Explorative data analysis of heavy metal contaminated soil using multidimensional spatial regression

To obtain data on heavy metal contaminated soil requires laborious and time-consuming data sampling and analysis. Not only has the contamination to be measured, but also additional data characterizing the soil and the boundary conditions of the site, such as pH, land use, and soil fertility. For an integrative approach, combining the analysis of spatial distribution, and of factors influencing the contamination, and its treatment, the Mollifier interpolation was used, which is a non-parametric kernel density regression. The Mollifier was capable of including additional independent variables (beyond the spatial dimensions x and y) in the spatial interpolation and hence explored the combined influence of spatial and other variables, such as land use, on the heavy metal distribution. The Mollifier could also represent the interdependence between different heavy metal concentrations and additional site characteristics. Although the uncertainty measure supplied by the Mollifier at first seems somewhat unusual, it is a valuable feature and supplements the geostatistical uncertainty assessment.     

An approach for assessing soil health: a practical guide for optimal ecological management

How to integrate environmental geographic information and biodiversity data combined with management measures to effectively assess soil health is still an unresolved problem. This paper suggests an approach for systematically estimating soil quality and guiding ecological management. First, canonical correspondence analysis is used to predict the distributions of plant species or microorganism communities, principle pollutants and environmental variables from which spatial and environmental data are extracted by the geographic information system (GIS). Secondly, geostatistical methodologies are then used to estimate and quantify the spatial distribution characteristic of the species and pollutants and to create maps of spatial uncertainty and hazard assessment through ArcGis technology. Finally, redundancy analysis provides a suggestion about better management strategy and environmental factor for improving soil health and biodiversity. The combination of these methods with “3S” techniques as an assessment approach effectively meets the challenges for estimation and management in different soil environments.     

An Integrated Geostatistical Approach for Contaminated Site and Soil Characterisation

For several years, abandoned or stopped industrial sites increasingly arouse the interest of politics and investors. Having a high social and economic estate value, these sites generally reveal contaminated soils that must be remediated first before receiving any new use. Due to financial, environmental or human health stakes, heuristic methods appear inappropriate because they do not provide reliable estimations of contaminated soil volumes and ignore spatial uncertainties. Problems at hand may be very complex, involving multiple correlated contaminants for which spatially varying pollutant grades are to be estimated and confronted to various regulatory thresholds, depending on redevelopment target areas. In such conditions, geostatistics provides effective methods to quantify local and global uncertainties about soil contamination and contaminated soil volumes. By quantifying uncertainties, geostatistical models are useful as support for decision-making about redevelopment scenarios or remediation techniques. Specific approaches are required, however, to overcome particular modelling issues as related to the skewness of pollutant grade distributions or change of support. Making use of our practical experience, such an integrated geostatistical approach is proposed for modelling contaminated sites. It is illustrated by application to a recent actual case study.     

Representing Spatial Uncertainty Using Distances and Kernels

Assessing uncertainty of a spatial phenomenon requires the analysis of a large number of parameters which must be processed by a transfer function. To capture the possibly of a wide range of uncertainty in the transfer function response, a large set of geostatistical model realizations needs to be processed. Stochastic spatial simulation can rapidly provide multiple, equally probable realizations. However, since the transfer function is often computationally demanding, only a small number of models can be evaluated in practice, and are usually selected through a ranking procedure. Traditional ranking techniques for selection of probabilistic ranges of response (P10, P50 and P90) are highly dependent on the static property used. In this paper, we propose to parameterize the spatial uncertainty represented by a large set of geostatistical realizations through a distance function measuring “dissimilarity” between any two geostatistical realizations. The distance function allows a mapping of the space of uncertainty. The distance can be tailored to the particular problem. The multi-dimensional space of uncertainty can be modeled using kernel techniques, such as kernel principal component analysis (KPCA) or kernel clustering. These tools allow for the selection of a subset of representative realizations containing similar properties to the larger set. Without losing accuracy, decisions and strategies can then be performed applying a transfer function on the subset without the need to exhaustively evaluate each realization. This method is applied to a synthetic oil reservoir, where spatial uncertainty of channel facies is modeled through multiple realizations generated using a multi-point geostatistical algorithm and several training images.     

Early-indicator signals of groundwater contamination: the case of seawater encroachment

 An early indication of groundwater contamination occurs when pollutant concentrations start to fluctuate and exceed background values of ambient fresh groundwater. An analysis of a characteristic situation of this type uses data from Israel's coastal phreatic granular aquifer. The pollutant is generally seawater, and the contamination process involves replacement of freshwater by encroaching sea- or other saltwater, a process augmented by human activity. The contamination process involves three stages: (1) groundwater composition remains relatively stable with small salinity content; (2) small salinity changes are perceptible with reversible fluctuations; and (3) salinity concentration increases at a sharply higher rate. The second stage is a useful early-indicator signal of contamination. Early-indicator signals of groundwater pollutant concentrations involve "minor" fluctuations in water chemistry at the advent of the contamination process. The intensity and magnitude of such a salinization/pollution process at any given location depends upon lithologic matrix, aquifer heterogeneity, and resultant flow domain characteristics, as well as contaminant properties. If such "signs" are detected at a sufficiently early stage, appropriate management steps may be taken to rectify further seawater and/or saltwater encroachment. Received: 23 July 1996 · Accepted: 25 June 1997     

Air pollution prediction models of particles,As, Cd,Ni and Pb in a highly industrialized area in Castellón (NE,Spain)

The objective of this study was to elaborate a series of mathematical models with the aim of short-term prediction of TSP, PM10, As, Cd, Ni and Pb in ambient air. These pollutants depend on some known variables (meteorological variables). The reason for choosing this pollutant type is that particulate matter may present a much higher potential risk despite its low representativeness as compared with the gas pollutant group. A positive correlation between high particle concentrations and deterioration in public health has been shown in recent studies. The elements As, Cd, Ni and Pb in PM10 were also analyzed to determine the toxicity of these particles. The goal is to provide a useful instrument to alert the population facing possible episodes of high concentrations of atmospheric pollutants. The study was carried out in a highly industrialized area in the ceramic cluster of Castellón for 5 years (2001–2005). The origin of the contamination in this area is both natural and anthropogenic. The natural origin is due to the resuspension of mineral materials from the surrounding mountains and from the long-range transport of materials from North Africa. The anthropogenic contamination sources that stand out include the non-metallic mineral material industries (ceramic production), chemical industries (color, frit and enamel manufacturing), as well as vehicular traffic. Once the particle samples were collected in quartz fiber filters, the concentration levels of TSP and PM10 were determined gravimetrically. The chemical analysis of the filters was carried out by ICP-MS. Predictive models have been constructed by using multiple regression analysis together with time series models (ARIMA). The SPSS 14.0 statistical software has been employed to analyze the obtained experimental data.     

Quantification of organic pollutant degradation in contaminated aquifers using compound specific stable isotope analysis – Review of recent developments

Compound specific stable isotope analysis (CSIA) has been established as a viable tool for proving, characterizing and assessing degradation of organic pollutants within contaminated aquifers. The fractionation of stable isotopes during contaminant degradation leads to observable shifts in stable isotope ratios which can serve as an indicator for in situ pollutant degradation and allow for a quantitative assessment by means of the so-called Rayleigh (distillation) equation.This review highlights the recent developments of the Rayleigh equation approach for quantifying in situ degradation of organic pollutants in contaminated aquifers. The advantages and limitations of the Rayleigh equation approach are discussed and suggestions for improvements are given. Concepts are provided to estimate the uncertainty due to errors or variability of input parameters and how to deal with such uncertainty. Moreover, the applicability of the Rayleigh equation approach is evaluated regarding the heterogeneity and complexity of groundwater systems. For such systems, the review discusses the relevance of non-destructive processes, which affect the concentration (e.g., dispersive mixing) and potentially also the stable isotope ratio of contaminants (e.g., sorption, volatilization), and the resulting implications for the Rayleigh equation approach.     

Quantification of organic pollutant degradation in contaminated aquifers using compound specific stable isotope analysis - Review of recent developments

Compound specific stable isotope analysis (CSIA) has been established as a viable tool for proving, characterizing and assessing degradation of organic pollutants within contaminated aquifers. The fractionation of stable isotopes during contaminant degradation leads to observable shifts in stable isotope ratios which can serve as an indicator for in situ pollutant degradation and allow for a quantitative assessment by means of the so-called Rayleigh (distillation) equation.This review highlights the recent developments of the Rayleigh equation approach for quantifying in situ degradation of organic pollutants in contaminated aquifers. The advantages and limitations of the Rayleigh equation approach are discussed and suggestions for improvements are given. Concepts are provided to estimate the uncertainty due to errors or variability of input parameters and how to deal with such uncertainty. Moreover, the applicability of the Rayleigh equation approach is evaluated regarding the heterogeneity and complexity of groundwater systems. For such systems, the review discusses the relevance of non-destructive processes, which affect the concentration (e.g., dispersive mixing) and potentially also the stable isotope ratio of contaminants (e.g., sorption, volatilization), and the resulting implications for the Rayleigh equation approach.     

Distinguishing potential sources of arsenic released to groundwater around a fault zone containing a mine site

Arsenic (As) contamination in groundwater in mineralized areas typically results from the oxidation of As-rich sulfide minerals in aquifers, from hydrothermal alteration of geothermal systems, or as a result of anthropogenic influences such as mining activity. The primary goal of this study was to determine the spatial and temporal variance in As concentrations in shallow groundwater in a mineralized area and to identify the main As source controlling the concentration patterns. To this end, a combination of a geostatistical technique for space–time modeling of As concentrations and a numerical simulation, which models the transport of As in groundwater, is implemented. A study site in North Sulawesi, Sulawesi Island, Indonesia was selected as it was suitable for investigating the importance of fault lines and metal mining on As contamination. Initially, stable isotope analysis was used to ascertain the groundwater source and the mixing mechanism of the shallow and deep groundwater. Geostatistical modeling revealed consistent general patterns of As concentrations during the past 10 years, with high concentrations found along a NW–SE axis. By matching the geostatistical results with the distributions of As concentrations obtained through transport modeling, the deep-seated hydrothermal system along the fault zone was found to be the major As source. Wastewater from the mine was also observed to be a local As source. Another important influence on the As concentration pattern was a river, which acted as a boundary to separate the groundwater systems into two regions.     

Contamination,risk, and heterogeneity: on the effectiveness of aquifer remediation

The effectiveness of aquifer remediation is typically expressed in terms of a reduction in contaminant concentrations relative to a regulated maximum contaminant level (MCL), and is usually confirmed by sparse monitoring data and/or simple model calculations. Here, the effectiveness of remediation is re-examined from a more thorough risk-based perspective that goes beyond the traditional MCL concept. A methodology is employed to evaluate the health risk to individuals exposed to contaminated household water that is produced from groundwater. This approach explicitly accounts for differences in risk arising from variability in individual physiology and water use, the uncertainty in estimating chemical carcinogenesis for different individuals, and the uncertainties and variability in contaminant concentrations within groundwater as affected by transport through heterogeneous geologic media. A hypothetical contamination scenario is developed as a case study in a saturated, alluvial aquifer underlying an actual Superfund site. A baseline (unremediated) human exposure and health risk scenario, as induced by contaminated groundwater pumped from this site, is predicted and compared with a similar estimate based upon pump-and-treat exposure intervention. The predicted reduction in risk in the remediation scenario is not an equitable one—that is, it is not uniform to all individuals within a population and varies according to the level of uncertainty in prediction. The importance of understanding the detailed hydrogeologic connections that are established in the heterogeneous geologic regime between the contaminated source, municipal receptors, and remediation wells, and its relationship to this uncertainty is demonstrated. Using two alternative pumping rates, we develop cost-benefit curves based upon reduced exposure and risk to different individuals within the population, under the presence of uncertainty.     

Gaseous air pollution background estimation in urban,suburban, and rural environments

There is a great demand for estimating the ambient air pollutant background concentrations in order to assess the effectiveness of different emission control strategies. In this paper, the background concentrations of four pollutants, namely sulfur dioxide (SO₂), nitrogen oxides (NOx), carbon monoxide (CO), and ozone (O₃) pollutants in urban, suburban, and rural environments were investigated using Kolmogorov–Zurbenko (KZ) filter technique. Air quality data from monitoring stations over a period of 4 years (2007–2010) was analyzed for three locations in Kuwait, namely urban, suburban, and rural. The spatial and temporal (daily, weekly, and monthly) variations of the four pollutants were analyzed. The results show that the levels of ambient air pollutant background concentrations were high in the urban site compared to suburban and rural area. The diurnal variation of SO₂ concentration showed an early morning peak, while the diurnal variation of NOx concentration constituted has two peaks, one was in the early morning hours (5 to 8 a.m.) and the second was in nighttime hours (8 to 11 p.m.). These two peaks were observed at all three locations. The monthly background NOx concentration reached a maximum in winter and minimum in summer. Diurnal variation of CO concentration showed a similar trend to SO₂ concentrations in all three locations. Because of the photochemical reactions that occur in the atmosphere, the background concentration of O₃ showed an inverse relation with respect to background concentration of NOx.     

Application of sequential Gaussian simulation to quantify the surface wave magnitude uncertainty within the faulted regions

Geostatistical simulations have been recently widely used in the geological and mining investigations. Variogram, the fundamental tools of geostatistics, can identify the spatial distribution of the regionalized variable within the area. One of the important issues of geostatistical simulation in seismotectonics is producing uncertainty maps, which could be applicable to predict earthquake parameters through the site locations especially for civil structures like bridges. It can help engineers to design the structure of interest better. Earthquake parameters as for example seismic fault and surface wave magnitude (Ms) have significant impact on the feasibility study of the civil structures. In this research, a method is presented to produce uncertainty maps for seismic fault and surface wave magnitude, Ms. For this aim, information related to surface wave magnitude and fault trace in Zagros region (SW of Iran) has been collected. Then, the relationships between them through the site location have been investigated and analyzed by conditional geostatistical simulation. In order to quantify the uncertainty of each parameter, the uncertainty formula after generating the E-type maps has been provided and discussed. Finally, in “Talgah Bridge” site, these uncertainty maps were produced to interpret the impact of the surface wave magnitude and fault trace in this specific civil structure.     

Conditional Latin Hypercube Simulation of (Log)Gaussian Random Fields

In earth and environmental sciences applications, uncertainty analysis regarding the outputs of models whose parameters are spatially varying (or spatially distributed) is often performed in a Monte Carlo framework. In this context, alternative realizations of the spatial distribution of model inputs, typically conditioned to reproduce attribute values at locations where measurements are obtained, are generated via geostatistical simulation using simple random (SR) sampling. The environmental model under consideration is then evaluated using each of these realizations as a plausible input, in order to construct a distribution of plausible model outputs for uncertainty analysis purposes. In hydrogeological investigations, for example, conditional simulations of saturated hydraulic conductivity are used as input to physically-based simulators of flow and transport to evaluate the associated uncertainty in the spatial distribution of solute concentration. Realistic uncertainty analysis via SR sampling, however, requires a large number of simulated attribute realizations for the model inputs in order to yield a representative distribution of model outputs; this often hinders the application of uncertainty analysis due to the computational expense of evaluating complex environmental models. Stratified sampling methods, including variants of Latin hypercube sampling, constitute more efficient sampling aternatives, often resulting in a more representative distribution of model outputs (e.g., solute concentration) with fewer model input realizations (e.g., hydraulic conductivity), thus reducing the computational cost of uncertainty analysis. The application of stratified and Latin hypercube sampling in a geostatistical simulation context, however, is not widespread, and, apart from a few exceptions, has been limited to the unconditional simulation case. This paper proposes methodological modifications for adopting existing methods for stratified sampling (including Latin hypercube sampling), employed to date in an unconditional geostatistical simulation context, for the purpose of efficient conditional simulation of Gaussian random fields. The proposed conditional simulation methods are compared to traditional geostatistical simulation, based on SR sampling, in the context of a hydrogeological flow and transport model via a synthetic case study. The results indicate that stratified sampling methods (including Latin hypercube sampling) are more efficient than SR, overall reproducing to a similar extent statistics of the conductivity (and subsequently concentration) fields, yet with smaller sampling variability. These findings suggest that the proposed efficient conditional sampling methods could contribute to the wider application of uncertainty analysis in spatially distributed environmental models using geostatistical simulation.     

Copper contamination of soils and vegetables in the vicinity of Jiuhuashan copper mine,China

Copper contamination in soils and vegetables in the vicinity of an abandoned copper mine in China was investigated. The Cu concentrations of 93 soil samples ranged from 30.4 to 3,191 mg kg⁻¹ soil for a mean of 816.8 mg kg⁻¹ soil. Among 15 samples from a 0 to 20-cm soil layer used for the toxicity characteristic leaching procedure (TCLP) test, the highest value of Cu-TCLP was 133.8 mg kg⁻¹ soil and the TCLP values were positively correlated with the total Cu content of the soils. The sequential extraction of soils in the 0–20-, 20–40-, and 40–60-cm soil layers showed that Cu existed mainly in the Fe–Mn oxide fraction, sulfide/organic fraction, and residual fraction. The copper contamination of 21 species of vegetables from in situ sampling was also examined. Cu concentrations in the edible portions of Brassica chinensis and Solanum melongena were higher than the FAO/WHO standard (40 mg kg⁻¹ DW). The health risk of copper for local inhabitants from consuming these vegetables was assessed on the basis of the target hazard quotient. Enriched concentrations of copper were also found in situ in eight cultivars of B. chinensis planted in the fields, with two levels of Cu concentration. The results showed that there is severe copper contamination in this mine area, and the pollutant in soils show a high risk of leaching into the groundwater and diffusing through the food chain.     

Increased As load to the Uchen stream due to mine drainage and soils in the abandoned Kangwon mining district of Korea

To determine the appropriate allocation of resources for the future restoration of the abandoned mining district of Kangwon in Korea, identification of the main pollutants and the main sources discharging these pollutants is crucial. Therefore, a 2-year study was undertaken to quantify the amount of pollutants in the Uchen stream (a potential sink for contamination), which runs through the district, and to determine the potential sources of these pollutants, including mine drainage and soil. Arsenic (As) was the main pollutant in mine drainage and soils showing concentrations above the Korean regulated standard levels of 50 μg L⁻¹ and 50 mg kg⁻¹ for water and soil, respectively. In addition, the pollution index (PI) showed that mine drainages were polluted by As to a moderate (2 ≤ PI < 3) or strong (4 ≤ PI < 5) degree. Consequently, As load in mine drainage and soil contributed to increased amounts of As in the stream. The As loads in mine drainages (11 and 587 g month⁻¹ for mine adit 1 and 2, respectively) accounted for only 9% of the total As load to the stream (6,378 g month⁻¹); and the influence of mine drainages on As contents in the stream was more reliant on the total volume of mine drainage generated rather than the As concentration in the mine drainage. Approximately 91% of the As in the stream was derived from the soils within the study area.     

饮用水源地地下水氨氮特殊脆弱性研究

特殊脆弱性分析评价是实现定量研究地下水受某种特定污染物威胁的有效手段。文章以佳木斯市七水源地为研究对象,在分析区域水文地质条件的基础上,选取地下水防污性能评价模型（DRASTIC）用于研究区固有脆弱性的评价,侧重分析区内土地利用类型、稳定开采条件下的地下水水位降深等人为因素,以及典型污染物氨氮在特定介质中的通量这一特殊人类活动因素,构建了水源地特殊脆弱性评价模型。以研究区内28组浅层地下水样品中氨氮浓度和对应采样点特殊脆弱性指数之间的相关性来评估模型的可靠性,计算结果显示二者相关系数为0.67,具有较好的相关性,说明该评价系统可靠。特殊脆弱性计算结果显示研究区内以中等以下脆弱性为主,其中水源开采区和西南丘陵区特殊脆弱性较高,计算结果有助于实现水源地的科学管理。     

Geographical Information Systems and Geostatistics for Modelling Radioactively Contaminated Land Areas

The Chernobyl plume contaminated vast lands of Europe with radiocaesium (¹37Cs) in 1986 because of the deposition of radionuclides on the ground by wet and dry deposition processes. Nevertheless, in a nuclear emergency, contamination data may be very sparse and there is need to make rapid and scientifically supported decisions. Here we analyze the rainfall field, an important precursor of the wet deposition, during the passage of the plume. Thus, estimating rainfall spatial variability can help to identify possible contaminated areas and associated risks when rainfall exceeded a given threshold. In this paper, we show that the conditional probabilities of exceeding threshold rainfall values could be spatially assessed using the mutual benefits of linking geostatistical and geographical information system (GIS) to quantify the evaluation of the risk involved in decision making. In particular, the non-parametric geostatistic technique, termed Indicator Kriging (IK), enables one to efficiently estimate the probability that the true value exceeds the threshold values by means of the indicator coding transform. Afterward, GIS has been used to find the areas probably affected by wash-out (probability >0.5 that rainfall is above a certain threshold). The experimental study has been focused on a test site in Beneventan agroecosystem (Southern Italy) to model the spatial uncertainty over a continuous area from sparse rainfall data. This enabled to generate probability maps delineating area potentially affected by to contamination to be monitored after wet deposition of Chernobyl releases.     

Using Multiple-Point Geostatistics for Tracer Test Modeling in a Clay-Drape Environment with Spatially Variable Conductivity and Sorption Coefficient

This study investigates the effect of fine-scale clay drapes on tracer transport. A tracer test was performed in a sandbar deposit consisting of cross-bedded sandy units intercalated with many fine-scale clay drapes. The heterogeneous spatial distribution of the clay drapes causes a spatially variable hydraulic conductivity and sorption coefficient. A fluorescent tracer (sodium naphthionate) was injected in two injection wells and ground water was sampled and analyzed from five pumping wells. To determine (1) whether the fine-scale clay drapes have a significant effect on the measured concentrations and (2) whether application of multiple-point geostatistics can improve interpretation of tracer tests in media with complex geological heterogeneity, this tracer test is analyzed with a local three-dimensional ground-water flow and transport model in which fine-scale sedimentary heterogeneity is modeled using multiple-point geostatistics. To reduce memory needs and calculation time for the multiple-point geostatistical simulation step, this study uses the technique of direct multiple-point geostatistical simulation of edge properties. Instead of simulating pixel values, model cell edge properties indicating the presence of irregularly shaped surfaces are simulated using multiple-point geostatistical simulations. Results of a sensitivity analysis show under which conditions clay drapes have a significant effect on the concentration distribution. Calibration of the model against measured concentrations from the tracer tests reduces the uncertainty on the clay-drape parameters. The calibrated model shows which features of the breakthrough curves can be attributed to the geological heterogeneity of the aquifer and which features are caused by other processes.     

考虑污染物扩散风险的场地地下水污染多层次风险评估方法

地下水中污染物具有迁移性和扩散性,会对下游敏感受体造成威胁,目前场地地下水风险评估主要关注人体健康风险,还未能综合考虑地下水污染的整体风险,尤其是忽视了污染物迁移引起的对下游敏感受体的风险。本研究基于“源-径-汇”模型构建了考虑污染物扩散风险的场地地下水污染风险评估的指标体系与风险评估模式。在指标体系构建方面,重点考虑场地地下水的污染源、迁移路径和敏感受体3个方面。在风险评估模式方面,根据场地不同地下水污染状态开展3个层次的风险评估。基于假想的铬污染场地开展了案例分析,设置了地下水污染状态的4种情景,利用Wexler溶质运移模型计算了地下水污染羽的时空变化,并针对地下水污染的不同层次开展了风险评估。结果表明,在场地地下水污染羽未到达场地边界的2种情形中,场地地下水的风险评分分别为4.0,6.2,分别属于低风险与中风险。在场地地下水污染羽到达或超出场地边界的2种情形中,场地地下水的风险评分分别为7.0,8.8,分别属于中风险与高风险。综合而言,本研究构建的方法能够用来对场地地下水进行系统全面的评估和对比,能够根据风险结果对污染场地进行有效的分级管控,为污染场地的风险管控提供技术支撑。     

基于未确知信息的河流水质模拟预测研究

基于河水流速、污染物综合衰减系数等参量信息不确定性的特点,提出了运用未确知数学理论进行河流水质模拟预测的未确知数学模型。该模型不仅能得到以区间形式表示的河流污染物预测浓度,也能获得各浓度可能值区间对应的主观可信度,这为水污染控制系统规划和水环境管理决策提供了更为丰富、科学的浓度信息。实例研究表明,运用未确知数学理论进行河流水质模拟预测,理论上是可行的,预测结果是可靠的。