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1.
Rapid population growth, industrialization, and agricultural expansion in the Khoy area (northwestern Iran) have led to its dependence on groundwater and degradation of groundwater quality. This study attempts to decipher the major processes and factors that degrade the groundwater quality of the Khoy plain. For this purpose, 54 groundwater samples from unconfined and confined aquifers of the plain were collected in July 2017 and analyzed for major cations and anions (Na, K, Ca, Mg, HCO3, SO4, and Cl), minor ions (NO3 and F), and Al. Magnesium and bicarbonate were identified as the dominant cation and anion, respectively. Several ionic ratios and geochemical modeling using PHREEQC indicated that the most important hydrogeochemical processes to affect groundwater quality in the plain were weathering and dissolution of evaporitic and silicate minerals, mixing, and ion exchange. There were smaller effects from evaporation and anthropogenic factors (e.g., industries). Results showed that the high salinity of the groundwater in the northeast area of the plain was due to the high solubility of the evaporitic minerals, e.g., halite and gypsum. Reverse ion exchange and the contribution of mineral dissolution were more significant than ion exchange in the northeastern part of the plain. Elevated salinity of the groundwater in the southeast was attributed mostly to reverse ion exchange and somewhat to evaporation.  相似文献   
2.
Stochastic Environmental Research and Risk Assessment - Water quality monitoring is an important component of water resources management. In order to predict two water quality variables, namely...  相似文献   
3.
The accuracy of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), wavelet-ANN and wavelet-ANFIS in predicting monthly water salinity levels of northwest Iran’s Aji-Chay River was assessed. The models were calibrated, validated and tested using different subsets of monthly records (October 1983 to September 2011) of individual solute (Ca2+, Mg2+, Na+, SO4 2? and Cl?) concentrations (input parameters, meq L?1), and electrical conductivity-based salinity levels (output parameter, µS cm?1), collected by the East Azarbaijan regional water authority. Based on the statistical criteria of coefficient of determination (R2), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency coefficient (NSC) and threshold statistics (TS) the ANFIS model was found to outperform the ANN model. To develop coupled wavelet-AI models, the original observed data series was decomposed into sub-time series using Daubechies, Symlet or Haar mother wavelets of different lengths (order), each implemented at three levels. To predict salinity input parameter series were used as input variables in different wavelet order/level-AI model combinations. Hybrid wavelet-ANFIS (R2 = 0.9967, NRMSE = 2.9 × 10?5 and NSC = 0.9951) and wavelet-ANN (R2 = 0.996, NRMSE = 3.77 × 10?5 and NSC = 0.9946) models implementing the db4 mother wavelet decomposition outperformed the ANFIS (R2 = 0.9954, NRMSE = 3.77 × 10?5 and NSC = 0.9914) and ANN (R2 = 0.9936, NRMSE = 3.99 × 10?5 and NSC = 0.9903) models.  相似文献   
4.
A detailed hydrogeological investigation was carried out in the Tabriz plain in Iran using conventional hydrogeological field investigations and hydrochemistry. The study was carried out because the aquifers are of particular importance as they are more or less the only source of water supply available to the rural population and for agricultural and industrial activities. Analytical and numerical methods were applied to the constant rate pumping test data from the Tabriz airport and the Tabriz Power Station well fields. Two types of aquifers of different water quality were identified in the study area: an unconfined aquifer that extends over the plain and confined aquifers that are found in the deeper layers of the multilayered sediment terraces of the Aji-Chay River course. Therefore, the central part of the Tabriz plain contains both unconfined and confined aquifers, while close to the highlands, there is only an unconfined aquifer. There was evidence of minor leakage in the confined aquifers when the numerical method was used for analysis. The groundwater in the area can be identified by three main geochemical facies: Na-Cl, Ca-HCO3, and mixed Ca-Mg-Cl-SO4. The processes responsible for the hydrochemical evolution in the area fall into five categories: dissolution of evaporate minerals, precipitation of carbonate minerals, evaporation, ion exchange, and anthropogenic activity.  相似文献   
5.
Aji-Chay River is one of the most important surface reservoirs of northwest of Iran, because it passes through Tabriz city and discharges to Urmia Lake, one of the largest permanent salty lakes in the world. The main objectives of the present study are to evaluate its overall water quality and to explore its hydrogeochemical characteristics, including the potential contamination from heavy metals and metalloids such as Co, Pb, Zn, Cd, Cu, Cr, Al and As. For this purpose, 12 water samples were collected from the main river body and its tributaries within Tabriz plain. The Piper diagram classified water samples mainly into Na–Cl and secondary into Ca–HCO3 and mixed Ca–Mg–Cl types, denoting a profound salinization effect. The cross-plots showed that natural geochemical processes including dissolution of minerals (e.g., carbonates, evaporites and silicates), as well as ion exchange, are the predominant factors that contribute to fluvial hydrogeochemistry, while anthropogenic activities (industrial and agricultural) impose supplementary effects. Cluster analysis classified samples into two distinct clusters; samples of cluster B appear to have elevated electrical conductivity (EC) values and trace metals concentrations such as Co, Pb and Cd, while SiO2 and Zn are low in comparison with the samples of the cluster A. The main processes controlling Aji-Chay River hydrogeochemistry and water quality were identified to be salinization and rock weathering. Both are related with geogenic sources which enrich river system with elevated values of Na+, Cl?, Ca2+, Mg2+, K+, SO4 2? and EC as a direct effect of evaporites leaching and elevated values of Pb and Cd as an impact from the weathering process of volcanic formations. According to the US salinity diagram, all of the water samples are unsuitable for irrigation as having moderate to bad quality.  相似文献   
6.
The main aims of the present study are to identify the major factors affecting hydrogeochemistry of groundwater resources in the Marand plain, NW Iran and to evaluate the potential sources of major and trace elements using multivariate statistical analysis such as hierarchical clustering analysis (HCA) and factor analysis (FA). To achieve these goals, groundwater samples were collected in three sampling periods in September 2013, May 2014 and September 2014 and analyzed with regard to ions (e.g., Ca2+, Mg2+, Na+ and K+, HCO3 ?, SO4 2?, Cl?, F? and NO3 ?) and trace metals (e.g., Cr, Pb, Cd, Mn, Fe, Al and As). The piper diagrams show that the majority of samples belong to Na–Cl water type and are followed by Ca–HCO3 and mixed Ca–Na–HCO3. Cross-plots show that weathering and dissolution of different rocks and minerals, ion exchange, reverse ion exchange and anthropogenic activities, especially agricultural activities, influence the hydrogeochemistry of the study area. The results of the FA demonstrate that 6 factors with 81.7% of total variance are effective in the overall hydrogeochemistry, which are attributed to geogenic and anthropogenic impacts. The HCA categorizes the samples into two clusters. Samples of cluster C1, which appear to have higher values of some trace metals like Pb and As, are spatially located at the eastern and central parts of the plain, while samples of cluster C2, which express the salinization of the groundwater, are situated mainly westward with few local exceptions.  相似文献   
7.
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8.
Groundwater is an important source of freshwater for domestic, agricultural and industrial uses in Iran. Groundwater quality assessment and environmental evaluation are considered as critical issues in recent years. Intensive human activities have resulted in significant changes in environment leading to serious groundwater contamination. This research proposes a two-part systematic approach to tackle heavy metals contamination problem in Rayen Basin (southeast Iran). The first part consists of determining geochemical characteristics and evaluating groundwater quality through application of water quality index and heavy metal pollution indices (i.e. HPI and MI). The second part includes ranking sampling stations based on heavy metals concentration in groundwater using linear assignment method. Six types of water could be identified according to the dominant cations and anions in samples: Ca–HCO3, Ca–SO4, Na–Cl, Na–HCO3, Na–SO4 and mixed water type. Calculation of indices revealed that natural and anthropogenic activities are playing a vital role in degrading groundwater quality in the study area. The proposed methodology can help in groundwater resource management and preventative activities by identifying risk factors and recognizing their pollution level. The results of this research provide useful and effective information for water pollution control and management and can be used in environmental studies in order to protect groundwater resources in the future.  相似文献   
9.
Construction of a highway almost in the middle portion of Lake Urumieh affected the pattern of sedimentation process in the Lake. The resulting effects were studied by the use of multisensor, multitemporal and multiformat satellite data. Results obtained from this study clearly show the possibility of partitioning of this important waterbody in the near future.  相似文献   
10.
While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance-based K-nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree-based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na+ and Ca2+ showed the greatest positive and negative correlations with fluoride (r = 0.59 and −0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources.  相似文献   
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