1. REGIONAL GEOCHEMICAL MAPPING OF TOPSOIL HEAVY METALS: A SCORPAN KRIGING APPROACH CONDITIONAL ON SOIL MAP DELINEATIONS AND LAND USE
F. Ungaro 1 N. Marchi 2 C. Calzolari 1 M. Guermandi 2
1 CNR - IRPI UOS Firenze Italy
2 Regione Emilia Romagna, Servizio Geologico Sismico e dei Suoli, Bologna, Italy

2. Aims of the work
The spatial variability of heavy metal topsoil contents is generally affected by soil parent materials and anthropogenic sources.
The major problem associated with the characterization of heavy metals in most sites is given by multiple pollution sources.
In the last decades the increase of industrial activities and the massive use of pesticides in agriculture, enhanced the risk of topsoil contamination by pollutants.
An accurate knowledge of the “usual” background content of metals in soils and its spatial distribution over a given area is then necessary to assess their contamination state and one of the principal objectives of heavy metal concentration assessment is to distinguish the usual and natural background levels from human induced pollution.
Information on potentially hazardous levels of heavy metal in soils has potential implications for agricultural management and land use.
Aims of this work are:
to investigate topsoil metal interrelations,
to elucidate factors that affect their spatial distribution and
to estimate and map the “usual” background content of heavy metals using a geostatistical approach which explicitly takes into account exhaustive secondary information on soils and land use.
7th Congress on Regional Geological Cartography and Information Systems Bologna - Italy June 12th 15th, 2012

3. Material & Methods. I Data
For a variable number ( ) of representative sites topsoil (20-30 cm) total content of As, Cd, Cu, Cr, Hg, Ni, Pb, Sb, Sn and Zn was determined through acid extraction with aqua regia (UNI/EN method C) and detection by ICP (Inductive Coupled Plasma, EPA 6020). Sampling points (avg. 1/20 km2) have been selected following a “typological approach” (ISO/DIS 19258, 2004) based on i) texture, ii) degree of weathering and iii) parent material.

4. Material & Methods. I Descriptive StatisticsSn
1 mg/kg Cu
120 mg/kg Pb Zn Ni Cr
150 mg/kg
150 mg/kg
120 mg/kg

5. Material & Methods. I Correlations
0.32
0.38
0.35
0.20
0.48
0.79
0.34
0.40
0.14
0.24
0.43
0.12
0.32
0.20
0.13
0.21
0.51
0.42
0.32
0.45
0.17
0.59
-0.11
The relationships between the metals are quite complex, differentiated for the different metals and difficult to explain on an individual basis, but they would suggest a different origins for most of the considered metals.

6. Material & Methods. I PCA
A fine to medium fine textured, B moderately fine to moderately coarse textured, C coarse textured
All low to moderate alteration excepted A1 high alteration (Luvisols)
Ophiolites: 1 absent < 3 moderate < 4 < 5 high As Pb Cu Cr Sb Zn Ni Sn Cd

7. Material & Methods. II Soils and Land UseCr
150 mg/kg Ni Sn
120 mg/kg
A fine to medium fine texture,
B moderately fine to moderately coarse texture
C coarse texture
All low-medium degree of weathering, excepted
A1 High degree of weathering (Luvisols)
Ophiolites: 1 absent < 3 moderate < 4 < 5 high
1 mg/kg Pb Zn Cu
120 mg/kg

8. Material & Methods. II Soils and Land Use1 4 7 10 13 16 19 22 25 Pb Zn Ni Cu Sn Cr

9. Material & Methods. III Mapping Approach
Trends in metals concentrations are clearly detectable as related to soil texture, parent material and land use. Given the low data density, these provide then a valuable information to be integrated into the procedure to assess HMs spatial distribution and eventually to map the probability to exceed contamination thresholds.
MWS 25 km overlap 50% Zn Cu Ni Cr Sn Pb

10. Material & Methods. III Mapping Approach
In order to take into account 2ndary information and to deal with data non-stationarity, a Scorpan kriging approach conditional on GFUs and Land use (i.e. kriging with varying local means) was implemented resorting to Sequential Gaussian Simulations (SGS).
The geostatistic-scorpan model is partially deterministic and partially stochastic and the value of a soil property Z to be estimated at a given point in space u in the ith map delineation can be described as:
Z(u)i = m(u)i + R(u) + 
where m(u)i is a deterministic function describing the structural component of Z at u, which in this case is given by the local mean value of the considered metal for the ith delineation:
R(u) is a stochastic, locally varying but spatially dependent term that represents the residual from m(u), and  is a residual, spatially independent Gaussian noise term having zero mean and variance σ2.
The locally varying structural components were calculated from available data; the locally varying but spatially dependent residual term was estimated with a simple kriging of the normal score transform of the mean residuals.

11. Material & Methods. III Mapping Approach
Local means Cr
Residuals
E-type map
Back-transforms
SGS (N = 1000)
NS variogram
Normal scores
Residuals

12. Material & Methods. III Mapping ApproachCu
γ(|h|) Ni Zn
1.0 Pb Cr Cr Ni
0.8 Sn
0.6
0.4
0.2
6000
18000
30000
42000
54000 Zn Cu
Distance, m
Nugget C1 C2 A1 A2 Cr
0.20
0.45
0.34
6600
27498 Sn
0.18
0.48
0.38
5400
27600 Pb
0.33
0.43
0.26
6558
21320 Cu
0.30
0.42
4800
52200 Zn
0.28
0.40
0.39
40200 Ni
0.35
0.44
0.25
18000 Sn Pb

13. Results. II SGS Results Cr & Ni E-type Maps
276 km2
2.9% Cr
401 km2
4.2% Ni

14. Results. II SGS Results Cr & Ni E-type Maps
Cr topsoil
subsoil
Cr subsoil
topsoil

15. Results. II SGS Results Zn & Cu E-type Maps
341 km2
3.6% Zn
20 km2
0.2 %

16. Results. II SGS Results Zn & Cu vs. Pig Husbandry
Bi-LISA Clusters p <0.01

17. Results. II SGS Results Pb & Sn E-type Maps
9480 km2
99% Pb
0 km2 0%

18. Results. II SGS Results Sn vs Sugar Factories
Probability Sn > 1 mg/kg (avg. 0.97%)
Probability Sn > 2 mg/kg (avg. 0.44%)

19. Conclusions
Given the different nature of the factors determining heavy metal presence and accumulation is soils, a mapping approach taking into account soil and land use pattern is necessary to produce reliable outputs.
This goal can be achieved with a Scorpan Kriging approach in combination with Sequential Gaussian Simulation (SGS) of the local land use/soil genetic-functional units residuals, which describe the local deviation from the overall spatial pattern
Furthermore SGS can be used to model the uncertainty of mapping heavy-metal concentrations in soil and a number of SGS realizations can be used to explore possible spatial patterns and to model mapping uncertainty at the level of a single location or jointly at multiple locations.

20. Conclusions
Uncertainty can be expressed in terms of probability of heavy-metal concentrations being higher than the defined threshold level of contamination, and then used to highlight contaminated areas.
In the study area this resulted in the assessment of :
Local geogenic enrichment above regulatory thresholds for Cr (3%) and Ni (4%)
Local anthropogenic enrichment above regulatory thresholds for Zn (3%) and Cu (4%) due to pig husbandry
Diffuse anthropogenic enrichment above regulatory thresholds for Sn (99%) due to the massive use of organo-tin fungicides in sugar beet cultivation to control Cercospora leaf spot and of acaricides used in orchards and vineyards.

21. ThaSnk for your attention….