Spatio-temporal variability of some metal concentrations in the soil of eastern England, and implications for soil monitoring

被引:53
|
作者
Lark, R. M.
Bellamy, P. H.
Rawlins, B. G.
机构
[1] Silsoe Res Inst, Bedford MK45 4HS, England
[2] Cranfield Univ, Natl Soil Resources Inst, Silsoe MK45 4DT, Beds, England
[3] British Geol Survey, Nottingham NG12 5GG, England
基金
英国生物技术与生命科学研究理事会;
关键词
geostatistics; pseudo cross-variogram; cokriging; robust estimation; soil monitoring; heavy metals; pedometrics;
D O I
10.1016/j.geoderma.2005.08.009
中图分类号
S15 [土壤学];
学科分类号
0903 ; 090301 ;
摘要
Previous workers have proposed the use of multivariate geostatistics for the problem of estimating temporal change in soil properties for soil monitoring, but this has yet to be evaluated. We present a case study of this approach from the Humber-Trent region in North East England. We extracted data from two sources on cobalt, nickel and vanadium concentrations in the topsoil on two dates. Auto-variograms were estimated for each metal on each date, and pseudo cross-variograms for each metal on the two dates. It was shown that robust estimators of the auto and pseudo cross-variograms were needed for the analysis of these data. A linear model of coregionalization was then fitted to describe the spatio-temporal variability of each metal. While the concentration of each metal in the soil showed pronounced spatial dependence that we know is driven by parent material, the change over time was only spatially structured for cobalt and vanadium. This shows that information on spatial variability from a single date may be a poor guide to the design of a monitoring scheme. We showed how the cokriging variance of the change in concentration of cobalt and vanadium depends on sampling effort and strategy. The change in these particular variables between two dates is best estimated by sampling with equal intensity at the same sites on both dates; and when resampling an existing baseline survey it is best to sample them at rather than between the original sites. The best strategy in any case depends on how the variable is coregionalized over time. (c) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码:363 / 379
页数:17
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