Multivariate Curve Resolution:
theory and applications
Romà Tauler, Abril, 2005
Multivariate Curve Resolution
Mixed information
Pure component information
λ
s1
tR
è
sn
c1
D
cn
ST
C
Retention times
Wavelengths
Pure concentration profiles
Pure signals
Chemical model
Process evolution
Compound contribution
Compound identity
Flowchart of MCR-ALS
Journal of Chemometrics, 1995, 9, 31-58; Chemomet.Intel. Lab. Systems, 1995, 30, 133-146
Journal of Chemometrics, 2001, 15, 749-7; Analytica Chimica Acta, 2003, 500,195-210
D = C ST + E
ST
Data
Matrix
D
Data matrix
decomposition
according to a
bilinear model
SVD
or
PCA
Initial
Estimation
ALS
optimization
Resolved
Concentration
profiles
(bilinear model)
C
Estimation of
the number
of
components
Initial
estimation
+
E
ALS optimization
CONSTRAINTS
ˆ Sˆ T
ˆ PCA − C
min
D
ˆ
C
Resolved
Spectra
profiles
Results of the ALS optimization
procedure:
Fit and Diagnostics
ˆ PCA − C
ˆ Sˆ T
min
D
T
S
Hard + soft modelling constraints
MCR-ALS hybrid (grey) models
Multiway data : Multiple measurement
orders/modes/directions/ways
Example: Three-way data
D
(I x J x K)
Regular data cube
D
D
Other three-way arrangements
A series of two-way data sets with common
information in one or more modes.
Data augmentations in MCR
The same
experiment
monitored
with different
techniques
S1 T
D1
D2
D3
=
D
D2
C1
=
ST
Row and column-wise
S1T
D1
D2
D3
D
C
C1
S2T
S3T
ST
=
D4
C3
Row-wise
ST
C2
D3
S3T
C
Column-wise
D1
S2T
D5
D
Several experiments
monitored with the
same technique
D6
C2
C Several experiments
monitored with several
techniques
Trilinearity Constraint (flexible to every species)
Extension of MCR-ALS to multilinear systems
D1
D2
=
ST
D3
D
Selection of species profile
Substitution of species profile
C
Trilinearity
Constraint
Unique
Solutions!
C
Folding
species
profile
R.Tauler, I.Marqués and E.Casassas.
Journal of Chemometrics,
1998; 12, 55-75
loadings
1st score
PCA,
SVD
1st score
gives the
common
shape
Unfolding species profile
Loadings give the
relative amounts!
Quality assesment of MCR-ALS results
MCR solutions are not unique
Evaluation of rotation ambiguities
R.Tauler. Journal of Chemometrics, 2001, 15, 627
•0.5
D = C ST + E = D* + E
Tmax
•0.4
•0.3
STnew = T ST
Cnew = C T-1
D*
=C
ST =
C
T-1T
ST
=
Tmin
•0.2
•0.1
CnewSTnew
•0
•0
•5
•10
•15
•20
•30
•35
•40
•45
•50
Tmax
•1.5
Rotation matrix T is not
unique. It depends on the
constraints.
Tmax and Tmin may be found by a
non-linear constrained
optimization?
•25
•1
•0.5
•0
•0
•5
•10
•15
Tmin
•20
•25
•30
•35
•40
Extensión to ‘multiway’ data: 4 chromatographic
runs of 4 coeluting components Trilinear data
N
∑
d ijk =
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
00
n=1
c in s
jn
t kn + e ijk
a) Matrix augmentation, non-negativity and
spectra normalization constraints
0.6
0.4
3
0.2
2
Run1 Run 2
Run 3
1
Run 4
50
100
150
200
0
0
00
0.5
0.4
0.3
0.2
0.1
00
50
100
150
200
5
10
15
20
25
30
35
40
45
50
4
Run 3
Run2
Run 1
Run 4
3
2
20
40
1
0
0
b) Matrix augmentation, non-negativity, spectra
normalization and selectivity constraints
20
40
60
80
100
120
140
160
180
200
c) Matrix augmentation, non-negativity, spectra
normalization and trilinearity constraints
0.6
0.35
0.5
0.3
0.4
0.25
0.2
0.3
0.15
0.2
0.1
0.1
0.05
0
0
5
10
15
20
25
30
35
40
45
50
4
0
0
10
20
30
40
50
60
20
40
60
80
100
120
70
80
90
100
3
2.5
3
2
2
1.5
1
1
0.5
0
0
20
40
60
80
100
120
140
160
180
200
0
0
140
160
180
200
Quality assesment of MCR-ALS results.
Error propagation and Confidence intervals
J.Jaumot, R.Gargallo and R.TaulerJ. of Chemometrics, 2004, 18, 327–340
Resampling Methods
Theoretical
Data
Montecarlo
Simulation
pK1
Experimental
Data
Noise
Addition
Jackknife
Noise 1%
Mean, bands and confidence range of concentration profiles
pK2
Noise
added
Value
Std.
dev
Value
Std.
dev
0%
3.6539
2e-14
4.9238
2e-14
0.1 %
3.6540
6e-4
4.9226
0.0022
1%
3.6592
0.0061
4.9134
0.0264
2%
3.6656
0.0101
4.9100
0.0409
5%
4.0754
0.4873
5.3308
1.1217
Mean, bands and confidence range of spectra
0.8
1
0.9
0.7
0.8
0.6
Absorbance /a.u.
0.7
Rel. conc
0.6
0.5
0.4
0.5
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
3
4
5
6
pH
7
8
0
240
250
260
270
280
Wavelength /nm
290
300
310
320
Mean, bands and confidence range of the spectra
0.8
0.7
0% noise
0.6
Absorbance /a.u.
0.5
0.4
0.3
0.2
0.1
0
240
250
260
270
280
290
300
310
320
Wavelength /nm
Noise Addition Simulations
Spectra profiles:
Mean, max and min profiles
Confidence range profiles
Mean, bands and confidence range of spectra
0.9
0.7
0.8
0.1% noise
0.6
0.6
0.5
0.4
0.3
0.5
0.4
0.3
0.2
0.2
0.1
0.1
0
240
2 % noise
0.7
Absorbance /a.u.
Absorbance /a.u.
Mean, bands and confidence range of spectra
0.8
0
250
260
270
280
Wavelength /nm
290
300
310
320
240
250
Mean, bands and confidence range of spectra
1.6
0.7
1.4
1 % noise
290
300
310
320
5 % noise
1
Absorbance /a.u.
Absorbance /a.u.
280
Wavelength /nm
1.2
0.5
0.4
0.3
0.2
0.8
0.6
0.4
0.2
0.1
0
240
270
Mean, bands and confidence range of spectra
0.8
0.6
260
0
250
260
270
280
Wavelength /nm
290
300
310
320
-0.2
240
250
260
270
280
Wavelength /nm
290
300
310
320
Until now
MCR-ALS input has to be typed in
the MATLAB command line
Troublesome and difficult in complex cases where several data
matrices are simultaneously analyzed and/or different constraints
are applied to each of them for an optimal resolution
Now
A new graphical user-friendly
interface for MCR-ALS
Joaquim Jaumot, Raimundo Gargallo, Anna de Juan and Roma Tauler,
Chemometrics and Intelligent Laboratory Systems, 2005, 76(1) 101-110
Multivariate Curve Resolution
Home Page
http://www.ub.es/gesq/mcr/mcr.htm
Example : Analysis of a single experiment
Melting experiment of an oligonucleotide (2 components)
monitored by UV-VIS spectroscopy
Example 2. Analysis of multiple experiments. Analysis of 4
HPLC-DAD runs each of them containing four compounds
Spectroscopic Imaging Data
Coupling microscopy and spectroscopy
λ
λ
2000
Number of pixels (x x y)
2000
1000
1000
y
000
ç
0
0
50
100
150
200
250
50
100
150
200
250
3000
2000
x
2000
1000
Pixel
1000
000
0
0
Scanned surface
D
Data set
Chemical measurement
Example: Industrial spectroscopic images
RAMAN intensity
Oil-in-water emulsion
(60 × 60 pixels)
Spectroscopic technique
RAMAN (229 wavenumbers)
Compounds in the emulsion
overlap.
The interphase is complex.
Wavenumber
Oil-in-water emulsion monitored
at different depths
(24 x 23 x 10 pixels)
(*) Andrew, J.J., Hancewicz, T.M. Appl. Spec. 50 (1996) 263.
Resolution of augmented image data sets
Pure spectrum
Layer 1
ST
Layer 2
Layer 3
L1
L2
=
L3
Layer 4
Distribution maps
L4
Layer 5
D
C
L5
Bulk quantitative
information
A. de Juan, R. Dyson, C. Marcolli, M. Rault, R. Tauler and M.Maeder. Trends
in Analytical Chemistry 2004, 23, 70-79
Study of the cis platination reaction between:
O
N
O
NH
O
NH
O
P
N
O
NH
O
-
dG
O
OH
Methionine
S
Oligopeptide methionine-guanine conjugate
Phac-Met-linker-p5dG (Phac = phenylacyl)
H3C
15N
H3
N
O
Cl
+
Pt
cisplatin
[15N]-cis-dichlorodiammineplatinum(II)
Cl
15NH
3
J.Jaumot, V.Marchán, R.Gargallo, A.Grandas and R.Tauler, Analytical Chemistry, 2004, 76, 7094-7101
NH 2
2D-NMR reaction monitoring
Experimental conditions of the reaction
[1H,15N]-HSQC NMR
correlated spectroscopy
Cl
Met
+
cisplatin
dG
-reaction time: hours (slow reaction)
H315N
Pt
Cl
15NH
3
15N
labelled cisplatin
751 15N chemical shifts
156 1H chemical shifts
-at several times one 2D-NMR spectrum
- 23 2D NMR correlated spectra were measured
Study of this
reaction with time
48 h
D (23,156,751)
S adduct
cisplatin
S adduct
cisplatin
N adduct
guanine
N adduct
guanine
chelate
chelate
final product
final product
cisplatin
cisplatin
chelate
chelate
chelate
chelate
751 δ
156 δ1H
15N
2D-spectrum
t1
751 δ
Evolving
Reaction
156
x 751
15N
2D-spectrum t2
156 δ1H
δ1H
Data structure:
multiple 2D data
matrices at different
reaction stages
δ15N
751 δ
156 δ1H
15N
2D-spectrum t22
751 δ
D “tube-wise”
156 δ1H
D1
D2
.................................................................
................................................................
D23
t
15N
2D-spectrum t23
MCR-ALS applied to multiple 2D NMR data matrices
ST 1
ST 2
ST 2
ST 4
D1
D2
=
..........................................
..........................................
..........................................
t
Refolding
D23
C
156 δ1H x 751 δ15N
156
δ1H
S1
751 δ15N
D “tube-wise”
Kinetic information:
distribution diagram and 2D “pure” NMR spectra
=
S2
S3
ST
S4
NH3 Trans Met
Monofuntional
Final Product
NH3 Trans dG
Monofuntional
NH3 Trans
Metal Chelate
NH3 Trans Met
Monofuntional
NH3 Trans dG
Monofuntional
NH3 Trans
Metal Chelate
Final Product
S.Navea, A. de Juan and R.Tauler Analytical Chemistry, 2002, 74, 6031-9
α-lactalbumin
(Ca2+ presente)
250-300 nm
200-250 nm
α-apolactalbumin
(Ca2+ ausente)
Id the presence of
Ca(II) affecting the
protein folding
mechanism?
The protein folding pathway (Far- and near-UV CD)
D1
S1T
è
D2
ST
C
D
α-apolactalbumin
α-lactalbumin
1
0.1
0.4
0.2
0.04
D
0.02
0 0
10
20
30
40
50
60
Temperature (ºC )
70
200
D
0
-200
N
-400
-600
260
280
300
320
Wavelengths (nm)
CD near UV
-800
D
I
0.6
0.06
-200
-250
N
0.8
N
0.08
50
0
-50
-100
-150
S2T
D
N
200 210 220 230 240 250
Wavelengths (nm)
CD far UV
0
10
5
I
0
-5
- 10
- 15
- 20
- 25 250
20
30
40
50
60
70
Temperature (oC)
80
0
I
- 20
D
D
- 40
D
D
- 60
6
N
N
I
- 80
8
300
Wavelengths ( nm )
CD near UV
I
N
N
200
250
Wavelengths ( nm )
CD far UV
T-induced transitions of β-lactoglobulin
1.2
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
1200 1600 2000 2400
Wavelength (nm)
0 1900 1700 1500
Wavenumber (cm -1)
1.4
1.4
1.2
1.2
1
1
0.8
0.8
0.6
0.6
0.4
0.4
=
D1
0.6
ST NIR
0.4
0.2
D2
20 30 40 50 60 70 80
Temperature (ºC)
CD2O and Cprotein
1
0.2
0.2
0
0 1900 1700 1500
Wavenumber (cm -1)
1200 1600 2000 2400
Wavelength (nm)
0.8
0
Concentració (u.a.)
Absorbance
0
1
P1
0.8
0.6
0.4
D1
D2
P3
1
0.9
D1
0.8
D2
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0 1200 1600 2000
Wavelength (nm)
STMIR
Absorbance (u.a.)
1.2
Absorbance (u.a.)
1.4
Tesis doctoral de
Susana Navea
CD2O
Concentration (u.a.)
1.4
Absorbance
Absorbance
NIR/MIR results
0.9
0.8
0.7
D1
0.6
D2
0.5
0.4
0.3
0.2
0.1
0 1900
1700
1500
Wavenumber (cm -1)
P2
0.2
0
20 30 40 50 60 70 80
Temperature (ºC)
The protein contributions are successfully modelled
in the presence of the evolving solvent background.
T-induced transitions of β-lactoglobulin
Protein process description
P3
P1
P2
63 ºC
46 ºC
20
30
40
50
60
70
80
0.8
P2
P3
P1
0.7
0.6
0.5
R-type state
(p2)
P1
0.3
0.2
0
P3
1900 1800 1700 1600 1500 1400
Wavenumber (cm -1)
Wavelength (nm)
46ºC
P2
0.4
0.1
1200 1400 1600 1800 2000 2200 2400
Temperature (ºC)
Native protein
(p1)
MIR
0.9
Absorbance (a.u.)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Absorbance (a.u.)
Concentration (a.u.)
NIR
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
63ºC
Molten globule
(p3)
• The proposed mechanism is confirmed.
• Only the combined use of MIR/NIR defines all the protein
conformations.
S.Navea, A. de Juan and R.Tauler Analytical Chemistry, 2003, 75, 5592-5601
MCR-ALS applied to DNA microarray data
DNA microarray technology has made possible to monitor gene
expression levels for thousands of genes in a single experiment.
Information about the existence of
patterns and relationships between
samples (cell lines) and variables
(genes) can be obtained.
Cell
lines
(cancer
samples)
Because of the huge amount of data
generated in a single experiment, data
compression and data analysis methods
are needed to extract and understand
the information contained in the data
Genes expression
(variables)
MCR-ALS applied to DNA microarray data
D
Data Matrix
information
about the
cancer
samples
(cell lines)
Experimental data
Initial Estimation
K-means Centroids
ALS
C
Samples profiles
Gene profiles
information
about the
gene expression
(variables)
ST
EXTRACTING BIOMEDICAL INFORMATION
FROM GENE EXPRESSION MICROARRAY DATA
BY MULTIVARIATE CURVE RESOLUTION
J.Jaumot, R.Tauler and R.Gargallo (work in progress)
MCR-ALS results
shows that each group
is characterized by:
MCR
Component
Type of
cancer
1
CNS, OV,
RE, LC
2
ME
3
LE
4
Not well
defined
5
CO
LE leukemia, ME melanoma
CO colon, other carcinomas
C
ST
(cell lines)
(genes)
m/z
(A)
t
MCR-ALS
D
Daug
Quantitative
information
Cunk
Dstd1
Cstd1
Estd1
Eaug
Estd2
...
EstdN
QUANTITATION USING MCR-ALS
Cunk Cstd1 Cstd2
...
CstdN
+
CstdN
Elution
profile 1
Cstd2
Eunk
...
=
...
Cstd1
Qualitative information
Cstd2 Caug
MCR-ALS
DstdN
Cunk
E
ST
Dunk
Dstd2
+
C
=
MS data
(B)
ST
CstdN
...
Aunk Astd1 Astd2 AstdN
hunk hstd1 hstd2 hstdN
Astd or hstd
hunk
Cunk
Cstd
MSD2 TIC, MS File (E:\20PPM.D)
API-ES, Pos, Scan, 90
(A)
Reconstructed TIC MS chromatograms
12000000
10000000
8000000
Standard mixture of 13 biocides at 20 ppm
6000000
4000000
2000000
0
10
MSD2 TIC, MS File (E:\MINAB10.D)
20
30
40
min
API-ES, Pos, Scan, 90
(B)
8000000
7000000
6000000
Aznalcollar sediment sample spiked
with 13 biocides at 10 ppm
5000000
4000000
3000000
2000000
1000000
0
10
MSD2 TIC, MS File (E:\LLAGE10.D)
20
30
40
API-ES, Pos, Scan, 90
In-WWTP water sample from La LLagosta sample spiked
with 13 biocides at 10 ppm
10000000
8000000
min
(C)
6000000
4000000
2000000
0
10
20
30
40
min
MULTIVARIATE CURVE RESOLUTION
λ & m/z + wavelets
MS
DAD
MCR-ALS
tR
DAD
=
MS
ST
C
D
Elution profiles
900
DAD and MS Spectra
0.4
1000
chlopyrifos-oxon
terbutryn
DAD
0.3
800
0.2
700
0.1
600
0
200
220
240
260
280
300
320
340
?
500
400
solvent
gradient
alachlor
300
0.4
0.2
200
tR
m/z
?
SCAN mode
0.1
100
0
35
MS
0.3
35.5
36
36.5
37
37.5
38
38.5
tR
0
50
100
150
200
250
300
350
400
m/z
Aznalcollar sediment
In-WWT water sample
900
900
(A)
800
(A)
800
700
700
600
600
500
500
400
400
300
300
200
200
100
100
0
34.5
35
35.5
36
36.5
37
37.5
38
38.5
tR
0
34.5
35
35.5
36
36.5
37
37.5
tR
0.2
0.2
0.18
0.18
(B)
0.16
(B)
0.16
0.14
0.14
0.12
0.12
0.1
0.1
0.08
0.08
0.06
0.06
0.04
0.04
0.02
0.02
0
0
200
220
240
260
280
300
320
340
200
220
240
260
280
300
320
340
?
?
0.35
0.35
(C)
0.3
(C)
0.3
0.25
0.25
0.2
0.2
0.15
0.15
0.1
0.1
0.05
0.05
0
50
100
150
200
250
300
350
Solvent gradient
Alachlor
Chlorpyrifos-oxon
Terbutryn
400
m/z
0
50
100
150
200
250
300
350
Solvent gradient
Alachlor
Chlorpyrifos-oxon
Terbutryn
Impurity
400
m/z
Determinació directa d’analits a partir de mesures
espectrofluorimètriques en matrius naturals complexes
sense necesitat de separació prèvia per métodes
cromatogràfics
Determinació de trifenilestany en aigua de mar mitjançant
fluorescència i resolució multivariant
Espectres d’excitacioemissió per una mostra de
aigua de mar amb
trifenilestany
Analytica Chimica Acta, 2000, 409, 237-245
MCR-ALS resolution of [U;S;R;B] augmented matrix
5
(b)
3
1
3
0
450
540
500
460
Emission Wavelength (nm)
580
(c)
500
550
Emission wavelength (nm)
b
4
2
2
1
1
Fluorescence Intensity
2
(d)
500
550
Emission wavelength (nm)
(f)
e
1
c
2
2
2
0
450
1
580
(e)
3
4
400
540
500
460
Emission Wavelength (nm)
excitation
4
2
0
B
3
450
Relative intensity
R
0
300
Excitation 305
Wavelength (nm)310315
420
4
300
Excitation 305
Wavelength (nm)310315
580
MCR
-ALS
2
0
Relative intensity
Fluorescence Intensity
0
300
Excitation 305
Wavelength (nm)310
540
500
315 420
460
Emission Wavelength (nm)
2
1
1 TPhT flavonol complex
2 Flavonol reagent
3 sea-water background
2
S
4
a
2
4
Arbitrary intensity
Fluorescence Intensity
(a)
U
Relative intensity
9
8
7
6
5
4
3
2
1
300
305
Excitation
310
Wavelength (nm)
315 420
Relative intensity
Fluorescence Intensity
emission
500
550
Emission wavelength (nm)
405
410
415
Excitation wavelength (nm)
d
4
2
3
0
420
540
500
460
Emission Wavelength (nm)
450
500
550
Emission wavelength (nm)
a) 3-D plots of the EEM fluorescence of the unknown sample U, standard S, flavonol reagent R and
sea-water background B;
b) emission spectra for the unknown sea-water sample; c) emission species spectra for the standard;
d) emission species spectra for flavonol reagent; e) emission species spectra for sea-watere background;
f) excitation spectra
3
Standards
Response
2.5
Synthetic
See Water A
2
See Water B
1.5
See Water C
1
See Water D
0.5
Comparación valores verdaderos y calculados
por MCR-ALS en muestras de aguas de mar
Calculated concentration (ppt)
Resolución y quantificación por
MCR-ALS de datos EEM
45
40
35
30
25
20
15
10
5
0
0
See Water E
10
20
30
40
50
Real Concentration (ppt)
10
20
30
40
Concentration (pg/l)
50
Areas de los espectros de emisión resuleltos por
MCR-ALS respecto a las concentraciones de TPhT
Estratègias de Calibración
i.
using pure standards
ii.
using sea-water standards
iii. using the standard addition method
Analytica Chimica Acta, 2000, 409, 237;
2001, 432, 245-255
The Analyst, 2000, 125, 2038-43
cU = [Area(yU) / Area(yS)] cS
errores de predicción siempre por debajo del 13%!
Relative Area
0
0
2.5
2
1.5
1
0.5
-20
0
0
20
concentration TPhT
(µg / L)
40
Standard
addition
calibration
graph in
a sea-water
analyte
determination
(sea-water
sample U4)
Environmental data tables (two-way data)
J variables
Conc. of chemicals
Physical Properties
Biological properties
Other .....
I samples
<LOD
X(I,J)
Data table or
data matrix
12 13 45 67 89 42 35 0 0.3 0.005 111 33 5 67 90 0.06 44 33 1 2
‘m’
350
350
300
300
250
200
Plot of samples
(rows)
200
150
150
100
100
50
50
0
0
-50
0
5
10
15
Plot of variables
(columns)
250
20
25
30
-50
0
5
10
15
20
25
30
35
40
45
50
Environmental source resolution and apportioment
20
Bilinearity!
0.2
15
0.15
0.1
10
0.05
5
0
0
0
0
5
10
15
20
25
10
20
30
40
50
60
70
80
90
100
FT
0.2
0.15
30
0.1
20
0.05
0
0
10
10
20
30
40
50
60
70
80
90
100
G
0.4
0
0.3
0
5
10
15
20
25
0.2
20
0.1
0
0
15
10
0
5
10
15
20
20
30
40
50
60
70
80
90
100
NR
identification
of contamination
sources
(composition)
5
0
10
25
distribution of
conatamination
sources
+
NR
E
NC
6
22 samples
5
4
NR
X
N
x = ∑ g f +e
ij n=1 in nj ij
3
2
1
0
0
10
20
30
40
50
60
70
80
concn. of 96 organic compounds
90
100
xij concentration of chemical contaminant j in
sample i; f iin contribution of source n in sample j;
gnj contribution of compound j in source n
Projecte ACA
E. Peré-Trepat, M. Terrado, R.Tauler,
Contaminación general
?
AQUATERRA
Sub-Project BASIN
Workpackage: R2 – EBRO river basin
Integration Monitoring-Chemometrics-GIS
M.Terrado, A.Navarro, S.Lacorte and R.Tauler
VITORIA
T3
T2
R1
R4
T8
T7
R6
PAMPLONA
T9
SABIÑÁNIGO
T12
T5 LOGROÑO
WP Leader: D. BARCELO
IIQAB-CSIC, Barcelona (E)
Partners:
ACA, Barcelona (E)
AGBAR, Barcelona (E)
AGH, Kracow (Pl)
HUESCA
TUDELA
T13
MONZÓN
T15
ZARAGOZA
T10
T11
R14
LLEIDA
T16
R17
TORTOSA
R18
SURFACE WATER
AQUATERRA (Surveillance
monitoring)
R0: Ebro in Reinosa (Cantabria)
R1: Ebro in Miranda de Ebro (Burgos)
T2: Zadorra in Audinaka (Álava)
T3: Zadorra in Villodas (Álava)
R4: Ebro in Haro (La Rioja)
T5: Najerilla in Najera (La Rioja)
R6: Ebro in Logroño (La Rioja)
T7: Ega in Estella (Navarra)
R7: Ebro in Tudela (Navarra)
T8: Araquil in Alsasua (Navarra)
T9: Arga in Puente la Reina (Navarra)
T10: Jalon in Grisen (Zaragoza)
T11: Huerva in Zaragoza (Zaragoza)
T12: Gállego in Caldearenas (Huesca)
T13: Gállego in San Mateo de Gállego (Zaragoza)
R14: Ebro in Presa de Pina (Zaragoza)
R15: Ebro in Sástago (Zaragoza)
T15: Cinca in Alcolea de Cinca (Huesca)
T16: Segre in Torres de Segre (Lleida)
R17: Ebro in Flix (Tarragona)
R18: Ebro in Tortosa (Tarragona)
R19: Ebro in Amposta (Tarragona)
R20: Ebro in Delta de l’Ebro (Tarragona)
GAR1: Gállego in Villanueva de Gállego (Zaragoza)
24 sample points
DIFFERENT SOURCES
RED ICA àCHE
(Red Integral de
Control de Aguas)
210 sample points
RCSPàCHE (Red de control
de sustancias peligrosas)
SP-1:
Gállego en Jabarrella
SP-2:
Ebro en Presa Pina
SP-3:
Ebro en Ascó
SP-4:
Segre en Torres de Segre
SP-5:
Cinca en Monzón (aguas abajo)
SP-6:
Arga en Puente La Reina
SP-7:
Ebro en Miranda de Ebro
SP-8:
Zadorra en Vitoria Trespuentes
SP-9:
Ebro en Tortosa
SP-10:
Araquil en Alsasua-Urdiaín
SP-11:
Ebro en Conchas de Haro
SP-12:
Ebro en Logroño (aguas abajo)-Varea
SP-13:
Ega en Arinzano
SP-14:
Gállego en Villanueva
SP-15:
Huerva en Fuente de la Junquera
SP-16:
Jalón en Grisén
SP-17:
Najerilla en Nájera (aguas abajo)
SP-18:
Zadorra en Salvatierra
18 sample points
SURFACE WATER à
HISTORICAL DATA
2
1
DANGEROUS SUBSTANCES CONTROL NETWORK
(Red de Control de Sustancias Peligrosas)
18 sampling points; 3 compartments comparison
WATER
(2002-04)
SEDIMENTS
(2002-03)
BIOTA
(2002-03)
ICA NET
OF SURFACE WATER CONTROL
(Red Integrada de Control de la Calidad de las
Aguas Superficiales)
Selection of some points from this net common
with the AQUATERRA’s surveillance monitoring;
1 compartment
WATER
(2002-04)
DATA BASE
Physical
parameters
Chemical parameters
Other
parameters
-Water temperature
-pH
METALS: As, Cd, Pb, Zn, Cu
-Air temperature
-Dissolved oxygen
-LAND USES à grouping
points according to their
land use, to obtain common
features and differences
ORGANIC COMPOUNDS:
-Conductivity at 20ºC
-Suspended materials
-QUALITY INDEXES :
-Aspect
-COD (chemical oxygen
demand), BOD5...
-pesticides of agricultural
origin (HCB, SDDTs, SHCHs)
ISQA (simplified index
for water quality)
-Flow
-Chlorides, sulphates,
phosphates
-SPCBs
-PAHs
BIOLOGIC index
SURFACE WATER AND
GROUNDWATER DATABASES
PCA (Principal Component Analysis)
CHEMOMETRICS
Other methods (Multivariate data
Analysis)
IDENTIFICATION
contamination sources
Loading plots
DISTRIBUTION
contamination sources
Geographical
Temporal
GIS
ENVIRONMENTAL
DISCUSSION
ØKey zone delimitation according to
the pollution degree.
ØRelation between land use
(agricultural, industrial, urban and
protected areas) and distribution of
the contamination sources
ØRepresentation of quality indexes
ØComparison of the contribution of
the different contamination sources
in each environmental compartment
(water, sediments and biota)
Acknowledgements
• Chemometrics Group UB
– Staff: Anna de Juan, Javier Saurina, Raimundo Gargallo (RyC)
– PhD : Susana Navea, Joaquim Jaumot, Emma Peré-Trepat
– Master and DEA: Gloria Muñoz, Silvia Mas
• Environmental Chemometrics Group IIQAB-CSIC
– Staff: Romà Tauler
– Post-doc: Montse Vives (JdC), Mónica Felipe (I3P)
– Master and DEA Marta Terrado, Xavier Puig
• Elisabeth Teixido (ACA), Silvia Termes (LAG)