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.
Reis
A P
AP
PD grant, Fundação para a Ciência e a Tecnologia, Portugal. pmarinho@geo.ua.pt
Sousa
A J
AJ
Da Silva
E Ferreira
EF
Fonseca
E Cardoso
EC
D001151:Arsenic / Q000032:analysis*; D055593:Geological Phenomena; D005844:Geology; D019216:Metals, Heavy / Q000032:analysis; D015233:Models, Statistical; D011174:Portugal; D018570:Risk Assessment; D012989:Soil Pollutants / Q000032:analysis*
D019216:Metals, Heavy; D012989:Soil Pollutants; D001151:Arsenic