Groundwater quality in Imphal West district, Manipur, India, with multivariate statistical analysis of data

Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak

Loktak Lake, one of the largest freshwater lakes in Manipur, India, is critical for the eco-hydrology and economy of the region, but faces deteriorating water quality due to urbanisation, anthropogenic activities, and domestic sewage. Addressing the urgent need for effective pollution management, this study aims to assess the lake's water quality status using the water quality index (WQI) and develop advanced machine learning (ML) tools for WQI assessment and ML model interpretation to improve pollution management decision making. The WQI was assessed using entropy-based weighting arithmetic and three ML models - Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) - were optimised using a grid search algorithm in the H2O Application Programming Interface (API). These models were validated by various metrics and interpreted globally and locally via Partial Dependency Plot (PDP), Accumulated Local Effect (ALE) and SHapley Additive exPlanations (SHAP). The results show a WQI range of 72.38-100, with 52.7% of samples categorised as very poor. The RF model outperformed GBM and DNN and showed the highest accuracy and generalisation ability, which is reflected in the superior R2 values (0.97 in training, 0.9 in test) and the lower root mean square error (RMSE). RF's minimal margin of error and reliable feature interpretation contrasted with DNN's larger margin of error and inconsistency, which affected its usefulness for decision making. Turbidity was found to be a critical predictive feature in all models, significantly influencing WQI, with other variables such as pH and temperature also playing an important role. SHAP dependency plots illustrated the direct relationship between key water quality parameters such as turbidity and WQI predictions. The novelty of this study lies in its comprehensive approach to the evaluation and interpretation of ML models for WQI estimation, which provides a nuanced understanding of water quality dynamics in Loktak Lake. By identifying the most effective ML models and key predictive functions, this study provides invaluable insights for water quality management and paves the way for targeted strategies to monitor and improve water quality in this vital freshwater ecosystem.

Drinking water quality evaluation in supply systems in Wuhan, China: application of entropy weight water quality index and multivariate statistical analysis

In this study, source water, finished water, and tap water were sampled monthly from two large drinking water treatment plants in Wuhan city, China for 12 months where physicochemical and microbiological parameters were measured, and the complex monitoring data was analyzed using single-factor assessment method, entropy weight water quality index (EWQI), and multivariate statistical techniques (i.e., cluster analysis (CA), discriminant analysis, and correlation analysis). The results of the single-factor assessment method showed that the total nitrogen pollution was the main problem in the source water quality, and the finished and tap water met the required quality standards. The EWQI values indicated that the overall quality of the source, finished, and tap water samples was "Excellent." In addition, strengthening monitoring of parameters with high entropy weights, including Pb, Hg, sulfide, Cr in surface water and Hg, aerobic bateria count, and As in drinking water, were suggested, as they were prone to drastic changes. Spatial CA grouped the finished and tap water samples from the same plant into a cluster. Temporal CA grouped 12 sampling times of source water into Cluster 1 (June), Cluster 2 (April-May, and July-November), and Cluster 3 (December-March). Concerning finished and tap water, except the October was regrouped, the result of temporal CA was consistent to that of the source water. Based on similar characteristics of water samples, monitoring sites and frequency can be optimized. Moreover, stepwise discriminant analysis indicated that the spatiotemporal variations in water quality among CA-groups were enough to be explained by four or five parameters, which provided a basis for the selection of monitoring parameters. The results of correlation analysis showed that few pairwise correlations were both significant (P < 0.05) and stable across sampling sites, suggesting that the number of monitoring parameters was difficult to reduce through substitution. In summary, this study illustrates the usefulness of EWQI and the multivariate statistical techniques in the water quality assessment and monitoring strategy optimization.

Geospatial assessment and hydrogeochemical characterization of groundwater resources of Manipur Valley, India

Spatio-temporal hydrogeochemical assessments of groundwater resources were carried out for the valley region of Manipur in India to investigate its quality status and suitability criteria for irrigational and potable uses. The assessment was carried out for 140 spatially distributed groundwater samples collected during 2018-2021 for pre- and post-monsoon seasons. To assess and comprehend the hydrogeochemical behavior of underlying aquifers and controlling factors for groundwater quality in the region, groundwater quality indices and statistical tools were used. Assessment of in situ parameters such as pH, TDS, salinity, and EC under field conditions reveal that the values exceed concentration in many locations. Most of the samples fall in the category of hard to very hard water. Moreover, observation of both positive and negative oxidation-reduction potential (ORP) and low dissolved oxygen (DO) concentration in groundwater samples indicates aquifers are of recent unstable geologic formations. Similarly, elevated concentrations of F-, Cl-, HCO3-, and Fe3+ greater than the prescribed standards of the World Health Organization in many samples indicate the unsuitability of the groundwater sources for potable uses. The geochemical interactions were found to be dominated and controlled by the rock-weathering geochemical process that contributes to HCO3--Ca2+-type water, followed by HCO3--Na+, Cl--Ca2+, and mixed-type water in both seasons. Spatio-temporal geospatial vulnerable groundwater zone mapping using interpolation techniques carried out in the ArcGIS platform identifies the aquifers based on the water quality and pollution indices. The study's significant findings can provide baseline information that can supplement the government's planning and management initiatives to deal with the current water security challenges in the region as groundwater uses are increasing due to various hydroclimatic phenomena in the state.

Appraisal of groundwater quality for drinking and irrigation suitability using multivariate statistical approach in a rapidly developing urban area, Tirunelveli, India

The quantitative and qualitative stress on groundwater resources has been witnessed across the globe. The current study assesses the groundwater quality of Tirunelveli district which faces the hazard of groundwater contamination through seepage of toxins, considering the open dumping of huge volumes of solid waste. The findings from this study confirmed the presence of more than 20% samples in the "poor to very poor" quality with high concentrations of TDS, Cl-, and NO3-, unfit for drinking, and other domestic purposes. The spatial distribution of TDS and NO3- highlighted the potential impact of solid waste dumping in the nearby landfill sites. K-means hierarchical clustering and multivariate analysis suggested that salinization and nitrate pollution was highly influenced by anthropogenic sources in comparison to geogenic sources. Rock water interaction and evaporation processes emerged as the two major dominant natural mechanisms controlling the groundwater chemistry. Four hydro-chemical facies were identified in the order of Ca-HCO3 > Ca-Mg-Cl > Na-Cl > mixed Ca-Na-HCO3. Thus, this study creates an urgent need of mitigation measures towards curbing and management of solid waste disposal and hence, the potential hazard of contaminant seepage into the groundwater.

Groundwater quality assessment using water quality index and multivariate statistical analysis case study: East Matrouh, Northwestern coast, Egypt

Rapid urbanisation has had a significant negative influence on the water bodies that flow through and around urban areas. This study aims to evaluate the water quality and analyse the suitability for drinking and irrigation uses. This study envisaged assessing the water quality status of the groundwater using the pollution index of groundwater (PIG), ecological risk index (ERI) and multivariate statistical techniques, namely cluster analysis (CA) and principal component analysis (PCA), that were applied to differentiate the sources of water quality variation and determine the cause of pollution in the study area. Most groundwater is unsuitable for drinking and irrigation consumption, depending on analyses. PIG values indicated high pollution levels in the studied water body, rendering it unsuitable for any practical purpose. CA results showed the impact of surface water and treatment plant on groundwater. PCA was used to identify four important factors in the groundwater, including mineral and nutrient pollution, heavy metal pollution, organic pollution and faecal contamination. The deteriorating water quality of the groundwater was demonstrated to originate from vast sources of anthropogenic activities, especially municipal sewage discharge. Study wells had greater concentrations of Cl^- and Na⁺ in their water because seawater flows into the aquifer system and mixes with the marine aquifer matrix. Thus, the current work reveals how to employ the PIG and multivariate statistical approaches to obtain more accessible and more meaningful information about the water quality of groundwater and to identify the sources of pollution.

An Ensemble Kalman Filter approach to assess the effects of hydrological variability, water diversion, and meteorological forcing on the total phosphorus concentration in a shallow reservoir

Total phosphorus (TP) is a vitally important water quality index in shallow reservoirs and is closely connected with hydrological variability, anthropogenic water diversion and meteorological forcing. However, it is still unclear to what extent the TP concentration in a complex shallow reservoir system attributes to each type of forcing. To resolve this issue, this study proposed a TP concentration contribution index (TPI) to assess the contribution of each forcing, using the data assimilation (DA) method, the Ensemble Kalman Filter (EnKF), which was applied in the shallow Yuqiao Reservoir, China. The EnKF model was conducted based on the Vollenweider model and logistic regression models with datasets of 1989-2015. The results showed that human-originated activities forcing (water diversion) contributed the maximum TPI (40%), followed by hydrological variability forcing (37%). Finally, meteorological forcing (air temperature and wind included) only accounted for 23%. Furthermore, the seasonal analyses also showed that the TPI of hydrological variability dominated in spring and winter, with 65% and 73% respectively. However, the contributions of meteorological forcing (air temperature and wind) accounted for a larger proportion of 63% and 57% in summer and autumn. The benefit of our EnKF model denoises the Gaussian noise contained in observation and simulation, which offers a chance to isolate and identify even a minor driving factor (i.e., meteorological forcing) from a complex river and lake system with limited data. The study provides a method to assess the influence of direct and indirect forcing on TP concentration in shallow reservoirs from a quantitative perspective. Thus, it may serve as a useful tool for water quality management in water-receiving systems.

Seasonal changes in groundwater quality deterioration and chemometric analysis of pollution source identification in South India

Hydrogeochemical understanding of groundwater is essential for the effective management of groundwater. This study has been carried out to have concrete data for the seasonal variations in hydrogeochemistry of groundwater in central Tamilnadu forming a complex geological terrain with a varied lithology. A total of 244 groundwater samples were collected during four different seasons, viz, southwest monsoon (SWM), summer (SUM), postmonsoon (POM), and northeast monsoon (NEM) from bore wells. The physical parameters such as pH, temperature, TDS, ORP, humidity, and electrical conductivity (EC) were measured insitu, whereas major ions were analyzed in the lab adopting standard procedures. Overall, higher EC and NO₃ values were observed and exceeded the WHO permissible limit irrespective of seasons, except for NO₃ in SWM. Na and HCO₃ are the dominant cation and anion in the groundwater irrespective of seasons. The highest average values of Na (65.06 mg L^-1) and HCO₃ (350.75 mg L^-1) were noted during SWM. Statistical analysis was carried out to elucidate the hydrogeochemistry of the region. Initially, to understand the ionic relationship, correlation matrix was used followed by factor analysis for determination of major geochemical control and later factor scores were derived to understand the regional representations. An attempt has also been made to identify the samples influenced by multiple geochemical processes and to understand their spatial variation in the study period. Correlation of geochemical parameters reveals a excellent positive correlation between Ca and NO₃ in SUM, SWM, and NEM due to the dominant of anthropogenic sources and minor influence of weathering process. Strongly loaded factor scores are found to be mostly in the following order POM > NEM > SWM > SUM. Principal component analysis of different seasons indicates the interplay of natural weathering and anthropogenic factors. Overall, the predominant geochemical processes in this region, irrespective of seasons are weathering and, ion exchange and anthropogenic activities.

Arsenic-Iron Relationships in Aquifers of North East India: Implications for Public Health and the Environment

Arsenic (As) contamination of groundwater in the Ganga-Brahmaputra-Meghna basin of India and Bangladesh poses severe risk to human health and the environment. Arsenic in the groundwater of this area is largely geogenic, derived from the microbial reductive dissolution of iron (Fe) oxyhydroxide and subsequent release of the sorbed As. The present study examined the correlation between As and Fe in groundwater sources in four districts of Manipur state, and in Cachar, Hailakandi, and Karimganj districts in the Barak Valley region of south Assam. Both the study areas are located in the northeastern region of India. The results reveal that As-Fe correlations in Manipur were more significant and positive, when compared to those in Barak Valley. The significance of these findings in the mobilization and transport of arsenic in the fluvial environment of these two areas of North East India, and in the prediction of arsenic-safe aquifers, are discussed and compared with existing literature. These findings, therefore, have implications in the sustainable management of groundwater resources in the study areas.

Use of water quality index and multivariate statistical techniques for the assessment of spatial variations in water quality of a small river

Rapid urban development has led to a critical negative impact on water bodies flowing in and around urban areas. In the present study, 25 physiochemical and biological parameters have been studied on water samples collected from the entire section of a small river originating and ending within an urban area. This study envisaged to assess the water quality status of river body and explore probable sources of pollution in the river. Weighted arithmetic water quality index (WQI) was employed to evaluate the water quality status of the river. Multivariate statistical techniques namely cluster analysis (CA) and principal component analysis (PCA) were applied to differentiate the sources of variation in water quality and to determine the cause of pollution in the river. WQI values indicated high pollution levels in the studied water body, rendering it unsuitable for any practical purpose. Cluster analysis results showed that the river samples can be divided into four groups. Use of PCA identified four important factors describing the types of pollution in the river, namely (1) mineral and nutrient pollution, (2) heavy metal pollution, (3) organic pollution, and (4) fecal contamination. The deteriorating water quality of the river was demonstrated to originate from wide sources of anthropogenic activities, especially municipal sewage discharge from unplanned housing areas, wastewater discharge from small industrial units, livestock activities, and indiscriminate dumping of solid wastes in the river. Thus, the present study effectively demonstrates the use of WQI and multivariate statistical techniques for gaining simpler and meaningful information about the water quality of a lotic water body as well as to identify of the pollution sources.

Natural and anthropogenic factors affecting the shallow groundwater quality in a typical irrigation area with reclaimed water, North China Plain

In this study, the hydrochemical characteristics of shallow groundwater were analyzed to get insight into the factors affecting groundwater quality in a typical agricultural dominated area of the North China Plain. Forty-four shallow groundwater samples were collected for chemical analysis. The water type changes from Ca·Na-HCO₃ type in grass land to Ca·Na-Cl (+NO₃) type and Na (Ca)-Cl (+NO₃+SO₄) type in construction and facility agricultural land, indicating the influence of human activities. The factor analysis and geostatistical analysis revealed that the two major factors contributing to the groundwater hydrochemical compositions were the water-rock interaction and contamination from sewage discharge and agricultural fertilizers. The major ions (F, HCO₃) and trace element (As) in the shallow groundwater represented the natural origin, while the nitrate and sulfate concentrations were related to the application of fertilizer and sewage discharge in the facility agricultural area, which was mainly affected by the human activities. The values of pH, total dissolved solids, electric conductivity, and conventional component (K, Ca, Na, Mg, Cl) in shallow groundwater increased from grass land and cultivated land, to construction land and to facility agriculture which were originated from the combination sources of natural processes (e.g., water-rock interaction) and human activities (e.g., domestic effluents). The study indicated that both natural processes and human activities had influences on the groundwater hydrochemical compositions in shallow groundwater, while anthropogenic processes had more contribution, especially in the reclaimed water irrigation area.

Impact of fresh tailing deposition on the evolution of groundwater hydrogeochemistry at the abandoned Manitou mine site, Quebec, Canada

The abandoned Manitou mine site has produced acid mine drainage (AMD) for several decades. In order to limit the detrimental environmental impacts of AMD, different rehabilitation scenarios were proposed and analyzed. The selected rehabilitation scenario was to use fresh tailings from the neighboring Goldex gold mine as monolayer cover and to maintain an elevated water table. In order to assess the impact of the Goldex tailing deposition on the hydrogeochemistry of the Manitou mine site, a network of 30 piezometers was installed. These piezometers were used for continuous measurement of the groundwater level, as well as for water sampling campaigns for chemical quality monitoring, over a 3-year period. Hydrochemical data were analyzed using principal component analysis. Results clearly showed the benefic impact of fresh tailing deposition on the groundwater quality around the contaminated area. These findings were also confirmed by the evolution of electrical conductivity. In addition to the improvement of the physicochemical quality of water on the Manitou mine site, new tailing deposition induced an increase of water table level. However, at this time, the Manitou reactive tailings are not completely submerged and possible oxidation might still occur, especially after ceasing of the fresh tailing deposition. Therefore, complementary rehabilitation scenarios should still be considered.

Hydrochemistry and coal mining activity induced karst water quality degradation in the Niangziguan karst water system, China

Hydrogeochemical analysis, statistical analysis, and geochemical modeling were employed to evaluate the impacts of coal mining activities on karst water chemistry in Niangziguan spring catchment, one of the largest karst springs in Northern China. Significant water quality deterioration was observed along the flow path, evidenced from the increasing sulfate, nitrate, and TDS content in karst water. Karst water samples are Ca-Mg-HCO3 type in the recharge areas, Ca-Mg-HCO3-SO4 type in the coal mining areas, and Ca-Mg-SO4-HCO3/HCO3-SO4 type in the rural areas and discharge areas. A four-factor principal component analysis (PCA) model is conducted which explains over 82.9% of the total variation. Factor 1, which explained the largest portion (45.33%) of the total variance, reveals that coal mining activities and natural water-rock interaction as the primary factors controlling karst water quality. Anthropogenic effects were recognized as the secondary factor with high positive loadings for NO3 (-) and Cl(-) in the model. The other two factors are co-precipitation removal of trace elements and silicate mineral dissolution, which explained 20.96% of the total variance. A two-end mixing modeling was proposed to estimate the percentage of coal wastewater giving on karst water chemistry, based on the groundwater sulfate chemistry constrains rather than sulfur isotopes. Uncertainty of sulfur isotope sources led to an overestimation of coal mining water contribution. According to the results of the modeling, the contribution of coal mining waste on karst water chemistry was quantified to be from 27.05 to 1.11% which is ca. three times lower than the values suggested using a sulfur isotope method.

Combined multivariate statistical techniques, Water Pollution Index (WPI) and Daniel Trend Test methods to evaluate temporal and spatial variations and trends of water quality at Shanchong River in the Northwest Basin of Lake Fuxian, China

Understanding spatial and temporal variations in river water quality and quantitatively evaluating the trend of changes are important in order to study and efficiently manage water resources. In this study, an analysis of Water Pollution Index (WPI), Daniel Trend Test, Cluster Analysis and Discriminant Analysis are applied as an integrated approach to quantitatively explore the spatial and temporal variations and the latent sources of water pollution in the Shanchong River basin, Northwest Basin of Lake Fuxian, China. We group all field surveys into 2 clusters (dry season and rainy season). Moreover, 14 sampling sites have been grouped into 3 clusters for the rainy season (highly polluted, moderately polluted and less polluted sites) and 2 clusters for the dry season (highly polluted and less polluted sites) based on their similarities and the level of pollution during the two seasons. The results show that the main trend of pollution was aggravated during the transition from the dry to the rainy season. The Water Pollution Index of Total Nitrogen is the highest of all pollution parameters, whereas the Chemical Oxygen Demand (Chromium) is the lowest. Our results also show that the main sources of pollution are farming activities alongside the Shanchong River, soil erosion and fish culture at Shanchong River reservoir area and domestic sewage from scattered rural residential area. Our results suggest that strategies to prevent water pollutionat the Shanchong River basin need to focus on non-point pollution control by employing appropriate fertilizer formulas in farming, and take the measures of soil and water conservation at Shanchong reservoir area, and purifying sewage from scattered villages.

The spatial and seasonal variability of the groundwater chemistry and quality in the exploited aquifer in the Daxing District, Beijing, China

The aquifer in the Beijing Plain is intensively used as a primary source to meet the growing needs of the various sectors (drinking, agricultural, and industrial purposes). The analysis of groundwater chemical characteristics provides much important information useful in water resources management. To characterize the groundwater chemistry, reveal its spatial and seasonal variability, and determine its quality suitability for domestic and agricultural uses, a total of 200 groundwater samples were collected in June and October 2012 from 100 exploited wells in Daxing District, Beijing, China. All of the indices (39 items) listed in the Quality Standard for Groundwater of China (QSGC) as well as eight additional common parameters were tested and analyzed for all samples, based on which research target was achieved. The seasonal effect on the groundwater chemistry and quality was very slight, whereas the spatial changes were very obvious. The aquifer is mainly dominated by HCO3-Ca·Mg-type water. Of the 39 quality indices listed in QSGC, 28 indices of all of the samples for the 2 months can be classified into the excellent level, whereas the remaining 11 indices can be classified into different levels with the total hardness, NO3, NO2, and Fe being the worst, mainly distributed in the residential and industrial land. According to the general quality index, the groundwater can be classified from good to a relatively poor level, mainly from southeast to northwest. Furthermore, the relatively poor-level area in the northwest expands to the southeast more than in the past years, to which people should pay attention because this reverse spatial distribution relative to the natural law indicates an obvious, anthropogenic impact on the groundwater. In addition, the groundwater in this area is generally very suitable for irrigation year-round. Nevertheless, we recommend performing agricultural water-saving measures for the sustainable development of water and urbanization, groundwater recovery, and ecological safety.

Assessment of Yamuna and associated drains used for irrigation in rural and peri-urban settings of Delhi NCR

The present study assessed the quality of Yamuna River and the Najafgarh drain water for irrigational purposes in the Delhi region in terms of spatial variations in the physicochemical characteristics as well as heavy metal concentrations. The monitoring was done for the period July 2012-August 2013 representing pre-monsoon, monsoon, and post-monsoon sessions and considering six physicochemical parameters. Heavy metals such as cadmium, chromium, copper, nickel, zinc, and lead have been found in the river due to rampant discharge of industrial effluents into the river. The mean metal concentrations in the 15 sampling sites were in the range of (mg L(-1)) 0.02-0.64 (Cu), 0-0.42 (Cr), 0.13-2.22(Zn), 0.03-0.27 (Pb), 0-0.07 (Cd), and 0.01-0.13 (Ni). Multivariate statistics (PCA and HCA) were used to identify the possible sources of metal contamination and to examine the spatial changes in the Yamuna River as well as in the Najafgarh drain. This study reveals the occurrence of mean Cd concentration above the safe limit at Palla, Christian Ashram and Jagatpur of the Yamuna river while Punjabi Bagh of the Najafgarh drain necessitate treatment in terms of heavy metals such as Cd, Cu, Cr, Ni, Pb, and Zn before it could be rendered useful for irrigation.

Evaluation of factors influencing the groundwater chemistry in a small tropical island of Malaysia

Groun in a very complex way. In this work, multivariate statistical analysis was used to evaluate the factors controlling the groundwater chemistry of Kapas Island (Malaysia). Principal component analysis (P dwater chemistry of small tropical islands is influenced by many factors, such as recharge, weathering and seawater intrusion, among others, which interact with each other CA) was applied to 17 hydrochemical parameters from 108 groundwater samples obtained from 18 sampling sites. PCA extracted four PCs, namely seawater intrusion, redox reaction, anthropogenic pollution and weather factors, which collectively were responsible for more than 87% of the total variance of the island’s hydrochemistry. The cluster analysis indicated that three factors (weather, redox reaction and seawater intrusion) controlled the hydrochemistry of the area, and the variables were allocated to three groups based on similarity. A Piper diagram classified the island’s water types into Ca-HCO₃ water type, Na-HCO₃ water type, Na-SO₄-Cl water type and Na-Cl water type, indicating recharge, mixed, weathering and leached from sewage and seawater intrusion, respectively. This work will provide policy makers and land managers with knowledge of the precise water quality problems affecting the island and can also serve as a guide for hydrochemistry assessments of other islands that share similar characteristics with the island in question.