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 appraisal and zone mapping for agriculture utilities in Wadi Fatima, Saudi Arabia using water quality indices, boron and trace metals.

Groundwater quality in Wadi Fatimah is evaluated and demarcated for agriculture utilities using comprehensive approaches namely, international standards, agricultural water quality (AWQ) indices, irrigation water quality index (IWQI), and trace metals. Groundwater samples were collected (n = 59) and analysed for EC, pH, major and minor ions and trace metals. According to FAO recommendations, 42% of samples (EC > 3000 µS/cm) are inappropriate for agricultural uses. AWQ indices including salinity hazard, Kelly's ratio and Na% show that 50%, 19% and 37% of samples, respectively, are unsuitable for agricultural uses. USSL classification reveals that groundwater is preferable only for high-permeability soils and salt-tolerant crops. IWQI suggests that 88% of samples are moderately usable for agriculture. The interrelationship between water salinity and crop yield justified that 73%, 59%, 51% and 25% of samples are desirable to yield 90% in date palm trees, sorghum, rice and citrus fruits, respectively. Groundwater is appropriate for date palm trees except in downstream regions. Boron concentration suggests that 52%, 81% and 92% of samples are suitable for sensitive, semi-tolerant and tolerant crops, respectively. Groundwater in the central part (suitable for sensitive crops), central and upstream regions (semi-tolerant crops) and all regions except downstream (tolerant crops) are suitable for cultivation. Trace metals contents illustrate that 36%, 34%, 22%, 8%, 5% and 100% of samples are inappropriate for agriculture due to high concentrations of Cr, Cu, Ni, V, Mn and Mo, respectively in the groundwater. Further, AWQ indices, IWQI, USSL classifications and trace metals ensure that groundwater in the downstream, and a few pockets in the upstream are unfit for agricultural uses. This study recommends that groundwater in this basin is more suitable for tolerant crops (ie. date palm, sorghum) followed by semi-tolerant and sensitive crops.

Water reuse of treated domestic wastewater in agriculture: Effects on tomato plants, soil nutrient availability and microbial community structure.

The reuse of treated wastewater (TWW) in agriculture for crop irrigation is desirable. Crop responses to irrigation with TWW depend on the characteristics of TWW and on intrinsic and extrinsic soil properties. The aim of this study was to assess the response of tomato (Solanum lycopersicum L.) cultivated in five different soils to irrigation with TWW, compared to tap water (TAP) and an inorganic NPK solution (IFW). In addition, since soil microbiota play many important roles in plant growth, a metataxonomic analysis was performed to reveal the prokaryotic community structures of TAP, TWW and IFW treated soil, respectively. A 56-days pot experiment was carried out. Plant biometric parameters, and chemical, biochemical and microbiological properties of different soils were investigated. Shoot and root dry and fresh weights, as well as plant height, were the highest in plants irrigated with IFW followed by those irrigated with TWW, and finally with TAP water. Plant biometric parameters were positively affected by soil total organic carbon (TOC) and nitrogen (TN). Electrical conductivity was increased by TWW and IFW, being such an increase proportional to clay and TOC. Soil available P was not affected by TWW, whereas mineral N increased following their application. Total microbial biomass, as well as, main microbial groups were positively affected by TOC and TN, and increased according to the following order: IFW > TWW > TAP. However, the fungi-to-bacteria ratio was lowered in soil irrigated with TWW because of its adverse effect on fungi. The germicidal effect of sodium hypochlorite on soil microorganisms was affected by soil pH. Nutrients supplied by TWW are not sufficient to meet the whole nutrients requirement of tomato, thus integration by fertilization is required. Bacteria were more stimulated than fungi by TWW, thus leading to a lower fungi-to-bacteria ratio. Interestingly, IFW and TWW treatment led to an increased abundance of Proteobacteria and Acidobacteria phyla and Balneimonas, Rubrobacter, and Steroidobacter genera. This soil microbiota structure modulation paralleled a general decrement of fungi versus bacteria abundance ratio, the increment of electrical conductivity and nitrogen content of soil and an improvement of tomato growth. Finally, the potential adverse effect of TWW added with sodium chloride on soil microorganisms depends on soil pH.

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).

Reflecting on changes in the drinking and irrigation water quality of rivers Beas, Satluj and confluence waters.

Prevention and control of water pollution for maintaining and restoring the wholesomeness of rivers are unavoidable. The current water quality approach of designated best use has some limitations such as it is non-integrative and inflexible with regard to the consideration of variables and does not provide a separate rating scale for a given designated use. We thus used water quality index approach proposed by the Canadian Council of Ministers of the Environment (CCME WQI) to evaluate and develop a separate rating system for drinking and irrigation purposes of rivers Beas, Satluj and their confluence water of the Indian Punjab using information collected over 4 years (2016 to 2019). River Beas exhibited better water quality compared to river Satluj for irrigation as well as for drinking. The overall drinking water quality index (DWQI) for Beas was marginal (45.5), whereas it was poor for Satluj (37.7) and confluence waters (40.1). The spatial variation in DWQI was greater for Satluj compared to Beas and confluence waters reflecting the effect of dumping of untreated industrial and domestic waste waters. Variables such as Total coliform (T. coli), dissolved oxygen (DO), turbidity and biological oxygen demand (BOD) contributed to the deterioration of DWQI. The irrigation water quality index (IWQI) was good for Beas (86), marginal for Satluj (60.1) and fair for confluence waters (71.2). Faecal coliform (F. coli), Kelly ratio (KR) and %Na contributed to the deterioration of IWQI. Calcium-magnesium-bicarbonate (Ca-Mg-HCO3) was the dominant water type in Beas and confluence waters, whereas for Satluj, in addition to Ca-Mg-HCO3, sodium-potassium-chloride-sulphate and mixed water types were also prevalent. The river waters witnessed salinity hazard but did not pose sodicity hazard except at a few locations of Satluj. The study indicates the need to take location specific measures for improving river water quality for drinking as well as irrigation purposes. The current status of water quality calls for an urgent need to formulate stringent policy regulations to maintain the surface water quality.

Hydrochemical analysis and groundwater suitability for drinking and irrigation in an arid agricultural area of the Northwest China.

Groundwater is the foremost water source in the arid and semiarid regions of Northwest China. Assessing groundwater's drinking and irrigation quality is essential for protecting these valuable groundwater resources. In this study, a total of 24 confined groundwater samples and 54 phreatic groundwater samples were collected in the southern and central Ningxia area for hydrochemical analysis and quality assessment. The hydrochemical results revealed that hydrochemical types of phreatic and confined groundwater consistently belonged to Na-SO4-Cl and Na-Mg-SO4-Cl types. The driving forces of groundwater chemistry were determined by gypsum dissolution, silicate dissolution, and positive cation exchange for phreatic and confined aquifers. The entropy-weighted water quality index (EWQI) and irrigation water quality index (IWQI) showed that the drinking water quality and irrigation quality were better in phreatic groundwater than in confined groundwater due to the Neogene-Paleogene groundwater system recharge and strong evaporation. Measures such as controlling groundwater extraction and optimizing well placement need to be implemented. The achievements would be helpful for groundwater management and protection in agricultural areas under semi-arid and arid climates.

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 targeted water purification programmes should be implemented to protect the public health and sustainability of water resources in Baluchistan.

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.

Integrating GIS and irrigation water quality index approaches for identifying groundwater irrigation potential zones in Central Gujarat, India.

In this study, groundwater irrigation potential zones are identified by measuring an integrated irrigation water quality index using a GIS platform in the Vishwamitri River Basin (VRB) region, Gujarat, India. Classification of different irrigation potential zones is about the collective paraphernalia of thematic maps, namely, electrical conductivity (EC), sodium adsorption ratio (SAR), Na+, Cl-, and HCO3-, which principally regulate the groundwater quality for irrigation. The EC, SAR, and chloride values are > 3000 µS/cm, > 9 meq/L, and > 10 meq/L, respectively, indicating 98.78%, 41.78%, and 84.88% of the total area, respectively, belong to groundwater both hazardous and unsuitable for irrigation. Similarly, 39.55% of the total area is unsuitable for irrigation due to its high Na+ concentration. The permeability, porosity, and infiltration risk usually appear when excessive sodium ions reduce the rate of flow of irrigation water which moves in the lower soil zones. Similarly, high bicarbonate and chloride concentrations have been denoted to hamper the development of several crops causing symptoms analogous to lime-induced chlorosis due to toxic effects of the salt. The final irrigation potential map is marked by four levels, such as "most suitable," "moderately suitable," "moderately unsuitable," and "unsuitable for irrigation." Approximately, 56.69% of the total VRB region are covered with unsuitable for irrigation purposes. In such areas, irrigation must be avoided in usual conditions, and the halophytes, namely, Khati palak (Atriplex stocksii), Lamb's quarters (Chenopodium album), and Rudravanti (Cressa cretica) should be recommended with high soil permeability and proper drainage facilities. The soil must be highly permeable for groundwaters with high salinity and sodicity. Based on this study, policymakers and environmental managers can estimate the resource implications for drinking and irrigation.

QMRA of Ralstonia solanacearum in potato cultivation: Risks associated with irrigation water recycled through managed aquifer recharge.

Agricultural aquifer storage recovery and transfer (ASTR) stores excess fresh water for later reuse in irrigation. Moreover, water quality improves because chemical pollutants and pathogens will be removed by degradation and attachment to the aquifer material. The source water may contain the bacterial plant pathogen Ralstonia solanacearum which causes plant infections and high yield losses. We used quantitative microbial risk assessment (QMRA) to investigate the removal of R. solanacearum during ASTR to predict infection risks of potato plants after irrigation with the recovered water. Laboratory experiments analyzed the ASTR treatment by investigating the bacterial die-off in the water phase and the removal by attachment to the aquifer sediment. Die-off in the water phase depends on the residence time and ranged between 1.3 and 2.7 log10 after 10 or 60 days water storage, respectively. A subpopulation of the bacteria persisted for a prolonged time at low concentrations which may pose a risk if the water is recovered too early. However, the natural aquifer sand filtration proofed to be highly effective in removing R. solanacearum by attachment which depends on the distance between injection and abstraction well. The high removal by attachment alone (18 log10 after 1 m) would reduce bacterial concentrations to negligible numbers. Upscaling to longer soil passages is discussed in the paper. Infection risks of potato plants were calculated using a dose-response model and ASTR treatment resulted in negligible infection risks of a single plant, but also when simulating the irrigation of a 5 ha potato field. This is the first QMRA that analyzed an agricultural ASTR and the fate of a plant pathogen focusing on plant health. QMRA is a useful (water) management tool to evaluate the treatment steps of water reclamation technologies with the aim to provide safe irrigation water and reduce risks disseminating plant diseases.

Water quality and health risk assessment of heavy metals in groundwater of Ranbir Singh Pura tehsil of Jammu and Kashmir, India.

Groundwater is one of the important sources available for drinking, agricultural, domestic, and various other purposes in the study area. Study area is having agricultural importance and is famous for Basmati rice production in the world. In order to assess water suitability for irrigation and drinking purposes, 25 sampling sites were selected and water samples were collected from handpumps, borewells and motors from May 2022 to June 2022. Fifteen physico-chemical parameters and water quality index (WQI) was calculated to assess the drinking water suitability. The results obtained then compared with the BIS (2012) and WHO drinking water guidelines. For irrigation water suitability, irrigation water quality index (IWQI) and other indices were calculated. Heavy metal health risk assessment was also evaluated using target hazard quotient (THQ), carcinogenic risks (CR), non-carcinogenic risks, heavy metal pollution index (HPI), etc. Study found 60% of water samples under poor category of WQI. All water samples were found suitable for irrigation purposes according to different indices except for permeability index for which only 32% samples were found suitable. IWQI classifies 52%, 32%, and 12% of water samples under moderate, low, and no restriction category respectively. Groundwater of the study area found to be contaminated with copper (Cu), iron (Fe), lead (Pb), and chromium (Cr) while low contamination of zinc (Zn) and arsenic (As) was found according to heavy metal evaluation index (HEI). High contamination of chromium (HPI= 9740.8) and lead (HPI=188) was recorded as per HPI. HQ value for men, women, and children in case of zinc were found safe while HQ values for copper and lead in all population groups were found at risk. Overall, the study area was found highly contaminated with the lead, copper, and chromium concentrations. Thus, study recommends regular monitoring of the groundwater of study area as well as treatment before using this water for drinking purposes.

Identifying factors affecting irrigation metrics in the Haor basin using integrated Shannon's entropy, fuzzy logic and automatic linear model.

The sustainability of agricultural practices is seriously threatened by the quality of water used for irrigation. This paper aims to evaluate the suitability of irrigation water and identify the region suitable for agricultural use in the Haor basin of Bangladesh using conventional irrigation indices such as sodium adsorption ratio (SAR), percent sodium (Na%), magnesium hazard ratio (MHR), permeability index (PI), and Kelly's ratio (KR), as well as novel irrigation indices such as, Shannon's entropy index for irrigation water quality (EWQ) and fuzzy logic index for irrigation water quality (FIWQI). The main influences of groundwater and surface water parameters on irrigation indices were predicted using automatic linear modeling (ALM). Forty water samples were collected from shallow tube wells, rivers, canals, ponds, and drainage systems within agricultural land sampled and analyzed for cations and anions. SAR and KR show that 52.5% and 60% of the samples exceeded the allowable level, respectively, indicating that they were unsuitable for irrigation. According to EWQI, about 55% of the analyzed samples were of good quality, while 45% were of medium quality. ALM predicted that KR (0.98), Na% (0.87), and MHR (0.14) were the main significant factors affecting SAR and KR. ALM shows that elevated sodium, magnesium, and calcium are the most important factors affecting irrigation water suitability. The EWQI and FIWQI integrated models showed that water from nearly 30% of the sampling sites would need treatment before use. A new suitability map created by overlaying all parameters showed that surface water and some groundwater in the western and southwestern portions are suitable for agriculture. The north-central part is unsuitable for irrigation due to excessive sodium and magnesium levels. This paper will highlight the irrigation pattern for regional water resource use, identify new suitable regions, and improve sustainable agricultural practices in the Haor basin.

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

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

Identifying the factors controlling surface water and groundwater chemical characteristics and irrigation suitability in the Yarkant River Basin, northwest China.

This study investigated the geochemical processes and the suitability for irrigation purposes of surface water and groundwater in the Yarkant River Basin, northwest China. The results showed that the surface water was moderately alkaline and neutral to slightly alkaline in the groundwater. The mean values for most ions in the groundwater were higher than those in the surface water. Geochemical method and hydrogen and oxygen isotope revealed that hydrochemical were mainly affected by dissolved evaporite minerals, ion exchange processes, and anthropogenic activities. Still, the geochemical processes of in surface water and groundwater were different. Additionally, three and four factors were extracted by factor analyses of surface water and groundwater, respectively, which distinguished the hydrochemical from natural origins and anthropogenic activities in more detail. The surface water and some groundwater in the south of the study area were suitable for irrigation processes. However, many groundwaters in the north were unsuitable for irrigation, because of their high sodium levels and salinity. The study results provide a theoretical basis for the sustainable utilization of regional water resources.

Impact of COVID-19 lockdown on the water quality of the Damodar River, a tributary of the Ganga River in West Bengal.

The COVID-19 (SARS-CoV-2) pandemic is wreaking havoc on the planet, yet control of waste materials comforted the ecosystem during the lockdown restricting human activities. Damodar is the most important tributary of the lower Ganga River in West Bengal. It flows through an industrially developed, agriculturally flourished populated area. Different methods are applied to identify the changing pattern of water quality during the lockdown. BOD graph shows an increase in pollution levels in residential areas but a sharp decline in coliform levels in urban residential sites. The National Sanitation Foundation Water Quality Index (NSFWQI) shows the same pattern of water quality throughout the course. Irrigation suitability of water is examined using sodium percentage (%Na), sodium absorption ratio (SAR), potential salinity (PS), magnesium absorption ratio (MAR), and Kelly's ratio (KR). Mujhermana (received maximum pollutants from industries and residential areas) station shows a decrease in ions concentration and subsequent improvement in agriculture water quality during the COVID-19 period. According to Kelly's ratio, the water at this sample site is unfit for agricultural use; however, the water quality improved and became acceptable for cultivation during the lockdown period. Cluster analysis is used to understand the similar pollution concentration of eleven sampling stations in different periods. Mujhermana site makes a separate cluster due to its high pollution load compared to other sampling sites before the COVID-19. But during the lockdown period, this site was clustered with the most petite contaminated sites. Supplementary Information: The online version contains supplementary material available at 10.1007/s40899-022-00790-2.

Assessment of impacts of industrial effluents on physico-chemical and microbiological qualities of irrigation water of the Fez Rriver, Morocco.

Global water consumption has grown twice as fast as the population. Wastewater is therefore a valuable and renewable source and provides additional water for priority uses. Wastewater can also be a source of pollution; thus, its physico-chemical and biological compositions can present major risks to the environment and human health. The objective of this study was to assess the status of irrigation waters in terms of salinization, accumulation of metallic elements, and microbiological contamination by parasites and pathogenic bacteria. The study focused on the surface water of Oued Fès used for irrigation located downstream of the industrial zone of Doukkarat and upstream of the industrial zone of Ain Noukbi (wastewater) before the confluence with the Oued Sebou, as well as on the treated wastewater of the wastewater treatment plant. The physico-chemical and microbiological analyses were carried out in two periods: summer and winter. Metals were analyzed by ICP-AES. The chemical and bacteriological quality of the wastewater and treated wastewater was found to be poor. These were characterized by organic pollution, including biodegradable pollutants, while upstream the organic residues were not biodegradables. COD, BOD5, Kjeldahl nitrogen, as well as chloride ion (Cl-) are above the standard values. The highest concentrations of Cd, at 850 µg/l, Cu, at 690 µg/l and Mn, at 470 µg/l, largely exceed the international standards and requirements. In addition to fecal contamination, characterized by total coliforms and thermo-tolerant coliforms, other pathogens were present, including helminth eggs, both in the wastewater and in the treated wastewater. Other pathogens, such as Vibrio cholera, were found at all three sites whether in winter or summer, with the exception of the downstream of Oued Fez in winter. As for Salmonella, it was present in treated wastewater during the winter only. The water used for irrigation upstream of Oued Fez and the treated wastewater have poor to very poor quality. Therefore, for a better use of these waters, it is necessary to ensure their regular treatment in order to minimize the impacts on the environment and human health.

Evaluation and prediction of irrigation water quality of an agricultural district, SE Nigeria: an integrated heuristic GIS-based and machine learning approach.

Poor irrigation water quality can mar agricultural productivity. Traditional assessment of irrigation water quality usually requires the computation of various conventional quality parameters, which is often time-consuming and associated with errors during sub-index computation. To overcome this limitation, it becomes critical, therefore, to have a visual assessment of the irrigation water quality and identify the most influential water quality parameters for accurate prediction, management, and sustainability of irrigation water quality. Therefore, in this study, the overlay weighted sum technique was used to generate the irrigation water quality (IWQ) map of the area. The map revealed that 29.2% of the area is suitable for irrigation (low restriction), 41.7% is moderately suitable (moderate restriction); and 29.1% is unsuitable (high restriction), with the irrigation water quality declining towards the central-southeastern direction. Multilayer perceptron artificial neural networks (MLP-ANNs) and multiple linear regression models (MLR) were integrated and validated to predict the IWQ parameters using Cl-, HCO3- SO42-, NO3-, Ca2+, Mg2+, Na+, K+, pH, EC, TH, and TDS as input variables, and MAR, SAR, PI, KR, SSP, and PS as output variables. The two models showed high-performance accuracy based on the results of the coefficient of determination (R2 = 0.513-0.983). Low modeling errors were observed from the results of the sum of square errors (SOSE), relative errors (RE), adjusted R-square (R2adj), and residual plots, further confirming the efficacy of the two models; although the MLP-ANNs showed higher prediction accuracy for R2. Based on the sensitivity analysis of the MLP-ANN model, HCO3, pH, SO4, EC, and Cl were identified to have the greatest influence on the irrigation water quality of the area. This study has shown that the integration of GIS and machine learning can serve as rapid decision-making tools for proper planning and enhanced agricultural productivity.

Geospatial assessment of groundwater quality using entropy-based irrigation water quality index and heavy metal pollution indices.

Groundwater contamination has become a serious environmental threat throughout the world in the era of Anthropocene. Thus, the present study examined the groundwater quality for irrigation purposes based on the entropy method and heavy metal pollution indices. To compute the entropy-based groundwater irrigation quality index (EIWQI), physicochemical parameters such as pH, chloride (Cl-) and nitrate (NO3-), irrigation indices including electrical conductivity (EC), sodium absorption ratio (SAR), sodium percentage (%Na), soluble sodium percentage (SSP), residual sodium carbonate (RSC), magnesium hazard (MH), Kelley's ration (KR), permeability index (PI) and heavy metals such as manganese (Mn), iron (Fe), zinc (Zn) and arsenic (As) have been employed for the 37 sample wells of the Damodar fan delta (DFD), India, which is a semi-critical agriculture-dominated region. Shannon's entropy method has been used to assign the weights of the different parameters for constructing the EIWQI. The results portray a spatial variation of the irrigation water quality in the DFD. The EIWQI revealed that 27.03%, 59.46%, 8.11%, 2.7% and 2.7% of the sample wells, respectively, contain excellent, good, moderate, poor and very poor quality of irrigation water. On the other hand, heavy metal pollution indices (modified degree of contamination, pollution load index, Nemerow index and modified heavy metal pollution index) show that 15-20% of sample wells of the DFD are contaminated by heavy metal pollution. The pockets of pollution are concentrated in the southwestern, northeastern and central parts of the DFD. The study found that the spatial variation in groundwater quality is controlled by the higher sodium concentration, carbonate weathering and expansion of agricultural and urban-industrial areas.

Prediction of irrigation water suitability using geospatial computing approach: a case study of Agartala city, India.

An increase in population expansion, urban sprawling environment, and climate change has resulted in increased food demand, water scarcity, environmental pollution, and mismanagement of water resources. Groundwater, i.e., one of the most precious and mined natural resources is used to address a variety of environmental demands. Among all, irrigation is one of the leading consumers of groundwater. Various natural heterogeneities and anthropogenic activities have impacted the groundwater quality. As a result, monitoring groundwater quality and determining its suitability are critical for the sustainable long-term management of groundwater resources. In this study, groundwater samples from 35 different sampling stations were collected and tested for various parameters associated with irrigation water quality. Hybrid MCDM (fuzzy-AHP) method was used to determine the groundwater suitability for irrigation purposes. The suitability map obtained using spatial overlay analysis was classified into low, moderate, and high irrigation water suitability zones. Along with suitability analysis, various regression-based machine learning models such as multiple linear regression (MLR), random forest (RF), and artificial neural network (ANN) were used and compared to predict irrigation water suitability. Results depicted that the ANN model with the highest R2 value of 0.990 and RMSE value near to zero (0) has outperformed all other models. The present methodology could be found useful to predict irrigation water suitability in the region where regular sampling and analysis are quite challenging.

Effects of COVID-19 era on a subtropical river basin in Bangladesh: Heavy metal(loid)s distribution, sources and probable human health risks.

The COVID-19 era has profoundly affected everyday human life, the environment, and freshwater ecosystems worldwide. Despite the numerous influences, a strict COVID-19 lockdown might improve the surface water quality and thus provide an unprecedented opportunity to restore the degraded freshwater resource. Therefore, we intend to investigate the spatiotemporal water quality, sources, and preliminary health risks of heavy metal(loid)s in the Karatoya River basin (KRB), a tropical urban river in Bangladesh. Seventy water samples were collected from 35 stations in KRB in 2019 and 2022 during the dry season. The results showed that the concentrations of Ni, Cu, Zn, Pb, Cd, and Cr were significantly reduced by 89.3-99.7 % during the post-lockdown period (p < 0.05). However, pH, Fe, Mn, and As concentrations increased due to the rise of urban waste and the usage of disinfectants during the post-lockdown phase. In the post-lockdown phase, the heavy metal pollution index, heavy metal evaluation index, and Nemerow's pollution index values lessened by 8.58 %, 42.86 %, and 22.86 %, respectively. Besides, the irrigation water quality indices also improved by 59 %-62 %. The total hazard index values increased by 24 % (children) and 22 % (adults) due to the rise in Mn and As concentrations during the lockdown. In comparison, total carcinogenic risk values were reduced by 54 % (children) and 53 % (adults) in the post-lockdown. We found no significant changes in river flow, rainfall, or land cover near the river from the pre to post-lockdown phase. The results of semivariogram models have demonstrated that most attributes have weak spatial dependence, indicating restricted industrial and agricultural effluents during the lockdown, significantly improving river water quality. Our study confirms that the lockdown provides a unique opportunity for the remarkable improvement of degraded freshwater resources. Long-term management policies and regular monitoring should reduce river pollution and clean surface water.