Mahsa Sameti; Seied Hosein Sanaei-Nejad; Firoozeh Rivaz; Bijan Ghahraman
Abstract
Introduction: Drought is a very complex natural phenomenon which changes with time and space. Spatial and temporal variations of drought are analyzed separately. Geostatistical methods can be used for spatiotemporal analyses to find related spatial and temporal pattern changes. These methods, ...
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Introduction: Drought is a very complex natural phenomenon which changes with time and space. Spatial and temporal variations of drought are analyzed separately. Geostatistical methods can be used for spatiotemporal analyses to find related spatial and temporal pattern changes. These methods, which use the spatio-temporal data, considering the spatial position of the data relative to each other, also take into account their temporal dependence. If needed, they can estimate values of their variable at any location and any time. Moreover, the drought spatial variations in the studied region can be drawn at every desired period. On the other hand, it is expected that intervening of the time dimension in the equations of these methods, as compared to the purely spatial methods, provide more precision in estimating the values of drought indices, which is studied in this research.
Materials and Methods: Monthly rainfall data of 48 stations in the northeast of Iran for the period of 1981-2012 were used in this study. The SPI drought index is calculated for the 12-month time scale. Data were divided into two groups of training data from 1981-2011 and experimental data of 2012. After analyzing the data regarding their stationarity and isotropic assumptions, the spatiotemporal data were formed and their spatiotemporal empirical variogram was drawn. Furthermore, the purely spatial and temporal variograms for the zero space and time steps were also drawn. Then, four models of the spatiotemporal variogram functions were applied on the training data. The performance of these models was tested and compared by estimating the parameters of the model based on the Square Error (MSE). Moreover, three-dimensional fitted variograms were drawn for different models. Mean The best spatiotemporal variogram model was selected by comparing the models prediction with experimental data using the Mean Square Prediction Error (MSPE). Using spatiotemporal kriging method, the predicted values of experimental data were interpolated and that of the observed values were interpolated by kriging method. Cross validation on experimental data was also performed using RMSE, MAE, ME and COR. Then spatiotemporal and purely spatial variogram models were investigated and compared.
Results and Discussion: The results showed that the 12-month SPI index had no spatial trend but had a decreasing trend against the time. Hence, a simple regression equation was used for fitting the trend of the data. After detrending the data, the SPI index values were considered as the dependent variable, while the time was taken as the independent variable. On the other hand, drawing the variogram in different directions (0°, 45°, 90°, and 135°) had no significant effect relative to each other, and the hypothesis of isotropic state was accepted. The plots of purely spatial and temporal variograms showed that the spherical variogram for space and the linear variogram for the time would have the best fitting. The empirical 3-D and 2-D spatiotemporal variograms of the training data were plotted. The empirical 3-D variogram showed that the data had reached to its temporal sill in a 1-year time lag, and had reached to its spatial sill, in about 25-kilometers, which are in conformity with the purely spatial and temporal variograms. The comparison of different variogram functions showed that the MSE values of the separable, metric, product-sum and sum-metric models were 0.00139, 0.00295, 0.00111, and 0.00112, respectively, the last two of which had fewer errors. Drawing the spatiotemporal variogram of these functions showed that the spatiotemporal variogram of product-sum and sum-metric models have more similarity to the sample one. Regarding the selection of the best model, the MSPE statistics of the product-sum and sum-metric models were 0.281 and 0.389, respectively. Therefore, the product-sum model could be selected as the best model. The least rate of errors was found in the exponential variogram model for space, and in the linear variogram for the time. The parameters of the nugget effect, partial sill and range for the spatial variogram would be 0.00, 0.063, and 5.78, and for the temporal variogram would be 0.00, 0.635, and 1.044, respectively. After predicting values of 12-month SPI in 2012 by the product-sum variogram model and adding the values of the trend, they were interpolated by using the spatiotemporal kriging, and the observed values were interpolated by the use of kriging. The obtained plot from the predicted values had great similarity with that of the observed values, which indicates the appropriate capability of the model in predicting the unobserved values. The cross-validation of different spatiotemporal and the spatial models with 25 and 47 neighborhoods showed that the performance of the models had no significant differences relative to each other, and they also had no better performance relative to the purely spatial model.
Conclusion: The results of this study showed that the product-sum model had a better performance among different spatiotemporal variogram models in predicting the 12-month SPI values of 2012. However, the performances of different spatiotemporal models were quite close to each other. There is no significant difference that could be observed between spatiotemporal and purely spatial models. Also, it is proposed to use the dynamic spatiotemporal models and the results to be compared with the classical models.
shokrollah asghari; Mahmood Shahabi
Abstract
Introduction: Over the last few years, due to the depletion of Lake Urmia located in the northwest of Iran, the proportion of surrounding saline agricultural lands increased at a fast pace. Digital mapping of regional soils affected by salt is essential when monitoring the dynamics of soil salts and ...
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Introduction: Over the last few years, due to the depletion of Lake Urmia located in the northwest of Iran, the proportion of surrounding saline agricultural lands increased at a fast pace. Digital mapping of regional soils affected by salt is essential when monitoring the dynamics of soil salts and planning land development and reclamation schemes. The soil hydraulic and mechanical parameters are very important factors that affect water and chemical transport in soil pores. In the salt-affected soils, saturated hydraulic conductivity (Ks) is very low due to the high contents of sodium and weak aggregate stability. Penetration resistance (PR) indicates soil mechanical strength to penetration of a cone or flat penetrometer; it is important in seedling, root growth and tillage operations. Generally, PR values exceed 2.5 MPa, while root elongation is significantly restricted. The analysis of spatial variability of Ks and PR is essential to implement a site-specific soil management especially in the salt-affected lands. The objective of this study was to evaluate the influence of two different bare and agricultural land uses on the spatial variability of Ks and PR in the salt-affected soils around Lake Urmia.
Materials and Methods: This study was conducted in the agricultural and bare lands of Shend Abad region located at the 15 km of Shabestar city, northwest of Iran (45° 36ʹ 34ʺ to 45° 36ʹ 38ʺ E and 38° 6ʹ 37ʺ to 38° 7ʹ 42ʺ N). Totally, 100 geo-referenced samples were taken from 0-10 cm soil depth with 100×100 m intervals (80 ha) in agricultural (n=49) and bare (n=51) land uses. Sand, silt, clay, organic carbon (OC), mean weight diameter of aggregates (MWD), sodium adsorption ratio (SAR) and electrical conductivity (EC), were measured in the collected soil samples. The EC and SAR were measured in 1:2.5 (soil: distilled water) extract. Ks was measured using constant or falling head method. Bulk density (BD) and field water content (FWC) were measured in the undisturbed soil samples taken by steal cylinders with 5 cm diameter and height. Total porosity calculated from BD and particle density (PD). PR was directly measured at the field using a cone penetrometer. The best fit semivariograms model (Gaussian, spherical and exponential) was chosen by considering the minimum residual sum of square (RSS) and maximum coefficient of determination (R2). Ordinary Kriging (OK) and inverse distance weighting (IDW) interpolation methods were used to analyze the spatial variability of Ks and PR. Spatial distribution maps of soil variables were provided by Arc GIS software. The accuracy of OK and IDW methods in estimating Ks and PR was evaluated by mean error (ME), mean absolute error (MAE), root mean square error (RMSE) and concordance correlation coefficient (CCC) criteria.The CCC indicates the degree to which pairs of the measured and estimated parameter value fall on the 45° line through the origin.
Results and Discussion: According to coefficient of variation (CV) from the study area, the most variable soil indicator was Ks (CV=155.6%), whereas the least variable was PD (CV= 3.05%) both in bare land use. The Lognormal distribution was found for Ks data in the studied region. The Pearson correlation coefficients (r values) indicated that there are significant correlations between Ks and OC (r=0.36), sand (r=0.60), SAR (r=-0.35), EC (r=-0.22), BD (r=-0.52), TP (r= 0.31), silt (r=-0.60), and clay (r=-0.43). Also, significant correlations were obtained between PR and FWC (r=-0.32), BD (r=0.21), and TP (r=-0.21). The spatial dependency classes of soil variables were determined according to the ratio of nugget variance to sill expressed in percentages: If the ratio was >25% and <75%, the variable was considered moderately spatially dependent; if the ratio was >75%, variable was considered weakly spatially dependent; and if the ratio was <25%, the variable was considered strongly spatially dependent. The strong spatial dependences with the effective ranges of 2443m were found for Ks. The PR and PD variables had the least (335 m) and the highest (2844 m) effective range, respectively. The range of influence indicates the limit distance at which a sample point has influence over another points, that is, the maximum distance for correlation between two sampling point. The models of fitted semivariograms were spherical for Ks and exponential for PR. According to RMSE and CCC criteria, there was not found significant difference between Ks estimates by OK and IDW interpolation methods. The high CCC and low RMSE values for OK compared with IDW indicated the more precision and accuracy of OK in estimating PR in the studied area. Generally, the spatial maps showed that from agricultural to bare land use by nearing to Lake Urmia, the BD and PR increased and consequently TP and Ks decreased.
Conclusion: The results showed that Ks negatively related to the SAR, EC, BD, silt and clay and positively related to the OC, sand, MWD and TP in the study area. Also, PR negatively related to the FWC and TP and positively related to the BD and silt. The spatial dependency was found strong for Ks. The PR revealed the smallest effective range (335 m) among the studied variables. As a suggestion, for subsequent study, soil sampling distance could be taken as 335 m instead of 100 m in order to save time and minimize cost.
Morteza Bahmani; jahangard mohammadi; Isa Esfandiarpour Borujeni; Hamidreza Mottaghian; Keramatollah Saeidi
Abstract
Introduction: The importance and the presence of spatial variability in soil properties is inevitable, however, the understanding of causes and sources of the variability is not complete. Spatial variation of soil attributes can affect the quality and quantity of plants. Investigation of the soil variability ...
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Introduction: The importance and the presence of spatial variability in soil properties is inevitable, however, the understanding of causes and sources of the variability is not complete. Spatial variation of soil attributes can affect the quality and quantity of plants. Investigation of the soil variability at the small scale can be evaluated by classic statistics and geospatial statistics. The present study was conducted to investigate the spatial variability of yield characteristics of rose (Rosa Damasceneea Mill) and soil characteristics in two main cultivated fields of rose (Negar- Golzar) with different climatic and topographic characteristics located in Bardsir city, Kerman Province.
Materials and Methods: In order to achieve the objectives of the present study, 100 soil and plant samples were collected from each farm. The soil samples were taken from 0 to 25 cm depth and analyzed. The measured soil properties at each location were including fragment, clay, silt, sand, and organic matter contents, CEC, calcium carbonate equivalent, EC, pH, total nitrogen, available phosphorus, and available potassium. Moreover, some plant characteristics (yield, plant height, and plant crown diameter) were measured at each point. Then, maps of soil properties and plant induces were prepared using Geoeas, Variowin, and surfer software. Descriptive statistics were applied using Statistica software (version 20). Kolmogorov-Smirnov test was also used to test the tolerance of variables distribution.
Results and Discussion: The results of Kolmogorov-Smirnov test showed that all characteristics of the plant and soil in both farms follow the normal distribution. Statistical analysis showed that coefficient of variation of soil properties was as follows: total nitrogen (54.47%) and pH (3.16%) in Negar farm, and EC (46.09%) and pH (35.3%) in Golzar farm. The variability of nutrients in both farms had similar trends, so that total nitrogen, phosphorus and potassium have the highest to lowest coefficients of variation, respectively. Analysis of variograms indicated that all of the variables in both fields have a strong and moderate spatial variability. Ranges for variograms were from 122.16m (for yield) to 218.46 m (for silt) in Negar farm and from 115.1m (for available K) to 228 m for (total nitrogen) in Golzar unit. The distribution conditions and spatial variations of the soil properties in the study area were not uniform due to variation of the range of the variograms. The results also showed that the yield characteristics of the rose with some soil characteristics have a closer spatial relationship. About this, in the Negar farm, the range of the rose flower yield was close to the clay, available potassium and calcium carbonate contents. In the Golzar farm, the range of rose flower yield was close to the range of clay, silt, fragments and available phosphorus contents. The spatial correlation ratio showed that all plant characteristics including plant yield, plant height and plant diameter had a strong spatial correlation in the Golzar farm, and all characteristics of the soil were in the medium spatial correlation. Also, in the Negar farm, the product yield characteristics were in a strong spatial correlation class, and all other characteristics were in the medium spatial correlation. Kriging maps showed that soil characteristics and product yield in the study area had spatial distribution. The similarity of the spatial distribution pattern of some variables was one of the important features that these maps showed.
Conclusion: The results of this study showed the characteristics of plant yield and soil characteristics have a moderate to strong spatial dependency even in small scales. Kriging maps illustrated that the pattern and distribution of soil properties even within a farm can be varied. However, the spatial pattern of some soil characteristics such as organic matter and total nitrogen with the spatial pattern of plant characteristics and the dimensions of the farms showed conformity. This indicates that the variability of these characteristics is mainly under the management of farmers, and in order to optimize the use of nutrients, inputs should be re-evaluated by farm managers. In general, the results of this study indicated geostatistical method can be used to recognize of control factors of plant production and use its information in order to improve management.
Leila Kashi Zenouzi; Mohammadreza Yazdani; Mohammad Khosroshahi; Mohammad Rahimi
Abstract
Introduction: Groundwater is the only major source of water for drinking, agricultural and industrial purposes in the Marand city, and its vital importance makes sure that its quality is seriously considered. With qualitative zoning, the process of underground water quality changes is determined at any ...
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Introduction: Groundwater is the only major source of water for drinking, agricultural and industrial purposes in the Marand city, and its vital importance makes sure that its quality is seriously considered. With qualitative zoning, the process of underground water quality changes is determined at any time, place, and condition. It is possible to save time and cost by removing the stations with similar quality status and install new stations at times that are different or critical. In this paper, using the observational data of wells in Marand watershed, the spatial distribution of some groundwater quality parameters has been studied and analyzed using land-based methods. Geostatistical methods for estimating the unknown are remarkably effective.
Materials and Methods: In order to predict the spatial distribution of groundwater quality, data was collected from 48 water wells, semi-deep wells, springs, and others from the Water Resources Management Company. In this research, the spatial variation process of five qualitative parameters of water include EC, electrical conductivity, chlorine and sulfate (SO42-) anions, and Sodium Rate Absorption (SAR) and soluble solids (TDS): Total Dissolved Salts) were studied. After reviewing, some of them were omitted due to statistical deficiencies. Common time base was selected for studying the Blue Years 2003-2005, and the years 1388-88 and 1391-1391. Data homogeneity was evaluated for the statistical period between 1384-1384 by the sequencing test method. According to the mentioned method, there was no heterogeneity in the data. Statistical deficits were determined according to the correlation coefficient of a variable. Data were normalized using SPSS 18.0 software using logarithmic transformation method and their elongation and bending values were obtained in the range -2 and 2. In this study, for estimation of groundwater quality parameters including EC, TDS, Cl-, SAR and SO42-, piezometric wells data were used during the years 84, 88 and 91. Statistical analysis methods consisted of conventional Kriging method in Spherical, Gaussian and exponential modes and Weighted Inverse Distance (IDW) methods with power from 1 to 3 were studied. Cross-validation, G statistics (GetisOrd General G) and Morans Index were used to select the best and most suitable interpolation method. The values of all three evaluation methods were calculated and analyzed using Arc / GIS 10.3 software.
Results and Discussion: Based on the cross-evaluation method, the Kriging method is less effective than RMSE and ME in comparison with the Inverse Distance Weighting method. The zonation map of anion SO42- in year 2012 with G statistics and Moran index was 21.41 and 0.99 %, had the highest interaction in spatial structure and EC zonation map in year 2005 with Moran index and G statistic was 0.16 and 45 respectively has the least interaction of spatial structure. Charts of Changes in Quality Parameters showed that, water quality in latitude and longitude, values which were Cl, EC, SAR, and TDS and SO42 anions between the years 2005-2009 in the western-eastern part have been intangible and have been steeply sloping in the year 91. But in the North-South direction of 84 to 91 increased and then decreased in the middle of basin. Finally, by disconnecting the map of land use and geology of the watershed with the zoning maps of each of the parameters, it is concluded that due to the distribution of villages, residential areas and agricultural lands around them in the center and east of the watershed, the trend of groundwater quality parameters had been changed. The underground waters of Marand country watershed were influenced by human activities. Also, some geological formations and gypsum and dolomite minerals in the area in groundwater quality have led to an increase in TDS values and sulfidation of water resources in the eastern parts of the basin.
Conclusion: Groundwater quality is always influenced by various factors such as flow direction, groundwater level, climatic factors (precipitation, evapotranspiration, etc.), type and composition of geological formations of the region and human factors (land use, extraction of groundwater resources, Entry of household wastewater and agriculture into groundwater resources, etc.). Therefore, due to the importance of the use of groundwater resources and the limitations of its use, it is suggested that continuous monitoring of groundwater quality changes should be carried out using ground-based methods and in order to evaluate the effective factors of water quality parameters spatial distribution maps was prepared and analyzed. In the present study, based on the previous studies, two geology formation and land use types were selected to prepare map of water quality parameters and it turned out that both of these factors are the most important factors affecting the groundwater quality in the Marand country watershed.
azam gholamnia; mohammadhosein mobin; atefe jebali; hamid alipor
Abstract
Introduction: Solar radiation (Rs) energy received at the Earth's surface is measured usingclimatological variables in horizontal surface and is widely used in various fields. Domination of hot and dry climates especially in the central regions of Iran results from decreasing cloudiness and precipitation ...
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Introduction: Solar radiation (Rs) energy received at the Earth's surface is measured usingclimatological variables in horizontal surface and is widely used in various fields. Domination of hot and dry climates especially in the central regions of Iran results from decreasing cloudiness and precipitation and increasing sunshine hours, which shows the high potential of solar energy in Iran. There is a reasonable climatic field and solar radiation in most of regions and seasons which have provided an essential and suitable field to use and extend new and pure energy.
Materials and Methods: One of the common methods to estimate the solar energy received by the earthis usingtemperature variables in any place . An empirical model is proposed to estimate the solar energy as a function of other climatic variables (maximum temperature) recorded in 50 climatological, conventional stations; this model is helpful inextending the climatological solar-energy estimation in the study area. The mean values of both measured and estimated solar energy wereobjectively mapped to fill the observation gaps and reduce the noise associated with inhomogeneous statistics and estimation errors. This analysis and the solar irradiation estimation method wereapplied to 50 different climatologicalstations in Iran for monthly data during1980–2005. The main aim of this study wasto map and estimate the solar energy received in four provinces of Yazd, Esfahan, Kerman and Khorasan-e-Jonoubi.The data used in this analysis and its processing, as well as the formulation of an empirical model to estimate the climatological incident of solar energy as a function of other climatic variables, which is complemented with an objective mapping to obtain continuous solar-energy maps. Therefore, firstly the Rswasestimated using a valid model for 50 meteorological stations in which the amounts of solar radiation weren't recorded for arid and semi-arid areas in Iran. Then, the appropriate method was selected to interpolate by GS+ software and after that, the seasonal maps of the received solar energy over the ground surface were produced by GIS software. The best fitof the Gaussian model was determined in winter with the lowest residual error and the highest correlation 1.87 and 0.913respectively, in spring with the lowest RSS and highest R23.87 and 0.86 respectively and during summer with RSS and R2, 5.9 and 0.851 and the exponential model in autumn withthe RSS and R2, 3.61 and 0.88..
Results and Discussion: Naturally, some of the differences in the mean solar energy among the stations may be related to inter annual variability rather than to differences in the climatic, radiative regimes. If different periods for the climatological estimations are used, the resulting mean values can be representative of the regional climatic regime of solar energy. The results showed that 53% of Yazd province Received 26 Mj / m2.day, in summer.In winter, more than 80% of Yazd province received 15 Mj / m2.day radiation. Kerman compared to other provinces received high solar radiation, especially this feature wasmore pronounced in winter because in this season compared to Yazd, Kerman radiation didnot only showed greater range, but also about 40% of the province's total area received 16 Mj / m2.day radiation, whereas Yazd no radiation was received during this season. Because Kerman is located in the southeast of region and itreceived more solar radiation than other provinces.In this study, the amount of solar energy in surface of 4 provinces including Yazd, Esfahan, Kerman and South Khorasan in arid and semiarid regions of Iran was estimated by the geostatistic. Seasonal mean values of solar energy absorbed at the surface of 4 stationswascalculated. The results showed that Kerman with receiving 27.25 (Mj m-2. D-1) averagely has the most received solar energy and Esfahan with 21.54 (Mj m-2. D-1) during the summer has received the least solar energy. The limited records of solar energy used in thisanalysis madethe analysis of long-term variations impossible. This paper wasthe first stage of a more extensive study which involvedmonitoring the behavior of photocells under real environmental conditions, which allowedto obtain efficiency curves used in the mapping of actual photovoltaic potential inarid and semiarid regions of Central Iran. This analysis must be complemented by better, higher resolution estimates of the incident solar energy; a viable alternative for such a task is the use of satellite observations. However, a better photovoltaic prospection, with high quality data, is necessary.
Ahmad Gholamalizadeh Ahangar; F. Sarani; M. Hashemi; A. Shabani
Abstract
Knowledge of organic carbon spatial variations in different land uses will help to interpret and simulate the behavior of terrestrial ecosystems facing environmental and climate changes. The purpose of this study is comparing regression, geostatistics and artificial neural network (ANN) methods for predicting ...
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Knowledge of organic carbon spatial variations in different land uses will help to interpret and simulate the behavior of terrestrial ecosystems facing environmental and climate changes. The purpose of this study is comparing regression, geostatistics and artificial neural network (ANN) methods for predicting organic carbon content in 192 samples of surface soil (0 to 30 cm) of Sistan plain (Miankangi region). In this study, Only 25% of organic carbon variations were explained by variables used in linear regression model in the study area (R2= 0.25). Moreover, simple co-kriging (with clay as co-variable) which was the best geostatistical method in the current study, predicted organic carbon content weakly (R2= 0.23 and RMSE= 0.127). However, using latitude and longitude parameters, ANN performed much better than linear regression and geostatistical methods for predicting organic carbon content (R2= 0.79 and RMSE= 0.044).
M. Zahedifar; S.A.A. Moosavi; M. Rajabi
Abstract
Management and chemical quality of groundwater is very important in arid regions. Fasa plain (in Fars province) is an arid-semi arid region in Iran, that almost all of its residents are using groundwater in agricultural activities. Recent water shortages resulted in deepens water table, salinization ...
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Management and chemical quality of groundwater is very important in arid regions. Fasa plain (in Fars province) is an arid-semi arid region in Iran, that almost all of its residents are using groundwater in agricultural activities. Recent water shortages resulted in deepens water table, salinization and reduced groundwater quality in this area. Studying the spatial variability and zoning of the chemical quality attributes of water in order to optimum utilization and management of soil and water resources is one of the practical methods in conservation of these resources. Therefore, the spatial variability for some of groundwater quality attributes in 80 wells located in Fasa plain of Fars province including total hardness, total dissolved solids, electrical conductivity, pH, soluble cations (calcium, magnesium, sodium, and potassium) and anions (sulfate, chloride, and bicarbonate) concentration was studied and attributes were estimated by applying geostatistical methods. The suitable estimation method was determined and zoning of the studied area was done for each studied attributes. The spatial variability structure of studied attributes followed the spherical and exponential models having the range parameters of 6700 to 140600 m belonging to the moderate to strong spatial correlation classes. The Ordinary Point Kriging was determined as the suitable estimating method that used for preparing the maps of water quality zoning. The quality of groundwaters in the southern half of the studied area was lower than that of the northern half, therefore, the more sensitive management in utilization of water resources and in using of agricultural systems is needed in order to avoiding the deterioration of water quality and worsening of groundwater status that is directly related to the residents livelihood.
salman naimi marandi; shamsollah Ayoubi; B. Azimzadeh
Abstract
Soil pollution by heavy metals is an important environment issue throughout the world. Heavy metals’ origin, accumulation, and distribution in soil have been the focus of much attention by many researchers. The objective of this study was to recognize the sources of some heavy metals in surface soils ...
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Soil pollution by heavy metals is an important environment issue throughout the world. Heavy metals’ origin, accumulation, and distribution in soil have been the focus of much attention by many researchers. The objective of this study was to recognize the sources of some heavy metals in surface soils in Zob-Ahan industrial district, Isfahan province, using multivariate geostatistical techniques. A total of 202 surface (0–30 cm) soil samples were collected. Total lead (Pb), zinc (Zn), manganese (Mn), iron (Fe), copper (Cu), nickel (Ni), cobalt (Co) and chromium (Cr) contents of the samples were determined. A multivariate geostatistical analysis was performed to identify the common source of heavy metals. The results of principal component analysis led to the identification of three components. So, these components were explained 31, 27, and 16 % of total variance of heavy metal concentration, respectively. The distribution of scores of each components were shown that the quantities of Fe, Mn, Pb and Zn were found to be associated with anthropogenic activities, corresponding to the first factor was termed the “anthropogenic component”. The quantities of Co were found to be associated with parent rocks, corresponding to the second factor was termed the “lithologic component”. Also, the third factor was mainly attributed to Cu, Ni and Cr which also comprised the first and third factors, indicating a mixed source both from lithologic and anthropogenic inputs.
M. Akbarzadeh; B. Ghahraman
Abstract
In geo-statistics, prediction of an unknown value of random field has been performed in specified time and position, using spatio-temporal Kriging. In some circumstances, a suitable covariate increase the estimation prediction. Geo-statistical methods of Universal Kriging (UK) and Kriging with External ...
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In geo-statistics, prediction of an unknown value of random field has been performed in specified time and position, using spatio-temporal Kriging. In some circumstances, a suitable covariate increase the estimation prediction. Geo-statistical methods of Universal Kriging (UK) and Kriging with External Trend (KwET) were applied to Mashhad plain water quality data sets. The optimal network to monitor groundwater quality was presented, using Entropy. All wells ranked based on the criterion of Entropy and mutual information. Then, the optimal network was determined based on the percentages of acquired information and relying on the spatio-temporal Kriging. Based on UK and KwET, electrical conductivity (EC) was the best covariate. KwET with EC as a covariate was the superior Kriging method. A network covering 111 wells showed to be as informative as the existing monitoring network with a total of 237 wells.
salman naimi marandi; shamsollah Ayoubi; H. Khademi
Abstract
Soil pollution by heavy metals from the manufacturing process due to metal smelting plants closely related to human health is very important. Given the importance of the province to industrial and agricultural activities, this study was conducted to explore the vertical and horizontal variability of ...
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Soil pollution by heavy metals from the manufacturing process due to metal smelting plants closely related to human health is very important. Given the importance of the province to industrial and agricultural activities, this study was conducted to explore the vertical and horizontal variability of lead and nickcl metals in contaminated soils around the Zobahan melting factory, in nearby of Isfahan city. For this purpose, 202 profiles were dug and described in the green landscapes of Zobahan industrial site by a manner of gird sampling method. Five hundred soil samples were taken from depths of 0–30, 60–90, and 120–150 cm. Conccntration of total lead (Pb) and nickel (Ni) were measured in the soil samples. To explore the vertical distribution of selected metals, the mean values of Ni and Pb were compared statistically. The horizontal variability of selected metals was evaluated by variography analysis and the spatial distributions of them were constructed by kriging method. The overall results of study showed that Pb content in surface horizons is controlled by industrial activity, otherwise the concentration of Ni mainly attributed to parent material.
A. Lakzian; M. Fazeli Sangani; Alireza Astaraei; A. Fotovat
Abstract
This study was conducted to evaluate using terrain attributes derived from digital elevation model (DEM) as ancillary data to predict soil organic carbon (SOC) by implementing different statistical and geostatistical techniques. A linear regression model (LR), Artificial Neural Network model (ANN), ordinary ...
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This study was conducted to evaluate using terrain attributes derived from digital elevation model (DEM) as ancillary data to predict soil organic carbon (SOC) by implementing different statistical and geostatistical techniques. A linear regression model (LR), Artificial Neural Network model (ANN), ordinary kriging (OK), ordinary co-kriging (OCK), regression kriging (RK) and kriging with an external drift (KED) were performed to predict spatial distribution of SOC in an area of 2400 km2 in mashhad, iran. The SOC was measured for 200 soil samples of the study area and their corresponding Terrain attributes value was extracted from derived from 10-m resolution DEM. correlation between measured SOC and individual terrain attributes was determined, the number of 160 data were used for model development and 40 as validation data set. Resulting maps of different interpolation methods were compared to evaluate map quality using MAE and R2 criteria calculated from plotting measured versus estimated data. The results showed that there is a significant but not strong correlation between SOC and terrain attributes. The comparison of estimation techniques showed that the KED technique with wetness index as ancillary data has the best performance (MAE=0.18 %, R2=0.67) of all, but no significant difference with RK. There were modest differences between maps created with geostaistical technique but sensible difference with LR and ANN ones. The results of this study propose that although there is a significant correlation between SOC and terrain attributes therefore It can be use for enhancing the quality of map, but it is not able to express the spatial variability of SOC as it is necessary for detailed soil map. Because there is other factors controlling SOC spatial distribution
