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.     

Catchment scale geostatistical simulation and uncertainty of soil erodibility using sequential Gaussian simulation

Soil erodibility (K) affects sediment delivery to streams and needs to be appropriately quantified and interpolated as a fundamental geographic variable for implementing suitable catchment management and conservation practices. The spatial distribution of K for erosion modelling at non-sampling grid locations has traditionally been estimated using interpolation algorithms such as kriging which do not adequately represent the uncertainty of estimates. These methods cause smoothing effects through overestimating the low values and underestimating the large values. In this study observed values were used to implement a sequential Gaussian simulation (SGS) procedure to evaluate the certainty of modelled data. Soil erodibility values were computed using 41 soil samples taken from the top 10 cm soil layer regularly distributed across four catchments, 367–770 ha in area, within Kangaroo River State forest, New South Wales (NSW). One hundred realisations were applied in the simulation process to provide spatial uncertainty and error estimates of soil erodibility. The results indicated that values simulated by the SGS algorithm produced similar K values for the neighbouring cells. At the pixel level, the SGS approach generated a reliable estimation of soil erodibility in most areas. Spatial variation of the K factor in this study was strongly related to soil landscape differences across the catchments; within catchments slope gradient did not have a substantial impact on the numerical values of the K factor using pixel-by-pixel comparisons of raster grid maps.     

基于拉丁方抽样及K-S检验的边坡可靠性分析

边坡稳定性是受复杂因素影响下的多维非线性问题,大多数边坡工程都存在着不确定性。大量试验和工程实践证明,影响边坡状态的因素中有许多具有显著的随机性,参数具有变异性。从概率的角度出发,结合实际工程,对于原始数据采用K-S检验法进行假设验证,确定参数变量的分布类型,克服了人为假设的误差,并分别采用Latin方抽样(LHS)法和Monte Carlo法对参数进行抽样,得到状态函数值,确定安全系数及可靠指标,对比两种抽样方法,基于LHS法模拟次数要少于Monte Carlo法,而且破坏概率的收敛性也优于Monte Carlo法,明显节省了计算时间     

基于拉丁超立方抽样的有限元可靠度程序开发及应用

在地下工程结构可靠性分析中,岩土体参数离散性大,功能函数非线性程度高,常为隐式函数,因此,对可靠度计算方法提出了很高的要求。针对传统蒙特卡罗抽样方法计算量非常大,难以满足工程要求的问题,基于拉丁超立方抽样技术,结合了Matlab和有限元软件Abaqus各自的优点,编制了Matlab-Abaqus联合计算的有限元可靠度程序,该程序具有节省样本空间、提高抽样效率的显著特点。通过对某圆形隧道的结构可靠性分析,表明该程序能很快达到收敛,能够满足地下工程结构可靠性计算分析的要求。     

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.     

Assessing the risk of soil vulnerability to wind erosion through conditional simulation of soil water content in Sistan plain, Iran

Surface soil water content (SWC) is one of the key factors controlling wind erosion in Sistan plain, southeast of Iran. Knowledge of the spatial variability of surface SWC is then important to identify high-risk areas over the region. Sequential Gaussian simulation (SGSIM) is used to produce a series of equiprobable models of SWC spatial distribution across the study area. The simulated realizations are used to model the uncertainty attached to the surface SWC estimates through producing a probability map of not exceeding a specified critical threshold when soil becomes vulnerable to wind erosion. The results show that SGSIM is a suitable approach for modelling SWC uncertainty, generating realistic representations of the spatial distribution of SWC that honour the sample data and reproduce the sample semivariogram model. The uncertainty model obtained using SGSIM is compared with the model achieved through sequential indicator simulation (SISIM). According to accuracy plots, goodness statistics and probability interval width plots, SGSIM performs better for modelling local uncertainty than SISIM. Sequential simulation provided a probabilistic approach to assess the risk that SWC does not exceed a critical threshold that might cause soil vulnerability to wind erosion. The resulted risk map can be used in decision-making to delineate “vulnerable” areas where a treatment is needed.     

土壤重金属元素迁移模拟方法在矿集区适用性比较

选择合适的模拟方法预测重金属的迁移对于防治重金属污染具有重要意义。介绍土壤重金属的垂向迁移和水平迁移2种方式,比较了垂向迁移中的经验模型、分相模型和整体模型,以及水平迁移中的SWAT模型、随风迁移模型的优缺点及其影响因素,探讨了这些模型在矿集区重金属迁移模拟中的适用性,为矿集区选择合适的土壤重金属迁移模拟方法提供了科学依据。     

土壤重金属元素迁移模拟方法在矿集区适用性比较

选择合适的模拟方法预测重金属的迁移对于防治重金属污染具有重要意义。介绍土壤重金属的垂向迁移和水平迁移2种方式,比较了垂向迁移中的经验模型、分相模型和整体模型,以及水平迁移中的SWAT模型、随风迁移模型的优缺点及其影响因素,探讨了这些模型在矿集区重金属迁移模拟中的适用性,为矿集区选择合适的土壤重金属迁移模拟方法提供了科学依据。     

Visible and near infrared reflectance spectroscopy for measuring soil heavy metal content as a quick method

Conventional methods for investigating soil heavy metal contamination based on raster sampling and chemical analysis are time consuming and relatively expensive. Reflectance spectroscopy within the visible-near-infrared （VNIR） region is known for being rapid, convenient, and accurate. Due to low abundance, heavy metals in soils cannot exhibit their inherently spectral features. The objective of this study were （1） to examine the possibility of reflectance spectra as a rapid method to assess Ni, Cr, Cu, Hg, Zn, As, Pb, and Cd in soils, and （2） to explore the mechanism by which to predict spectrally featureless heavy metals. Two research areas located in the south （120 samples） and north （61 samples） of Nanjing City were researched. Reflectance spectra of the samples were recorded on a Perkin-Elmer Lambda 900 spectrophotometer. Partial least-square regression （PLSR） approach was used to develop calibration equations between the spectral data and measured values for heavy metals. The prediction results of the two areas were consistent. Except for Cd all the other elements could be successfully predicted using the reflectance spectra. The prediction accuracy for Ni, Cr, and Cu was highest, while that for Hg and Cd was lowest. In order to further explore the physicochemical mechanism behind the relations between reflectance spectra and heavy metals, one more principal component analysis （PCA） was done for nine elements （eight heavy metals and Fe）. The results of PCA for the both areas were also consistent. Loading plot of factors 1 and 2 from the results of PCA showed that Ni, Cr and Cu, the prediction accuracy of which was highest, were clustered together with Fe. For the other elements that were less correlated with Fe, their prediction accuracy was also lower than that of the three elements. Fe is spectrally active and can exhibit its absorption features. Therefore, the inter-correlation between heavy metals and spectrally active total Fe is the major mechanism by which to predict spectrally featureless trace metals. This study for the two areas showed the potential of reflectance spectra to predict microelements in soils.     

土壤重金属污染潜在风险评价

以北方某河流河床及河流两侧农田中土壤为研究对象,采用地累积指数法和潜在生态危害指数法对土壤中Cd 和Ni 的污染状况进行风险评价。结果表明,废弃冶炼中心处河床及河流两侧土壤中 Cd 和Ni 污染严重,均超过当地土壤背景值; 河床中土壤重金属富集程度为Cd ＞ Ni,其中Cd 富集程度达5 级,为重污染。以当地土壤背景值作为参比值进行评价,该河流处于重度的潜在生态危害程度,重金属的生态危害顺序为Cd ＞ Ni。     

四酸消解-电感耦合等离子体发射光谱法(ICP-OES)测定土壤中6种重金属元素

建立了四酸消解-电感耦合等离子发射光谱法(ICP-OES)同时测定土壤中Cu、Pb、Zn、Cr、Mn、Ni 6种重金属元素含量的方法.通过试验确定了6种重金属元素的特征分析谱线,同时确定了仪器的最佳工作参数为:高频发生器射频功率1 300 W,等离子体气流速15 L/min,辅助气流速0.3 L/min,雾化器流量0.55 L/min,泵流速1.5 L/min,观测方式为水平观测.根据仪器参数,方法中6种重金属元素的检出限为0.25～1.00 μg/g,工作曲线的相关系数均大于0.9990.选取土壤标准物质GBW07406(gss-6)验证方法,6种重金属元素准确度和精密度均能达到分析测试的要求.     

A geostatistical approach for mapping and uncertainty assessment of geogenic radon gas in soil in an area of southern Italy

Spatial distribution of concentrations of radon gas in the soil is important for defining high risk areas because geogenic radon is the major potential source of indoor radon concentrations regardless of the construction features of buildings. An area of southern Italy (Catanzaro-Lamezia plain) was surveyed to study the relationship between radon gas concentrations in the soil, geology and structural patterns. Moreover, the uncertainty associated with the mapping of geogenic radon in soil gas was assessed. Multi-Gaussian kriging was used to map the geogenic soil gas radon concentration, while conditional sequential Gaussian simulation was used to yield a series of stochastic images representing equally probable spatial distributions of soil radon across the study area. The stochastic images generated by the sequential Gaussian simulation were used to assess the uncertainty associated with the mapping of geogenic radon in the soil and they were combined to calculate the probability of exceeding a specified critical threshold that might cause concern for human health. The study showed that emanation of radon gas radon was also dependent on geological structure and lithology. The results have provided insight into the influence of basement geochemistry on the spatial distribution of radon levels at the soil/atmosphere interface and suggested that knowledge of the geology of the area may be helpful in understanding the distribution pattern of radon near the earth’s surface.     

小秦岭金矿带农田土壤重金属的时空变异趋势及其意义

研究农田土壤重金属累积的时空变异特征及定量化预测变异趋势,是探讨土壤重金属地球化学累积效应及预警方法的关键。收集整理小秦岭金矿区以往调研资料和陆续的动态监测数据,对比分析了近28年来研究区农田土壤重金属特征污染物Hg、Pb、Cd、Cu的时空变异特征和扩散速率,并利用年均增长量和总变化率2个参数,定量计算了研究区土壤Hg、Pb、Cd、Cu的累积速率,探讨其环境意义。分析表明,1985年至今研究区农田土壤重金属Hg、Pb、Cd含量平均值持续增长,Cu有所波动,但总体也呈增长趋势。在区域分布上,Hg、Pb、Cd基本以双桥河流域为浓集中心,随着时间的推移,污染范围均逐渐扩大,且向下游有加重的趋势,Cu和Pb的污染范围虽然也在扩大,但极重污染区正在减小甚至消失。     

小秦岭金矿带农田土壤重金属的时空变异趋势及其意义

研究农田土壤重金属累积的时空变异特征及定量化预测变异趋势,是探讨土壤重金属地球化学累积效应及预警方法的关键。收集整理小秦岭金矿区以往调研资料和陆续的动态监测数据,对比分析了近28年来研究区农田土壤重金属特征污染物Hg、Pb、Cd、Cu的时空变异特征和扩散速率,并利用年均增长量和总变化率2个参数,定量计算了研究区土壤Hg、Pb、Cd、Cu的累积速率,探讨其环境意义。分析表明,1985年至今研究区农田土壤重金属Hg、Pb、Cd含量平均值持续增长,Cu有所波动,但总体也呈增长趋势。在区域分布上,Hg、Pb、Cd基本以双桥河流域为浓集中心,随着时间的推移,污染范围均逐渐扩大,且向下游有加重的趋势,Cu和Pb的污染范围虽然也在扩大,但极重污染区正在减小甚至消失。     

黏土固化注浆帷幕控制重金属污染物运移的数值模拟

针对南昌市麦园垃圾卫生填埋场渗滤液防渗的实际情况,分析了重金属污染物在黏土固化注浆帷幕中的迁移转化特征,建立了地下水渗流模型和重金属污染物运移的耦合动力学模型。利用该模型对黏土固化注浆帷幕控制重金属污染物的迁移行为进行了模拟研究。数值模拟结果表明,在填埋场设计使用年限内黏土固化注浆帷幕对重金属污染物的阻滞率可以达到94.5 %以上,重金属污染物在防渗系统中的浓度范围沿地下水水流方向逐渐扩展,同一位置的浓度值随时间的延续呈逐渐增大的趋势,但距离污染源越远,污染物的浓度越低。结合现场检测情况,证明了黏土固化注浆帷幕对重金属污染物具有较强的污染控制能力。     

不同采样点数量下土壤有机质含量空间预测方法对比

运用普通克里格、泛克里格、协同克里格和回归克里格4种方法,结合由DEM获取的高程因子以及土壤全氮和阳离子交换量（CEC）,预测了黑龙江省海伦市耕地有机质含量的空间分布。不同样点数量下海伦市土壤有机质含量的空间变异结构分析表明,样点数量多并不一定能够识别土壤有机质含量的结构性连续组分,最优化的布置采样点位置可能比单纯增加...     

土壤重金属生物有效性研究进展

土壤重金属污染具有巨大环境风险。笔者围绕土壤重金属元素生物有效性(可给性)概念、元素形态与有效性影响因素、有效态(可给态)实验技术,综述了国内外研究现状。土壤金属元素形态和生物有效性取决于其地球化学行为、元素成因来源、土壤理化条件(p H值、有机质、粘土矿物与化学活性矿物、土壤粒级组成等)以及植物根际效应等。选择性单步提取和连续提取是检测土壤元素形态、有效态的有效和可行方法。针对手—口是土壤铅等污染物在儿童群体暴露的重要途径,发展了生物可给态体外试验方法。土壤污染物生物有效性和可给性已成为土壤污染风险评价的重要指标参数,实验成果也为土壤污染修复提供了重要依据。     

高光谱遥感在土壤重金属污染监测中的应用

高光谱技术的快速发展为宏观、快速获取土壤中的重金属元素信息提供了有力支撑。选择云南大红山铜矿作为研究区,对其土壤进行采样,其中水样、底泥样和土壤样各30个,共90个样本。使用ASD光谱仪对样品进行光谱分析,借助ENVI软件对原始测量数据进行平滑与去噪处理,利用与重金属相关的官能团在VNIR和SWIR波段光谱吸收特征的变化提取光谱吸收特征参数。通过与预处理后研究区的HyMap遥感图像数据进行光谱相似度及光谱参数相似度的对比,验证了将ASD实测光谱与HyMap高光谱遥感数据结合监测研究区重金属污染的可行性。认为高光谱遥感在土壤重金属监测方面有着广泛的应用前景。     

雄安新区土壤重金属污染特征及健康风险

土壤质量状况直接关系到农产品安全和人类健康,是农业种植和土地规划重要的参考依据。四川成都唐昌镇作为成都市重要的菜篮子基地,农业种植发达,目前该地区土壤质量状况尚不清楚。为了准确掌握当前唐昌镇农业种植区表层土壤元素含量特征和质量等级,本文通过开展1∶5万土壤质量地球化学调查,采集表层土壤810件,采用X射线荧光光谱法、电感耦合等离子体质谱法、原子荧光光谱法等方法测定了N、P、K、As、Cd、Hg、Pb、Cr、Ni、Cu、Zn、Se等元素含量和pH值, 进行土壤养分等级、土壤环境质量等级、土壤地球化学综合等级评价。结果表明：①与全国表层土壤相比,研究区表层土壤略富集Zn、Pb,较富集Se,强烈富集N、P、Cd、Hg,土壤总体呈弱酸性。②土壤养分综合等级以二等较丰富为主,面积占比88.0%,土壤肥力较丰富;土壤8项重金属元素环境质量等级中的一等土壤面积占比均大于96.0%,综合环境质量等级以一等清洁型为主,占比达98.06%。土壤综合质量以一等优质级为主,占比85.85%。③区内富硒土壤面积2.49km²,占比3.7%,零星分布于研究区中部;富铜土壤面积63.13km²,占94.0%;富锌土壤面积63.31km²,占比94.17%。综上所述,唐昌镇地区表层土壤养分较丰富,土壤环境整体清洁,土壤综合质量属优质级。