闽江福州段沉积物中多环芳烃的空间分布异质性研究

多环芳烃(PAHs)是环境中一类典型的持久性有机污染物,为探明PAHs在闽江福州段沉积物中的空间分布规律,采用传统统计学和地统计学相结合的方法对PAHs的空间异质性进行了研究.结果表明,PAHs含量变异系数(CV)在39%～90%之间,属于中等强度变异.3环PAHs含量的块金值与基台值之比[C0/(C+C0)]为0.307,具有中等强度的空间相关性,其余不同环数PAHs以及PAHs总量的C0/(C+C0)值都小于0.25,具有强烈的空间相关性.低环PAHs的分维数D值高于高环PAHs,空间异质性较高.3环及3环以上PAHs的Moran’s I系数随距离的增大而下降,且在0～25.0 km之间其空间自相关性随间隔距离的增大而减弱;而Moran’s I系数分析结果进一步说明了2环PAHs的空间变异特征的复杂性.受到水流影响,PAHs含量在南-北方向的半方差函数值与其它方向有所不同,但在东北-西南、东南-西北和东-西方向上的半方差函数值重合性较好,近似各向同性.用交叉验证法检验的结果表明,普通克里插值法能够很好地预测闽江福州段沉积物中PAHs的空间分布情况,PAHs含量沿水流流向大致呈下降的趋势.     

Spatial distribution and pollution assessment of mercury in sediments of Lake Taihu, China

Spatial distribution patterns of total mercury (THg) in 36 surficial sediment samples representing five regions of Lake Taihu were assessed using the ArcGis geostatistical analyst module. The pollution levels of THg were also evaluated from the same five lake regions. Concentrations of THg were in a ranged of 23-168 ng/g (mean 55 ng/g) in surfical sediments, which was significantly higher than those established baseline levels of the lake. Results of THg indicated that the northern region exhibited notably higher values, the bay regions showed elevated values relative to open areas, and the lakeside regions were higher than those observed in the central area. Lake Taihu suffered moderate to high Hg pollution, and expressed clear Hg enrichment status according to monomial pollution index I geo and human activity factors. The concentrations of THg in the surficial sediments of Lake Taihu showed moderate-strong variation (coefficient of variation 52%). Geostatistical analysis indicated a weak spatial self-correlation, suggesting the contamination of Hg in Lake Taihu is primarily the result of anthropogenic activities.     

黑土区小流域土壤氮素空间分布及主控因素研究

在经典统计学和地统计学的基础上,结合"3S"技术,对黑土区海沟河小流域土壤表层(0~20 cm)中全氮(TN)、碱解氮(AN)的空间变异、分布特征及主控因素进行深入探讨.结果表明:海沟河小流域土壤TN含量处于较高水平,AN含量为中等水平;TN和AN的变程分别为900 m和1282 m,其空间变异均受地形、成土母质等结构性要素影响较大,在东-西(E-W)方向的空间变异相对剧烈;TN含量与坡度等地形指标显著性相关,AN含量与高程等地形要素显著性相关;回归协同克里格插值结果显示,TN自东向西呈现"高-低"交替的带状格局,与土地利用方式在东西方向上的演替相近,AN的高值在东部山区,低值在中部旱地分布集中的区域,呈现"两边高,中间低"的特征;水系、居民点等环境要素对TN和AN的空间分布存在明显的作用距离,土地利用方式及坡位对TN和AN含量分布影响显著,且存在较大差异.     

Sampling The Soil In Long-Term Forest Plots: The Implications Of Spatial Variation

Long-term monitoring of forest soils as part of a pan-European network to detect environmental change depends on an accurate determination of the mean of the soil properties at each monitoring event. Forest soil is known to be very variable spatially, however. A study was undertaken to explore and quantify this variability at three forest monitoring plots in Britain. Detailed soil sampling was carried out, and the data from the chemical analyses were analysed by classical statistics and geostatistics. An analysis of variance showed that there were no consistent effects from the sample sites in relation to the position of the trees. The variogram analysis showed that there was spatial dependence at each site for several variables and some varied in an apparently periodic way. An optimal sampling analysis based on the multivariate variogram for each site suggested that a bulked sample from 36 cores would reduce error to an acceptable level. Future sampling should be designed so that it neither targets nor avoids trees and disturbed ground. This can be achieved best by using a stratified random sampling design.     

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.     

基于遥感和地统计分析的滑坡监测研究评述

在滑坡监测过程中,常规技术方法往往会受到气候与地形的限制,且连续观测能力较差,实施困难较大.而遥感技术可以克服观测条件的限制,并可针对滑坡不同阶段实行监测.此外,地统计分析方法可充分利用遥感数据本身具有的空间相关性,可以提高影像信息提取的准确性.因此,遥感与地统计方法集成用于滑坡监测具有广阔的应用前景.     

Assessment of soil salinisation in the Ejina Oasis located in the lower reaches of Heihe River,Northwestern China

Recognition of the spatial-temporal distribution characteristics of soil salinity has become an important basis for the formulation of strategies required in the utilisation and sustainable development of soil resources in arid and semi-arid area. In this paper, based on vegetation cover dataset during 1998–2015 in Ejina region combined with the collected soil salinity data, geostatistical methods were used to explore the temporal and spatial dynamic characteristics of soil salinity and its impact on vegetation in the study area. The results showed significant differences in soil salinisation characteristics with a large variability in the soil salinity among the different soil depths, with a variation coefficient ranging from about 0.97–1.47. Soils are represented by a continuous variation, both in space and time. Soil salinity showed an obvious spatial autocorrelation, with a plaque type distribution. The areas of the salinised soil found for the years 2003, 2011 and 2015 were approximately 18,565, 23,206 and 17,721?km², respectively. From the relationships deduced between the normalised difference vegetation index (NDVI) and soil moisture and salt content in different soil depths, the present study ascertains that the soil moisture content is the most important limiting factor of vegetation growth in Ejina Oasis.     

Hierarchical modeling of count data with application to nuclear fall-out

We propose a method for a Bayesian hierarchical analysis of count data that are observed at irregular locations in a bounded domain of R². We model the data as having been observed on a fine regular lattice, where we do not have observations at all the sites. The counts are assumed to be independent Poisson random variables whose means are given by a log Gaussian process. In this article, the Gaussian process is assumed to be either a Markov random field (MRF) or a geostatistical model, and we compare the two models on an environmental data set. To make the comparison, we calibrate priors for the parameters in the geostatistical model to priors for the parameters in the MRF. The calibration is obtained empirically. The main goal is to predict the hidden Poisson-mean process at all sites on the lattice, given the spatially irregular count data; to do this we use an efficient MCMC. The spatial Bayesian methods are illustrated on radioactivity counts analyzed by Diggle et al. (1998).     

Estimating the area affected by phosphorus runoff in an Everglades wetland: a comparison of universal kriging and Bayesian kriging

Phosphorus-enriched agriculture runoff is believed to be the leading cause of ecosystem changes of Everglades wetlands. To study this effect, it is necessary to estimate the area of the affected region. In this study, Bayesian kriging and universal kriging were used to estimate the area by analysing the data collected by Reddy et al. (1991). The background level of the soil's total phosphorus concentration is usedto determine whether the region is affected by the agriculture runoff, through an indicator function. The area of the affected region was represented by the integration of the indicator function over the entire wetland. The expected value of the affected area was calculated using the results derived from Bayesian and universal kriging. The outcome indicates that universal kriging is sensitive to specification of thecovariance model. It was observed that universal kriging and Bayesian kriging yield comparable results, if the specified covariance structures are of similar nature.     

Application of geostatistical methods to arsenic data from soil samples of the Cova dos Mouros mine (Vila Verde-Portugal)

A total of 286 soil samples were collected in the Cova dos Mouros area. All samples were dry sieved into the <200 mesh size fraction and analysed for Fe, Cu, Zn, Pb, Co, Ni, Bi and Mn by atomic absorption spectrometry (AAS) and for As, Se, Sb and Te by atomic absorption spectrometry-hydrid generation (AAS-HG). Only the results of arsenic are discussed in this paper although the survey was extended to all analysed chemical elements. The purpose of this study was to make a risk probability mapping for arsenic that would allow better knowledge about the vulnerability of the soil to arsenic contamination. To achieve this purpose, the initial variable was transformed into an indicator variable using as thresholds the risk-based standards (intervention values) for soils, as proposed by [Swartjes 1999. Risk based assessment of soil and groundwater quality in the Netherlands: Standards and remediation. J. Geochem. Explor.73 1–10]. To account for spatial structure, sample variograms were computed for the main directions of the sampling grid and a spherical model was fitted to each sample variogram (arsenic variable and indicator variables). The parameters of the spherical model fitted to the arsenic variable were used to predict arsenic concentrations at unsampled locations. A risk probability mapping was also done to assess the vulnerability of the soil towards the mining works. The parameters of the spherical model fitted to each indicator variable were used to estimate probabilities of exceeding the corresponding threshold. The use of indicator kriging as an alternative to ordinary kriging for the soil data of Cova dos Mouros produced unbiased probability maps that allowed assessment of the quality of the soil.