Data on groundwater quality, scaling potential and corrosiveness of water samples in Torbat-e-Heydariyeh rural drinking water resources, Khorasan-e-Razavi province, Iran

Dataset on the assessment of water quality of ground water in Kalingarayan Canal, Erode district, Tamil Nadu, India

This data article aimed to investigate the quality of ground water in Kalingarayan Canal for the analysis of pollution level, Tamil Nadu. In order to understand the pollution status of the canal, nine ground water samples (GW1- GW9) were collected from the downstream side of the canal during the period between January 2014 – December 2016. Nine stations were selected along the Kalingarayan Canal, and ground water samples were collected on a monthly basis from these stations. The parameters like pH, electrical conductivity (EC), total dissolved solids (TDS), chlorides, total hardness (TH) nitrates, sulphates, sodium, calcium and magnesium were analyzed to observe the current status of the groundwater quality. Also, the groundwater quality is expressed in terms of Water Quality index (WQI). The APHA method was applied to determine the physico chemical parameters of the water samples. From the investigation, WQI reflects a low quality of groundwater in sampling stations Kolathupalayam (GW3) and Perumparai (GW6) which is mainly contaminated with nitrate and the water is found to be very hard in nature. Also, it was observed that calcium and magnesium content in groundwater is very high at certain stations. Most of the groundwater from this place cannot be used for any kind of industrial processes and human consumption without proper treatment.

Data on estimation for sodium absorption ratio: Using artificial neural network and multiple linear regressions

In this article the data of the groundwater quality of Aras catchment area were investigated for estimating the sodium absorption ratio (SAR) in the years 2010–2014. The artificial neural network (ANN) is defined as a system of processor elements, called neurons, which create a network by a set of weights. In the present data article, a 3-layer MLP neural network including a hidden layer, an input layer and an output layer had been designed. The number of neurons in the input and output layers of network was considered to be 4 and 1, respectively, due to having four input variables (including: pH, sulfate, chloride and electrical conductivity (EC)) and only one output variable (sodium absorption ratio). The impact of pH, sulfate, chloride and EC were estimated to be 11.34%, 72.22%, 94% and 91%, respectively. ANN and multiple linear regression methods were used to estimate the rate of sodium absorption ratio of groundwater resources of the Aras catchment area. The data of both methods were compared with the model׳s performance evaluation criteria, namely root mean square error (RMSE), mean absolute error (%) and correlation coefficient. The data showed that ANN is a helpful and exact tool for predicting the amount SAR in groundwater resources of Aras catchment area and these results are not comparable with the results of multiple linear regressions.

Data on health risk assessment of fluoride in water distribution network of Iranshahr, Iran

The main of this data was determine the concentrations and health risks of fluoride in 66 drinking water samples collected from villages of the Iranshahr city, Sistan and Baluchestan Province in Iran. Fluoride concentration was measured by the standard SPADNS method. Data indicated that fluoride concentration in drinking water ranged from 0.25 to 1.72 mg L⁻¹ and average of fluoride concentration was 0.27 mg L⁻¹. The mean estimated daily intake (EDI) values for fluoride in different groups of infants, children, teenagers and adults were 0.0021, 0.0151, 0.0107 and 0.0086 mg/kg, respectively. Also, risk assessment data indicated that hazard quotient (HQ) value of groundwater samples is more than 1 in 6% of groundwater samples in age groups of children and teenagers.