Numerical evaluation of multicomponent cation exchange reactive transport in physically and geochemically heterogeneous porous media

Experimental evidence and stochastic studies strongly show that the transport of reactive solutes in porous media is significantly influenced by heterogeneities in hydraulic conductivity, porosity, and sorption parameters. In this paper, we present Monte Carlo numerical simulations of multicomponent reactive transport involving competitive cation exchange reactions in a two-dimensional vertical physically and geochemically heterogeneous medium. Log hydraulic conductivity, log K, and log cation exchange capacity (log CEC) are assumed to be random Gaussian functions with spherical semivariograms. Random realizations of log K and log CEC are used as input data for the numerical simulation of multicomponent reactive transport with CORE^2D, a general purpose reactive transport code. Longitudinal features of the fronts of reactive and conservative species are computed from the temporal and spatial moments of depth-averaged concentrations. Monte Carlo simulations show that: (1) the displacement of reactive fronts increases with increasing variance of log K, while it decreases with the variance of log CEC; (2) second-order spatial moments increase with increasing variances of log K and log CEC; (3) uncertainties in the mean arrival time are largest (smallest) for negatively (positively) correlated log K and Log CEC; (4) cations undergoing competitive cation exchange exhibit different apparent velocities and retardation factors due to both physical and geochemical heterogeneities; and (5) the correlation between log K and log CEC affects significantly apparent cation retardation factors in heterogeneous aquifers.     

Development from within: an evaluative research on economic development strategies

Research on the practice of adopting local economic development (LED) strategies is important to understand our local world as it is and what it might be made to be as a place for community. This article on economic development strategies (EDS) highlights the results from a survey of 82 small communities representing the Yukon Territory and ten provinces in Canada. The purpose was to evaluate the positive and additive effects of past local action and community initiatives so as to understand the variation in the adoption of local economic development strategies of Canada’s small towns and local municipalities. Using a questionnaire as instrument for data collection, the study employed hierarchical regression analysis and principal component analysis (PCA) as method for factor extraction and composite assessment on the effects of adopting EDS for community. The PCA solution was applied to evaluate the structure of correlation between the community characteristics as control variables in the baseline model for regression analysis and the past local action and community initiatives as independent variables. The results of the hierarchical regression analysis showed that local initiatives have significant and additive effects on the adoption of EDS by small communities. The study findings offer some insights into some perspectives of ‘development from within’ to mean the local economic development practice in Canadian communities.     

Evaluation of probabilistic flow in two unsaturated soils

A variably saturated flow model is coupled to a first-order reliability algorithm to simulate unsaturated flow in two soils. The unsaturated soil properties are considered as uncertain variables with means, standard deviations, and marginal probability distributions. Thus, each simulation constitutes an unsaturated probability flow event. Sensitivities of the uncertain variables are estimated for each event. The unsaturated hydraulic properties of a fine-textured soil and a coarse-textured soil are used. The properties are based on the van Genuchten model. The flow domain has a recharge surface, a seepage boundary along the bottom, and a no-flow boundary along the sides. The uncertain variables are saturated water content, residual water content, van Genuchten model parameters alpha (α) and n, and saturated hydraulic conductivity. The objective is to evaluate the significance of each uncertain variable to the probabilistic flow. Under wet conditions, saturated water content and residual water content are the most significant uncertain variables in the sand. For dry conditions in the sand, however, the van Genuchten model parameters α and n are the most significant. Model parameter n and saturated hydraulic conductivity are the most significant for the wet clay loam. Saturated water content is most significant for the dry clay loam. Electronic Publication     

Stable carbon isotopic composition of soil organic matter in the karst areas of Southwest China

This study dealt with the distribution characteristics of soil organic carbon （SOC） and the variation of stable carbon isotopic composition （δ^13C values） with depth in six soil profiles, including two soil types and three vegetation forms in the karst areas of Southwest China. The δ^13C values of plant-dominant species, leaf litter and soils were measured using the sealed-tube high-temperature combustion method. Soil organic carbon contents of the limestone soil profiles are all above 11.4 g/kg, with the highest value of 71.1 g/kg in the surface soil. However, the contents vary between 2.9 g/kg and 46.0 g/kg in three yellow soil profiles. The difference between the maximum and minimum δ^13C values of soil organic matter （SOM） changes from 2.2‰ to 2.9‰ for the three yellow soil profiles. But it changes from 0.8‰ to 1.6‰ for the limestone soil profiles. The contrast research indicated that there existed significant difference in vertical pattems of organic carbon and δ^13C values of SOM between yellow soil and limestone soil. This difference may reflect site-specific factors, such as soil type, vegetation form, soil pH value, and clay content, etc., which control the contents of different organic components comprising SOM and soil carbon turnover rates in the profiles. The vertical variation patterns of stable carbon isotope in SOM have a distinct regional character in the karst areas.     

Investigation of spatial variability and pattern analysis of soil properties in the northwest of Iran

Analysis of the spatial variability of soil properties is important to explain the site-specific ecosystems. Spatial patterns of some soil properties such as soil texture, exchangeable sodium percentage (ESP), electrical conductivity (ECe), soil pH and cation exchange capacity (CEC) were analyzed in salt and sodic affected soils in the south of the Ardabil province, in the northwest of Iran, to identify their spatial distribution for performance of a site-specific management. Soil samples were collected from 0 to 30, 30 to 60, 60 to 90, 90 to 120 and 120 to 150 cm soil depths at sampling sites. Data were investigated both statistically and geostatistically on the basis of the semivariogram. The spatial distribution model and spatial dependence level varied in the study area. Among the considered parameters, maximum and minimum spatial variability were observed in EC and pH parameters, respectively. Soil properties showed moderate to strong spatial dependence, except for a few. ECe was strongly spatially dependent in the total soil depth and clay was strongly spatially dependent at the first depth. Sand and pH were moderately spatially dependent for three of the five depths. ESP was strongly spatially dependent and silt was moderate in the total soil depths, except at 90–120 cm depth. Furthermore, CEC had strong spatial dependence for three of the five depths. All geostatistical range values were >1,389 m in this study. It was concluded that the strong spatial dependency of soil properties would lead to extrinsic factors such as bedrock, agricultural pollution, drainage and ground water level.     

Application of Continuous Wavelet Transform in?Examining Soil Spatial Variation: A?Review

An adequate understanding of soil spatial variation as a function of space and scale is necessary in ecological modeling, environmental prediction, precision agriculture, soil quality assessment and natural resources management. Soil spatial variation can be partitioned into frequencies (scale) and positions (location) by the wavelet transform. This review focuses mainly on different applications of the continuous wavelet transform (CWT) for the identification of the scale and location dependence of soil attributes. We discussed both wavelet spectra and wavelet coherence in our analysis of soil spatial variation. Global wavelet spectra, being the sum of wavelet spectra over all spatial locations at a scale, can be used to examine the dominant scale of variation. Furthermore, some variations at a particular scale persist over all locations (termed global features), whereas others are present at only a few locations (localized features). Wavelet spectra can be used to identify both localized and global features. The combination of localized and global features provides a complete picture of the scale-location information of different processes in the field and may provide better guidance in designing efficient management practices. Wavelet coherency partitions the total correlation between two variables into correlations at different scales and locations, while also revealing the scale- and location-specific relationship between those two variables. This relationship may be helpful in developing predictive links between one property and another.     

Integrating multivariate statistical analysis with GIS for geochemical assessment of groundwater quality in Shiwaliks of Punjab,India

The dependency of people on groundwater has increased in the past few decades due to tremendous increase in crop production, population and industrialization. Groundwater is the main source of irrigation in Shiwaliks of Punjab. In the present study the samples were collected from predetermined location as was located on satellite image on basis of spectral reflectance. Global positioning system was used to collect samples from specific locations. Principal components analysis (PCA) together with other factor analysis procedures consolidate a large number of observed variables into a smaller number of factors that can be more readily interpreted. In the present study, concentrations of different constituents were correlated based on underlying physical and chemical processes such as dissociation, ion exchange, weathering or carbonate equilibrium reactions. The PCA produced six significant components that explained 78% of the cumulative variance. The concentration of the few trace metals was found to be much higher indicating recharge due to precipitation as main transport mechanism of transport of heavy metals in groundwater which is also confirmed by PCA. Piper and other graphical methods were used to identify geochemical facies of groundwater samples and geochemical processes occurring in study area. The water in the study area has temporary hardness and is mainly of Ca–Mg–HCO₃ type.     

Joint Consistent Mapping of High-Dimensional Geochemical Surveys

Geochemical surveys often contain several tens of components, obtained from different horizons and with different analytical techniques. These are used either to obtain elemental concentration maps or to explore links between the variables. The first task involves interpolation, the second task principal component analysis (PCA) or a related technique. Interpolation of all geochemical variables (in wt% or ppm) should guarantee consistent results: At any location, all variables must be positive and sum up to 100 %. This is not ensured by any conventional geostatistical technique. Moreover, the maps should ideally preserve any link present in the data. PCA also presents some problems, derived from the spatial dependence between the observations, and the compositional nature of the data. Log-ratio geostatistical techniques offer a consistent solution to all these problems. Variation-variograms are introduced to capture the spatial dependence structure: These are direct variograms of all possible log ratios of two components. They can be modeled with a function analogous to the linear model of coregionalization (LMC), where for each spatial structure there is an associated variation matrix describing the links between the components. Eigenvalue decompositions of these matrices provide a PCA of that particular spatial scale. The whole data set can then be interpolated by cokriging. Factorial cokriging can also be used to map a certain spatial structure, eventually projected onto those principal components (PCs) of that structure with relevant contribution to the spatial variability. If only one PC is used for a certain structure, the maps obtained represent the spatial variability of a geochemical link between the variables. These procedures and their advantages are illustrated with the horizon C Kola data set, with 25 components and 605 samples covering most of the Kola peninsula (Finland, Norway, Russia).     

Trace elements in sub-alpine forest soils on the eastern edge of the Tibetan Plateau,China

Industrial development has increased fast in China during the last decades. This has led to a range of environmental problems. Deposition of trace elements to forest ecosystems via the atmosphere is one potential problem. In this paper, we report the results from a pilot study where the trace element levels of the sub-alpine forest soils on the eastern edge of the Tibetan Plateau have been measured. Possible relationships between soil properties and trace element concentrations have also been investigated. The obtained concentrations (mg kg⁻¹) were boron (B) 48.06–53.70, molybdenum (Mo) 1.53–2.26, zinc (Zn) 68.18–79.53, copper (Cu) 36.81–42.44, selenium (Se) 0.33–0.49, cadmium (Cd) 0.16–0.29, lead (Pb) 25.80–30.71, chromium (Cr) 96.10–110.08, nickel (Ni) 30.16–45.60, mercury (Hg) 0.05–0.11, and arsenic (As) 3.09–4.17. With a few exceptions, the element concentration can be characterized as low in the investigated sub-alpine forest soils. No clear differences in trace element levels were found between topsoil and subsoil samples, indicating that the atmospheric deposition of trace element has been low. The soil parent material plays a key role to determine trace element levels. Soil properties, including pH_w, organic carbon (OC), clay fraction, cation-exchange capacity (CEC), total iron (Fe), and total aluminum (Al) concentrations were related to trace element concentration using correlation analysis. Total Fe and Al showed the strongest relationships with concentrations of most trace elements in the sub-alpine forest soils. PCA analyses indicated that a significant increase in the number of cars with the fast development of local tourism may result in higher Pb concentration in the future.     

长春地区粉质黏土导热系数与其物性参数相关性研究

为研究导热系数与影响因素之间的相关关系,建立导热系数的推算公式,以长春地区粉质黏土为研究对象,对原状土样的导热系数与其物理参数之间的相关性进行回归分析。制作9个重塑土样,测其相关的参数值,以验证回归方程的适用性。结果表明,回归分析建立导热系数与2个物理参数之间的关系式不成立;考虑天然密度、含水率和孔隙度为自变量,其分别对应的相关性系数T检验显著值(Sig)都0. 05,复决定系数为0. 886,建立的回归方程成立,自变量能准确解释因变量的变化,且含水率与导热系数呈负相关,天然密度和孔隙度呈正相关。重塑土样相关参数代入回归方程得到的导热系数值与实验实测值之间相对误差低于4%,验证了该回归方程的普遍性和适用性。     

Topographic controls on the depth distribution of soil CO2 in a small temperate watershed

Accurate measurements of soil CO₂ concentrations (pCO₂) are important for understanding carbonic acid reaction pathways for continental weathering and the global carbon (C) cycle. While there have been many studies of soil pCO₂, most sample or model only one, or at most a few, landscape positions and therefore do not account for complex topography. Here, we test the hypothesis that soil pCO₂ distribution can predictably vary with topographic position. We measured soil pCO₂ at the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO), Pennsylvania, where controls on soil pCO₂ (e.g., depth, texture, porosity, and moisture) vary from ridge tops down to the valley floor, between planar slopes and slopes with convergent flow (i.e., swales), and between north and south-facing aspects. We quantified pCO₂ generally at 0.1–0.2 m depth intervals down to bedrock from 2008 to 2010 and in 2013. Of the variables tested, topographic position along catenas was the best predictor of soil pCO₂ because it controls soil depth, texture, porosity, and moisture, which govern soil CO₂ diffusive fluxes. The highest pCO₂ values were observed in the valley floor and swales where soils are deep (≥0.7 m) and wet, resulting in low CO₂ diffusion through soil profiles. In contrast, the ridge top and planar slope soils have lower pCO₂ because they are shallower (≤0.6 m) and drier, resulting in high CO₂ diffusion through soil profiles. Aspect was a minor predictor of soil pCO₂: the north (i.e., south-facing) swale generally had lower soil moisture content and pCO₂ than its south (i.e., north-facing) counterpart. Seasonally, we observed that while the timing of peak soil pCO₂ was similar across the watershed, the amplitude of the pCO₂ peak was higher in the deep soils due to more variable moisture content. The high pCO₂ observed in the deeper, wetter topographic positions could lower soil porewater pH by up to 1 pH unit compared to porewaters equilibrated with atmospheric CO₂ alone. CO₂ is generally the dominant acid driving weathering in soils: based on our observations, models of chemical weathering and CO₂ dynamics would be improved by including landscape controls on soil pCO₂.     

Compaction characteristics of granular soils in United Arab Emirates

Most of the topsoils encountered in United Arab Emirates and in the Arabian Peninsula are granular soils with small percentages of silt and clay. Determination of the compaction characteristics of such soils is an essential task in preparing for construction work. The accumulating experience over many years of soil testing in our laboratories suggested that there exists an underlying trend that governs the compaction characteristics of such soils. As such, a study was undertaken to assess the compaction characteristics of such soils and to develop the governing predictive equations. For the purposes of this study, 311 soil samples were collected from various locations in the United Arab Emirates, and tested for various including grain-size distribution, liquid limit, plasticity index, specific gravity of soil solids, maximum dry density of compaction, and optimum moisture content following ASTM D 1557-91 standard procedure C. Following the development of the predictive equations, a new set of 43 soil samples were collected and their compaction results were used to test the validity of predictive model. The range of variables for these soils were as follows: percent retained on US sieve #4 (R#4): 0–68; Percent passing US sieve #200 (P#200): 1–26; Liquid limit: 0–56; Plasticity index: 0–28; Specific gravity of soil solids: 2.55–2.8. Based on the compaction tests results, multiple regression analyses were conducted to develop mathematical models and nomographic solutions to predict the compaction properties of soils. The results indicated that the nomographs could predict well the maximum dry density within ±5% confidence interval and the optimum moisture content within ±3%. This revised version was published online in July 2006 with corrections to the Cover Date.     

Variable rainfall limits the germination of upper intertidal marsh plants in Southern California

Temporal variation in rainfall created a germination window for seedling establishment in the upper intertidal marshes of southern California. In this highly variable climate, total annual rainfall was highly variable, as was the timing and size of rainfall during the wet season. Daily rainfalls>3.0 cm were rare in the long-term record but created germination opportunities that had two components: low salinity and high moisture. During the 1996–1997 wet season, only one-day rainfalls>3.0 cm resulted in large increases in soil moisture and decreases in soil salinity. Germination in the upper intertidal marsh of three wetlands followed two large (>3.0 cm) rainfall events in the relatively dry 1996–1997 season and multiple medium and small rainfall events in the wetter 1997–1998 season. In addition to rainfall, plant cover and soil texture influenced, spatial and temporal variation in soil salinity and moisture. Daily and weekly sampling adequately described soil moisture and salinity so that germination could be predicted; monthly sampling would have missed the low-salinity and high-moisture events that trigger germination.     

Mineralogical characterization of saprolite at the FRC background site in Oak Ridge,Tennessee

The Field Research Center (FRC) including five contaminated sites and a clean background area was established in Oak Ridge, Tennessee, as a part of the U.S. Department of Energy’s Natural and Accelerated Bioremediation Research (NABIR) program. This study investigates the mineralogy and mineralogical pathways of saprolite at the FRC background site to provide a fundamental basis for the remediation strategy for contaminated sites. The background site is underlain interbedded shales, siltstones, and limestones with nearly identical characteristics to the contaminated sites. Bulk samples of saprolite were collected by hand picking approximately at 1 m depth (C horizon) from the soil surface. The soil pH of 4.3 and cation exchange capacity (CEC) of 10.5 cmol/kg measured are in the range of the typical shallow depth saprolite layer in this area. Total Fe by citrate-bicarbonate-dithionate (CBD) and ammonium oxalate extractable (amorphous) were 17.6 and 0.61 g/kg, respectively. Total Mn extracted by NH₂OH·HCl was 0.17 g/kg. X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses indicate that quartz, illite, and microcline (K-feldspar) are the dominant minerals, occupying 95% of mineral composition. The saprolite samples analyzed have shown characteristics of oxic conditions overall, and the degrees of weathering for three sampling locations were various, most for S1 and least for S3, likely influenced either by the flow channels developed through saprolite or by seasonal fluctuation of the groundwater table. The source of the manganese oxide that observed from the site is likely to be Mn-rich muscovite in the shale or Mn-rich biotite in the blackish band in the limestone. The results such as abundant Mn and Fe contents identified encouraging prospects for conducting remediation projects in FRC sites.     

The use of correspondence analysis and principal component analysis as a tool for deducing metamorphic mineral crystallization in Mn-rich lithologies

Principal component analysis (PCA) and correspondence analysis (CA) have been used to identify several critical mineral crystallization factors during metamorphism of Mn-rich lithologies from the Iberian Massif, Spain. Three types of variables have been considered in the system: mineralogical variables, bulk composition variables and one physical variable (oxygen fugacity). PCA was applied to the compositional variables to obtain four components, which were used as new compositional variables. These, together with the mineralogical and physical variables, were treated in the CA. The plot of the variables on the factor 1-factor 2 plane reveals that tephroite crystallization is controlled by a compositional variable representing low values of ratio Si/(Mn+Fe) in the rocks. Moreover, it is possible to deduce from this plot the importance of high oxygen fugacity conditions for the crystallization of piemontite. The third factor shows that the crystallization of spessartine does not require very restrictive physical-chemical conditions.     

Abiotic regulators of soil respiration in desert ecosystems

Soil temperature and soil moisture are the most important environmental factors controlling soil respiration in mesic ecosystems. However, soil respiration and associated abiotic regulators have been poorly studied in desert ecosystems. In this study, soil respiration was measured using an automated CO₂ efflux system (LI-COR 8100), and the effects of soil temperature and moisture on the rate of soil respiration were examined in six desert sites [three communities—Haloxylon ammodendron, Halostachys caspica and Anabasis aphylla at high (B) and low (A) vegetation coverage respectively]. It was found that soil respiration was significantly and positively correlated with soil surface temperature. A multi-variable model of soil temperature and soil moisture could explain 61.9% of temporal variation in soil CO₂ efflux at a larger scale. There were significantly negative correlations between soil respiration and soil moisture in Haloxylon ammodendron B and Halostachys caspica B sites, which represented the driest and wettest sites, respectively. The results also showed that soil respiration displayed obvious diurnal and seasonal patterns during the growing season. The Q₁₀ values for Haloxylon ammodendron A and B, Halostachys caspica A and B, and Anabasis aphylla A and B sites were 1.3, 1.34, 1.58, 1.65, 1.31 and 1.17, respectively, with a cross-site average of 1.39. The results showed that soil respiration was not positively correlated with soil moisture unlike in most mesic ecosystems. However, soil respiration in desert ecosystems is less sensitive to temperature variation than most mesic ecosystems as indicated by the lower Q₁₀ values possibly due to energy limitation.     

Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation

Soil erosion is one of most widespread process of degradation. The erodibility of a soil is a measure of its susceptibility to erosion and depends on many soil properties. Soil erodibility factor varies greatly over space and is commonly estimated using the revised universal soil loss equation. Neglecting information about estimation uncertainty may lead to improper decision-making. One geostatistical approach to spatial analysis is sequential Gaussian simulation, which draws alternative, equally probable, joint realizations of a regionalised variable. Differences between the realizations provide a measure of spatial uncertainty and allow us to carry out an error analysis. The objective of this paper was to assess the model output error of soil erodibility resulting from the uncertainties in the input attributes (texture and organic matter). The study area covers about 30 km² (Calabria, southern Italy). Topsoil samples were collected at 175 locations within the study area in 2006 and the main chemical and physical soil properties were determined. As soil textural size fractions are compositional data, the additive-logratio (alr) transformation was used to remove the non-negativity and constant-sum constraints on compositional variables. A Monte Carlo analysis was performed, which consisted of drawing a large number (500) of identically distributed input attributes from the multivariable joint probability distribution function. We incorporated spatial cross-correlation information through joint sequential Gaussian simulation, because model inputs were spatially correlated. The erodibility model was then estimated for each set of the 500 joint realisations of the input variables and the ensemble of the model outputs was used to infer the erodibility probability distribution function. This approach has also allowed for delineating the areas characterised by greater uncertainty and then to suggest efficient supplementary sampling strategies for further improving the precision of K value predictions.     

Assessment of aquifer vulnerability to contamination in Khanyounis Governorate, Gaza Strip—Palestine, using the DRASTIC model within GIS environment

Groundwater is a very important natural resource in Khanyounis Governorate (the study area) for water supply and development. Historically, the exploitation of aquifers in Khanyounis Governorate has been undertaken without proper concern for environmental impact. In view of the importance of quality groundwater, it might be expected that aquifer protection to prevent groundwater quality deterioration would have received due attention. In the long term, however, protection of groundwater resources is of direct practical importance because, once pollution of groundwater has been allowed to occur, the scale and persistence of such pollution makes restoration technically difficult and costly. In order to maintain basin aquifer as a source of water for the area, it is necessary to find out, whether certain locations in this groundwater basin are susceptible to receive and transmit contamination. This study aims to: (1) assess the vulnerability of the aquifer to contamination in Khanyounis governorate, (2) find out the groundwater vulnerable zones to contamination in the aquifer of the study area, and (3) provide a spatial analysis of the parameters and conditions under which groundwater may become contaminate. To achieve that, DRASTIC model within geographic information system (GIS) environment was applied. The model uses seven environmental parameters: depth of water table, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity to evaluate aquifer vulnerability. Based on this model and by using ArcGIS 9.3 software, an attempt was made to create vulnerability maps for the study area. According to the DRASTIC model index, the study has shown that in the western part of the study area the vulnerability to contamination ranges between high and very high due to the relatively shallow water table with moderate to high recharge potential, and permeable soils. To the east of the previous part and in the south-eastern part, vulnerability to contamination is moderate. In the central and the eastern part, vulnerability to contamination is low due to depth of water table. Vulnerability analysis of the DRASTIC Model indicates that the highest risk of contamination of groundwater in the study area originates from the soil media. The impact of vadose zone, depth to water level, and hydraulic conductivity imply moderate risks of contamination, while net recharge, aquifer media, and topography impose a low risk of aquifer contamination. The coefficient of variation indicates that a high contribution to the variation of vulnerability index is made by the topography. Moderate contribution is made by the depth to water level, and net recharge, while impact of vadose zone, hydraulic conductivity, soil media, and Aquifer media are the least variable parameters. The low variability of the parameters implies a smaller contribution to the variation of the vulnerability index across the study area. Moreover, the “effective” weights of the DRASTIC parameters obtained in this study exhibited some deviation from that of the “theoretical” weights. Soil media and the impact of vadose zone were the most effective parameters in the vulnerability assessment because their mean “effective” weights were higher than their respective “theoretical” weights. The depth of water table showed that both “effective” and “theoretical” weights were equal. The rest of the parameters exhibit lower “effective” weights compared with the “theoretical” weights. This explains the importance of soil media and vadose layers in the DRASTIC model. Therefore, it is important to get the accurate and detailed information of these two specific parameters. The GIS technique has provided an efficient environment for analysis and high capabilities of handling large spatial data. Considering these results, DRASTIC model highlights as a useful tool that can be used by national authorities and decision makers especially in the agricultural areas applying chemicals and pesticides which are most likely to contaminate groundwater resources.     

Screening of extraction methods for Cd and As bioavailability prediction in rhizospheric soil using multivariate analyses

Heavy metals in tailings and mining wastes from abandoned mines can be released into adjacent agricultural field and bioaccumulated in crops or vegetables. Therefore, prediction of metal bioavailability has become an important issue to prevent adverse effect of bioaccumulated metals on human health. In this study, single and sequential extraction methods were compared using multivariate analysis to predict the bioavailability of Cd and As in contaminated rhizosphere soils. Single extraction using 0.1 M HCl for Cd and 1.0 M HCl for As had an extraction efficiency of 8–12% for soil Cd and 14–17% for soil As compared to total concentration extracted with aqua regia. Using sequential extraction, Fe–Mn-bound Cd (FR3) and residual Cd (FR5) were the dominant fractions representing 43 and 41% of total Cd concentration. For As, the strongly absorbed form (FR2) was the most abundant chemical fraction showing 45–54% of the total As concentration in soil. Multivariate analyses showed that single extraction with HCl and total concentration of Cd and As in soil were significantly correlated to potato and green onion plant tissue metal concentration. Although little information was obtained with multiple regression analysis because of multicollinearity of variables, the result of principle component analysis (PCA) revealed that the highest positive loading was obtained using total concentration of Cd and As in soil in the first principle component (PC1). In addition, total concentration of Cd and As in soil was independently grouped with other chemical fractions by cluster analysis. Therefore, the overall result of this research indicated that total concentrations of Cd and As in rhizosphere soils were the best predictors of bioavailability of heavy metals in these contaminated soils.