Natural Resources Research

, Volume 28, Issue 1, pp 109–124 | Cite as

Multivariate Mapping of Heavy Metals Spatial Contamination in a Cu–Ni Exploration Field (Botswana) Using Turning Bands Co-simulation Algorithm

  • Peter N. Eze
  • Nasser MadaniEmail author
  • Amoussou Coffi Adoko
Original Paper


With a mining-driven economy, Botswana has experienced increased geochemical exploration of minerals around existing mining towns. The mining and smelting of copper and nickel around Selibe-Phikwe in the Central Province are capable of releasing heavy metals including Pb, Fe, Mn, Co, Ni and Cu into the soil environments, thereby exposing humans, plants and animals to health risks. In this study, turning bands co-simulation, a multivariate geostatistical algorithm, was presented as a tool for spatial uncertainty quantification and probability mapping of cross-correlated heavy metals (Co, Mn, Fe and Pb) risk assessment in a semiarid Cu–Ni exploration field of Botswana. A total of 1050 soil samples were collected across the field at a depth of ~ 10 cm in a grid sampling design. Rapid elemental concentration analysis was done using an Olympus Delta Sigma portable X-ray fluorescence device. Enrichment factor, geoaccumulation index and pollution load index were used to assess the potential risk of heavy metals contamination in soils. The partially heterotopic nature of the dataset and strong correlations among the heavy metals favors the use of co-simulation instead of independent simulation in the probability mapping of heavy metal risks in the study area. The strong correlation of Co and Mn to iron infers they are of lithogenic origin, unlike Pb which had weak correlation pointing to its source in the area being of anthropogenic source. Manganese, Co and Fe show low enrichment, whereas Pb had high enrichment suggesting possible lead pollution. We, however, recommend that speciation of Pb in the soils rather than total concentration should be ascertained to infer chances of possible bioaccumulation, and subsequent health risk to human by chronic exposure.


Probability mapping Gaussian random field Semiarid soils Portable XRF device Uncertainty quantification 



We are grateful to Dr. John Carranza and the two anonymous reviewers for their comments, which substantially helped improving the final version of the manuscript. The second author acknowledges the Nazarbayev University for supporting this work through Faculty Development Competitive Research Grants for 2018–2020 under Contract No. 090118FD5336.


  1. Addo, M. A., Darko, E. O., Gordon, C., Nyarko, B. J. B., Gbadago, J. K., Nyarko, E., et al. (2012). Evaluation of heavy metals contamination of soil and vegetation in the vicinity of a cement factory in the Volta Region, Ghana. International Journal of Science and Technology, 2(1), 40–50.Google Scholar
  2. Agency for Toxic Substance and Disease Registry (ATSDR) (2007). Toxicological profile for lead.
  3. Anderson, R. H., & Kravitz, M. J. (2010). Evaluation of geochemical associations as a screening tool for identifying anthropogenic trace metal contamination. Environmental Monitoring and Assessment, 167(1), 631–641. Scholar
  4. Bogaert, P. (1999). On the optimal estimation of the cumulative distribution function in presence of spatial dependence. Mathematical Geology, 31(2), 213–239. Scholar
  5. Bourennane, H., Douay, F., Sterckeman, T., Villanneau, E., Ciesielski, H., King, D., et al. (2010). Mapping of anthropogenic trace elements inputs in agricultural topsoil from Northern France using enrichment factors. Geoderma, 157(3), 165–174. Scholar
  6. Bourgault, G. (1997). Spatial declustering weights. Mathematical Geology, 29(2), 277–290. Scholar
  7. Buat-Menard, P., & Chesselet, R. (1979). Variable influence of atmospheric flux on the trace metal chemistry of oceanic suspended matter. Earth and Planetary Science Letters, 42, 398–411.Google Scholar
  8. Burgos, P., Madejón, E., Pérez-de-Mora, A., & Cabrera, F. (2008). Horizontal and vertical variability of soil properties in a trace element contaminated area. International Journal of Applied Earth Observation and Geoinformation, 10(1), 11–25. Scholar
  9. Carr, J. R., & Myers, D. E. (1985). COSIM: A FORTRAN IV program for coconditional simulation. Computers & Geosciences, 11(6), 675–705. Scholar
  10. Chilès, J.-P., & Delfiner, P. (2012). Geostatistics: Modeling spatial uncertainity (2nd ed.). New York: Wiley.Google Scholar
  11. Chilès, J.-P., & Lantuéjoul, C. (2005). Prediction by conditional simulation: Models and algorithms. In M. Bilodeau, F. Meyer, & M. Schmitt (Eds.), Space, structure and randomness: Contributions in honor of Georges Matheron in the field of geostatistics, random sets and mathematical morphology (pp. 39–68). New York, NY: Springer.Google Scholar
  12. Cloquet, C., Carignan, J., Libourel, G., Sterckeman, T., & Perdrix, E. (2006). Tracing source pollution in soils using cadmium and lead isotopes. Environmental Science and Technology, 40(8), 2525–2530. Scholar
  13. Davis, J. C. (1986). Statistics and data analysis in geology (2nd ed.). New York: Wiley.Google Scholar
  14. Desaules, A. (2012). Critical evaluation of soil contamination assessment methods for trace metals. Science of the Total Environment, 426, 120–131. Scholar
  15. Deutsch, C. V., & Journel, A. (1998). GSLIB: Geostatistical software and user’s guide (2nd ed.). New York: Oxford University Press.Google Scholar
  16. Emery, X. (2004). Testing the correctness of the sequential algorithm for simulating Gaussian random fields. Stochastic Environmental Research and Risk Assessment, 18(6), 401–413. Scholar
  17. Emery, X. (2005). Variograms of order ω: A tool to validate a bivariate distribution model. Mathematical Geology, 37(2), 163–181. Scholar
  18. Emery, X. (2007). Conditioning simulations of Gaussian random fields by ordinary kriging. Mathematical Geology, 39(6), 607–623. Scholar
  19. Emery, X. (2008). A turning bands program for conditional co-simulation of cross-correlated Gaussian random fields. Computers & Geosciences, 34(12), 1850–1862. Scholar
  20. Emery, X. (2012). Cokriging random fields with means related by known linear combinations. Computers & Geosciences, 38(1), 136–144. Scholar
  21. Emery, X., & Lantuéjoul, C. (2006). TBSIM: A computer program for conditional simulation of three-dimensional Gaussian random fields via the turning bands method. Computers & Geosciences, 32(10), 1615–1628. Scholar
  22. Evans, D. W., Cutshall, N. H., Cross, F. A., & Wolfe, D. A. (1977). Manganese cycling in the Newport River estuary, North Carolina. Estuarine and Coastal Marine Science, 5(1), 71–80. Scholar
  23. Eze, P. N., Mosokomani, V. S., Udeigwe, T. K., & Oyedele, O. F. (2016a). Quantitative geospatial dataset on the near-surface heavy metal concentrations in semi-arid soils from Maibele Airstrip North, Central Botswana. Data in Brief, 8, 1448–1453. Scholar
  24. Eze, P. N., Mosokomani, V. S., Udeigwe, T. K., Oyedele, O. F., & Fagbamigbe, A. F. (2016b). Geostatistical analysis of trace elements PXRF dataset of near-surface semi-arid soils from Central Botswana. Data in Brief, 9, 764–770. Scholar
  25. Eze, P. N., Udeigwe, T. K., & Stietiya, M. H. (2010). Distribution and potential source evaluation of heavy metals in prominent soils of Accra Plains. Ghana. Geoderma, 156(3), 357–362. Scholar
  26. Gneiting, T. (1999). The correlation bias for two-dimensional simulations by turning bands. Mathematical Geology, 31(2), 195–211. Scholar
  27. Gómez-Hernández, J. J., & Cassiraga, E. F. (1994). Theory and practice of sequential simulation. In M. Armstrong & P. A. Dowd (Eds.), Geostatistical simulations (pp. 111–124). Dordrecht: Springer.Google Scholar
  28. Gómez-Hernández, J. J., & Journel, A. G. (1993). Joint sequential simulation of multigaussian fields. In A. Soares (Ed.), Geostatistics Tróia’92 (Vol. 1, pp. 85–94). Dordrecht: Springer, Netherlands.Google Scholar
  29. Goovaerts, P. (1997). Geostatistics for natural resources evaluation. New York: Oxford University Press.Google Scholar
  30. Gupta, S. K., Vollmer, M. K., & Krebs, R. (1996). The importance of mobile, momilisable and pseudo total heavy metal fractions in soil for three-level risk assessment and risk management. Science of the Total Environment, 178, 11–20.Google Scholar
  31. Hani, A., & Pazira, E. (2011). Heavy metals assessment and identification of their sources in agricultural soils of Southern Tehran, Iran. Environmental Monitoring and Assessment, 176(1), 677–691. Scholar
  32. Hasan, A. B., Kabir, S., Selim Reza, A. H. M., Nazim Zaman, M., Ahsan, A., & Rashid, M. (2013). Enrichment factor and geo-accumulation index of trace metals in sediments of the ship breaking area of Sitakund Upazilla (Bhatiary–Kumira), Chittagong, Bangladesh. Journal of Geochemical Exploration, 125, 130–137. Scholar
  33. Johnson and Wichern. (1998). Applied multivariate statistical analysis (4th ed.). New York: Prentice-Hall.Google Scholar
  34. Jørgensen, K., & Jensen, L. S. (2009). Chemical and biochemical variation in animal manure solids separated using different commercial separation technologies. Bioresource Technology, 100(12), 3088–3096. Scholar
  35. Journel, A. G., & Xu, W. (1994). Posterior identification of histograms conditional to local data. Mathematical Geology, 26(3), 323–359.Google Scholar
  36. Kampunzu, A. B., Tombale, A. R., Zhai, M., Bagai, Z., Majaule, T., & Modisi, M. P. (2003). Major and trace element geochemistry of plutonic rocks from Francistown, NE Botswana: evidence for a Neoarchaean continental active margin in the Zimbabwe craton. Lithos, 71(2), 431–460. Scholar
  37. Lantuéjoul, C. (1994). Non conditional simulation of stationary isotropic multigaussian random functions. In M. Armstrong & P. A. Dowd (Eds.), Geostatistical simulations (pp. 147–177). Dordrecht: Springer.Google Scholar
  38. Lantuéjoul, C. (2002). Geostatistical simulation, models and algorithms (p. 256). Berlin: Springer.Google Scholar
  39. Lee, C. S.-L., Li, X., Shi, W., Cheung, S. C.-N., & Thornton, I. (2006). Metal contamination in urban, suburban, and country park soils of Hong Kong: A study based on GIS and multivariate statistics. Science of the Total Environment, 356(1), 45–61. Scholar
  40. Leuangthong, O., & Deutsch, C. V. (2003). Stepwise conditional transformation for simulation of multiple variables. Mathematical Geology, 35(2), 155–173. Scholar
  41. Likuku, A. S., Gaboutloeloe, G. K., & Mmolawa, K. B. (2013). Determination and source apportionment of selected heavy metals in aerosol samples collected from Sebele. American Journal of Environmental Sciences, 9(2), 188–200.Google Scholar
  42. Liu, X., Wu, J., & Xu, J. (2006). Characterizing the risk assessment of heavy metals and sampling uncertainty analysis in paddy field by geostatistics and GIS. Environmental Pollution, 141(2), 257–264. Scholar
  43. Luoma, S. N. (1990). Processes affecting metal concentrations in estuarine and coastal marine sediments. In R. W. Furness & P. S. Rainbow (Eds.), Heavy metals in the marine environment (pp. 51–66). Boca Raton: CRC Press Inc.Google Scholar
  44. Manta, D. S., Angelone, M., Bellanca, A., Neri, R., & Sprovieri, M. (2002). Heavy metals in urban soils: a case study from the city of Palermo (Sicily), Italy. Science of the Total Environment, 300(1), 229–243. Scholar
  45. Mantoglou, A. (1987). Digital simulation of multivariate two- and three-dimensional stochastic processes with a spectral turning bands method. Mathematical Geology, 19(2), 129–149. Scholar
  46. Matheron, G. (1973). The intrinsic random functions and their applications. Advances in Applied Probability, 5(3), 439–468. Scholar
  47. Matheron, G. (1989). Estimating and choosing: An essay on probability in practice. Berlin: Springer.Google Scholar
  48. Müller, G. (1969). Index of geoaccumulation in sediments of the Rhine River. GeoJournal, 2, 108–118.Google Scholar
  49. Myers, D. E. (1989). Vector conditional simulation. In M. Armstrong (Ed.), Geostatistics (pp. 283–293). Dordrecht: Springer.Google Scholar
  50. Paravarzar, S., Emery, X., & Madani, N. (2015). Comparing sequential Gaussian and turning bands algorithms for cosimulating grades in multi-element deposits. Comptes Rendus Geoscience, 347(2), 84–93. Scholar
  51. Pardo-Igúzquiza, E., & Chica-Olmo, M. (1993). The Fourier Integral Method: An efficient spectral method for simulation of random fields. Mathematical Geology, 25(2), 177–217. Scholar
  52. Pardo-Igúzquiza, E., & Chica-Olmo, M. (1994). Spectral simulation of multivariable stationary random functions using covariance fourier transforms. Mathematical Geology, 26(3), 277–299. Scholar
  53. Pebesma, E. J. (2004). Multivariable geostatistics in S: The gstat package. Computers & Geosciences, 30(7), 683–691. Scholar
  54. Qi, J., Zhang, H., Li, X., Lu, J., & Zhang, G. (2016). Concentrations, spatial distribution, and risk assessment of soil heavy metals in a Zn–Pb mine district in southern China. Environmental Monitoring and Assessment, 188(7), 413. Scholar
  55. Qu, C., Sun, K., Wang, S., Huang, L., & Bi, J. (2012). Monte Carlo simulation-based health risk assessment of heavy metal soil pollution: A case study in the Qixia Mining Area, China. Human and Ecological Risk Assessment: An International Journal, 18(4), 733–750. Scholar
  56. Reimann, C., & de Caritat, P. (2005). Distinguishing between natural and anthropogenic sources for elements in the environment: Regional geochemical surveys versus enrichment factors. Science of the Total Environment, 337(1), 91–107. Scholar
  57. Rivoirard, J. (1994). Introduction to disjunctive Kriging and nonlinear geostatistics. Oxford: Oxford University Press.Google Scholar
  58. Rule, J. H. (1986). Assessment of trace metal element geochemistry of Hampton Roads and lower Chesapeake Bay area sediments. Environmental Geology and Water Sciences, 8, 209–219.Google Scholar
  59. Sakizadeh, M., Sattari, M. T., & Ghorbani, H. (2017). A new method to consider spatial risk assessment of cross-correlated heavy metals using geo-statistical simulation. Journal of Mining and Environment, 8(3), 373–391. Scholar
  60. Tomlinson, D. L., Wilson, J. G., Harris, C. R., & Jeffrey, D. W. (1980). Problem in the assessment of heavy metals levels in estuaries and the formation of a pollution index. Helgoländer Meeresuntersuchungen, 33, 566–575.Google Scholar
  61. Tran, T. T. (1994). Improving variogram reproduction on dense simulation grids. Computers & Geosciences, 20(7), 1161–1168. Scholar
  62. Udeigwe, T. K., Young, J., Kandakji, T., Weindorf, D. C., Mahmoud, M. A., & Stietiya, M. H. (2015). Elemental quantification, chemistry, and source apportionment in golf course facilities in a semi-arid urban landscape using a portable X-ray fluorescence spectrometer. Solid Earth, 6(2), 415–424. Scholar
  63. Wackernagel, H. (2003). Multivariate geostatistics—An introduction with applications. Berlin: Springer.Google Scholar
  64. Xie, Y., Chen, T.-B., Lei, M., Yang, J., Guo, Q.-J., Song, B., et al. (2011). Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: Accuracy and uncertainty analysis. Chemosphere, 82(3), 468–476. Scholar
  65. Zhang, C. (2006). Using multivariate analyses and GIS to identify pollutants and their spatial patterns in urban soils in Galway, Ireland. Environmental Pollution, 142(3), 501–511. Scholar
  66. Zhang, C., & Selinus, O. (1998). Statistics and GIS in environmental geochemistry—Some problems and solutions. Journal of Geochemical Exploration, 64(1), 339–354. Scholar
  67. Zimmerman, A. J., & Weindorf, D. C. (2010). Heavy metal and trace metal analysis in soil by sequential extraction: A review of procedures. International Journal of Analytical Chemistry. Scholar

Copyright information

© International Association for Mathematical Geosciences 2018

Authors and Affiliations

  • Peter N. Eze
    • 1
  • Nasser Madani
    • 2
    Email author
  • Amoussou Coffi Adoko
    • 2
  1. 1.Department of Earth and Environmental ScienceBotswana International University of Science and TechnologyPalapyeBotswana
  2. 2.Department of Mining Engineering, School of Mining and GeosciencesNazarbayev UniversityAstanaKazakhstan

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