Multivariate Curve Resolution:theory and applications
Romà Tauler, Abril, 2005
Multivariate Curve Resolution
Pure component information
è
C
STsn
s1
c nc 1
WavelengthsRetention times
Pure concentration profilesChemical model
Process evolutionCompound contribution
Pure signalsCompound identity
D
Mixed information
tR
λ
DataMatrix
InitialEstimation
SVDor
PCA
ALSoptimization
ResolvedSpectraprofiles
Re
solv
ed
Co
nce
ntr
atio
np
rofi
les
Estimation of the number
of components
Initial estimation ALS optimization
CONSTRAINTS Results of the ALS optimization procedure:
Fit and Diagnostics
E+
Data matrix decomposition according to a bilinear model
Flowchart of MCR-ALS
DC
ST
TPCAC
SCDmin ˆˆˆˆ
− TPCA
SSCDmin
Tˆˆˆ −
D = C ST + E(bilinear model)
Journal of Chemometrics, 1995, 9, 31-58; Chemomet.Intel. Lab. Systems, 1995, 30, 133-146Journal of Chemometrics, 2001, 15, 749-7; Analytica Chimica Acta, 2003, 500,195-210
Hard + soft modelling constraintsMCR-ALS hybrid (grey) models
D(I x J x K)
Regular data cube
A series of two-way data sets with commoninformation in one or more modes.
D
D
Other three-way arrangements
Multiway data : Multiple measurementorders/modes/directions/ways
Example: Three-way data
Data augmentations in MCR
D
=D1 D2 D3
S1 T S2
T S3T
ST
CD
=D1 D2 D3
S1 T S2
T S3T
ST
CD
=D1 D2 D3D1 D2 D3
S1 T S2
T S3TS1
T S2T S3
T
ST
C
The same experiment monitored with different techniques
C1
C2
C3
=
STD1
D2
D3
D C
C1
C2
C3
=
STD1
D2
D3
D CSeveral experiments monitored with the same technique
=
S1T S2
T S3T
D1 D2 D3 C1
D4 D5 D6 C2
D C
ST
=
S1T S2
T S3TS1
T S2T S3
T
D1 D2 D3 C1
D4 D5 D6 C2
D C
ST
Several experiments monitored with several
techniques
Row-wise
Column-wise Row and column-wise
ST
C
=
D
D1
D2
D3
Trilinearity Constraint (flexible to every species) Extension of MCR-ALS to multilinear systems
1st scoreloadings
PCA,SVD
Foldingspeciesprofile
1st scoregives thecommonshape
Loadings give therelative amounts!
TrilinearityConstraint
Unfolding species profile
UniqueSolutions!
Substitution of species profile
C
Selection of species profile
R.Tauler, I.Marqués and E.Casassas. Journal of Chemometrics, 1998; 12, 55-75
D = C ST + E = D* + E
STnew = T ST
Cnew = C T-1
D* = C ST = C T-1T ST = CnewSTnew
Quality assesment of MCR-ALS resultsMCR solutions are not uniqueEvaluation of rotation ambiguities
Rotation matrix T is notunique. It depends on theconstraints.Tmax and Tmin may be found by a non-linear constrainedoptimization?
•0 •5 •10 •15 •20 •25 •30 •35 •40 •45 •50•0
•0.1
•0.2
•0.3
•0.4
•0.5
•0 •5 •10 •15 •20 •25 •30 •35 •40•0
•0.5
•1
•1.5
Tmax
Tmin
Tmax
Tmin
R.Tauler. Journal of Chemometrics, 2001, 15, 627
Extensión to ‘multiway’ data: 4 chromatographic runs of 4 coeluting components Trilinear data
∑=
+=N
1nijkknjninijk etscd
0 50 100 150 20000.20.40.60.8
11.21.41.61.8
Run 2Run1Run 3
Run 4 0 50 100 150 2000
1
2
3
0.5
0 20 4000.10.20.30.4
0 5 10 15 20 25 30 35 40 45 500
0.2
0.4
0.6
0 20 40 60 80 100 120 140 160 180 2000
1
2
3
4
Run 1 Run2 Run 3 Run 4
a) Matrix augmentation, non-negativity andspectra normalization constraints
0 10 20 30 40 50 60 70 80 90 1000
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0 20 40 60 80 100 120 140 160 180 2000
0.5
1
1.5
2
2.5
3
c) Matrix augmentation, non-negativity, spectranormalization and trilinearity constraints
0 5 10 15 20 25 30 35 40 45 500
0.1
0.2
0.3
0.4
0.5
0.6
0 20 40 60 80 100 120 140 160 180 2000
1
2
3
4
b) Matrix augmentation, non-negativity, spectranormalization and selectivity constraints
3 4 5 6 7 8
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Mean, bands and confidence range of concentration profiles
pH
Rel
. con
c
240 250 260 270 280 290 300 310 3200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8Mean, bands and confidence range of spectra
Wavelength /nm
Abs
orba
nce
/a.u
.
2e-144.92382e-143.65390 %
0.00224.92266e-43.65400.1 %
0.02644.91340.00613.65921 %
1.12175.33080.48734.07545 %
0.04094.91000.01013.66562 %
Std. dev
ValueStd. dev
ValueNoiseadded
pK2pK1
MontecarloSimulation
JackknifeNoise Addition
Resampling Methods
TheoreticalData
ExperimentalData
Noise 1%
Quality assesment of MCR-ALS results. Error propagation and Confidence intervals
J.Jaumot, R.Gargallo and R.TaulerJ. of Chemometrics, 2004, 18, 327–340
240 250 260 270 280 290 300 310 3200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Wavelength /nm
Abs
orba
nce
/a.u
.
Mean, bands and confidence range of the spectra
240 250 260 270 280 290 300 310 3200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8Mean, bands and confidence range of spectra
Wavelength /nm
Abs
orba
nce
/a.u
.
240 250 260 270 280 290 300 310 3200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8Mean, bands and confidence range of spectra
Wavelength /nm
Abs
orba
nce
/a.u
.
240 250 260 270 280 290 300 310 320
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9Mean, bands and confidence range of spectra
Wavelength /nm
Abs
orba
nce
/a.u
.
240 250 260 270 280 290 300 310 320-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6Mean, bands and confidence range of spectra
Wavelength /nm
Abs
orba
nce
/a.u
.
Noise Addition SimulationsSpectra profiles: Mean, max and min profilesConfidence range profiles
0% noise
0.1% noise
1 % noise 5 % noise
2 % noise
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
NowA new graphical user-friendly
interface for MCR-ALSJoaquim Jaumot, Raimundo Gargallo, Anna de Juan and Roma Tauler, Chemometrics and Intelligent Laboratory Systems, 2005, 76(1) 101-110
Multivariate Curve ResolutionHome Page
http://www.ub.es/gesq/mcr/mcr.htm
Example : Analysis of a single experimentMelting 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
0 50 100 150 200 2500
000
1000
1000
2000
2000
0 50 100 150 200 2500
000
1000
1000
2000
2000
3000
λ
y
x
Scanned surface
Pixel
λ
Num
ber
of p
ixel
s (x
x y
)
D Chemical measurementData set
Spectroscopic Imaging DataCoupling microscopy and spectroscopy
ç
(*) Andrew, J.J., Hancewicz, T.M. Appl. Spec. 50 (1996) 263.
Oil-in-water emulsion(60 × 60 pixels)Spectroscopic techniqueRAMAN (229 wavenumbers)Compounds in the emulsion overlap.The interphase is complex.
Wavenumber
RA
MA
N in
tens
ity
Example: Industrial spectroscopic images
Oil-in-water emulsion monitoredat different depths(24 x 23 x 10 pixels)
Resolution of augmented image data sets
Layer 1
D C
ST
Pure spectrum
Distribution maps
=
Bulk quantitativeinformation
Layer 2
Layer 3
Layer 4
Layer 5
L1
L2
L3
L4
L5
A. de Juan, R. Dyson, C. Marcolli, M. Rault, R. Tauler and M.Maeder. Trendsin Analytical Chemistry 2004, 23, 70-79
Study of the cis platination reaction between:
Oligopeptide methionine-guanine conjugatePhac-Met-linker-p5dG (Phac = phenylacyl)
cisplatin[15N]-cis-dichlorodiammineplatinum(II)
+
PtH315N
15NH3
Cl
Cl
NH
O
S
CH3
NH
O
O N
OO
OH
O-
O
P N
NHN
NH2
O
Methionine
dG
J.Jaumot, V.Marchán, R.Gargallo, A.Grandas and R.Tauler, Analytical Chemistry, 2004, 76, 7094-7101
2D-NMR reaction monitoring
[1H,15N]-HSQC NMRcorrelated spectroscopy
15N labelled cisplatin751 15N chemical shifts156 1H chemical shifts
Experimental conditions of the reaction
cisplatin
Met dG+
-reaction time: hours (slow reaction)
-at several times one 2D-NMR spectrum
- 23 2D NMR correlated spectra were measured
D (23,156,751)
PtH315N
15NH3
Cl
Cl
Study of thisreaction with time
48 h
cisplatinS adduct
N adductguanine
cisplatinS adduct
N adductguanine chelate
chelate
chelate
chelate
cisplatin
final product
chelatechelate
cisplatin
final product
Data structure:multiple 2D data matrices at differentreaction stages
2D-spectrum
751 δ 15N
156 δ1H t1
EvolvingReaction
2D-spectrum
751 δ 15N
156 δ1H t2
2D-spectrum
751 δ 15N
156 δ1H t23
2D-spectrum
751 δ 15N
156 δ1H t22
t
156 δ1H x 751 δ15N D “tube-wise”
D1
D2
D23
.................................................................
................................................................
MCR-ALS applied to multiple 2D NMR data matrices
=
Refolding
S1 S3S2 S4
=
Kinetic information:distribution diagram and 2D “pure” NMR spectra
D1
D2
D23
..........................................
..........................................
..........................................
D “tube-wise”
ST1
ST2
ST2
ST4
C
t
156 δ1H x 751 δ15N 156 δ1H
751 δ15N
ST
Final Product
NH3 Trans MetMonofuntional
NH3 Trans dGMonofuntional
NH3 TransMetal Chelate
NH3 TransMetal Chelate
Final Product
NH3 Trans MetMonofuntional
NH3 Trans dGMonofuntional
S.Navea, A. de Juan and R.Tauler Analytical Chemistry, 2002, 74, 6031-9
α-lactalbumin(Ca2+ presente)
α-apolactalbumin(Ca2+ ausente)
Id the presence of Ca(II) affecting the protein folding mechanism?
200-250 nm
250-300 nm
The protein folding pathway (Far- and near-UV CD)
D1 D2
D
S1T S2
T
è
CST
α-lactalbumin
260 280 300 320-250-200
-150-100-50
050
Wavelengths (nm)
D
N
250200 210 220 230 240-800
-600
-400
-200
0
200
Wavelengths (nm)
ND
Temperature (ºC )0 10 20 30 40 50 60 700
0.02
0.04
0.06
0.08
0.1
N
D
α-apolactalbuminN
ID
10 20 30 40 50 60 70 800
0.20.4
0.6
0.8
1
250-25-20-15-10
-505
Wavelengths ( nm )
N
D
I
300-25-20-15-10
-505
N
D
I
- 25-20-15-10
-505
N
D
I
200 250Wavelengths ( nm )
- 8
-6
-40
-20
0
NI
D
- 80
-60
-40
-20
0
NI
D
Temperature (oC)
CD near UV CD far UV CD far UVCD near UV
T-induced transitions of β-lactoglobulinNIR/MIR results
Co
nce
ntr
ació
(u.a
.)
1200 1600 2000 24000
0.2
0.4
0.6
0.8
1
1.2
1.4
Wavelength (nm)
Ab
sorb
ance
1500170019000
0.2
0.4
0.6
0.8
1
1.2
1.4
Wavenumber (cm-1)
=C
on
cen
trat
ion
(u.a
.)
20 30 40 50 60 70 800
0.2
0.4
0.6
0.8
1
Temperature (ºC)
CD2O and Cprotein
P3
P1
P2
D2D1
20 30 40 50 60 70 800
0.2
0.4
0.6
0.8
1
Temperature (ºC)
CD2O
D1
D2
15001700190000.10.20.30.40.50.60.70.80.9
Wavenumber (cm-1)
Ab
sorb
ance
(u.a
.)
STMIR
D2
D1
1200 1600 200000.10.20.30.40.50.60.70.80.91
Wavelength (nm)
Ab
sorb
ance
(u.a
.)
STNIR
D2D1
1200 1600 2000 24000
0.2
0.4
0.6
0.8
1
1.2
1.4
Wavelength (nm)
Ab
sorb
ance
Ab
sorb
ance
1500170019000
0.2
0.4
0.6
0.8
1
1.2
1.4
Wavenumber (cm-1)
The protein contributions are successfully modelledin the presence of the evolving solvent background.
Tesis doctoral deSusana Navea
T-induced transitions of β-lactoglobulin
• The proposed mechanism is confirmed.• Only the combined use of MIR/NIR defines all the protein
conformations.
1200 1400 1600 1800 2000 2200 240000.10.20.30.40.50.60.70.80.91
Wavelength (nm)
Ab
sorb
ance
(a.u
.)P1
P2
P3
1400150016001700180019000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Wavenumber (cm-1)
Ab
sorb
ance
(a.u
.)
P1
P2
P3
20 30 40 50 60 70 8000.10.20.30.40.50.60.70.80.91
Temperature (ºC)
Co
nce
ntr
atio
n(a
.u.)
P1
P2
P3
46 ºC 63 ºC
Native protein Molten globule63ºCR-type state46ºC
(p1) (p2) (p3)
Protein process descriptionNIR MIR
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 ofpatterns and relationships betweensamples (cell lines) and variables (genes) can be obtained.
Because of the huge amount of data generated in a single experiment, data compression and data analysis methodsare needed to extract and understandthe information contained in the data
Celllines
(cancersamples)
Genes expression(variables)
Experimental data
Gene profilesSamples profiles
ALS
Initial EstimationK-means Centroids
DData Matrix
C ST
informationabout the
cancersamples
(cell lines)
informationabout the
gene expression(variables)
MCR-ALS applied to DNA microarray data
EXTRACTING BIOMEDICAL INFORMATION FROM GENE EXPRESSION MICROARRAY DATA
BY MULTIVARIATE CURVE RESOLUTION J.Jaumot, R.Tauler and R.Gargallo (work in progress)
MCR-ALS resultsshows that each group
is characterized by:
Type ofcancer
MCR Component
CO5
Not welldefined
4
LE3
ME2
CNS, OV, RE, LC
1
C(cell lines)
ST
(genes)
LE leukemia, ME melanomaCO colon, other carcinomas
ST
+
Elutionprofile 1
=MCR-ALS
Dunk
Dstd1
Dstd2
DstdN
...
Cunk
Cstd1
Cstd2
CstdN
...
Eunk
Estd1
Estd2
Cstd
Astd or hstd
Cunk
hunk
EstdN
...
Aunk
hunk
...Astd1 Astd2
hstd1 hstd2
CunkCstd1 Cstd2 CstdN
AstdN
hstdN
Cunk
Cstd1
Cstd2
...
CstdN
ST
C +=MCR-ALS
ED
Quantitativeinformation
Qualitative information
m/z
t
MS data
(A)
(B)
Daug Caug Eaug
QUANTITATION USING MCR-ALS
min10 20 30 40
0
2000000
4000000
6000000
8000000
10000000
12000000
MSD2 TIC, MS File (E:\20PPM.D) API-ES, Pos, Scan, 90
min10 20 30 40
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
MSD2 TIC, MS File (E:\MINAB10.D) API-ES, Pos, Scan, 90
min10 20 30 40
0
2000000
4000000
6000000
8000000
10000000
MSD2 TIC, MS File (E:\LLAGE10.D) API-ES, Pos, Scan, 90
(A)
(B)
(C)
Reconstructed TIC MS chromatograms
Standard mixture of 13 biocides at 20 ppm
Aznalcollar sediment sample spikedwith 13 biocides at 10 ppm
In-WWTP water sample from La LLagosta sample spikedwith 13 biocides at 10 ppm
35 35.5 36 36.5 37 37.5 38 38.50
100
200
300
400
500
600
700
800
900
1000
D
Elution profiles
alachlor
chlopyrifos-oxonterbutryn
solventgradient
CST
=MCR-ALS
MULTIVARIATE CURVE RESOLUTION
tR
λ & m/z + wavelets
DAD MS
DAD MS
tR
200 220 240 260 280 300 320 3400
0.1
0.2
0.3
0.4
50 100 150 200 250 300 350 4000
0.1
0.2
0.3
0.4
?
?m/ztR
?m/ztR
DAD and MS Spectra
m/z
DAD
MS
SCAN mode
50 100 150 200 250 300 350 4000
0.05
0.1
0.15
0.2
0.25
0.3
0.35
34.5 35 35.5 36 36.5 37 37.5 38 38.50
100
200
300
400
500
600
700
800
900
200 220 240 260 280 300 320 3400
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
?
m/z
tR
Terbutryn
Solvent gradientAlachlorChlorpyrifos-oxon
(B)
(C)
(A)
Aznalcollar sediment
34.5 35 35.5 36 36.5 37 37.50
100
200
300
400
500
600
700
800
900
50 100 150 200 250 300 350 4000
0.05
0.1
0.15
0.2
0.25
0.3
0.35
200 220 240 260 280 300 320 3400
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
?
m/z
tR
ImpurityTerbutryn
Solvent gradientAlachlorChlorpyrifos-oxon
(B)
(C)
(A)
In-WWT water sample
Determinació directa d’analits a partir de mesuresespectrofluorimètriques en matrius naturals complexes sense necesitat de separació prèvia per métodescromatogràfics
Espectres d’excitacio-emissió per una mostra de
aigua de mar amb trifenilestany
Analytica Chimica Acta, 2000, 409, 237-245
Determinació de trifenilestany en aigua de mar mitjançant fluorescència i resolució multivariant
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
Rel
ativ
e in
tens
ity
450 500 5500
2
4
Emission wavelength (nm)
2
1
3
a
Emission wavelength (nm)450 500 550
Rel
ativ
e in
tens
ity
0
2
4b
1
2
Emission wavelength (nm)450 500 550
0
2
4
Rel
ativ
e in
tens
ity
2
c
Emission wavelength (nm)450 500 550
0
2
4
Rel
ativ
e in
tens
ity
d
3
415400 405 410
0
2
4
Excitation wavelength (nm)
Arb
itrar
y in
tens
ity
e
1
2
3
MCR-ALS
(e)
(d)
(c)
(b)
(f)
1 TPhT flavonol complex2 Flavonol reagent3 sea-water background
MCR-ALS resolution of [U;S;R;B] augmented matrix
300305
310315420 460 500 540 580
123456789
Excitation Wavelength (nm)
Emission Wavelength (nm)
Fluo
resc
ence
Inte
nsity
U
300305
310315420 460 500 540 580
0
1
2
3
4
5
ExcitationWavelength (nm)
Emission Wavelength (nm)
Fluo
resc
ence
Inte
nsity
300305
310315 420 460 500 540
1
2
3
4
ExcitationWavelength (nm)
Emission Wavelength (nm)
Fluo
resc
ence
Inte
nsity
300305
310315420 460 500 540 580
0
1
2
ExcitationWavelength (nm)
Emission Wavelength (nm)
Fluo
resc
ence
Inte
nsity
(a)
S
R
B
a) 3-D plots of the EEM fluorescence of the unknown sample U, standard S, flavonol reagent R andsea-water background B;
emission
excitation
0
0.5
1
1.5
2
2.5
3
0 10 20 30 40 50Concentration (pg/l)
Res
pons
e
Standards
Synthetic
See Water A
See Water B
See Water C
See Water D
See Water E
Areas de los espectros de emisión resuleltos por MCR-ALS respecto a las concentraciones de TPhT
Resolución y quantificación porMCR-ALS de datos EEM
0
5
10
15
20
25
30
35
40
45
0 10 20 30 40 50
Real Concentration (ppt)
Cal
cula
ted c
once
ntr
atio
n (
ppt)
Comparación valores verdaderos y calculados por MCR-ALS en muestras de aguas de mar
cU = [Area(yU) / Area(yS)] cSerrores de predicción siempre por debajo del 13%!
0
0.5
1
1.5
2
2.5
-20 0 20 40concentration TPhT
(µg / L)
Rel
ativ
e A
rea
Estratègias de Calibracióni. using pure standardsii. using sea-water standardsiii. using the standard addition method
Analytica Chimica Acta, 2000, 409, 237;2001, 432, 245-255
The Analyst, 2000, 125, 2038-43
Standardadditioncalibrationgraph ina sea-wateranalytedetermination(sea-watersample U4)
I sam
ples
J variables
0 5 10 15 20 25 30-50
0
50
100
150
200
250
300
350
0 5 10 15 20 25 30 35 40 45 50-50
0
50
100
150
200
250
300
350
Data table ordata matrix
Plot of samples(rows)
Plot of variables(columns)
12 13 45 67 89 42 35 0 0.3 0.005 111 33 5 67 90 0.06 44 33 1 2X(I,J)
Environmental data tables (two-way data)
Conc. of chemicalsPhysical PropertiesBiological propertiesOther .....
‘m’
<LOD
1
Nx g f eij in nj ijn= +∑
=
Bilinearity!
NR
NC
X
+G
FT
ENR NR
Environmental source resolution and apportioment
0 10 20 30 40 50 60 70 80 90 1000
1
2
3
4
5
6
concn. of 96 organic compounds
0 1 0 20 30 4 0 50 60 7 0 80 90 1000
0.05
0.1
0.15
0.2
0 1 0 20 30 4 0 50 60 7 0 80 90 1000
0.05
0.1
0.15
0.2
0 1 0 20 30 4 0 50 60 7 0 80 90 1000
0.1
0.2
0.3
0.4
0 5 1 0 1 5 20 250
5
10
15
20
0 5 1 0 1 5 20 250
10
20
30
0 5 1 0 1 5 20 250
5
10
15
20
22 samples
identificationof contamination
sources(composition)
distribution ofconatamination
sources
xij concentration of chemical contaminant j in sample i; fiin contribution of source n in sample j;gnj contribution of compound j in source n
Projecte ACAE. Peré-Trepat, M. Terrado, R.Tauler,
Contaminación general
?
T12
R14
R17
T16
T15
T9R1T3
R18
T8
R4R6T7
T13
T11T10
T5
T2
TORTOSA
LLEIDAZARAGOZA
HUESCAMONZÓN
SABIÑÁNIGO
PAMPLONA
LOGROÑO
VITORIA
TUDELA
AQUATERRASub-Project BASIN
Workpackage: R2 – EBRO river basinIntegration Monitoring-Chemometrics-GIS
M.Terrado, A.Navarro, S.Lacorte and R.Tauler
WP Leader: D. BARCELOIIQAB-CSIC, Barcelona (E)Partners:ACA, Barcelona (E)AGBAR, Barcelona (E)AGH, Kracow (Pl)
SURFACE WATERDIFFERENT SOURCES
RED ICA àCHE(Red Integral de Control de Aguas)
210 sample points
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
RCSPàCHE (Red de control de sustancias peligrosas)
Zadorra en SalvatierraSP-18:
Najerilla en Nájera (aguas abajo)SP-17:
Jalón en GrisénSP-16:
Huerva en Fuente de la JunqueraSP-15:
Gállego en VillanuevaSP-14:
Ega en ArinzanoSP-13:
Ebro en Logroño (aguas abajo)-VareaSP-12:
Ebro en Conchas de HaroSP-11:
Araquil en Alsasua-UrdiaínSP-10:
Ebro en TortosaSP-9:
Zadorra en Vitoria TrespuentesSP-8:
Ebro en Miranda de EbroSP-7:
Arga en Puente La ReinaSP-6:
Cinca en Monzón (aguas abajo)SP-5:
Segre en Torres de SegreSP-4:
Ebro en AscóSP-3:
Ebro en Presa PinaSP-2:
Gállego en JabarrellaSP-1:
Zadorra en SalvatierraSP-18:
Najerilla en Nájera (aguas abajo)SP-17:
Jalón en GrisénSP-16:
Huerva en Fuente de la JunqueraSP-15:
Gállego en VillanuevaSP-14:
Ega en ArinzanoSP-13:
Ebro en Logroño (aguas abajo)-VareaSP-12:
Ebro en Conchas de HaroSP-11:
Araquil en Alsasua-UrdiaínSP-10:
Ebro en TortosaSP-9:
Zadorra en Vitoria TrespuentesSP-8:
Ebro en Miranda de EbroSP-7:
Arga en Puente La ReinaSP-6:
Cinca en Monzón (aguas abajo)SP-5:
Segre en Torres de SegreSP-4:
Ebro en AscóSP-3:
Ebro en Presa PinaSP-2:
Gállego en JabarrellaSP-1:
18 sample points
SURFACE WATER à HISTORICAL DATA
DATA BASE
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)
1ICA 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
2
WATER(2002-04)
Physical parameters
-Flow
-Water temperature
-Air temperature
-Conductivity at 20ºC
-Aspect
Chemical parameters
-pH
-Dissolved oxygen
-Suspended materials
-COD (chemical oxygen demand), BOD5...
-Chlorides, sulphates, phosphates
METALS: As, Cd, Pb, Zn, Cu
ORGANIC COMPOUNDS:
-pesticides of agricultural origin (HCB, SDDTs, SHCHs)
-SPCBs
-PAHs
Other parameters
-LAND USES à grouping points according to their land use, to obtain common features and differences
-QUALITY INDEXES :
ISQA (simplified index for water quality)
BIOLOGIC index
SURFACE WATER AND GROUNDWATER DATABASES
CHEMOMETRICSPCA (Principal Component Analysis)
Other methods (Multivariate data Analysis)
IDENTIFICATIONcontamination sources
Loading plots
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)
DISTRIBUTIONcontamination sources
Geographical Temporal
GIS
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)