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Combining GIS and stochastic simulation to estimate spatial patterns of variation for lead at the Lousal mine, Portugal
Author(s) -
Reis A. P.,
Da Silva E. Ferreira,
Sousa A. J.,
Matos J.,
Patinha C.,
Abenta J.,
Fonseca E. Cardoso
Publication year - 2005
Publication title -
land degradation and development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 81
eISSN - 1099-145X
pISSN - 1085-3278
DOI - 10.1002/ldr.662
Subject(s) - geostatistics , kriging , tailings , environmental science , stochastic simulation , mining engineering , field (mathematics) , grid , soil science , geology , hydrology (agriculture) , spatial variability , statistics , mathematics , geotechnical engineering , materials science , geodesy , pure mathematics , metallurgy
Lousal is one of the many massive sulphide deposits of the Iberian Pyrite Belt. The mine is closed at present, but the heavy metal enriched tailings remain at the surface in the open air. Applying geostatistics to the diagnosis of this potentially contaminated site we aim to assess the quality of the soil. The uncertainty attached to the unknown concentrations of the contaminant was first modelled by a non‐parametric predictor (indicator kriging), and then taken into account in the prediction of the concentrations at unsampled locations by a method of stochastic simulation ( p ‐field simulation). At each grid node, the N simulated values resulting from the N equi‐probable simulations were transformed into probabilities of exceeding a regulatory threshold, which allowed assessment of the quality of the soil. The probability maps were then used to identify contaminated areas within the mining field. Copyright © 2005 John Wiley & Sons, Ltd.