Assessment of groundwater from an industrial coastal area of south India for human health risk from consumption and irrigation suitability

Integrated approach to understand the multiple natural and anthropogenic stresses on intensively irrigated coastal aquifer in the Mediterranean region

Understanding the major factors influencing groundwater chemistry and its evolution in irrigation areas is crucial for efficient irrigation management. Major ions and isotopes (δD-H2O together with δ18O-H2O) were used to identify the natural and anthropogenic factors contributing to groundwater salinization in the shallow aquifer of the Wadi Guenniche Plain (WGP) in the Mediterranean region of Tunisia. A comprehensive geochemical investigation of groundwater was conducted during both the low irrigation season (L-IR) and the high irrigation season (H-IR). The results show that the variation range and average concentrations of almost all the ions in both the L-IR and H-IR seasons are high. The groundwater in both seasons is characterized by high electrical conductivity and CaMgCl/SO4 and NaCl types. The dissolution of halite and gypsum, the precipitation of calcite and dolomite, and Na-Ca exchange are the main chemical reactions in the geochemical evolution of groundwater in the Wadi Guenniche Shallow Aquifer (WGSA). Stable isotopes of hydrogen and oxygen (δ18O-H2O and δD-H2O) indicate that groundwater in WGSA originated from local precipitation. In the H-IR season, the δ18O-H2O and δD-H2O values indicate that the groundwater experienced noticeable evaporation. The enriched isotopic signatures reveal that the WGSA's groundwater was influenced by irrigation return flow and seawater intrusion. The proportions of mixing with seawater were found to vary between 0.12% and 5.95%, and between 0.13% and 8.42% during the L-IR and H-IR seasons, respectively. Irrigation return flow and the associated evaporation increase the dissolved solids content in groundwater during the irrigation season. The long-term human activities (fertilization, irrigation, and septic waste infiltration) are the main drives of the high nitrate-N concentrations in groundwater. In coastal irrigation areas suffering from water scarcity, these results can help planners and policy makers understand the complexities of groundwater salinization to enable more sustainable management and development.

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, and health risk model

An in-depth understanding of nitrate-contaminated surface water and groundwater quality and associated risks is important for groundwater management. Hydrochemical characteristics and driving forces of groundwater quality and non-carcinogenic risks of nitrate were revealed by the integrated approaches of self-organizing map analysis, spatial visualization by geography information system, entropy and irrigation water quality indices, and human health risk model. Groundwater samples were categorized into two clusters by SOM analysis. Cluster I including three samples were Ca-SO4 type and cluster II of remaining 136 samples were Ca-HCO3 type. Hydrochemical compositions of two cluster samples were dominated by water-rock interaction: (1) calcite and gypsum dissolution for cluster I samples and (2) calcite dissolution, silicate weathering, and positive cation exchange for cluster II samples. Nitrate contamination occurred in both cluster I and II samples, primarily induced by agricultural nitrogen fertilizer. The EWQI results showed that 90.97% in total groundwater samples were suitable for drinking purpose, while the IWQI results demonstrated that 65.03% in total groundwater samples were appropriate for irrigation purpose. The HHR model and Monte Carlo simulation indicated that the non-carcinogenic nitrated risk was highest in children. Exposure frequency was the most sensitive factor (86.33% in total) influencing the total non-carcinogenic risk, indicated by sensitivity analysis. Compared with the two clusters of groundwater, surface water has a shorter circulation cycle and lower ion concentrations resulting in better water quality. This study can provide scientific basis for groundwater quality evaluation in other parts of the world.

Spatial distribution and hydrogeochemical evaluations of groundwater and its suitability for drinking and irrigation purposes in kaligonj upazila of satkhira district of Bangladesh

Groundwater is a significant water resource for drinking and irrigation in Satkhira district, Bangladesh. The depletion of groundwater resources and deterioration in its quality are the results of the confluence of factors such as industrialization, intensive irrigation, and rapid population growth. For this reason, this study focused on the evaluation of tubewell water of six unions of Kaligonj upazila in Satkhira district, which is situated in the coastal southwest part of Bangladesh. Major and trace elemental concentrations were assimilated into positive matrix factorization (PMF) to identify potential sources and their respective contributions. Principal component analysis (PCA) revealed that groundwater salinization and manmade activities were the primary causes of heavy metals in the coastal groundwater. Its average pH value was found to be 7.5, while Dissolved oxygen, Total dissolved solids, salinity, and conductivity, with values ranging from 1.18 to 7.38 mg/L, 0.5-4.88 g/L, 0.4-5%, and 0.95 to 8.56 mS/cm, respectively. The total hardness average value was 561.7 mg/L, classified into the very hard water categories, which is why 90% of the tubewell water samples were unfit for household purposes. All samples had an excessive level of arsenic present. The iron concentration of fifteen (15) samples crossed the standard limit according to WHO 2011 value. Around 63% of the samples were of the Na+-K+-Cl--SO42- type, and about 72% were sodium-potassium and alkali types. 98% of samples were covered in chloride and bicarbonate. The findings showed that 45.83% of the groundwater samples had negative Chloroalkaline index (CAIs), while 54.16% had positive. The permeability index (PI) was an average of 73%, and residual sodium carbonate (RSC) averaged 260.2 mg/L, and the findings clearly showed that 80% of the samples weren't appropriate for irrigation. According to the sodium adsorption ratio (SAR) value, 65% of the samples fell into the unsuitable category. These calculations indicated a high overall salinity hazard in the study area, which may be caused by the intrusion of sea water given that the study area is close to the coastal region. Findings compared to standards revealed that the majority of the samples were deemed unfit for drinking and irrigation purposes. Hence, additional attention must be paid to this area to ensure the availability of drinkable water and to preserve sustainable farming practices.

An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy

Groundwater salinization in coastal aquifers is a major socioeconomic challenge in Oman and many other regions worldwide due to several anthropogenic activities and natural drivers. Therefore, assessing the salinization of groundwater resources is crucial to ensure the protection of water resources and sustainable management. The aim of this study is to apply a novel approach using predictive optimized ensemble trees-based (ETB) machine learning models, namely Catboost regression (CBR), Extra trees regression (ETR), and Bagging regression (BA), at two levels of modeling strategy for predicting groundwater TDS as an indicator for seawater intrusion in a coastal aquifer, Oman. At level 1, ETR and CBR models were used as base models or inputs for BA in level 2. The results show that the models at level 1 (i.e., ETR and CBR) yielded satisfactory results using a limited number of inputs (Cl, K, and Sr) from a few sets of 40 groundwater wells. The BA model at level 2 improved the overall performance of the modeling by extracting more information from ETR and CBR models at level 1 models. At level 2, the BA model achieved a significant improvement in accuracy (MSE = 0.0002, RSR = 0.062, R2 = 0.995 and NSE = 0.996) compared to each individual model of ETR (MSE = 0.0007, RSR = 0.245, R2 = 0.98 and NSE = 0.94), and CBR (MSE = 0.0035, RSR = 0.258, R2 = 0.933 and NSE = 0.934) at level 1 models in the testing dataset. BA model at level 2 outperformed all models regarding predictive accuracy, best generalization of new data, and matching the locations of the polluted and unpolluted wells. Our approach predicts groundwater TDS with high accuracy and thus provides early warnings of water quality deterioration along coastal aquifers which will improve water resources sustainability.

Hydrochemical evaluation of groundwater for drinking and irrigation purposes using multivariate indices along Indus Suture Zone, North Pakistan

The present study is aimed to investigate the hydrochemical characteristics, spatial distribution and suitability of groundwater for drinking and irrigation purposes along the Indus Suture Zone (ISZ), north Pakistan. Physicochemical parameters and hazardous trace elements (HTEs) like Cd, Co, Cu and Mn were determined following standard methods. The mean and median concentrations were found below the World Health Organization (WHO) drinking water guidelines values. Hydrochemical results indicate that groundwater sources were mainly attributed to rock-water interaction category. Piper diagram shows that most of the groundwater samples fall in Ca-HCO₃^─ class presenting weak-alkaline proportion type. The drinking water quality index (DWQI) ranking was categorized as good to excellent, indicating the overall quality of the groundwater may pose no health hazard concern. Based on irrigation WQI (SAR, Na%, MAR, KR), the groundwater was found fit for irrigation except SAR whereas 36% of the groundwater samples fall within the poor class. The total HI values through dermal contact exceeded the safe non-carcinogenic threshold of HI = 1. Therefore, there is required an effective groundwater monitoring and management facility in the study area to safeguard residents from various illnesses associated with varying HTEs concentrations in drinking water. The major response actions needed for groundwater bodies restoration are including the installation of a continuous groundwater monitoring network and control of agricultural fertilizers that seems to be the most effective and tangible for immediate action.

Novel machine learning algorithms to predict the groundwater vulnerability index to nitrate pollution at two levels of modeling

The accurate mapping and assessment of groundwater vulnerability index are crucial for the preservation of groundwater resources from the possible contamination. In this research, novel intelligent predictive Machine Learning (ML) regression models of k-Neighborhood (KNN), ensemble Extremely Randomized Trees (ERT), and ensemble Bagging regression (BA) at two levels of modeling were utilized to improve DRASTIC-LU model in the Miryang aquifer located in South Korea. The predicted outputs from level 1 (KNN and ERT models) were used as inputs for ensemble bagging (BA) in level 2. The predictive groundwater pollution vulnerability index (GPVI), derived from DRASTIC-LU model was adjusted by NO₃-N data and was utilized as the target data of the ML models. Hyperparameters for all models were tuned using a Grid Searching approach to determine the best effective model structures. Various statistical metrics and graphical representations were used to evaluate the superior predictive performance among ML models. Ensemble BA model in level 2 was more precise than standalone KNN and ensemble ERT models in level 1 for predicting GPVI values. Furthermore, the ensemble BA model offered suitable outcomes for the unseen data that could subsequently prevent the overfitting issue in the testing phase. Therefore, ML modeling at two levels could be an excellent approach for the proactive management of groundwater resources against contamination.

Understanding the factors contributing to groundwater salinity in the coastal region of Andhra Pradesh, India

The present study focused on understanding the factors responsible for groundwater salinity in the coastal region, Prakasam district, Andhra Pradesh, India. Groundwater samples were collected and analysed for pH, EC, TDS, TA, TH, CH, NCH, EA, Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃^-, Cl^-, SO₄^2-, NO₃^-, and F^-. Groundwater quality was assessed using entropy weighted water quality index (EWQI), Chadha and Gibbs diagrams, ionic ratios, chloro-alkaline indices (CA), saturation indices (SI), principal component analysis (PCA), and hierarchical cluster analysis (HCA). TDS vs TH indicated that 86% of groundwater samples fall under brackish-cum-very hard water-quality type, while TA and TH relationship showed that 70% and 30% of groundwater samples fall under non‑carbonate hardness (NCH) and excess alkalinity (EA), respectively. EWQI classified groundwater samples into medium (22%), poor (40%), and exremely poor (38%) water quality types, indicating that most samples are not suitable for drinking purposes. Hydrogeochemical types in Chadha diagram showed saline water (Na⁺-Cl^-) type in 92% of groundwater samples. Ionic ratios indicated that anthropogenic activities resulting from the leaching of surface water pollutants are the main source of groundwater pollution. Base ion exchange was indicated as the main process in CA indices. SI revealed precipitation of the calcite phase and dissolution of the gypsum and halite phases in groundwater. Evaporation appeared in Gibb's diagram as a primary process rather than a geogenic origin. PC1 (Na⁺, Cl^-, SO₄^2-, Mg²⁺, K⁺, and NO₃^-) and PC2 (HCO₃^- and F^-) were considered salinity-process and alkalinity-process, respectively. The main sources of salinity in groundwater are brackish-water aquaculture and salt-making activities with household waste, septic tank spills, irrigation-return-flows, and chemical fertilizers being secondary sources. HCA classified groundwater samples into Group-I (46%), which represents domestic water, agricultural activities, etc., Group-II (30.24%), which shows the influence of both Group-I and Group-II, and Group-III (23.76%), which specifies brackish-water aquaculture and salt-making activities. Consequently, the present study obviously indicated that the groundwater quality of anthropogenic origin has largely overcome the influence of geogenic sources. The EWQI classification spatially delineated the study region into medium, high, and very high vulnerable zones, covering 28.69%, 32.75%, and 38.56%, respectively. Therefore, it is suggested to control the dumping of domestic waste and septic tank leaks, limit irrigation-return-flows and chemical fertilizers, ban brackish water aquaculture and salt production activities, and strictly implement an aquifer recharge management strategy to ensure human health. This study will assist decision-makers in addressing groundwater salinity issues in coastal regions.

New research on water, waste and energy management, with special focus on antibiotics and priority pollutants

The Editors of the Virtual Special Issue (VSI) "New Research on Water, Waste and Energy Management, with Special Focus on Antibiotics and Priority Pollutants" (VSI WWEM-20) here present details corresponding to papers that have been accepted, as well as further comments on the matter. It should be noted that the VSI should be associated to a Conference that had been initially programmed to be held in Rome during the summer of 2020, Unfortunately, it was postponed due to the COVID-19 pandemic. That conference was one of those within the series called "International Congress on Water, Waste and Energy Management". Although the Conference was postponed, the Call for Papers for the VSI was maintained by this journal. As a result, a set of very interesting papers were accepted after a careful peer-review process. We hope that it will be complemented with additional VSIs associated to future conferences corresponding to the series, increasing the knowledge on the topic.