『Abstract
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 unite (NUTS level3) with highest overall
concentrations are: (1) Liege (Arrondissement) (BE), Attiki (GR),
Darlington (UK), Coventry (UK), Sunderland (UK), Kozani (GR),
Grevena (GR), Hertlepool & 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.
Keywords: Soil mapping; Regression-kriging; MODIS; Night lights
image; Geochemical database; Pan-European monitoring』
1. Introduction
2. materials and methods
2.1. The FOREGS dataset
2.2. The study area
2.3. Auxiliary GIS layers
2.4. Geostatistical analysis
3. Results
3.1. Preliminary data screening
3.2. Variogram modeling and regression analyses
3.3. Regression-kriging - maps of HMCs
3.4. Validaton
3.5. Factor analysis - the overall concentrations
4. Discussion and conclusions
References