Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)--a case study

Identifying the spatio-seasonal pattern of hydrochemical evolution and surface water-groundwater interaction in a large urban river basin, Northwest China

There is insufficient understanding of the spatio-temporal evolution of surface water-groundwater quality and hydraulic connection under both natural and human influences in urban river basins. To this end, this paper investigated the spatio-seasonal pattern of hydrochemical evolution and surface water-groundwater interaction in a typical urban river basin (Dahei River basin) based on isotopic and hydrochemical data of 132 water samples collected during three seasons (normal, wet and dry seasons). From the normal season to the wet season, surface water in the Dahei River basin was dominated by the impacts of evaporation and groundwater discharge processes. During this period, the precipitation and agricultural activities (canal irrigation) were frequent. Thus, groundwater was affected by irrigation infiltration of surface water and precipitation from high-altitude areas. From the wet season to the dry season, precipitation decreased and irrigation methods changed (canal irrigation → well irrigation). In this case, groundwater discharge had a stronger impact on surface water, and shallow groundwater was recharged by deep groundwater through the well irrigation. Under this hydrological pattern, the hydrochemical characteristics of surface water were mainly influenced by evaporation, human activities (agricultural irrigation and sewage treatment) and groundwater discharge. In contrast, the hydrochemical characteristics of groundwater were main influenced by water-rock interactions (dissolution of evaporites and silicates, and cation exchange) and human activities. This study contributed to a better understanding of the hydrochemical and hydrological processes in urban river basins and provided a theoretical basis for the sustainable management of water resources.

Evaluating long-term impacts of land use/land cover changes on pollution loads at a catchment scale

Evaluating how pollutant loads react to changes in land use/land cover (LULC) is a challenging task due to the intricate relationships among the many elements within a watershed. However, the difficulty in connecting LULC change and nonpoint source (NPS) pollution loads to streams may be lessened by combining hydrological modeling with geospatial tools and multivariate statistics. The objective of this study was to investigate the long-term effects of LULC change on NPS pollution loads in a highly human-dominated catchment, in central Ethiopia. In the study, hydrologic modeling was used to estimate the NPS parameters from multispectral Landsat images, and multivariate statistical techniques were then used to extract major LULC types that explain the variances of NPS loads between 1981 and 2020. The results demonstrated that there were human-induced LULC changes in the area, as the built-up and agricultural landscapes are rising (186.4% and 5.8%, respectively), and shrub and forest lands are decreasing (67.1% and 41%, respectively). As a result of these changes, the concentrations of nitrate (NO3), total P, total N, organic N, and organic P loads were increased by 69.41, 19.83, 18.45, 18.88, and 24.05%, respectively. Reductions in natural vegetation, as well as agriculture intensification, are the major contributors to the NPS pollutant losses to surface water sources. The result also revealed that pollution nutrients are strongly related to deforestation and agricultural land expansion. Proper adaptation strategies should be implemented to minimize the negative impact of LULC changes in the area.

Characterization of groundwater salinity by hydrogeochemical and multivariate statistical analysis in the coastal aquifer of Nagapattinam district, Southern India

The impact of seawater intrusion from coast to inland terrain in the Cauvery River Basin (CRB) and Uppanar River Basin (URB) was evaluated based on major ion groundwater chemistry. TDS ranges from 229 to 2260 mg/l, and 408 to 3732 mg/l; Na+ range from 67 to 560 mg/l, and 74 to 1600 mg/l, and Cl- range from 120 to 906 mg/l, and 110 to 3260 mg/l for CRB and URB respectively. Piper Diagram, Hydrochemical Facies Evolution Diagram (HFE-D), rock-water interaction (Gibbs Plots), various bivariate plots viz., TDS vs. Cl-; Na+ vs. Cl-; Ca2+ vs. Cl-; Ca2+ vs. SO4 2-; TH vs. TDS and Principal Component Analysis (PCA) (Cluster and Factor analysis) were used to identify the seawater intrusion from coast to inland aquifers and to understand hydrogeochemical characterization and salinization processes. Piper diagram shows that most of the samples are Na+-Cl- type, HFE-D diagram also shows that most of the samples were saline intrusion type and mixing behavior, while TH vs. TDS plot shows hard fresh to hard brackish type from both the basins. PCA results clearly show the three factors, explaining 84.02 % and 76.67 % variance in URB and CRB. Factor-1 records 53.03 % alteration, with a strong confidence loading of TDS, Na+, Cl-, Ca2+, K+, SO4 2, Total Alkalinity (TA), and Total Hardness (TH) in URB indicating saline nature. A total variance of 46.23 % in CBR is more positively loaded with TH, Mg2+, Ca2+, and SO4 2- indicating rock-water interaction. Cluster analyses of these parameters illustrate the cluster distribution in CRB and URB. In URB, TDS, Na+, and Cl- ions make a cluster with a linkage distance of 5000 m, whereas in CRB, the TDS, Na+, Cl-, and TA ions make a cluster with a linkage distance of 2800 m. The factor and cluster analysis fetched out an effect of intensive use of fertilizers, aquaculture activities, and excessive groundwater exploitation. This technique gave the relationship between various chemical parameters in groundwater. Factor and cluster analysis have proven highly effective in groundwater quality studies. The study concluded that the study area has the threat of saline water intrusion in shallow aquifers with continuous influences of seawater mixing.

A framework model to integrate sources and pathways in the assessment of river water pollution

Metal and nutrient pollution, soil erosion, and alterations in climate and hydrology are prevalent issues that impact the water quality of riverine systems. However, integrated approaches to assess and isolate causes and paths of river water pollution are scarce, especially in the case of watersheds impacted by multiple hazardous activities. Therefore, a framework model for investigating the multiple sources of river water pollution was developed. The chosen study area was the Paraopeba River basin located in the Minas Gerais, Brazil. Besides multiple agriculture, industrial, and urban pollution sources, this region was profoundly affected by the rupture of the B1 tailings dam (in January 2019) at the Córrego do Feijão mine, resulting in the release of metal-rich waste. Considering this situation, thirty-nine physicochemical and hydromorphological parameters were examined in the Paraopeba River basin, in the 2019-2023 period. The analysis involved various statistical techniques, including bivariate and multivariate methods such as correlation analysis, principal component analysis, and clustering. The Paraopeba River was mainly impacted by metal contamination resulting from the dam collapse, whereas nutrient contamination, mainly from urban and industrial discharges, predominantly affected its tributaries. Additionally, the elevated concentrations of aluminum, iron, nitrate, and sulfate in both main river and tributaries can be attributed to diffuse and point source pollution. In terms of hydromorphology and soil type, the interaction between woody vegetation and erosion-resistant soils, especially latosols, contributes to the stability of riverbanks in the main river. Meanwhile, in the tributaries, the presence of neosols and sparse vegetation in urbanized areas promoted riverbank erosion potentially amplifying pollution. While the study was conducted in a particular watershed, the findings are based on a methodology that can be applied universally. Hence, the insights on surface water quality from this research can be a valuable resource for researchers studying watersheds with diverse pollution sources.

Review: The application of source analysis methods in tracing urban non-point source pollution: categorization, hotspots, and future prospects

The contribution of urban non-point source (NPS) pollution to surface water pollution has gradually increased, analyzing the sources of urban NPS pollution is of great significance for precisely controlling surface water pollution. A bibliometric analysis of relevant research literature from 2000 to 2021 reveals that the main methods used in the source analysis research of urban NPS pollution include the emission inventory approach, entry-exit mass balance approach, principal component analysis (PCA), positive matrix factorization (PMF) model, etc. These methods are primarily applied in three aspects: source analysis of rainfall-runoff pollution, source analysis of wet weather flow (WWF) pollution in combined sewers, and analysis of the contribution of urban NPS to the surface water pollution load. The application of source analysis methods in urban NPS pollution research has demonstrated an evolution from qualitative to quantitative, and further towards precise quantification. This progression has transitioned from predominantly relying on on-site monitoring to incorporating model simulations and employing mathematical statistical analyses for traceability. This paper reviews the principles, advantages, disadvantages, and the scope of application of these methods. It also aims to address existing problems and analyze potential future development directions, providing valuable references for subsequent related research.

Evaluation of groundwater quality by adopting a multivariate statistical approach and indexing of water quality in Sagar Island, West Bengal, India

In the vicinity of the coast, predominantly groundwater is the sole reliable resource for potable purposes as the surface water sources are highly saline and unfit for human consumption. However, the groundwater in Sagar Island is highly vulnerable to saltwater intrusion. The majority of drinking water comes from government-owned hand pump-equipped tube wells. But during the summer season, many of these tube wells yield significantly less water. Hence, in the current scenario, water quality assessment has become important to the quantity available. Total of 31 samples of deep tube wells (groundwater) are collected at variegated locations during pre-monsoon season throughout Sagar, and then, the physical and chemical quality parameters of these water samples are analysed. Furthermore, a multivariate statistical technique is executed with the aid of the SPSS program. The hydro-chemical parameters that are taken into account for the quality analysis are pH, salinity, electrical conductivity (EC), total dissolved solids (TDS), total hardness, aluminium, arsenic, bi-carbonate, cadmium, iron, chloride, copper, chromium, cobalt, lead, magnesium, manganese, nickel, potassium, sulphate, zinc, and sodium. Then, the analysed data evaluates the water quality index (WQI). Five components are identified through the principal component analysis (PCA) technique, and 82.642% total variance is found. The outcomes of the quality assessment study illustrate that about 54.84% of collected samples come in the "excellent" water quality class when calculated by the "weighted arithmetic WQI method," and 90.32% of collected groundwater samples come in the "good" water quality class when computed using the "modified weighted arithmetic WQI method." This study helps for the interpretation of WQI to assess groundwater quality.

Determining recovery of marine water quality of the Rio Doce using statistical and temporal comparisons with nearby river systems

The Fundão dam breach affected the Rio Doce's estuarine and marine environments with water, tailings, scoured soil and/or sediments, and other debris. Time series and standard exceedance analyses are typically used to assess water quality recovery to baseline conditions after deteriorating water quality events. In the absence of historical measurements, impacts to water quality from the Fundão Event were compared with measurements of nearby rivers. Similar river systems with available water quality measurements were grouped into affected and unaffected estuarine and marine waters. Statistical and temporal comparisons of marine waters unaffected by the Rio Doce with those affected by the Rio Doce were evaluated for systematic differences. Multivariate statistical techniques were also used to assess water quality differences. Our results demonstrate that the Fundão dam breach had a short-term water quality impact on marine waters near the Rio Doce mouth. Principal component and comparative time series analyses clearly demonstrated this impact during the 2015/2016 and 2016/2017 wet seasons. The ephemeral effects of the breach, however, did not compromise marine water quality. Exceedances of CONAMA standards for metals remained either at zero or at very low levels during the affected period (<5.7%). Before the start of the next wet season in October 2017, water quality impacts from the Event were statistically indistinguishable from unaffected marine waters, indicating recovery to baseline conditions. Integr Environ Assess Manag 2024;20:99-116. © 2023 NewFields Companies, LLC. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Applying multivariate techniques to fingerprint water quality impact of the Fundão Dam breach within the Rio Doce basin

The Fundão Dam breach on 5 November 2015 (the "Event") released tailings, water, soil and/or sediments, and other debris to downstream watercourses. This breach included both direct and indirect impacts from scouring of soils and sediments along and within the affected courses. Multivariate statistical techniques were used to determine the potential of fingerprinting the impact of the breach compared to pre-Event water quality conditions and unaffected watercourses. The selection of key parameters is an important first step for multivariate analyses. Analysis of too many parameters can mask important trends and relationships, while analysis of too few may miss significant water quality indicators. A two-phased selection process was used to identify key parameters that indicated impact from the Event: (a) unbiased, principal component analysis to extract chemically dominant profiles among all measured parameters and (b) comparison of metals' concentrations between unaffected soils and/or sediments and tailings samples. Radar charts of key parameters along with statistical comparisons to pre-Event and not-affected waterways were then aggregated over space and time to assess impact and potential recovery to pre-Event conditions. Nine parameters were identified that characterize tailings-related (direct) and background soil and/or sediment-related (indirect) impacts. Spatially and temporally aggregated radar charts and nonparametric Mann-Whitney U tests were used to assess the statistical significance of these impacts during each wet season since the breach. Indirect parameters, like aluminum and lead, returned to pre-Event levels in the first wet season after the Event. By the 2018/2019 wet season, most of the direct and indirect parameters had returned to pre-Event levels. Integr Environ Assess Manag 2024;20:133-147. © 2023 NewFields Companies, LLC. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Application of unsupervised clustering model based on graph embedding in water environment

Surface water monitoring data has spatiotemporal characteristics, and water quality will change with time and space in different seasons and climates. Data of this nature brings challenges to clustering, especially in terms of obtaining the temporal and spatial characteristics of the data. Therefore, this paper proposes an improved TADW algorithm and names it RTADW to obtain the spatiotemporal characteristics of surface water monitoring points. We improve the feature matrix in TADW and input the original time series data and spatial information into the improved model to obtain the spatiotemporal feature vector. When the improved TADW model captures watershed information for clustering, it can simultaneously extract the temporal and spatial characteristics of surface water compared with other clustering algorithms such as the DTW algorithm. We applied the proposed method to multiple different monitoring sites in the Liaohe River Basin, analyzed the spatiotemporal regional distribution of surface water monitoring points. The results show that the improved feature extraction method can better capture the spatiotemporal feature information between surface water monitoring points. Therefore, this method can provide more potential information for cluster analysis of water environment monitoring, thereby providing a scientific basis for watershed zoning management.

Water quality, criticality, and sustainability of mountain springs-a case study from the Nepal Himalaya

In the hilly terrain of Nepal, springs are the main source of drinking water. With the increasing population, rapid urbanization, and unabated climate change, springs are under the risk of survival. There is increasing evidence that springs in the mid-hills of Nepal are drying up, or are reducing in discharge, or are deteriorating by water quality. As a result, local communities are facing unprecedented water stress. On this background scenario, assessment of water quality of the springs in the Marin Watershed of Sindhuli district of Nepal was carried out in this study. Spring water samples were collected during the pre-monsoon and post-monsoon of 2020 and their hydrochemistry was analyzed. Further, springs' discharge was measured in situ and their criticality and sustainability were studied through community interaction. Results have revealed seasonal variation in spring discharge and spring water quality. Out of 39 flowing springs, water quality of 13, 8, and 6 springs, respectively, was very poor, poor, and unsuitable for drinking in the pre-monsoon. The count of springs with very poor, poor, and unsuitable drinking water quality during the post-monsoon was 8, 3, and 1, respectively. Occurrence of pathogenic bacteria (Escherichia coli) was identified in five spring samples which makes the spring water unsafe for drinking. Again, out of 51 springs studied, 12 are already dried up, and another 12 of the 39 flowing springs have become seasonal with very low discharge (0.03-0.07 L/s). The study highlights and emphasizes the need to protect the flowing springs and rejuvenate the dying springs of the Himalaya and of other parts of the world.

Accelerated eutrophication alters fish and aquatic health: a quantitative assessment by using integrative multimarker, hydrochemical, and GIS modelling method in an urban lake

The ramifications of anthropogenic activities on the environment and the welfare of aquatic life in lakes worldwide are becoming increasingly alarming. There is a lack of research in the Indian Himalayas on fish biomarker responses to stressful aquatic conditions and the use of environmetric modelling in GIS. Our research evaluates the environmental health of urban lakes in multiple basins using multi-biomarker endpoints (13 features) in Schizothorax niger and hydrochemical characterization (9 features) of water. The study covers 31 grids, each at a distance of 1 km2. This study demonstrated a statistically significant (P = 0.001) increase in white blood cells (WBC), mean cell size (MCH), helminth infection, and health assessment index score (HAIS) score in fish from a highly eutrophic cluster or basin compared to a reference cluster, which is indicative of environmental stress in fish. Based on hydrochemical similarities, the lake water datasets were divided into three categories using hierarchical cluster analysis (HCA). In the PCA analysis, the first three principal components were responsible for 78.1% of the data's variance. The first principal component (PC1) accounted for 57.4% of the variance and had a strong positive loading from ammonia, total phosphate, pH, nitrates, and total alkalinity for water quality parameters. Additionally, PC1 had a favourable loading from WBC, helminth infection (%), and the health assessment index score (HAIS) for biological endpoints. These findings are in alignment with the results of the multivariate analysis. The trophic state index (TSI) showed a significant (P < 0.05) increase in Cluster 1, which includes the peripheral areas of Hazratbal and Gagribal side (> 70), compared to the reference cluster. The multiple regression model indicates that ammonia, phosphate, and nitrate significantly impact the general health of fish (R2 > 0.7). A novel methodology for monitoring water quality fluctuations across different basins and clusters is presented in this study. By integrating fish health biomarkers and GIS technology, we have developed a comprehensive approach to evaluate the overall well-being of aquatic habitat. This technique may prove beneficial in the management of urban lentic water bodies in the Kashmir Himalayas and other comparable water systems around the globe, while also supporting sustainable practices.

Data fusion strategy based on ultraviolet-visible spectra and near-infrared spectra for simultaneous and accurate determination of key parameters in surface water

Chemical oxygen demand (COD), ammonia nitrogen (AN) and total nitrogen (TN) are the key parameters to reflect the degree of surface water pollution. Ultraviolet - visible (UV-Vis) spectroscopy and near - infrared (NIR) spectroscopy are ideal techniques for rapid monitoring of these indicators. In this study, a strategy based on the fusion of UV-Vis and NIR spectral data (UV-Vis-NIR) for water quality detection was proposed to further improve the quantitative analysis accuracy of spectroscopic methods. Seventy river samples with different levels of pollution were used for spectroscopic analysis. The UV-Vis-NIR fusion spectrum of each water sample was obtained by directly splicing sample's UV-Vis spectrum and NIR diffuse transmission spectrum. The UV-Vis-NIR fusion models were optimized through using different variable selection algorithms. The results show that the UV-Vis-NIR fusion models for surface water COD, AN and TN achieves better prediction results (the root mean square errors of prediction are 6.95, 0.195, and 0.466, respectively) than single-spectroscopic based models. Since better prediction performances were shown under different optimization conditions, the robustness of fusion models were also better than the single-spectroscopic based models. Therefore, the data fusion strategy proposed in this study has a promising application prospect for further accurate and rapid monitoring of surface water quality.

Spatio-temporal dynamics of nutrients at the water-sediment interface: case of the Nokoue lagoon in Southern Benin

Lake Nokoué, a major component of the hydrographic system of the Ouémé delta, is the largest lagoon-estuarine ecosystem in Benin. Recent studies have shown that benthic processes would actively participate in the maintenance of the eutrophication and biogeochemical cycle of this ecosystem. In order to understand the implication of the bottom on the quality of the waters of the lake, a monthly follow-up of ten (10) parameters of quality of the waters of the bottom was undertaken from July 2020 to December 2021 on a network of nineteen (19) stations distributed on the whole lake. Univariate and multivariate analysis techniques were used to assess the spatial and temporal dynamics of these waters. The Kruskal-Wallis test, PCA, correlation analysis and discriminant analysis all showed a very marked influence of the hydrological regime on the concentration of nutrients compared to the influence of anthropogenic activities around the lake and an influence of climatic conditions on internal processes. Indeed, water inflows from the Ouémé watershed are the main contributors of phosphorus in the lake while benthic processes are the most important contributors of nitrogen. Cluster analysis defined three significantly different areas in Lake Nokoué: the channel, the centre of the lake and the river mouth. A single station in each cluster could be used for a spatial assessment of water quality over the entire lake.

Prediction of total phosphorus in reservoir cascade systems

Reservoir cascade systems have attracted the attention of scientists worldwide. The present study investigates the cascade of five reservoirs (R1, R2, R3, R4, and R5) along a 192-km water channel system located in the state of Ceará, in the Brazilian semiarid region. This cascade system was implemented in 2012 to promote water availability and security to the capital of Ceará and the strategic industry and port complex of the region. However, these reservoirs have faced a progressive degradation of water quality, which has resulted in intense eutrophication and high-water treatment costs. The study evaluates the dynamics of water quality from 2013 to 2021 along this reservoir cascade (from R1 to R5). The results revealed that water quality did not improve along the cascade system, differently from previous studies on reservoirs interconnected by natural rivers. This was attributed to the low water residence time and low capacity of pollutant removal along the man-made water channel system, as well as to the high internal phosphorus loads of the reservoirs. Multiple regression models involving the explanatory variables of total phosphorus, total nitrogen, chlorophyll-a, cyanobacteria, transparency, rainfall, and volume from upstream reservoirs were obtained to determine total phosphorus concentration in downstream reservoirs, considering different combinations of reservoir pairs in the cascade and different time delays. A clear trend of R2 decline with the distance between the upstream and downstream reservoirs was observed. For example, the R2 values for the correlations adjusted between R1 and R2 (48 km), R1 and R3 (172 km), R1 and R4 (178 km), and R1 and R5 (192 km) were 0.66, 0.32, 0.22, and 0.12, respectively. On the other hand, the adoption of time delays of the order of the cumulative residence times of the reservoirs promoted a significant improvement in the R2 values. For instance, the best correlation adjusted between R1 and R5 improved from R2 = 0.12 to 0.69 by considering a time delay of 21 months. This suggests that previous data from upstream reservoirs can be used to predict current and future total phosphorus concentration in downstream reservoirs. The results from this study are important to better understand the spatiotemporal dynamics of water quality in reservoir cascade systems and thus improve water resources management, especially in drylands.

Metagenomic analysis reveals diverse microbial community and potential functional roles in Baner rivulet, India

BACKGROUND

The health index of any population is directly correlated with the water quality, which in turn depends upon physicochemical characteristics and the microbiome of that aquatic source. For maintaining the water quality, knowledge of microbial diversity is a must. The present investigation attempts to evaluate the microflora of Baner. Metagenomics has been proven to be the technique for examining the genetic diversity of unculturable microbiota without using traditional culturing techniques. The microbial profile of Baner is analyzed using metagenomics for the first time to the best of our knowledge.

RESULTS

To explore the microbial diversity of Baner, metagenomics analysis from 3 different sites was done. Data analysis identified 29 phyla, 62 classes, 131 orders, 268 families, and 741 genera. Proteobacteria was found to be the most abundant phylum in all the sampling sites, with the highest abundance at S3 sampling site (94%). Bacteroidetes phylum was found to be second abundant in S1 and S2 site, whereas Actinobacteria was second dominant in sampling site S3. Enterobacteriaceae family was dominant in site S1, whereas Comamonadaceae and Pseudomonadaceae was abundant in sites S2 and S3 respectively. The Baner possesses an abundant bacterial profile that holds great promise for developing bioremediation tactics against a variety of harmful substances.

CONCLUSION

Baner river's metagenomic analysis offers the first insight into the microbial profile of this hilly stream. Proteobacteria was found to be the most abundant phylum in all the sampling sites indicating anthropogenic interference and sewage contamination. The highest abundance of proteobacteria at S3 reveals it to be the most polluted site, as it is the last sampling site downstream of the area under investigation, and falls after crossing the main city, so more human intervention and pollution were observed. Despite some pathogens, a rich profile of bacteria involved in bioremediation, xenobiotic degradation, and beneficial fish probiotics was observed, reflecting their potential applications for improving water quality and establishing a healthy aquaculture and fishery section.

Multi-spatial-scale land/use land cover influences on seasonally dominant water quality along Middle Ganga Basin

Studying spatiotemporal water quality characteristics and their correlation with land use/land cover (LULC) patterns is essential for discerning the origins of various pollution sources and for informing strategic land use planning, which, in turn, requires a comprehensive analysis of spatiotemporal water quality data to comprehend how surface water quality evolves across different time and space dimensions. In this study, we compared catchment, riparian, and reach scale models to assess the effect of LULC on WQ. Using various multivariate techniques, a 14-year dataset of 20 WQ variables from 20 monitoring stations (67,200 observations) is studied along the Middle Ganga Basin (MGB). Based on the similarity and dissimilarity of WQPs, the K-means clustering algorithm classified the 20 monitoring stations into four clusters. Seasonally, the three PCs chosen explained 75.69% and 75% of the variance in the data. With PCs > 0.70, the variables EC, pH, Temp, TDS, NO2 + NO3, P-Tot, BOD, COD, and DO have been identified as dominant pollution sources. The applied RDA analysis revealed that LULC has a moderate to strong contribution to WQPs during the wet season but not during the dry season. Furthermore, dense vegetation is critical for keeping water clean, whereas agriculture, barren land, and built-up area degrade WQ. Besides that, the findings suggest that the relationship between WQPs and LULC differs at different scales. The stacked ensemble regression (SER) model is applied to understand the model's predictive power across different clusters and scales. Overall, the results indicate that the riparian scale is more predictive than the watershed and reach scales.

Spatiotemporal evaluation and assessment of shallow groundwater quality for irrigation of a tropical coastal groundwater basin

The present study aims at analyzing groundwater quantity and quality simultaneously to identify its availability and suitability for irrigation. Various water quality indices were used to assess (i) origin of the groundwater sources (Gibbs diagram); (ii) salinity, alkalinity, and sodium hazard (sodium adsorption ratio, exchangeable sodium percentage, Kelly's ratio, US Salinity Laboratory diagram, Wilcox diagram); (iii) magnesium hazard (magnesium absorption ratio); (iv) carbonate and bicarbonate hazard (residual sodium carbonate); (v) hydro-chemical facie and evaluation (Piper diagram and Expanded Durov diagram); and (vi) statistical relationship among the variables, sample sites, and spatiotemporal grouping (principal component analysis and cluster analysis). The overall objective is to quantify the irrigation suitability of groundwater reserves. Gibb's diagram suggests that the groundwater quality is mainly controlled by rock-water interaction. Piper trilinear showed the presence of various types of hydro-chemical facies such as Ca-Mg-HCO3, mixed, and sodium bicarbonate. The expanded Durov diagram revealed the hydro-chemical evolution, grouping, and areal distribution of the groundwater samples. USSL diagram, Wilcox diagram, Kelly's ratio, magnesium hazard, and permeability index suggest that the groundwater quality is suitable for irrigation. Kaiser-Meyer-Olkin (KMO) and Bartlett's tests confirmed the applicability of principal component analysis (PCA), which indicates that groundwater quality is controlled by rock-water interaction mainly. It also suggests that the groundwater has carbonate dissolution, which indicates the groundwater's hardness increased. Cluster analysis (CA) from the year 2000 to 2010 shows 4 to 8 groups present within the study area. Irrigation water quality suitability map and predicted groundwater potential zone map together act as a master tool for deciding tube well location, pumping schedule, and crop planning for the sustainability of the agriculture eco-system in the study area. The implementation of the aforementioned activities in the study area will further stop the advancement of the seawater intrusion front. The methodology shows the potential applicability for similar coastal groundwater basins worldwide with or without modification.

A comprehensive health risk assessment associated with bioaccumulation of heavy metals and nutrients in selected macrophytes of Loktak Lake, Manipur, India

The Loktak Lake, a Ramsar site in Northeast India, is known for its rich biodiversity that includes a variety of macrophyte species, most of which have not been studied for their phytoremediation capacities and potential toxicity via consumption of the edible species. Therefore, a comprehensive assessment was conducted to evaluate the accumulation of selected heavy metals and nutrients in 10 dominant macrophyte species growing in Loktak Lake and to assess the potential health risks associated with consumption of the edible plants. The concentrations of nutrients such as total phosphorus (TP), total nitrogen (TN), potassium (K), calcium (Ca), magnesium (Mg), and heavy metals such as copper (Cu), manganese (Mn), zinc (Zn), and iron (Fe) were found to be in the order of plant > sediment > water. The bioaccumulation factors (BAFs) revealed high efficiency of most plants to accumulate heavy metals and nutrients in their tissues from the lake water and sediments, indicating their potential to be used as phytoremediators. Translocation factors (TFs) were also estimated to determine the efficiency of the plants to translocate elements from root to shoot. Colocasia esculenta and Polygonum perfoliatum exhibited the highest BAF values, whereas Colocasia esculenta, Hedychium flavum, Phragmites karka, and Oenanthe javanica exhibited the highest TF values for most elements. Target hazard quotients (THQs) revealed potential health risks associated with one or more heavy metals in the plants, except for Zn, whose THQ values were below the level of concern in all the edible plant species. The hazard index (HI) signifying potential non-carcinogenic health risk from the combined effects of all the heavy metals was highest for Polygonum perfoliatum, indicating a potentially higher risk to health if this edible macrophyte is regularly consumed in higher quantities and may pose long-term health effects to the exposed population.

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.

A holistic review on the assessment of groundwater quality using multivariate statistical techniques

Water is an essential element in nature. It is used in drinking, irrigation, and industry mainly. Human health is directly linked to groundwater quality and is affected by poor groundwater quality caused by excessive fertilizer application and unhygienic circumstances. Because of increased pollution, investigating the water quality became a point of research for many researchers. There are numerous approaches to assessing water quality, and statistical methods are essential among them. This review paper discusses Multivariate Statistical Techniques, including Cluster Analysis, Principal Component Analysis, Factor Analysis, Geographical Information System, and Analysis of Variance, to name a few. We have presented the significance of each method concisely and how it is being used. In addition, an extensive table is prepared to demonstrate the individual technique along with the computational tool, the type of water bodies, and their respective regions. The advantages and disadvantages of the statistical techniques are also discussed therein. It is found that Principal Component Analysis and Factor Analysis are widely explored techniques.

Temporal and spatial relationships of CrAssphage and enteric viral and bacterial pathogens in wastewater in North Carolina

Enteric disease remains one of the most common concerns for public health, particularly when it results from human exposure to surface and recreational waters contaminated with wastewater. Characterizing the temporal and spatial variation of enteric pathogens prevalent in wastewater is critical to develop approaches to mitigate their distribution in the environment. In this study, we aim to characterize pathogen variability and test the applicability of the human-associated wastewater indicator crAssphage as an indicator of enteric viral and bacterial pathogens. We conducted weekly samplings for 14 months from four wastewater treatment plants in North Carolina, USA. Untreated wastewater samples were processed using hollow fiber ultrafiltration, followed by secondary concentration methods. Adenovirus, norovirus, enterovirus, Salmonella, Shiga toxin 2 (stx2), Campylobacter, and crAssphage were measured by quantitative polymerase chain reaction (qPCR) and reverse transcriptase (rt)-qPCR. Our results revealed significant correlations between crAssphage and human adenovirus, enterovirus, norovirus, Salmonella, and Campylobacter (p<0.01). Pathogens and crAssphage concentrations in untreated wastewater showed distinct seasonal patterns, with peak concentrations of crAssphage and viral pathogens in fall and winter, while bacterial pathogens showed peaked concentrations in either winter (Campylobacter), fall (Salmonella), or summer (stx2). This study enhances the understanding of crAssphage as an alternative molecular indicator for both bacterial and viral pathogens. The findings of this study can also inform microbial modeling efforts for the prediction of the impact of wastewater pathogens on surface waters due to increased flooding events and wastewater overflows associated with climate change.

A multivariate statistical approach and water quality index for water quality assessment for the Rokel river in Sierra Leone

The main aim of the study was to use multivariate statistical approach to determine the relationship between parameters, identify the factors affecting the quality of water and interpret and group the water quality parameters. Water quality data was collected during two seasons; wet season spanning from June to August 2019 and dry season spanning from February to April 2019. The physiochemical and microbial parameters measured from the sampling process were turbidity, temperature, pH, electric conductivity, total hardness, calcium carbonates, total dissolved solid (TDS), dissolved oxygen (DO), total suspended solids (TSS), iron, nitrate, phosphate, potassium, sulphate, chromium, fluoride, e. coli and coliform. A total of 406 data set were collected and analysed using Principal Component Analysis, water quality index, cluster analysis and analysis of variance (ANOVA). These data sets were tested for sampling adequacy using Kaiser-Meyer-Olkin and Bartlett's Test and the result on the Kaiser-Meyer-Olkin Measure of Sampling Adequacy obtained was 0.615. The analysis yields Five PCs extraction with eigenvalues >1. These components explained 82.628% of the total variance of the entire components. The maximum water quality index 13 which indicated a grade A and can be treated for water supply. The following parameters Chromium 0.39 mg/l, Iron 1.88 mg/l, turbidity 18.66NTU, Phosphates 26.00 mg/l and fluorides 1.75 mg/l exceeded the WHO guidelines for drinking water. The mean values electrical conductivity is 12.26 μS/cm, 31.8 μS/cm for rain and dry seasons respectively., The following parameters Turbidity, Total Dissolved Solid, Total Suspended solids, Iron, Phosphate, Fluoride and Sulphate shows variation with High during the rain and low during the dry season with significant statical difference with a p value < 0.05. Whereas there is difference between the seasonal values of chromium, Nitrate and Potassium. The ANOVA resulted in P-value >0.05 which indicated no statistically significant different for chromium, Nitrate and Potassium. The seasonal variation was corroborated by cluster analysis with two clusters of C1 and C2. The PCs analysis, cluster analysis and ANOVA gave detailed characterization of the source and group correlation amongst the physiochemical and microbial parameters.

Water quality and hydrochemical assessments of thermal springs, Gandaki Province, Nepal

Thermal springs are the sites where the water temperature lies above ambient temperature. They are widely used for power generation, hot water spas, balneotherapy, agriculture, laundering, and aquaculture. In Nepal, many thermal springs are reported but scientific understanding on water quality and hydrogeochemistry of the springs is very limited. In this study, a total of 28 physico-chemical parameters were measured in water samples collected from 12 thermal springs from Gandaki Province, Nepal. Correlation matrix and multivariate statistical analysis such as principal component analysis (PCA) and cluster analysis were used to understand the water quality and hydrogeochemistry of the hot water springs. The pH, temperature, electrical conductivity, total dissolved solids, and turbidity in the hot water springs ranged from 7.3 to 8.8, 31.6-64.3 °C, 206-16270 μS/cm, 115-6637 mg/L, and 0.21-63.7 NTU; respectively. The dominance order of major anions and cations were: Cl^- > HCO₃^- > SO₄^2- > NO₃^- > F^- and Na⁺ > Ca²⁺ > K⁺ > Mg²⁺; respectively. Comparison of the water quality parameters with the WHO and National Water Quality Standards suggested that the majority of the parameters were within the safe limit. Out of 9 heavy metals and trace elements analyzed Zn²⁺, Ni²⁺, Cr³⁺, Cd⁺², Hg (total), and Pb²⁺ were found below the safe limit but Fe (total), As (total) and Cu (total) were found higher than the WHO safe limit in total of 3, 5 and 1 sampling sites; respectively. The water quality index (WQI), sodium absorption ratio (SAR), and magnesium hazard (MH) ratio in the hot water springs ranged from 40.9 to 573, 2.2-49.3, 7.1-70.8; respectively. The result of PCA analysis showed that four principal components are required to explain hydrogeochemistry. Cluster analysis suggested that the sampling sites can be grouped into three distinct clusters based on total dissolved solids. Interestingly, the classification of hydrochemical facies using a Piper diagram suggested that 7 out of 12 thermal springs have Na-Cl type water. Finally, a perspective on the suitability of the hot springs for hot water spas and balneotherapy and policy recommendation is provided.

Geochemical characterisation and geostatistical evaluation of groundwater suitability: a case study in Perambalur District, Tamil Nadu, India

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^-  > SO₄^2-  > 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.

Spatiotemporal analysis and prediction of water quality in Pearl River, China, using multivariate statistical techniques and data-driven model

Identifying spatiotemporal variation patterns and predicting future water quality are critical for rational and effective surface water management. In this study, an exploratory analysis and forecast workflow for water quality in Pearl River, Guangzhou, China, was established based on the 4-h interval dataset selected from 10 stations for water quality monitoring from 2019 to 2021. The multiple statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), correlation analysis (CoA), and redundancy analysis (RDA), as well as data-driven model (i.e., gated recurrent unit (GRU)), were applied for assessing and predicting the water quality in the basin. The investigated sampling stations were classified into 3 categories based on differences in water quality, i.e., low, moderate, and high pollution regions. The average water quality indexes (WQI) values ranged from 38.43 to 92.63. Nitrogen was the most dominant pollutant, with high TN concentrations of 0.81-7.67 mg/L. Surface runoff, atmospheric deposition, and anthropogenic activities were the major contributors affecting the spatiotemporal variations in water quality. The decline in river water quality during the wet season was mainly attributed to increased surface runoff and extensive human activities. Furthermore, the short-term prediction of river water quality was achieved using the GRU model. The result indicated that for both DLCK and DTJ stations, the WQI for the 5-day lead time were predicted with accuracies of 0.82; for the LXH station, the WQI for the 3-day lead time was forecasted with an accuracy of 0.83. The finding of this study will shed a light on an effective reference and systematic support for spatio-seasonal variation and prediction patterns of water quality.

Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques

The impact of land use on water quality is becoming a global concern due to the increasing demand for freshwater. This study aimed to assess the effects of land use and land cover (LULC) on the surface water quality of the Buriganga, Dhaleshwari, Meghna, and Padma river system in Bangladesh. To determine the state of water, water samples were collected from twelve locations in the Buriganga, Dhaleshwari, Meghna, and Padma rivers during the winter season of 2015 and collected samples were analysed for seven water quality indicators: pH, temperature (Temp.), conductivity (Cond.), dissolved oxygen (DO), biological oxygen demand (BOD), nitrate nitrogen (NO₃-N), and soluble reactive phosphorus (SRP) for assessing water quality (WQ). Additionally, same-period satellite imagery (Landsat-8) was utilised to classify the LULC using the object-based image analysis (OBIA) technique. The overall accuracy assessment and kappa co-efficient value of post-classified images were 92% and 0.89, respectively. In this research, the root mean squared water quality index (RMS-WQI) model was used to determine the WQ status, and satellite imagery was utilised to classify LULC types. Most of the WQs were found within the ECR guideline level for surface water. The RMS-WQI result showed that the "fair" status of water quality found in all sampling sites ranges from 66.50 to 79.08, and the water quality is satisfactory. Four types of LULC were categorised in the study area mainly comprised of agricultural land (37.33%), followed by built-up area (24.76%), vegetation (9.5%), and water bodies (28.41%). Finally, the Principal component analysis (PCA) techniques were used to find out significant WQ indicators and the correlation matrix revealed that WQ had a substantial positive correlation with agricultural land (r = 0.68, P < 0.01) and a significant negative association with the built-up area (r =  - 0.94, P < 0.01). To the best of the authors' knowledge, this is the first attempt in Bangladesh to assess the impact of LULC on the water quality along the longitudinal gradient of a vast river system. Hence, we believe that the findings of this study can support planners and environmentalists to plan and design landscapes and protect the river environment.

Prevalence of organic micropollutants in the Yamuna River, Delhi, India: seasonal variations and governing factors

This work primarily emphases on evaluating the prevalence of organic micropollutants (OMPs) in the perennial Yamuna River (YR) that flow through the national capital of India, Delhi. Sixteen sampling campaigns (non-monsoon, n = 9; monsoon n = 7) were organized to understand the seasonal variations with special emphasis on monsoon. We have found fifty-five OMPs in the monsoon; while forty-seven were detected in non-monsoon. Fifty-seven screened and quantified OMPs in the most polluted stretch of River Yamuna included the pharmaceutically active compounds, pesticides, endocrine-disrupting chemicals, phthalates, personal care products, fatty acids, food additives, hormones, and trace organics present in hospital wastes. During monsoon months, compounds for which concentrations exceeded 50 μg/L were: adenine (64.6 μg/L), diethyl phthalate (62.9 μg/L), and octamethyltrisiloxane (56.9 μg/L); and the same for non-monsoon months was only for 1-dodecanethiol (52.3 μg/L). The average concentration of OMPs in non-monsoon months indicate PhACs>PCPs>Pesticides>Fatty acids>Hospital waste>Hormones>Pesticides>EDCs. In monsoon months due to surface runoff and high volume of untreated wastewater discharges few more OMPs concentrations were detected which mainly includes PhACs (clofibric acid, diclofenac sodium, gemfibrozil, ketoprofen), pesticides (aldrin, metribuzin, atrazine, simazine). Due to dilution effect in the monsoon months, average concentrations of 3-acetamido-5-bromobenzoic acid (PhACs) was reduced from 45.22 μg/L to 14.07 μg/L, whereas some EDCs such as 2,4- Di-tert-amylphenol, 3,5- di-tert-butyl-4-hydroxybenzyl alcohol, Triphenylphosphine oxide, Benzophenone were found in much higher concentrations in the monsoon months. Octamethyltrisiloxane (PCPs) was detected 50 times higher in concentration in the monsoon months. Interestingly, the concentration of about 50 % of the OMPs was more in the monsoon samples than in non-monsoon samples which is contrary to the general understanding that monsoon-induced dilution lowers the concentrations of OMPs. In RY water higher magnitude of diclofenac sodium, ibuprofen, ketoprofen, and clofibric acid was found than Europe and North America rivers. Hormones such as estriol and estrone in RY water are found 70 to 100 times higher than the maximum reported concentrations in the US streams. Finally, various OMPs responded differently to the monsoon season as evident from multivariate analyses.

Understanding the hydrochemical functioning of glacierized catchments of the Upper Indus Basin in Ladakh, Indian Himalayas

Recent studies have endorsed that surface water chemical composition in the Himalayas is impacted by climate change-induced accelerated melting of glaciers. Chemical weathering dynamics in the Ladakh region is poorly understood, due to unavailability of in situ dataset. The aim of the present study is to investigate how the two distinct catchments (Lato and Stok) drive the meltwater chemistry of the Indus River and its tributary, in the Western Himalayas. Water samples were collected from two glaciated catchments (Lato and Stok), Chabe Nama (tributary) and the Indus River in Ladakh. The mildly alkaline pH (range 7.3-8.5) and fluctuating ionic trend of the meltwater samples reflected the distinct geology and weathering patterns of the Upper Indus Basin (UIB). Gibbs plot and mixing diagram revealed rock weathering outweighed evaporation and precipitation. The strong associations between Ca²⁺-HCO₃^-, Mg²⁺-HCO₃^-, Ca²⁺-Mg²⁺, Na⁺-HCO₃^-, and Mg²⁺-Na⁺ demonstrated carbonate rock weathering contributed to the major ion influx. Principal component analysis (PCA) marked carbonate and silicates as the most abundant minerals respectively. Chemical weathering patterns were predominantly controlled by percentage of glacierized area and basin runoff. Thus, Lato with the larger glacierized area (~ 25%) and higher runoff contributed low TDS, HCO₃^-, Ca²⁺, and Na⁺ and exhibited higher chemical weathering, whereas lower chemical weathering was evinced at Stok with the smaller glacierized area (~ 5%). In contrast, the carbonate weathering rate (CWR) of larger glacierized catchments (Lato) exhibits higher average value of 15.7 t/km²/year as compared to smaller glacierized catchment (Stok) with lower average value 6.69 t/km²/year. However, CWR is high in both the catchments compared to silicate weathering rate (SWR). For the first time, in situ datasets for stream water chemical characteristics have been generated for Lato and Stok glaciated catchments in Ladakh, to facilitate healthy ecosystems and livelihoods in the UIB.

Evaluation of heavy metal contaminants in prepared noodles: source allocation and health risk assessment

In recent years, special attention has been given to emission research that led to the deposition of toxicants from road traffic. Thus, it is imperative to focus on heavy metal (HM) stressors in food items, their source contribution, and health risk assessment providing insight into their spatial role at the population level. In this study, heavy metal in the street vended noodles was studied while correlating the quality of noodle with different environmental origins. The samples were prepared using acid digestion and analysed by flame atomic absorption spectrophotometer, except Hg which was analysed by direct mercury analyser. The results showed that some heavy metals like Cr, Pb, Mn, Cd, and Hg exceed their permissible limits established by the international legislation for food products. In the noodle samples, the concentration of heavy metal ranged from < 0.1 to 0.904 mg/kg for Pb, < 0.09 to 0.843 mg/kg for Ni, < 0.004 to 0.201 mg/kg for Cd, < 0.0001 to 0.004 mg/kg for Hg, < 0.01 to 1.388 mg/kg for Cu, < 0.015 to 8.049 mg/kg for Mn, and < 0.02 to 16.514 mg/kg for Cr. Noodle samples vended on high traffic density streets are directly associated with increased HM content due to atmospheric deposition from the surrounding. Source apportionment study determines that HM contamination belongs to the same source of origin, except Cr. Based on the cluster analysis, these samples fall into three major groups that were further validated by the canonical discriminant function. Health risk prediction by Monte Carlo simulation revealed an elevated non-carcinogenic health hazard risk to consumers with a hazard index (HI) shift from 71 to 75%. Health hazard analysis showed that consumers of high traffic density street vended food are at higher risk of developing health-related issues. This study is important to evaluate the health risk of the population exposed to heavy metals due to ingestion of street vended food.

Assessment of Gualaxo do Norte River water quality (Minas Gerais, Brazil) affected by the dam breach of Fundão utilizing exploratory multivariate techniques

At the end of 2015, the Fundão dam belonging to the Samarco S.A. mining company was ruptured, releasing a flood of mud into the Gualaxo do Norte River, which advanced into the Doce River. The aim of the present study was to apply exploratory multivariate approaches to water quality data obtained during sampling campaigns at the Gualaxo do Norte River during the dry and rainy seasons, between July 2016 and June 2017. A total of 27 locations along the river were sampled, covering unaffected areas and regions influenced by the tailings waste from the dam. Determinations of chemical, physical, and microbiological water quality parameters were performed. Application of principal component analysis (PCA) resulted in the first two components together explaining 39.49% and 37.91% of the total variance for the dry and rainy season data, respectively. In both cases, the PCA groups were related to variables such as turbidity and total solids, which both presented higher values in regions affected by the mud flow. These results are in agreement with those obtained by the Kohonen neural network method, where two-dimensional maps confirmed the samples according to the affected and unaffected area by the disaster.

Impact of COVID-19 lockdown on the water quality of the Damodar River, a tributary of the Ganga River in West Bengal

The COVID-19 (SARS-CoV-2) pandemic is wreaking havoc on the planet, yet control of waste materials comforted the ecosystem during the lockdown restricting human activities. Damodar is the most important tributary of the lower Ganga River in West Bengal. It flows through an industrially developed, agriculturally flourished populated area. Different methods are applied to identify the changing pattern of water quality during the lockdown. BOD graph shows an increase in pollution levels in residential areas but a sharp decline in coliform levels in urban residential sites. The National Sanitation Foundation Water Quality Index (NSFWQI) shows the same pattern of water quality throughout the course. Irrigation suitability of water is examined using sodium percentage (%Na), sodium absorption ratio (SAR), potential salinity (PS), magnesium absorption ratio (MAR), and Kelly's ratio (KR). Mujhermana (received maximum pollutants from industries and residential areas) station shows a decrease in ions concentration and subsequent improvement in agriculture water quality during the COVID-19 period. According to Kelly's ratio, the water at this sample site is unfit for agricultural use; however, the water quality improved and became acceptable for cultivation during the lockdown period. Cluster analysis is used to understand the similar pollution concentration of eleven sampling stations in different periods. Mujhermana site makes a separate cluster due to its high pollution load compared to other sampling sites before the COVID-19. But during the lockdown period, this site was clustered with the most petite contaminated sites.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40899-022-00790-2.

Exploring fish assemblage structure, feeding guild, and water quality in a typical river-reservoir interface of tropical large reservoir environment, Central India

The present study aims to understand the spatio-temporal dynamics in habitat ecology and fish assemblage and determine the relationship of habitat transition and fish distribution in a large riverine-reservoir cascade system (Sardar Sarovar Reservoir part in Madhya Pradesh). This study was carried out along a 125-km riverine-reservoir interface (RRI) during 2018-2020. The study showed presence of a total of 110 fish species (104 spp. in upstream main Narmada River stretch excluding reservoirs, 56 in RRI, and 39 in Sardar Sarovar reservoir). We recorded 50 species common in upstream riverine stretch of Narmada and the RRI while RRI uniquely harbored 3 native species (Chitala chitala, Labeo boga, Salmostoma phulo) not previously reported from Narmada River stretch upstream. The similarity percentage (SIMPER) analysis showed presence of S. phulo causing 27.91%, Schistura dayi (3.71%), and Rita pavimentata (3.41%) of dissimilarity among Maheshwar (S1), Rajghat (S2), Koteshwar (S3) non-confluence zone, and Kakrana (S4)-confluence zone. The analysis of data indicated disappearance of some species like deccan peninsular carp Labeo fimbriatus, Mahseer Tor khudree which was earlier reported but was absent in recent years. The statistical correlation of environmental variables with observed abundances of fish feeding guilds showed significant positive correlation with specific conductivity and total dissolved solid content of the water. The canonical correspondence analysis (CCA) indicated association among omnivorous fishes of the reservoir with water temperature; total dissolved solids; specific conductivity; total alkalinity; chlorophyll; and NO₃-N. The herbivore fishes were found to be more associated with magnesium, dissolved oxygen, and phosphorus content of the water while insectivorous fishes were more associated with the silicate and transparency of the water. The abundance of omnivores species indicated dominance of generalist fish species rather than specialists in the interface region of reservoir showing conservation and ecological importance of the ecosystem. The baseline information generated on fish assemblage and ecological perspectives of the river reservoir interface and science-based management recommendations formulated in the study are critical for sustaining fish diversity, promoting fisheries enhancement, and management planning of tropical large reservoirs.

Identifying trends and driving factors of spatio-temporal water quality variation in Guanting Reservoir Basin, North China

Assessment of river quality has been attracting a great deal of attention because of its important implications for the living environment of human beings and aquatic organisms. This study investigated the spatial and temporal variation of water quality and its possible driving factors of Guanting Reservoir Basin. For this purpose, water quality was assessed with the Canadian Council of Ministers of the Environment Water Quality Index, together with nutrient inputs and social-economic data which were collected and analyzed during the period 2009-2019. The results showed that the overall water quality in Guanting Reservoir increased over time and was rated as "good" during the studied decade. Spatially, water quality in tributary upstream of Yanghe River and Sanggan River was better than that in Guishui River, resulting in better quality in West Reservoir than in East Reservoir. Among water quality indices, total nitrogen was the main pollutant affecting the water quality of the studied area. In addition, principal component analysis and correlation analysis were applied to evaluate the relationships between the socio-economic factors and water quality variation. Urbanization and industry structure were significantly correlated with the water quality variation in upstream tributaries. The findings provide insightful understanding into the spatio-temporal variations of water quality and the associated driving factors of Guanting Reservoir basin, which would help managers in executing theoretical reference for water quality protection.

Spatial Distribution, Source Analysis and Health Risk Study of Heavy Metals in the Liujiang River Basin in Different Seasons

Three high-frequency sampling and monitoring experiments were performed at the Lutang and Luowei transects of the Liujiang River entrance and at the southeast exit of the Liuzhou during 2019 for the purpose of assessing physico-chemical variables and human health hazards of water heavy metals in different rainfall processes. There were significant seasonal variations in concentrations of 11 heavy metals and most variables showed higher levels during the dry season. The distribution of heavy metals in the Liuzhou area varied significantly by region. Pollution source analysis indicated distinct seasons of wetness and dryness. The dry season is dominated by anthropogenic activities, while the wet season is dominated by natural processes. The results of hazard quotient (HQ) and carcinogenic risk (CR) analysis showed that the health risk of non-carcinogenic heavy metals in the wet season is slightly higher than that in the dry season. Seasonal changes in carcinogenic risk are the opposite; this is due to the combined influence of natural and human activities on the concentration of heavy metals in the river. Among them, Al was the most important pollutant causing non-carcinogenic, with As being a significant contributor to carcinogenic health risk. Spatially, the downstream Luowei transect has a high health risk in both the dry and rainy seasons, probably due to the fact that the Luowei transect is located within a major industrial area in the study area. There are some input points for industrial effluent discharge in the area. Therefore, high-frequency monitoring is essential to analyze and reduce the heavy metal concentrations in the Liujiang River during dry and wet seasons in order to protect the health of the residents in the area.

Spatial Comparative Analysis of Landscape Fragmentation Metrics in a Watershed with Diverse Land Uses in Iran

Knowledge of landscape fragmentation is known to be important in ecological integrity, hydrological processes, urban planning, sustainable land management, and policymaking. Recent anecdotal studies reveal a need for analytical quantification of landscape fragmentation at different levels. Therefore, the present study was conducted at KoozehTopraghi Watershed, Ardabil Province, Iran, where covers by different land uses/covers, to (a) explore the spatial pattern of landscape fragmentation metrics comprehensively in different scales, (b) distinguish the landscape fragmentation hot spots, and (c) investigate the spatial clustering of landscape fragmentation metrics. The behaviors of 7, 10, and 13 fragmentation metrics concerning three levels of patch, class, and landscape across 36 sub-watersheds were explored using principal component analysis (PCA) and expert elicitation. The Getis-Ord Gi* and local Moran’s I indices were also used to analyze the hot spots and clusters of landscape fragmentation, respectively. The results verified the high degree of spatial variability of the metrics in the three levels of fragmentation analysis. The class-level fragmentation analysis showed that the watershed is characterized by high-fragmented residential land use and low-fragmented dry farming land use. The spatial trend analysis at the landscape level further indicated that sub-watersheds 1, 2, 11, 21, to 26, and 34 to 36, mainly located in lowlands and central parts, allocated better status considering the fragmentation metrics rather than other parts of the watershed. The significant hot spots and high clusters of fragmentation also were distributed in different parts of the watershed in terms of various landscape metrics.

Using EEM-PARAFAC to identify and trace the pollution sources of surface water with receptor models in Taihu Lake Basin, China

The identification and apportionment of the multiple pollution sources are essential and crucial for improving the effectiveness of surface water resources management. In this study, the surface water samples were collected from Taihu Lake Basin, and the optimal water quality parameters for the receptor models were selected firstly with multivariate statistical analyses. In order to identify the potential pollution sources in surface water, dissolved organic matter (DOM) was analyzed with the excitation-emission matrix coupled with parallel factor analysis (EEM-PARAFAC). Through the Pearson correlation analysis of water quality parameters and DOM components, the pollution sources were further verified, i.e., agricultural activities, domestic sewage, phytoplankton growth/terrestrial input and industrial sources. In addition, principal component analysis (PCA) combined with the absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models were employed to quantify pollution sources. Compared with PCA-APCS-MLR model, PMF model resulted in higher performance on evaluation statistics and lower proportion of unexplained variability, thus showed more realistic and robust representation. The results of PMF showed that agricultural activities (42.08%) and domestic sewage (21.16%) were identified as the dominant pollution sources of surface water in the study area. This study highlights the effectiveness of EEM-PARAFAC in identifying the pollution sources, and the applicability of PMF in apportioning the contributions of each potential pollution source in surface water.

Hydrogeochemical characterization based water resources vulnerability assessment in India's first Ramsar site of Chilka lake

A limnological site is significantly characterized by rich biological, chemical, and physical properties of the environment and is also described as the epitome of a large aquatic ecosystem. During the last few decades, the Chilka lake Ramsar site has experienced substantial degradation of water quality with associated deterioration of aquatic biodiversity. Our study aims to quantify the VWRM of the Chilka lake Ramsar region using the most reliable MLAs, namely ANN and RF, with the help of seventeen hydro-chemical properties of lake water. The produced map is validated through six validating measures (ROC-AUC- 0.89, Sensitivity-0.90, Specificity-0.78, PPV-0.78, NPV-0.88, Taylor diagram (r)-0.94), which depict that ANN is the most reliable ML algorithm in assessing the VWRM of the concerned region followed by RF. The prepared map of our study revealed that the eastern part was remarkably high to very high vulnerable zone covered area with 22.41 % and 7.19 %, respectively.

Assessment of groundwater salinity using principal component analysis (PCA): a case study from Mewat (Nuh), Haryana, India

In the present study, principal component analysis (PCA) is used to investigate the processes controlling groundwater salinity in the Mewat (Nuh) district, Haryana, India. Twenty groundwater samples were collected from salinity-affected areas in the March-April months of years 2018 and 2019 and were analyzed for chemical variables pH, EC, Ca²⁺, Mg²⁺, Na⁺, K⁺, [Formula: see text], Cl^-, SO₄^2-, [Formula: see text], TDS, and total hardness. Three principal components were selected based on the eigen value, which explains 79.58% and 85.08% of the total variation in the years 2018 and 2019, respectively. The first principal component (PC-1) is identified with salinity, the second principal component (PC-2) with alkalinity, and the third principal component (PC-3) described the pollution. When the yearly comparison was made, the samples collected in 2019 were found to have an increased salinity compared to 2018, which shows an increased vulnerability to the aquifer of Mewat on account of the decline in rainfall recharge. It was also evident that declining recharge also triggered the recharge from other sources; thus, the impact of pollution is more pronounced in 2019 compared to 2018.

Slight flow volume rises increase nitrogen loading to nitrogen-rich river, while dramatic flow volume rises promote nitrogen consumption

Concentrated rainfall and water transfer projects result in slight and dramatic increases in flow volume over short periods of time, causing nitrogen recontamination in the water-receiving areas of nitrogen-rich rivers. This study coupled hydrodynamic and biochemical reaction models to construct a model for quantifying diffusive transport and transformation fluxes of nitrogen across the water-sediment interface and analysed possible changes in the relative abundance of microbial functional genes using high-throughput sequencing techniques. In this study, the processes of ammonium (NH₄⁺-N) and nitrate (NO₃^--N) nitrogen release and sedimentation with resuspended particles, as well as mineralisation, nitrification, and denitrification processes were investigated at the water-sediment interface in the Fu River during slight and dramatic increases in flow volume caused by concentrated rainfall and water diversion projects. Specifically, a slight flow volume rise increased the release of NH₄⁺-N from the sediment, inhibited sedimentation of NO₃^--N, decreased the mineralisation rate, increased the nitrification rate, and had little effect on the denitrification process, ultimately increasing the nitrogen load to the river water. A dramatic increase in flow volume simultaneously increased NH₄⁺-N and NO₃^--N exchange fluxes, inhibited the mineralisation process, promoted nitrification-denitrification processes, and increased inorganic nitrogen consumption in the river. This study provides a solution for the re-pollution of rivers that occurs during the implementation of reservoir management and water diversion projects. Furthermore, these results indicate a potential global nitrogen sink that may have been overlooked.

Determining groundwater quality using indices and multivariate statistical techniques: a study of Tosham block, Haryana, India

Water is the most important component for human survival and often the most misused one. The present study deals with the assessment of groundwater quality of Tosham block, Bhiwani District, Haryana, India, and its nearby villages. A total of 23 samples were collected from different groundwater sources and were analysed for 16 different physico-chemical parameters. Correlation coefficients were calculated to identify highly correlated and interrelated water quality parameters. It provides an excellent tool for the prediction of parameter values within the reasonable rank of exactness. A strong correlation was observed between total hardness, magnesium and total dissolved solids, especially between total dissolved solids and electrical conductivity. The obtained results were compared with Indian Standard Drinking water specifications IS: 10500-2012. Concentration of total hardness, iron and chloride was found above the permissible limit in all the samples. Multivariate statistical techniques, namely cluster analysis and principal component analysis, were used to find the relationship between studied parameters. Water quality index (WQI) was also calculated. The WQI of groundwater samples ranged from 125 to 556. The water quality index revealed that out of 23 samples, 13 were found to have poor water quality and 6 samples were of very poor water quality. High value of WQI was found mainly from total dissolved solids, electrical conductivity, total hardness, Mg, Cl and Fe. Groundwater analysis of the studied water samples indicated that there is a need to treat the water before its use for drinking and other domestic activities. Necessary precautions should be taken to prevent the groundwater from being contaminated.

Application of synthetic data to establish the working framework for multivariate statistical analysis of river pollution traceability - the heavy metals in Nankan River, Taiwan

This study applied multivariate statistical analysis (MSA) to synthetic data simulated by a river water quality model to verify whether the MSA can correctly infer the pollution scenario assigned in the river water quality model. The results showed that when assessing the number and possible locations of pollution sources based on the results of cluster analysis (CA), two instead of three pollution point source were identified when considering the hydraulic variations of surface water. When discussing the principal component analysis (PCA) result, the second principal component (PC2) and the Pearson correlation coefficients among the pollutants should also be considered, which can infer that Cu, Pb, Cr, and Ni are contributed by the same pollutant point source, and Cu is also influenced by another pollutant point source. This result also implies that the solid and liquid partition coefficients (K_d) of pollutants can affect the interpretation of the PCA results, so the K_d values should be determined before tracing the pollution sources to facilitate the evaluation of the source characteristics and potential targets. This study established a working framework for surface water pollution traceability to enhance the effectiveness of pollution traceability.

Temporal and spatial variation in water quality in the Yangtze Estuary from 2012 to 2018

Water quality plays an important role in estuarine habitats and affects aquatic organisms. The focus of this study was to understand the temporal-spatial variation of water quality and reveal the natural and anthropogenic drivers by using multiple analysis approaches. Herein, during 2012-2018, six water quality constituents (pH, electrical conductivity (EC), dissolved oxygen (DO), ammonia nitrogen (NH₃N), total nitrogen (TN), and total phosphorus (TP) were examined for eight sites in the Yangtze Estuary, and the hydro-sediment data, i.e., discharge (Q) and sediment transport rate (STR), was collected from the upstream hydrological station Datong. The cluster analysis (CA), principal component analysis (PCA)/factor analysis (FA), Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), and the Mann-Kendall (MK) test were applied. The eight sampling sites were geographically grouped into cluster I, cluster II, and cluster III based on the characteristics of water quality changes, which are under the influence of the upstream runoff, the anthropogenic activities, and seawater. The results are as follows: (1) NH₃N, TN, and DO were key constituents representing the water quality of cluster I and cluster III, and those were EC, TN, and DO for cluster II; (2) The monthly-average Q and STR of Datong were negatively correlated to water quality constituents with seasonal variation except for pH; (3) The impact of anthropogenic activities on water quality was especially reflected in cluster III which is close to the Huangpu River Estuary; upstream runoff was the natural driver of water quality changes for cluster I while that was seawater for cluster II. Seawater acts a key role in affecting the water quality of cluster II situated at the North Branch. Revealing the key drivers of water quality change in the Yangtze Estuary provides a reference for water quality management.

Hydrochemical characterization of groundwater quality using chemometric analysis and water quality indices in the foothills of Himalayas

Groundwater pollution of the watershed is mainly influenced by the multifaceted interactions of natural and anthropogenic process. To analyse the spatial-temporal variation and pollution source identification and apportionment, the dataset was subjected to a globally acknowledged coherent technique using water quality indices and chemometric techniques (principal component analysis (PCA) and cluster analysis. The bulk of the samples tested were below the BIS's permissible levels. Groundwater samples from the pre- and post-monsoon seasons mostly contained the anions HCO- 3 > Cl- > SO2- 4 > NO- 3, while the primary cations were Ca2+ > Mg2+ > Na+ > K+. Groundwater was alkaline and hard at most of the sites. According to hydro-geochemical facies and relationships, Piper diagrams, and principal component analysis, weathering, dissolution, leaching, ion exchange, and evaporation were the key mechanisms influencing groundwater quality. The hydrochemical facies classified the groundwater samples into the Ca-Mg-HCO3 type. For all the sampling locations, PIG was determined to be 0.43, 0.52, 0.47, 0.48, 1.00, and 0.70; respectively. The majority of the test locations fell into the low to medium contamination zone, as determined by the groundwater pollution index (PIG) and contamination index. Three principal components, which together account for 93.8% of the total variance, were identified via PCA. The study's findings confirm the value of these statistical techniques in interpreting and understanding large datasets and offering reliable information to reduce the time and expense of programmes for monitoring and evaluating water quality.

Can water composition and weather factors predict fecal indicator bacteria removal in retention ponds in variable weather conditions?

Retention ponds provide benefits including flood control, groundwater recharge, and water quality improvement, but changes in weather conditions could limit the effectiveness in improving microbial water quality metrics. The concentration of fecal indicator bacteria (FIB), which is used as regulatory standards to assess microbial water quality in retention ponds, could vary widely based on many factors including local weather and influent water chemistry and composition. In this critical review, we analyzed 7421 data collected from 19 retention ponds across North America listed in the International Stormwater BMP Database to examine if variable FIB removal in the field conditions can be predicted based on changes in these weather and water composition factors. Our analysis confirms that FIB removal in retention ponds is sensitive to weather conditions or seasons, but temperature and precipitation data may not describe the variable FIB removal. These weather conditions affect suspended solid and nutrient concentrations, which in turn could affect FIB concentration in the ponds. Removal of total suspended solids and total P only explained 5% and 12% of FIB removal data, respectively, and TN removal had no correlation with FIB removal. These results indicate that regression-based modeling with a single parameter as input has limited use to predict FIB removal due to the interactive nature of their effects on FIB removal. In contrast, machine learning algorithms such as the random forest method were able to predict 65% of the data. The overall analysis indicates that the machine learning model could play a critical role in predicting microbial water quality of surface waters under complex conditions where the variation of both water composition and weather conditions could deem regression-based modeling less effective.

Spatio-temporal dynamics of water quality in river sources of drinking water in Uttarakhand with reference to human health

Any contamination in potable water leads to high risk on human health. Hence, it is inevitable to characterize water quality and assess temporal change with reference to human health. In this paper, nineteen water quality parameters from 50 sources of drinking water supply operated by state agency Uttarakhand Jal Sansthan (UJS) were analysed. The water samples were collected for pre-monsoon (May) and post-monsoon (November) seasons for the year 2010, 2011, 2018 and 2019. Multivariate analysis such as Pearson's correlation coefficient (r), K-mean, hierarchical clustering and principle component analysis (PCA) were applied to examine the drinking water quality at source sites. The strength of correlation among the parameters is reduced over the time. Cluster analysis provides six major hydro-chemical clusters in the study region. Clusters were compact during 2010-2011 and converted to overlapping structure in later period. The size of clusters was change from two clusters to six clusters during 2018-2019. These hydro-chemical clusters were examined through PCA and established six major hydro-chemical components of water quality. The physico-chemical parameters, namely, pH, alkalinity, TH, TDS, Na, K, Ca, Mg, Cl and SO₄, were in PC-1, the heavy metals in PC-3, turbidity in PC-4, TC in PC-5 and PC-6 is combination of turbidity, NO₃ and TC, whereas PC-2 was a mix of pH and Na and K salts. Study highlighted that the water quality has changed over the time due to runoff of plant debris, erosion, agricultural fertilizer, development activities and local geology hosts. Each PCs was mapped with associated human health issues. Result reveals that structures and pattern of PCs indicate several human health diseases over the later period with seasonal effects.

Evaluation of polluted urban river water quality: a case study of the Xunsi River watershed, China

Urbanization has caused severe negative impacts on intra-urban river water bodies. In this paper, 22 physicochemical parameters were measured at 20 locations in the level period and 29 locations in the wet and dry period using nearby urban area surface water samples from a medium-sized polluted river (a lake-river continuum, i.e., Xunsi River, Qingling River and Tangxun Lake) in the Yangtze River watershed in Wuhan, China. In this study, the environmental quality standards for surface water of China (GB3838-2002) and the WHO drinking water standard (2011) were used as reference standards. After the basic physicochemical parameters were evaluated, the possible sources of pollution in the study area under urban pressure were examined by applying multivariate statistical techniques. The factor analysis method and entropy weighting method identified three critical factors describing the type of surface water pollution in the watershed: (1) pollution from nutrients, mainly nitrogen pollution, (2) trace metals pollution, mainly from Fe and Pb, and (3) organic pollution. Based on multivariate analysis, the weighted arithmetic water quality index (WQI) was used to assess the water quality of each period. The WQI results show that the water near the urban area of the watershed temporally shows light to moderate pollution overall in the level and wet periods, and sever pollution in the dry period. The worst water quality indicating water that is not suitable for any practical use occurs in the dry period. Spatially distributed in clusters and strips visualized in kriging method, the water quality of the lake source and upstream is better than that of the downstream. In addition, Escherichia coli was detected at consistently high levels, and although the river is not a source of drinking water, it could pose a threat to downstream waterworks.

Source Identification and Health Risk Assessment of Heavy Metals in Soil: A Case Study of Lintancang Plain, Northeast China

To investigate the concentration, source, and potential health risk of soil heavy metals (V, Cr, Ni, Cu, Zn, Pb, Hg), this study determined the concentration of these seven metals in 37 soil samples from Linyi City, southeast of Shandong Province, China. The mean concentrations of the investigated heavy metals followed the sequence: Cr (76.2 mg/kg) > V (70.5 mg/kg) > Zn (70.1 mg/kg) > Ni (34.0 mg/kg) > Pb (31.4 mg/kg) > Cu (23.2 mg/kg) > Hg (1.7 mg/kg). The enrichment factor (EF) and geo-accumulation index (Igeo) indicated an extreme enrichment of Hg (EF > 10, Igeo > 4) within the study area, while a slight enrichment of other metals. According to the toxic risk index (TRI), Hg accounted for the strongest soil toxicity (TRI = 8.07, 64.3%). The risk assessment with hazard index (HI) suggested that the health risks of all metals were acceptable, and the HI of adults was generally lower compared with that of the children. In addition, two principal components (PC) calculated by principal component analysis (PCA) were used to identify the sources of these heavy metals, which were 57.73% for PC 1 (Pb, Cr, Zn, Ni, Hg, Cu and V) and 21.63% for PC 2 (Hg, Cu and V), respectively. Moreover, PC 1 was mainly controlled by anthropogenic inputs, while PC 2 was contributed to by natural sources. Combined with the correlation matrix, it was concluded that there were three different sources for all seven heavy metals.

Assessment of ecohydrogeochemical status of freshwater Loktak Lake of Manipur, India

The present study has been carried out to assess the ecohydrogeochemical status of Loktak Lake, the largest freshwater lake in the Northeastern region of India, based on the water quality parameters, hydrogeochemistry, water quality indices (WQI) and trophic state index (TSI). The spatio-temporal variations of physicochemical parameters have been assessed, and it was found that parameters such as pH, turbidity, dissolved oxygen, biological oxygen demand, iron, fluoride and coliform concentrations in the water exceeded the permissible limits prescribed by the World Health Organization (WHO) and Bureau of Indian Standards (BIS) during both pre-monsoon (PM) and post-monsoon (PoM) seasons. The water hardness lies within the soft category, except for a few samples found to be moderately hard. WQI values of lake water ranged between 38.19 and 155.47 during PM and 39.48 and 432.26 during PoM. Based on the WQI classification during PM, 8.6% of the samples were in the unsuitable category, 14.3% very poor, 45.7% poor and 31.4% in the good category. During PoM, 22.9% of the samples were in the unsuitable category, 25.7% very poor, 31.4% poor and 20% in the good category. The irrigation water quality was evaluated using indices such as sodium percentage, sodium adsorption ratio, residual sodium carbonate, permeability index and Kelly's ratio, and the results indicated that the lake water could be used safely for agricultural purposes. The trophic state evaluation revealed an oligotrophic condition of the lake waters during PM (TSI 37.9) and a mesotrophic condition during PoM (TSI 46.9).