Hydrology
Shahrokh Norallahi; A. Asghari Moghaddam; Fijani Elham; Rahim Barzegar
Abstract
In recent decades, due to growth of population and qancequently increasing demand for drinking, agriculture and industry purposes has led to consider the groundwater as the most important resource of water in the area. Therefore, it is necessary to pay attention to the quality of the groundwater in the ...
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In recent decades, due to growth of population and qancequently increasing demand for drinking, agriculture and industry purposes has led to consider the groundwater as the most important resource of water in the area. Therefore, it is necessary to pay attention to the quality of the groundwater in the area, along with its quantity. The objectives of this study are to investigate the possible origin of some heavy metals in the groundwater of Mashgin-Shahr plain using multivariate statistical methods including cluster analysis and factor analysis along with correlation coefficient as well as identification of factors affecting groundwater quality in the area. For this purpose, 25 groundwater samples were collected in October 2016, and measured with respect to pH, electrical conductivity, major (calcium, magnesium, sodium, potassium, chloride, sulfate, carbonate, and bicarbonate) and minor (nitrate, fluoride and silica) ions and some heavy metals/metalloid such as iron, manganese, aluminum, zinc, chromium, copper, cadmium, lead and arsenic. The analyzes show that processes such as weathering and dissolution of evaporatic and silicate formations, ion exchange and agricultural activities are effective on the groundwater quality of the area. The results of multivariate analysis show that most of the heavy are originated from volcanic formations in the area and salinity and acidity play an important role in releasing them into the groundwater. Factor analysis indicates that geogenic processes with a total of 79.9 % of variance and anthropogenic factors with a total of 6.6 % of variance control the groundwater chemistry.
A Asghari Moghaddam; E Fijani; A Nadiri
Abstract
Aquifer vulnerability assessment to define critical zones of pollution risk is an important method for groundwater resource management. By applying the DRASTIC model in this study, groundwater vulnerability in the Maragheh-Bonab Plain aquifer was evaluated. The DRASTIC model uses seven environmental ...
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Aquifer vulnerability assessment to define critical zones of pollution risk is an important method for groundwater resource management. By applying the DRASTIC model in this study, groundwater vulnerability in the Maragheh-Bonab Plain aquifer was evaluated. The DRASTIC model uses seven environmental parameters (Depth to water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity) as seven layer in GIS media and finally a groundwater vulnerability map was created by overlaying the available hydrogeological data and categorized to low, moderate, and high risk. The DRASTIC index value was evaluated 81 to 116 for the study area. The vulnerability map created by DRASTIC is compared to nitrate data and the results indicate a relative correlation between the nitrate level and vulnerability index. In order to improve the model, four artificial intelligence (AI) models are adopted by optimizing the weights of the DRASTIC parameters. The four AI models are the Sugeno fuzzy logic (SFL), the Mamdani fuzzy logic (MFL), the artificial neural network (ANN), and the neurofuzzy (NF). For this purpose, the AI model input (the DRASTIC parameters), output (the vulnerability index), and nitrate concentration data was divided into two categories for training and test steps. The output of model in training step was corrected by related nitrate concentration, and after model training, the output of model in test step was verified by nitrate concentration. The results show that the four AI models are applicable to improve the correlation between nitrate level and vulnerability index using DRASTIC model for groundwater vulnerability assessment. The NF model by taking advantage of FL and ANN has the best results that high nitrate level at observation well location has high vulnerable index and was selected as a final model. According to the final model, the western areas of the aquifer are classified as high pollution risk. In conclusion, the AI approach proved to be an effective way to improve the DRASTIC model and provides a confident estimate of pollution risk for the study area.
A. Asghari Moghaddam; E. Fijani
Abstract
Maku area is located in the north of West Azarbaijan, northwest of Iran. In this area, groundwater supplies main water demands for different purposes such as drinking, agriculture and industry. The aim of this research is to study the groundwater hydrochemistry, hydrogeological relation between karstic ...
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Maku area is located in the north of West Azarbaijan, northwest of Iran. In this area, groundwater supplies main water demands for different purposes such as drinking, agriculture and industry. The aim of this research is to study the groundwater hydrochemistry, hydrogeological relation between karstic and basaltic aquifers, determination of probable hydrochemical anomalies and their genesis and suitable methods for removal of these anomalies. For this purpose, in adition to available hydrochemical data, 72 water samples were collected from wells and springs in high and low level groundwater durations and were been analyzed for some trace elements. The results indicate fluoride anomaly in this area. In order to examine the hydrochemistry of the study area, graphical and mass balance methods were used. Both of these methods confirm the basaltic origin of fluoride anomalies. Consequently, hydrogeological relation between karstic and basaltic aquifer is established. Petrologic studies show that basaltic rocks of the area have appropriate conditions for occurrence of fluoroapatite; as a result, fluoroapatitic origin for high concentration of fluoride is identified. The most suitable methods for removal of the fluoride proposed to be adsorption on Defluoron2 and exchanging Cl- with F- by anionic resin in the study area.
