A framework to estimate the distribution of heavy metals in … · 2008. 9. 19. · 1 JRC Ispra -...
Transcript of A framework to estimate the distribution of heavy metals in … · 2008. 9. 19. · 1 JRC Ispra -...
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JRC Ispra - IES1
A framework to estimate the distribution of heavy metals in
European Soils VIENNA, August 27th 2007
Luis Rodríguez-Lado, H. Reuter & T. Hengl
Eurosoil 2008 / Vienna
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Soil data
• FOREGS database• Association of the Geological Surveys
of the European Union
• Geochemical Atlas of Europe
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Maps of Heavy Metals: Situation at present
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JRC Ispra - IES4
Why geostatistics?
• It gives an objective estimate:– of the HMC values– of the associated uncertainty
• A high number of auxiliary predictors can be used
• An in-depth analysis of HMC sources is possible (is it random?)
• It can be automated (R scripts)
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FOREGS soil database
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Targeted variables
• 8 heavy metals in soils:– As, Cd, Cr, Cu, Hg, Ni, Pb, Zn
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JRC Ispra - IES7
FOREGS soil database: Exploratory analysisAS
foregs$AS
Freq
uenc
y
0 100 300
050
010
0015
00
CD
foregs$CD
Freq
uenc
y
0 5 10 15 20
050
010
0015
00
CR
foregs$CRFr
eque
ncy
0 500 1500
050
010
0015
00
CU
foregs$CU
Freq
uenc
y
0 100 300
050
010
0015
00
HG
foregs$HG
Freq
uenc
y
0 1 2 3 4
050
010
0015
00
NI
foregs$NI
Freq
uenc
y
0 1000 2000
050
010
0015
00
PB
foregs$PB
Freq
uenc
y
0 2000 4000
050
010
0015
00
ZN
foregs$ZN
Freq
uenc
y
0 1000 2000 3000
050
010
0015
00
logAS
log(foregs$AS)
Freq
uenc
y
1 2 3 4 5 6
020
040
060
0
logCD
log(foregs$CD)
Freq
uenc
y
0.0 1.0 2.0 3.0
050
100
150
logCR
log(foregs$CR)
Freq
uenc
y
0 2 4 6 8
010
020
030
040
0logCU
log(foregs$CU)
Freq
uenc
y
0 1 2 3 4 5 6
010
020
030
040
0
logHG
log(foregs$HG)
Freq
uenc
y
0.0 0.4 0.8 1.2
05
1015
logNI
log(foregs$NI)
Freq
uenc
y
0 2 4 6 8
010
020
030
0
logPB
log(foregs$PB)
Freq
uenc
y
2 4 6 8
010
020
030
040
0
logZN
log(foregs$ZN)
Freq
uenc
y
1 2 3 4 5 6 7 8
010
020
030
040
0
TAS
foregs$TAS
Freq
uenc
y
-13 -11 -9 -8
020
040
060
0
TCD
foregs$TCD
Freq
uenc
y
-18 -16 -14 -12
020
040
060
0
TCR
foregs$TCR
Freq
uenc
y
-14 -12 -10 -8 -6
020
040
060
0
TCU
foregs$TCU
Freq
uenc
y
-14 -12 -10 -8
020
040
060
0
THG
foregs$THG
Freq
uenc
y
-20 -18 -16 -14 -12
020
040
060
0
TNI
foregs$TNIFr
eque
ncy
-14 -12 -10 -8 -6
020
040
060
0
TPB
foregs$TPB
Freq
uenc
y
-12 -10 -8 -6
020
040
060
0
TZN
foregs$TZN
Freq
uenc
y
-12 -10 -8 -6
020
040
060
080
0
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JRC Ispra - IES8
FOREGS soil database: Exploratory analysis
TAS
foregs$TAS
Freq
uenc
y
-13 -11 -9 -8
020
040
060
0
TCD
foregs$TCD
Freq
uenc
y
-18 -16 -14 -12
020
040
060
0
TCR
foregs$TCR
Freq
uenc
y-14 -12 -10 -8 -6
020
040
060
0
TCU
foregs$TCU
Freq
uenc
y
-14 -12 -10 -8
020
040
060
0
THG
foregs$THG
Freq
uenc
y
-20 -18 -16 -14 -12
020
040
060
0
TNI
foregs$TNI
Freq
uenc
y
-14 -12 -10 -8 -6
020
040
060
0
TPB
foregs$TPB
Freq
uenc
y
-12 -10 -8 -6
020
040
060
0
TZN
foregs$TZN
Freq
uenc
y
-12 -10 -8 -6
020
040
060
080
0
TAS = ln(ASstand/1-ASstand)
ASstand= (AS-ASmin)/(ASmax-ASmin)
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JRC Ispra - IES9
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50.
000.
050.
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Principal Component Analysis
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FOREGS soil database: Exploratory analysis
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Principal Component Analysis
PC1
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-40 -20 0 20
-40
-20
020
ASCD
CR
CU
HG
NI
PBZN
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JRC Ispra - IES10
Regression-kriging
Multiple Linear Regression
Yj = a1 X1 + a2X2 + … + an Xn + εj
Soil variable j residuals j
Kriging
Yj
...
... ..... ...
.
. .. ..
..
∑ aiXii
.
..
...
. .... .. ..
γεj
distance (m)
Sem
i-var
ianc
e
(interpolation process according to spatial autocorrelations of the variable)
Auxiliary data i
Spatially continuous Punctual
Summation of the two maps
regression
kriging
regression-kriging
auxiliary data
residuals
soil variables
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JRC Ispra - IES11
Predictors
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JRC Ispra - IES12
Principal Component Analysis
PCA of the auxiliary variables
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JRC Ispra - IES13
Multiple Regression Analysis
PCA of the auxiliary variables
-
JRC Ispra - IES14
Regression-kriging
Multiple Linear Regression
Yj = a1 X1 + a2X2 + … + an Xn + εj
Soil variable j residuals j
Kriging
Yj
...
... ..... ...
.
. .. ..
..
∑ aiXii
.
..
...
. .... .. ..
γεj
distance (m)
Sem
i-var
ianc
e
(interpolation process according to spatial autocorrelations of the variable)
Auxiliary data i
Spatially continuous Punctual
Summation of the two maps
regression
kriging
regression-kriging
auxiliary data
residuals
soil variables
-
JRC Ispra - IES15
Spatial dependency:Semivariograms
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JRC Ispra - IES16
Results
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JRC Ispra - IES17
Results #1 Pb
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JRC Ispra - IES18
Results of RK and OK
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JRC Ispra - IES19
Results #2 Ni
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JRC Ispra - IES20
Correlations for Ni
3DPlot of Ni content over PC2, PC4 & PC14
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JRC Ispra - IES21
Results #3 Cd
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JRC Ispra - IES22
Overall concentration of HM in European soils
(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, Cu, Hg and Pb)
(2) central Greece and Liguria region in Italy (Cr and Ni).
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JRC Ispra - IES23
Estimation errors
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JRC Ispra - IES24
Validation
• Comparison OK vs RK
• Moderate good: Ni, Pb• Medium: As, Cd, Hg• Poor: Cr, Cu, Zn
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JRC Ispra - IES25
Conclusions• FOREGS is an examplary pan-european
dataset that is well-suited for geostatistical analysis.
• In many cases, spatial distribution of HMCs is closely connected with relief, urbanization, vegetation cover.
• All variables also show distinct spatial autocorrelation.
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JRC Ispra - IES26
Conclusions
• As far as we have more detailed auxiliary maps, more accurate results are expected from these models.– Additional data (other existing soil
samples, field data description)– Accurate GIS datasets (geological map of
Europe)
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JRC Ispra - IES27
Thanks for your attention
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JRC Ispra - IES28
Maps of Heavy Metals: Situation at present
Soil Organic Carbon derived from PTR using soil type, land use and temperature as covariates.
Soil dataMaps of Heavy Metals: Situation at present Correlations for NiMaps of Heavy Metals: Situation at present