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Showing 5 results for Water Quality
Assessment of groundwater quality index (GQI) for Lenjanat aquifer using GIS
Majid Dashti Barmaki, Mohsen Rezaei, Amir Saberi Nasr,
Volume 8, Issue 2 (11-2014)
Abstract
This paper has evaluated the groundwater quality index of Lenjanat aquifer. Water quality index as a unique index is presented to describe overall water quality conditions using multiple water quality variables. Physical and chemical data of 66 water samples were used in this study. The results have been obtained by Comparing the qualitative features with the World Health Organization (WHO) standard and Industrial Research of Iran (ISIRI) standards. In calculating GQI, 7 parameters, including calcium (Ca), magnesium (Mg), sodium (Na), chlorine (Cl), sulfate (SO4), total dissolved solids (TDS) and nitrate (NO3) have been used. Groundwater quality index shows the medium to relatively high groundwater quality in the study area. Minimum and maximum value of the index is calculated as respectively 55 and 93. Land use map shows that along the Zayanderood River and around the location of rice paddies, water quality reaches to the lowest quantity. Optimum index factor technique allows the selection of the best combination of parameters dictating the variability of groundwater quality.
Evaluating groundwater hydrochemistry in the Zarabad aquifer (Sistan and Baluchistan province) to determine the quality of the water for agricultural use
Miss Masoumeh Nikbakht, Prof Mohammad Nakhaei, Prof Ata Shakeri, Dr Vahab Amiri,
Volume 16, Issue 4 (12-2022)
Abstract
In this study, the hydrogeochemical and qualitative status of groundwater resources of the Zarabad coastal aquifer in southeast Iran has been investigated. The decreasing order of cations and anions is Na+>Ca2+>Mg2+>K+ and Cl->SO42->HCO3-, respectively. The two most water type are Na-Cl (78%) and Ca-Mg-Cl (22%). The water type, chlorine-alkalinity index, ion ratios, and position of the samples on the Gibbs diagram show that cation exchange (direct and reverse), weathering of silicates and evaporites, and seawater intrusion are the main controlling processes of water chemistry. The ionic ratios of SO42-/Cl-, B/Cl-, and Na+/Cl- indicate that saltwater infiltration increases as the distance from the Rabach River increases, particularly in the northwest and southeast regions. This can lead to a decrease in the quality of water resources. Moreover, the water quality for agricultural use is assessed based on some indices, including electrical conductivity (EC), sodium percentage (Na%), sodium absorption ratio (SAR), residual sodium carbonate (RSC), magnesium absorption ratio (MAR), permeability index (PI), Kelly’s ratio (KR), and USSL and Wilcox diagrams. The results showed that about 60% of the samples had unsuitable quality for irrigation. These samples were located in the northwestern and southeastern parts of the plain. About 40% of the samples have suitable quality for irrigation and are located in the vicinity of the Rabach River.
 

Prediction of Chloride in Groundwater Using Supervised Artificial Intelligence Committee Machine (SAICM) (Case Study: Sari Plain)
Tahereh Azari, Sakineh Dadashi, Fatemeh Kardel,
Volume 17, Issue 2 (9-2023)
Abstract
Qualitative assessment of coastal waters affected by seawater salinity can be done using the parameter of chloride in groundwater. This research proposes a supervised artificial intelligence committee machine (SAICM) method for accurate prediction of chloride concentration in groundwater of Sari plain. SAICM predicts chloride concentration as the output of the model by non-linear combination of artificial intelligence models. In this research, Principal Component Analysis (PCA) method was used to identify effective hydrochemical parameters related to chloride concentration as input components to artificial intelligence models. Based on the results of PCA, parameters (Na, K, EC, TDS, SAR) were selected as input components of artificial intelligence models. Firstly, four artificial intelligence models, Sogno fuzzy logic, Mamdani fuzzy logic, Larsen fuzzy logic and artificial neural network were designed to predict chloride concentration. Based on the modelling results, all the models showed a good fit with the chloride data in Sari Plain. Then, the combined SAICM model was built, which combines the prediction results of 4 separate AI models using the nonlinear ANN combiner and determines the chloride concentration more accurately. The results show that the proposed SAICM can estimate chloride concentration with much higher accuracy than individual methods.

Assessment of hydrochemical characteristics and quality of the Garmabdasht River, Golestan Province, NE Iran
Shaghayegh Samiee-Rad, Giti Forghani, Hadi Jafari,
Volume 18, Issue 3 (12-2024)
Abstract
The Garmabdasht River as the first tributary of the Qarasu River, flows through the city of Gorgan and eventually  flows into Gorgan Bay. In order to study the hydrochemistry and to assess the water quality, 10 water samples were collected in June 2022. Physicochemical properties (pH, electrical conductivity, total dissolved solids), major ion concentrations, and microbiological  parameters (dissolved oxygen content, biological oxygen demand, chemical oxygen demand, and coliform bacteria) were measured by standard methods. The obtained results show that the pH of the water samples varies between 7.5 and 8.5 and the electrical conductivity of water samples varied between 376 and 665 µs/cm.  In terms of hardness, water samples were classified as hard and very hard. The concentrations of the major ions, phosphate and nitrate were within the permissible range for drinking usage. By calculating the ionic ratios and drawing the Durov diagram, it was found that the water chemistry was mainly controlled by the dissolution process. The position of the samples on the Piper diagram shows that the type and facies of the river water samples were calcium bicarbonate, magnesium bicarbonate and calcium sulphate. According to the Wilcox diagram, the Garmabdasht river water was suitable for irrigation. The residual sodium carbonate and sodium percentage values confirm this conclusion; however, based on the magnesium hazard index, the studied samples were not suitable for irrigation. The values of dissolved oxygen in all samples were within the permissible limit. The amounts of biological oxygen demand and chemical oxygen demand in some stations exceeded the permissible limit due to the influx of livestock and agricultural effluents. The obtained results show that the samples were microbially polluted, which may induce the health problem in the local population. The values of NSFWQI also shows that, except for the upstream samples of S1 and S2, the quality of the studied samples for drinking is in the bad to medium class.

Water quality assessment of Qarasu River using IRWQIsc index
Dr Eisa Hajiradkouchak, Dr Behzad Rahnama, Dr Hasan Nasrollahzadeh, Mr Ali Shahbazi, Mr Reza Raeiji, Mr Kazem Babaei,
Volume 18, Issue 3 (12-2024)
Abstract
Many researchers believe that providing safe water, sanitary disposal and optimal management are the three axes of health, and in all these cases, while paying attention to the process of doing work, continuous control should also be done. This study was designed and implemented with the aim of seasonally investigating the physicochemical and microbial water quality of Qarasu River in Golestan province using the IRWQIsc index. 6 sampling stations were identified for Qarasu River and sampling was done once every month in four seasons of 1400. The measured parameters include pH, BOD, COD, dissolved oxygen (DO), electrical conductivity (EC), ammonium (NH4), nitrate (NO3), phosphate (PO4), total hardness (TH), turbidity and total suspended solids. It was a stool form. According to the measured parameters, Iran's surface water quality index IRWQISC was calculated. The results of the study based on the index showed that the quality of this index for all stations in all seasons was between 70.5 and 14.7 and according to the IRWQISC index, it was in five good categories (70-1.85), relatively good. (55-1/70), relatively bad (30-44-9), bad (15-29-9) and very bad (less than 15). The influencing parameters were total suspended solids, turbidity, nitrate, temperature and fecal coliform. It can be concluded that the amount of 70.5 with good quality is related to (Tuskestan village) in winter and the amount of 7.14 with very bad quality is related to (Pol Qara Tepe) in summer that the quality of the river water in The Gorgan to Aqqla road bridge station (Qorban Abad) is in bad condition in all seasons due to the entry of urban and industrial pollutants into this station, and Tuskestan village station has good and relatively good quality in most seasons because Tuskestan is in It is located in high altitudeand the entrance of clean running water  into thisarea is more and it is far from industrial and urban pollutants.

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