Rodriguez Lado, L; Hengl, T; Reuter, HI
This paper presents the results of modeling the distribution of eight critical heavy metals (arsenic, cadmium, chromium, copper, mercury, nickel, lead and zinc) in topsoils using 1588 georeferenced samples from the Forum of European Geological Surveys Geochemical database (26 European countries). The concentrations were mapped using regression-kriging (RK) and accuracy of predictions evaluated using the leave-one-out cross validation method. A large number of auxiliary raster maps (topographic indexes, land cover, geology, vegetation indexes, night lights images and earth quake magnitudes) were used to improve the predictions. These were first converted to 36 principal components and then used to explain spatial distribution of heavy metals. The study revealed that this database is suitable for geostatistical analyses: the predictors explained from 21% (Cr) to 35% (Pb) of variability; the residuals showed spatial autocorrelation. The Principal Component Analysis of the mapped heavy metals revealed that the administrative units (NUTS level3) with highest overall concentrations are: (1) Liege (Arrondissement) (BE), Attiki (GR), Darlington (UK), Coventry (UK), Sunderland (UK), Kozani (GR), Grevena (GR), Hartlepool & Stockton (UK), Huy (BE), Aachen (DE) (As, Cd, Hg and Pb) and (2) central Greece and Liguria region in Italy (Cr, Cu and Ni). The evaluation of the mapping accuracy showed that the RK models for As, Ni and Pb can be considered satisfactory (prediction accuracy 45-52% of total variance), marginally satisfactory for Cr, Cu, Hg and Zn (36-41%), while the model for Cd is unsatisfactorily accurate (30%). The critical elements limiting the mapping accuracy are: (a) the problem of sporadic high values (hot-spots); and (b) relatively coarse resolution of the input maps. Automation of the geostatistical mapping and use of auxiliary spatial layers opens a possibility to develop mapping systems that can automatically update outputs by including new field observations and higher quality auxiliary maps. This approach also demonstrates the benefits of organizing standardized joint European monitoring projects, in comparison to the merging of several national monitoring projects. (English)
Soil mapping; Regression-kriging; MODIS; Night lights image; Geochemical database; Pan-European monitoring; Europe; Concentration; Analyse factorielle; Couche arable; Zinc; Plomb; Nickel; Mercure; Cuivre; Chrome; Cadmium; Arsenic; Distribution spatiale; Modélisation; Carte; Base de données; Krigeage; Analyse spatiale; Géostatistique; Sol; Métal lourd; factor analysis; Top soil; lead; mercury; copper; chromium; spatial distribution; Modeling; maps; data bases; kriging; Spatial analysis; geostatistics; soils; heavy metals; Europa; Concentración; Análisis factorial; Capa arable; Plomo; Niquel; Mercurio; Cobre; Cromo; Cadmio; Arsénico; Distribución espacial; Modelización; Mapa; Base dato; Análisis espacial; GeoestadÃstica; Suelo; Metal pesado