Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey)

Innovative interpretable AI-guided water quality evaluation with risk adversarial analysis in river streams considering spatial-temporal effects

Water security remains a critical issue given the looming threats of industrial pollution, necessitating comprehensive assessments of water quality to address seasonal fluctuations and influential factors while formulating effective strategies for decision makers. This study introduces a novel approach for evaluating water quality within a complex riverine zone in South Korea: Han River that encompasses five river streams situated at each junction of North and South streams (including Gyeongan Stream) that ultimately leading towards Paldang Lake. By utilizing the monthly water characteristic data from the year 2013-2022 across 14 different locations, the significant seasonal trends and potential influences on water quality are identified. The water quality here is calculated with the proposed method of sub-index water quality index (s-WQI). A combinatorial prediction approach of s-WQI for each location is conducted through a collective of data preprocessing approaches including Hampel filtering and feature selection in prior to the machine learning predictions. In return, light gradient boosting (LGB) is the most accurate predictor by outperforming other prediction algorithms, especially through LGB-Pearson and LGB-Spearman combinations for North and South stream intersections, and LGB-Pearson for Paldang Lake. To further evaluate the robustness of this evaluation and extending the results to a foreseeable scenario, a seasonal based Monte-Carlo Simulation with 10,000 attempts targeting the water characteristic distributions obtained from each location considered are carried out to identify the risk bounds within. The results are further interpreted with SHAP analysis on identifying the contributions of each water characteristics towards the water quality through local and global spectrum. This research yields practical implications, offering tailored strategies for water quality enhancement and early warning systems. The integration of AI-based prediction and feature selection underscores the transformative potential of computational techniques in advancing data-driven water quality assessments, shaping the future of environmental science research.

Water quality evaluation based on water quality index and multiple linear regression: A research on Hanyuan Lake in southern Sichuan Province, China

This study aims to understand the changes in the water quality of Hanyuan Lake and to show these changes over time. In this study, monthly sampling was conducted at three sampling sites in Hanyuan Lake, and water samples were measured for water quality indicators in the laboratory according to the methods specified in the Environmental Quality Standards for Surface Water (GB3838-2002). Based on the monitoring data from January to December 2019, the WQI comprehensive evaluation method was used to conduct multiple linear stepwise regression analysis, extract key indicators, and establish the WQImin model. The results show that according to the WQI comprehensive evaluation method, the WQI values of Hanyuan Lake are all above 90, and the grade is excellent. The overall water quality of Hanyuan Lake is excellent, and most of the water quality indexes reach the Class I standard in the Environmental Quality Standards for Surface Water (GB3838-2002). WQImin1 (R2 = 0.86, p < 0.001, PE = 4.28) as the best WQImin model. In this study, a model with fewer parameters was established by multiple linear regression method, which is conducive to better monitoring of water quality at monitoring stations while saving costs. PRACTITIONER POINTS: According to the WQI comprehensive evaluation method, the WQI values of Hanyuan Lake are all above 90, the rating is excellent. From January 2019 to September 2020, the monthly change trend of each section is roughly the same, showing a trend of first decreasing, then rising, then decreasing, and finally rising and flattening. The WQImin model was developed to completely describe the change in the water body.

Distribution, source identification, water quality, and risk assessment of trace elements in the surface-groundwater-sediments multifunctional system in Guohe River Basin

Within the Huaihe River Basin, Guohe River, as its second-largest tributary, serves as a critical water supply source. Recent industrial and agricultural advancements have led to increased trace element contamination, adversely impacting the water quality within Guohe River Basin. Therefore, this study aimed to investigate the distribution characteristics, sources, water quality and risk assessment of trace elements in the surface water, groundwater, and sediments across the basin. The results showed that the spatial distribution of trace elements in the surface water and groundwater of Guohe River Basin was that most of the high concentrations appeared in Qiaocheng District of Bozhou City, the mean concentration of Fe in Guohe River sediments was the highest, the mean concentration of Sb was the lowest. The PMF source analysis results showed that the main source of trace elements in Guohe River Basin was natural geological processes, followed by human activities. The sodium adsorption ratio (SAR) indicated that the surface water samples of Guohe River in two seasons had high sodium and salinity hazards. The water quality index (WQI) showed that surface water and groundwater samples in the northwestern of Guohe River Basin had poor water quality. The results of the risk assessment showed that As and Mn posed great ecological risks to surface water and groundwater, respectively, and that F- was the pollutant with the most potential health risk hazard in the basin. The Geo-accumulation index (Igeo) results showed that Cd, Se and As should be taken seriously as the main contaminants of the sediments in Guohe River Basin. KEYWARDS: Trace elements; Source analysis; Sodium adsorption ratio; Water quality index; Risk assessment; Geo-accumulation index.

Water quality assessment of Bheemasandra Lake, South India: A blend of water quality indices, multivariate data mining techniques, and GIS

An integrated approach combining water quality indices (WQIs), multivariate data mining, and geographic information system (GIS) was employed to examine the water quality of Bheemasandra Lake, located adjacent to a sewage treatment plant (STP) in Tumakuru city, India. The analysis of 22 lake water samples, examined before and after the monsoons, revealed that the physicochemical parameters namely - electrical conductivity, biochemical oxygen demand, turbidity, total dissolved solids, ammoniacal nitrogen, nitrates, phosphates, magnesium, total hardness, total alkalinity, and calcium - exceeded the acceptable limits stipulated by national and international standards. The Canadian Council of Ministers of the Environment WQI (pre-monsoon: 25.3; post-monsoon: 33.9) and weighted arithmetic WQI (pre-monsoon: 3398; post-monsoon: 2093) designated the water as unsafe for drinking. Irrigation WQIs (sodium adsorption ratio, sodium percentage, residual sodium carbonate, magnesium hazard, permeability index, and potential salinity) implied water's suitability for irrigation. However, electrical conductivity indicated otherwise. Industrial WQIs (Larson-Skold Index, Langelier Index, Aggressive Index, and Puckorius Scaling Index) illustrated scaling propensity and the chloride sulfate mass ratio alluded galvanic corrosion potential. Hierarchical cluster analysis gathered 22 sampling points into two clusters (cluster 1: relatively lower polluted regions; cluster 2: highly polluted regions) for each season based on similarities in water features. Principal component analysis extracted four (79.07% cumulative variance) and six (87.14% cumulative variance) principal components before and after the monsoons, respectively. These components identified the primary pollution sources as urban sewage and natural lithological processes. WQI maps, created using the inverse distance weighted interpolation technique, enhanced the visualization of spatial-temporal variations. This study highlights the dire consequences of urbanization, STP pollution, and sewage management failures, necessitating that concerned authorities should implement policies and measures to curb the negative impacts on the environment and public health.

A water quality assessment model involving novel fluorescence technology

The reasonable utilization of water resources and real-time monitoring of water pollution are the core tasks of current world hydrological and water conservancy work. Novel technologies and methods for monitoring water pollution are important means to ensure water health. However, the absence of intuitive and simple analysis methods for the assessment of regional pollution in large-scale water bodies has prevented scientists from quickly grasping the overall situation of water pollution. In this study, we propose a strategy based on the unique combination of fluorescence technology and simple kriging (SK) interpolation (FL-SK) for the first time. This strategy could present the relative magnitude and distribution of the physicochemical indicators of a whole natural lake intuitively and accurately. The unique FL-SK model firstly offers a simple and effective water quality method that provides the pollution index of different sampling points in lakes. The macroscopic evaluation of large-scale water bodies by the FL-SK model primarily relies on the fluorescence response of the RDM-TPE to the comprehensive indicators of the water body, as experimental results have revealed a good correlation between fluorescent responses and six normalized physicochemical indicators. Multiple linear regression and fluorescence response experiments on RDM-TPE indicate that to some extent, the fluorescence signals of the FL-SK model may originate from a certain type of sulfide in the water body. Pattern discovery could enable the analysis of pollution levels in other ecosystems and promote early pollution assessment in the future.

Geospatial quality assessment of locally available ice for heavy metals and metalloids and their potential risks for human health in Karachi, Pakistan

Extreme hot conditions during summers, high poverty rate and continuous electricity load shedding affect commercial manufacturing and sale of ice in many countries. The vendors prepared ice using untreated piped water, tanker water and ground water. These waters may contain hazardous pollutants and ice made from them will pose a potential human health risk. Thus, it is important to regularly monitor the chemical composition of water sources and the quality of the manufactured ice. A contemporary examination was carried out to evaluate the physico-chemical properties and heavy metals and metalloids in the ice sold in all the districts of Karachi, Pakistan. This pioneering study was an innovative effort to assess the ice quality in relation to potential pollutant hazards to human health; with concomitant geospatial information. The geospatial distribution of ice quality and major constituents were among the measured parameters; carefully associated with further geospatial information, determined using GIS (Geographic Information Systems) and PCA (Principal Component Analysis) techniques. Interestingly, the physico-chemical analyses revealed that the ice quality was marginally adequate and the total mean metal-metalloid contents were in the sequence of Pb > Ni > Zn > Fe > Cr > As. The concentrations of these metals were above the upper allowable limits with reference to the recommended WHO guidelines. We observed that 57.1% and 35.7% ice samples had good physico-chemical properties assessed using the Ice Quality Index (IQI). Conversely, the IQI for metals showed that the ice was unsafe for human consumption. In terms of health risk assessment, the overall mean CDI (Chronic Daily Intake) and HQ (Hazard Quotient) values were in the order of Pb () > Ni (3.2) > Zn (2.3) > Fe (2.1) > Cr (1.6) > As (0.5) and Pb (7.4) > As (1.7) > Cr (0.5) > Ni (0.4 > Zn (0.008) > Fe (0.003), respectively. This study highlighted that routine monitoring of the water supplies available for making ice is required to protect public health.

Deep optimization of water quality index and positive matrix factorization models for water quality evaluation and pollution source apportionment using a random forest model

Effective evaluation of water quality and accurate quantification of pollution sources are essential for the sustainable use of water resources. Although water quality index (WQI) and positive matrix factorization (PMF) models have been proven to be applicable for surface water quality assessments and pollution source apportionments, these models still have potential for further development in today's data-driven, rapidly evolving technological era. This study coupled a machine learning technique, the random forest model, with WQI and PMF models to enhance their ability to analyze water pollution issues. Monitoring data of 12 water quality indicators from six sites along the Minjiang River from 2015 to 2020 were used to build a WQI model for determining the spatiotemporal water quality characteristics. Then, coupled with the random forest model, the importance of 12 indicators relative to the WQI was assessed. The total phosphorus (TP), total nitrogen (TN), chemical oxygen demand (CODCr), dissolved oxygen (DO), and five-day biochemical oxygen demand (BOD5) were identified as the top five significant parameters influencing water quality in the region. The improved WQI model constructed based on key parameters enabled high-precision (R2 = 0.9696) water quality prediction. Furthermore, the feature importance of the indicators was used as weights to adjust the results of the PMF model, allowing for a more reasonable pollutant source apportionment and revealing potential driving factors of variations in water quality. The final contributions of pollution sources in descending order were agricultural activities (30.26%), domestic sewage (29.07%), industrial wastewater (26.25%), seasonal factors (6.45%), soil erosion (6.19%), and unidentified sources (1.78%). This study provides a new perspective for a comprehensive understanding of the water pollution characteristics of rivers, and offers valuable references for the development of targeted strategies for water quality improvement.

Potential toxic heavy metals in village rainwater runoff of antimony mining area, China: Distribution, pollution sources, and risk assessment

The potential toxic heavy metal runoff from antimony mining areas poses a serious threat to the water environment and the health of residents in the village. The study found that the average concentrations of As, Sb, Cr, Pb, and Cd in the runoff were 0.1237, 0.1148, 0.0332, 0.0140, and 0.0013 mg/L, which were higher than the normal average concentrations in the water environment of 0.018, 0.0009, 0.05, 0.012, and 0.000013 mg/L, respectively.Sb and As are severely polluted, while Cd, Pb, and Cr have lower pollution levels. The coefficients of variation for As, Sb, Cr, Pb, and Cd range from 0.079 to 1.051, with Sb showing exceptionally high variability. Heavy metal elements Pb, Cd, and Sb accumulate in the southeastern area of the village, with Sb concentrations decreasing from the southeast to the northwest. As is mainly distributed in the northeastern part of the village, while Cr is primarily found in the central-western region. Source analysis indicates that As and Sb originate from mining and industrial activities, dust deposition, and domestic sewage. Cr comes from the natural geological background and metal product industry, Pb from lead-acid batteries, industrial activities, and gasoline additives, and Cd from fertilization in residential green areas and pesticide use. Health risk analysis reveals that the hazard index (HI) values for As and As in the water environment are 1.49 and 2.31, respectively, both exceeding 1, posing a serious threat to the health of village residents. The HI values for Pb, Cr, and Cd elements are all below 1, indicating lower risks. This study identified that Sb in the antimony ore area and its associated metal element As are the main elements leading to potential heavy metal pollution in the runoff of village residential areas, providing direction for subsequent water environment restoration work.

Groundwater quality and hydrochemical characteristics in the upper Seybouse sub-basin, Northeast Algeria

The present study aims to evaluate the groundwater quality in an area characterized by significant human anthropic activities within the upper Seybouse. In order to assess the quality, a total of 20 samples were analyzed to identify the chemical and bacteriological composition of the water, its variations, and their potential impacts on the environment and human health. The results revealed concentrations of the chemical and bacteriological elements exceeding the WHO standards, with high levels of electrical conductivity (EC) (peak = 4210 μS/cm), Ca2+ (peak = 340.68 mg/L), Na+ (peak = 360 mg/L), HCO3- (peak = 287 mg/L), Cl- (peak = 542 mg/L), SO42- (peak = 687 mg/L), NO3- (pek = 65.91 mg/L), fecal coliforms (FC) (peak = 160 UFC/mL), fecal Streptococcus (FS) (peak = 43 UFC/mL), and Clostridium perfringens (CP) (peak = 29 UFC/mL). Within the basin, two different facies have been identified: Cl-SO4-Na type and Cl-SO4-Ca type. The calculated Water Quality Index (WQI) indicates that none of the groundwater samples are suitable for drinking or human consumption. The detection of pathogenic microorganisms through diverse molecular methods has revealed the existence of eight distinct species, encompassing pathogenic strains that can affect human health. Moreover, the dissolution of geologic formations can influence the water's chemistry. In this region, groundwater pollution seems to be influenced by anthropogenic and agricultural factors such as fertilizer application, irrigation practices, and the release of domestic sewage.

Investigation of fluoride concentrations, water quality, and non-carcinogenic health risks of borehole water in bongo district, northern Ghana

Access to potable water is a significant concern due to the increasing global threat posed by fluoride contamination in groundwater sources. This study investigated the concentrations of fluoride (F-), the suitability of groundwater for human consumption, the physicochemical characteristics affecting the water quality, and non-carcinogenic adverse health risks to both children and adults in the Bongo district in Northern Ghana. The findings revealed that the groundwater had a mean pH, salinity, TDS, conductivity, and turbidity below the WHO guideline values with a mean fluoride concentration of 1.76 mg/L above the guideline limit of 1.5 mg/L. The study also found that there was no strong relationship between fluoride and the measured water parameters, which may be attributed to poor control of distribution, transport mechanisms, and sources. The WQI scores ranged from 42.62% to 70.72%, indicating that all borehole water samples were of good and excellent quality. The average chronic daily intake showed that children are often more exposed to the harmful impact of fluoride than adults. The average HQ > 1 indicates the probability of dental and skeletal fluorosis after continuous exposure over time in adults and children. The study recommends taking immediate action to mitigate high groundwater fluoride concentrations, implementing appropriate water management strategies, and raising public awareness of the health risks. These measures can guide future groundwater management practices and help policymakers address contamination and protect local communities.

A new assessment method for water environment safety and its application

The Three Gorges Reservoir area is recognized by its vast size, dense population, bustling economic and social activities along its banks, and by the significant volume of waterway traffic. These factors make it with a high risk of water pollution accidents, posing a serious threat to water environmental safety. Therefore, it is imperative to conduct a water environmental safety assessment in this region to ensure the safety of the water environment. In the present work, the Driving-Pressure-State-Impact-Response-Risk Water Environmental Safety model was proposed, and a comprehensive water environmental safety assessment system was established. The Water Environment Safety Index was introduced to measure the degree of water environment safety. This model synthesized multiple factors and their interrelationships, enabling a more accurate assessment of water environment safety. By adopting scientifically rigorous evaluation criteria, this assessment method enhanced the reliability and credibility of the results obtained. The water environment safety in the 22 counties and districts of the Three Gorges Reservoir area of Chongqing region from 2017 to 2021 was assessed in terms of temporal changes and spatial differentiation. In general, the overall water environment safety situation in the Three Gorges Reservoir area of Chongqing region is relatively safe, but a few counties/districts (such as Wanzhou District, Jiangjin District, etc.) are still in Warning. Spatially, the water environmental safety condition was relatively better in the northeast compared to the southwest. The main factors threatening water environment safety include: 1) the consequence of the Three Gorges Project, 2) severe soil erosion, 3) industrial, agricultural, and domestic pollution, and 4) frequent water pollution accidents. The present work provided a new method for conducting water environment safety assessments, which is expected to positively contribute to further research in this field.

Rapid assessment of surface water quality using statistical multivariate analysis approach: Oder River system case study

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.

Temporal variation of water quality parameters in the lacustrine of the Thrace Region, Northwest Türkiye

Thrace is a part of the Marmara Region northwest of Türkiye. This region hosts several lentic ecosystems used for irrigation and drinking water supply. The present study was conducted to analyze the temporal distributions of water quality parameters (WQPs) of lentic ecosystems (lacustrine habitats), including lakes (L1-L2), reservoirs (R1-R12), and ponds (P1-P19) of the Thrace Region. Thirty-three lacustrine habitats were identified in the region. Freshwaters were collected in the wet (end of winter) and dry (end of summer) seasons of 2021-2022 and tested for 12 WQPs. Data was evaluated for the water quality index (WQI) and nutrient pollution index (NPI) and their overall quality level. For the evaluation of non-carcinogenic health risk indices of WQPs, the chronic daily index (CDI), hazard quotient (HQ), and hazard index (HI) were applied. Cluster analysis (CA), Pearson correlation index (PCI), and principal component analysis (PCA) were used to classify the lacustrine habitats and identify the source of WQPs. The average values were as follows: 9.28 mg/L for dissolved oxygen (DO), 94.6% for oxygen (O2) saturation, 9.29 for pH, 613 μS/cm for electrical conductivity (EC), 3.96 NTU for turbidity, 358 mg/L for total dissolved solids (TDS), 3.17 mg/L for nitrate (NO3), 0.05 mg/L for nitrite (NO2), 1.01 mg/L for phosphate (PO4), 78.5 mg/L for sulfate (SO4), and 102 mg/L for chloride (Cl). Results showed a significant increase in WQPs, including NO3, NO2, and PO4, in the wet season, while the salinity decreased from the dry to wet season. Results revealed that HI values of water contaminants in lacustrine habitats were noted to be less than one. Based on determined WQPs, the present study recommends using lacustrine water habitats for irrigation, drinking, and other domestic and industrial purposes.

Evaluation of some heavy metals and selenium pollution in Karataş Lake (Burdur/Türkiye) using various pollution indices and statistical analysis

Wetland face environmental problems due to some reasons such as drought, rapid population and rapid developments in technology. Karataş Lake is located within the Lakes Region in Türkiye and has also of great economically, ecologically and ornithologically importance. Unfortunately, it completely dried out in 2021 due to sufficient rainfall, groundwater withdrawal and global climate change. In 2022, the lake started to hold water again. This study is important because it is one of the last studies before the lake dries up and first study to evaluate some heavy metals and selenium pollution using some indices. In this study, which was carried out between October 2019-July 2020, some heavy metals and selenium concentrations were determined in water of Karataş Lake seasonally and evaluated pollution degree of lake water using some indices and statistical analysis. Water temperature, pH, electrical conductivity and dissolved oxygen levels were measured in situ using YSI 556 MPS. For heavy metal analysis, water samples were taken, acidified to a pH of <2, stored at 4 °C and analyzed directly using ICP-OES (Agilent 5110). Pb and Se in all seasons were below detection limit. The analyzed metals sorted as; Cd < Mo < Cr < Cu < Mn < Zn < Ni < Fe. Generally, heavy metals were increased in autumn and decreased in spring. According to some water quality standards for drinking water, Fe levels were higher than the permissible levels for drinking water in all seasons (TSE, 2005; EPA, 2018; EU, 2020), Mn in autumn (TSE, 2005; EPA, 2018; EU, 2020), Ni in autumn and summer (TSE, 2005; EU, 2020). Water Quality Index (WQI), Heavy Metal Pollution Index (HPI) and Heavy Metal Evaluation Index (HEI) values were calculated in Karataş Lake to determine pollution degree. Depend on WQI results, lake water was in good category in autumn, winter and spring while poor category in summer. The lake water samples based on HPI and HEI values are in the low contamination category. The lake is still struggling with environmental problems. If necessary precautions are not taken for the future of the lake, the lake may dry out again. The results of this study will help the authorities in terms of preventing re-drying, sustainability of the lake and management of the region. This study will be also a reference for future studies.

Geostatistical appraisal of water quality, contamination, source distribution of potentially toxic elements (PTEs) in the lower stretches of Subarnarekha River (Odisha), India, and health risk assessment by Monte Carlo simulation approach

In the present study, the status of water quality, environmental contamination in the lower stretch of Subarnarekha River with respect to potentially toxic elements (PTEs), its seasonal distribution, and ecotoxicological health impacts were investigated. For this purpose, a combination of indexing approaches and geospatial methods was used. The estimated water quality index (WQI) has shown that the river water falls under "moderate to very poor" category during the pre-monsoon and "moderate to poor" category in the post-monsoon season. The abundance of PTEs (Pb, Cu, Ni, Cd, Fe, and Cr) was on the higher side during the pre-monsoon in comparison with the post-monsoon season. The results of contamination index (Cd) and heavy metal evaluation index (HEI) explain that Subarnarekha River has low-to-moderate levels of contamination with PTEs in the majority of sampling sites. However, HPI indicated that the river water is moderate-to-highly contaminated with PTEs in both seasons. Principal component analysis (PCA) and cluster analysis (CA) reveal that anthropogenic sources are prime contributors to PTEs contamination in Subarnarekha River. The potential non-cancerous health concerns for child and adults due to Cr and Pb in some sampling stations along the river stretch have been observed. The carcinogenic risk (CR) has been established for Cr, Pb, and Cd in Subarnarekha River with Cr (> 10-4) as the most unsafe element. Monte Carlo simulation (MCS) indicates a high risk of cancer hazards due to Cr (values > 1E-04) in present as well as future for both child and adults.

Contamination and health risks brought by arsenic, lead and cadmium in a water-soil-plant system nearby a non-ferrous metal mining area

Heavy metal(loid)s contamination prevails in the water-soil-plant system around non-ferrous metal mining areas. The present study aimed to evaluate the heavy metal(loid)s contamination in Nandan Pb-Zn mining area (Guangxi, China). A total of 36 river water samples, 75 paired paddy soil and rice samples, and 128 paired upland soil and plant samples were collected from this area. The concentrations of arsenic (As), lead (Pb), and cadmium (Cd) in these samples were measured. Results showed that the average water quality indexes (WQIs) at the 12 sampling sites along the main river ranged from 41 to 5008, indicating the water qualities decreasing from "Excellent" to "Undrinkable". The WQIs nearby tailings or industrial park were significantly higher than those at the other sites. 34.0% and 64.5% of soil samples exceeded the risk screening values for As and Cd. The Pb and Cd concentrations in all rice samples exceeded the Chinese food safety limits by 18.7% and 82.7%, respectively. Leafy vegetables had a higher concentration of As, Pb, and Cd than other vegetables, exceeding the maximum permissible limits by 14.1%, 61.2%, and 40.0%, respectively. The biological accumulation coefficient (BAC) of Cd was the highest in rice and lettuce leaves. The hazard quotients (HQs) of As and Cd, indicating non-carcinogenic risks, were 4.15 and 1.76 in adult males, and 3.40 and 1.45 in adult females, all higher than the permitted level (1.0). The carcinogenic probabilities of As and Cd from rice and leafy vegetables consumption were all higher than 1 × 10-4. We conclude that metal(loid)s contamination of the water-soil-plant system has posed great non-carcinogenic and carcinogenic risks to the local population.

Ecosystem health evaluation of urban rivers based on multitrophic aquatic organisms

The ecosystem health evaluation method of urban rivers is significantly different from natural rivers, because of intensive human interferences and ecological restoration measures. Biotic integrity index (IBI) provides a method to quantify the response of aquatic organisms to environmental stress. Multi-trophic aquatic organisms may exhibit different responses and sensitivities to stress factors, which affects the reliability of the IBIs. This study proposed a hypothesis that the biota with the higher trophic level (whose habitat was not completely destroyed) or that of the biota with the shorter life cycle would be more sensitive in urban rivers. To prove the above hypothesis, the ecosystem health status of urban rivers was evaluated by the IBIs across multitrophic groups, including benthic invertebrates, zooplankton, phytoplankton, periphyton algae and microorganisms. The reliability of the IBIs was assessed by estimating their relationship with water quality index (WQI). The spatial distribution differences of the IBIs were distinguished by spatial autocorrelation analysis. The results showed that the IBI based on benthic invertebrates cannot mask the effects of dredging. Compared with the IBIs from other trophic groups, the correlation coefficients between the IBIs based on zooplankton and microorganisms and WQI were higher. Moreover, the evaluation results of Z (Zooplankton)-IBI and M (Microorganism)-IBI were able to discriminate the least, medium and highly impaired site groups divided by WQI. For the spatial response mode, Z-IBI and M-IBI could identify the high-value river sections under ecosystem restoration projects, and Z-IBI could also identify the low-value river sections under intensive human interferences. Therefore, Z-IBI and M-IBI could be recommended as the priority application in urban rivers. The constructed ecosystem health evaluation framework for urban rivers would play a guiding role in reducing impairments and restoring water ecosystem quality.

Groundwater quality index development using the ANN model of Delhi Metropolitan City, India

Groundwater is widely recognized as a vital source of fresh drinking water worldwide. However, the rapid, unregulated population growth and increased industrialization, coupled with a rise in human activities, have significantly harmed the quality of groundwater. Changes in the local topography and drainage systems in an area have negative impacts on both the quality and quantity of groundwater. This underscores the critical need to assess the susceptibility of groundwater to pollution and implement measures to mitigate these risks. The water quality index (WQI) is an approach that simulates the water quality at peculiar locations for a particular period of time. The artificial neural network (ANN) model approach is such an idealistic methodology that can be utilized for WQI development and provides better results for specific locations in optimum time. Therefore, the goal of the current study is to provide a unique way for using artificial neural networks (ANN) to characterize the groundwater quality of Delhi Metropolitan City, India. In order to make the water fit for residential and drinking use, the research also pinpoints the geographical variability and spots where the contaminated region has to be sufficiently cleaned. A minimum WQI of 41.51 was obtained at the Jagatpur location while a maximum value of 779.01 was at the Peeragarhi location. During the training phase, the results obtained using the ANN model were highly favorable, demonstrating a strong association with an R-value of 98.10%, thus highlighting the program's exceptional efficiency. However, in accordance with the correlation regression findings, the prediction outcomes of the ANN model in testing are observed to be an R-value of 99.99-100%. This study confirms the promise and advantages of employing advanced artificial intelligence in managing groundwater quality in the studied area.

Assessment of the water quality using multivariate statistics and the water quality index: a case study of the Yağlıdere Stream (Giresun) in the Eastern Black Sea region, Turkey

In this study, the water quality of the Yağlıdere Stream passing through Espiye (Giresun-NE Turkey) and Yağlıdere districts, where old and new mining activities are present, was evaluated, and characterized by using the water quality index (WQI), multivariate statistical, and GIS techniques. The downstream aquifer of the Yağlıdere Stream, which originates from Erimez Mountains and reaches the sea from the west of Espiye district, meets the domestic and drinking water needs of Yağlıdere and Espiye districts. In addition, activities such as energy production and fish farming are carried out along the stream. Therefore, it is of great importance to investigate the water quality of the stream. In order to evaluate the water quality, 50 water samples were taken from 10 sampling points (5 periods) along the flow direction of the stream. Parameters other than T, DO, Mg, F, NH3, CN, and HS show significant spatial variations indicating the influence of geogenic and anthropogenic activities. Some of the investigated parameters (T, DO, Ca2+, Mg2+, SO42-, NH3, CN, Fe, Se) exhibit important seasonal variations due to high seasonality in water temperature and water flow. Principal component analysis/factor analysis reveals that the parameters responsible for water quality changes in the Yağlıdere Stream are mainly related to the geogenic structure, mining wastes, agricultural activities, and domestic wastes. According to the WQI values, the water samples taken from the upstream and middle part of the stream are in the water quality between "Excellent" and "Good", on the other hand, in the downstream regions where anthropogenic and geogenic effects are dominant, the water samples are in the quality of "Poor water" and "Unsuitable for drinking". The most effective water quality parameters on WQI are Al, Fe, and Mn and the water quality in the basin is negatively affected by geogenic and anthropogenic effects.

Spatial distribution of drinking and irrigation water quality in different climatic zones of Baluchistan, Pakistan

Baluchistan's water profile was developed by dividing it into seven zones (Northern Highlands-NH, Southern Highlands-SH, Quetta Valley-QV, Desert-D, Sibbi Plains-SP, Coastal Lasbella-CL, Coastal Gwadar-CG) based on geography, water availability, and climate of the area. A total of 106 water samples were collected from karaiz, spring water, and tube wells. Spatial distribution of EC, TDS, TH, SO42-, Cl-, Na+, and K+ showed an increasing trend in concentration from the highlands towards the desert and coastal zones. For anion, HCO3- is predominant in NH, SH, and QV, Cl- in D, CL, and CG and only SO42- in SP, whereas the cationic trend in overall zones is Na+>Ca2+>Mg2+>K+. In the NH, SH, QV, and SP zones, the physicochemical parameters met the drinking water quality guidelines; however, D, CL, and CG exceeded in almost all quality parameters. Furthermore, the drinking water quality index (WQI) shows excellent to good water quality in NH, SH, QV, and D zones, while CL and CG fall in poor to unsuitable water classes. In terms of hydrogeochemical facies, maximum water samples from NH fall in Ca-Mg-HCO3, and SH, QV, and SP in Ca-Mg-Cl type, where major ion chemistry is controlled by rock-weathering, while D, CL, and CG fall in the NaCl type, where evaporation is dominant. Similarly, irrigation water quality parameters (EC, SAR, RSC, Na%, MH%, PI, SSP, and KR) reveal that NH, SH, QV, and SP have suitable water for irrigation, and D, CL, and CG require proper treatment. Additionally, USSL and Wilcox's diagrams indicated that NH, SH, QV, and SP have "excellent to permissible"; however, D, CL, and CG have "permissible to unsuitable" class water, requiring special management practices. Consequently, appropriate control measures and targeted water purification programmes should be implemented to protect the public health and sustainability of water resources in Baluchistan.

Assessment of groundwater vulnerability for seawater intrusion using DRASTIC model in coastal area at Patuakhali District, Bangladesh

Seawater intrusion is a growing concern for coastal regions in Bangladesh. Overexploitation of groundwater, low rainfall, a high rate of evaporation, rising sea level, cyclones, storm surges, and shrimp cultivation are the causes of seawater intrusion that polluted the groundwater near the coastal area. High values of EC, TDS, Na+, and Cl- ions in groundwater indicate the seawater intrusion. Piper's Trilinear and Durov diagrams of groundwater indicated that most gathered groundwater show predominantly Na-Cl-type of water. Based on the USSL diagram, it is observed that the salinity of the study area is medium to high and the alkalinity is medium to very high. Also, in view of the Wilcox outline, most of the samples in the area of interest are permissible to doubtful, and doubtful to unsuitable. Chloride is moderately correlated with Na+ (R2 = 0.590) and poorly correlated with (HCO3-  + CO32-) (R2 = 0.128), which indicates seawater intrusion. According to a comparison of the levels of Fe and Mn in the groundwater of the study area with those recommended by the World Health Organization (WHO) and Department of the Environment (DOE), twenty-two out of thirty-one samples exceed the acceptable limit for Fe and thirty out of thirty-one samples fall within the acceptable limit for Mn. The calculated water quality index reveals that most samples are of low drinking water quality. The research area's poor water type is found in the north-central and southern parts. The WQI geographic variation map reveals that the study's water has a WQI of more than 100, making it unsuitable for consumption. The final seawater intrusion vulnerability map clearly identifies the moderate to highly vulnerable zone of the study area using DRASTIC index parameters. DRASTIC index parameters revealed that some portions of the northwestern part of the sample area show a moderately vulnerable zone. And the rest of the sampling area indicates the highly vulnerable zone.

Linking multivariate statistical methods and water quality indices to evaluate the natural and anthropogenic geochemical processes controlling the water quality of a tropical watershed

The improvement of water management requires monitoring techniques that accurately evaluate water quality status and detect the effects of land use changes on water chemistry. This study aimed to evaluate how multivariate statistical methods and water quality indices can be applied together to evaluate the processes controlling water chemical composition and the overall water quality status of a tropical watershed. Thirty-four water samples were collected in the Formoso River basin, located on the border of the Amazon Forest. Water parameters were measured in situ using a multiparameter and in the lab using spectroscopic and volumetric techniques. The water quality dataset was interpreted through principal component analysis, multivariate linear regression, and water quality indices. Statistical methods allowed us to identify the sources and geochemical processes controlling water quality chemistry, which were carbonate dissolution, runoff/erosion, nutrient input due to anthropogenic activities, and redox reactions in flooded zones. They were also used to create linear functions to evaluate the effects of land use changes on the geochemical processes controlling water chemistry. Conversely, the water quality indices provide information about the overall condition of the water. The Weight-Arithmetic Quality Index correctly evaluates water suitability for its multiple uses, according to the Brazilian guidelines. Conversely, the Ontario Water Quality Index is not suitable to evaluate the water quality of tropical rivers, since the usual higher water temperature and the low oxygen contents associated with tropical environments result in biased water quality evaluations by this index.

Combined Multivariate Statistical Techniques and Water Quality Index (WQI) to Evaluate Spatial Variation in Water Quality

In present study, Water Quality Index (WQI) has been assessed of the Rawal Lake which is a major source of drinking water for people in the Federal Capital, Islamabad, and its adjacent city Rawalpindi in Pakistan. For this, the principal component analysis (PCA) and WQI were applied as an integrated approach to quantitatively explore difference based on spatial variation in 11 water quality parameters of the five major feeding tributaries of the Rawal Lake, Pakistan. The results of temperature in water, total dissolved solids, pH, electrical conductivity, chlorides and sulfates were well within the allowable World Health Organisation's (WHO) limits. However, the heavy metals like cadmium and lead were above permissible limits by the WHO in tributaries of Bari Imam and Rumli. Moreover, this has been proven by the Pearson correlation which suggested strong positive correlation (0.910*) between lead and cadmium. The results of present study were subjected to statistical analysis, i.e., PCA which gave three major factors contributing 96.5% of the total variance. For factor 1, pH, TDS, alkalinity, chlorides, sulfates and zinc have highest factor loading values (>0.60) and presented that these parameters were among the most significant parameters of first factor. As per the WQI results, the water was categorised in two major classes indicating that water of Bari Imam and Rumli is highly contaminated with heavy metals and totally unsuitable for drinking purposes. Based on the results of the present study, it is suggested to make heavy metals consideration as an integrated component in future planning for maintaining water quality of the Rawal Lake and its tributaries.

Water quality assessment and pollution source apportionment using multivariate statistical and PMF receptor modeling techniques in a sub-watershed of the upper Yangtze River, Southwest China

Rapid industrial and agricultural development as well as urbanization affect the water environment significantly, especially in sub-watersheds where the contaminants/constituents present in the pollution sources are complex, and the flow is unstable. Water quality assessment and quantitative identification of pollution sources are the primary prerequisites for improving water management and quality. In this work, 168 water samples were collected from seven stations throughout 2018-2019 along the Laixi River, a vital pollution control unit in the upper reaches of the Yangtze River. Multivariate statistics and positive matrix factorization (PMF) receptor modeling techniques were used to evaluate the characteristics of the river-water quality and reveal the pollution sources. Principal component analysis was employed to screen the crucial parameters and establish an optimized water quality assessment procedure to reduce the analysis cost and improve the assessment efficiency. Cluster analysis further illustrates the spatiotemporal distribution characteristics of river-water quality. Results indicated that high-pollution areas are concentrated in the tributaries, and the high-pollution periods are the spring and winter, which verifies the reliability of the evaluation system. The PMF model identified five and six potential pollution sources in the cold and warm seasons, respectively. Among them, pollution from agricultural activities and domestic wastewater shows the highest contributions (33.2% and 30.3%, respectively) during the cold and warm seasons, respectively. The study can provide theoretical support for pollutant control and water quality improvement in the sub-watershed, avoiding the ecological and health risks caused by the deterioration of water quality.

Assessment of groundwater hydrochemistry, water quality, and health risk in Hainan Island, China

Groundwater is an important source of water for human sustenance. The determination of groundwater quality at island sites is an urgent priority in China, but there are lacking systematic reports relating to them. Here, 63 groups of groundwater samples were collected and analyzed of Hainan Island. The groundwater in the study area is weakly alkaline, mainly comprising hard and soft freshwater. The predominant anions and cations are HCO₃^-, and Ca²⁺ and Na⁺, respectively, and the main water chemistry types are HCO₃-Cl-Na and HCO₃-Cl-Na-Ca. The chemical evolution of groundwater is mainly affected by water-rock interactions, cation exchange, and human activity. The groundwater is mostly of high quality and, in most areas, is suitable for drinking and irrigation. Contrastingly, the water quality in the west of the island is relatively poor. The spatial distribution of the risk coefficient (HQ) is consistent with the spatial variation in the NO₃^- concentrations in the groundwater. Notably, there are unacceptable health risks for different groups of people, with infants having the greatest level of impact, followed by children, teenagers, and adults. This study provides a valuable reference for the development and utilization of groundwater resources, as well as the improvement of aquatic ecological conditions on Hainan Island and other island areas worldwide.

A comprehensive review of water quality indices for lotic and lentic ecosystems

Freshwater resources play a pivotal role in sustaining life and meeting various domestic, agricultural, economic, and industrial demands. As such, there is a significant need to monitor the water quality of these resources. Water quality index (WQI) models have gradually gained popularity since their maiden introduction in the 1960s for evaluating and classifying the water quality of aquatic ecosystems. WQIs transform complex water quality data into a single dimensionless number to enable accessible communication of the water quality status of water resource ecosystems. To screen relevant articles, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed to include or exclude articles. A total of 17 peer-reviewed articles were used in the final paper synthesis. Among the reviewed WQIs, only the Canadian Council for Ministers of the Environment (CCME) index, Irish water quality index (IEWQI) and Hahn index were used to assess both lotic and lentic ecosystems. Furthermore, the CCME index is the only exception from rigidity because it does not specify parameters to select. Except for the West-Java WQI and the IEWQI, none of the reviewed WQI performed sensitivity and uncertainty analysis to improve the acceptability and reliability of the WQI. It has been proven that all stages of WQI development have a level of uncertainty which can be determined using statistical and machine learning tools. Extreme gradient boosting (XGB) has been reported as an effective machine learning tool to deal with uncertainties during parameter selection, the establishment of parameter weights, and determining accurate classification schemes. Considering the IEWQI model architecture and its effectiveness in coastal and transitional waters, this review recommends that future research in lotic or lentic ecosystems focus on addressing the underlying uncertainty issues associated with the WQI model in addition to the use of machine learning techniques to improve the predictive accuracy and robustness and increase the domain of application.

Impacts of seasonal variations and wastewater discharge on river quality and associated human health risks: A case of northwest Dhaka, Bangladesh

Surface water pollution caused by the discharge of effluents from industrial estates has become a major concern for Dhaka (Bangladesh). This study aims to have a concise look at the severe river water pollution, mainly from effluents discharged from the tannery village. Effluent samples were collected from five ejected points, including the central effluent treatment plant (CETP), twenty adjacent river water, and two pond water nearby Hemayetpur, Savar. Thirty-one parameters have been observed at these sampling points for three seasons, from April 2021 to January 2022. The results obtained from water quality indices, i.e., water quality index (WQI), entropy water quality index (EWQI), and irrigation water quality index (IWQI), show that most studied surface water samples ranked "unsuitable" for consumption, irrigation, and anthropogenic purposes. The highest health risk was observed downstream of Hemayetpur city at the Savar CETP discharge site, indicating higher levels of heavy metal in the river water following the tannery village. Carcinogenic and non-carcinogenic human health risks could be triggered mainly by water consumption as concentrations of arsenic (As), chromium (Cr), nickel (Ni), and lead (Pb) exceeded the upper benchmark of 1 × 10^-4 for adults and children. The results of the carcinogenic risk assessment revealed that children were more vulnerable to health hazards, and quick corrective action is required to control the increased levels of heavy metals at all sample locations. Therefore, through bioaccumulation, human health and the environment are affected in these areas. Using river water for consumption, household work, or even irrigation purposes is not advisable. This study's result highlighted that properly implementing compatible policies and programs is required to improve effluent treatment methods and provide biodegradability to the Dhaleshwari River.

Assessing and forecasting water quality in the Danube River by using neural network approaches

The health and quality of the Danube River ecosystems is strongly affected by the nutrients loads (N and P), degree of contamination with hazardous substances or with oxygen depleting substances, microbiological contamination and changes in river flow patterns and sediment transport regimes. Water quality index (WQI) is an important dynamic attribute in the characterization of the Danube River ecosystems health and quality. The WQ index scores do not reflect the actual condition of water quality. We proposed a new forecast scheme for water quality based on the following qualitative classes very good (0-25), good (26-50), poor (51-75), very poor (76-100) and extremely polluted/non-potable (>100). Water quality forecasting by using Artificial Intelligence (AI) is a meaningful method of protecting public health because of its possibility to provide early warning regarding harmful water pollutants. The main objective of the present study is to forecast the WQI time series data based on water physical, chemical and flow status parameters and associated WQ index scores. The Cascade-forward network (CFN) models, along with the Radial Basis Function Network (RBF) as a benchmark model, were developed using data from 2011 to 2017 and WQI forecasts were produced for the period 2018-2019 at all sites. The nineteen input water quality features represent the initial dataset. Moreover, the Random Forest (RF) algorithm refines the initial dataset by selecting eight features considered the most relevant. Both datasets are employed for constructing the predictive models. According to the results of appraisal, the CFN models produced better outcomes (MSE = 0.083/0,319 and R-value 0.940/0.911 in quarter I/quarter IV) than the RBF models. In addition, results show that both the CFN and RBF models could be effective for predicting time series data for water quality when the eight most relevant features are used as input variables. Also, the CFNs provide the most accurate short-term forecasting curves which reproduce the WQI for the first and fourth quarters (the cold season). The second and third quarters presented a slightly lower accuracy. The reported results clearly demonstrate that CFNs successfully forecast the short-term WQI as they may learn historic patterns and determine the nonlinear relationships between the input and output variables.

Method for screening water physicochemical parameters to calculate water quality index based on these parameters' correlation with water microbiota

Water quality index (WQI) plays a crucial role in guiding water resource management. However, WQI calculation methods are not uniform, especially the selection of water parameters and the weighting given to each water parameter (P_i). To optimize WQI calculation, 132 water samples from seven rivers and from Chaohu Lake (33 sampling sites in Chaohu Lake Basin) in four seasons were collected, and the water parameters and microbiota composition were analyzed using high-throughput sequencing of 16 S rDNA. The correlation coefficient R² between water parameters and microbiota composition using redundancy analysis with the Monte Carlo method were calculated, and the water parameters that significantly correlated with the microbiota composition were selected to calculate WQI_min. The results showed that TP, COD, DO, and Chl a correlated significantly with water microbiota composition. WQI_b calculated by substituting R² for P_i was more consistent with the similarity between the microbiota compositions. WQI_minb calculated using TP, COD, and DO was consistent with WQI_b. The results of WQI_b and WQI_minb were more consistent than those of WQI and WQI_min. These results imply that using R² instead of P_i could help obtain a more stable WQI_b that could better reflect the biological characteristics of the Chaohu Lake Basin.

Levels, origins and probabilistic health risk appraisal for trace elements in drinking water from Lhasa, Tibet

Due to the lack of monitoring systems and water purification facilities, residents in western China may face the risk of drinking water pollution. Therefore, 673 samples were collected from Lhasa's agricultural and pastoral areas to reveal the status quo of drinking water. We used inductively coupled plasma-mass spectrometry to determine trace elements concentrations for water quality appraisal, source apportionment, and health risk assessment. The results indicate that concentrations of V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, Ba, and Pb are below the guidelines, while As concentrations in a few samples exceed the standard. All samples were classified into "excellent water" for drinking purpose based on Entropy-weighted water quality index. Thereafter by principal component analysis, three potential sources of trace elements were extracted, including natural, anthropogenic, and mining activities. It is worth noting that geotherm and mining exploitation does not threaten drinking water safety. Finally, health risks were assessed using Monte Carlo technique. We found that the 95th percentiles of hazard index are 1.80, 0.80, and 0.79 for children, teenagers, and adults, indicating a non-carcinogenic risk for children, but no risks for the latter two age groups. In contrast, the probabilities of unacceptable cautionary risk are 7.15, 2.95 and 0.69% through exposure to Cr, Ni, As, and Cd for adults, children, and teenagers. Sensitivity analyses reveal As concentration and ingestion rate are most influential factors to health risk. Hence, local governments should pay more attention to monitoring and removal of As in the drinking water.

Assessing water quality in the Dong Nai River (Vietnam): implications for sustainable management and pollution control

Dong Nai River provides essential water resources for millions of people across 11 provinces and cities in Vietnam. However, the different pollution sources such as household, farming, and industrial operations have caused the river water quality to deteriorate over the past decade. To gain a comprehensive understanding of the river's surface water quality, this study employed the water quality index (WQI) across 12 different sampling sites. In total,144 water samples with 11 parameters were analyzed in accordance with the Vietnamese standard 08:2015/MONRE. Results revealed a range of surface water quality, from poor to good according to the VN-WQI (Vietnamese standard), and a medium even bad level in some months according to the NS-WQI (American standard). The study also identified temperature, coliform, and dissolved oxygen (DO) as strong contributors to WQI values (VN_WQI standard). Principal component analysis/factor analysis was used to determine pollution sources, with the results highlighting agricultural and domestic activities as the main contributors to river pollution. In conclusion, this study underscores the importance of effective planning and management of infrastructure zoning and local activities to improve the river's surface water quality and surrounding areas, as well as safeguard the well-being of the millions who depend on it.

COVID-19 pandemic lockdown modulation of physico-chemical parameters of surface water, Karamana river basin, Southwest India: A weighted arithmetic index and geostatistical perspective

The coronavirus disease or COVID-19 pandemic continues imposing restrictions on the human population from full-scale normal/routine activities all over the world. This study primarily spotlights the consequences of the COVID-19-pandemic-lockdown on physicochemical parameters of water (samples) of the Karamana river system (KRS) during the pre-monsoons (or January) of 2021 and 2022, using the Weighted Arithmetic Index method and Geostatistical analysis (ArcMap 10.2). Even though the Karamana river supported the water needs of the people during the past several decades, the quality of water deteriorated due to the rising population and consequent anthropogenic activities. Hence, it is imperative to evaluate the water quality during the post-COVID-19 lockdowns and document the spatial distribution of parameters listed in the BIS (Bureau of Indian standard) IS10500, 2012. This was accomplished by establishing a water quality index (WQI), Geostatistical analysis, and weighted overlay analysis (WOA). The estimated WQI suggested that about 45.11km2 (6.43%) area has declined from the excellent category of water quality between 2021 and 2022. Similarly, WOA results deciphered that the area under the poor category has drastically and negatively changed from 27.85 km2 (4.0%) to 60.42 km2 (8.6%) after revoking of lockdown restrictions. The lessons learned from syn-Covid-19, the spike or uptrend of the water quality compared to the past decades, offer ample scientific basis to policymakers, administrators, and environmentalists for restoration of river system health from huge anthropogenic stress.

New insights into pollution source analysis using receptor models in the upper Yangtze river basin: Effects of land use on source identification and apportionment

To effectively control pollution and improve water quality, it is essential to accurately analyze the potential pollution sources in rivers. The study proposes a hypothesis that land use can influence the identification and apportionment of pollution sources and tested it in two areas with different types of water pollution and land use. The redundancy analysis (RDA) results showed that the response mechanisms of water quality to land use differed among regions. In both regions, the results indicated that the water quality response relationship to land use provided important objective evidence for pollution source identification, and the RDA tool optimized the procedure of source analysis for receptor models. Positive matrix decomposition (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models identified five and four pollution sources along with their corresponding characteristic parameters. PMF attributed agricultural nonpoint sources (23.8%) and domestic wastewater (32.7%) as the major sources in regions 1 and 2, respectively, while APCS-MLR identified mixed sources in both regions. In terms of model performance parameters, PMF demonstrated better-fit coefficients (R²) than APCS-MLR and had a lower error rate and proportion of unidentified sources. The results show that considering the effect of land use in the source analysis can overcome the subjectivity of the receptor model and improve the accuracy of pollution source identification and apportionment. The results of the study can help managers clarify the priorities of pollution prevention and control, and provide a new methodology for water environment management in similar watersheds.

Spatial-temporal distribution, source identification, risk assessment and water quality assessment of trace elements in the surface water of typical tributary in Yangtze River delta, China

As China's first cross-province ecological compensation mechanism pilot area in the hinterland of the Yangtze River Delta, Xin'an River has been hotspot in the study of rational utilization of ecological resources, and the functional value of its ecosystem services has been widely concerned. As an important tributary of the upper reaches of Xin'an River, Fengle River may affect the whole basin. The spatial-temporal distributions, occurrence, water quality and risk assessment of trace elements were studied in Fengle River in three seasons. High element concentrations were found in the downstream. Traceability models results showed that the major sources of trace elements were related to different human activities. The water quality was worse downstream in the wet season, and was more suitable for irrigation in the dry season. Risk assessment results showed that Zn, Cu, Mn, Co, and As were able to pose the risk to the ecological environment and human.

Hydrogeochemical characterisation and geospatial analysis of groundwater for drinking water quality in Ludhiana district of Punjab, India

This study characterises the quality of groundwater for the Ludhiana district of Punjab, India by analysing water samples collected from 152 locations spread across 3767 km². The samples were analysed for 18 parameters consisting of pH, EC, TDS, TA, TH, major anions and cations. The parameter values have been used to calculate the drinking water quality index of the study area which suggests that 2.6, 57.9, 32.9, 4 and 2.6% of the samples fall under the excellent, good, poor, very poor and unsuitable categories, respectively. The sequence of abundance for ions (in meq/l) as revealed from the laboratory tests is Na⁺ (37.1%) > Ca²⁺ (30.8%) > Mg²⁺ (29.1%) > K⁺ (2.8%) for cations and HCO₃^- (80%) > Cl^¯ (8.9%) > CO₃^2- (6.5%) > SO₄^2- (3.9%) > NO₃^-, F^-, PO₄^3- (< 1%) for anions. The spatial variability of these parameters has been depicted through the use of interpolation maps. Evaluation of different ionic ratios indicates that carbonate weathering and silicate weathering are both significantly affecting the groundwater chemistry with a slight dominance of carbonate weathering. Also, the ion exchange process is taking place in the area as confirmed by CAI index values. In terms of saturation index, the groundwater is undersaturated with respect to halite, fluorite and sylvite, whereas it is supersaturated for calcite, dolomite and aragonite minerals. The principal components in PCA explained 75.4% of the total variance with 29.1 and 28.3% contributions from PC1 and PC2. Both of these components indicate towards the geogenic and anthropogenic influence on groundwater mineralization of the area. The analysis suggests that groundwater for the study area is suitable for drinking in most of the region expect in a few places. Such a study could be used to understand the current status of groundwater quality in the area, the results of which can be used to prevent further contamination and sustain the resource for the future.

Spatial-temporal distribution, occurrence, water quality, and risk assessment of trace elements in ten rivers surrounding Chaohu Lake in China

As one of the five great lakes in China, the Chaohu Lake Basin is the main water source for regional economic and ecological development in Hefei city and is considered a source of drinking water. The spatial-temporal distributions and occurrence of soluble trace elements were studied in the surrounding ten rivers in the Chaohu Lake Basin as well as water quality and risk assessment during the normal and wet seasons. Principal component analysis (PCA) showed that the main source of river pollution during the two seasons was the mining industry. High values of most elements were found in the northwestern rivers in the two seasons. The temporal changes in the elements showed that the distributions of As, Mn, Cd, and Cu in the two seasons were very different, but the trends of Ni, Co, and V were basically the same. The sodium adsorption ratio (SAR) showed that almost all river samples needed to be treated before irrigation, and the water quality index (WQI) showed that most samples were of excellent water quality for drinking. The ecological risk assessment results showed that the risks in the two seasons were all slight. The results of the health risk evaluation suggested that no noncarcinogenic risks were found in the normal season and that the carcinogenic risks from Cr and As reached their highest levels in the normal and wet seasons, respectively. This research can provide vital data for rational water control and water quality conservation, offer a scientific basis for ecological environment safety, and offer a reference for carcinogenic and noncarcinogenic health risks to regional residents.

Appraisal of groundwater quality with WQI and human health risk assessment in Karamık wetland and surroundings (Afyonkarahisar/Turkey)

Groundwater in Karamık wetland and surroundings, which is one of the important wetlands in Turkey, was examined and the chemical properties and quality of groundwater were determined in the present study. In addition, the possible risks to human health as a result of groundwater usage were investigated along with spatial analyses carried out using Geographic Information Systems (GIS). Physicochemical analyses were carried out on 25 samples taken from groundwater. The dominant ions in the study area are Ca, Mg, and HCO₃. When the results are compared with the limit values in the valid drinking water guidelines such as TSI-266 (Standards for drinking waters, 2005) and WHO (Guidelines for drinking-water quality, 2011), the groundwaters were not suitable for use as drinking water in terms of NO₃, Fe, Pb and As. The analysis results were evaluated together with GIS and Water Quality Index (WQI) methods. In the evaluations with the WQI method, 76% of the samples were in the "poor water" class. The groundwater in the study area is polluted by both geogenic and anthropogenic sources. For this reason, health risk assessment was performed due to the use of groundwater in the region by the local people in different areas, especially as drinking water, and the negative effects of water quality. Accordingly, there are non-carcinogenic negative effects on health in terms of NO₃, As, Pb and Fe parameters from the use of groundwater by children. In addition, Pb and As concentrations are at carcinogenic levels for both children and adults in all groundwater samples collected from the study area. Therefore, it is not recommended to use groundwater as drinking water without treatment.

Groundwater quality, heavy metal pollution, and health risk assessment using geospatial techniques and index methods in Eber wetland and surroundings (Afyonkarahisar/Turkey)

The continuous increase in the demand for water and the scarcity of water to be used as drinking water have made groundwater even more important. The study area, Eber wetland, is located in the Akarçay river basin, which is one of the most important river basins in Turkey. The groundwater quality and heavy metal pollution were investigated in the study using index methods. In addition, health risk assessments were performed. Ion enrichment was determined at locations E10, E11, and E21 related to water-rock interaction. In addition, nitrate pollution was observed in many samples due to agricultural activities and also fertilizer application in the areas. The water quality index (WOI) values of the groundwaters vary between 85.91 and 201.77. In general, groundwater samples located around the wetland were in the "poor water" class. According to the values for the heavy metal pollution index (HPI), all the groundwater samples are suitable for use as drinking water. They are also classified as "low pollution" according to the heavy metal evaluation index (HEI) and the value/degree of contamination (Cd). In addition, since the water is been used for drinking by the people in the area, a health risk assessment was performed to ascertain As and NO₃. It was determined that the R_cancer values calculated for As were considerably higher than the tolerable/acceptable values for both adults and children. The results obtained clearly show that the groundwater should not be used as drinking water.

Integrated machine learning-based model and WQI for groundwater quality assessment: ML, geospatial, and hydro-index approaches

The demands upon the arid area for water supply pose threats to both the quantity and quality of social and economic activities. Thus, a widely used machine learning model, namely the support vector machines (SVM) integrated with water quality indices (WQI), was used to assess the groundwater quality. The predictive ability of the SVM model was assessed using a field dataset for groundwater from Abu-Sweir and Abu-Hammad, Ismalia, Egypt. Multiple water quality parameters were chosen as independent variables to build the model. The results revealed that the permissible and unsuitable class values range from 36 to 27%, 45 to 36%, and 68 to 15% for the WQI approach, SVM method and SVM-WQI model respectively. Besides, the SVM-WQI model shows a low percentage of the area for excellent class compared to the SVM model and WQI. The SVM model trained with all predictors with a mean square error (MSE) of 0.002 and 0.41; the models that had higher accuracy reached 0.88. Moreover, the study highlighted that SVM-WQI can be successfully implemented for the assessment of groundwater quality (0.90 accuracy). The resulting groundwater model in the study sites indicates that the groundwater is influenced by rock-water interaction and the effect of leaching and dissolution. Overall, the integrated ML model and WQI give an understanding of water quality assessment, which may be helpful in the future development of such areas.

Assessment of the water quality of Bartın Kışla (Kozcağız) Dam by using geographical information system (GIS) and water quality indices (WQI)

This study was conducted to evaluate the water quality of the Kışla (Kozcagiz) Dam located in the province of Bartın in the Western Black Sea Region of Turkey. Water samples were collected monthly from 5 stations for a year and analyses were conducted using 27 water quality parameters. The quality of the dam and the water quality parameters were evaluated using different indices in comparison to the limits determined according to the standards set by the World Health Organization (WHO) and Turkey Surface Water Quality Regulation (SWQR). Water quality index (WQI), organic pollution index (OPI), sodium adsorption ratio (SAR), magnesium adsorption ratio (MAR), permeability index (PI), and metal pollution index (MPI) were calculated and spatial assessment of pollution was made seasonally by making use of the geographic information system (GIS). A piper diagram was used in determining the facies of the water. The types of Ca²⁺-Mg²⁺-HCO₃^- predominated in the dam water. Moreover, statistical analyses were used in order to determine if there was a significant difference between the parameters. WQI results generally indicate that the water quality was good in all seasons; however, only in the autumn, sampling points S1 (101.58), S2 (100.59), S4 (102.31), and S5 (102.12) showed poor water characteristics. According to the OPI results, while winter and spring yielded good water quality, summer samples were lightly polluted and autumn samples were moderately polluted. Given SAR results, it can be stated that the water of Kışla Dam could be used as irrigation water. Considering the standards specified by WHO and SWQR, the parameters generally exceeded the threshold values, but the water hardness value was much higher than 100 mg L^-1 specified in SWQR as very hard water. The principal component analysis (PCA) results showed that the pollution sources were anthropogenic. Thus, for the dam water to not be affected by the increasing pollutant factors, it should be continuously monitored, and attention should be paid to the irrigation methods used in agricultural activities.

Impacts of land uses on spatio-temporal variations of seasonal water quality in a regulated river basin, Huai River, China

Land use impacts from agriculture, industrialization, and human population should be considered in surface water quality management. In this study, we utilized an integrated statistical analysis approach mainly including a seasonal Mann-Kendall test, clustering analysis, self-organizing map, Boruta algorithm, and positive matrix factorization to the assessment of the interactions between land use types and water quality in a typical catchment in the Huai River Basin, China, over seven years (2012-2019). Spatially, water quality was clustered into three groups: upstream, midstream, and downstream/mainstream areas. The water quality of upstream sites was better than of mid-, down-, and mainstream. Temporally, water quality did not change significantly during the study period. However, the temporal variation in water quality of up-, down-, and mainstream areas was more stable than in the midstream. The interactions between land use types and water quality parameters at the sub-basin scale varied with seasons. Increasing forest/grassland areas could substantially improve the water quality during the wet season, while nutrients such as phosphorus from cropland and developed land was a driver for water quality deterioration in the dry season. Water area was not a significant factor influencing the variations of ammonia nitrogen (NH₃-N) and total phosphorus (TP) in the wet or dry season, due to the intensive dams and sluices in study area. The parameters TP, and total nitrogen (TN) were principally linked with agricultural sources in the wet and dry seasons. The parameters NH₃-N in the dry season, and chemical oxygen demand (COD_Cr) in the wet season were mainly associated with point source discharges. Agricultural source, and urban point source discharges were the main causes of water quality deterioration in the study area. Collectively, these results highlighted the impacts of land use types on variations of water quality parameters in the regulated basin.

A preliminary assessment of spatial variation of water quality of Ratuwa river

This work helps to identify the source of pollution in water and characterize the water quality which is essential to water management for sustainable development. Therefore, the main objective of this work is to evaluate the spatial distribution of the water quality of Ratuwa river and its tributaries. The water samples were collected from six discrete sampling locations and fifteen parameters were tested using respective well-calibrated equipment and standard APHA methods. The physicochemical analysis, water quality index, and correlation matrix method were employed to evaluate the spatial variation of the water quality of Ratuwa river. Turbidity was the most polluting factor in river water. The results showed the spatial variation of the water quality index (WQI) from 39.3 to 70.5, which fell in the range of "good" to "poor" water quality status. None of the water samples was either "excellent" or "unsuitable for drinking." The water quality was "Poor" upstream and downstream of Ratuwa river due to the high value of turbidity. Chaju river was found to have unpolluted whereas Dipeni river was slightly polluted due to domestic and municipal wastes. Hence, the deterioration of water quality can be attributed to natural and anthropogenic sources.

Impact of gold mining on the water quality of the lom river, Gankombol, Cameroon

The aim of this paper is to assess the impact of gold mining on the water quality of the Lom River at Gankombol (Adamawa Cameroon). Forty-eight (48) water samples are systematically collected during the dry and the rainy season. These water samples are characterized to determine the physico-chemical parameters, major ions and metals. A local geological study is conducted to determine the relationship between the geological units encountered and water contamination. Hydrochemical assessment, multivariate statistical analysis (MSA) and geostatistical modeling (GM) are used to assess contamination. The results indicate that the waters of the Lom River draining the gold mining site are acidic to neutral (5.3-6.9), very turbid (117-510 NTU) with high concentrations of suspended solids (22.89-471 mg/L). The mean concentrations of Fe, Pb, and As exceed the limits set by the World Health Organization (WHO) standards. Pb, As, Cd and Hg concentrations decrease in the rainy season mainly due to dilution by rainwater. The predominant water type is Ca-Mg-HCO₃. This surface water is unsuitable for drinking purpose (997.5, Water Quality Index >300) with high level of metal pollution. MSA reveal strong linear correlations between EC-TDS, EC-Na⁺, TDS-Na⁺, Pb-As, Cl^- -SO₄ ^2- and TSS-Cd suggesting that the correlated parameters can have common origin. Finally, GM reveal that the lowest values of metals and pollution indices are found upstream of the gold mining site. The weathering of geological units encountered, mining activities and seasons have a major influence on the water quality. Therefore, it appears that decision-makers must take immediate action to decrease pollution and adopt suitable and sustainable remedial solutions.

Investigation of Wabe River water fitness for agricultural and industrial purposes

The Wabe River is bordered by 74.84% agricultural area, and farmers rely solely on rainfall. The present research made an attempt to investigate the suitability of the Wabe river water for Agricultural and Industrial purpose. The suitability of river water for agricultural use was evaluated using the sodium adsorption ratio (SAR), potential salinity (PS), magnesium ratio (MR), Kelly index (KI), permeability index (PI), residual sodium carbonate (RSC), sodium percentage (%Na), and heavy metal pollution index (HPI). Additionally, the Ryznar Stability Index (RSI), Aggressive Index (AI), and Langelier Saturation Index (LSI) were used to evaluate the river water's suitability for industrial uses. Furthermore, plot the Gibb's diagrams to identify the sources of pollution and Piper diagrams to determine the hydrochemical composition of Wabe water. According to the HPI, pollution levels in the wet and dry seasons ranged from 53.34 (low) to 317.58 (medium) and 32.24 to 102.42 (low), respectively. The results showed that the Wabe River has very acceptable water quality characteristics and that the trace elements identified did not surpass thresholds that made them dangerous for agricultural usage. The findings showed that domestic wastewater and leachate contamination at sampling point W4 is the cause of the water quality deterioration in the downstream zone. The Ryznar Stability Index (RSI), Aggressive index, and Langelier saturation index readings were less than 9 at all sampling locations during both seasons, suggesting that the river water was corrosive, highly aggressive and unusable for industrial use without treatment.

Evaluating the water quality characteristics and tracing the pollutant sources in the Yellow River Basin, China

Evaluating water quality characteristics (WQC) and tracing pollutant sources (PS) have gradually attracted worldwide attention. This study was conducted to develop an integrated method framework for evaluating WQC, tracing PS, and improving understanding of their relationship to efficiently managing the water environment. The single-factor index, comprehensive water quality index (CWQI), and hazard quotient and hazard index (HQ and HI) were used to evaluate the characteristics of single pollutant concentration, comprehensive concentration, and human health risk, respectively. These evaluation methods combined with relevant standards selected data from the original sampling data. These selected data were used for tracing PS by principal component analysis and Pearson correlation methods. 3384 sampling data were collected in the Yellow River Basin in 2021, and the WQC assessment and pollutant traceability were carried out by using the above-integrated method framework. The results showed that TN(total nitrogen) was the primary pollutant with an average concentration of 4.54 mg/L, followed by CODcr(dichromate oxidizability), NH₄⁺-N(ammonia nitrogen), and TP(total phosphorous). The CWQI values ranged from 1.26 to 110.03, with an average of 7.74, indicating the pollution level of trace elements was excellent. The HQ and HI max values of As(arsenic) and Cr⁶⁺(hexavalent chromium) elements were over 1, meaning the elements have negatively affected local human health. Furthermore, the anthropogenic input was the primary pollutant source for TN. The anthropogenic input and agricultural source pollution emission could be considered for CODcr, NH₄⁺-N, TP, and BOD5(five-day biological oxygen demand). The anthropogenic input and the weathering and leaching of loess could be considered for As elements. For Cr⁶⁺, F(fluorine), Anionic, and Petroleum, the anthropogenic activities were the primary pollutant sources, including the metal mining and production and the coal mining and processing industry. Our results could provide effective information to support adaptive management measures to improve water environment conditions and protect human health.

Dissolved Heavy Metal Pollution and Assessment of a Karst Basin around a Mine, Southwest China

Karst water quality is one of the most important environmental issues in karst areas. The study's purpose was to investigate dissolved heavy metal pollution and health risk assessment in karst water basins around mines. River water and groundwater samples were analyzed by principal component analysis, correlation analysis, water quality index, hazard quotient, and hazard index. Median concentrations of dissolved heavy metals in the Sidi River were similar to the world average with a slightly alkaline characteristic. The concentrations of most dissolved heavy metals in river water were higher than those in groundwater. The concentrations of Zn, Pb, and Cd around the mine exceeded the limits of drinking water indicators. The poor water quality samples with high water quality index values were distributed around the mine. Lead (Pb), Zn, As, Cd, and Cr were potentially threatening metals in the study area. The pollution level of dissolved heavy metals in the Sidi River was at a medium level compared with other rivers worldwide. Principal component analysis and correlation analysis showed that Cu, Pb, Zn, Cd, Mn, Fe, As, and Sr mainly came from mine drainage; Ca²⁺, Mg²⁺, and Cr mainly came from the contribution of carbonate rocks; Na⁺ and K⁺ were related to local human agricultural activities. The concentrations of dissolved heavy metals in groundwater were affected by karst aquifers. The results of this study can provide a data reference for water resources prevention and human health protection in the Sidi River's karst basin and similar karst basins.

Evaluation of water quality using water quality index, synthetic pollution index, and GIS technique: a case study of the river Gomti, Lucknow, India

The river Gomti, one of India's most polluted rivers, passing through Lucknow, Uttar Pradesh, India, has been selected for this study. An attempt has been made to assess its water quality status by combining the water quality index (WQI) and synthetic pollution index (SPI). Further, the data integration with the geographic information system (GIS) along with twelve water quality parameters for the seven sampling stations (S1 to S7) over 5 years (2013-2017) has been performed. The study area showed a variation of WQI from 78.993 to 249.388 and SPI from 0.868 to 2.096 in 5 years. The map interpolated through GIS revealed that the WQI falls into the category of severely polluted (76-100) and unsuitable for human consumption (> 100), while SPI lies in the category of moderately polluted (0.5-1.0) and severally polluted (1.0-3.0). The BOD and COD were found to significantly influence the WQI and SPI scores. With the constant release of waste effluents into the river, all selected parameters increased from S1 to S7. Based on the study, effective wastewater management is immediately required to improve water quality and support any sustainable river restoration plan.