The high amount of Electrical Conductivity (EC) in the groundwater is one of the major negative Geo-environmental problems which has a considerable effect on the quality of drinking water. To address this challenging problem we proposed an intelligent Machine Learning (ML) based approach to predict EC in urban areas. We applied the deep learning technique as one of the most applicable ML techniques with high capabilities for intelligent predictions. Five different deep neural networks (Net 1 to Net 5) were developed in this study and their reliability to predict EC with an emphasis on different settings of inputs, features, functions, and the number of hidden layers was evaluated. The achieved results showed that deep neural networks can predict EC parameters using minimum and economic input parameters. Results showed parameters Cl and SO4 with a high range of correlation and pH with a low range of Pearson correlation properties are influential parameters to be used as the input of neural networks. Activation function Relu, optimization function Adam with a learning rate of 0.0005 and loss function Mean Squared Error with the minimum of two hidden dense layers from Keras laboratory of Tensor Flow developed an efficient and fast network to predict the EC parameter in urban areas. Maximum epochs for developed networks were defined up to 2000 iterations while epochs are reducible up to 200 to drive minimum loss function outcome. The maximum training and testing R2 for developed networks was 0.99 in both the training and testing parts.

The present study investigates groundwater quality in terms of heavy metals level and microbial contamination as well as the impact of bleaching powder on microbial load of groundwater samples in close proximity to a surface water body inside selected areas of Ibadan Nigeria. To do so, it collects nine water samples from three boreholes and six hand-dug wells from six locations, namely Eleyele, Wofun-Olodo, Oluyole Industrial Estate, Ogunpa, Olorunsogo, and Ojoo, keeping them in 750 mL plastic bottles. The samples are then divided by two, giving a total amount of 18 samples, with 3 and 6 duplicates apiece being treated with bleaching powder and the rest remaining untreated. Both sample sets have been analysed for water quality parameters such as pH, biochemical oxygen demand, and chemical oxygen demand, assessed using standard methods. The coliform count has been determined, using the pour plate method while heavy metal has been set by means of Atomic Absorption Spectrophotometer (AAS) after nitric acid digestion. Results show that the pH ranges within 6.0-6.5, BOD within 1.67-4.33mg of O2/L, and COD  within 2.93-9.43, while heavy metal concentration is from 0.013 to 0.047 mg/L for lead, ND to 0.023 mg/L for chromium, and ND to 0.010 for cadmium. What is more, the coliform count in the samples is between 0.00 and 913.33 CFU/mL. Most of the samples exceed the WHO limits for heavy metals in drinking water, having significant levels of microbial contamination. Bleaching powder treatment alleviated the level of pollution to varying degrees; therefore, constant monitoring of groundwater source and treatment before drinking is of utmost importance.

Groundwater is a major source of water for domestic, industrial, and agricultural sectors in many countries. The main objective of this research was to provide an overview of present groundwater quality using parameters such as calcium, magnesium, sodium, chloride, sulfate, pH, and electrical conductivity (EC) in the Mehran plain, Ilam province using GIS and geostatistical techniques. A total of 23 deep and semi-profound wells were selected based on the classified randomized sampling method. The sampling locations were obtained by GPS. Plastic containers were used for the collection of water samples. These samples were transferred to the laboratory for analyzing water quality parameters. Statistical characteristics, qualitative data interpolation, and zoning were investigated using SPSS 20 ،GS+5.3 and ArcGIS10.1. Kolmogorov–Smirnov test were used to test data normality. In order to normalize parameters, logarithm, and 1/x were used for sulfate, EC, cation, and anion. Then the variogram analysis was performed to select the appropriate model. Results showed that co-kriging is the best method for cation and anion, whereas local polynomial interpolation is suitable for sulfate. The results of the interpolation of groundwater quality factors showed that there is approximately good adaption among groundwater factors and geomorphology and topology of the region. Because of inappropriate irrigation system, the highest concentration is in the northwest and western parts of the region, where there is the minimum height and maximum agricultural land. Growth of arable land and agricultural activities has caused increasing concentrations of studied elements, especially EC.

Groundwater quality regarding major anions and cations in the Birjand Plain located in the largest desert in Eastern Iran was monitored in this study. Fifteen boreholes were considered as sampling stations and the parameters pH, TDS, EC and major anions and cations were measured in groundwater samples. The dominant groundwater types can be introduced as sodium-chloride and magnesium-sulphate. The majority of samples were within the not-suitable category for drinking uses. Regarding agricultural use, around 80 and 50 per cent of samples indicated a very high salinity hazard and a very high sodium alkali hazard, respectively. Spatial distribution of salinity was also monitored within the study area. If the study area was considered to be a semicircle, the centre appeared to be the least polluted area, while towards the peripheral surroundings, an increasing behaviour was observed. Intrusion of salt water from eastern and western parts of the study area caused severe groundwater degradation. The relatively better quality of groundwater in southern areas may be attributed to a chain of mountains located along south of the study area. The prevention of uncontrolled groundwater withdrawal must be regarded to cease the salinization trend and to prepare the required infrastructure for implementing the artificial recharge projects.

A student-centric research education program with the active participation of undergraduate students is initiated. The aim is to imbibe ―responsible citizenship behavior‖ in them so that each member becomes conscious and well trained to take up environmental-related issues and challenges for long-term sustainability of the ecosystem. In this work, we report spectrophotometer-based estimation of hexavalent chromium (57-268 gL-1) and lead (34–158 gL-1) concentrations in different surface waters and groundwater samples in and around the city of Hyderabad, India. Our results indicate that the studied surface water bodies and aquifers are contaminated to variable degrees and pose a serious threat to the ecosystem. In view of low geochemical baseline values for chromium and lead, the origin of heavy metal pollution is inferred to be anthropogenic, mainly originating from industrial effluents. The toxicological data are integrated with health data for risk assessment and impending health hazard. Finally, the novelty of this student-centric research program is highlighted.

This study was undertaken to evaluate concentration of Uranium (U) in the drinking water of the Tonk district of Rajasthan (India). The main objective of the study is to determine the distribution of Uranium concentration and the geochemical behavior of Uranium in pre-monsoon (PRM) and post-monsoon (POM) drinking water samples. Uranium was measured by LED fluorimeter. Total 318 drinking water samples were collected for both seasons. It is observed that the water quality of all the samples is within the limits prescribed by WHO (30 µg/L) except a few, and can be used for domestic purposes. The Uranium concentration was found to be in the range 0.21 to 173.72 µg/L with a mean value of 8.58 µg/L in pre-monsoon and 0.21 to 162.34 µg/L with a mean value of 11.22 µg/L in post-monsoon samples. The geochemistry of the study area shows rock-water interaction. The order of average anionic concentration is found to be HCO3 – > Cl – > SO4 2– > NO3 –. Although no definite trend of seasonal variation in the concentration of U was observed, large samples have higher Uranium concentrations in post-monsoon than pre-monsoon.

A comprehensive investigation was engaged to determine the spatial distribution of Uranium (U) and the consequential chemical and radiological health risk associated due to the consumption of groundwater containing U, in Panchkula district. A well-accepted technique of fluorescence of U estimation in an aqueous medium was employed having a detection limit of 0.50 µgL-1. The chemo-radiological health risk and water quality index was computed using a standard equation of concerned agencies to determine the suitability for human health. The concentration of U was observed to vary from 1.70 – 12.28 µgL-1 with the mean value of 5.89 µgL-1 The concentration of U was far below the standard prescribed limits as per World Health Organisation, Atomic Energy Regulatory Board, and United Nation Environmental Protection Agency. Except  nitrate and total alkalinity in few samples, all water quality paramters were within the recommended limit of BIS. The annual effective dose (AED), excess cancer risk (ECR), and lifetime average daily dose (LADD) indicated no potential health issue due to the consumption of groundwater of studied locations. The correlation was computed between U and various macro-anions and cations present in water samples. U was observed to have a significant weak positive correlation with total dissolved solids (TDS), electrical conductivity (EC), and salinity.

This study derives an analytical solution of a one-dimensional (1-D) Advection-Dispersion Equation (ADE) for solute transport with two contaminant sources incorporating the source term. Groundwater velocity is considered as a linear function of space while the dispersion as a nth power of velocity and analytical solutions are obtained for , and . The solution is derived using the Generalized Integral Transform Technique (GITT) with a new regular Sturm-Liouville Problem (SLP). Analytical solutions are compared with numerical solutions obtained in MATLAB pedpe solver and are found to be in good agreement. The obtained solutions are illustrated for linear combination of exponential input distribution and its particular cases. The dispersion coefficient and temporal variation of the source term on the solute distribution are demonstrated graphically for the set of input data based on similar data available in the literature. As an illustration, model predictions are used to estimate the time histories of the radiological doses of uranium at different distances from the sources boundary in order to understand the potential radiological impact on the general public for such problem.

The article presents the results of a study of heavy metals in snow and groundwater within the industrially developed Arkhangelsk agglomeration, which is the largest among urban formations in the Arctic zone of Russia. This article describes the results of research on the territories of three suburban community garden plots used by residents of the cities of the Arkhangelsk, Severodvinsk and Novodvinsk agglomeration for recreation, growing fruits and vegetables, picking wild berries and mushrooms, and short-term residence. In groundwater samples taken from wells, the average concentrations of heavy metals decrease in the following order: Fe > Mn > Zn > Cr > Ni > Cu > Ti > V > Pb > U > As > Co > Mo > Sb > Cd. A comparison of metal concentrations in groundwater with WHO and SanPiN standards showed that only Fe and Mn exceeded the permissible limits, for the rest of the studied metals, the concentrations were significantly below the permissible limits. The study of heavy metals in the snow showed a similar order of decrease in concentrations to groundwater and total concentrations of soluble metal fractions. This fact indicates the migration of heavy metals into groundwater after the spring snowmelt and the fact the main source of groundwater pollution is the atmospheric channel. According to the values of the total areal pollution of the snow cover with heavy metals, the most polluted are suburban garden plots in the area of the Arkhangelsk city – 216.91 mg/m2. The results of the principal component analysis showed that the main sources of snow cover pollution with heavy metals in the suburban areas of the Arkhangelsk agglomeration were thermal power plants, machine-building and metallurgical plants, a solid waste landfill, and vehicles. The calculation of the heavy metal pollution index for water did not reveal a significant anthropogenic impact. However, the indices assessing the amount of metals (heavy metal evaluation index), toxicity (heavy metal toxicity load), non-carcinogenic risk (hazard index), and carcinogenic risk indicate a high level of heavy metal pollution of the studied waters, as well as the unsuitability of groundwater and melted snow as drinking water. Metals such as Fe, Mn, Ni, Cu, and Pb make the greatest contribution to the quality indices of the studied waters.

This work aims to assess the Water Quality Index (WQI) of the groundwater-based public drinking water supply system of Kamrup District (Rural) of Assam, India. For assessing WQI, water samples have been collected, both raw water and treated water, from seventy-eight public drinking water supply projects over the district for comprehensive physicochemical analysis. The WQI was calculated based on the weightage derived from the literature survey and based on the doctors’ weightage. The derived WQI showed that the water quality falls from poor to very poor quality. However, the concentration of the water quality parameters except Iron, Fluoride, and Manganese are within the permissible limit in all the water supply projects. It shows that the WQI calculated based on the weightage derived, as stated above, is not displaying the actual water quality of the supplied water. As such, a modified method is proposed to calculate the WQI of the supplied water considering the permissible limit of the parameters in deriving the weightage for the parameters. The WQI values calculated using the modified method falls in the range of good water quality to poor water quality and shows the true water quality of the supplied water. The statistical analysis of the water quality parameters and WQI shows that the WQI has a very high correlation with Manganese with a coefficient of correlation value of 0.86, followed by 0.4 with Chloride and 0.34 with Fluoride.