Sustainable development analyses to evaluate groundwater quality and quantity management

Evaluation of groundwater quality for drinking water using a quality index in Abyi Adi, Tigrai, Northern Ethiopia

High quality, safe and sufficient drinking water is essential for public health and well-being. However, the war on Tigrai damaged the water sources of communities and pose people to health problems. Therefore, the aim of the present study was to assess the quality of water of the town of Abyi Adi, Tigrai, Northern Ethiopia using physicochemical and biological parameters and water quality indices. A total of 36 water samples were collected from four major water sources. The physicochemical and biological parameters were determined using standard analytical procedures for water analysis. The mean values of electrical conductivity and pH ranged from 273.63 to 881.27 μS/cm and 6.68 to 7.42, respectively. Moreover, the experimental results of major cations (Na+ = 3.70-14.77 mg/L and Ca2+ = 8.50-15.77 mg/L), and anions (HCO3 - = 21.52-40.77 mg/L, Cl- = 13.56-40.29 mg/L, NO3 - = 0.14-0.25 mg/L, NO2 - = 0.24-0.76 mg/L and PO4 3- = 0.34-1.32 mg/L) were recorded below the World Health Organization (WHO) permissible limits set for drinking water. The water quality index (WQI) that is determined using a weighted arithmetic water quality index method (WAWQIM) was also found in the range of 5.3-37.2. Subsequently, all groundwater sources except Adibakla are classified as excellent or "A" rating. However, the total coliform of Maylomin and Chiny water sources were found to be 6.33 MPN/100 mL and 3.67 MPN/100 mL, respectively. Both are higher than the WHO permissible limit set for drinking water. Considering the susceptibility of groundwater to pollution and its impact to human health, regular monitoring and supervision should be performed to keep the water safe for drinking. Accordingly, chlorination water treatment process is recommended to provide safe drinking water.

A cumulative-risk assessment method based on an artificial neural network model for the water environment

To analyze the cumulative risks to the water environment, the backpropagation artificial neural network (BP-ANN), a self-adapting algorithm, was proposed in this study. A new comprehensive indicator of cumulative risks was formed by combining the water risk assessment tool proposed by the World Wide Fund for Nature or World Wildlife Fund (WWF), Deutsche Investitions und Entwicklungsgesellschaft mbH (DEG), and the cumulative environmental risk assessment system proposed by the US Environmental Protection Agency (USEPA). Eleven training algorithms were selected and optimized based on the mean square error (MSE) of prediction results. Data concerning evaluating indicators and cumulative risk indexes of the Liao River collected from 2005 to 2017 in the cities of Tieling, Shenyang, and Panjin, China, were used as input and output data to train, validate, and test the BP-ANN. Levenberg Marquardt backpropagation was the most accurate algorithm, with an MSE of 3.33 × 10^-6. After optimization, there were six hidden layers in the model. The correlation coefficient of the BP-ANN with LM exceeded 80%. These findings suggest that the BP-ANN model is applicable to prediction of cumulative risks to the water environment. The model was sensitive to the number of wastewater treatment facilities and the wastewater treatment rate along the river. Based on the sensitivity analysis, the contributing factors can be controlled to reduce the cumulative risk.