کریجینگ

The aim of this study was to investigate the spatial variation of some soil properties such as soil texture, organic carbon content, soil pH and electrical conductivity (EC) using geostatistical methods in Margon town, Kohgiluyeh and Boyer-Ahmad province, Iran. For this purpose, 58 surface soil samples (0-30 cm depth) were prepared from the studied area and some physical and chemical characteristics of the soils were measured. The studied properties were estimated by various interpolation methods including ordinary kriging, simple kriging, disjunctive kriging, co-kriging, and inverse distance weighting (IDW) using GIS software. The capability of these methods was evaluated by the mean absolute error (MAE), and the root mean square error (RMSE). Based on the absolute mean error and the squared mean squared error, the spherical model for pH, salinity, sand percentage and soil silt percentage and the exponential model for clay and organic carbon percentage had the highest accuracy. Moreover, the results indicated that conventional co-kriging method for organic carbon percentage content and soil pH, simple co-kriging method for clay and silt percentage, simple kriging method for soil EC, and conventional kriging method for sand percentage were the most suitable methods as compared to the other studied methods.

The significant reduction of surface water resources and recent recurrent droughts have increased the reliance on groundwater sources, leading to a decline in their quality. In this study, 28 samples of well water from different locations in Khanmirza, Lordegan, Borujen, Ardal and Kiar were collected in 2020-2021 and underwent chemical analysis in the laboratory. To evaluate the quality of the samples for use in agricultural, drinking and industrial sectors, the Wilcox diagram, Schuler diagram and Langlier index were used. Using interpolation methods, the chemical properties of the samples taken within the study area were examined and qualitative zoning maps were prepared using Kriging and inverse distance weighting methods. The results showed that according to the Wilcox diagram, most of the samples are within the range of C2S1 and are suitable for agricultural purposes. According to the Schuler diagram, these samples are within the range of good quality for drinking and based on the Langlier index, they are within the range of precipitating and consumable. This study helps decision-makers to have a clear view and plan comprehensively and effectively for the exploitation and preservation of groundwater resources, taking into account the qualitative maps of groundwater.

Updated information in quantitative and qualitative properties of forests are useful in describing ecosystem sustainability, and designing management and conservative plans. According to importance of canopy cover parameter in the Zagros region and cost and time consuming processes of field measurement methods, in this study performance of remote sensing and geostatistics techniques to estimate forest canopy cover of Baghan region, Marivan city, were investigated.

 First, the number of 89 plots (each 0.1 Hectare) were selected based on random sampling method. In each plot, information of tree crown and center geographic coordinates of that plot were recorded. Remote sensing method was carried out using Landsat satellite images (TM) and multiple linear regression model. After image processing, spectral values of the corresponding field plots were extracted from the original images and synthetic bands composed of vegetation indices and principle component analysis. In geostatistic method, the estimation was performed using ordinary kriging from a fitted exponential model to the semivariogram.

 The calculated variograms of canopy cover showed relatively strong spatial autocorrelation fitted by exponential models and cross-validation results showed an unbiased estimation of canopy estimation. Compared with the remote sensing method (with R2= 0/52 and RMSE= 16/47), the results indicated that Kriging model (RMSE= 9.21, R2= 0.69) showed a more accurate estimation of forest canopy cover.

The results showed that geostatistics techniques can be used as an efficient tool for mapping the forest canopy in the same regions (Zagros Forest).

Increasing world population and need to more production, limited water resources and the indiscriminate use of them specially in arid and semi-arid regions and also the huge amount of sullage produced in cities and the necessity for proper disposal of them, increased the urgency (necessity) of using wastes in order to farming & feeding the under- ground aquifers. Cadmium, lead and nickel are such pollutants which can enter to water, soil and plant and finally to human and animals’ food chain from different resources.

This Study was performed to evaluate the amount of soil contamination in the south Tehran farms and mapping the studied area using geostatistical methods. From the 2000 hectares of the agricultural farms around Firooz Abad stream in Tehran south in 2014, 175 soil samples were selected with 300 meters intervals. Two methods of mapping including kriging and Inverse distance weighting (IDW) were used.

Based on mean concentration, the metals in studied area were in following decreasing order: nickel> lead> cadmium. According to the standards proposed by the environmental protection Agency of Iran, 13 and 9 % of soil samples are contaminated to total lead and nickel, respectively.

Due to the importance of point spatial variation of soil pollutants, the IDW method was identified as the suitable method for mapping the concentration of total lead and nickel. The total lead and nickel concentration in about 161 and 14 hectares of the agricultural lands, respectively, were more than allowed limits.

In the present study, simple and ordinary kriging methods, inverse distance and linear regression based on digital elevation model of the earth were evaluated for estimating annual rainfall using twenty-year statistics of precipitation data (1998-2018) in 33 rainfall stations in Chaharmahal and Bakhtiari province. For this purpose, first in ArcMAP, for each model in Kriging method, its variogram was calculated and using two-way evaluation technique, the error of the maps was estimated. The best method among geostatistical methods was conventional kriging method with Gaussian model. MAE, MBE and RMSE statistical indices for this method were 74.44, 0.48 and 93.72, respectively. Then, rainfall and altitude data of the stations were used using a linear regression model in Curve Expert environment. Finally, in order to determine the best model for spatial distribution of precipitation as well as comparing statistical and geostatistical methods, linear regression and ordinary kriging models were compared with each other and the MAE, MBE and RMSE statistical indices for regression method obtained were 115, 3 and 155, respectively. As a result, due to the accuracy, precision and error rate of the prepared maps, the most suitable method for interpolation of annual precipitation is the conventional kriging method with Gaussian model.

Knowledge and awareness of temporal and spatial changes of quality parameters, especially salinity in groundwater and surface water resources and the preparation of zoning maps can be an important step in proper management and planning in the use of water resources. In this research, the trend of spatial and temporal changes of some groundwater quality parameters in Guilan province has been evaluated. Measurement data of some cations and anions sampled from 150 wells in September and March of 2011-2018 and water level data of 282 piezometric wells were collected. The zoning maps obtained by ordinary kriging method were examined. These maps show that in terms of quality parameters, groundwater resources in the central part of Gilan province have higher values ​​and in the growing season due to agricultural activities and fertilization, groundwater quality decreases. Effect of groundwater depth on the concentration of the parameters was assessed. According to the map of groundwater depth changes, the largest area of ​​Gilan province has a water level of 0.23 to 5.5 m. The results of classification of qualitative parameters show that in shallow groundwater depths the concentration of qualitative parameters increases.

Groundwater is the main source of drinking water in the semi-arid regions of Iran. Therefore, monitoring the quality of this valuable resource is vital. Hence, the purpose of this study was to investigate and prepare a groundwater quality map of Birjand located in the semi-arid region of eastern Iran using GIS-based geostatistical analysis with Drinking Groundwater Quality Index (DGWQI). For this purpose, the collected groundwater quality data of 27 agricultural wells during the years 2014-2019 were used. The results of spatial analysis show that 63% of the aquifer area was in the appropriate quality category, 18% in the poor category, 10% in the very poor category and 9% of the aquifer area was in the non-drinking category. Sensitivity analysis indicated that Mg2+, EC, and TDS parameters with the highest mean change index of 18.98, 20.68 and 19.04 were the most sensitive parameters in calculating DGWQI, respectively. Error evaluation calculated by R2 and RMSE methods on conventional kriging and spherical variogram models showed good performance for spatial analysis of all parameters including DGWQI, Mg2+, EC, and TDS. The DGWQI map shows that the western and southwestern parts of the aquifer do not have good quality conditions for extracting drinking water.

Using unsuitable quality water for agricultural purposes has  negative consequences on plant growth and the soil physical properties. The aim of this study was to investigate the spatial changes in ground water quality with agricultural applicability in central Chabahar and Kenark Distircts. For this purpose, we first performed interpolation for EC and SAR parameters from 40 wells in the study area in 2019. Then we employed  inverse distance weighting , global polynomial, local polynomial, radial basis function and kriging with circular, spherical, exponential, gaussian, as well as quadratic equation semivariograms; the most suitable method for interpolation was selected baed on the Root Mean Square Error (RMSE) and coefficient of determination (R2) criteria. The research found that kriging interpolation method with a Gaussian variogram having RSME=13.85 and R2=0.77 was the most suitable methods for interolating with the SAR paramete and the kriging interpolation method with as spherical variogram having RSME=797.89 and R2=0.68 was considered as the most suitable methods for interpolating EC. Then, based on Wilcox method, the groundwater quality map for agricultural and irrigation uses was prepared using the spatial variation map of two parameters, EC and SAR. The results showed that the quality classification of the groundwater in the study area was: about 10% as good, 51% as fair, 16% as poor, and about 23% as unusable water.

The number of stormy days is determined by various factors such as wind speed, rainfall, soil moisture and so on. The study of this index in the country can be considered in various plans. The purpose of this research is mapping of the number of dusty stormy days in Iran and selecting the best model based on the climatic data of 150 meteorological stations for the period of 25 years (1986-2010).

Dust stormy days’ data of the studied stations were analyzed using variogram curves to represents their spatial correlation. Gaussian variogram (R2=0.96) shows the highest correlation between the data. Then, map of the number of dust stormy days in Iran were prepared using different geostatistical and mathematical methods. For this purpose, several mathematical interpolation methods including Inverse Distance Method (IDW), Global Polynomial Interpolation (GPI), Radial Basis Function (RBF), Local Polynomial Interpolation (LPI), and geostatistical method of Kriging were used. To select the best interpolation method among several geostatistical and mathematical methods, statistical indicators of Root Mean Square (RMS) and correlation coefficient between observed and predicted data were used.

Results show that the highest correlation between predicted and observed data (R2 = 0.74) was found in kriging indicator method. The southeast and southwest of the country have the highest number of dust storm days.

High number of dust stormy days in the southeast is resulting from drying of Hammon lakes and blowing of 120-day winds in Sistan plain, and entering of dust from Arabic countries form the direction of southwest. North part of the country has the lowest number of dust storm days.

The climate change phenomenon affects water storage in aquifers through changes in rainfall volume. In this study, the aim is to estimate the average groundwater level using the most appropriate geostatistical method and then investigate the relationship between groundwater index and one of the drought indices on monthly and annual scale in order to specify the importance of rainfall in groundwater recharge.

The watershed of this study was Sharifabad watershed located in Qom province with an aquifer area of ​​ 27035/2 hectares. There are 16 observation wells in this area. In this study, during the period of 1384-1384, the water level of observation wells in the area was investigated using variogram models. The average water level was determined using the best model per year, then the SWI index at the watershed was calculated. The SPI index, one of the drought indicators, was also used to investigate precipitation variation and then, using Pearson coefficient, the correlation of these indices was investigated on monthly and annual scale.

The results showed that among the variogram models that were used to interpolate the monthly water level data, Kriging model is the most appropriate. Also, Pearson correlation coefficient between SWI and SPI indicators is not significant on monthly basis at confidence level of 99% in both observations of Mozafarabad and Alborz. Moreover; this correlation coefficient on the annual scale is not meaningful either.

In this study, the insignificance relationship between SPI and groundwater index in the study area indicates that precipitation has a small proportion in groundwater recharge in the water balance of these areas on monthly and annual scale. Therefore, it can be concluded that the most part of rainfall is out of reach due to excessive evapotranspiration and is not able to recharge the aquifer  in the region.

Soil characteristics have spatial variations. Understanding spatial variations of soil characteristics is among the effective factors in sustainable land management. A better understanding the effects of management factors on soil characteristics need to quantify their heterogeneity and variability. This research was conducted with the aim of investigating spatial variations of some soil characteristics such as soil texture (clay, silt and sand), calcium carbonate (CaCo3), soil acidity (pH), and soil salinity using geostatistic methods. For this purpose, 252 soil samples (from 0 to 20 cm depth) were prepared from the study area and physical and chemical properties of soil were measured. After normalizing the data, the half-shift of each of the studied characteristics was calculated and the best model was fitted to them. Then, the characteristics of the study were estimated through different methods of conventional Kriging, simple Kriging, discrete Kriging and Inverse Distance Weighted (IDW) using ArcGIS software. The accuracy of the estimation was evaluated using the mean absolute error (MAE), the mean bias error (MBE), and the root mean square error (RMSE). The results showed that the best model for the acidity was the spherical model and for the other measured variables was the exponential model. Moreover, the conventional CoKriging method for clay, calcium carbonate and acidity (pH), IDW method for silt and soil salinity, and conventional Kriging method for sand were better than other methods used and provided more accurate estimates.

Soil contamination produced by Cd and Pb released from vehicles is a serious environmental problem. Lead as a result of using leaded petrol fuels and cadmium in the car tire is introduced into the environment through vehicle depreciation and traffic. This study aimed to investigate the spatial distribution of heavy metals such as Pb and Cd at the roadside soils of Zabol - Zahedan route. 252 soil samples were taken from a depth of 0 -20 cm and the concentration of total lead and cadmium metals, physicochemical properties of soil including pH, EC and organic matter were measured. The contamination assessment of soil was done through using enrichment factor and geoaccumulation index. Metals’ spatial distribution maps for heavy metals concentration were used kriging methods. The mean concentration at 0, 50 and 100 meters, respectively, was 2.12, 2.03 and 2 mg kg-1 for lead, which is less than the global mean (25 mg kg-1) for this metal, and respectively 0.21, 0.21 and 0.20 mg kg-1 for cadmium, which was lower than the global average (0.53 mg kg-1). The results of the geospatial analysis showed that both variables were anisotropic and the exponential model for lead and Gaussian model for cadmium had the best fit.

One method of groundwater quality assessment is applying Groundwater quality index (GWQI), which is a combinational method of different quality parameters of groundwater. The aim of this study was to estimate the Marvdasht-Kharameh Plain groundwater quality for drinking by employing GWQI along with applying geostatistics technology in GIS environment. For this purpose, the norm of five years quality data of 49 wells were used. GWQI was estimated by applying inverse distance weighting method to quality parameters. Kriging geostatistics pattern was used for assessing spatial changes and mapping the quality parameters and GWQI. Spherical variogram pattern had the best fitting on quality parameters and GWQI. The results illustrated that spherical variogram pattern with 19200 m effective range is appropriate for GWQI. Based on sensitivity analysis, EC, TDS, and Mg had the most and pH, sulphate and TA had the least influence on GWQI. The GWQI range was variable from 47 to 1000. Upon to GWQI map, the north-western parts of Marvdasht-Kharameh Plain aquifer had suitable quality for drinking while it was not suitable to use in southern parts of aquifer.

Determination of corrosion and scaling potential is one of the most important quality aspects of groundwater resources in different uses. Therefore, the aim of this research work was to determine the corrosion and scaling potential of Marvdasht Plain groundwater using Langelier saturation and Ryznar stability indices and their mapping through GIS. For this purpose, 49 agricultural wells were sampled for five years and total hardness (TH), total dissolved solids (TDS), total alkalinity (TA) and pH were measured. The ordinary kriging geostatistical method was applied for mapping the Langelier saturation and Ryznar stability indices and their components. The results showed that the spherical model was most fitted for these indices. The eefective domin varied from 15752 to 32711 m for the components and the spatial correlation class changed from good to fair. The mean Langelier index was 0.58 showing low to fair scaling potential. The mean Ryznar index was 6.2 indicating low corrosion potential of Marvdasht Plain Aquifer. Based on the zoning maps, the northern parts of the area showed the highest TH and TDS components followed by Langelier index, showing fair scaling potential. In this part, the effect of carbonate formations was evaluated as the probable contributor of the increasing the Langelier index. In general, the Marvdasht groundwater has fair quality in terms of corrosion and scaling potential.

Understanding chemical changes in groundwater and their mapping play a substantial role in optimal management of groundwater in an area. There are various methods for investigation and classification of groundwater chemical features, and selection of appropriate method depends on the purpose, conditions of the area and available information. Geostatistical methods and GIS can be useful tools in this regard. The aim of his study is to investigate the spatial variations of groundwater quality and select the best mapping method for the management of groundwater resources in Segsi plain. In this paper, the distribution of quality pollutants pH, TDS, Hco3, EC, Ca, Mg, TH, Na and So4 in groundwater of Segsi plain was investigated using inverse distance and geostatistical methods, distance and bearing functions, local, general and ordinary Kriging estimator software ARCGIS9.3. Accordingly, the sample from 445 groundwater wells, springs and canals were examined. After evaluating variograms and determining the spatiality of the changes in the studied parameters, interpolation of parameters was performed and the best evaluation model with lowest RMSE was selected through mutual evaluation technique and the root-mean-square error. Findings: The results showed that all parameters had the lowest RMSE using the ordinary Kriging method and it was used for mapping the spatial distribution of water quality parameters. Results from mapping indicated that Na, Ca, Mg, So4, Hco3 and pH had no limitation and EC, TH and TDS had limitations in terms of drinkability. According to the results, application of the ordinary Kriging method is recommended for more precision, less calculation and less data demand among various interpolation methods for the groundwater mapping.

It is inevitable to analyze the existing contaminant elements in soil as the most important source of nutrition for human. Heavy metals are considered and referred among these elements. The contaminated soils of urbanized regions are directly related to human, while these elements are absorbed by plants in agricultural fields, through which they enter the human food cycle. The excessive increase of these elements in soil results in the growing incidence of certain diseases such as cancer. Moreover, the irresolvability of these materials may have dangerous impacts on the human digestion as well as nervous systems. Contamination, due to heavy metals in soil, have even resulted in some animals’ death.
There are various factors involved in soil contamination. One of main factors is the incorrect use and increasingly irregular exploitation of nature by human beings. In Iran, inappropriate economic exploitation of agricultural fields and aberrant use of chemicals have led to the higher concentration of heavy metals in soil, turning to a crucial problem in certain areas. The most significant cause of soil contamination is the wrong use of chemical fertilizers, resulting in the agglomeration of heavy metals in soil. Therefore, it is inevitable to identify the spatial distribution of these elements. The geo-statistical instruments have facilitated the quantification of soil spatial characteristics, whereby leading to the probability of spatial interpolation.
Objective of the study are: (i)To prepare zoning maps of elements including Zinc, Copper, Iron, Manganese, Potassium, and Lead using mathematics and geo-statistics methods as well as choosing the most optimum technique of spatial interpolation by comparing RMSE, MAE and MBE;(ii)To analyze the probable spatial correlation between the concentration of these elements in agricultural soils;(iii)To distinguish the regions affected by the excessive contamination higher than the threshold limit of general index and WHO standard and also Iran’s Soil and Water Research Institute’s standard.
Harris Town is located in the north-western Iran between 38°04° and 38°24° latitudes of northern hemisphere and of 46°22° and 47°22° geographical longitudes of eastern hemisphere.
Statistical Analyses
In this research, first of all, 370 samples of soil were randomly selected from this region and the concentrations of abovementioned elements were measured in the laboratory. Then, the accuracy of the measured data was examined by applying 3Sigma Validation Test. At the next stage, the spatial distribution of the elements of Zinc, Iron, copper, Manganese, and Potassium was examined using Mathematics and geo-statistics interpolation models. These methods which are based on the first principle of geography include certain mathematical methods such as the Inverse Distance Weighting, Local Polynomial, and Radial Basis Functions and geo-statistics methods such as Ordinary Kriging (OK), Simple Kriging (SK), and Universal Kriging (UK) with Circular, Globular, Gaussian, and Exponential Variogram Models.
Kriging is one of the geo-statistical interpolation methods, which is based on Weighted Moving Average. One of the features distinguishing it from other interpolation methods is that it is regarded as the Best Linear Unbiased Estimator. The first stage in statistical analyses is the computation and drawing of variogram. In fact, variogram represents the variability of samples based on their distance. The next stage is the selection of the best theoretical model for fitting to experimental variogram. The modeled variogram shows the spatial self-correlation of data, and describes range, sill and nugget. The Kriging interpolator is divided into different methods. The models being tested in this research are Ordinary, Universal and Simple Kriging.
Inverse Distance Weighting
The Inverse Distance Weighting is a radix point method which acts based on the first principle of geography. In this interpolation method, the weighting parameter operates according to the criterion that an increase in the distance from the sampled points to the passive point leads a decrease in its effect in the expected (predicted) value.
Local Polynomial Polynomial interpolation is a method for finding a formula, the figure of which passes through the data. The Universal Polynomials identify the fitted surface to data by considering all existing data in the analysis, while local polynomials performs this action for a limited number of points within a considered oval. This method allocates the least proportional squares among the identified points in the oval shape area as the point weight. Thus, interpolation is performed based on the allocated weight of the relations of the first, second and/or third grades among the variables in the neighboring points of X, Y, and Z along with the minimization of estimated errors.
Radial Basis Functions
This method is a manner of artificial neural networks in which the predicted values are higher than the maximum observed and lower than the existing minimum observed. Indeed, the surface fitting by these functions acts like a plastic membrane so as to minimize the total surface curvature. The Radial Basis Functions performs interpolation based on five principal functions. The main core of these functions is the sum of squares values. In this method, a function is considered for each location (place), where the linear combination of these functions is used for predicting the amount of function in the passive location as the final function. In this study, five functions including Spline Function, Quite Regular, Spline with tension, Multi-Quadric function, Reverse Multi-Quadric Function and Spline Function with the thin surface were used.
Validation Criterion of Interpolation In this research, the observed data were divided in two groups of experimental and control, with the proportion of 80 to 20. At the beginning, by applying the experimental data, a surface was fitted for the data using each interpolation method. Jackknife Cross-Validation Method was used to determine errors vector. The basis of this method is that when fitting the surface, one of the data is omitted each time, fitting is performed by using other data and the data deviation from the predicted value is recorded. Then, the control data is inserted in the fitted model and the amount of data deviation from the fitted model is being reported.
In order to examine the accuracy and validation of each method in the zoning of the aforementioned heavy metals, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Basin Error (MBE) were applied. The closer the values of (RMSE), (MAE), and (MBE) to zero, the more efficient (effective) the performance of the fitted model.
All obtained numbers for the spatial correlation between the elements are of positive value, and this represents a direct relation in the reciprocal transformations of elements. Among the results obtained, the spatial correlation between two metals of Zinc and Copper is more intense, as with 46%, include the most intense relation as compared to each pair of other elements. Meanwhile, the correlation between the two elements of Manganese and Potassium has the least value with 1%.
By using the QQ chart, it was found the data of Potassium, Zinc, Iron and Manganese have a relatively high skewedness and far from normal distribution.
As it was previously indicated, variogram explains the relation between sample variability as well as their interaction. In order to choose the best model of experimental variogram, the proportion of nugget to sill was used, in which the least value represents the best model (Figure 3).
By using Arc GIS Software, the most optimum model for the preparation of zoning maps was examined. Among the various models, deviation vectors were drawn from the measured value for the fitted surface. These vectors were drawn for both sets of data. By comparing the measured data and the global index of soil contamination and WHO standard, it was found that the highest volume of elements of Iron, Copper, and Manganese are not located in the permissible range, while the amount if this element is located in the impermissible range in the whole region under study. (Figures 4 to 8).

IntroductionHeavy metals in the soil are an important indicator of global environmental contamination and constitute an important part of the pollutants, which due to toxic and aggregate properties, even at low concentrations, have high environmental significance. Therefore, the distribution of their contamination is highly noticeable. Population growth, urban expansion, industrial development and distribution without management in agricultural inputs are among the most important factors in increasing the concentration of heavy metals. To improve the management recommendations for controlling and eliminating these pollutants, it is necessary to determine the spatial pattern of pollutants. In practice, the precise separation of ground contaminated with heavy elements is difficult due to the complexity of the pattern of spatial variations, the severe changes, and the presence of local contaminants. But there are several studies for the interpolation and determination of spatial distribution of heavy metals concentration in the soil. The analysis of the spatial distribution of heavy metals in soils is of fundamental importance in a vast number of applications, including general soil surveying and characterization, delineation of potentially polluted spots at unsampled sites, or planning remediation strategies. The relevance of heavy metal soil pollution has favoured the application of advanced geostatistical techniques such as kriging in their different varieties. Kriging techniques have also been widely applied in other earth sciences applications such as mapping of precipitation, air temperature or solar radiation at different temporal and spatial scales. Vanadium is a trace element, which is widely distributed in nature. The average abundance of vanadium in the crust is approximately 0.01% Vanadium in the environment comes from :( 1) weathering of parental rocks; (2) combustion of fossil fuels; (3) mining and high-temperature industrial activities including steel-iron refining, electronics and dyeing, etc. While in the soil, vanadium is derived from parental rocks and deposits Human activities, such as the disposal of vanadium-contained waste and oil leakage, may also provide a certain amount of vanadium to the soil. Consequently, there are few countries, where standards and regulations for environmental pollution in soil with vanadium are accepted, for example, Russia, where the maximum of 150 g g−1 allowed in agricultural soil Because of industrial activities and anthropogenic emissions, the concentrations of vanadium in soil have increased significantly in recent years, and the number of people affected by vanadium pollution is also increased. The purpose of this paper were to: (i) estimate the upper baseline concentration of As in topsoils of Isfahan Province (central Iran); (ii) evaluate factors controlling the spatial distribution of soil V concentrations and (iii) delineate polluted from unpolluted areas using geostatistic methods.
Materials & MethodsThis research was conducted in Isfahan province, central Iran. Isfahan has an arid climate and is about 6800 km2 around Zayandehroud River. Mean annual precipitation and temperature are 120 mm and 14.5 oC, respectively and direction of dominant wind in study area is NW-SE (Fig 2b). the area extends from easting of 51o15' to 52o41' longitude and northing of 32o7' to 32o59' latitude. The area covers different land uses including agricultural, industrial, urban and uncultivated lands. There are several big steel factories in the study area. The underlying geology consists mainly of recent terraces, resent alluvial deposit and undifferentiated terraces, all of quaternary age. In addition, lower Cretaceous grey limestone containing Orbitalin and Jurassic shale are found in the south west and south of region.
In this study, soil sampling strategy was random stratify. A total of 207 soil samples (0-20 cm) were collected. At each sampling point the coordinates were obtained using a portable GPS and its land use was recorded the distances.
Soil samples were air dried and ground to pass through a 2 mm sieve. Total V concentration in the soil was determined by XRF.
Statistics including mean, variance, maximum, minimum, coefficient of variation (CV) were calculated using SPSS 11.0 for Windows. Based on the land use information, data sets were grouped into three subsets: industrial and urban, agricultural and uncultivated lands. After calculation, 46.5% of the sampling locations occurred in agricultural lands, 43.5% in uncultivated lands and 10% in industrial and urban areas. The ANOVA was then used to compare the effect of land use.
The spatial variability analysis is a necessary step prior to applying kriging techniques In this work several theoretical variogram functions including linear, exponential and spherical forms considering nugget effects were evaluated using the VARIOWIN and WINGSLIB software. With the sets of fitted variogram functions, the next step is cross validation of the prediction models with MSE. Distribution maps of V was produced using the ordinary kriging procedure and mapped. Correlation coefficient (Pearson) computed between real and estimated data with ordinary kriging that this parameter is identifier of relatively high accuracy estimation in maps. Maps were produce with Surfer (version 10) and ILWIS (version 3.3).
Discussion & ResultsThe average V concentration was 82.9 mg/kg with range of 19.2-140.1 mg/kg in Isfahan surface soils. Total V concentration was scanned for trend determination and no trend was apparent. Therefore, V concentration roughly followed a normal distribution indicating that the data is from a single statistical population. Total V concentrations in different land uses were compared using one-way ANOVA. Total V concentrations were significantly affected by land use and Total V concentrations in agricultural lands also industrial and urban areas are higher significantly. Directional variograms for V concentration computed along twelve directions of azimuth 30 degree with tolerance ± 15 degree. The best variograms fitted in directions of 90 degree and a spherical theoretical covariance model suitable for spatial fields was fitted. Present of spatial dependence (sill/ C0) indicate that distribution of V have Intermediate spatial dependence class. Distribution maps of V indicate high concentration of V in parts of east Isfahan province and industrial and mining activity are important factor to concentration of vanadium in this regions and direction of dominant wind which has affected spread of V in east of the study area. Pearson coefficient in V concentration was rather high and this parameter is identifier of relatively high accuracy estimation in maps.
ConclusionThe overall distribution of heavy metals in surface soil can be due to local changes in rock, land use, and climatic processes. The average V concentration is 82.9 mg/kg in Isfahan surface soils and this is not higher than global average concentration. Land use did have significant effect on V concentration. Agricultural and industrial activities are probably effective factors causing increase the V concentration in the region. Vanadium is one of the impurities of earth crust deposits. Considering the wide expansion of industries such as Isfahan Iron Ore in the Lenjan and Mobarakeh Steel Area in Mobarakeh, and also the significant difference in the average concentration of vanadium in urban land-industrial areas with incult land, the role of industrial activities in these areas can be increased in extend of vanadium. Direction of dominant wind have affected in spread of Vanadium in the high concentration spot in study area.

One of the most important steps in plain zoning groundwater quality is the quality management of groundwater resources. In this study, the use of geostatistical methods, the chemical properties of groundwater of Ilam city is examined. Water from 78 wells in a 13year period from 2002 to 2014 and the removal of sodium, chlorideelectrically conducting, sulfate, TDS, EC and TH were chemically analysed. The results showed that the quality of groundwater in this area has a strong correlation and spatial structure of the model follows the Gaussian and spherical, then the kriging interpolation was used by GIS software. Finally, using fuzzy logic, classification of Schoeller and Wilcox and metod FAHP Buckley multi-criteria decision-classification map of the study area were collected for drinking and agriculture. Zoning map shows the region's groundwater, according to Schuler, 21% of the area of water quality, medium quality, 36% and 43% of the poor quality of drinking water the according to Wilcox, 15% of the area of water quality, 8% and 77% of the average quality of water for agriculture are the poor quality. So we can say that the quality of drinking water in the study area is intermediate and unfavorable for agriculture.

Ambient air quality is a major concern in highly urbanized and industrialized regions such as Tehran.
In this paper, the spatial distribution of 4 air pollutants in Tehran atmosphere was analyzed. The analyzed air pullutants were Carbon monoxide (CO), Nitrogen Dioxide (NO2), Ozone (O3) and atmospheric particulate matters less than 10 micrometers in diameter (PM10). For this purpose, 4 common geostatistical interpolation methods namely: Ordinary Kriging (OK), Universal Kriging (UK), Sample Kriging (SK), and Ordinary Cokriging (COK) with Gaussian modeled semivariogram, were used to estimate the continuous surface for the 4 mentioned air pollutants. The data were collected from 21 air quality monitoring stations located in different districts of Tehran during a 2-year period from 2012 to 2013. The Kriging interpolation schemes are stochastic, local, gradual and exact interpolators. After preprocessing the collected data, they were imported to GIS by using metric coordinate system (UTM Zone 39). Finally, the Kriging predicted map was evaluate using 3 statistical indices of validation namely: Mean Absolute Error (MBE), Mean Bias Error (MAE), and Root Mean Square Error (RMSE) that can be divide into systematic and unsystematic errors (RMSEs, RMSEu).
Findings: The results indicated that, using 2 auxiliary variables, the OCK is the optimum schema for spatial estimation of CO and NO2 pollutants in Tehran. Moreover, SK is found out as the best model for spatial estimation of NO2 and CO. According to optimal model, the highest concentrations of ozone (O3) and particulate matters greater than 10 microns (PM10) are observed in the marginal areas of Tehran, while the highest concentrations of CO, NO2 are observed in the central and northern districts of Tehran such as districts 1 to 4.
The developed optimized model can be used for real time estimation of any pollutants in Tehran atmosphere by updating the observed data.

Accumulation of heavy metals in soil has resulted in several environmental problems in different parts of the world. High concentrations of these elements in soils may lead to high absorption by plants and this is the main route into the human food chain and animal and biological cycles. Consequently, investigations are necessary to find out how accumulation of these elements can be prevented that creates soil and ecosystem contamination. Among heavy metals, cadmium is one of the most dangerous due to its high mobility in biological systems and ability to accumulate in plant tissues without causing any visible symptoms. Considering the high mobility of Cd in acid soils and the optimal growth of rice in such soils, and according to the role of rice in public health, this study was conducted to assess of the contamination of paddy soils with Cd in Guilan Province. For this purpose, 100 soil samples were randomly taken from 0-20 cm depths using GPS devices from different paddy farms. The soil samples were then passed through a 2-mm sieve after drying. The total cadmium concentration and some soil characteristics such as EC, pH, bulk density, particle density and soil organic matter were then measured. The spatial distribution of cadmium was investigated using different geo-statistical methods including IDW, Spline, and Kriging with a GIS software. The results of the soil physical and chemical analyses showed that the average pH of the soils and saturated electrical conductivity were 6.48 and 1.59 ds/m, respectively, indicating no salinity hazard for rice production. The sand, silt and clay contents of soil samples were between 4 to 35, 28 to 49 and 39 to 65, respectively, so the soil textures were in the range of clay loam, silty clay and clay. These soil textures were moderately heavy to heavy and so are suitable for rice cultivation. The average amount of organic matter was 1.78 and the total Cd concentration before cropping varied between 0.65 to 1.40 and, after harvest, was 1.97 to 11.05. From the geo-statistical methods studied, the ordinary Kriging with spherical model was selected as the best method. According to the results obtained, part of the study area becomes contaminated after harvesting, based on the Kelly index which can show the effect of chemical fertilizers use and incorrect land management, and so the use of these fertilizers should be carried out with care.