Soluble Sodium Percentage Research Articles

Spatial variation analysis of groundwater quality for irrigation purpose using GIS

Water is a precious resource for all the needs of human being on earth. The present investigation aims at identifying the appropriateness of groundwater (GW) sources for irrigation use in Rasipuram Taluk, South India. A total of 45 GW samples are collected from Rasipuram Taluk during pre-monsoon (May 2015) and post-monsoon (January 2016) seasons. The samples are analyzed for Electrical Conductivity, Calcium, Sodium, Potassium, Magnesium, Carbonate and Bicarbonate. Salinity hazard, Sodium Absorption Ratio, Soluble Sodium Percent, Residual Sodium Carbonate, Sodium Percentage, Kelley’s Ratio and Magnesium Hazard are calculated for the assessment of irrigational suitability. Except the sodium hazard all other calculated parameters shows that the groundwater samples in Rasipuram Taluk is fit for irrigation use. This study will be helpful for the water resources managers in the study area to make suitable decisions on irrigation water management practices.

Integration of Geochemical Modeling, Multivariate Analysis, and Irrigation Indices for Assessing Groundwater Quality in the Al-Jawf Basin, Yemen

Water quality monitoring is crucial in managing water resources and ensuring their safety for human use and environmental health. In the Al-Jawf Basin, we conducted a study on the Quaternary aquifer, where various techniques were utilized to evaluate, simulate, and predict the groundwater quality (GWQ) for irrigation. These techniques include water quality indices (IWQIs), geochemical modeling, multivariate statistical analysis, geographic information systems (GIS), and adaptive neuro-fuzzy inference systems (ANFIS). Physicochemical analysis was conducted on the collected groundwater samples to determine their composition. The results showed that the order of abundance of ions was Ca2+ > Mg2+ > Na+ > K+ and SO42− > Cl− > HCO3− > NO3−. The assessment of groundwater quality for irrigation based on indices such as Irrigation water quality index (IWQI), sodium adsorption ratio(SAR), sodium percent (Na%), soluble sodium percentage (SSP), potential salinity (PS), and residual sodium carbonate RSC, which revealed moderate-to-severe restrictions in some samples. The Adaptive Neuro-Fuzzy Inference System (ANFIS) model was then used to predict the IWQIs with high accuracy during both the training and testing phases. Overall, these findings provide valuable information for decision-makers in water quality management and can aid in the sustainable development of water resources.

Combination of discretization regression with data-driven algorithms for modeling irrigation water quality indices

Forecasting water quality parameters helps plan crop selection and irrigation strategies but is often costly because many parameters are required, particularly in developing nations. Therefore, the current research objective is to estimate the irrigation water quality indices in the Nand Samand catchment by developing machine learning models. To accomplish this objective, six machine learning models (Regression by discretization REGD; Regression by discretization-Bagging, REGD-Bagging; Regression by discretization-Random Subspace, REGD-RSS; Regression by discretization-Additive Regression, REGD-AR; Regression by discretization-M5 Pruned, REGD-M5P; Regression by discretization-Random Forest, REGD-RF) were developed and the accuracy of these model's were checked by ten statistics models and validated for predicting four irrigation water quality indices (soluble sodium percentage, SSP; sodium adsorption ratio, SAR; magnesium hazards, MH; Kelly's ratio, KR). Eleven physicochemical variables were analyzed from 95 open wells of the research area for two different seasons in 2020, pre-monsoon and post-monsoon, respectively. Results revealed that the REGD-M5P model showed the best fit for all irrigation indices based on the correlation coefficient(r) for the SSP, MH, KR, and SAR index (0.947, 0.975, 0.950, and 0.956 during the training phase and 0.868, 0.966, 0.930, and 0.979 during the testing phase, respectively). Similarly, root mean square error (RMSE) values showed the best fit for the REGD-M5P model for all four irrigation indices, i.e., SSP, MH, KR, and SAR (3.696, 3.100, 0.115, and 0.393 in the training phase; 3.446, 3.772, 0.125 and 0.207 in the testing phase, respectively). It may be concluded that machine learning models may improve the parameters of irrigation water quality, and such findings are vital to farmers for crop selection and irrigation application planning. Additionally, the proposed machine learning models in predicting the irrigation water quality index and being fast decision tools for modeling irrigation water quality are also very important.

Application of fuzzy C-means clustering and fuzzy EDAS to assess groundwater irrigation suitability and prioritization for agricultural development in a complex hydrogeological basin.

The current research is focused on detecting a river basin suitable for agriculture and priority for management using a new clustering tool of groundwater quality with fuzzy logic technique in R and Geographical Information System. A new fuzzy clustering-soft computing technique has been executed to determine the different hydrochemical zones considering 13 essential parameters such as electrical conductivity, hardness, chloride, sodium adsorption ratio, residual sodium carbonate, soluble sodium percent, magnesium hazard, permeability index, potential salinity, residual sodium bicarbonate, Kelly's ratio, synthetic harmful coefficient, and exchangeable sodium percentage. The derived fuzzy C-mean clustering (FCM) outperformed other available hard computing techniques like hierarchical clustering, K-means clustering, and agglomerative clustering. It divided the sampling sites into 2 clustering groups (FCM I and FCM II) which has been validated using fuzzy silhouette index (0.85), the partition coefficient (0.76), the partial entropy (0.68), and the modified partition coefficient (0.52). The hydrogeochemical analysis confirmed that the rock-water interaction, chemical weathering, and ion exchange process are predominant in the aquifer system of the study area. According to the correlation plots, the studied groundwater samples largely evolved from [Formula: see text], mixed [Formula: see text] types, and [Formula: see text] types. The spatial distribution map and the hydrochemical analysis also gives a clear depiction of the fluoride (> 1.0mg/l) and high iron (> 0.3mg/l) contamination in groundwater quality, making it unsuitable for both drinking and irrigation. A fuzzy EDAS priority map has been prepared based on all the irrigation suitability parameters which concludes that the groundwater at the upstream and downstream section of the basin requires the most attention. Based on the highest priority for management, five zones have been delineated: very high (5.98%), high (22.31%), medium (16.39%), low (32.30%), and very low (23.02). The findings of this study will be beneficial to planners and policymakers as they can develop schemes to solve similar problems across the country.

Assessment of Groundwater Quality using Water Quality Index, and Geo-Spatial Tools

Groundwater is considered a significant component of valuable freshwater resources for the living beings living in the arid and semi-arid regions of Pakistan, factors such as climate change, landfill deposits, application of fertilizers and pesticides on agricultural lands, leakages from septic tanks, industrial effluents, and urbanization jeopardizing the groundwater quality that makes its timely assessment necessary for human survival to protect people from water-borne diseases. This research study was carried out to analyze the 13 physicochemical parameters in the 38 groundwater samples that were taken from nearby different locations of Akram, Pinyari, and Phuleli canal within the jurisdiction of district Hyderabad. 26 samples were taken from hand pumps, electric motors and 12 samples were taken from tube wells to investigate the variability in groundwater quality. The water quality index and kelly’s ratio, magnesium hazard, residual sodium carbonate, sodium absorption ratio, soluble sodium percentage, and permeability index were used to assess the suitability of groundwater for drinking, and agricultural purposes. The result of the study revealed that alkalinity, bicarbonates, carbonates, magnesium, PH, potassium, and sodium were within the permissible limit of WHO Standards in all the samples, while the concentration of calcium was crossing the permissible limit only in one sample. Moreover, the availability of Chloride (Cl) was found in 16 samples that were above the limit ranges from 274.9 to 2549, High concentration of EC was found in 14 samples than the permissible limit having values from 2212 to 8360 (P26), and Total Hardness (TH) were found only in 6 samples slightly high from the permissible limit ranges from 509 to 651, and TDS were present in excessive amounts than the allowable limit from 1100 to 4180 mg/l (P26) in 10 samples. Considering the Water Quality Index (WQI) it was observed that 8 sample falls in the good category, 2 samples in the poor category, 11 samples in the very poor category, and 17 samples in the unsuitable category of water quality.

Detection of Groundwater Quality Changes in Minia Governorate, West Nile River

The need for freshwater supplies is increasingly rising according to the increase in the inhabitants’ expansion and economic growth. Available water resources are reduced by pollution and overpumping. This research’s prime objective is to study changes in the water quality of the Pleistocene aquifer in Minia Governorate. Historical hydro-chemical data of the groundwater in two years 2009 and 2019 were used to study the changes in the groundwater quality of the Pleistocene aquifer under the impact of the recharge and discharge processes. The Nile River, and the Al-Ibrahimia and Bahr Youssef Canals are considered the main sources of aquifer recharge. Collected data from 53 groundwater wells in the Pleistocene aquifer were used to calculate the sodium adsorption ratio (SAR), sodium percentage (Na%), Kelly index (KI), Soluble Sodium Percentage (SSP), magnesium ratio (MR%), permeability index (PI) and chloro-alkaline index (CAI). These data were used to evaluate and detect the quality and changes in groundwater through the years 2009 and 2019 using spatial mapping in the geographic information system (GIS). The values of SAR, KI and Na% varied between 0.06–1.22, 0.02–0.57 meq/L and 3.7–37.63%, respectively, in the year 2009, but these values changed to 0.4–0.75, 0.16–0.28 meq/L and 15.07–23.44% in the year 2019. The calculated MR and PI values indicate that 100% of the groundwater samples were in the “suitable” category. The calculated SSP reflects no changes in groundwater alkalinity between the years 2009 and 2019. The hydro-chemical analysis of the studied groundwater (G.W.) samples shows high pollution levels caused by Pb and Fe in some parts of the study area. Pb was found to be >40 µg/L in the middle parts, whereas Fe was found with high levels in 27% of the studied groundwater samples. The localities of these samples were affected by pollution from the industrial wastewater from the sugar factory of Abou-Qarqas city (e.g., El-Moheet drain), the fertilizer leaching process and pesticides seeping into groundwater from soils and agricultural wastewater.

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.

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.

Assessment of shallow groundwater aquifer in an arid environment, Western Saudi Arabia

In arid and semi-arid regions like the Kingdom of Saudi Arabia (KSA), groundwater is considered the main source for drinking and agricultural uses due to low precipitation and limited surface water resources. The aim of this research is to evaluate the hydrogeological and hydrochemical characteristics of groundwater in Wadi Marawani basin for sustainable development. To achieve this goal, standard analytical methods have been used to determine the physicochemical parameters in sixty-four samples. Groundwater quality and controlling factors were evaluated using diagrams (Piper, Gibbs, Chadha, and HEF) and irrigation indices such as Sodium percentage (Na%), Soluble Sodium Percentage (SSP), Permeability Index (PI), Residual Sodium Bicarbonate (RSBC) and Kelly Index (KI). Also, the Support Vector Machine for Regression (SVMR) and the Multiple Linear Regression (MLR) models have been applied to predict the irrigation parameters. The results revealed that the Quaternary aquifer of unconfined condition is the main source of groundwater in Wadi Marawani basin. Depth to water measurements in sixty-two wells indicated that the groundwater flows from east to west towards the Red Sea coast. The ionic composition of the analyzed water samples showed that, Ca–HCO3, Na–Cl, mixed Ca–Mg–Cl–SO4, and Na–Ca–HCO3 are the main water types. Assessment of groundwater quality indicates that, 33%, 86%, 100%, 100% and 69% of groundwater samples are suitable for irrigation based on the calculated irrigation indices Na%, SSP, PI, RSBC and KI respectively. The SVMR model is outperformed the MLR in prediction of the irrigation indices. There was no overfitting or underfitting in the datasets used to measure, calibrate, and validate the SVMR model of four irrigation indices. Finally, this study can provide a holistic view of groundwater management for agricultural uses in the Wadi Marawani basin.

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.

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.

Assessing the potential of a surface water body for multiple uses. Case study: the Anambra River Basin, south-eastern Nigeria

Anthropogenic activities causing deterioration in water quality and a decrease in meeting the ecosystem service potentials of surface water are considered one of the major basic environmental challenges. Water samples were collected from three sampling stations (Ogurugu, Otuocha, and Otu- Nsugbe) in the Anambra River Basin and analyzed following standard protocols and procedures for nine months. The cosystem services potentials of the Anambra River Basin were evaluated using physico-chemical parameters and some indices: Comprehensive pollution index (CPI), nutrient pollution index (NPI), salinity potential, and soluble sodium percentage (SSP). All the water quality parameters were within the standard level for drinking water, aquaculture practice, recreation (swimming/bathing) and agricultural purposes, with the exception of pH and phosphate, whereas dissolved oxygen met aquaculture criteria only in station II. Comprehensive pollution index (0.150 – 0.449), nutrient pollution index (0.07 – 0.46), and soluble sodium percentage (6.0% -26.92%) were within the category of non-polluted or excellent to sub-clean. Potential salinity (39.39 - 97.19) was high, indicating water from the Anambra River Basin contained crystallized solutes. Conclusion: the Anambra River Basin is not at risk of eutrophication but may not support irrigation program due to the high potential salinity. However, the river needs to be monitored, and anthropogenic activities need to be regulated.

ASSESSMENT OF IRRIGATION AND WATER QUALITY INDICES OF INDUSTRIAL EFFLUENT AND SURFACE WATER IN ALETO ELEME, RIVERS STATE

x The study assessed the irrigation water quality indices of industrial surface water in Aleto Eleme, Rivers State, Nigeria. A total of 9 samples of water effluents and surface water were collected and taken to laboratory to test for the level of concentrations of the physico-chemical parameters using standard laboratory analyses as recommended by the American Society for Testing and Materials for physico-chemical properties and descriptive statistics were employed for the data analysis. Findings revealed that Sodium Absorption Ration (SAR) values ranged from 0.32 to 12.39; Soluble Sodium Percentage (SSP) values ranged from 23.39 to 80.62; PI values ranged from 24.79 to 889.8; KR values ranged from 0.39 to 4.16, Residual Sodium Carbonate(RSC) values ranged from -585.3 to 9.9. Also, the different classes of salinity hazard in the USSL diagram include low, C1 (EC < 250 μS/cm); medium, C2 Electrical Conductivity (EC 250–750 μS/cm); high, C3 (EC 750 -2250 μS/cm); and very high, C4 (EC > 2250 μS/cm), as shown in figure 4. Also, the sodium hazard classes include: low, S1 Sodium Absorption Ratio (SAR < 10); medium, S2 Sodium Absorption Ratio (SAR 10 -18); high, S3 (SAR 18–26); and very high, S4 (SAR > 26). Thus, the quality of river water in the area had WQI values (WQI > 300), rating the water as unsuitable for drinking. The study recommended that the water quality should be improved for domestic purposes by making sure that regulatory bodies monitor the level of water pollution everywhere in Rivers State. More studies on water quality should also be conducted periodically.

Water Quality, Source Identification, and Risk Assessment of Heavy Metals Using Multivariate Analysis in the Han River Watershed, South Korea

This study aimed to investigate the irrigation water quality, major pollution sources, and human health hazards by focusing on heavy metal concentrations in the surface water of the Han River watershed, South Korea that supplies water for consumption and irrigation. Here, Mn was found to have the highest mean concentration, with the maximum concentration recorded at IH-2. The heavy metal concentrations were higher during summer and fall than that during spring. The mean concentration of heavy metals was relatively high in the downtown area (1.8 times) and downstream of the wastewater treatment facilities (1.3 times), indicating that the wastewater treatment facilities (WTFs) may be the primary source of pollution. Water at most of the sites were found to be suitable for irrigation. However, the sodium absorption ratio and soluble sodium percentage indicated that IH-2 was unsuitable. The results of the principal component analysis indicated that anthropogenic (vehicle and industrial) activities were the primary sources of metal pollution. Ingestion was identified as the primary exposure pathway in terms of health risks. However, the hazard quotients and hazard index for all pathways were below the safety limit (<1) for children and adults.

Groundwater quality for irrigation in an arid region-application of fuzzy logic techniques.

Groundwater is the main source to answer the irrigation supply in several arid and semi-arid areas. In the present work, groundwater quality for irrigation purposes in the arid region of Menzel Habib (Tunisia) for thirty-six groundwater samples is assessed considering the application of different conventional water quality indicators, particularly, electrical conductivity (EC), sodium absorption ratio (SAR), soluble sodium percentage (SSP), magnesium adsorption ratio (MAR), Kelly ratio (KR), and permeability index (PI). The results obtained indicate a variability for EC: 3.06 to 14.98 mS.cm-1; SAR: 4.08 to 19.30; SSP: 35.78 to 71.53%; MAR: 34.19 to 56.01; PI: 38.47 to 72.74; and KR: 0.56 to 2.47. These results suggest that groundwater from Menzel Habib aquifer system is classified between excellent to unsuitable according to the applied water quality indices. Furthermore, the groundwater samples are also plotted in the Richards diagram classification system, based on the relation between SAR and EC, suggesting that almost groundwater samples present a harmful quality. Moreover, fuzzy logic model has been proposed and created to assess groundwater quality for irrigation. The membership functions are constructed for six significant parameters such as EC, SAR, SSP, MAR, KR, and PI and the rules are, then, fired to get a simple Fuzzy Irrigation Water Quality Index (FIWQI). The obtained groundwater quality results suggest that 3% of the samples from Menzel Habib region are considered as "good" for irrigation, 3% are classified as "good to permissible", 33% with a "permissible" quality, 36% "permissible to unsuitable", while 25% of groundwater present an "unsuitable" quality. Thus, the use of fuzzy logic techniques has more reliable and robust results by overcoming the uncertainties in the decision-making attributed to the conventional methods by the creation of new classes (excellent to good, good to permissible, and permissible to unsuitable) in addition to the classes proposed by Richards diagram classification (excellent, good, permissible, and unsuitable) to assess the groundwater quality suitability for irrigation purposes.

Surface Water and Groundwater Suitability for Irrigation Based on Hydrochemical Analysis in the Lower Mayurakshi River Basin, India

The present work investigates the hydrochemical properties of the surface and groundwater of the Mayurakshi River Basin (India) for assessing their irrigation suitability with respect to irrigation hazards. The study involves 72 water samples classified as 48 surface water samples (pre-monsoon: 24; post-monsoon: 24) and 24 groundwater samples (pre-monsoon:12; post-monsoon: 12). Regarding the specific irrigation hazard, percent of sodium and soluble sodium percentage have demonstrated the groundwater vulnerability to sodium while the surface water is observed to be free from this kind of hazard. Similar findings have also been retained for magnesium hazard and the potential salinity hazard. Moreover, regarding the seasonality of the hazards, the post-monsoon season has depicted a higher level of irrigation hazards compared to the pre-monsoon season. The study found that the general evolution of groundwater hydrochemistry and the suitability of water for irrigation are principally governed by carbonate weathering, sand mining, stone crushing, and the development of brick kiln industries. Our methodology can be a good example for similar contexts, especially in developing and tropical countries.

Water quality assessment of Noyyal river using water quality index (WQI) and multivariate statistical techniques

ABSTRACT Noyyal River is an important river that flows through the districts of Coimbatore, Tirupur, and Erode in Tamil Nadu, India. This river is the prime source of water for agricultural and domestic purposes in the above districts. This study evaluates the surface water quality of the Noyyal river and its suitability for drinking and agricultural uses. For this study, samples were collected from 27 locations along the Noyyal river from its source to sink. The water quality index (WQI) values were designated and, except for two samples out of 27, all the other samples were categorized as poor, very poor, and unfit for consumption. The parameters evaluated to assess the water quality of the Noyyal river for irrigation include Electrical conductivity (EC), Sodium adsorption ratio (SAR), Soluble sodium percentage (SSP), Residual sodium carbonate (RSC), Magnesium hazard (MH), Kelly’s ratio (KR), and Permeability index (PI). USSL (U.S. Salinity Laboratory) diagram analysis helped to classify the samples 1 and 2 of the Noyyal river in the C1S1 and C2S1 category, which indicates low salinity-low sodium and medium salinity-low sodium, respectively. The rest of the samples fall under the category of very high salinity and very high sodium waters. Other indices like RSC, magnesium hazard, soluble sodium percentage, Kelly’s ratio, and permeability index signified that majority of the collected samples fall in the category of unsuitable for agricultural use and they need special management practices. The Principle component analysis determined the four PCs that explained 84.88% of the total variance in the data structure. The present study concludes that we cannot use the surface water of the Noyyal river directly without treatment and strict management practices should be implemented for the sustainable use of the resource.

Chambal River Water Quality Assessment at Kota Metropolis Through the Irrigation Water Quality Indices from 2017-2021

The Chambal river plays an essential role in satisfying all water demands for the two million population of Kota and nearby districts of Rajasthan (India). This research aims to analyse the Chambal river water quality for irrigation in the upstream and downstream of Kota dam, Rajasthan (India), for five years, from 2017 to 2021. The irrigation water quality of the Chambal river at Kota is estimated with the help of five indices, namely, the permeability index (PI). Kelly ration (KR), magnesium hazard (MH), soluble sodium percentage (SSP), and sodium absorption ratio (SAR). Calcium, magnesium, potassium, sodium, and bicarbonate were analysed from 2017 to 2021 to complete irrigation water quality indices (IWQIs) for the Chambal river upstream and downstream of the Kota dam. Akalgarh is situated upstream of the Kota dam, while Rangpur, SRRT, and Keshoraipatan in the downstream. The irrigation water quality indices (IWQIs) are estimated to access river water quality for agricultural use. SAR, SAP, KR, MH, and PI had higher values in downstream sampling points than upstream during the observation period of five years. Hence, upstream river water is more suitable for use in agricultural activities. The evaluated IWQIs are the best rating tool for Chambal river water quality. Different characteristics of Chambal river water may be assessed through these IWQIs. The best management program and pollution control strategies can be determined, which ultimately helps stack holders for decision making.

ASSESSMENT OF IRRIGATION WATER QUALITY AS DETERIORATED BY EFFLUENT DISCHARGED FROM MAJOR SHRIMP PROCESSING INDUSTRIES OF RUPSHA UPAZILA, KHULNA, BANGLADESH

A Comparative study was carried out in different shrimp processing industries at Rupsha Upazila under Khulna district from January 2010 to December 2010. In the experiment, some water quality parameters were determined to sketch out the nutrient dynamics of the experimental area. The nutrient contents of water were analyzed for measuring temperature, pH, EC, salinity, sodium adsorption ratio (SAR), soluble sodium percentage (SSP), hardness and some major ionic concentrations. The temperature of the water samples were ranged from 29.1°C to 29.3 °C and the pH varied from 8.0 to 8.12 which reflected neutral to alkaline in nature. The values of SAR, SSP and hardness were ranged from 4.28 to 8.65, 71.24 to 81.847 percent and 239.1 to 312.3 mg L-1, respectively. Among the five industries the hazardous classes of the four industries reflected High sodium (alkali) content with medium salinity hazard. So, from the result, it may be concluded that appropriate sustainable management technologies need to be incorporated to control pH, hardness and salinity of that water.

Assessment of soil and water quality status of rose growing areas of Rajasthan and Uttar Pradesh, India

Rose is a commercial flower crop widely grown across India. It is highly sensitive to salinity and alkalinity. In the process of identification of salt and alkalinity resistant rootstocks of rose cultivars, a survey was conducted in the rose growing areas of Uttar Pradesh (UP) and Rajasthan. Total of 28 representative surface soil samples were collected from rose fields of these regions, processed and analyzed for the soil quality parameters. Similarly water samples (20 samples) from the bore wells of these fields were collected and analyzed. The results revealed that most of the soils of rose growing fields in UP were alkaline (pH >8.0) with normal salt content (electrical conductivity, EC < 0.5 dS m-1). Many of these soils also had higher bicarbonates (> 3 meq 100 g-1). In case of Rajasthan, few samples had higher pH, EC, chloride (>2 meq 100 g-1) and bicarbonate contents. Exchangeable sodium percentage (ESP) of UP and Rajasthan samples ranged from 5.21-20.7% and 2.94-24.9%, respectively. In case of water parameters in these areas, pH was slightly in alkaline range, EC of some of the samples were high (>1 dSm-1). Sodium content was slightly higher than other cations. Soluble sodium percentage (SSP) of water samples was also slightly higher than normal range (0-50%). Few samples had slightly higher chloride above the threshold limit. From the results, it is concluded that soil and water quality of the rose growing areas of UP and Rajasthan is marginal and proper management/reclamation measures need to be carried out for sustaining the production system.