Geostatistical assessment of Pb in soil around Paris, France

This paper presents a survey on soil Pb contamination around Paris (France) using the French soil monitoring network. The first aim of this study is to estimate the total amount of anthropogenic Pb inputs in soils and to distinguish Pb due to diffuse pollution from geochemical background Pb. Secondly, this study tries to find the main controlling factors of the spatial distribution of anthropogenic Pb. We used the technique of relative topsoil enhancement to evaluate the anthropogenic stock of Pb and we performed lognormal kriging to map Pb regional distribution. The results show a strong gradient of anthropogenic stock of Pb around the urban Paris area. We estimate a total amount of anthropogenic stock of Pb close to 143,000 metric tons, which corresponds to an average accumulation of 5.9 t km(-2). Our study suggests that a grid-based survey can help to quantify diffuse Pb contamination by using robust techniques of calculation and that it might also be used to validate predictions of deposition models.     

Analytic science for geospatial and temporal variability in renewable energy: A case study in estimating photovoltaic output in Arizona

To assess the electric power grid environment under the high penetration of photovoltaic (PV) generation, it is important to construct an accurate representation of PV power output for any location in the southwestern United States at resolutions down to 10-min time steps. Existing analyses, however, typically depend on sparsely spaced measurements and often include modeled data as a basis for extrapolation. Consequentially, analysts have been confronted with inaccurate analytic outcomes due to both the quality of the modeled data and the approximations introduced when combining data with differing space/time attributes and resolutions. This study proposes an accurate methodology for 10-min PV estimation based on the self-consistent combination of data with disparate spatial and temporal characteristics. Our Type I estimation uses the nearby locations of temporally detailed PV measurements, whereas our Type II estimation goes beyond the spatial range of the measured PV incorporating alternative data set(s) for areas with no PV measurements; those alternative data sets consist of: (1) modeled PV output and secondary cloud cover information around space/time estimation points, and (2) their associated uncertainty. The Type I estimation identifies a spatial range from existing PV sites (30-40 km), which is used to estimate accurately 10-min PV output performance. Beyond that spatial range, the data-quality-control estimation (Type II) demonstrates increasing improvement over the Type I estimation that does not assimilate the uncertainty of data sources. The methodology developed herein can assist the evaluation of the impact of PV generation on the electric power grid, quantify the value of measured data, and optimize the placement of new measurement sites.     

Space–time modelling of air quality for environmental-risk maps: A case study in South Portugal

Since the 1960s, there has been a strong industrial development in the Sines area, on the southern Atlantic coast of Portugal, including the construction of an important industrial harbour and of, mainly, petrochemical and energy-related industries. These industries are, nowadays, responsible for substantial emissions of SO₂, NO_x, particles, VOCs and part of the ozone polluting the atmosphere. The major industries are spatially concentrated in a restricted area, very close to populated areas and natural resources such as those protected by the European Natura 2000 network. Air quality parameters are measured at the emissions’ sources and at a few monitoring stations. Although air quality parameters are measured on an hourly basis, the lack of representativeness in space of these non-homogeneous phenomena makes even their representativeness in time questionable. Hence, in this study, the regional spatial dispersion of contaminants is also evaluated, using diffusive-sampler (Radiello Passive Sampler) campaigns during given periods. Diffusive samplers cover the entire space extensively, but just for a limited period of time.In the first step of this study, a space–time model of pollutants was built, based on a stochastic simulation—direct sequential simulation—with local spatial trend. The spatial dispersion of the contaminants for a given period of time—corresponding to the exposure time of the diffusive samplers—was computed by ordinary kriging. Direct sequential simulation was applied to produce equiprobable spatial maps for each day of that period, using the kriged map as a spatial trend and the daily measurements of pollutants from the monitoring stations as hard data.In the second step, the following environmental risk and costs maps were computed from the set of simulated realizations of pollutants: (i) maps of the contribution of each emission to the pollutant concentration at any spatial location; (ii) costs of badly located monitoring stations.     

Spatial analysis in ecological risk assessment: Pollutant bioaccumulation in clams Tapes philipinarum in the Venetian lagoon (Italy)

Exposure characterization is a central step in Ecological Risk Assessment (ERA). Exposure level is a function of the spatial factors linking contaminants and receptors, yet exposure estimation models are traditionally non-spatial. Non-spatial models are prone to the adverse effects of spatial dependence: inflated variance and biased inferential procedures, which can result in unreliable and potentially misleading models. Such negative effects can be amended by spatial regression modelling: we propose an integration of geostatistics and multivariate spatial regression to compute efficient spatial regression parameters and to characterize exposure at under-sampled locations. The method is applied to estimate bioaccumulation models of organic and inorganic micropollutants in the tissues of the clam Tapes philipinarum. The models link bioaccumulation of micropollutants in clam tissue to a set of environmental variables sampled in the lagoon sediment. The Venetian lagoon case study exemplifies the problem of multiple variables sampled at different locations or spatial units: we propose and test an effective solution to this common and serious problem in environmental as well as socio-economic multivariate analysis.     

矿产资源估算中定量邻域分析与优化研究

在应用线性、非线性地质统计学进行矿产资源/储量估算或品位条件模拟过程时,邻域分析是其中非常关键和重要的一个环节。通常情况下,在进行资源/储量估算中邻域参数通过人为控制和设定,这导致在一定程度上具有主观因素。研究认为估计邻域的大小,应由各个方向变异函数变程及搜索椭球体等参数来确定,优化邻域需选择足够多的样本,且得到最大回归斜率,同时具有最小负克里金权重。克里金邻域参数的合理设置将直接影响最终块体估值结果,这为资源量估算和矿床评价提供了理论和实践基础。     

地质统计学及其在中国矿业领域中的应用

地质统计学是研究那些展布于空间并呈现出一定的结构性和随机性的自然现象的科学,它已成功地应用于矿业工作的许多领域。本文在论述了地质统计学的基本理论后,较为仔细地论述了诸如普通克立格法、泛克立格法、对数正态克立格法、指示克立格法、加权中位数法以及多元地质统计学方法在矿产储量计算,勘探方法以及地质、物化探数据处理中的应用。     

Human health risk assessment in regions surrounding historical mining activities: the effect of change of support

This paper presents a probabilistic exposure model and its adaptation for use with spatially explicit information: soil contaminant concentrations and pH levels, predicted by geostatistical simulation; and population data mapped according to place of residence. Sequential indicator simulation (SIS) is used to provide 1000 plausible maps of soil contaminant concentrations, and results are fed into the exposure model to produce risk maps. Distributions of exposure values are closely related to uncertainty in the soil contaminant values. Using a different support for the estimations has a large effect on the results when comparing exposure values to regulatory cut-offs. Mapping the number of overexposed people allows effective targeting of clean up to reduce efficiently the number of overexposed individuals. Two areas of historical mining activity, a case study from the Southern Urals region of Russia for metal mining and another study from the Tula coal mining region of Russia, are used to demonstrate the importance of the support in the human health risk evaluation.     

Constraining distance-based multipoint simulations to proportions and trends

In the last years, the use of training images to represent spatial variability has emerged as a viable concept. Among the possible algorithms dealing with training images, those using distances between patterns have been successful for applications to subsurface modeling and earth surface observation. However, one limitation of these algorithms is that they do not provide a precise control on the local proportion of each category in the output simulations. We present a distance perturbation strategy that addresses this issue. During the simulation, the distance to a candidate value is penalized if it does not result in proportions that tend to a target given by the user. The method is illustrated on applications to remote sensing and pore-scale modeling. These examples show that the approach offers increased user control on the simulation by allowing to easily impose trends or proportions that differ from the proportions in the training image.     

Feature-preserving interpolation and filtering of environmental time series

We propose a method for filling gaps and removing interferences in time series for applications involving continuous monitoring of environmental variables. The approach is non-parametric and based on an iterative pattern-matching between the affected and the valid parts of the time series. It considers several variables jointly in the pattern matching process and allows preserving linear or non-linear dependences between variables. The uncertainty in the reconstructed time series is quantified through multiple realizations. The method is tested on self-potential data that are affected by strong interferences as well as data gaps, and the results show that our approach allows reproducing the spectral features of the original signal. Even in the presence of intense signal perturbations, it significantly improves the signal and corrects bias introduced by asymmetrical interferences. Potential applications are wide-ranging, including geophysics, meteorology and hydrology.     

A review of recent developments in geostatistics

This paper reviews developments in geostatistics in the period 1987 to mid-1991. The developments which are regarded as significant by the author fall broadly under six headings: simulation, indicator kriging, interval estimation, applications to hydrocarbon reservoirs and hydrology, incorporation of prior information in spatial estimation, and fuzzy kriging. A summary of significant contributions under each of these headings is given together with an assessment of their importance and application.