1. Assessment of the behaviour of Potentially Toxic Elements (PTEs) in soil from the Sarno River Basin through compositional data analysis Thiombane Matar1*, De Vivo Benedetto1, Josep-Antoni Martín-Fernández2, Albanese Stefano1, Lima Annamaria1 (1) Università degli Studi di Napoli Federico II, Dipartimento di Scienze della Terra, dell’Ambiente e delle Risorse, Complesso Universitario di Monte Sant'Angelo, – Napoli,Italy.
This study shows the applicability and importance of compositional data transformation, which relates perfectly relationships and dependencies between elements which can be lost when univariate and classical multivariate analyses are employed on raw or lognormal geochemical data.
Due to the fact that geochemical data give relative information ( ratio of components) and mostly are right skewness and present outliers, we should apply a log ratio transformation to tend to a normal distribution of variables and hold the real correlation between relationships between them.
Geochemical data
Clustering Analysis
Factor Analysis
Clr biplot
Compositional data transformation
Factor score maps
Correlation and origins of the PTEs
Geogenic features
Anthropogenic activities

2. Features of the study areas
Sarno River Basin (500 Km2)
Campagna region (Southern Italy)
Mts. Somma-Vesuvius
Mt.Lattari
Mts. Picentini
Pyroclastic and limestones deposits
Sarno river
High urbanisation and
Industrial activities
Intensive agriculture
the wide extent of local industrial and agricultural activities release PTEs into the basin.

3. Sampling procedure and preparation
319 samples in two saisons, specific grid sampling design
SAR-SOB: 283 soil samples (density of 1S/ 2 Km2)
SAR-SAR: 36 soil samples (1S/ 600m)
Sampling procedure follows UE protocole ( Salminen et al., 1998)
1 sample (5 subsamples) in 100 squared meter and 0.5 kg in each corner and the center.
Much of this variability can be attributed to the underlying parent material on which a soil has formed
each sample is made up of five subsamples from corners and center of a 20 m square and are sampled from the uppermost 15 cm of mineral soil (nominally a depth of 5 – 20 cm allowing for vegetation cover and surface litter.

4. Sampling procedure and preparation
Homogenize, drying and sieved samples in < 2mm seize
50g sample sent to Vancouver ACME Laboratory (Canada)
Acqua regia extraction , ICP-MS spectrometry
53 inorganic Elements: Ag, Al, As, Au, B, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Ge, Hf, Hg, In, K, La, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Pd, Pt, Rb, Re, S, Sb, Sc, Se, Sn, Sr, Ta, Te, Th, Ti, Tl, U, V, W, Y, Zn

5. Compositional data Analysis
Compositional data are parts of some whole which carry relative information (ratios of components)
Usual units of measurement: parts par unit, percentages, ppm, ppb, concentrations..
Sum of the data subject is constant (100%, 1 millions ppm, etc..)
spurious correlation may arise when the raw data is used (Karl Pearson, 1897)
Lognormal approach was created to resolve this problem ( normalization )
The sample space of compositional data (D-part simplex S) should be moved to real space RD or RD−1
(John Aitchison, 1982, 1986) using log-ratio transformations

6. Compositional data Analysis
Log-ratio Transformations
Additive log-ratio transformation
(John Aitchison, 1986)
Centered log-ratio transformation
Euclidean geometry in real space is not a proper geometry for compositional data (orthogonality between vectors is taken into account, distances between data-points are considered). The results of the procedure to construct such a new basis are called sequential binary partitions, and the constructed basis vectors are called balances
Isometric log-ratio transformation
(Egozcue et al, 2003)

7. Multivariate Analysis
Hierarchical Clustering Analysis
Ordinary Euclidean distance between vectors of clr coefficients are equal to the Aitchison distance of their corresponding compositions. This also holds for the inner product and the norm, i.e.
Clustering analysis using clr transformed data enhances groups of elements and the correlation between variables (use of Aitchison distance)

8. Multivariate Analysis
Clr biplot
Association 1: Ca-Mg
Association 2: Na-K-P-B-Rb-Ba
Association 3: Zn-Cd-Pb-Hg –Ag-Au
Association 4: As-U-Th-Zr-La-Li
Association 5: Fe-Ti-Co-V
Association 6 : Ni, Cr

9. Multivariate Analysis
Clr biplot (Amalgamation)
Most of the SAR-SAR samples ( samples along Sarno river) are linked to the Clr-Amag (Cr-Ni) vertex
As-U-Th-Zr-La-Li and Fe-Ti-Co-V Associations might be related to the same origin

10. Multivariate Analysis and factor score maps
Factor Analysis
Raw data
Centered log-transformed data
It reduces attribute space from a larger number of variables to a smaller number of factors (each factor carries correlation between the variables which may have the save origin).
Total variance for Clr transformation (74.99%) greater than that the use of raw data (69.54%)

11. Multivariate Analysis and factor score mapping
Factor score maps
ArcGIS and GeoDAS (Fractal and multifractal IDW maps)
Concentration-area (C-A) fractal plot (spatial association and singularity)
This method tries to locate and group pixels on the map sharing concentrations
apparently varying according to a spatial law
Factor score ranges and thresholds
Fractal and multifractal IDW: It is a method that preserves high frequency information, which is lost in any conventional moving average methods such as kriging and ordinary IDW. This method takes into account both spatial association and local singularity. This method tries to locate and group pixels on the map sharing concentrations apparently varying according to a spatial law.

12. Multivariate Analysis and factor score maps
Raw data
Elevated factor scores
near the slopes of
Mts. Somma–Vesuvius
Clr data
Factor score association of elements is related to features which settle to the slope of the Somma.vesuvius volcano and decrease gradually in inner basin.
In fact it’s related to the Potassic and ultrapotassic rocks of the Mt.Somma Vesuvius
Factor score values decrease
gradually from the slope of
the Mt.Vesuvius to inner basin
Factor 1 element associations is related to the igneous formations and volcanic soils
Potassic and ultrapotassic lavas and pyroclastic materials

13. Multivariate Analysis and factor score maps
Raw data
Elevated factor scores
is mapped near Solofra
and surrounding
Clr data
Low factor scores values
correspond to the main
urban cities
Sedimentary materials dominated by silico-clastic deposits
Anthropogenic sources (high rates of fossil fuel combustion and vehicle emissions)

14. Multivariate Analysis and factor score maps
Raw data
Elevated factor scores values correspond to the main Urban cities
Clr data
Low factor scores values
Map geological features (Mts. Lattari mostly) inner the basin
Anthropogenic sources (high rates of fossil fuel combustion and vehicle emissions)
Limestone and Dolostone of the Mts. Picentini–Taburno and Mts.Lattari

15. Multivariate Analysis and factor score maps
Raw data
Elevated factor scores values are located near Solofra and surrounding
Clr data
Highest factor scores values
Are found all along the Sarno River and Solofra tributaries
Cr-Ni association is related to anthropogenic activities which release these PTEs into the River
Solofra Tannery industries are the main industries which use Chromium and Nickel in the Tanning processes.
It might be confirmed that these Industries release their waste water into the Sarno river

16. Summarize of the main points
Compositional data need a logratio transformation for correlation analysis
Logratio transformation enhances better the interrelationships between variables
Clr logratio transformation enables to correlate the main geological features and anthropogenic activities which control the elements associations into the basin
Factor score maps lay out the origin of PTEs
release of wastewater from Solofra tanneries district (Chromium and Nickel) into the Sarno River
vehicle emission (Pb, Zn, Cd, Co)

17. Assessment of the behaviour of Potentially Toxic Elements (PTEs)
in soil from the Sarno River Basin through compositional data analysis
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