Deciphering pollution vulnerability zones of River Yamuna in relation to existing land use land cover in Panipat, Haryana, India

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.

A multivariate approach to assessing the water quality of the Bamako reach of the Niger River in Mali as irrigation water

Agricultural production in the Bamako region has been raised, and its output quality has been questionable due to the discharge of wastewater into the Niger River. This study assessed the Niger River water body variations for irrigation application temporally and spatially. Thirteen parameters, potential of hydrogen, electrical conductivity, nitrate, total dissolved solids, phosphate, sulfate, chloride, ammonium, calcium, magnesium, potassium, sodium, and bicarbonate, were analyzed at the 15 sampling locations. Parameters examination indicated that most pollutants had higher concentrations over the high-flow phase than in the low-flow period. All parameters were within the Food and Agriculture Organization's recommended values levels. Irrigation variables, sodium adsorption ratio, sodium percentage, soluble sodium percentage, residual sodium bicarbonate, Kelly's ration, permeability index, total hardness, and potential salinity showed the water samples' convenience for irrigation. However, the magnesium hazard concentration exceeded the recommended values levels. Besides, the chloroalkaline indices indicated a trend of degradation that should be addressed. Therefore, a river management plan and regular irrigation water quality monitoring are needed to reduce water hardness in Bamako. The Niger River's sustainable management process must be thrived on all actors' participation. A scientific assessment will be conducted using appropriate methods to identify pollution sources in Bamako. The results of this study will serve as a cornerstone for future investigations concerning the quality of surface water, which is essential for irrigation purposes. PRACTITIONER POINTS: Human activities affected the Niger River water bodies in Bamako city. Quantitative and qualitative assessments reveal the pollution status and trend of the Niger River. The water quality trend is better in the low-flow season, which is an ideal period for vegetable production in Bamako. Most multivariate approaches indicated that the Niger River water is healthy for irrigation purposes. Magnesium hazard exceeded the standard levels, and the chloroalkaline indices indicated a trend of the Niger River water quality deterioration.

New insights into pollution source analysis using receptor models in the upper Yangtze river basin: Effects of land use on source identification and apportionment

To effectively control pollution and improve water quality, it is essential to accurately analyze the potential pollution sources in rivers. The study proposes a hypothesis that land use can influence the identification and apportionment of pollution sources and tested it in two areas with different types of water pollution and land use. The redundancy analysis (RDA) results showed that the response mechanisms of water quality to land use differed among regions. In both regions, the results indicated that the water quality response relationship to land use provided important objective evidence for pollution source identification, and the RDA tool optimized the procedure of source analysis for receptor models. Positive matrix decomposition (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models identified five and four pollution sources along with their corresponding characteristic parameters. PMF attributed agricultural nonpoint sources (23.8%) and domestic wastewater (32.7%) as the major sources in regions 1 and 2, respectively, while APCS-MLR identified mixed sources in both regions. In terms of model performance parameters, PMF demonstrated better-fit coefficients (R²) than APCS-MLR and had a lower error rate and proportion of unidentified sources. The results show that considering the effect of land use in the source analysis can overcome the subjectivity of the receptor model and improve the accuracy of pollution source identification and apportionment. The results of the study can help managers clarify the priorities of pollution prevention and control, and provide a new methodology for water environment management in similar watersheds.

Multivariate statistical and hydrochemical approaches for evaluation of groundwater quality in north Bahri city-Sudan

Groundwater has recently been considered one of the primary sources of water supply in Sudan. However, groundwater quality is continuously degraded due to overexploitation and long-term agricultural operations. The fossilized Cretaceous Nubian sandstone is the principal aquifer in the study area. This research aims to determine the major factors influencing groundwater quality and detect the suitability of groundwater for drinking and irrigation purposes by integrating hydrochemical and multivariate statistical methods. Hydrochemical plots such as Piper, Chadha, and Durov diagrams were applied to detect the groundwater facies and hydrochemical processes controlling the groundwater quality. They indicated Ca–Mg–HCO₃ water type as a dominant groundwater facies followed by Na–HCO₃ and Na–Cl types. Gibbs plots suggested that the dissolution of the minerals is the main factor influencing the water quality. The results of the Gibbs plot were further interpreted using saturation indices (SI). The SI values indicated that aragonite, calcite, and dolomite precipitated respectively in 58.33%, 75%, and 75% of groundwater samples. Multivariate statistical analyses, including Pearson's correlation analysis, hierarchical cluster analysis (HCA), and principal component analyses (PCA), were jointly employed to identify the structure of water quality data and deduce the main factors controlling groundwater quality. The statistical analysis revealed the effect of the physical and human-induced activities as the main factors influencing groundwater chemistry. These factors are rock-water interaction, agricultural practice, and organic contamination from septic tanks. Further, the suitability of groundwater for irrigation is determined using sodium adsorption ratio (SAR) and sodium percent (Na⁺%) indices. They carefully indicated that 75% of the groundwater samples in the study area are excellent for irrigation except for some sample location where the salinity hazard is stimulated by ion exchange. This integrated approach was effective in calibrating water quality assessment methodologies. The current research concluded that the implication of a groundwater quality monitoring scheme is crucial to ensure water supply sustainability in north Bahri city.

Physicochemical and biological analysis of river Yamuna at Palla station from 2009 to 2019

Yamuna is one of the main tributaries of the river Ganga and passes through Delhi, the national capital of India. In the last few years, it is considered one of the most polluted rivers of India. We carried out the analysis for the physiochemical and biological conditions of the river Yamuna based on measurements acquired at Palla station, Delhi during 2009–19. For our analysis, we considered various physicochemical and biological parameters (Dissolved Oxygen (DO) Saturation, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Alkalinity, Total Dissolved Solids (TDS), and Total Coliform. The water stats of river Yamuna at Palla station were matched with Water Standards of India, United Nations Economic Commission for Europe (UNECE), and World Health Organization (WHO). Maximum changes are observed in DO saturation and total coliform, while BOD and COD values are also seen higher than the upper limits. Total alkalinity rarely meets the minimum standards. TDS is found to be satisfactory as per the standard limit. The river quality falls under Class D or E (IS2296), Class III or IV (UNECE), and fails to fulfill WHO standards for water. After spending more than 130 million USD for the establishment of a large number of effluent treatment plants, sewage treatment plants, and common effluent treatment plants, increasing discharges of untreated sewage, partially treated industrial effluents and reduced discharge of freshwater from Hathnikund are causing deterioration in water quality and no major improvements are seen in water quality of river Yamuna.