Sodium Adsorption Ratio Research Articles

Evaluation and Prediction of Groundwater Quality for Irrigation Using an Integrated Water Quality Indices, Machine Learning Models and GIS Approaches: A Representative Case Study

Agriculture has significantly aided in meeting the food needs of growing population. In addition, it has boosted economic development in irrigated regions. In this study, an assessment of the groundwater (GW) quality for agricultural land was carried out in El Kharga Oasis, Western Desert of Egypt. Several irrigation water quality indices (IWQIs) and geographic information systems (GIS) were used for the modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM)) were developed for the prediction of eight IWQIs, including the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), potential salinity (PS), residual sodium carbonate index (RSC), and Kelley index (KI). The physicochemical parameters included T°, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, and they were measured in 140 GW wells. The hydrochemical facies of the GW resources were of Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, and mixed Na-Ca-HCO3 types, which revealed silicate weathering, dissolution of gypsum/calcite/dolomite/ halite, rock–water interactions, and reverse ion exchange processes. The IWQI, SAR, KI, and PS showed that the majority of the GW samples were categorized for irrigation purposes into no restriction (67.85%), excellent (100%), good (57.85%), and excellent to good (65.71%), respectively. Moreover, the majority of the selected samples were categorized as excellent to good and safe for irrigation according to the SSP and RSC. The performance of the simulation models was evaluated based on several prediction skills criteria, which revealed that the ANFIS model and SVM model were capable of simulating the IWQIs with reasonable accuracy for both training “determination coefficient (R2)” (R2 = 0.99 and 0.97) and testing (R2 = 0.97 and 0.76). The presented models’ promising accuracy illustrates their potential for use in IWQI prediction. The findings indicate the potential for ML methods of geographically dispersed hydrogeochemical data, such as ANFIS and SVM, to be used for assessing the GW quality for irrigation. The proposed methodological approach offers a useful tool for identifying the crucial hydrogeochemical components for GW evolution assessment and mitigation measures related to GW management in arid and semi-arid environments.

Feasibility of conjunctive use of canal and alkali groundwater for toria–chicory crop rotation in western Uttar Pradesh, India

The response of toria–chicory crop rotation under different conjunctive water use modes of good-quality canal water with an electrical conductivity (ECCW) of 1.8 dS/m, residual sodium carbonate (RSCCW) of 0 and sodium adsorption ratio (SARCW) of 5.97 and alkali water with an ECAW of 2.81 dS/m, RSCAW of 10.12 meq/L and SARAW of 11.45 was evaluated for 5 years (2015–2020) on a well-drained sandy loam soil. The initial pH of soil saturated paste (pHs), EC of saturated paste extract (ECe) and exchangeable sodium percentage (ESP) values were 8.1, 2.3 dS/m and 5.5, respectively. The pHs values under different modes varied from 7.0 to 9.1, ECe varied from 3.4 to 4.7, and ESP varied from 8.9 to 13.5. The ECe and ESP build-up affected yields. The highest and lowest yields were recorded in the canal water and alkali water treatments, respectively. Alkali water irrigation at the initial stage adversely affected yield, while canal water irrigation reduced adverse effects. The relative yields and economic returns were better under different conjunctive modes than under irrigation with alkali water alone. Farmers should prefer conjunctive use of canal water with alkali water. The use of canal water at the initial stages of crops is preferable. La réponse de l'assolement de toria-chicorée sous différents modes d'utilization combinée de l'eau de canal de bonne qualité ayant une conductivité électrique (ECCW) de 1,8 dS/m, le carbonate de sodium résiduel (RSCCW) étant nul et le taux d'adsorption de sodium (SARCW) étant de 5,97 et une eau alcaline ayant une ECAW de 2,81 dS/m, RSCAW de 10,12 meq/L et SARAW de 11,45 a été évaluée au cours de 5 ans (2015–2020) sur un sol argileux sableux bien drainé. Les valeurs de pH initial de la pâte saturée du sol (pHs), de la CE de l'extrait de pâte saturée (ECe) et du pourcentage de sodium échangeable (ESP) étaient respectivement de 8,1, 2,3 dS/m et 5,5. Les valeurs de pH sous différents modes variaient de 7,0 à 9,1, l'ECe de 3,4 à 4,7, et l'ESP de 8,9 à 13,5. L'accumulation d'ECe et d'ESP a affecté les rendements. Les rendements les plus élevés et les plus faibles ont été enregistrés dans les traitements à l'eau du canal et à l'eau alcaline, respectivement. L'irrigation à l'eau alcaline au stade initial exerce un effet négatif sur le rendement, tandis que l'irrigation à l'eau du canal a réduit les effets négatifs. Les rendements relatifs et les rendements économiques étaient meilleurs sous les différents modes conjonctifs que sous l'irrigation à l'eau alcaline seule. Les agriculteurs devraient préférer l'utilization combinée de l'eau du canal et de l'eau alcaline. Il est préférable d'utiliser l'eau du canal aux stades initiaux. The original data are available with first author.

Groundwater quality assessment in western Saudi Arabia using GIS and multivariate analysis

In arid and semi-arid regions, assessment of groundwater quality and potentially toxic elements is essential issue for health of the human being. Groundwaters were collected from sixty-eight wells in Harrat Khaybar, Saudi Arabia to evaluate their suitability for drinking and irrigation purposes and to document the potential sources of contamination. Several contamination indices and inverse distance weighted technique were applied for assessing contamination and generate spatial maps for the potentially toxic elements (PTEs). The results showed that the average values of the ions, Cl–, SO42–, HCO3–, NO3–, Na+, Ca2+, Mg2+, and the total dissolved solids (TDS) were greater than the permissible limit for drinking water while the average values of PTEs were less than the permissible limit, with exceeding limits of Cr, Se, As, Zn, and Pb in some individual samples. Piper diagram indicated that 47.10% of the water samples are of Na-K-SO4-Cl type, 23.51% of Ca-Mg-CO3-HCO3 type, 23.51% of Ca-Mg-SO4-Cl type, and 5.88% of Na-K-CO3-HCO3 type. Based on the groundwater quality index (GWQI), 29 of the groundwater wells were categorized as excellent and good water for drinking purposes, while 29 wells fell under poor, very poor water, and unsuitable for drinking. Additionally, results of heavy metal pollution index (HPI) indicated that all waters fell within the low pollution category, while results of the metal index (MI) indicated that 35 wells fell within very pure, pure, and slightly affected categories, while 33 wells fell in the moderately, strongly, and seriously affected categories. Results of sodium adsorption ratio (SAR), sodium percentage (%Na), and magnesium ratio (MR) revealed that 33.82–98.5 % of the water samples are suitable for irrigation depending on the parameter type. Ions exchange reactions and dissolution of rock forming minerals, as well as industrial and domestic effluents and intensive use of fertilizers and pesticides were the natural and anthropogenic factors controlling the groundwater geochemistry in the study area and PTE contamination in some wells.

Enzyme activities and organic matter mineralization in response to application of gypsum, manure and rice straw in saline and sodic soils

Saline and alkaline soils are a challenge for sustainable crop production. The use of organic and inorganic amendments is a common practice to increase the fertility of salt-affected soils that can trigger faster carbon (C) and nitrogen (N) cycling. We examined the effects of gypsum (Gyps), farm manure (Manure) and rice straw (Straw) on enzyme activities, organic matter mineralization and CO2 emissions in two salt-affected soils [Solonchak (saline); pH: 8, electrical conductivity (EC): 6.5, sodium adsorption ratio (SAR): 2.5, and Solonetz (alkaline sodic); pH: 8.9, EC: 1.6, SAR: 17]. Gypsum addition decreased soil pH up to 0.62 and 0.30 units, SAR 1.2 and 5.2 units, and EC 2.9 and 1.4 units in Solonchak and Solonetz, respectively. Dissolved organic C, microbial biomass C, dissolved organic N, mineral N (NO3− and NH4+), enzyme activities (urease, invertase, catalase, phosphatase, phenol-oxidase), alkali extractable phenols, and available phosphorous increased with the application of all amendments in both soils. Solonetz released more CO2 than Solonchak, whereas maximum CO2 emissions were common after manure application (3140 mg kg−1 in Solonchak, and 3890 mg kg−1 in Solonetz). We conclude that high SAR and low EC increase CO2 emissions through accelerated C and N cycling and manure decomposition in Solonetz soils.

Comparative evaluation of classical and SARIMA-BL time series hybrid models in predicting monthly qualitative parameters of Maroon river

In the present study, seasonal autoregressive integrated moving average (SARIMA) time series models, nonlinear BL model, multi-layer perceptron artificial neural network and SARIMA-bilinear hybrid models were employed to predict the quality parameters of total dissolved solids (TDS), sodium adsorption ratio (SAR), electrical conductivity (EC) in the Maroon basin in Khuzestan Province. For fitting the mentioned models, the monthly data were used for the calibration (1970–2000), confirmation (2001–2011) and prediction of the model (2012–2018). The appropriate models of SARIMA, the bilinear BL and SARIMA-BL hybrid models of the rationalized parameters of the above-mentioned quality parameters were selected based on the adequacy tests, such as the Akaike criterion and the independence test of the model residuals (Ljung–Box). To determine the effective input parameters of the network, the partial mutual information (PMI) algorithm was used to model the three monthly EC, TDS, and SAR parameters. Also, for input and output layers linear transfer function and for hidden layer various active functions with the back-propagation learning algorithm were used for modeling and predicting this water quality parameters. Comparison of the models showed the tangible superiority of SARIMA-bilinear hybrid models than the artificial neural network with effective input parameters based on PMI algorithm, SARIMA and bl models in performed prediction of the all three monthly qualitative parameters in all three stages, training-validation-test in Maroon basin.

Efficacy of GIS-based AHP and data-driven intelligent machine learning algorithms for irrigation water quality prediction in an agricultural-mine district within the Lower Benue Trough, Nigeria.

Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water resource management and sustainability. In this study, the geographical information system (GIS), analytical hierarchy process (AHP) technique, and machine learning models were integrated to assess and predict the irrigation water quality (IWQ) suitability of the Okurumutet-Iyamitet agricultural-mine district. To achieve this, six water quality criteria were reclassified into four major hazard groups (permeability and infiltration hazard, salinity hazard, specific ion toxicity, and mixed effects) based on their sensitivity on crop yield. The normalized weights of the criteria were computed using the AHP pairwise comparison matrix. Eight thematic maps based on IWQ parameters (electrical conductivity, total dissolved solids, sodium adsorption ratio, permeability index, soluble sodium percentage, magnesium hazard, hardness, and pH) were generated and rasterized in the ArcGIS environment to generate an irrigation suitability map of the area using the weighted sum technique. The derived IWQ map showed that the water in 28.2% of the area is suitable for irrigation, 43.7% is moderately suitable, and 28.1% is unsuitable, with the irrigation water quality deteriorating in the central-southeastern direction. Two machine learning models-multilayer perceptron neural networks (MLP-NNs) and multilinear regression (MLR)-were integrated and validated to predict the IWQ parameters. The coefficient of determination (R2) for MLR and MLP-NN ranged from 0.513 to 0.858 and 0.526 to 0.861 respectively. Based on the results of all the metrics, the MLP-NN showed higher performance accuracy than the MLR. From the results of MLP-NN sensitivity analysis, HCO3, Cl, Mg, and SO4 were identified to have the highest influence on the irrigation water quality of the area. This study showed that the integration of GIS-AHP and machine learning can serve as efficient and rapid decision-making tools in irrigation water quality monitoring and prediction.

Suitability evaluation of groundwater for drinking and agriculture purpose in Una District, Himachal Pradesh (India)

The study’s goal is to analyze the suitability of the sub-surface water of Una district for drinking and agriculture purposes. The data related to physico-chemical parameters of groundwater of district Una was obtained from Central Groundwater Board’s website and compared to the BIS standard (10500:2012). The values of Ca2+, Cl−, F−, and SO4 2- were found to be below the acceptable limit. pH, TDS, Alkalinity, Mg2+, TH, and NO3 − had values that were above the acceptable limit at a few sites, but within the maximum permissible limit. Furthermore, as per the observed values of Total dissolved solids (TDS), Electric conductivity (EC), Sodium adsorption ratio (SAR), Percentage sodium (Na%) and Permeability Index (PI), the groundwater of Una district is suitable for irrigation purposes. However, Kelly’s Ratio (KR), Magnesium ratio (MR) and Residual sodium-carbonate (RSC) values revealed that groundwater is unfit for irrigation at few sites only. The value of Corrosivity Ratio (CR) was more than 01 for 11 sites indicating that the groundwater of these sites is corrosive in nature and can be transported only via non-corrosive pipes in these locations.

Suitability Assessment of Surface Water Quality for Irrigation: A Case Study of Modjo River, Ethiopia.

Water quality change due to industrial pollution is one of the major environmental concerns in developing countries. The majority of industries in Ethiopia release their wastewaters into the nearby water bodies with limited or without any forms of treatment. The main objective of this study was to assess the suitability of Modjo River for irrigation use based on the assessment of salinity, reduced water infiltration rate, specific ion toxicity, and miscellaneous chemicals as water quality-related problems in irrigated agriculture. Water samples were collected from six sampling stations along the river, and the relevant physicochemical parameters were determined in the laboratory by the standard procedures. Besides, the sodium adsorption ratio (SAR) and irrigation water quality index (IWQI) were determined to evaluate the suitability of the river water for irrigation. Cluster and principal component analyses of the data set were carried out. The results of this study showed that the river water in the vicinity of industries was saline and thus affects sensitive crops. The SAR value varied from 57.6 to 122.3. The concentration of chromium in the lower reaches of the river was also above the standard value of 0.1 mg/L. Among the miscellaneous chemicals, concentration of potassium, carbonate, and bicarbonate at all the sampling stations were above the standard values set by FAO for irrigation use. Based on the computed IWQI of 30.6, Modjo River is in the medium class of suitability for irrigation. Effluents from industrial establishments, namely, tannery, abattoir houses, and poultry farms and domestic waste dumping, were identified as the main sources of water pollution in the study area. Based on the findings of the study, Modjo River water is not suitable for irrigation use without some forms of physical and chemical treatment.

Delineation of seawater intrusion and groundwater quality assessment in coastal aquifers: The Korba coastal aquifer (Northeastern Tunisia)

The aim of this study is to determine the general state of the Korba aquifer (northwestern Tunisia) with respect to seawater intrusion and to assess the suitability of groundwater for drinking and irrigation purposes. A total of 60 groundwater samples were collected and analysed for physicochemical parameters (pH, EC, TDS, Na+, K+, Ca2+, Mg2+, HCO3−, Cl−, NO3−, SO42− and Br−). Major ionic ratios highlighted the dominance of the reverse ion exchange process triggered by the marine intrusion. Br−/Cl− ratio suggested that the irrigation with saline water and wastewater were potential additional sources of salinization. Hydrochemical Facies Evolution Diagram coupled with a GIS-based framework revealed that most of samples are located beneath the mixing line, showing dominant marine intrusion process. Based on the water quality index, most of groundwater samples were unsuitable for drinking. In addition, according to high sodium adsorption ratio, the groundwater quality was limited for irrigation purposes.

Assessing data mining algorithms to predict the quality of groundwater resources for determining irrigation hazard.

This study aims to compare three popular machine learning (ML) algorithms including random forest (RF), boosting regression tree (BRT), and multinomial logistic regression (MnLR) for spatial prediction of groundwater quality classes and mapping it for salinity hazard. Three hundred eighty-six groundwater samples were collected from an agriculturally intensive area in Fars Province, Iran, and nine hydro-chemical parameters were defined and interpreted. Variance inflation factor and Pearson's correlations were used to check collinearity between variables. Thereinafter, the performance of ML models was evaluated by statistical indices, namely, overall accuracy (OA) and Kappa index obtained from the confusion matrix. The results showed that the RF model was more accurate than other models with the slight difference. Moreover, the analysis of relative importance also indicated that sodium adsorption ratio (SAR) and pH have the most impact parameters in explaining groundwater quality classes, respectively. In this research, applied ML algorithms along with the hydro-chemical parameters affecting the quality of ground water can lead to produce spatial distribution maps with high accuracy for managing irrigation practice.

An evaluation of waste and well water quality for agriculture production around Erbil city, Iraq

Erbil city is located in the northern Iraq with a population of over one million people. Due to water crises farmers usually use wastewater and well water for the agricultural production. In this study six stations were designed to sample waste water and three from well water to define waste water and ground water characteristics. In this study, Residual Na+ Carbonate, Mg++ hazard, salinity hazard, Kelley index, %sodium, total hardness, permeability index, potential salinity, sodium adsorption ratio, and Irrigation Water Quality Index (IWQI) were determined. The order of average cation concentrations in water was Mg2+> Ca2+ > Na+ > K+. While the proportion of main anions in water were HCO3 >SO4 > Cl. The highest concentrations of Ca2+, Mg2+ and Na+ were found in well water, while the highest concentration of K+ was found in wastewater. The maximum concentration of HCO3 and Cl recorded in well water, while the highest concentration SO4 recorded in wastewater. Moreover, the order of trace elements was Pb > Al > Fe > Cd > As > Mn >Cr > Ag > Ni. Keeping in mind metal concentration set by US EPA and FAO (1999) and (1994) guidelines the levels of Pb, Al, Fe, Mn, Cr, Ag and Ni in the waste and well water were within the admissible limitations for irrigation schemes. Moreover, limitations of As and Cd were beyond permissible limitation need to be reduced. The IWQI ranged from 88.92 to 95.09 in the waste water samples. Overall assessment reveals that cultivated agriculture plants were secured from toxic compounds.

Determination of pollution status of Nilüfer river by water and bottom sediment analysis

Abstract This study aims to evaluate the Nilüfer river in terms of water quality standards. Bottom sediment and water samples were taken from six different locations. The analysis of the physicochemical parameters of water compared with FAO for irrigation water quality standards. In addition, the SAR (sodium adsorption ratio), % Na, MAR (magnesium adsorption ratio), RSC (residual sodium carbonate), KR (Kelley's ratio) and % PI (permeability index) parameters were evaluated in water samples. The bottom sediment pollution situation was evaluated in terms of pollution index such as TEC (threshold effect concentration) and PEC (probable effect concentration). According to the results of the study, the irrigation water classes of the samples were determined as C2S1 and C3S1. Especially, sample sites S4, S5 and S6 were not suitable for irrigation in terms of RSC, KR. The bottom sediment exceeded the TEC and PEC limit values in terms of Zn, Cu, Cr and Ni contents. This may cause adverse effects on the majority of sediment-dwelling organisms.The use of alternative water sources should be preferred. The river can be used for irrigation with precaution but extensive treatment is required before use for irrigation purposes to prevent adverse public health effects.

Groundwater quality assessment using the WQI and GIS mapping: Suitability for drinking and irrigation usage in the Sirdala block of Nawada district

Abstract In the present study, the suitability of groundwater for drinking and irrigation purpose were analyzed. A total of 65 groundwater samples were collected and analyzed for relevant water quality parameters from Sirdala block of Nawada district Bihar (India). To estimate the WQI for the drinking purpose, various parameters such as pH, TDS, TH, AS and major ions (Ca2+, Mg2+, Na+, K+, Cl−, F−, HCO3−, NO3−, SO42−) were considered. The WQI for irrigation, various irrigation indices namely soluble sodium percent (Na%), sodium adsorption ratio (SAR), residual sodium bicarbonate (RSCB), permeability index (PI), magnesium hazards ratio (MHR), Kelly's ratio (KR), potential salinity (PS), Cl−: HCO3−, Mg(2+): Ca(2+) and Na+: Ca(2+) were employed and discussed in addition with Wilcox and USSL diagram. Plots, namely Gibbs ratio and Piper diagram, were used to understand chemical characteristics of groundwater. The WQI for drinking purpose was observed between 57.67 and 929.90 in which most samples (70.76%) were unsuitable for drinking. However, the WQI for irrigation purpose varied from 87.88 to 434.67 in which 50% of samples were suitable. Gibb's plots determined in this study reveal that evaporation-precipitation was the dominant factor that influences hydrochemistry. Piper plots suggest that Na-HCO3− water type was major hydrochemical facies in groundwater.

Application of Water Quality Indices, Machine Learning Approaches, and GIS to Identify Groundwater Quality for Irrigation Purposes: A Case Study of Sahara Aquifer, Doucen Plain, Algeria

In order to evaluate and project the quality of groundwater utilized for irrigation in the Sahara aquifer in Algeria, this research employed irrigation water quality indices (IWQIs), artificial neural network (ANN) models, and Gradient Boosting Regression (GBR), alongside multivariate statistical analysis and a geographic information system (GIS), to assess and forecast the quality of groundwater used for irrigation in the Sahara aquifer in Algeria. Twenty-seven groundwater samples were examined using conventional analytical methods. The obtained physicochemical parameters for the collected groundwater samples showed that Ca2+ > Mg2+ > Na+ > K+, and Cl− > SO42− > HCO3− > NO3−, owing to the predominance of limestone, sandstone, and clay minerals under the effects of human activity, ion dissolution, rock weathering, and exchange processes, which indicate a Ca-Cl water type. For evaluating the quality of irrigation water, the IWQIs values such as irrigation water quality index (IWQI), sodium adsorption ratio (SAR), Kelly index (KI), sodium percentage (Na%), permeability index (PI), and magnesium hazard (MH) showed mean values of 47.17, 1.88, 0.25, 19.96, 41.18, and 27.87, respectively. For instance, the IWQI values revealed that 33% of samples were severely restricted for irrigation, while 67% of samples varied from moderate to high restriction for irrigation, indicating that crops that are moderately to highly hypersensitive to salt should be watered in soft soils without any compressed layers. Two-machine learning models were applied, i.e., the ANN and GBR for IWQI, and the ANN model, which surpassed the GBR model. The findings showed that ANN-2F had the highest correlation between IWQI and exceptional features, making it the most accurate prediction model. For example, this model has two qualities that are critical for the IWQI prediction. The outputs’ R2 values for the training and validation sets are 0.973 (RMSE = 2.492) and 0.958 (RMSE = 2.175), respectively. Finally, the application of physicochemical parameters and water quality indices supported by GIS methods, machine learning, and multivariate modeling is a useful and practical strategy for evaluating the quality and development of groundwater.

Evaluation of hydrogeochemical characteristics, spatial distribution of major ions, and water quality for drinking and irrigation in six districts of the Dodoma region in Tanzania.

The present study was carried out to evaluate the hydrogeochemical characteristics, spatial distribution of major ions, and quality of water for drinking and irrigation purposes in six districts of the Dodoma region. The results show that mineralization levels exist along the gradient and are concentrated in the southern part of the region. The order of Electrical Conductivity (EC) was found to be Kongwa > Mpwapwa > Chamwino > Dodoma Urban > Kondoa > Chemba. The percentage of samples in Kongwa district that exceeded the WHO guideline was discovered to be 68.42% for EC (<2500 μS/cm), 85.90% for Ca2+ (75 mg/l),89.50% for Mg2+ (50 mg/l),89.50% for K+ (12 mg/l), 52.63% for Na+ (200 mg/l),46.67% for SO42− (250 mg/l), 94.74% for Cl− (300 mg/l) and 84.21% for HCO3−(250 mg/l). The mean value for some of the parameters in Kongwa district are; 3045.58 ± 1107.64 μS/cm for Electrical Conductivity (EC), 225.91 ± 160.52 mg/l for Ca2+, 116.09 ± 54.64 mg/l for Mg2+,11.85 ± 3.17 mg/l for K+,264.33 ± 144.04 mg/l for Na+, 278.07 ± 335.39 mg/l for SO42−, 608.93 ± 296.17 mg/l for Cl−0.295.86 ± 100.59 mg/l for HCO3−. The order of the cations and anions was found to be Na+>Ca2+>Mg2+>K+ and Cl− > HCO3− > SO4−2. The dominant hydrochemical facies was found to be a mixed Ca–Mg–Cl–SO4 type in all Dodoma districts. Wilcox diagram, Sodium Adsorption Ratio (SAR), Kellys Ratio (KR), %Na, Magnesium Absorption Ratio (MAR) indicate that most of the water in Dodoma is suitable for irrigation purposes.

Hydrogeo-Chemical Processess and Evaluation of Groundwater Quality for Drinking and Irrigation Purposes in Afzalpur Taluk, Karnataka, India.

To determine the geochemical processes impacting groundwater geochemistry and its appropriateness for drinking and irrigational uses, a thorough examination of groundwater is conducted in some areas of the Afzalpur taluk. According to the results of a thorough chemical analysis, the research area's groundwater has a pH average between 6.7 and 6.3, making it fairly acidic. Mg2+ and Ca2+ cations and HCO3- anions are also present in high amounts. Ion-exchange and rock weathering mechanisms were perceived to dominate the groundwater chemistry, with anthropogenic sources playing a minor role. Particularly, the electrical conductivity, total hardness (TH), and concentrations of Na+ and HCO3- exceeded the BIS and WHO water quality standards' acceptable limits. Groundwater suitability for irrigation was assessed on the basis of sodium adsorption ratio, Na%, magnesium hazard (MH), residual sodium carbonate (RSC), and Kelley's index (KI). Above parameters revealed that nearly 40 to 50 % of the groundwater in the sampling sites was of high-quality for irrigation purpose.

Differential responses of hydrochemical factors and LULC changes on the spatial and temporal hydrogeochemistry of the eco-sensitive Baraila wetland, Bihar, India.

Freshwater scarcity, deterioration and associated water management remain to be one of the most challenging aspects of high-population density economies especially in subtropical/tropical regions. The present research deals with hydrogeochemical analysis of the eco-sensitive Baraila wetland and possible sources of anthropogenic pollution. The hydrochemical characterization was performed in GIS environment, considering meteorologically induced parameters and spatial variability. Temporal variations were assessed through different seasons, namely pre-monsoon, monsoon and post-monsoon with the help of multivariate statistics. The changes in water depth across the seasons showed significant hydrochemical variations in the vertical profile of the wetland apart from thermal demarcation. Seasonal variations in the hydrogeochemistry were induced by multiple physicochemical parameters, geochemical processes, geomorphology of the surrounding area and land use and land cover (LULC) changes. The pre-post flooding changes revealed that aquatic vegetation was increased by 15.36% whereas a major decrease in water bodies (- 73.2%) occurred. Dissolved oxygen (DO), pH, temperature and carbonates are fundamental towards establishing wetland's water chemistry. The water type is primarily of Ca-HCO3 type, mostly derived from rock-water interactions and cation exchange processes. Irrigational quality of water was assessed through multiple indices (sodium adsorption ratio (SAR), %Na, residual sodium carbonate (RSC), magnesium hazard (MH) and total hardness (TH)) and plots. The heavy aquatic vegetation abundance and eutrophication because of agricultural run-off is currently the major issue with the Baraila wetland and may be playing a simultaneous role in regulating the water chemistry to a large extent apart from other geochemical processes. The hydrogeochemical interactions between sediment and overlying water have created distinct effects on biota and land use/land cover changes. Their role in the landscape is prominent in this respect and may be utilized for environmental management, eco-tourism and employment boost. Serious lack of hydrogeochemical studies in this important floodplain wetland and its rapid deterioration deems it necessary to focus on the comprehensive research and wetland management options for its conservation and sustainable usage in future.

Suitability of the Lower Ganga basin groundwater for irrigation, using hydrogeochemical parameters and land-use dynamics.

The northern Ganga basin is one of the most densely populated basins in the world. Most agricultural and industrial contaminants drained in the river length are likely to be accumulated in the lower part of the Ganga basin. In this study, we have used ten parameters obtained from 495 sampling locations, besides using long-term climate data (GLDAS_NOAH025_M) to understand the irrigation suitability using the TOPSIS model. Multi-criteria decision making (MCDM) model using TOPSIS has been used to make the best choices from the available finite number of alternatives based on their ranking. The entropy weights for the irrigation suitability parameters such as electrical conductivity (Ec), sodium adsorption ratio (SAR), magnesium hardness (MH), sodium percent (Na%), total hardness (TH), Kelly's ratio (KR), permeability index (PI), chloride concentration (Cl-), groundwater level fluctuation (GWLF), and the Lang factor (Df) are found to be 0.08, 0.14, 0.02, 0.02, 0.04, 0.08, 0.01, 0.32, 0.29, and 0.01, respectively. We find that SAR, Cl-, and GWLF control the water quality for irrigation in the Lower Ganga basin since these parameters have relatively higher entropy weights (more than 0.10). The results obtained from the computed performance index or the closeness coefficient show that the area percent having very good and good groundwater quality for irrigation in the Lower Ganga basin is 77.03% and 22.97% respectively. The land-use change dynamics for the between 2000 and 2015 estimated using the transition matrix shows a positive percentage change for settlement (133.50%), wetland (35.04%), and bare area (0.98%); however, several other classes such as the agriculture (- 0.85%), forest (- 0.49%), grassland (- 14.38%), sparse vegetation (- 11.39%), and water (- 4.12%) show a decreasing trend. The highest amount of percentage change was observed in settlement areas which were contributed by other land-use classes such as agriculture (694.43 km2), water (41.61 km2), forest (16.77 km2), and grassland (1.86 km2). The results may be useful to the concerned organization for the proper planning and management of water resource for sustainable development.

Evaluation of Groundwater Quality for Irrigation in Deep Aquifers Using Multiple Graphical and Indexing Approaches Supported with Machine Learning Models and GIS Techniques, Souf Valley, Algeria

Irrigation has made a significant contribution to supporting the population’s expanding food demands, as well as promoting economic growth in irrigated regions. The current investigation was carried out in order to estimate the quality of the groundwater for agricultural viability in the Algerian Desert using various water quality indices and geographic information systems (GIS). In addition, support vector machine regression (SVMR) was applied to forecast eight irrigation water quality indices (IWQIs), such as the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), soluble sodium percentage (SSP), potential salinity (PS), Kelly index (KI), permeability index (PI), potential salinity (PS), permeability index (PI), and residual sodium carbonate (RSC). Several physicochemical variables, such as temperature (T°), hydrogen ion concentration (pH), total dissolved solids (TDS), electrical conductivity (EC), K+, Na2+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, were measured from 45 deep groundwater wells. The hydrochemical facies of the groundwater resources were Ca–Mg–Cl/SO4 and Na–Cl−, which revealed evaporation, reverse ion exchange, and rock–water interaction processes. The IWQI, Na%, SAR, SSP, KI, PS, PI, and RSC showed mean values of 50.78, 43.07, 4.85, 41.78, 0.74, 29.60, 45.65, and −20.44, respectively. For instance, the IWQI for the obtained results indicated that the groundwater samples were categorized into high restriction to moderate restriction for irrigation purposes, which can only be used for plants that are highly salt tolerant. The SVMR model produced robust estimates for eight IWQIs in calibration (Cal.), with R2 values varying between 0.90 and 0.97. Furthermore, in validation (Val.), R2 values between 0.88 and 0.95 were achieved using the SVMR model, which produced reliable estimates for eight IWQIs. These findings support the feasibility of using IWQIs and SVMR models for the evaluation and management of the groundwater of complex terminal aquifers for irrigation. Finally, the combination of IWQIs, SVMR, and GIS was effective and an applicable technique for interpreting and forecasting the irrigation water quality used in both arid and semi-arid regions.

The influence of geological setting and land use on the physical and chemical properties of the soil at the Fruska Gora Mountain

Soil erosion is a problem that affects the landscape at different scales and represents a serious challenge for land management and soil conservation in both natural forests and meadows. The aim of this study was to determine how the parent material and land use affect the physical and chemical properties of the soil in the area of the Fruska Gora Mountain. The soils were separated into five bedrock types: serpentinite, marl, trachyte, shale, loess and two land use types: forest and meadow. Twenty-three forest soil and 24 meadow soil from a depth of 0?20 cm were sampled from the Fruska Gora Mt. The following properties were determined: pH, electrical conductivity, oxidationreduction potential, content of organic carbon, sodium adsorption ratio, aggregate size and stability. There is no statistically significant difference in pH values, redox potential (Eh), electrical conductivity (EC) and sodium adsorption ratio (SAR) values between the analysed forest and meadow soils, but there is a statistically significant difference in the content of organic carbon (Corg). It can be concluded that both the parent material, and to a slightly less extent, land use have a great influence on physico-chemical properties of the soil.