Irrigation Indices Research Articles

Suitability of treated wastewater for irrigation and its impact on groundwater resources in arid coastal regions: Insights for water resources sustainability

Water scarcity threatens agriculture and food security in arid regions like Saudi Arabia. The nation produces significant quantities of municipal wastewater, which, with adequate treatment, could serve as an alternative water source for irrigation, thereby reducing reliance on fossil and non-renewable groundwater. This study assessed the appropriateness of using treated wastewater (TWW) for irrigation in a dry coastal agricultural region in Eastern Saudi Arabia and its impact on groundwater resources. Field investigations were conducted in Qatif to collect water samples and field measurements. A multi-criteria approach was applied to evaluate the TWW's suitability for irrigation, including complying with Saudi Standards, the Irrigation Water Quality Index (IWQI), the National Sanitation Foundation water quality index (NSFWQI), and the individual irrigation indices. In addition, the impact of TWW on groundwater was assessed through hydrogeological and isotope approaches. The results indicate that the use of TWW in the study area complied with the Saudi reuse guidelines except for nitrate, aluminum, and molybdenum. However, irrigation water quality indices classify TWW as having limitations that necessitate the use for salt-tolerant crops on permeable and well-drained soils. Stable isotopic analysis (δ2H, δ18O) revealed that long-term irrigation with TWW affected the shallow aquifer, while deep aquifers were minimally impacted due to the presence of aquitard layer. The application of TWW irrigation has successfully maintained groundwater sustainability in the study area, as evidenced by increased groundwater levels up to 2.3 m. Although TWW contributes to crop productivity, long term agricultural sustainability could be enhanced by improving effluent quality, regulating irrigation practices, implementing buffer zones, and monitoring shallow groundwater. An integrated approach that combines advanced wastewater treatment methods, community involvement, regulatory oversight, and targeted monitoring is recommended to be implemented.

Evaluating hydrogeochemistry and heavy metal contamination of groundwater at Ranipet environs: employing multivariate statistics, agricultural indices, and health risk assessment.

Water plays an essential role in sustaining life on Earth as an indispensable natural resource. In recent decades, dependence on groundwater for domestic and industrial purposes has become inevitable. The Ranipet industrial environs (RIE) has many tanneries and chemical industries, which affects the groundwater quality. This study assessed groundwater quality and its suitability for domestic, agricultural, and human health risk assessments. 40 groundwater samples (28 open wells and 12 bore wells) were collected during pre-monsoon 2022 and analyzed by employing multivariate statistics, standard scatter plots, irrigation indices, and health risk assessment. The results of hydrogeochemical analysis and multivariate statistics affirmed that electrical conductivity (EC), total dissolved solids (TDS), calcium (Ca2+), and magnesium (Mg2+) have controlled the hydrochemistry of RIE. Cadmium (Cd) at 46% and chromium (Cr) at 33% have contaminated the groundwater in the study area, making it unsuitable for human consumption and irrigation. The agricultural indices analysis results show groundwater quality ranging from very poor to unsuitable making it unsuitable and also affects crop productivity. Hazard index (HI) results infer that Cr and Cd severely contaminated the RIE's groundwater, encompassing 14 villages, making the groundwater unfit for drinking, domestic use, and irrigation. Hazard quotient (HQ) and incremental lifetime cancer risk (ILCR) analysis revealed that 2 in 100 infants and 3 in 1000 people over the age of 63 are likely to develop cancer due to Cr and Cd in the REI. This is a need-of-the-hour problem, addressing this issue with preventive measures to ensure the protection of groundwater sources will lead to achieving the Sustainable Development Goal 6 (Clean Water and Sanitation).

Entropy-weighted water quality index, hydrogeochemistry, and Monte Carlo simulation of source-specific health risks of groundwater in the Morava River plain (Serbia)

Population growth, urbanization, industry, floods, and agriculture globally degrade groundwater in river plains, necessitating action for its quality assessment and management. Hence, a comprehensive methodology, including hydrogeochemical facies (Piper, Gibbs), irrigation indices (SAR, Wilcox), entropy-weighted water quality index (EWQI), positive matrix factorization (PMF), and Monte Carlo simulation of source-specific health risks was used in this study to analyze groundwater in the Morava river plain (Serbia). The results revealed a prevalent Ca–Mg–HCO3 groundwater type, influenced by water–rock interactions. Although groundwater was found suitable for irrigation, only 66.7 % of the samples were considered drinkable. Agricultural activities, natural processes, and municipal wastewater were identified as primary pollution sources. The incremental lifetime cancer risk (ILCR) and hazard index (HI) threshold exceedance for adults and children ranged from 8.5 % to 39 % of the samples, with arsenic identified as the most risk-contributing contaminant. These findings provide valuable insights for researchers studying groundwater vulnerability in river plains.

Analysis of Dynamic Evolution and Driving Factors of Low-Carbon Utilization Efficiency of Cultivated Land in China

In order to cope with global climate warming, measurement of the low-carbon utilization efficiency (LCUE) of cultivated land, considering carbon sink and carbon emission effects, is proposed. To address this, based on the data of 30 provinces in China, this study conducts a LCUE evaluation system by the MinDS-U-M productivity index model in order to analyze the spatiotemporal patterns and driving factors of LCUE with the geographic detector model and GTWR model. The results show the following: (1) Over the past 20 years, the average LCUE value exhibits a slow increasing trend from 2001 to 2021, which ranges from 0.9864 to 1.0272. Provinces with mid-level LCUE ranging from 1.0000 to 1.0990 account for the highest proportion in each period. (2) The annual growth rate of LCUE in the central region is the highest, where the promotion of green technology and farmland protection policies have played important roles. (3) According to the Geodetector analysis, urbanization rate (UR), irrigation index (IR), grain output value (GOV), precipitation (PR), arable land area (ALA), and environmental pollution control (EPC) are important drivers of the spatial difference of LCUE. (4) The GTWR model shows that the positive effects of ALA and SRT have always been concentrated in the main grain-producing areas over time. UR and PR have strong explanatory power for the space/time differentiation of LCUE, especially in eastern coastal regions. IR has an increasing effect on LCUE in the Western region, and the positive effect of EPC on the LCUE is concentrated in the central region. In order to coordinate regional LCUE contradictions, it is suggested to be wary of land resource damage caused by economic development, warn about the impacts of climate change, and strengthen the supervision of land remediation projects in order to achieve sustainable land management.

Estimating Impact of Conventional and System of Rice Intensification (SRI) Methods of Rice Production Management on Sustainable Livelihood Generation in the Context of Climate Change in Maharashtra, India: A Factor Analysis

The global agricultural community has been preparing to face the imminent threat posed by climate change as projections by scientific communities complement the fact that food production will be affected adversely in the near future. The Indian subcontinent has not been explicit about the effects of natural disasters like floods and cyclones caused due to climatic changes. The aberrant weather patterns along with the inconsistent output of monsoon create unprecedented challenges for the rice-dominated farming system to thrive. It directly impacts the livelihood of the farmers making them economically vulnerable. Thus, it has become necessary for the research community to explore different causal dimensions related to rice production and suggest remedies for the constantly changing agroecosystem. This study aimed to investigate different indicators of impact on sustainable livelihood generation through rice production management in light of climate change in both conventional as well as SRI methods of rice production. An ex post facto research design was adopted for conducting the study. Purposive sampling was used to select the state, district, and talukas keeping in view the backwardness and agricultural status of the area of the study. Using a simple random sampling method, two hundred (200) respondents were selected from two talukas of the Bhandara district of Maharashtra. In case of results found in conventional method of rice production, the variables, Water management, Farm size, Education, Irrigation index%, Selling% and Risk orientation have contributed 9.996 per cent of variance and clubbed into Factor 1. On the other hand, in the case of SRI method, Factor 1 has the following variables i.e., Farm size, Family income primary, Education, Primary occupation, Mass media exposure, Family education status and Sowing time which has contributed 9.54 per cent of variance and has been renamed as ‘Family capability’.

Evaluation of groundwater quality indices using multi-criteria decision-making techniques and a fuzzy logic model in an irrigated area

The quality of water for irrigation presents a fundamental challenge to the sustainability of agricultural practices. This study, conducted in the Ras Jbel (RJ) aquifer of Tunisia, aims to evaluate the suitability of groundwater for both drinking and irrigation purposes and to identify regions suitable for agriculture. In this research, various indices were employed, including water quality index (WQI), entropy water quality index (EWQI), and Fuzzy logic for irrigation water quality index (FLIWQ). The conventional irrigation indices, such as sodium adsorption ratio (SAR), magnesium hazard (MH), sodium percentage (Na%), Kelly's ratio (KR), permeability index (PI), residual sodium carbonate (RSC), and potential salinity (PS) were converted into fuzzy membership and produced dry and wet seasons maps. The dry and wet season fuzzy logic maps were used as input for final FLIWQI. Eighty-nine groundwater samples were collected from shallow wells and analyzed for major cations and anions. Most parameters exceeded the recommended values for drinking, according to both WHO and Tunisian standards, across all samples. The results of the WQI and EWQI indicated that, during the wet season, over 90% and 86% of the water samples were classified as poor and very poor quality, respectively, compared to 93.33% and 49% during the dry season. For IWQI, the wet season fuzzy logic map suggests 55.57% groundwater suitability, mainly in the central area. In the dry season, the map indicates 41.11% suitability for irrigation. The FLIWQI map confirms that the groundwater in the central part of the study area is suitable for agriculture. This study can help decision-makers initiate efficient water quality management plans in similar water-scarce environments worldwide.

Evaluation of Cropland Utilization Eco-Efficiency and Influencing Factors in Primary Grain-Producing Regions of China

Under the backdrop of the “double-carbon” target, the primary grain-producing regions in China are confronted with the tasks of mitigating pollution and carbon emissions and ensuring food security. This paper explores the eco-efficiency of cropland utilization and the factors influencing the primary grain-producing regions in China, utilizing panel data from 13 provinces spanning the period from 2000 to 2019. The analysis employs three models: the super-efficiency SBM model, the Malmquist index model, and the random-effect panel Tobit model. The findings suggest the following: (1) Although the eco-efficiency of cropland utilization in China’s primary grain-producing regions did not reach the production frontier during the period of 2000–2019, it exhibited a high level with an overall upward trend. The limiting factor inhibiting the growth of total factor productivity is lower technical efficiency. (2) There is evident spatial variation in the eco-efficiency of cropland utilization across China, displaying a dynamic evolution from northeast > western > central > eastern to northeast > western > eastern > central. Total factor productivity in each province demonstrates an upward trend, with the east > northeast > west > central ranking. (3) Regarding the influencing factors, the utilization of agricultural production chemicals exerts a negative influence, while the proportion of government financial input, labor input, and irrigation index have a positive impact.

Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

The evaluation of groundwater quality is crucial for irrigation purposes; however, due to financial constraints in developing countries, such evaluations suffer from insufficient sampling frequency, hindering comprehensive assessments. Therefore, associated with machine learning approaches and the irrigation water quality index (IWQI), this research aims to evaluate the groundwater quality in Naama, a region in southwest Algeria. Hydrochemical parameters (cations, anions, pH, and EC), qualitative indices (SAR,RSC,Na%,MH,and PI), as well as geospatial representations were used to determine the groundwater’s suitability for irrigation in the study area. In addition, efficient machine learning approaches for forecasting IWQI utilizing Extreme Gradient Boosting (XGBoost), Support vector regression (SVR), and K-Nearest Neighbours (KNN) models were implemented. In this research, 166 groundwater samples were used to calculate the irrigation index. The results showed that 42.18% of them were of excellent quality, 34.34% were of very good quality, 6.63% were good quality, 9.64% were satisfactory, and 4.21% were considered unsuitable for irrigation. On the other hand, results indicate that XGBoost excels in accuracy and stability, with a low RMSE (of 2.8272 and a high R of 0.9834. SVR with only four inputs (Ca2+, Mg2+, Na+, and K) demonstrates a notable predictive capability with a low RMSE of 2.6925 and a high R of 0.98738, while KNN showcases robust performance. The distinctions between these models have important implications for making informed decisions in agricultural water management and resource allocation within the region.

Hydrochemistry and Irrigation Quality of High-Altitude Lakes: A Case Study of the Ramaroshan Lake Complex, Nepal Himalayas

The Ramaroshan Lake Complex (RLC) in Sudurpaschim Province, Nepal, is a Himalayan lake cluster that holds significant ecological, economic, religious, and esthetic importance. This study aimed to provide a comprehensive characterization of the hydrochemical properties of water within the RLC and assess its suitability for irrigation purposes. A total of 38 water samples were collected from seven different lakes of the complex. The physicochemical parameters and major ions were then analyzed. The water samples from the RLC were alkaline, and based on total hardness, they ranged from soft to moderately hard categories. The presence of major ions included the following: Ca2+ > Na+ > Mg2+ > K+ > Fe3+ > NH4+ and HCO3− > Cl− > SO42− > NO3− > PO43−. The alkaline earth metals (Ca2+ and Mg2+) dominated the alkali metals (Na+ and K+) and weak acids (HCO3−) dominated the strong acids (Cl− and SO42−). The dominant hydrochemical facies of the lake water was a Ca-HCO3 type indicating a calcium carbonate type of lithology. Carbonate rock weathering was the most dominant process in influencing the hydrochemistry of the water. A high ratio of (Ca2++ Mg2+)/Tz+ and a lower ratio of (Na+ + K+)/Tz+ revealed the dominance of Ca2+ and Mg2+ resulting from carbonate weathering, with little contribution from silicate weathering. Different irrigation indices revealed the suitability of the RLC water for irrigation. The insights derived from this study are pivotal in safeguarding water quality and bolstering sustainability efforts. The study also furnishes foundational data crucial to an array of stakeholders including researchers and policymakers and significantly contributes to advancing water management strategies and fostering ecosystem conservation in the Himalayan freshwater lakes, particularly in the face of the overarching challenge posed by global climate change.

Deployment of entropy information theory in the Indian Sundarban region using hydrogeochemical parameters and GIS for assessment of irrigation suitability.

The evaluation of irrigation suitability plays a crucial role for the socio-economic development of the society, especially in the region of Sundarban. For sustainable agricultural practices, groundwater quality must be suitable for irrigation; otherwise, it can degrade soil and diminish crop yield. The entropy information theory, several irrigational indices, multivariate statistics, GIS, and geostatistics are used in this work to evaluate the geographical distribution and quality of groundwater in the Indian Sundarban region. In total, 33 groundwater samples were collected in 2018 (April and May), and they were evaluated for major cations, anions, as well as other parameters like electrical conductivity (EC), soluble sodium percentage (SSP), potential salinity (PS), total dissolved solids (TDS), Kelly ratio (KR), sodium absorption ratio (SAR), permeability index (PI), residual sodium carbonate (RSC), magnesium hazard (MH), and residual sodium bicarbonate (RSBC). The overall trend of the principal cations and anions is in the sequence of Na+ ≥ Mg2+ ≥ Ca2+ ≥ K2+ and HCO3- ≥ Cl- ≥ NO3- ≥ SO42- ≥ F-, respectively, whereas the spatial variation of %Na, SAR, RSBC, and MH demonstrate very poor irrigation water quality, and spatial variation of KR, RSC, SSP, PI, and PS signifies that the irrigation water quality is excellent to good. In order to identify the specific association and potential source of the dissolved chemical in the groundwater, statistical techniques like correlation and principal component analysis were also employed. The hydrochemical facies indicates that mixed type makes up the bulk (51.51%) of the water samples. Following the Wilcox plot, more than 75% of the water samples are good to doubtful; however, by the US salinity hazard map, roughly 60.60% of the samples had high salinity (C3-S1 zone). The EWQII reports that no samples fall into the very good (no restriction) category, whereas 30.30%, 30.30%, and 39.40% of the sample wells record good (low restriction), average (moderate restriction), and poor (severe restriction) irrigation water quality, respectively. Based on this study, the bulk of the groundwater samples taken from the study area are unsuitable for cultivation. The findings of this study will also help decision-makers develop adequate future plans for irrigation and groundwater resource management.

Evaluation of Tigris River Quality for Irrigation Purposes within Salah Al-Din province/Iraq

In arid and semi-arid regions around the world, water quality is essential in agricultural systems. The present study was evaluated Tigris River for suitability irrigation in Salah Al-Din province. Water samples were collected and analyzed for various Hydro-chemical parameters like EC, TDS, pH, total hardness (TH), Ca2+, Mg2+, CO32-, HCO3-, Cl-, SO42-, Na+, and K+, with different Irrigation indices such as Sodium percentage (Na%), Sodium adsorption ratio (SAR), Residual Sodium Carbonate (RSC), Permeability Index (PI), Kelly's Ratio (KR), Potential salinity (PS) and Hardness were also calculated. The Na%, SAR, RSC, PI, KR, PS and total hardness varied from 2.26 to 10.06 (mmol. l-1)1/2, 0.01 to 6.10 0 meq. ll-, 8.83 to 77.10%, 0.19 to 1.19 meq. ll, 1.00 to 15.16 meq. ll and 49.3 to 197.1 mg. L-1, respectively. Hence, the various parameters and indices results were concluded that the Tigris River quality was suitable to be used as a water source for irrigation. A strategic plan and regular monitoring are required to limit or reduce the pollution of the river water in order to improve the water quality in its natural condition.

Application of GIS-based machine learning algorithms for prediction of irrigational groundwater quality indices

Agriculture is considered one of the primary elements for socioeconomic stability in most parts of Sudan. Consequently, the irrigation water should be properly managed to achieve sustainable crop yield and soil fertility. This research aims to predict the irrigation indices of sodium adsorption ratio (SAR), sodium percentage (Na%), permeability index (PI), and potential salinity (PS) using innovative machine learning (ML) techniques, including K-nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and Gaussian process regression (GPR). Thirty-seven groundwater samples are collected and analyzed for twelve physiochemical parameters (TDS, pH, EC, TH, Ca+2, Mg+2, Na+, HCO3−, Cl, SO4−2, and NO3−) to assess the hydrochemical characteristics of groundwater and its suitability for irrigation purposes. The primary investigation indicated that the samples are dominated by Ca-Mg-HCO3 and Na-HCO3 water types resulted from groundwater recharge and ion exchange reactions. The observed irrigation indices of SAR, Na%, PI, and PS showed average values of 7, 42.5%, 64.7%, and 0.5, respectively. The ML modeling is based on the ion’s concentration as input and the observed values of the indices as output. The data is divided into two sets for training (70%) and validation (30%), and the models are validated using a 10-fold cross-validation technique. The models are tested with three statistical criteria, including mean square error (MSE), root means square error (RMSE), and correlation coefficient (R2). The SVR algorithm showed the best performance in predicting the irrigation indices, with the lowest RMSE value of 1.45 for SAR. The RMSE values for the other indices, Na%, PI, and PS, were 6.70, 7.10, and 0.55, respectively. The models were applied to digital predictive data in the Nile River area of Khartoum state, and the uncertainty of the maps was estimated by running the models 10 times iteratively. The standard deviation maps were generated to assess the model’s sensitivity to the data, and the uncertainty of the model can be used to identify areas where a denser sampling is needed to improve the accuracy of the irrigation indices estimates.

Assessment of hydrogeochemistry in groundwater using water quality index model and indices approaches

Groundwater resources around the world required periodic monitoring in order to ensure the safe and sustainable utilization for humans by keeping the good status of water quality. However, this could be a daunting task for developing countries due to the insufficient data in spatiotemporal resolution. Therefore, this research work aimed to assess groundwater quality in terms of drinking and irrigation purposes at the adjacent part of the Rooppur Nuclear Power Plant (RNPP) in Bangladesh. For the purposes of achieving the aim of this study, nine groundwater samples were collected seasonally (dry and wet season) and seventeen hydro-geochemical indicators were analyzed, including Temperature (Temp.), pH, electrical conductivity (EC), total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), total organic carbon (TOC), bicarbonate (HCO3−), chloride (Cl−), phosphate (PO43−), sulfate (SO42−), nitrite (NO2−), nitrate (NO3−), sodium (Na+), potassium (K+), calcium (Ca2+) and magnesium (Mg2+). The present study utilized the Canadian Council of Ministers of the Environment water quality index (CCME-WQI) model to assess water quality for drinking purposes. In addition, nine indices including EC, TDS, TH, sodium adsorption ratio (SAR), percent sodium (Na%), permeability index (PI), Kelley's ratio (KR), magnesium hazard ratio (MHR), soluble sodium percentage (SSP), and Residual sodium carbonate (RSC) were used in this research for assessing the water quality for irrigation purposes. The computed mean CCME-WQI score found higher during the dry season (ranges 48 to 74) than the wet season (ranges 40 to 65). Moreover, CCME-WQI model ranked groundwater quality between the “poor” and “marginal” categories during the wet season implying unsuitable water for human consumption. Like CCME-WQI model, majority of the irrigation index also demonstrated suitable water for crop cultivation during dry season. The findings of this research indicate that it requires additional care to improve the monitoring programme for protecting groundwater quality in the RNPP area. Insightful information from this study might be useful as baseline for national strategic planners in order to protect groundwater resources during the any emergencies associated with RNPP.

Human exposures to multiple water sources in the southwestern coastal region of Bangladesh: water quality, pollution sources, and preliminary health risks appraisals.

The coastal areas of Bangladesh have poor accessibility to fresh drinking water and the groundwater is not suitable for drinking, cooking, and other domestic uses due to high levels of salinity and potentially toxic elements. The current study focuses on understanding of the distribution of some physicochemical parameters (temperature, pH, EC, TDS, and salinity) and chemical elements (Fe, Mn, Zn, Ca, Mg, Na, K, Cu, Co, Pb, As, Cr, Cd, and Ni) with health perspective in drinking water from the southwestern coastal area of Bangladesh. The physicochemical properties of the water samples were examined with a multiparameter meter, while the elemental concentrations were analyzed using atomic absorption spectrometer. Water quality index (WQI) and irrigation indices were utilized to determine the drinking water quality and irrigation feasibility, respectively, whereas hazard quotients (HQs) and hazard index (HI) were used to assess the probable pathways and the associated potential risks to human health. The concentrations of some toxic elements in measured samples were relatively higher compared to drinking water guidelines, indicating that ground and surface water are not apt for drinking and/or domestic uses. The multivariate statistical approaches linked the source of the pollutants in the studied water body mostly to the geogenic origin including saline water intrusion. WQI values ranged from 18 to 430, reflecting excellent to unsuitable categories of water quality. The assessment of human health risks due to exposure to contaminated water demonstrated both carcinogenic and non-carcinogenic health risks in the exposed residents of the study area. Therefore, appropriate long-term coastal area management strategies should be adopted in the study region for environmental sustainability. The findings of this research will be supportive in understanding the actual situation of fresh drinking water in the area for policymakers, planners, and environmentalists to take effective necessary measures to ensure safe drinking water in the study area.

Fuzzy logic, geostatistics, and multiple linear models to evaluate irrigation metrics and their influencing factors in a drought-prone agricultural region

The quality of water used for irrigation is one of the major threats to maintaining the long-term sustainability of agricultural practices. Although some studies have addressed the suitability of irrigation water in different parts of Bangladesh, the irrigation water quality in the drought-prone region has yet to be thoroughly studied using integrated novel approaches. This study aims to assess the suitability of irrigation water in the drought-prone agricultural region of Bangladesh using traditional irrigation metrics such as sodium percentage (NA%), magnesium adsorption ratio (MAR), Kelley's ratio (KR), sodium adsorption ratio (SAR), total hardness (TH), permeability index (PI), and soluble sodium percentage (SSP), along with novel irrigation indices such as irrigation water quality index (IWQI) and fuzzy irrigation water quality index (FIWQI). Thirty-eight water samples were taken from tube wells, river systems, streamlets, and canals in agricultural areas, then analyzed for cations and anions. The multiple linear regression model predicted that SAR (0.66), KR (0.74), and PI (0.84) were the primary important elements influencing electrical conductivity (EC). Based on the IWQI, all water samples fall into the “suitable” category for irrigation. The FIWQI suggests that 75% of the groundwater and 100% of the surface water samples are excellent for irrigation. The semivariogram model indicates that most irrigation metrics have moderate to low spatial dependence, suggesting strong agricultural and rural influence. Redundancy analysis shows that Na+, Ca2+, Cl−, K+, and HCO3− in water increase with decreasing temperature. Surface water and some groundwater in the southwestern and southeastern parts are suitable for irrigation. The northern and central parts are less suitable for agriculture because of elevated K+ and Mg2+ levels. This study determines irrigation metrics for regional water management and pinpoints suitable areas in the drought-prone region, which provides a comprehensive understanding of sustainable water management and actionable steps for stakeholders and decision-makers.

Hydrogeochemical constraints on uranium contamination of groundwater for drinking water supplies and associated health risks

The groundwater quality of Punjab, India is of huge concern owing to the presence of various toxic contaminants that includes uranium and other trace elements. The focus of this study is to identify the hydrogeochemical processes responsible for uranium mobilization in groundwater and evaluation of groundwater quality in uranium contaminated zones for drinking, irrigation use along with associated health risks. The study was conducted using the water quality data obtained from 302 sampling locations covering the entire state of Punjab reported by Central Ground Water Board between 2019 and 2020. It was observed that around 50% of the samples were exceeding the drinking water acceptable limits for total alkalinity, magnesium, pH, and Total Dissolved Solids. Around 25% of the samples were above drinking water acceptable limit for uranium in groundwater. A comprehensive multiplicative water quality index (MED-WQI) well represented the consolidated perspective of all individual water quality parameters wherein 40 sampling locations in districts viz. Bathinda, Fazilka, Ferozepur, Jalandhar, Kapurthala, Ludhiana, Muktsar and Sangrur fall under heavily-polluted water quality class due to high levels of uranium, alkalinity, and hardness. The Piper plots and Gibbs diagram depicted that the majority of groundwater samples were characterized as Na+–HCO3−, Ca2+–Mg2+–HCO3− systems driven by rock-water interactions. Speciation analysis indicates that the dominant uranium species in groundwater of U-affected regions was primarily associated with calcium and alkalinity and exist in the form of CaUO2(CO3)32−, Ca2UO2(CO3)3(aq), UO2(CO3)34−, and UO2(OH)3−. Various irrigation indices depict that the groundwater in majority of the state, barring few districts in Malwa region, was suitable for irrigation. Further, a non-carcinogenic health risk assessment model showed association of high health risk with heavily-polluted water quality class. The results of this study would aid regulatory authorities in designing remedial strategies and frame policy actions toward groundwater quality improvement in the uranium-affected regions of Punjab.

Hydrochemistry, water quality and health risk assessment of streams in Bismil plain, an important agricultural area in southeast Türkiye

In this study, the water quality of Ambar, Kuruçay, Pamuk and Salat streams, which are the important tributaries of the Tigris River in the Bismil Plain (Diyarbakır, Türkiye) was assessed using 19 physicochemical parameters. Except for a few exceptions, all parameters in the water samples taken from the streams were below the drinking water limit values. Kuruçay Stream had significantly higher TOC, Na+, NO3−, NO2−, Cl− and SO42− levels and lower DO levels than other streams (p < 0.05) due to sewage water discharges, animal manure storage areas near the stream and irrigation return flows. In all streams, Ca–HCO3 was dominant water type. Gibbs diagram indicated that rock weathering is the major factor controlling the hydrochemistry of the streams. According to the results of water quality index (WQI), all sampling stations of Ambar, Pamuk and Salat streams and K1 station of Kuruçay Stream had “good” quality water for drinking purposes, while K2 station of Kuruçay Stream had “poor” quality water. Irrigation indices (permeability index, sodium percentage, magnesium hazard, residual sodium carbonate, Kelley's ratio, sodium adsorption ratio and potential salinity) revealed that all water samples taken from the streams were suitable for irrigation. The water samples from Ambar, Pamuk and Salat streams were in the C2S1 (medium salinity and low alkalinity) category, while the samples from Kuruçay Stream were in the C2S1 and C3S1 (high salinity and low alkalinity) categories. Both hazard quotient and hazard index values of NO3–N, NO2–N and F− for children and adults were found below 1, indicating that adverse health effects are not expected from exposure to these contaminants via water ingestion and dermal contact. The findings of this study showed that the water quality status of Kuruçay Stream is worse as it receives large amounts of irrigation return flows compared to other streams.

Groundwater quality in Zagora southeast of Morocco by using physicochemical analysis and geospatial techniques.

Groundwater in Morocco is restricted because of the semiarid to arid climatic conditions; it is under threat from organic and inorganic pollution. Furthermore, it is considered the only source of potable water as well as having different usages, making its quantitative and qualitative protection an urgent priority. The present study focused mainly on the anthropogenic impact on the natural resources and groundwater quality around Zagora city. Fifteen samples were collected from wells during rainy and dry seasons in 2years 2020-2021 and the analysis of the groundwater quality of studied stations. The suitability of the aquifer Fezouata was investigated using drinking and irrigation water quality indices. The results showed that sulfate and chloride are the dominant anions in the groundwater samples. While the mean abundance of major cations is Na + > Mg2+ > Ca2+ > K+, the sodium ion is dominant and K + is the least abundant. The physicochemical parameters show that conductivity, nitrate, and sulfate exceed the limit fixed by WHO.Hydrogeochemical plots indicate that 93% of samples belong to Na-Cl facies and only 7% are mixt Cl-Mg-Ca in 2021, the results are similar except for two samples which are 13% belong Cl-Mg-Ca.The Water Quality Index suggests that 28.55% are good quality water, 23.90-47.55% are poor and very poor quality water, respectively, and 40.24% are unsuitable in 2020. Furthermore, the WQI of the 2021 campaign showed that only 17.48% were considered good quality water and 38.94% (43.58%) were poor or very poor quality water, respectively. However, 33.21% are unsuitable. Based on irrigation indices, the majority of groundwater samples can be used for agricultural purposes, notably those of the upstream part of the study area.

Effects of Seawater Intrusion on the Groundwater Quality of Multi-Layered Aquifers in Eastern Saudi Arabia

The degradation of groundwater (GW) quality due to seawater intrusion (SWI) is a major water security issue in water-scarce regions. This study aims to delineate the impact of SWI on the GW quality of a multilayered aquifer system in the eastern coastal region of Saudi Arabia. The physical and chemical properties of the GW were determined via field investigations and laboratory analyses. Irrigation indices (electrical conductivity (EC), potential salinity (PS), sodium adsorption ratio (SAR), Na%, Kelly's ratio (KR), magnesium adsorption ratio (MAR), and permeability index (PI)) and a SWI index (fsea) were obtained to assess the suitability of GW for irrigation. K-mean clustering, correlation analysis, and principal component analysis (PCA) were used to determine the relationship between irrigation hazard indices and the degree of SWI. The tested GW samples were grouped into four clusters (C1, C2, C3, and C4), with average SWI degrees of 15%, 8%, 5%, and 2%, respectively. The results showed that the tested GW was unsuitable for irrigation due to salinity hazards. However, a noticeable increase in sodium and magnesium hazards was also observed. Moreover, increasing the degree of SWI (fsea) was associated with increasing salinity, sodium, and magnesium, with higher values observed in the GW samples in cluster C1, followed by clusters C2, C3, and C4. The correlation analysis and PCA results illustrated that the irrigation indices, including EC, PS, SAR, and MAR, were grouped with the SWI index (fsea), indicating the possibility of using them as primary irrigation indices to reflect the impact of SWI on GW quality in coastal regions. The results of this study will help guide decision-makers toward proper management practices for SWI mitigation and enhancing GW quality for irrigation.

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.