A probabilistic methodology to assess the risk of groundwater quality degradation

An approach to assess the risk of groundwater quality degradation with regard to fixed standards, based on DisjunctiveKriging (DK) is presented. The DK allows one to evaluate the Conditional Probability (CP) of overriding a given threshold of concentration of a pollutant at a given time, and at a generic point in a consideredgroundwater system. The result of such investigation over the considered area can be plotted in form of maps of spatial risk. By repeating this analysis at different times, severalspatial riskmaps will be produced, one for each consideredtime. By means of non-parametric statistics, the temporal trendof the CPs can be evaluated at every point of the considered area. The trend index, assessed by means of a sort of classification of the trend values obtained as described above,can be superimposed on the most recent values of the spatialrisk (i.e.: the most recent values of probability). Consequentlya classification of the risk of groundwater quality degradationresults with which to weigh both the spatial distribution and thetemporal behaviour of the probability to exceed a given standardthreshold. The methodology has been applied to values of nitrateconcentration sampled in the monitoring well network of theModena plain, northern Italy. This area is characterised by intensive agricultural exploitation and hog breeding along withindustrial and civil developments. The influence of agriculture on groundwater results in a high nitrate pollution that limitsits use for potable purposes.     

Multiple-point variance analysis for optimal adjustment of a monitoring network

The selection of sampling sites is one of the major tasks in thedesign of a monitoring network. Many environmental networkssuffer from either insufficient information or redundantinformation. This study presents a new, effective algorithm thataddresses the issues of insufficient and reduction information.The new algorithm is denoted as Multiple-Point Variance Analysis(MPV). MPV includes both Multiple-Point Variance ReductionAnalysis (MPVR) for adding information-effectives sites, andMultiple-Point Variance Increase Analysis (MPVI) for deletinginformation-redundant sites. The MPVR and MPVI equations areverified under two hypothetical cases. The optimal procedures ofthis new algorithm include determination of simultaneousadditions or deletions of groups of sampling points. Theseproposed optimization procedures eliminate the need for anyspatial discretizations or sequential selections. The efficiencyof these optimal procedures is tested under actual fieldconditions. The results show that the optimal MPV is aneffective tool for adjustment of existing sampling networks.     

Comparison of ordinary and lognormal kriging on skewed data of total cadmium in forest soils of Sweden

Spatial statistical analysis of georeferenced data of total cadmium (TCd) in forest soils of Sweden was assumed to providemore advantageous maps than traditional interpolated maps. However, 264 measurements of TCd in O-horizon of forest soils displayed skewed frequency distribution. Since atypicalobservations affect badly the variogram, outliers wereidentified, different data transformations were tested andordinary (OK) and lognormal kriging (LK) scenarios werecompared based on cross-validation. Results were comparedusing overall measures of predictors, e.g. traditionalmean squared prediction error (MSPE), mean of krigingvariances, variance ratio, median of internallystandardised residuals, and assessments of classificationaccuracy, such as percentage of correctly predictedsamples and within-class MSPE.One outlier was identified based on the absolute value of skewness of value differences less or equal to one in data pairs separated at certain lag classes. Mapping categories characterised by percentage of correct classification and within-class MSPE were found to be essential in comparison of kriging results additionally to the overall measures. In comparison of kriging methods, OK predicted high values moreaccurately and LK was more effective to predict low and mediumvalues. Thus, OK was suggested for mapping high concentration of TCd and other pollutants. Percentage of correctly predictedsamples and within-class MSPE were found to be dependent on kriging method, as well as on the number and limits of categories.     

某工业场地砷污染的加密布点优化与精度对比分析

场地污染调查是开展场地风险评估和修复的基础,随着我国颁布建设用地土壤风险管控标准等文件,提出风险筛选值和管制值的分级管控体系,我国土壤污染调查工作对于精确掌握污染物浓度梯度及空间分布有了更高的要求,因而如何构建高效采样布点方法变得尤为重要.本研究以南京市某砷工业污染场地为例,开展了优化布点方法与调查精度的研究.首先,在初查数据的基础上,运用指示克里格的方法预测土壤污染概率,参考城市土壤背景值开展阈值设定,提取加密布点区域,之后结合场地历史生产资料与自然信息布设加密点位,最终将插值预测污染结果与非加密、均匀加密、真实情况结果进行对比,验证方法精度.结果表明：本方法符合国际中基于设计抽样以达到场地调查简单、经济、客观的理念,适用于数据存在明显空间相关性的场地;该方法获得的污染区域面积占比为8.32%,与8.57%的现实情况较为接近,但普通法得到的污染面积比相对未加密情况不升反降,可能的原因是在非污染区域增设点位会造成阈值边缘区域在模拟时往清洁区域偏移,污染区域被误判为清洁,反而造成结果偏差;在目前我国环境管理对超阈范围重点关注的背景下,该方法既能够节约布点成本,又精确化预测场地污染超标范围,为后续的场地修复和治理工作提供帮助.     

西藏高温地热显示区氟分布及富集特征

通过对西藏高温地热显示区内12个主要地热田采集的地热、地表水样共30组测试和分析可知,研究区氟含量0.34~19.2mg/L,地下热水中氟含量均高于2.0mg/L,研究区南部氟含量较北区偏高.氟含量高区,地热显示较明显.氟离子在水化学类型为Cl-Na或Cl×SO4-Na等Na型水中富集程度高于HCO3-Na×Ca或HCO3-Ca等Ca型水.研究区高氟地热水的富集机制主要有:深部地热流体的升流混合作用,补给水向下渗流过程中含氟硅酸盐矿物、萤石的溶滤作用.两种机制相比,深部热流的混合作用对地下水形成高浓度F-的贡献更大,但矿物的溶滤作用相对而言更为普遍些.研究区砷的富集机制与氟相似,这也使得二者在空间分布上具有共生性.应加强高氟高砷区地热水的管理与防护,防止其污染地表水环境.     

Spatio-temporal variations of fluorescence properties of dissolved organic matter along the River Têt (Pyrénées-Orientales,France)

Fluorescence excitation-emission matrices showed the spectral signature of dissolved organic matter (DOM) downstream in the River Têt at all seasons corresponding to humic substances with maximum fluorescence emission λem=420–460 nm for excitations of λex=340–360 nm and the occasional presence of tryptophan, tracer of anthropogenic pressure, spectroscopically identified by λex/λem=310/350 nm. A factorial discriminant analysis, performed using the parameters selected (λex/λem pairs of wavelength), succeeded in a better discrimination of seasons than stations, and clearly showed the presence of two fluorophores. Fluorophore 1, with two absorption bands: λex=260–320 nm and 330–390 nm for λem=440–500 nm, and Fluorophore 2, with one absorption band: λex=300–360 nm for λem=410 nm, which are attributed to humic acid (HA) and fulvic acid (FA), respectively. Variations of DOM relative contents downstream in the River Têt, according to seasons and stations, showed high amounts of DOM (pedogenic character) along the river in the humid period, with the highest value obtained in the mountain section. In contrast, in summer, the DOM contents were low (aquagenic character). Moreover, DOM presents a relatively constant composition with a percentage of FA ranging from 40% at the mountain station to 48% at the mouth river, whatever the season.     

太湖西南部沉积物重金属的空间分布特征和污染评价

基于GIS地统计模块,对太湖西南部沉积物6种重金属的空间分布特征和污染状况进行了探讨. 结果表明,太湖西南部沉积物重金属含量显著高于太湖沉积物背景值,w(Cd),w(Cu),w(Zn),w(Pb),w(Ni)和w(As)分别超标4.67,1.27,2.89,2.29,1.94和1.80倍,具有明显的累积效应. 地统计分析表明,太湖西南部沉积物重金属的空间自相关性较弱,说明随机性因素和结构因素对重金属的空间分布有较大的影响. 重金属含量的克里格插值图反映了重金属污染的空间格局,太湖西南区域沉积物已明显受到了重金属Zn,Pb和Cd的污染,西南岸区污染最严重,其次是南岸区,污染最轻的是湖心区.      

Spatial methods for plot-based sampling of wildlife populations

Classical sampling methods can be used to estimate the mean of a finite or infinite population. Block kriging also estimates the mean, but of an infinite population in a continuous spatial domain. In this paper, I consider a finite population version of block kriging (FPBK) for plot-based sampling. The data are assumed to come from a spatial stochastic process. Minimizing mean-squared-prediction errors yields best linear unbiased predictions that are a finite population version of block kriging. FPBK has versions comparable to simple random sampling and stratified sampling, and includes the general linear model. This method has been tested for several years for moose surveys in Alaska, and an example is given where results are compared to stratified random sampling. In general, assuming a spatial model gives three main advantages over classical sampling: (1) FPBK is usually more precise than simple or stratified random sampling, (2) FPBK allows small area estimation, and (3) FPBK allows nonrandom sampling designs.     

Spatial interpolation of air temperature using environmental context: Application to a crop model

The air temperature is one of the main input data in models for water balance monitoring or crop models for yield prediction. The different phenological stages of plant growth are generally defined according to cumulated air temperature from the sowing date. When these crop models are used at the regional scale, the meteorological stations providing input climatic data are not spatially dense enough or in a similar environment to reflect the crop local climate. Hence spatial interpolation methods must be used. Climatic data, particularly air temperature, are influenced by local environment. Measurements show that the air above dry surfaces is warmer than above wet areas. We propose a method taking into account the environment of the meteorological stations in order to improve spatial interpolation of air temperature. The aim of this study is to assess the impact of these corrected climatic data in crop models. The proposed method is an external drift kriging where the Kriging system is modified to correct local environment effects. The environment of the meteorological stations was characterized using a land use map summarized in a small number of classes considered as a factor influencing local temperature. This method was applied to a region in south-east France (150×250 km) where daily temperatures were measured on 150 weather stations for two years. Environment classes were extracted from the CORINE Landcover map obtained from remote sensing data. Categorical external drift kriging was compared to ordinary kriging by a cross validation study. The gain in precision was assessed for different environment classes and for summer days. We then performed a sensitivity study of air temperature with the crop model STICS. The influence of interpolation corrections on the main outputs as yield or harvest date is discussed. We showed that the method works well for air temperature in summer and can lead to significant correction for yield prediction. For example, we observed by cross validation a bias reduction of 0.5 to 1.0°C (exceptionally 2.5°C for some class), which corresponds to differences in yield prediction from 0.6 to 1.5 t/ha.     

天津地区土壤多环芳烃的克里格插值与污染评价

应用克里格法研究了天津地区表层土壤中多环芳烃的含量水平及其空间分布规律.在此基础上,参照国外环境标准,对区域表层土壤中10种多环芳烃的污染现状进行了评价.结果显示,区域土壤已经受到一定程度的多环芳烃污染.萘超标倍数最大,强致癌物苯并(a)芘的超标情况也较为严重,需引起进一步的关注.西青区和市区是土壤多环芳烃含量超标最严重的区域.