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a CIFA "Las Torres-Tomejil", I.F.A.P.A.-C.I.C.E. (Junta de Andalucía), Ctra. Sevilla-Cazalla, km 12,2, 41200 Alcalá del Río, Sevilla, Spain
b CIFA "Alameda del Obispo", I.F.A.P.A.-C.I.C.E. (Junta de Andalucía), 14080 Córdoba, Spain
c Dep. of Agronomy, University of Córdoba, Avda. Menéndez Pidal, s/n, 14080 Córdoba, Spain
* Correfsponding author (karl.vanderlinden.ext{at}juntadeandalucia.es)
Received for publication October 18, 2004. Monitoring of soil chemical properties for pollution assessment generally requires destructive soil sampling and results in spatiotemporal datasets where data from different sampling dates are non co-located. The objective of this study was to assess the spatial distribution of residual pyrite sludge at a reclaimed site, using temporally non co-located data on pH; soil oxidizable fraction (SOF); and EDTA-extractable Fe, Zn, and Cu from six different sampling dates over a period of 2 yr. During this period spatially averaged pH and Zn concentrations ranged, respectively, from 4.4 to 6.6 and from 60 to 140 mg kg1, with minimum pH values of below 2.7. The data were merged into a single dataset for each chemical property after applying a normal score (ns) transform. Normal score pH was significantly negatively correlated with the ns metal concentrations. A principal component analysis (PCA) showed that normal score pH, Zn, and Fe were associated with the residual contamination, while ns Cu, SOF, and elevation were related with historic contamination. The spatial dependence between the properties was found to be scale-dependent. The best ns estimates were produced by ordinary kriging with an anisotopic variogram model, for the properties related with Principal Component (PC) I, while those associated with PC II were best estimated using simple kriging with varying local means. A classified ns pH map showed that 33% of the study area reached at least once values of below 4 during the 2-yr period. This part of the area should be excluded to ensure successful revegetation.
Abbreviations: m, stationary mean MAE, mean absolute error ME, mean error ml, varying local mean ns, normal score OKa, ordinary kriging with anisotropic variogram model OKi, ordinary kriging with isotropic variogram model PC, principal component PCA, principal component analysis RF, random function RMSE, root mean squared error SBF, sugar beet foam SKlm, simple kriging with varying local means SOF, soil oxidizable fraction
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