Irrigation Water Quality Parameters Research Articles

Appraising water resources for irrigation and spatial analysis based on fuzzy logic model in the tribal-prone areas of Bangladesh.

The lack of quality water resources for irrigation is one of the main threats for sustainable farming. This pioneering study focused on finding the best area for farming by looking at irrigation water quality and analyzing its location using a fuzzy logic model on a Geographic Information System platform. In the tribal-prone areas of Khagrachhari Sadar Upazila, Bangladesh, 28 surface water and 39 groundwater samples were taken from shallow tube wells, rivers, canals, ponds, lakes, and waterfalls. The samples were then analyzed for irrigation water quality parameters like electrical conductivity (EC), total dissolved solids (TDS), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), residual sodium bicarbonate (RSBC), magnesium hazard ratio (MHR), Kelley's ratio (KR), and permeability index (PI). Fuzzy Irrigation Water Quality Index (FIWQI) was employed to determine the irrigation suitability of water resources. Spatial maps for parameters like EC, KR, MH, Na%, PI, SAR, and RSBC were developed using fuzzy membership values for groundwater and surface water. The FIWQI results indicate that 100% of the groundwater and 75% of the surface water samples range in the categories of excellent to good for irrigation uses. A new irrigation suitability map constructed by overlaying all parameters showed that surface water (75%) and some groundwater (100%) in the northern and southwestern portions are fit for agriculture. The western and central parts are unfit for irrigation due to higher bicarbonate and magnesium contents. The Piper and Gibbs diagram also indicated that the water in the study area is magnesium-bicarbonate type and the primary mechanism of water chemistry is controlled by the weathering of rocks, respectively. This research pinpoints the irrigation spatial pattern for regional water resource practices, identifies novel suitable areas, and improves sustainable agricultural uses in tribal-prone areas.

Groundwater Quality and Suitability Assessment in Tirupur Region, Tamil Nadu, India

The study aims to understand the hydrochemical characteristics and groundwater suitability for agricultural and drinking purposes. For this purpose, 21 groundwater samples were collected, and major physicochemical parameters such as pH, EC, TDS, temp, salinity, Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, and SO42− were analyzed, followed by the standard analytical procedures. Different groundwater quality graphical representations were constructed by using Aqua Chem software. The results indicate groundwater samples were alkaline with fresh to moderate saline in nature, sixty-eight percent of the samples were suitable for drinking in accordance with WHO, and thirty-two percent of the samples were unsuitable due to the excess amount of different ionic concentrations derived from natural and various anthropogenic sources. Irrigation water quality parameters such as SAR, EC, PI, Na %, RSBC, MR, and KR were used to understand the irrigation suitability. The US salinity diagram exemplifies that most groundwater samples fall in the C3S1 category with high salinity hazard and low alkali hazard. The Wilcox plot reveals that 80% of the samples were found under very good to permissible limits, and few samples fall with doubtful to unsuitable quality due to the excess amount of alkali and salinity. Permeability index values show that groundwater is suitable for irrigation. Three major hydrochemical facies were identified with the dominance order of mixed CaMgCl, NaCl, and CaCl. Gibb’s plot suggests that evaporation and rock-water interaction are the dominant natural mechanisms controlling the groundwater chemistry in the present study area.

Index Based Irrigation Suitability of Ramsar Sites (Rara and Ghodaghodi) in Western Nepal

The present study highlights the water quality of two important Ramsar sites of western Nepal (Ghodaghodi and Rara lakes) in terms of irrigation use. Based on land use patterns and location accessibility, 13 sites in Ghodaghodi and 18 in Rara were considered and the samplings were performed in the pre-monsoon and post-monsoon seasons. Different physicochemical parameters such as pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), major cations, and anion (Na+, K+, Ca2+, Mg2+, and HCO3-) were measured. The calculated indices were permeability index percentage (PI), sodium percentage (%Na), sodium adsorption ratio (SAR), magnesium hazard ratio (MAR), residual sodium carbonate (RSC), and Kelly’s index (KI). Major ions were analyzed using ion chromatography including field and procedural blanks to maintain quality standards, whereas on-site parameters were measured by using standard multi-meter probes. The studied irrigation water quality parameters (pH, EC, TDS, TH) and indices (PI, MAR, RSC, KI, SAR, and %Na) fall within the permissible limit in both lakes, indicating the suitability of such water for irrigation purposes. In addition, based on the SAR vs. EC plot, all the results from both lakes fall in the S1 category, signifying low sodium hazard. Concerning EC, most of the samples demonstrate the C1 category and few are in the C2 category (in Ghodaghodi) whereas the C1 category predominates for Rara. According to IWQI, all water samples in both lakes fall in the class I category, which supports the results of other indices indicating the suitability of water for irrigation purposes.

Hydrogeochemical investigation of irrigation water in the vicinity of metallic ore deposits in Kiraz‐İzmir, Turkey: Understanding the crucial nexus between “geology and food safety”

AbstractThe increasing drought due to climate change poses a threat to issues such as safe and accessible drinking water, food safety, and protection from diseases. The provision of water supply is vital for agricultural and livestock activities, which are commonly practiced around natural ore deposits. Examining traditional “irrigation water quality” methods alone is insufficient; investigating potentially toxic metal content in the region's waters is vital, especially around metallic ore deposits. This study focused on the Kiraz district in Turkey, known for its agricultural activities, to assess the impact of geogenic water pollution on irrigation water quality and its implications for food safety and human health. Geology determines nutrient availability, water resources, and land suitability for agriculture. Conventional irrigation water quality parameters indicate groundwater suitability for irrigation in the study area, considering Na%, sodium adsorption ratio, residual sodium carbonate, permeability index, Kelly ratio, magnesium hazard, and potential salinity. However, when examining the potential toxic metal content in the region, it was determined that the values of Al ranged from 96 to 8676 ppb, Ni values ranged from 27 to 360 ppb, and Sb concentrations varied between 9 and 53 432 ppb. Utilizing geogenically contaminated water for irrigation and its indiscriminate use in livestock, dairy, and food industries can lead to foodborne illnesses (cancer, endocrine disruptors, tuberculosis, antimony spots, thyroid tumors, goiter, neurologic and cardiovascular diseases) that endanger human health. The use of low‐quality water throughout the agricultural sector and food production chain increases food safety risks.

A Comparative Analysis of Water Quality Parameters of Vembanad Lake, Kerala and Its Primary Inflows Based on the Seasonal Variations

The ecology of lakes is largely dependent on rivers. However, because research has frequently concentrated on rivers and lakes independently, the interconnectivity between them is poorly defined. During the present study, hydrochemical investigation of surface water from the Vembanad Lake and its primary inflows, Periyar, Muvattupuzha, Manimala, Meenachil, Pamba and Achenkovil were carried out. This study describes the quality and suitability of lake and river water for consumption and irrigation purposes. It also aids in determining the influence of six rivers over the Vembanad lake water quality. Total 80 samples were collected during pre-monsoon and post monsoon seasons. Various physico-chemical parameters such as Temperature, Turbidity, Total Dissolved Solids (TDS), Conductivity, pH, Alkalinity, Total Hardness, Calcium, Magnesium, Sodium, Potassium, Chloride, Bicarbonate and Sulphate were analysed. The chemical classification using Piper trilinear diagram reveals that sodium is the predominant cation in both seasons. The Water Quality Index value of the Vembanad Lake is found to be higher in the pre-monsoon season compared to the postmonsoon. Sodium hazard (Sodium Adsorption Ratio- SAR), Residual Sodium Carbonates (RSC), Sodium Percentage (Na%), Magnesium Hazard (MH), Kelly's Ratio (KR) and Permeability Index (PI) are the parameters used to identify the irrigation water quality. Based on the irrigation water quality parameters it can be concluded that water from southern portion of the lake and six inflows are suitable for irrigation purpose. The U.S. Salinity diagram indicates that the northern portion of the lake occupies the high sodium hazard zone. The water quality of the southern portion of the Vembanad Lake is chiefly controlled by its primary freshwater in flows and the northern portion by the saline water from the Arabian Sea. Keywords: Hydrogeochemistry, Water Quality, Sodium Adsorption Ratio,Vembanad Lake Kerala

Spatial distribution of drinking and irrigation water quality in different climatic zones of Baluchistan, Pakistan.

Baluchistan's water profile was developed by dividing it into seven zones (Northern Highlands-NH, Southern Highlands-SH, Quetta Valley-QV, Desert-D, Sibbi Plains-SP, Coastal Lasbella-CL, Coastal Gwadar-CG) based on geography, water availability, and climate of the area. A total of 106 water samples were collected from karaiz, spring water, and tube wells. Spatial distribution of EC, TDS, TH, SO42-, Cl-, Na+, and K+ showed an increasing trend in concentration from the highlands towards the desert and coastal zones. For anion, HCO3- is predominant in NH, SH, and QV, Cl- in D, CL, and CG and only SO42- in SP, whereas the cationic trend in overall zones is Na+>Ca2+>Mg2+>K+. In the NH, SH, QV, and SP zones, the physicochemical parameters met the drinking water quality guidelines; however, D, CL, and CG exceeded in almost all quality parameters. Furthermore, the drinking water quality index (WQI) shows excellent to good water quality in NH, SH, QV, and D zones, while CL and CG fall in poor to unsuitable water classes. In terms of hydrogeochemical facies, maximum water samples from NH fall in Ca-Mg-HCO3, and SH, QV, and SP in Ca-Mg-Cl type, where major ion chemistry is controlled by rock-weathering, while D, CL, and CG fall in the NaCl type, where evaporation is dominant. Similarly, irrigation water quality parameters (EC, SAR, RSC, Na%, MH%, PI, SSP, and KR) reveal that NH, SH, QV, and SP have suitable water for irrigation, and D, CL, and CG require proper treatment. Additionally, USSL and Wilcox's diagrams indicated that NH, SH, QV, and SP have "excellent to permissible"; however, D, CL, and CG have "permissible to unsuitable" class water, requiring special management practices. Consequently, appropriate control measures and targetedwater purification programmes should be implemented to protect the publichealth and sustainability of water resources inBaluchistan.

Groundwater quality assessment for domestic and agricultural purposes using GIS, hydrochemical facies and water quality indices: case study of Rafsanjan plain, Kerman province, Iran

This paper investigates the suitability of groundwater for domestic and agricultural use in the Rafsanjan plain, southwest part of the Daranjir–Saghand basin, Iran. Fifty-five groundwater samples were collected and analyzed by six methods including the water quality index (WQI), Schoeller diagram, irrigation water quality (IWQ) parameters, Piper diagram, US salinity diagram and Wilcox diagram. The spatial distribution maps of chemical parameters and groundwater quality indices were plotted using the IDW method in GIS. The results showed a low concentration of major ions in the southeastern part and a high concentration from the central part towards northwestern part of the plain. The concentration of major ions in groundwater was strongly affected by groundwater flow, geological setting and the existence of the evaporative layers in the studied area. Moreover, results revealed that most of samples exceeded the acceptable limits recommended by the WHO and ISIRI1053 standards for domestic and agricultural purposes. In most of the wells, groundwater was classified into saline and very hard categories. The analyses based on WQI values indicated that above 87% of water samples were unsuitable for drinking purposes. IWQ parameters expressed that 85%, 67%, 32%, 51%, 43% and 50% of samples had EC > 3000, Na% > 60%, MAR > 50%, KR > 1, SAR > 9 and Cl− > 350, respectively, which were unsuitable for irrigation use. The dominant hydrochemical facies of water was Na–Cl–SO4 type, and 63% and 22.8% of samples were categorized as C4S4 and C4S3 class, with very high-salinity–high-sodium hazards and very high-salinity–high-sodium hazards, respectively. It indicated that most irrigated lands in this study area were affected by different levels of salinity and sodicity hazards that caused decreases in plant growth and crop productivity. The results can assist decision-makers and planners in prioritizing groundwater resources management in the region.

Evaluation of multiple water quality indices for irrigation purposes for the Bheri and Babai River systems, Nepal

The main purpose of this study was to assess the irrigation water quality of the Bheri and Babai Rivers and their tributaries prior to the proposed inter-basin water transfer (IBWT) in western Nepal. A total of 40 water samples from five sites in each river system were collected from January (winter), March-April (spring), June (summer) and October (autumn) in 2018; and some important irrigation water quality parameters were assessed. All the assessed parameters viz. pH, Total Dissolved Solids (TDS), Total Hardness (TH), permeability index (PI), percent sodium (%Na), sodium adsorption ratio (SAR), magnesium hazard (MAR), residual sodium carbonate (RSC), Kelly’s index (KI) from all sites were observed suitable for irrigation. USSL diagram showed that water from both the rivers belongs to the S1-C2 category indicating good for irrigation purposes. The Wilcox diagram revealed that all samples fall into the excellent to a good class. Based on Irrigation Water Quality Index (IWQI), 3 sampling sites fall in the high restriction category and 7 sampling sites fall in the moderate restriction category, indicating anthropogenic impacts on irrigation water quality at some sites.

Estimation of groundwater quality using an integration of water quality index, artificial intelligence methods and GIS: Case study, Central Mediterranean Region of Turkey

Groundwater is one of the most important natural resources in the world and is widely used for irrigation purposes. Groundwater quality is affected by various natural heterogeneities and anthropogenic activities. Consequently, monitoring groundwater quality and assessing its suitability are crucial for sustainable agricultural irrigation. In this study, the suitability of groundwater for irrigation was determined by using sodium adsorption ratio (SAR), residual sodium carbonate (RSC), Kelly index (KI), percentage of sodium (Na%), magnesium ratio (MR), potential salinity (PS) and permeability index (PI). The groundwater samples were collected and analyzed from 37 different sampling stations for this purpose. Along with suitability analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict irrigation water quality parameters. The models were evaluated by comparing the measured values and the predicted values using the statistical criteria [coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and Nash–Sutcliffe efficiency (NS)]. In the estimation of all irrigation water quality parameters, the ANN model has performed much higher compared with the ANFIS model. Spatial distribution maps were generated for measured and ANN model-estimated irrigation water quality indices using the IDW interpolation method. Spatial distributions of groundwater quality indices revealed that MR was higher than the allowable limits in most of the study areas and the other quality criteria were within the permissible limits. It has been determined that the interpolation maps obtained as a result of artificial intelligence methods have appropriate sensitivity when compared with the observed maps. Based on the present findings, ANN models could be used as an efficient tool for estimating groundwater quality indices in unsampled sections of the study area and the other regions with similar conditions.

Estimation and uncertainty analysis of groundwater quality parameters in a coastal aquifer under seawater intrusion: a comparative study of deep learning and classic machine learning methods.

Excessive withdrawal of groundwater for agricultural irrigation can cause seawater intrusion into coastal aquifers. Such a case will in turn results in deterioration of irrigation water quality. Determination of irrigation water quality with traditional methods is a time-consuming and costly process. However, machine learning algorithms can be useful tools for modeling and estimating groundwater quality used for irrigation water purposes. In this study, TDS, PS, SAR, and Cl parameters of groundwater were estimated with models based on EC and pH variables. For this purpose, prediction performances of two different deep learning methods (convolutional neural network (CNN) and deep neural network (DNN)) and two different classical machine learning (Random Forest (RF) and extreme gradient boosting (XGBoost)) methods were compared. In addition, predictive uncertainty of the models was determined by quantile regression (QR) analysis. Performance criteria and results of uncertainty analysis revealed that CNN (in testing phase, NSE = 0.95 for TDS, NSE = 0.96 for PS, NSE = 0.67 for SAR and NSE = 0.93 for CI) and DNN (in testing phase, NSE = 0.91 for TDS, NSE = 0.91 for PS, NSE = 0.57 for SAR and NSE = 0.94 for Cl) models had quite a close performance in estimation of TDS, PS, SAR, and Cl parameters and higher than the other two classical machine learning methods. As a result, the CNN model can be considered the best performing model in estimating all quality parameters due to the highest NSE and lowest RMSE values. In addition, the Taylor diagram showed that the values estimated using the CNN model had the highest correlation with the measured data. It was determined that the model with the lowest uncertainty based on the PICP statistics was DNN, followed by the CNN model. However, the CNN model has predicted outliers more accurately. Present findings proved that deep learning models could offer efficient tools for predicting irrigation water quality parameters.

Comparison of classical and developed indexing methods for assessing the groundwater suitability for irrigation

AbstractIntroductionSeveral water quality indices exist to evaluate irrigation water quality (IWQ), but they have some limitations. This study attempted to develop a new Overall Irrigation Water Quality Index (Overall IWQIndex) using a rational subindex that is flexible enough and updated to signify irrigation water suitability. It considered the maximum verified desirable and permissible value of parameters, including the hazard class, a modified rating system of parameters in each hazard class, and diversified water parameters that have affected the soil and plants.Materials and MethodsA total of 40 calcite‐type groundwater samples were analyzed to evaluate the IWQ using the proposed Overall IWQIndex, some IWQ parameters, and different diagram‐based statistical methods.ResultsThe study results were correlated with each other critically, and finally, the suitability for IWQ was determined. The results revealed that the samples of the postmonsoon and premonsoon felt under the ‘good’ and ‘excellent’ categories, respectively.ConclusionThe study developed an indexing model that is easy to compute, rationalize and deliver accurate information on the suitability of irrigation water.

Appraisal of groundwater quality and health risk in the Yalamlam basin, Saudi Arabia.

Groundwater quality in Yalamlam basin, Saudi Arabia, was appraised for drinking, irrigation, livestock and poultry applications by international standards, drinking water quality index (DWQI), irrigation water quality (IWQ) parameters, and irrigation water quality index (IWQI) calculations. Potential non-carcinogenic health risks due to high NO3- and F- water were assessed for various age groups using the United States Environmental Protection Agency (USEPA) models. Groundwater samples (n = 40) were analyzed for pH, electrical conductivity (EC), and major and minor constituents. The average total dissolved solids (TDS), EC, and total hardness (TH) in the groundwater are 3478 µS/cm, 1739mg/l, and 1240mg/l, respectively. High salinity, TH, NO3-, and F- in this aquifer restrict the usage of groundwater for drinking. DWQI values suggest that only 47.5% of samples are potable. According to USEPA recommendation, 72.5%, 80%, and 100% of samples for NO3- and 22.5%, 32.5%, and 40% of samples for F- surpassed the limit (HQoral > 1) for adults, children, and infants, respectively, which creates non-carcinogenic health hazards to the respective age groups. The total hazard index is greater than one in 75%, 87.5%, and 100% of samples computed for adults, children, and infants, respectively. Due to high salinity, 53% of samples are not pertinent for irrigation. USSL classification reveals that groundwater samples in the study site are recommended only for salt-tolerant crops and coarse-textured high permeability soil. In this study, IWQI is reclassified using salinity, which suggests that 68% of samples are moderately suitable for irrigation. Based on EC alone, 83% are desirable for livestock and poultry uses whereas integration of multiple parameters with EC indicates that only 53% are acceptable for all kinds of livestock and poultry uses in the study site. Spatial distribution of major and minor ions, DWQI, HQoral, and IWQI imply that groundwater quality is degraded from upstream to downstream. High salinity groundwater in the downstream wells is unsuitable for any application, which needs a proper treatment before use. Spatial maps created for various parameters are useful for identifying the good quality groundwater zone for groundwater development potential for various stakeholders.

Effect of Hydrocarbon Contaminated Irrigation Water On Some Selected Soil Physical and Chemical Properties of Sudan Savannah Alfisols

Due to fresh water scarcity challenges around the world, high increase in the demand for agricultural irrigation water from different sources including hydrocarbon contaminated wastewater increases. However, these wastewaters can alter soil physical and chemical properties. The aim of this study is to evaluate the impact of the hydrocarbon contaminated irrigation water on some selected soil physical and chemical properties from three phases of Sharada industrial area, Kano, Nigeria. Soil and water samples were collected from three different phases of the industrial area and analyzed using standard laboratory procedures. The hydrocarbon irrigation water quality analyses indicated that the hydrocarbon concentration in all phases was low and below irrigation reuse standard. However, the major irrigation water quality parameters; chemical oxygen demand (COD), biochemical oxygen demand (BOD), nitrate nitrogen (NO3-N), hydrogen carbonate (HCO3), ammonium-nitrogen (NH4-N), potassium (K), chlorine (Cl) and orthophosphate phosphorus (PO4-P) in all phases were all high and above irrigation reuse standard in comparison to other parameters that were compliant to standard according to literature. Pertaining the impact of the hydrocarbon irrigation water on soil properties, results revealed that soil texture was sandy loam and loamy sand, pH was neutral while bulk density (BD), electrical conductivity (EC), organic carbon (OC), total nitrogen (TN), potassium (K), sodium (Na), calcium (Ca), magnesium (Mg), cation exchange capacity (CEC) and phosphorus (P) were increased and higher compared with control soils. However, they are low in terms of concentration when compared with literature rating standard except P. Moreover, Ca, Mg, CEC and P of the soils showed significant variation statistically (P<0.05) in comparison to the remaining soil properties that recorded no significant statistical difference (P>0.05) in all phases and control soils after irrigation with the hydrocarbon wastewater. Overall, the research indicated that, the hydrocarbon wastewater was not fit for irrigation and has impacted relatively on some physical and chemical soil properties. Hence, all industries in Sharada should be directed by authorities to avoid direct discharge of their effluents without thorough quality investigation. In addition, farmers should also be educated to periodically monitor and assess the irrigation water quality of the effluents prior to application as well as incorporation of organic and inorganic amendments in the irrigated soils to uplift the concentration of the soil properties with concomitant improvement in soil fertility and quality of the study area.

Suitability assessment of groundwater quality for irrigational use in Sagardighi block, Murshidabad district, West Bengal

Groundwater alone comprises nearly about 80% of the available water for irrigation purpose in Murshidabad and this usage of groundwater for irrigation is yet to increase further in the upcoming years. The present study was carried out to access the groundwater’s suitability for different agricultural purposes in and around Sagardighi block of Murshidabad district, West Bengal, India. Groundwater samples from thirty three different locations around Sagardighi were collected and then tested in the laboratory for various physical as well as chemical parameters such as electrical conductivity (EC), total dissolved solids (TDS), pH, some major cations (Ca2+, Mg2+, Na+, K+, Fe2+) and some anions (CO32−, HCO3−, Cl−). To further delineate the suitable groundwater zones for irrigation practices, the irrigation water quality parameters such as sodium adsorption ratio (SAR), sodium percentage (Na%), magnesium adsorption ratio (MAR), permeability index (PI), residual sodium carbonate (RSC), Kelly’s ratio (KR) were also calculated. Results show that most of the water samples had low to medium salinity, while only a very few of the samples show high salinity. Piper trilinear plot reveals that the water is primarily of Ca–Mg–HCO3− facies type. The SAR value of majority of the water samples in the study area are less than 10, which is within safe limits. RSC values indicate that the carbonate content is extremely low in the groundwater of the study area. Overall, the results indicate that majority of the samples are fit to be used for irrigational purpose. The findings of this study would help decision-makers in developing irrigation suitability planning and groundwater resource management in the future.

Hydrogeochemical assessment of groundwater quality and suitability for irrigation in the coastal part of Cuddalore district, Tamil Nadu, India

A study was undertaken to identify in the irrigational suitability of groundwater in the cuddalore district (coastal part), Tamil Nadu, India. An entire study, 132 shallow and deep groundwater samples was gathered during Pre monsoon period (PRM) year of 2017 and post monsoon (POM) period samples collected year of 2018. Rock water interaction, silicate weathering and domestic waste are dominant sources for the water quality in the study area. The groundwater classification and irrigational suitability of groundwater were performed for both seasons. From the results of Chloro-Alkaline Indices (CAI I), and Chloro-Alkaline Indices (CAI II), during both seasons and classes, the direct ions exchange processes are predominant when compare with reverse ions exchange processes. The r1 and r2 results represents that most of the samples found as saline sources as Na+ - SO42− facies and performing with Deep Meteoric Percolation (DMP) than shallow meteoric percolation During PRM season, r1 represents 65 (98%) samples for Na+ - SO42− facies and 1 (2%) sample represents Na+ - HCO3− facies and during POM season, r1 represents 63 (95%) samples for Na+ - SO42− facies and 3 (5%) samples represent Na+ - HCO3− facies respectively. Irrigation water quality parameters like and satisfied the analysed water's irrigation suitability. However, according to MAR parameters, 5% of water samples were unsuitable for irrigation in PRM based on KR parameters, 41% of samples collected in the PRM season only were unsuitable for irrigation. The Wilcox diagram showed that 35% of water samples are suitable for irrigation. Sodium Adsorption Ratio (SAR), Soluble Sodium Percentage (SSP), Magnesium Adsorption Ratio (MAR), Permeability Index (PI), Potential Salinity (PS), and Total Dissolved Solids (TDS) are indicating the irrigational appropriateness of the groundwater samples, which is more suitable in post monsoon season compare to pre monsoon due to enrichment of Na+ by seawater intrusion and other processes. During POM season, the most number of groundwater samples are representing excellent to good categories might be due to an effective rainfall recharge by the monsoonal rain in the study area. The parameters indicated the introduction of geogenic and anthropogenic pollutions. The coastal community's knowledge is crucial to the long conservation of coastal water resources.

Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models.

Forecasting the irrigation groundwater parameters helps plan irrigation water and crop, and it is commonly expensive because it needs various parameters, mainly in developing nations. Therefore, the present research's core objective is to create accurate and reliable machine learning models for irrigation parameters. To accomplish this determination, three machine learning (ML) models, viz. long short-term memory (LSTM), multi-linear regression (MLR), and artificial neural network (ANN), have been trained. It is validated with mean squared error (MSE) and correlation coefficients (r), root mean square error (RMSE), and mean absolute error (MAE). These machine learning models have been used and applied for predicating the six irrigation water quality parameters such as sodium absorption ratio (SAR), percentage of sodium (%Na), residual sodium carbonate (RSC), magnesium hazard (MH), Permeability Index (PI), and Kelly ratio (KR). Therefore, the two scenario performances of ANN, LSTM, and MLR have been developed for each model to predict irrigation water quality parameters. The first and second scenario performance was created based on all and second reduction input variables. The ANN, LSTM, and MLR models have discovered that excluding for ANN and MLR models shows high accuracy in first and second scenario models, respectively. These model's accuracy was checked based on the mean squared error (MSE), correlation coefficients (r), and root mean square error (RMSE) for training and testing processes serially. The RSC values are highly accurate predicated values using ANN and MLR models. As a result, machine learning models may improve irrigation water quality parameters, and such types of results are essential to farmers and crop planning in various irrigation processes.

Cascade Tank Water Quality Management: A Case Study in Thirappane Tank Cascade System, Sri Lanka

Tank cascade system (TCS) is a series of tanks located in a mesocatchment and has been accepted as a Globally Important Agricultural Heritage System found in Sri Lanka. Ecosystem components of the TCS play a major role in purifying water within the system. This study attempted to investigate the water quality status and the farmers’ willingness to rehabilitate the ecosystem components of the Thirappane TCS. Drinking and irrigation water quality parameters were tested in 34 locations and drinking and irrigation water quality indexes were calculated. Participatory rural appraisal and a questioner survey were conducted to gather social data. Water of TCS was observed to be appropriate for irrigation but not for drinking during the Maha cropping season. Based on the results of the Nitrate (as NO3 - ) and Total Phosphate (as PO4 3-), water of TCS can be categorized as eutrophic. Presence of ecosystem features of tank cascade system, annual income of the respondents, satisfaction on the quality of water for drinking, and the awareness about the tank cascade system significantly influenced the participatory decisions of the community on the rehabilitation of TCS. This study shall be an example and an eye opener to formulate sustainable tank cascade management plan.

Assessment of groundwater quality for irrigation in the arid regions using irrigation water quality index (IWQI) and GIS-Zoning maps: Case study from Abu Dhabi Emirate, UAE

The present study developed an Irrigation Water Quality Index (IWQI) and GIS-Zoning maps for assessing groundwater resources for irrigation purposes in Abu Dhabi Emirate, UAE. The calculated IWQI values varied from 8.78 to 77.44 and 52% of the samples categorized under severe irrigation restrictions, where groundwater can be used for irrigation of high salt tolerance plants only. While, 44% of the investigated wells located within the high restriction category, 3% within moderate restriction, 1% within the low irrigation restriction category, and 0% within unrestricted category. Moreover, Wilcox and USSL diagrams confirmed IWQI results as the groundwater was classified mostly as unsuitable for irrigation purposes without prior treatment. It shows that waters classified either as Permissible to Doubtful or Good to Permissible are forming only 3% of the overall investigated wells and are located east to Abu Dhabi Emirate along the border with Oman. While, none of the samples fall in Excellent to Good region.Moreover, the combination of the irrigation water quality parameters such as Sodium Adsorption Ratio (SAR), Electrical Conductivity (EC), Sodium Ratio (%Na), Chloride concentration, and Permeability Index (PI) provided a guidance for evaluating the problems that might be developed in soil due to current irrigation practices in the study area. The spatial distribution (GIS zoning-map) of the IWQI provided a clear visualization of irrigation groundwater quality in the study area and can be used along with other water quality parameters and indices as useful tools for the decision makers to identify the “Red Zones” that suffer from over-extraction and as a guide for action toward a sustainable groundwater management plan for agriculture purposes. Additionally, GIS maps can be used as a guide to recommend the range of suitable crops that can be cultivated and used for the range of soils in the study area.

Analysis of Contaminants in Sewage and Groundwater: Future Impact on Human and Animal Health

Irrigation Water Quality Index (IWQI) is applied based on the linear combination of the five different groups of irrigation water quality parameters: (a) salinity hazard, (b) permeability and infiltration problems, (c) specific ion toxicity, (d) trace element toxicity; and (e) miscellaneous problems that have potential negative impacts on soil quality and crop yield. A single index value is developed, which is then interpreted to determine the suitability of the irrigation water. Furthermore, the values of various quality parameters of sewage were compared with the quality of groundwater of villages in the Gandhinagar district of Gujarat State selected for the study. The comparison revealed that no significant difference had been observed among the values of water quality parameters analyzed for groundwater and sewage. Further, the IWQI calculated for sewage and groundwater was found to be in the moderate and high class of suitability for irrigation purpose. IWQI also considers the levels of trace heavy metals, therefore it serves as an excellent tool for monitoring their levels and assess their impact on human and animal health in future. Moreover, it is recommended to carry out continuous monitoring of sewage, crops, and soil for perpetual applications of sewage for irrigation.

Assessment of Water Quality Status of Chandrabhaga River at Rishikesh, Dehradun, Uttarakhand, India

In the background of the ambitious ‘National Mission for Clean Ganga’ to clean, conserve and protect the River Ganga in a comprehensive manner, this study was undertaken to evaluate the status of Water Quality of Chandrabhaga river at Rishikesh, district Dehradun, India, between the Dhalwala bridge area and Mayakund area where it merges with the river Ganga, affecting adversely its water quality. The characteristics of Chandrabhaga river water were assessed during July to September 2017 by monitoring the water quality at most garbeged and contaminated five locations by determining physicochemical and biological parameters and metal ions. The observed values of dissolved oxygen (DO) varied from 0.4 to 1.29 mg/L, such a low DO, high biological oxygen demand (BOD, 21 ± 2.64 to 56 ±6.08 mg/L) and much high Coliform (1760 ±13.23 to3180 ±27.61 MPN/ 100 ml) at all the locations reflects the poor water quality of Chandrabhaga River. Recorded values of total dissolved solid (TDS), electrical conductivity (EC), total hardness (TH), alkalinity, phosphate, sodium, potassium and calcium exceeded the WHO standards. Pearson’s correlation analysis revealed the highly positive correlations all the time between EC and TDS, TH and TDS, TH and EC, while during high flow period good correlations were recorded between alkalinity and pH, TDS, EC and TH. Irrigation water quality parameters such as soluble sodium percentage (SSP), sodium adsorption ratio (SAR), magnesium adsorption ratio (MAR) and Kelly’s Ratio was evaluated to test the suitability of river water for irrigation purpose.