1. 1 Dipartimento di Scienze della Terra, dell'Ambiente e delle Risorse, Università degli Studi di Napoli "Federico II", Complesso
Universitario Monte S. Angelo, Via Cintia snc, 80126, Naples, Italy
2 Benecon Scarl and Pegasus University, Environment and Territory Dept., Via S. Maria di Costantinopoli 104, 80138 Naples, Italy
Source patterns of Zn, Pb, Cr and Ni potentially toxic elements
(PTEs) through a compositional discrimination analysis: a case
study on the Campanian topsoil data
Matar Thiombane1*, Attila Petrik1*, Stefano Albanese1 , Annamaria Lima1, Benedetto De Vivo2

2. • Geochemical survey aims to enhance mineralisation and contamination through exploration
and environmental geochemistry, respectively
• Soil pollution by potentially toxic elements (PTEs) is one of the major challenges worldwide
due to increase of industrialisation and urbanisation.
Methods
and results
MotivationBackground Conclusion
Fig 1. Geogenic contamination Fig 2. Anthropogenic contamination

3. • For that, various geostatistical computations have been used to identify source patterns of
different elements related to underlying geological features and/or anthropogenic activities
Graphical and statistical methods
Reimann et al. (2005) Preziosi et al. (2014)
Methods
and results
MotivationBackground Conclusion

4. Methods
and results
MotivationBackground Conclusion
• The critical reviews of these indices were made by Reimann and de Caritat (2005) claiming that
their values vary and are dependent on the different parent rock materials and chosen reference
media as well as reference elements. In addition, these indices do not take into account the
different biogeochemical processes which may have remarkable impact on elemental
enrichment/contamination
• In addition, the question of anthropogenic or geogenic origin has been always in the interest of
investigations. Several indices have been invented to separate anthropogenic contamination from
geogenic background values like Enrichment Factor (Sutherland, 2000), Pollution Index
(Hakanson, 1980) or Geo-accumulation Index (Müller, 1979)

5. Albanese et al. (2007)
• Background/Baseline separation by using various fractal methods (e.g. Concentration-
Area) was given particular attention in mineral exploration, environmental health-risk
analysis in regional/local studies
Zuo et al. (2014)
Methods
and results
MotivationBackground Conclusion

6. • The main objective is to discriminate the possible sources of contamination/enrichment of Zn, Pb,
Cr and Ni potentially toxic elements (PTEs) in soil of the Campania region.
Methods
and results
MotivationBackground Conclusion
Geochemical Data
16 elements
Robust biplot Factor Analysis
Elemental association Correlation structure
Factor score maps (F1-F4)ilr balances (Z1-Z5)
Bivariate regression analysis
Zn, Pb, Cr, and Ni Possible sources of 16
elements
Contamination/ Enrichment
sources of Zn, Pb, Cr and Ni

7. • The Campania Region is located in the southern part of Italy, covering an area of about 13,600 km2,
• NW-SE trending Apennine chain and large number of industrial activities, with the majority being
involved in agriculture.
• 3669 samples were collected from topsoil of the Campania Region at a nominal density of 1
sample/3.2 km2
C
Methods and resultsMotivationBackground Conclusion

8. • From 2013 to 2015, 3669 samples were
collected from topsoil of the Campania
Region at a nominal density of 1
sample/3.2 km2
• Each top soil sample (from 0-20 cm) was
made by homogenizing 5 subsamples at
the corners and the centre of a 100m2
square
Methods and resultsMotivationBackground Conclusion

9. • At each sampling site, several physico-chemical parameters of the soil properties were
measured, including pH, total water content, conductivity, total organic content and the
geographical coordinates system recorded by geospatial positioning systems (GPS)
• Chemical analyses were carried out at an international accredited Laboratory, Acme Analytical
Laboratories Ltd (now Bureau Veritas, Vancouver, Canada)
• The samples were analysed after an aqua regia extraction, by inductively coupled plasma mass
spectrometry (ICP/MS) for “pseudototal” concentration of 53 elements
Methods and resultsMotivationBackground Conclusion
UV light samples dried Natural samples dried

10. • To better visualize the 16 elements distribution and possible natural or anthropogenic behaviour,
robust compositional biplot was generated
• This is a powerful statistical tool that displays both samples and variables of a data matrix in
terms of the resulting scores and loading (Gabriel, 1971)
Methods and resultsMotivationBackground Conclusion
G1: Al-Ti-Th, Fe, Mn, As, La, and Co
G2: Na and K
G3: Ca and Mg
G4: Cu and P
G5: Zn, Pb, Ni, and Cr
Volcanic deposits
Ultrapotassic rocks
Limestone and dolostone
Anthropogenic activities
Agriculture activities

11. Methods and resultsMotivationBackground Conclusion
• Factor analysis (FA) was the multivariate statistical tool to explain the correlation structure of the
variables through few numbers of factors (Reimann et al., 2002)
• Successfully used to reveal the 16 elements sources related to their main hypothetical origins
Variables Factors Communalities
F1 F2 F3 F4
Cu -0.12 -0.77 0.23 -0.14 0.67
Pb -0.28 0.10 0.09 -0.84 0.79
Zn 0.13 -0.21 -0.03 -0.83 0.75
Ni 0.90 -0.13 -0.28 0.09 0.91
Co 0.88 -0.10 0.01 0.37 0.92
Mn 0.69 0.41 0.04 0.05 0.65
Fe 0.79 0.31 0.20 0.27 0.83
As 0.01 0.72 0.25 -0.11 0.60
Th -0.08 0.72 0.50 0.25 0.84
Ca 0.17 -0.17 -0.87 -0.06 0.83
P -0.22 -0.72 0.00 0.03 0.57
La -0.16 0.81 0.37 0.08 0.82
Cr 0.80 -0.08 -0.32 -0.20 0.78
Mg 0.20 -0.03 -0.82 0.14 0.73
Ti -0.72 0.38 0.35 0.06 0.79
Al -0.01 0.51 0.53 0.52 0.81
Na -0.86 -0.24 0.17 0.14 0.84
K -0.62 -0.46 0.30 0.50 0.94
Eigenvalues 5.81 4.71 2.08 1.31
Total variance in % 32.26 26.14 11.57 7.30
Cum. of total variance (%) 32.26 58.40 69.97 77.26
Elemental Factor loading associations:
F1: Ni, Co, Cr, Fe, Mn, - (Na,Ti, K)
F2: La, As, Th, Al, - (Cu, P)
F3: Al, Th, - (Ca, Mg)
F4: Al, K, - (Pb, Zn)
From factor score values, interpolated
geochemical maps were produced through
GeoDAS (Cheng et al.,1994) and ArcGIS
(ESRI, 2012) software to show their spatial
distributions.

12. Methods and resultsMotivationBackground Conclusion
Factor scores were used to produce interpolated geochemical maps by means of GeoDAS™ (Cheng
et al. 1994; Lima et al., 2003) and ArcGIS.12 (ESRI, 2014) software.
F1: Ni, Co, Cr, Fe, Mn, - (Na,Ti, K)
Ni, Co, Cr, Fe, Mn Na,Ti, K
F2: La, As, Th, Al, - (Cu, P)
La, As, Th, Al Cu, P
siliciclastic deposits signatures Potassic deposits
Pyroclastics rocks agricultural practices using
phosphate fertilisers
A
B

13. Methods and resultsMotivationBackground Conclusion
Factor scores were used to produce interpolated geochemical maps by means of GeoDAS™ (Cheng
et al. 1994; Lima et al., 2003) and ArcGIS™.12 (ESRI, 2014) software.
F3: Al, Th, - (Ca, Mg)
Al-Th Ca-Mg
F4: Al, K, - (Pb, Zn)
Al-K Pb-Zn
Pyroclastics rocks Limestone and dolostone
Ultrapotassic pyroclastics Anthropogenic sources
A
B

14. • Based on the multivariate regression analysis, the same 6 elements (Zn, Pb, Cr, Ni, Fe and Mn)
have been chosen to perform sequential binary partition and obtain balances (specific ilr-
coordinates)
• To minimize and/or eliminate the presence of outliers and spurious correlation (Pawlowsky-Glahn
and Buccianti, 2011), isometric logratio transformation (ilr) was applied on raw-data prior to
multivariate analysis
Balances Zn Pb Cr Ni Fe Mn R S
y1 (ilr-1) + + + + - - 4 2
y2 (ilr-2) + + - - 2 2
y3 (ilr-3) + - 1 1
y4 (ilr-4) + - 1 1
y5 (ilr-5) + - 1 1
Egozcue et al. (2003)
Methods and resultsMotivationBackground Conclusion

15. Methods and resultsMotivationBackground Conclusion
Balance (ilr) maps
Zn-Pb-Cr-Ni are mapped around large
urban and industrial areas (e.g. Naples,
Salerno, and Castelvolturno)
Fe-Mn displayed Fe and Mn can be
observed close to large volcanic edifices
and siliciclastic zone
High proportion of Zn and Pb is
revealed in urban and industrial areas
(e.g. Naples and Salerno) but also close
to large volcanic complexes
Higher proportion of Cr and Ni can be
observed in topsoils over siliciclastic
deposits, in the eastern and southern
part of the study area
A
B
ilr1 (ZnPbCrNi/FeMn) ilr2 (ZnPb/CrNi)

16. Methods and resultsMotivationBackground Conclusion
B
Zn proportion zones are mainly related
to underlying siliciclastic deposits in the
eastern and southern part of the study
area
The higher Pb proportion zones are
located in the urbanized area of Naples
and some parts of Ischia and Sorrento
Peninsula where they are tied to intensive
traffic.
A
Higher Cr proportion areas is related to
the Sarno Basin (Solofra district) where
several factories (e.g. tannery industry)
and high Cr proportion patches are
observed around the Mt. Roccamonfina
and above some carbonate massifs.
Nickel reaches higher proportion
compared to Cr in the eastern part of the
study area in topsoils over claystone and
siltstone.
Balance (ilr) maps
ilr3 (Zn/Pb) ilr4 (Cr/Ni)

17. Methods and resultsMotivationBackground Conclusion
• We performed bivariate regression analysis using the ilr-1 (dependent) and normalised ilr-2
(independent) variables to discriminate and map the anthropogenic and geogenic parts of the
contamination and enrichment, respectively.
Zn-PbCr-Ni
Zn-Pb-Cr-Ni anthropogenic
Zn-Pb-Cr-Ni geogenic

18. Methods and resultsMotivationBackground Conclusion
Cr and Ni-enrichment
is mainly related to the
siliciclastic deposits
where their proportions
are influenced by Fe
and Mn-oxy-
hydroxides.
The large volcanic
complexes (e.g. Mt.
Somma-Vesuvius, Mt.
Roccamonfina) are all
characterised by
geogenic enrichment
of Zn and Pb.
The large urban and
industrial areas (e.g.
Naples, Salerno) are
mainly
contaminated by
Pb and Zn due to
heavy traffic and
alloy production.
Cr and Ni
contamination was
discerned in the
Sarno Basin due to
the release of waste
rich in Cr-Ni elements
deriving from tannery
industries

19. Methods and resultsMotivationBackground Conclusion
• This study demonstrates a comprehensive discrimination analysis carried out
on 4 PTEs (Zn, Pb, Cr and Ni) of the Campanian high-density topsoil data set.
• The large urban and industrial areas (e.g. Naples, Salerno) along the coastline
are mainly contaminated by Pb and Zn due to heavy traffic and alloy
production.
• Some Cr and Ni contamination was discerned in the Sarno Basin due to the
release of waste rich in Cr-Ni deriving from tannery industries (e.g. Solofra).
• In contrast, Cr and Ni-enrichment is mainly related to the siliciclastic deposits
where their proportion is not only influenced by Fe and Mn but also organic
matter, clay content and dispersion mechanism.
• The large volcanic complexes (e.g. Mt. Somma-Vesuvius, Mt. Roccamonfina)
are all characterised by geogenic enrichment of Zn and Pb.

20. Source patterns of Zn, Pb, Cr and Ni potentially toxic elements (PTEs) through a
compositional discrimination analysis: a case study on the Campanian topsoil data
Matar Thiombane1*, Attila Petrik1*, Stefano Albanese1 , Annamaria Lima1, Benedetto De Vivo2