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
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This study investigates the sources of potentially toxic elements (Zn, Pb, Cr, and Ni) in Campanian topsoil, highlighting contamination due to industrial activities and urbanization. A comprehensive compositional discrimination analysis was performed on 3669 soil samples, revealing significant contamination related to heavy traffic and industrial processes, notably from the tannery industry. The study concludes that while urban areas show contamination from Pb and Zn, Cr and Ni enrichment is linked to geological deposits, particularly in less urbanized zones.
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
- 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