement from 2016 to 2021. The qualification and excellent rates were 98.91% and 86.81% for finished water, and 97.89% and 78.07% for tap water, respectively. The drinking water quality was predicted to maintain satisfactory in 2022 and 2023. Additionally, principal component analysis revealed that the primary sanitary issues in the water were poor sensory properties, elevated metal contents, high levels of dissolved solids, and microbial contamination. These issues were likely attributable to domestic and industrial waste discharge and aging water pipelines. The health risks associated with the long-term consumption of this water have been steadily decreasing over the years, underscoring the effectiveness of Wuhan\'s ongoing water management efforts.

The environmental conditions of a lake are influenced by its type and various environmental forces such as water temperature, nutrients content, and longitude and latitude to which it is exposed. Due to population growth and development limits, former mining lakes are being converted to more lucrative land uses like those of recreational zones, agriculture, and livestock. The fungus Ganoderma lucidum has the potential to be utilised as a substitute or to perform synergistic bacteria-coupled functions in efficient contaminated lake water treatment. The purpose of this paper is to evaluate the water quality and water quality index (WQI) of an ex-mining lake named Main Lake in the Paya Indah Wetland, Selangor. Furthermore, the current work simulates the use of a Malaysian fungus in decolourising the contaminated ex-mining lake by the BioDeF system in a 300 mL jar inoculated with 10% (v/v) of pre-grown Ganoderma lucidum pellets for 48 h. According to the results, the lake water is low in pH (5.49 ± 0.1 on average), of a highly intense dark brownish colour (average reading of 874.67 ± 3.7 TCU), and high in iron (Fe) content (3.2422 ± 0.2533 mg/L). The water quality index of the lake was between 54.59 and 57.44, with an average value of 56.45; thus, the water was categorized as Class III, i.e., under-polluted water, according to the Malaysian Department of Environment Water Quality Index (DOE-WQI, DOE 2020). The batch bioreactor BioDeF system significantly reduced more than 90% of the water\'s colour. The utilization of Ganoderma lucidum as an adsorbent material offers a variety of advantages, as it is easily available and cultivated, and it is not toxic.

Many physicochemical and biological monitored parameters must be taken into consideration to fully evaluate the surface water environmental condition. However, there are situations where a simple and rapid assessment of the poor water quality situations is critically important. This work presents a universal methodology for monitoring of many parameters simultaneously and early detection out-of-control samples in a real-time mode. The approach uses multivariate statistical quality control chart based on Principal Component Analysis (PCA) model with two well-known measures of abnormal behaviour in a process or system: Hoteling\'s T2 statistics and Q-statistic. The proposed TQ_PCA quality index provides on-line assessment of the water sample quality, with no specific knowledge and assumptions about control limits for monitored parameters required. A water sample is assessed through the simple control chart using the PCA model established for training/reference samples. The power of the proposed index has been tested using water quality data from the Oder River, including the time of the largest ecological disaster in recent European river history. The proposed index showed excellent analysis performance for physicochemical water quality dataset from Polish stations and physicochemical and biological water quality dataset from German/Frankfurt station, confirming earlier reports. There were consecutive number of alarms reported by the statistical index, a month prior to the disaster when there were no evident changes in the individual parameters. The method presented in this study demonstrated capability of assessment of the major water quality parameters, whose changes preempt the uncommon event. The presented TQ_PCA index could be easily extended to any research involving a large dataset of monitoring parameters from any industrial chemical process.

Manchar Lake, Pakistan\'s biggest lake in the arid zone, faces human-induced salinity issues. This study investigated its effects on the multifaceted ecosystem services, including serving as a source of drinking and irrigation water and aquatic health through assessing fish diversity and characteristics. Analyses of 189 water samples from 21 sites revealed spatiotemporal variations in major ions contributing to lake water salinity. The study assessed water suitability for drinking and agriculture using the water quality index (WQI), sodium adsorption ratio (SAR), magnesium hazard (MH), sodium percent (Na%), and Kelly\'s ratio (KR). The WQI, ranging from 141 to 408, indicated that the lake water was unfit for drinking. In some seasons, such as the pre-monsoon period, the lake water was deemed unsuitable for irrigation due to high SAR values (18 ± 4 g/L, average ± standard deviation), consistently rising MH values exceeding 66 in all seasons and elevated sodium percentages surpassing 66% in both the pre-monsoon and monsoon seasons. The KR remained acceptable (averaging 0.8 to 2.5) in all seasons. Fish health in highly saline conditions was assessed using data from interviews, focus group discussions, and fish sampling (1684 fish from 10 sites). Results depicted that high salt contamination severely impacted fish length and weight. The study found low richness (Simpson\'s biodiversity: 0.697 and Shannon Weaver: 1.51) and evenness (Pielou\'s index: 0.48) among the fish populations. Since 1998, Manchar Lake has seen a decline in fish varieties from 32 to 23, with changes in fish species\' feeding habits. To improve lake water quality, the study recommends diverting saline water to the sea before and after the monsoon season while utilizing freshwater from alternative sources to fill any water deficit.

Groundwater is important for human survival and development, particularly in arid and semi-arid regions. This study aimed to analyze the hydrochemical characteristics, influencing factors, and the impact of human activities on groundwater in the semi-arid plains of western Jilin Province, northwest China. The study collected 88 and 151 phreatic and confined water samples, respectively, which were analyzed for 13 water quality indicators using statistical and graphical methods. In order to investigate the impact of anthropogenic activities on water quality and health risks, the improved combined weighted water quality index (ICWQI) based on the entropy weight, criteria importance though inter-criteria correlation (CRITIC), the coefficient of difference method, subjective weight based on quality grading criteria, and the water quality index (WQI) were proposed to evaluate the water quality of the study area. Meanwhile, the human health risk assessment (HHRA) model was used to assess the risks of nitrate to the health of humans in different ages and sex categories. The results indicated that the groundwater in the study area was weakly alkaline and the main hydrochemical types in the phreatic and confined water were HCO3-·Ca-Mg and HCO3--Na. Rock weathering was the dominant process responsible for the generation of groundwater ions, the ions in groundwater primarily originate from the dissolution of halite, gypsum, and feldspar, while dolomitization promotes an increase in Mg2+. Human activities lead to an increase in NO3- in groundwater and have an impact on water quality and human health risks. The ICWQI method was found to yield more precise and rational assessments of water quality. Groundwater quality is primarily affected by nitrate ions. The areas in which groundwater nitrate posed a higher risk to human health were found to be mainly in the saline-alkali lands of Qian\'an, Tongyu, and Zhenlai. Fertilizers, pesticides, and livestock farming activities contribute to the pollution of surface water. This surface contamination then infiltrates abandoned confined wells, leading to contamination of the confined aquifers. This study can improve the understanding of groundwater hydrochemical characteristics and the impact of human activities on groundwater in the study area. This study can also contribute to the study of groundwater in semi-arid regions.

Surface water pollution has always posed a serious challenge to water quality management. Improving water quality management requires figuring out how to comprehend water quality conditions scientifically and effectively as well as quantitatively identify regional pollution sources. In this study, Xianghai Lake, a typical lake-type wetland on the Northeast China Plain, was taken as the research area. Based on a geographic information system (GIS) method and 11 water quality parameters, the single-factor evaluation and comprehensive water quality index (WQI) methods were used to comprehensively evaluate the water quality of the lake-type wetland in the level period. Four key water quality parameters were determined by the principal component analysis (PCA) method, and more convenient comprehensive water quality evaluation models, the minimum WQI considering weights (WQImin-w) and the minimum WQI without considering weights (WQImin-nw) were established. The multiple statistical method and the absolute principal component score-multiple liner regression (APCS-MLR) model were combined to analyse the lake pollution sources based on the spatial changes in pollutants. The findings demonstrated that the WQImin-nw model\'s water quality evaluation outcome was more accurate when weights were not taken into account. The WQImin-nw model can be used as a simple and convenient way to comprehend the variations in water quality in wetlands of lakes and reservoirs. It was concluded that the comprehensive water quality in the study area was at a \"medium\" level, and CODMn was the main limiting factor. Nonpoint source pollution (such as agricultural planting and livestock breeding) was the most important factor affecting the water quality of Xianghai Lake (with a comprehensive contribution rate of 31.65%). The comprehensive contribution rates of sediment endogenous and geological sources, phytoplankton and other plants, and water diversion and other hydrodynamic impacts accounted for 25.12%, 19.65%, and 23.58% of the total impact, respectively. This study can provide a scientific method for water quality assessment and management of lake wetlands, and an effective support for migration of migratory birds, habitat protection and grain production security.

As the second largest reservoir in China, the Danjiangkou Reservoir (DJKR) serves as the water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC), i.e., the currently longest (1273 km) inter-basin water diversion project in the world, for more than eight years. The water quality status of the DJKR basin has been receiving worldwide attention because it is related to the health and safety of >100 million people and the integrity of an ecosystem covering >92,500 km2. In this study, basin-scale water quality sampling campaigns were conducted monthly at 47 monitoring sites in river systems of the DJKRB from the year 2020 to 2022, covering nine water quality indicators, i.e., water temperature (WT), pH, dissolved oxygen (DO), permanganate index (CODMn), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), and fluoride (F-). The water quality index (WQI) and multivariate statistical techniques were introduced to comprehensively evaluate water quality status and understand the corresponding driving factors of water quality variations. An integrated risk assessment framework simultaneously considered intra and inter-regional factors using information theory-based and the SPA (Set-Pair Analysis) methods were proposed for basin-scale water quality management. The results showed that the water quality of the DJKR and its tributaries stably maintained a \"good\" status, with all the average WQIs >60 of river systems during the monitoring period. The spatial variations of all WQIs in the basin showed significantly different (Kruskal-Wallis tests, P < 0.01), while no seasonal differences were found. The increase in built-up land use and agricultural water consumption revealed the highest contributions (Mantel\'s r > 0.5, P < 0.05) to the rise of nutrient loadings of all river systems, showing the intensive anthropogenic activities can eclipse the power of natural processes on water quality variations to some extent. The risks of specific sub-basins that may cause water quality degradation on the MRSNWDPC were effectively quantified and identified into five classifications based on transfer entropy and the SPA methods. This study provides an informative risk assessment framework that was relatively easy to be applied by professionals and non-experts for basin-scale water quality management, thus providing a valuable and reliable reference for the administrative department to conduct effective pollution control in the future.

Groundwater is a naturally occurring potential source for drinking, irrigation, agricultural and industrial purposes. The population growth and accelerated development of industries and agriculture activity degrade groundwater quality. The groundwater quality of an area was determined by the physical and chemical parameters, influenced by geology, soil, land use, land cover and anthropogenic activities. Perambalur district in Tamil Nadu has been selected as a study area with a total geographical area of around 1757 km2. In the study area, groundwater quality decreases due to the usage of chemical fertilisers and pesticides in agricultural land and mining activities. So, the hydrogeochemical assessment will help to determine the groundwater suitability for drinking. Forty-eight groundwater samples were collected from the study area during the pre-monsoon (July 2021) and post-monsoon season (January 2022). Samples were analysed using the standard methods prescribed by the American Public Health Association for pH, electrical conductivity (EC), total dissolved solids (TDS), calcium, magnesium, sodium, potassium, carbonate, bicarbonate, chloride, sulphate, nitrate and fluoride. The spatial distribution of major physiochemical parameters is mapped using the inverse distance weighted (IDW) interpolation technique. The evaluation of hydrochemical facies from piper plots revealed that the major cation and anion were in the order of Ca2+  > Mg2+  > Na+  > K+ and Cl-  > HCO3-  > SO42-  > NO3- in both seasons, respectively. Further, the plot explains the presence of both permanent and temporary hardness in the groundwater. The evaluation of hydrochemical facies from the piper plot emphasises that the reverse ion exchange controls groundwater chemistry. The assessment of chloro-alkaline indices reveals that the sodium and potassium in groundwater get substituted with magnesium and calcium in the parent rock, which determines the groundwater composition. The values of saturation indices reveal that calcite and dolomite are supersaturated and tend to precipitate. From principal component analysis, the principal components have an eigenvalue of more than 1, containing 79.8% and 79.2% in the total variance in pre-monsoon and post-monsoon, respectively. Most physiochemical parameters like TDS, EC, Na+, Mg2+, Cl- and SO42 - have strong positive loading and are responsible for the changes in groundwater chemistry. Finally, the calculation of the water quality index identified that groundwater quality in post-monsoon tends to decline compared to pre-monsoon.

Water is essential for human survival. Its quality must be maintained to prevent any potential health problems. Pollution and contamination are likely causes of the water quality decline. This may occur if the world\'s rapidly expanding population and industrial facilities fail to clean their effluent correctly. The Water Quality Index, often known as the WQI, is the indicator most frequently used to characterize surface water quality. This study emphasizes several WQI models that could be of use to us in determining the level of water quality available in the various areas. We have tried to cover multiple essential procedures and their corresponding mathematical counterparts. In this article, we also examine the applications of index models in different types of water bodies, such as lakes, rivers, surface water, and groundwater. The level of contamination in water due to pollution directly affects the overall quality of water. A pollution index is a valuable tool for measuring the level of pollution. Concerning this, we have discussed two approaches, namely, the Overall Index of Pollution and Nemerrow\'s Pollution Index, which demonstrate the most effective technique to evaluate the water standard. Examining the similarities and differences between these approaches may offer researchers a suitable starting point to delve further into assessing water quality.