Inconsistency of PCA-based water quality index - Does it reflect the quality?

Integrating principal component weighted water quality index (PCWQI) model with GIS for evaluation groundwater quality in Gangetic West Bengal, India

The Contaminated groundwater significantly affects human health and sustainable agricultural practices. The speedy growth of population, coupled with growing urbanization and intensive agricultural practices has resulted in rapid deterioration of groundwater quality in many nations, including India. Accordingly, the current study has been conducted to explore the suitability of groundwater for drinking and irrigation purposes in the Gangetic West Bengal. In this study, the principal component-weighted water quality index (PCWQI) model was introduced to predict the drinking water quality index (DWQI) and irrigation water quality index (IWQI). In addition, different standard indices (sodium absorption ratio, sodium percentage, magnesium hazard, residual sodium carbonate, residual sodium bicarbonate, permeability index, potential salinity and Kelly's ratio) and graphs (Wilcox's chart, Doneen graph and USSL plot) were used to evaluate the suitability of groundwater for irrigation. Results revealed that ∼26.1 % of groundwater samples have suffered from substandard drinking water with an area of 16.6 × 103 Km2, whereas ∼32.2 % of samples covering an area of 21.4 × 103 Km2 are not fully suitable for irrigation. The results of the Wilcox and USSL diagrams confirmed the IWQI findings and various indices display that the majority of groundwater samples was acceptable for irrigation with the exception of the magnesium hazard, which shows that 70.7 % of the samples are unsuitable for irrigation. Hydro-geochemically, most of the groundwater samples belong to Ca2+-HCO3- and mixed Ca2+-Mg2+-Cl- facies. Additionally, rock-water interaction is prevalent in the aquifer systems. The current study offers beneficial insights of aquifer systems in Gangetic West Bengal. Thus, this study not only enhances the scientific perception of groundwater quality but also provides an integrated approach that can be fitted worldwide to formulate effective management strategies of groundwater resources.

Applying the water quality indices, geographical information system, and advanced decision-making techniques to assess the suitability of surface water for drinking purposes in Brahmani River Basin (BRB), Odisha

Surface water is used for a variety of purposes, including agriculture, drinking water, and other services. Therefore, its quality is crucial for irrigation, human welfare, and health. Thus, the main objective is to improve surface water quality assessment and geochemical analysis to evaluate anthropogenic activities' impact on surface water quality in the Brahmani Watershed, Odisha. In the present paper, emerging techniques such as CRITIC (Criteria Importance Through Inter-criteria Correlation), Additive Ratio Assessment (ARAS), Weighted Aggregated Sum-Product Assessment (WASPAS), SHAP (Shapley Additive Explanation), and Geographical Information System (GIS) were used to locate the origins of pollution in the surface water. The 5-year (2018-2023) database was created by analysing samples that varied geographically over seven sampling locations. The dataset was categorized according to its intended usage. The study employed Inverse Distance Weighting (IDW) tool, to forecast quantities and their geographical arrangement. The water temperature detected at several locations along the river revealed minor variations. The pH variations indicate that the surface water in the studied area is alkaline. Notably, the water's lowest temperature ever recorded was 25.72 °C, at Q-(1). In addition, sufficient DO concentrations are monitored to ensure optimal water quality. The major parts of the study area were found to be majorly affected with high concentrations of PO43-, EC, Ca2+, Mg2+, and SO42-. To determine the degree of contamination, a basic standard reference is necessary to interpret the values, which range from the anthropogenic to the natural contribution. The statistical results reveal the dominant decreasing order amongst the cations, such as: Ca2+  > Mg2+  > Na+  > K+ and in anions, namely, SO42- > Cl- > NO3- > F- > PO43-, respectively. It displays seasonal variations in dissolved and specific phase metal fractions that are not statistically significant at any of the seven sites. Proceeding further, the water quality index showed that the four samples fall in the poor water quality class, whereas the rest, 3 samples, were of good water quality. The surface water is contaminated and negatively affected due to percolation of ions from landfill leachate as per the data of C-WQI. Based on ARAS and WASPAS, Q-(1) and Q-(2) were mainly not fit for consumption. Meanwhile, the SHAP-WQI showed an increase in the number of samples (71.43%) with unsuitable quality for drinking. This emphasizes on the importance of weathering, dissolution, terrigenous, leaching, ion exchange, lithological and evaporation as the primary processes. Human influences were the secondary factors. Overall, the findings indicate that the study area's surface water is safe to drink, with the exception of a few locations including, Q-(1), (2), (3), (4), and (7), in the river water. Integrating GIS using WQ methods gives a new knowledge on the spatial variation in surface water characteristics for designated use. When enforcing regulations and carrying out pollution control operations, this will help determine the precise sampling sites or the sections of the river that show significant degradation. Thus, the integrated model provides insightful data on surface watershed management for urban planners and decision-makers. In overall, these findings underscore the importance of coordinated efforts across administrative boundaries within the basin to reduce water governance costs, providing valuable insights for fostering the coordinated development of regional economies and environmental sustainability. As a result, future studies should be conducted in the area to precisely state the quality of water used for drinking and domestic purposes.

Assessing hydrogeochemical facies and Groundwater Quality Index in rapidly urbanizing coastal region: a GIS-based approach with machine learning for enhanced management

Groundwater is an essential freshwater source worldwide, but increasing pollution poses risks to its sustainability. This study applied a comprehensive approach to assess hydrogeochemical facies and groundwater quality in Odisha's large low-lying coastal regions. Analysis of 136 samples revealed that sodium (9.4%), potassium (40.8%), bicarbonate (2.1%), and chloride (2.1%) exceeded WHO limits. The Groundwater Quality Index (GQI) map classified 5.1% of samples as "excellent," 39.4% as "good," 31.3% as "poor," 13.8% as "very poor," and 10.2% as "unsuitable" for use. Additionally, the GQI values demonstrate a random spatial autocorrelation (- 0.06) likely due to diverse influences. The study identified the expansion of agricultural (43%) and built-up areas (13%) from the Land Use/Land Cover (LULC) map. Piper diagram and Gibbs plots suggest continued freshening, rock-water interaction, and seawater intrusion. Groundwater levels fall between 0 to 2 m below ground level (mbgl), primarily due to excessive groundwater extraction. The Sodium (Na+) vs. Chloride (Cl-) cross plot shows most samples align with the mixing line, with some deviations indicating multiple contamination sources. The strong correlation (> 0.90) between total dissolved salts (TDS), electrical conductivity (EC), Na+, and Cl- signals seawater intrusion, highlighting the complex interaction between human activities and natural processes. The proposed machine learning (ML) models like random forest (RF), artificial neural network (ANN), decision tree, and linear regression (LR) offer a reliable alternative to traditional GQI methods, addressing the challenges of extensive sampling and data management. Among these, RF exhibited the highest predictive accuracy (coefficient of correlation (R2) = 95%), surpassing ANN (R2 = 82%), decision tree (R2 = 81%), and LR (R2 = 67%) as the most effective model for GQI prediction. Potassium (K+) stands out as a key indicator of contamination. GQI, LULC map, and ML methods improve understanding of contamination sources and support systematic groundwater management.

Unraveling the impact of high arsenic, fluoride and microbial population in community tubewell water around coal mines in a semiarid region: Insight from health hazards, and geographic information systems

High arsenic (As), fluoride (F-), and microbial pathogens coexist in semiarid conditions afflicting > 240 million people worldwide including Pakistan. Groundwater quality has declined due to geogenic and manmade activities providing suitable ground for ubiquity, bioavailability, and toxicity of contaminants. We tested the health hazard, distribution, and apportionment of As, F-, and microbes in groundwater around coal mines in Quetta, Pakistan. The range of As, and F- concentrations in groundwater were 0.2-16.6 µg/L, 0.4-18.5 mg/L. Both, As and F- correlate with high HCO3-, pH, Na+, SO42-, Fe, and Mn, and negatively with Ca2+ water. The coalfield showed many folds higher As 15.8-28.5 µg/L, and F- 10.8-34.5 mg/L compared to groundwater-wells. Geochemical phases revealed saturation of groundwater with calcite, dolomite, fluorite, gypsum, and undersaturation with halite-mirabilite, and arsenopyrite minerals. The positive matrix factorization (PMF) model assessed five-factor solutions: geogenic, industrial, coal mining, sulfide & fluoride-bearing mineral-dissolution, and agriculture pollution delivered As, F-, and microbial contamination. About 24.6 % and 64.4 % of groundwater samples exceeded the WHO guidelines of As 10 µg/L, F- 1.5 mg/L. The carcinogenicity, and non-carcinogenicity of As, and F- were higher in children than adults. Therefore, health hazards in children are of great concern in achieving sustainable management goals.

Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maximize the precision of WQI prediction by a minimal set of water parameters. Ten statistical or machine learning models were developed to predict the WQI over four seasons using water quality dataset collected in a coastal city adjacent to the Yellow Sea in China, based on which the key water parameters were identified and the variations were assessed by the Seasonal-Trend decomposition procedure based on Loess (STL). Results indicated that model performance generally improved with adding more input variables except Self-Organizing Map (SOM). Tree-based ensemble methods like Extreme Gradient Boosting (XGB) and Random Forest (RF) demonstrated the highest accuracy, particularly in winter. Nutrients (Ammonia Nitrogen (AN) and Total Phosphorus (TP)), Dissolved Oxygen (DO), and turbidity were determined as key water parameters, based on which, the prediction accuracy for Medium and Low grades was perfect while it was over 80% for the Good grade in spring and winter and dropped to around 70% in summer and autumn. Nutrient concentrations were higher at inland stations; however, it worsened at coastal stations, especially in summer. The study underscores the importance of reliable WQI prediction models in water quality assessment, especially when data is limited, which are crucial for managing water resources effectively.

Identify the seasonal differences in water quality and pollution sources between river-connected and gate-controlled lakes in the Yangtze River basin

The river-connected Dongting Lake (DT) and Poyang Lake (PY), and the gate-controlled Taihu Lake (TH) and Chaohu Lake (CH) are the four important lakes in the Yangtze River Basin. The comprehensive Water Quality Index (WQI), the Eutrophication Integrated Index (TLI(Σ)), and the Positive Matrix Factorization (PMF) model were employed to evaluate water quality and the contribution of pollution sources for these lakes. The results show that WQI for all lakes indicated generally good water quality, with DT scoring 73.52-86.18, the highest among them. During the wet season, the eutrophication degree of river-connected lake was medium, and that of gate-controlled lakes was high. The surface runoff and agricultural non-point sources are the main pollution sources for both types of lakes, but their impact is more pronounced in gate-controlled lakes during the wet season. The study provides evidence support for scientific understanding of water quality problems and management strategies in these areas.

Multi-model exploration of groundwater quality and potential health risk assessment in Jajpur district, Eastern India

The presence of fluoride and nitrate is a serious groundwater quality issue in India impacting human health. In the present study, 14 different hydrochemical parameters for 76 groundwater samples collected from the Jajpur district of Odisha, India, were evaluated. Entropy-weighted water quality index (EWQI), fixed-weight groundwater quality index (GWQI), principal component analysis (PCA), and rotated factor loading-based water quality index (PCWQI) were employed to assess groundwater quality. About 65.79 ± 4.68%, 33.55 ± 3.95%, and 0.66 ± 0.76% of the samples were rated as "excellent," "good," or "medium" quality, respectively, across the four different water quality indices, with a nominal rating discrepancy of 13.15%. Though 86% of samples consistently received excellent or good ratings across all WQI frameworks, concentrations of F- and NO3- in 36.8% and 11.84% of the samples exceeded the WHO permissible limit. In health risk assessment, about 38.15% of samples surpassed the F- hazard quotient (HQ > 1) posing non-carcinogenic health risks for children. The non-carcinogenic health risks due to NO3- were evident in 55.26% and 11.84% of samples for children and adults, respectively. The higher concentration of NO3- in some of the water samples, together with its positive correlation with HCO3-, may worsen groundwater pollution. The moderate correlation between Ca2+ and HCO3- (r = 0.410) and the insignificant correlation between Mg2+ and HCO3- (r = 0.234) suggests calcite dissolution is far more common than dolomite.

Multi-dimensional landscape ecological risk assessment and its drivers in coastal areas

The eastern coastal areas of Japan are threatened by multiple ecological risks due to frequent natural disasters, climate changes, human activities, etc. Identification spatio-temporal variations and driving mechanisms of landscape ecological risk could be used as significant basis for policymakers. In this study, taking the eastern coastal areas of Japan affected by the 2011 Great East Japan Earthquake and Tsunami Disaster as the study area, the "Nature-Landscape Pattern-Human Society" (NA-LP-HS) multi-dimensional ecological risk assessment framework was established to analyze the spatio-temporal patterns, and identity driving factors using spatial cluster analysis and spatial principal component analysis (SPCA) based on ArcGIS from 2009 to 2021. The findings revealed the distinct geographic patterns in landscape ecological risk, with a noticeable decline from the southwest to the northeast. During the period from 2009 to 2015, the driving factors leading to a sharp risk increase were natural disasters and vegetation coverage. These high-risk areas were concentrated in Sendai Bay and its surroundings. From 2015 to 2021, ecological instability was primarily attributed to a reduction in vegetation coverage, the occurrence of natural disasters, and heightened rainfall erosion. These high-risk areas were mainly clustered within the Tokyo-centered urban agglomeration. Spatial clustering of ecological risks was obvious across all time periods. The key factors contributing to the clustering of high ecological landscape risks focused on the "landscape pattern" criterion, specifically including vegetation coverage, land use land cover. This study demonstrated the ability of multi-dimensional ecological risk assessment to identify high-risk areas and driving factors, and these results could provide a visual analysis and decision-making basis for sustainable development of coastal areas.

Assessing the evolution and attribution of watershed resilience in arid inland river basins, Northwest China

Water scarcity significantly limits the sustainable development of oasis economies in arid inland river basins. Quantifying watershed resilience and its drivers is a major focus in the fields of hydrology and water resources. In this study, the resilience indicator pi represents watershed resilience, while meteorological, hydrological, socioeconomic, and ecological factors are used to investigate the spatial and temporal patterns of resilience and important driving factors in the Hotan River Basin from 1958 to 2020 by combining principal component analysis and random forest model. Results show that the overall resilience of the Hotan River Basin is low, decreasing from the upper (upstream) to the middle and lower (downstream) reaches, and that the intensity of human activities has a negative impact on resilience. Rivers are more likely to reach maximum resilience after experiencing periods of wet and dry conditions, although there is a lag in this progress. The random forest machine learning algorithm was used to accurately predict the resilience levels of the two upstream tributaries Yurungkash and Karakash Rivers, and the downstream Hotan River, with classification accuracies of 84.2 %, 71.4 %, and 87 %, respectively. The factors affecting the resilience of the Yurungkash River are the 30-day maximum, base flow index, low pulse duration, median streamflow in May, median streamflow in August, median streamflow in October, and 7-day maximum. The set of factors used to classify the resilience of the Karakash River include the 7-day maximum, 1-day maximum, median streamflow in June, 30-day maximum, 3-day maximum, median streamflow in February, and autumn temperature. The factors affecting the resilience of the Hotan River are the watershed inflow, Xiaota station runoff, population growth rate, and effective irrigated area. The findings of this study provide a theoretical basis for integrated water resource management and the sustainable development of the oasis economy in the Hotan River Basin.