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.

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.

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.

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.

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.

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.

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.

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.

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.

Successive addition of rice straw biochar enhances carbon accumulation in soil irrigated with saline or non-saline water

Biochar has been evaluated globally to improve soil fertility and mitigate climate change. However, the long-term effects of successive biochar application on carbon (C) accumulation in soil irrigated with saline versus non-saline water (canal water) has not been investigated. A field experiment was conducted to study the effects of rice straw biochar addition rates (0, 2, 4, 8 Mg ha^-1) on C storage in soil irrigated with water of different electrical conductivity [EC, dS m^-1]; 0.3 (non-saline canal irrigation water; CIW), 5 (saline irrigation water; SIW₅), 10 (SIW₁₀), and 15 (SIW₁₅) in a cotton-wheat system. Long-term irrigation with saline water of variable EC levels adversely affected soil functions, reducing above-ground biomass in cotton (12-48%) and wheat (5-27%). In contrast, plots irrigated with saline water but amended with rice straw biochar showed significant improvement in aboveground biomass (both in cotton and wheat), possibly due to its beneficial effects on soil properties such as soil EC, organic carbon, microbial population, water and nutrient availability, bulk density, soil aggregation, and proliferation of roots. Interestingly, the change in total organic C (TOC) stocks (8.5, 17, and 27.5 Mg C ha^-1) after 5 years were found to be almost double the amount of biochar C added (4.3, 8.6, 17.2 Mg ha^-1), indicating stabilization of belowground C inputs from the root biomass by the applied biochar and also possibly through its physical interaction with aggregates and minerals. Even though biochar application to saline water irrigated plots increased the contribution of plant-derived C to overall soil TOC stocks, it was still lower compared with canal water irrigated plots. The study conclusively established that the long-term stabilization of biogenic C through biochar has essential implications for mitigating climate change by accumulating additional C beyond the recalcitrant C contained in biochar.

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.

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.

Spatial water quality assessment of a mountain stream in northwestern India using multivariate statistical techniques

The water quality of Himalayan rivers and streams is deteriorating due to multitude of anthropogenic and natural influences and the changes are more pronounced in smaller hill basins with high human influx. Inadequate data and lack of monitoring further exacerbate the situation. The surface water quality of one such hill stream, Neeru was evaluated for potability and irrigation quality. The water quality parameters were analysed at 25 sampling stations over a stretch of 30 km across four seasons during a 2-year study from January 2014 to December 2015. The CCME Water Quality Index (WQI) indicated excellent water quality in upstream stations, while the midstream and a few downstream stations revealed varying degrees of impaired water quality. Although the irrigation quality of the stream was excellent, pollution levels near urban areas were rather high, necessitating considerable treatment for human consumption. The factor analysis yielded seven components with Eigenvalues greater than one that accounted for 83.656% of the variance and enabled the grouping of selected parameters based on comparable characteristics. The hydrogeochemical characteristics of the water samples indicated that they lie within the zone of rock dominance, while the trilinear diagram depicted the Ca-bicarbonate rich stream water. The study concluded that the upstream stations revealed pristine water quality, which significantly degraded towards human settlements. It thus calls for regular and long-term monitoring of the surface water quality to ensure the continuous and sustainable use of this important water body.

Assessment of ecohydrogeochemical status of freshwater Loktak Lake of Manipur, India

The present study has been carried out to assess the ecohydrogeochemical status of Loktak Lake, the largest freshwater lake in the Northeastern region of India, based on the water quality parameters, hydrogeochemistry, water quality indices (WQI) and trophic state index (TSI). The spatio-temporal variations of physicochemical parameters have been assessed, and it was found that parameters such as pH, turbidity, dissolved oxygen, biological oxygen demand, iron, fluoride and coliform concentrations in the water exceeded the permissible limits prescribed by the World Health Organization (WHO) and Bureau of Indian Standards (BIS) during both pre-monsoon (PM) and post-monsoon (PoM) seasons. The water hardness lies within the soft category, except for a few samples found to be moderately hard. WQI values of lake water ranged between 38.19 and 155.47 during PM and 39.48 and 432.26 during PoM. Based on the WQI classification during PM, 8.6% of the samples were in the unsuitable category, 14.3% very poor, 45.7% poor and 31.4% in the good category. During PoM, 22.9% of the samples were in the unsuitable category, 25.7% very poor, 31.4% poor and 20% in the good category. The irrigation water quality was evaluated using indices such as sodium percentage, sodium adsorption ratio, residual sodium carbonate, permeability index and Kelly's ratio, and the results indicated that the lake water could be used safely for agricultural purposes. The trophic state evaluation revealed an oligotrophic condition of the lake waters during PM (TSI 37.9) and a mesotrophic condition during PoM (TSI 46.9).

Evaluation of groundwater quality for irrigation water supply using multi-criteria decision-making techniques and GIS in an agroeconomic tract of Lower Ganga basin, India

Groundwater irrigation has evolved the monocropping cultivation pattern to multi-cropping, especially in many arid/semi-arid tracts globally. Irrigation practices with the groundwater of poor quality can limit the selection of the crop, reduce crop yields and degrade the soil quality. The present study has been undertaken to identify the hydrogeochemical phenomena of groundwater systems in the south-western Birbhum district, India and to analyze groundwater suitability for irrigation during the pre-and post-monsoon cycles by adopting the Irrigation Water Quality Index (IWQI) using Multivariate Factor Analysis along with some traditional methods viz. sodium adsorption ratio, sodium percentage, magnesium hazards, residual sodium bicarbonate (RSBC) and carbonate (RSC), Wilcox's and USSL diagrams, permeability index and Kelly's index. The hydrogeochemical analysis revealed that chemical weathering and evaporation are predominant in the aquifer systems. Groundwater quality reflected soil salinity, sodicity and magnesium hazards risks and water toxicity to the sensitive plants at 0-46.4% of the post-monsoon samples and 0-38.4% of the pre-monsoon samples based on the individual traditional methods whereas about 97.73-98.88% of the total area was classified as moderate to severely unsuitable for irrigation during both seasons when integrated multiple parameters using the IWQI method. Prolonged use of such groundwater for irrigation is susceptible to causing moderate to severe infiltration problems at a greater extent of the study area. The study recommends adaptation of salinity, sodicity and RSC/RSBC reduction procedures (e.g., the use of acid and gypsum amendments in the irrigation lands and through water blending) and advanced irrigation practices (viz. drips, sprinklers and micro irrigations) to prevent soil degradation and increase crops productivity. Adopting Managed Aquifer Recharge procedures as well as rainwater harvesting in the areas bearing unsuitable water quality can dilute the ionic concentrations of the groundwater facies which in turn will improve the groundwater quality for irrigation.

Groundwater quality assessment for irrigation by adopting new suitability plot and spatial analysis based on fuzzy logic technique

This study was focused on identifying the region suitable for agriculture-based, using new irrigation groundwater quality plot and its spatio-temporal variation with fuzzy logic technique in a geographic information system (GIS) platform. Six hundred and eighty groundwater samples were collected during pre, southwest, northeast, and post monsoon periods. A new ternary plot was also attempted to determine the irrigation suitability of water by considering four essential parameters such as sodium adsorption ratio (SAR), permeability index (PI), Sodium percentage (Na %), and electrical conductivity (EC). The derived ternary plot was the most beneficial over other available plots, as it incorporated four parameters, and it differs from the US Salinity Laboratory (USSL) plot, such that the groundwater with higher EC could also be used for irrigation purposes, depending on the Na%. The ternary plot revealed that the groundwater predominantly manifested good to moderate category during post, northeast, and southwest monsoons. The assessment with the amount of fertilizer used during the study period showed that the NPK fertilizers were effectively used for irrigation during monsoon periods. Spatial maps on EC, Kelly's ratio, Mg hazard, Na%, PI, potential salinity (PS), SAR, residual sodium carbonate (RSC), and soluble sodium percentage (SSP) were prepared for each season using fuzzy membership values, integrated for each season. A final suitability map derived by an overlay of all the seasonal outputs has identified that the groundwater in the western and the eastern part of the study area are suitable for agriculture. The study recommends cultivation of groundwater-dependent short-term crops, along the western and northern regions of the study area during the pre-monsoon season.

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.

Identifying influencing groundwater parameter on human health associate with irrigation indices using the Automatic Linear Model (ALM) in a semi-arid region in India

Quality of water for the purposes of irrigation is a serious threat to the sustainable development of the agriculture sector. The main objective of this study is to evaluate the suitability of groundwater for irrigation purposes using various irrigation indices such as: Sodium Absorption Ratio (SAR), Residual Sodium Carbonate (RSC), Percentage Sodium (%Na), Magnesium Hazards (MH), Permeability Index (PI), Potential Salinity (PS), Residual Sodium Bicarbonate (RBSC), Kelly's Ratio (KR), Synthetic Harmful Coefficient (K), and Exchangeable Sodium Percentage (ESP). A total of 30 samples were collected from the bore well of agricultural farmland and analysed for cations and anions. MH reveal that 53.33 % of samples exceed the permissible level. PS shows that 43.33 % of samples are marginally affected and 33.33 % of samples are unsuitable for use in irrigation. About 76 % of the groundwater samples were suitable for irrigation and the remainder require treatment before use. Automatic Linear Modelling (ALM) is used to predict the major influence parameter for MH and PS are RBSC, RSC and K value of groundwater. ALM shows that excess magnesium concentration and salinity are the primary factors that affect the suitability of groundwater for irrigation use. This integrated technique showed that water from approximately 25 % of the sample locations would require treatment before use. This study will improve the pattern of irrigation, identify sources of contamination and highlight the importance of organic fertilizers to develop and enhance the sustainable practices in the study region.

Treatment of Rose Oil Processing Effluent with Chlorella sp. Using Photobioreactor and Raceway

The integration of treatment of wastewater from agro-based industries with microalgae cultivation can reduce costs associated with cultivation while treating wastewater to meet the discharge limits for chemical quality of irrigation in agriculture and to obtain biofertilizers. Rose Oil Processing Effluent (ROPE) can be utilized as a growth medium for Chlorella sp. and thus can be used for biofertilizer production. The present study is aimed at determining the feasibility of the cultivation of Chlorella sp. in ROPE using a tubular photobioreactor with a capacity of 50 L and a raceway to treat ROPE while consuming less energy. The optimum mixing ratio ([ROPE/(ROPE + Bold Basal Medium (BBM)] × 100) was determined as 50% using 2-L Erlenmeyer flasks based on the COD removal efficiency. Better removal efficiencies with regard to COD, BOD₅, NH₄⁺-N, and NO₃^--N were obtained from the raceway compared to the tubular photobioreactor. The effluents from both systems met the chemical quality of irrigation water. The results of the biomasses harvested from both systems in macro and microelements revealed that they have a potential as a biofertilizer in agriculture. The energetic analysis of the ROPE treatment using the tubular photobioreactor and raceway showed that the raceway system had a better net energy ratio while consuming less energy and producing more energy during cultivation. Overall, the raceway appeared to be a better option to treat ROPE with production of biofertilizer and irrigation water quality while consuming less energy.

Microbial Load and Prevalence of Escherichia coli and Salmonella spp. in Macadamia Nut Production Systems

ABSTRACT

This study evaluated the potential impact of environmental factors and harvesting practices on the microbial load of macadamia nuts. Three farms located in primary macadamia nut production regions, the Mbombela (A), Barberton (B) and White River (C) areas in Mpumalanga Province, South Africa, were sampled over a 2-year period. A total of 264 irrigation water (54), soil (30), and macadamia nut (180) samples were collected and evaluated for microbial load. All water samples had mean Escherichia coli loads below 1,000 MPN/100 mL, which is the standard regulatory requirement for agricultural water considered fit for irrigation in South Africa. Mean total aerobic plate counts of nut-in-husk on-tree samples (3.91 log CFU/g; n = 60) were higher after harvesting (5.98 log CFU/g; n = 60) but were lower after dehusking (to 4.89 log CFU/g; n = 60) on nut-in-shell samples. Salmonella spp. were only detected in water samples from farm B (67%; n = 18) and farm C (15%; n = 18). Neither Listeria monocytogenes nor Salmonella spp. were detected in the soil samples. E. coli was only detected in 20% (n = 10) of soil samples collected from two farms (farms A and B). None of the E. coli isolated in this study was positive for the eae, stx1, and stx2 enterohemorrhagic E. coli virulence genes. This study provides basic data that can be used in the development of macadamia nut-specific hazard assessment tools within primary production environments.

HIGHLIGHTS

Groundwater hydro-geochemistry, quality, microbiology and human health risk assessment in semi-arid area of Rajasthan, India: a chemometric approach

The present investigation focused on groundwater hydro-geochemistry of Alsisar block of Jhunjhunu district, India, aims on evaluating the quality of groundwater for drinking and irrigation purposes and assessing the human health risk from ingestion of groundwater. The groundwater of Alsisar block is neutral to alkaline, brackish and very hard in nature. Total dissolved solids, total hardness, Na⁺, Mg²⁺, HCO₃^-, F^- and NO₃^- in majority of the groundwater samples were exceeding the World Health Organization and Bureau of Indian Standards recommended limits. The drinking water quality index ranged from 111.53 to 492.84. None of the sample belonged to excellent and good categories of drinking water quality. Fluoride varied from 0.018 to 4.176 mg L^-1, and nitrate varied from 0.34 to 520.66 mg L^-1 in groundwater. The non-carcinogenic risk assessment for children, men and women owing to ingestion of fluoride and nitrate-enriched groundwater indicates human health risks in the entire study area. Irrigation with groundwater of Alsisar block is liable to cause salinity and magnesium hazard to agricultural crops grown in the area. Source apportionment using principal component analysis suggests the geogenic origin of fluoride and anthropogenic origin of nitrate. Na⁺-Mg²⁺-Cl^- followed by Na⁺-Mg²⁺-HCO₃^- are the predominant hydrochemical facies in the groundwater of Alsisar block. Silicate rock weathering, ion exchange and evaporation are the predominating processes governing ionic concentrations in the groundwater. Biochemical and molecular tests demonstrated the presence of Brevibacillus borstelensis strain DSM 6347 16s rRNA and Bacillus paramycoides strain MCCC 1A04098 16s rRNA in the groundwater of the area.

Free living amoebae isolation in irrigation waters and soils of an insular arid agroecosystem

The use of freshwater in agricultural systems represents a high percentage of total water consumption worldwide. Therefore, alternative sources of water for irrigation will need to be developed, particularly in arid and semi-arid areas, in order to meet the growing demand for food in the future. The use of recycled wastewater (RWW), brackish water (BW) or desalinated brackish water (DBW) are among the different non-conventional water resources proposed. However, it is necessary to evaluate the health risks for humans and animals associated with the microbiological load of these waters. Protozoa such as free-living amoebae (FLA) are considered an emerging group of opportunistic pathogens capable to cause several diseases in humans (e.g. cutaneous and ocular infections, lung, bone or adrenal gland conditions or fatal encephalitis). In the present study we evaluate FLA presence in three different irrigation water qualities (RWW, BW and DBW) and its survival in irrigated agricultural soils of an extremely arid insular ecosystem (Fuerteventura, Canary Islands, Spain). Samples were cultured on 2% Non-Nutrient Agar (NNA) plates covered with a thin layer of heat killed E. coli and checked daily for the presence of FLA. According to the prevalence of FLA, Vermamoeba vermiformis (53,8%), Acanthamoeba spp. (30,8%), Vahlkampfia avara (7,7%) and Naegleria australiensis (7,7%) were detected in the analysed water samples, while Acanthamoeba (83,3%), Cercozoa spp. (8,3%) and Vahlkampfia orchilla (8,3%) were isolated in irrigated soils. Only Acanthamoeba strains were isolated in no irrigated soils used as control, evidencing the capability of these protozoa to resist environmental harsh conditions. Additionally, all analysed water sources and the irrigated soils presented growth of several pathogenic bacteria. Therefore, the coexistence in water and soils of pathogenic bacteria and FLA, can mean an increased risk of infection in agroecosystems.

Multivariate assessment of spatial and temporal variations in irrigation water quality in Lake Uluabat watershed of Turkey

Irrigation water quality has important implications on salinity, ion toxicity, production cost, and crop failures. There is a need for a comprehensive analysis of spatial and temporal dynamics in parameters at a watershed scale. This information is critical for irrigation management in agricultural production. The Lake Uluabat watershed is a significant agricultural area of Turkey, which is studied using monitored water data. Multivariate assessment is performed using cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA), and factor analysis (FA) to evaluate temporal and spatial variations in water quality in the watershed. The data is processed by clustering, reducing data dimensionality, delineating indicator parameters, assessing source identification, and evaluating temporal changes and spatial patterns. The results show that the most representative discriminant parameters had more than 90.98% validity in both temporal and spatial analyses. Runoff rate (Q) and water temperature (WT) were identified in the temporal study, while spatial analysis showed bicarbonate (HCO₃^-), sulfate (SO₄^2-), and boron (B³⁺) as indicators. Salinity, sodicity, boron hazard, and alkalinity affect both spatial and temporal water quality patterns in the watershed. It is observed that continued use of poor-quality irrigation water can adversely affect agriculture and soil health in a watershed. Spatio-temporal relationships in parameters will be useful in sustainable irrigation management and farm planning for improving crop productivity and soil health.

Combined use of municipal solid waste biochar and bacterial biosorbent synergistically decreases Cd(II) and Pb(II) concentration in edible tissue of forage maize irrigated with heavy metal-spiked water

A pot experiment was carried out to evaluate the effect of a municipal solid waste (MSW) biochar and a bacterial strain on the forage maize growth and the concentration of lead (Pb) and cadmium (Cd) in the edible tissue of maize irrigated with water contaminated with Cd (5 mg L⁻¹) and Pb (100 mg L⁻¹). Experimental treatments included (i) bacterial strain at two levels: no bacterial strain and Enterobacter cloacae R7; (ii) MSW biochar at three levels: 0, 1, and 3% (w/w); and (iii) irrigation water quality at five levels: plants irrigated with 100% freshwater (FW), plants irrigated with 75%FW + 25% contaminated water (CW), plants irrigated with 50%FW + 50% CW, plants irrigated with 25%FW + 75% CW, and plants irrigated with 100% CW. The effect of various treatments on maize growth indices and concentration of Pb(II) and Cd(II) in the plant was significant at 5% level. The concentration of these metals in the shoot of plants irrigated with 75 and 100% CW was higher than the permissible limits for Cd(II) and Pb(II) in livestock feed. However, the concentration of these metals in the shoot of the plants irrigated with 25 and 50% CW was lower than the permissible limit for this use. In this study, the combined application of 3%biochar and E. cloacae R7 had a significant effect on increased root dry weight (ranging from 29 to 33%), shoot dry weight (ranging from 32 to 43%) and bacterial root colonization (ranging from 33 to 53%) and on reduced concentration of Pb (ranging from 78 to 80%) and Cd (ranging from 72 to 76%) of the shoot of maize plant (edible tissues used by livestock), which was below the permissible limits for livestock feed, compared to corresponding controls. According to the results of this study, to reduce the concentration of the heavy metals in forage maize shoot (below the permissible limits for livestock feed), it is suggested using heavy metal–contaminated water either in combination with freshwater (50 or 75% FW) or in combination with biochar and bacterial biosorbent, averting human/animal health risk.

Sediment re-suspension as a potential mechanism for viral and bacterial contaminants

Pathogenic enteric viruses and bacteria tend to occur in higher concentrations and survive longer in aquatic sediments than suspended in the water column. Re-suspension of these organisms can result in a significant degradation of overlying water quality. Additionally, the re-suspension of microbial pathogens in artificial irrigation canals could endanger the consumption of fresh and ready-to-eat produce. Irrigation water has been implicated in numerous fresh produce outbreaks over the last 30 years. This study aimed to quantify the proportions of bacterial and viral re-suspension from sediment in a recirculating flume with varying velocities. MS2 coliphage and Escherichia coli were found to re-suspend at rates that were not significantly different, despite organism size differences. However, E. coli re-suspension rates from sand and clay were significantly different. This suggests that likely sediment-associated particles were recovered with the organisms attached. Similar re-suspension rates are hypothesized to be due to the dynamics of sediment transport, rather than that of the organisms themselves. This study also indicated that the re-suspension of sediment at very low velocities (e.g., less than 10 cm/s), could impact the microbiological quality of the overlaying water. Results from this study conclude that sediment could be a viable mechanism for irrigation water contamination.

Groundwater quality assessment in an industrial hotspot through interdisciplinary techniques

Dependency on groundwater has increased due to unprecedented growth of industries as well as settlements. Therefore, assessment of groundwater quality to determine its impact on human and environment has become essential. The major objective of this study was to frame a methodology for complete assessment of groundwater quality in a highly industrialized area comprising of iron, steel, fertilizer, cement, chemical, heavy machinery manufacturing, thermal power, coal mining, and allied industries. Physico-chemical parameters of water samples were analyzed from strategic locations during pre- and post-monsoon seasons. The primary analysis through the water quality index showed 50% of the sampling locations in pre-monsoon and 65% in post-monsoon seasons have very poor quality. Hence, the health risk calculated through hazard index indicates that the water is unsafe for drinking. Chemical indices such as sodium percentage, sodium adsorption ratio, residual sodium carbonate, permeability index, and magnesium hazard suggest that the water can be used for irrigation. High corrosivity ratio at 90% sampling locations specifies its unsuitability for use in industrial production. Factor analysis and other statistical methods justified that the pollution of groundwater was attributed to geogenic, as well as anthropogenic, activities. This research demonstrates the usefulness of interdisciplinary techniques for complete assessment of groundwater quality and representation of complex data set into a presentable and understandable form for proper communication with public, regulatory authorities, as well as policy makers, responsible for water management.

Assessment of surface water quality using water quality index and multivariate statistical analyses in Saraydüzü Dam Lake, Turkey

In this study, observations were carried out in the surface waters of Saraydüzü Dam Lake within Sinop provincial borders for 1 year to determine water quality. The basic 28 variables used to determine water quality were measured monthly at six stations. Taking into account the World Health Organization's drinking water standards, the water quality index (WQI) and Turkey's Ministry of Forestry and Water Affairs Surface Water Quality Regulations (SWQR) were used in determining the water quality. In addition, irrigation water quality was examined. For this, sodium absorption rates (SAR), sodium percentage and residual sodium carbonate (RSC) values were calculated. WQI values in the lake were found to be between 17.62 and 29.88. Water quality parameters did not exceed the recommended limit values in all months and at all stations. According to these values, the Saraydüzü Dam Lake water belongs to the 'very good' class in terms of drinking water quality. The results obtained showed that there were no nitrogen or phosphate inputs that could harm the ecosystem in the lake and that there were no low/insufficient ambient oxygen conditions resulting from excessive oxygen consumption during the degradation process of organic matter. All water quality parametres are well below the permissible limits except some heavy metals according to SWQR. Cu, Zn and Fe were found to exceed the limit values. The water quality of irrigation water was found to be good in terms of SAR and sodium percentage, whereas RSC was observed to have varying qualities during the year and not be suitable for irrigation in some months. According to results of factor analysis (FA), pH, temperature, electrical conductivity, suspended solid matter (SSM), biological oxygen demand (BOD), total hardness (TH),total alkalinity (TA), calcium, nitrate, ammonium, mercury and dissolved oxygen are the main variables responsible for the processes in the ecosystem.

Hydrogeochemical investigations and groundwater quality assessment of Torbat-Zaveh plain, Khorasan Razavi, Iran

Hydrogeochemical investigations of groundwater in Torbat-Zaveh plain have been carried out to assess the water quality for drinking and irrigation purposes. In this study, 190 groundwater samples were collected and analyzed for physicochemical parameters and major ion concentrations. The abundance of major cations and anions was in the following order: Na(+) > Mg(2+) > Ca(2+) > K(+), and Cl(-) > [Formula: see text] > [Formula: see text] > [Formula: see text]. As a result, alkaline element (Na(+)) exceeds alkaline earth elements (Mg(2+) and Ca(2+)), and strong acids (Cl(-) and [Formula: see text]) dominate weak acids ([Formula: see text] and [Formula: see text]) in majority of the groundwater samples. Statistical analyses including Spearman correlation coefficients and factor analysis display good correlation between physicochemical parameters (EC, TDS and TH) and Na(+), Mg(2+), Ca(2+), Cl(-) and [Formula: see text]. The results display that rock-weathering interactions and ion-exchange processes play important role in controlling groundwater chemistry. Saturation index values also indicate that water chemistry is significantly affected by carbonate minerals such as calcite, aragonite and dolomite. US Salinity Laboratory(USSL) and Wilcox diagrams together with permeability index values reveal that most of the groundwater samples are suitable for irrigation purpose. However, in some regions, the water samples do not indicate required irrigational quality.