جستجوی مقالات مرتبط با کلیدواژه "کیفیت آب زیرزمینی" در نشریات گروه "آبخیزداری، بیابان، محیط زیست، مرتع"

Assessing the quality of water resources is an important aspect of improving their management. Given the importance of groundwater resources in the Qazvin Plain, and to have a better understanding of the status of these resources, this study has focused on examining the quality of groundwater for drinking and agricultural purposes. For this purpose, using hydrogeochemical parameters, two quality indicators for drinking water and irrigation were selected to investigate and study the quality of groundwater in the years 2012, 2016, and 2020. Based on the results, quality indicator maps for drinking and irrigation water were classified, and the percentage of each class's area and their average values were obtained for different land uses using ArcMap 10.8.2 software, to consider the mutual effect of land use on groundwater quality. The results showed that the average drinking water quality index in the years 2012, 2016, and 2020 were 135.02, 128.30, and 127.38 respectively, indicating an improvement in drinking water quality. The average groundwater quality index for irrigation in recent years was 62.21, 63.51, and 63.39 respectively. Generally, the quality of groundwater for drinking and agricultural purposes in the northern regions was better than in the southern regions, while the groundwater in the central and eastern parts of the plain, which includes abandoned and neglected lands, has become increasingly restricted for irrigation over time. The results demonstrated that the area of land with suitable quality groundwater for drinking is decreasing. During the study period, the area of good class has decreased, while the area of poor class has increased.

Groundwater, as one of the vital resources for supplying drinking water and agriculture, is significantly affected by climate change and human activities. Rising temperatures, changing precipitation patterns, and alterations in vegetation lead to changes in the chemical composition of water, which can have serious implications for the provision of drinking water and agricultural needs. The quality of groundwater, like that of surface water, is constantly changing, and in arid and semi-arid plains, identifying and analyzing the factors influencing groundwater quality is crucial for managing various quality zones. Researchers in this field have acknowledged that soil characteristics and land surface can directly impact groundwater quality, and proper soil management can contribute to improving water quality. Therefore, this study examines the impact of climatic variables and land surface characteristics on groundwater quality in the Kahorestan plain of Hormozgan Province, aiming to identify quality changes in water during irrigation and non-irrigation seasons. It is noteworthy that previous research primarily estimated the Water Quality Index (WQI) using interpolation algorithms within Geographic Information Systems (GIS) and delineated different groundwater quality levels. In the present study, in addition to investigating the significant relationships between climatic variables and land surface characteristics with groundwater quality, the spatial distribution of these variables within the study area is analyzed, and modeling is conducted using Geographically Weighted Regression (GWR) and Ordinary Least Squares (OLS) methods based on the influential independent variables.

The study area in the present research is the Kahorestan plain, covering an area of 273.09 square kilometers, which is one of the rare plains in the western part of Hormozgan Province. This study was conducted based on chemical data from 15 semi-deep wells in the Kahorestan plain during irrigation and non-irrigation periods in the years 2009 and 2018. The independent variables of the study included the Normalized Difference Salinity Index (NDSI), cumulative precipitation (Pr), Normalized Difference Vegetation Index (NDVI), soil water deficit (DEF), potential evapotranspiration (PET), land surface temperature (LST), Palmer Drought Severity Index (PDSI), and actual evapotranspiration (AET), which were extracted as monthly raster satellite images from the Google Earth Engine (GEE). The dependent variable of the study was the Groundwater Quality Index (WQI), which was modeled within a Geographic Information System (GIS). To assess the significance of the relationships between these variables at different time periods, the Chi-square test was employed. Subsequently, Geographically Weighted Regression (GWR) and Ordinary Least Squares (OLS) techniques were utilized to model the spatial variations in groundwater quality. To compare the validity or efficiency of the multiple regression models, criteria such as the coefficient of determination, root mean square error, and Akaike Information Criterion were used. Additionally, it is noted that the Local Moran's I statistic was employed to investigate the spatial distribution of groundwater quality zones during these time periods.

The chi-square test indicated a significant relationship between climatic variables and land surface characteristics with groundwater quality. Spatial autocorrelation analysis revealed that the spatial distribution of groundwater quality in the Khorasan plain is random. Among the two methods, GWR (Geographically Weighted Regression) and OLS (Ordinary Least Squares), the GWR technique provided better results with a root mean square error of 0.14, a residual sum of squares of 11.3, a coefficient of determination (R²) of 0.82, and an Akaike Information Criterion (AIC) of -570.19, compared to the OLS method. Additionally, NDVI (Normalized Difference Vegetation Index) and NDSI (Normalized Difference Snow Index) showed a coefficient of determination (R²) of 0.47 in the first time period (4/2009), with the NDSI variable showing an R² of 0.63 in the second time period (11/2009). In the third time period (5/2018), both NDVI and NDSI had an R² of 0.65, and in the fourth time period (12/2018), the two variables NDVI and NDSI again showed the highest impact among climatic variables and land surface characteristics on groundwater quality, with an R² of 0.55. The groundwater quality classes of the Kahorestan plain only included two categories: very poor and unsuitable for agricultural use. In the first-time frame, the "very poor" quality class of groundwater in the Khorasan plain covered an area of 2.44 Km2. In the second time frame, the only groundwater quality class in the Khorasan plain during that irrigation season was "unsuitable for agricultural use," with an area of 275.2 Km2. In the third time frame, May 2018, the "very poor" quality class of surface groundwater accounted for 14.91 Km2 of the plain's area. In the fourth time frame, December 2018, the area classified as "very poor" quality was estimated to be 8.51 Km2.

As the results indicated, groundwater quality during irrigation seasons (months 11 and 12 of the years 2009 and 2018) decreases compared to non-irrigation seasons (months 4 and 5 of the years 2009 and 2018), with the quality levels of groundwater also diminishing during irrigation periods. The findings revealed that increased vegetation cover and reduced soil salinity are key factors in improving groundwater quality in this region. A decrease in vegetation cover and an increase in soil salinity during irrigation seasons lead to a reduction in the extent and quality of good and moderate quality zones. Given the superiority of the GWR model over OLS, this model can serve as a useful tool in managing the quality zones of groundwater resources and analyzing changes in water quality. Ultimately, this research emphasizes that attention to climatic variables and land surface characteristics is essential for the optimal management of groundwater resources. This study not only utilized various interpolation methods for the WQI based on the chemical data from observation wells within a Geographic Information System (GIS), but also highlighted the prediction of different groundwater quality zones using Geographically Weighted Regression and Ordinary Least Squares techniques, considering the impact of various climatic variables and land surface characteristics. Additionally, obtaining independent variables at a monthly scale across different time periods of irrigation and non-irrigation seasons from the online Google Earth Engine (GEE) was another focus of the current study regarding the examination of groundwater quality changes.

Groundwater quality, especially siltation and corrosion, are of great importance considering economic and health concerns. Due to the growing population and increasing water demands in the Hable-Roud river basin and therefore increasing importance of groundwater, this research aims to assess the potential of corrosion and siltation in groundwater resources. In this research, the Langelier Saturation Index, Ryznar Stability Index, Puckorius Scaling Index and Aggressive Index indicators were used for corrosion and siltation assessment and geostatistical analyses tools in ArcGIS were applied for zonation of the indicators across the watershed. Given the average values obtained (0.67 for LSI; 6.51 for RSI; 6.09 for PSI; 12.89 for AI), a large portion of the study area (about 74%) is faced with a siltation condition. Considering the minimum values of RMSE, the two indices of LSI and RSI were interpolated using the Kriging method, while the IDW and RBF were considered for the interpolation of the PSI and AI indices, respectively. Results suggest that, based on the SI indicator, 75.3% of the total area encounters a siltation condition while the figures change for AI as 72.96%, for PSI as 56.42% and for RSI as 32.17%. In general, assessment of the corrosion and siltation indicators indicates a siltation status for the groundwater resources of the basin, which necessitates developing a suitable management plan to prevent its economic and health consequences. One of the appropriate ways to deal with the siltation issue is to identify critical areas and then adjust water pH to under saturation level.

Water quality is always one of the major challenges for managers and decision makers in water resource management. However, the problems of water quality are more important than quantity. One of the main ways in thorough review and assessment of water quality using multivariate statistical techniques are, the majority of changes to a system, in order to identify important factors that influence could explain. This study classified zone Groundwater Quality Mashhad plain terms of agricultural potential and its quality review process has been carried out in recent decades. Therefore, the quality of groundwater for agricultural use was studied and maps of quality classification for 2001-2011 years were prepared. Then, using geological map, the effect of geological formations on degradation of groundwater quality was assessed. For water quality analyses, 10 important water quality variables in 39 selected wells were measured and they were analyzed using multivariate statistical techniques. The statistical analyses which were used are: Factor analysis for determining the most important variables, cluster analysis for determination of variables homogenous groups and Pearson Correlation for investigation of relationships between variables. The results have shown the best relationship between geological formation and quality factors. Also, the results of Factor Analysis also showed that EC and TDS 71.02 of the total variance explained by factor loading 0.98 and pH 14.91 of the total variance explained by factor loading 0.93 are the most important variables affecting the quality of groundwater in the study area.

At present, Groundwater contamination by nitrate, serves as one of the most important environmental issues. In respect to various land uses of Silveh basin, its ground water quality parameters might vary spatially and temporally. For this, ground water samples taken from 145 points were evaluated. After determining nitrate spatial variations by varyogram, different methods involved distance inverse method and geo-statistics methods of radial estimator approaches, local estimator, ordinary kriging, simple kriging and global kriging were evaluated using GIS software and nitrate spatial distribution map were prepared in two time intervals (pre and post-harvest). Criteria based on the Root Mean Squared Error(RMSE), ordinary kriging method has the lowest error, and the accuracy considerably. Spatial distribution of nitrate in area groundwater indicated that there was high concentration of nitrate in land uses of agriculture and arid area. Of course, presence of shale-stone causes nitrate releases, intensifying issues. Comparison of nitrate samples concentration with national and international standards suggested that 1.38%(2 Point) of all samples have been nitrate-contaminated before harvesting, while 11.03%(16 Point) of them have been contaminated after harvesting.

Recognizing quality of groundwater as one of the most important and vulnerable sources of water supply in recent decades is a necessary and important issue. Water purification is sensitive and time consuming; therefore, investigation of the quality of groundwater supply is essential. In this research, in order to study chemical compounds of groundwater, 21 observed wells were sampled in the summer of 2013 year and important parameters of samples were measured and calculated for agricultural and drinking uses. Also Piper diagram was used to determine hydrochemical facies of the groundwater area. The results showed that dominant type of groundwater hydrochemical facies of region was calcium sulfate (Ca-S04). According to World Health Organization standard, among wells of region just H9, L15, O12, and L6C wells was suitable for drinking use and these wells in terms of sodium hazard and change of soil structure for agriculture use, had a good to admissible situation . Also, groundwater of that region had moderate permeability index; therefore, it should be used for soil irrigation with sufficient permeability and cultivation of salt tolerant plants.

Nowadays, with the increasing exploitation of groundwater resources, optimal use of these resources is more and more necessary. geostatistical methods can be used to assess and monitor the quality of groundwater resources. Hashtgerd Plain is the case study of this investigation. In this study firstly, by using data from qualitative data which were harvested from 41 Piezometric wells, different qualitative parameters were evaluated, then by using the geostatistical methods such as: Kriging, Co-kriging and IDW the best model for mapping for aquifer quality classification was selected. Results showed that most of the indicators are better simulated by Co-kriging method, based on mutual evaluation and RMSE. The parameters of SAR and EC were selected in order to determine the irrigation water quality parameters according to Wilcox diagram. Based on these two parameters by using ArcGIS v.10 software zoning maps were prepared. Results showed that 99% of the aquifer is classified in the category of good quality irrigation water (C2S1) and 1% level in the aquifer is classified as middle class (C3S1) based on Wilcox diagrams. The results of the study can be used in aquifer management and irrigation management in the agricultural purposes.

Determination of groundwater quality is an important issue in watershed management. The aim of this study was to investigate the spatial variability of some properties of Kerman groundwater using geostatistical methods. For this, groundwater quality data including soluble calcium, magnesium, sodium, bicarbonates, sulphates, pH, EC, SAR, TDS and TH for spring season of 2009 were collected. Interpolation analysis was done using Kriging, Co-Kriging, inverse distance weight (IDW) and normal distance weight (NDW) methods. The best interpolation method was chosen through root mean square error, mean absolute error and Pearson’s coefficient parameters. Result showed that the best fitted model for magnesium and pH was linear to sill, whereas for the other groundwater properties the Gaussian’s model was the best. Spatial dependency for bicarbonates and pH was moderate, while for the other ones, strong dependencies were observed. The result also indicated that for all the groundwater properties, Kriging with the Pearson’s coefficient ranging from 0.695 to 0.851 was the best algorithm. In addition to Kriging, the NDW and IDW methods found to be appropriate for the estimation of all the parameters except for EC and TDS. For these two latter ones, additional to Krging, the Co-Kriging method led to suitable estimates. The prepared maps showed that the amounts of bicarbonates and pH were higher at the north and south sides of the study area, respectively. The amounts of the other properties were minimum at the east and north east, while increased toward the west and south west.

Spatiality assessment of groundwater pollution is very important to determine water quality condition، pollution sources and management decisions. In this case، GIS and geostatistics methods can be useful tools. Spatiality of groundwater quality parameters، in relation with various land uses، can be very extremely. Therefore water samples from 52 wells in the Kurdan area were analyzed in this study. The results show that nitrate concentrations are less than maximum acceptable concentration in drinking water (i. e. 50 mg/L as nitrate recommended by ISIRI and WHO guideline values) except to one sample (2 percent of samples) in the study area. Various geostatistics methods، e. g. IDW (power 1-4)، ordinary Kriging and RBF (five Kernel functions) were compared after assessing the variograms and the spatiality of nitrate samples. Then the model parameters were calibrated and through the specific methods، predicted and standard errors maps were prepared. Errors criteria show that Kriging is the best fitting model in the study area. Finally، probability map of NO3 concentrations exceeding the threshold value of 50 mg/L، is generated using the Indictor Kriging method. Spatiality of NO3 show that Nitrate concentration is increased where the rock type is permeable، land use is agriculture and slope is enough low to infiltrate polluted water into the wells. This research also tries to describe how to assess the spatiality of groundwater parameters by GIS.

Changes in groundwater quality, is caused destruction of other resources, either directly or indirectly, due to bad management of groundwater extractions. Considering the importance of groundwater resources, especially in arid and semiarid regions, this research was done to modeling the spatial distribution of some groundwater qualitative factors with emphasis on drinking using geostatistical methods. Qualitative variables selected are: total hardness, chloride, sulfate and nitrate ions. First of all, normality of data investigated based on Kolmogrov-Smirnov test and non-normal variables were transformed to normal by using logarithmic transform. Then variography and cross-variogram analysis of variables was done. Accuracy assessment of results was investigated based on cross validation and root mean square error (RMSE). Results showed that Cokriging Method (using auxiliary variables) compared with other Geostatistical methods is a more accurate estimator, so interpolating and zoning of variables were done by using Cokriging Method and drinking water standards.