A Comparative GIS tree‑pollution analysis between arsenic, chromium, mercury, and uranium contents in soils of urban and industrial regions in Qatar

  • Rania Bou Kheir
  • Mogens Greve
  • Mette Greve
  • Yi Peng
  • Basem ShomarEmail author
Original Paper


This study compared the applicability of several geographic information systems (GIS) regression tree-based models (n = 136) to precisely define the most influencing environmental predictor parameters on arid soils. The study focused on the accumulation of arsenic, chromium, mercury, and uranium in the arid soils of Qatar using GIS tools. The rates of the built reference trees (based on environmental parameters) vary among chosen toxic metals as follows: Hg (80%), U (77%), As (72%), and Cr (70%); and this affects considerably the developed correlations (reflected as relative importance in %) between these toxic metals and the chosen environmental parameters. These parameters influence differently the investigated As/Cr/Hg/U, with higher quantitative impact (importance varying according to the metal into question) of anthropogenic parameters (distance to environmental hotspots 85–90%, land cover/use 78–82%, and proximity to roads 62–82%) than for the geopedological (soil type 30–76%, parent material 17–60%, and distance to geological structures 12–42%) and hydromorphological (elevation 7–53%, slope gradient 10–41%, distance to drainage line 15–30%, slope gradient 0–5%, and slope aspect 0–3%) parameters. The results can be used to prioritize the choice of remediation measures, and can be applied to other arid areas sharing analogous environmental/socio-economic conditions and pollution causes.


Soil pollution Toxic heavy metals GIS regression trees Pollution quantitative correlations Arid environments Urban and industrial areas 



This article was made possible by a NPRP Award [5-572-1-101] from the Qatar National Research Fund (a member of The Qatar Foundation). The study was conducted within the framework of collaboration between Aarhus University (Denmark) and Qatar Environment & Energy Research Institute (QEERI). The authors would like to thank Dr. Jalal Hawari for his input to improve the quality of the manuscript. They also thank the editor and reviewers for helpful reviews of the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that there are no conflicts of interest.


  1. Abu Sukar H, Almeeri F, Almurekki A (2007) Agro-hydro-meteorological data book for the State of Qatar. Doha, QatarGoogle Scholar
  2. Alloway BJ (1995) Heavy metals in soils, 2nd edn. Blackie Academic and Professional, Glasgow, p 200pCrossRefGoogle Scholar
  3. Anagu I, Ingwersen J, Utermann J, Streck T (2009) Estimation of heavy metal sorption in German soils using artificial neural networks. Geoderma 152:104–112CrossRefGoogle Scholar
  4. Arfsten DP, Still KR, Ritchie GD (2001) A review of the effects of uranium and depleted uranium exposure on reproduction and fetal development. Toxicol Indust Health 17:180–191CrossRefGoogle Scholar
  5. Awiplan Qatar, Jean-Geos (2005) Soil classification and land use specifications for the State of Qatar. Department of Agriculture and Water Research (DAWR), DohaGoogle Scholar
  6. Berk RA (2003) An introduction to ensemble methods for data analysis. UCLA Department of Statistics. Technical Report, AngelesGoogle Scholar
  7. Bou Kheir R, Chorowicz J, Abdallah C, Dhont D (2008) Soil and bedrocks distribution estimated from gully form and frequency: a GIS-based decision-tree model for Lebanon. Geomor. 93:482–492CrossRefGoogle Scholar
  8. Bou Kheir R, Greve MH, Bøcher PK, Greve MB, Larsen R, McCloy K (2010a) Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: the case study of Denmark. J Environ Manag 91:1150–1160CrossRefGoogle Scholar
  9. Bou Kheir R, Greve MH, Abdallah C, Dalgaard T (2010b) Spatial soil zinc content distribution from terrain parameters: a GIS-based decision-tree model in Lebanon. Environ Pollut 158:520–528CrossRefGoogle Scholar
  10. Bou Kheir R, Greve MH, Deroin J-P, Rebai N (2011) Implementing GIS regression trees for generating the spatial distribution of copper in Mediterranean environments: the case study of Lebanon. Inter J Environ Anal Chem. 2:1–18Google Scholar
  11. Breiman L (2001) Decision-tree forests. Mach Lear 5:5–32CrossRefGoogle Scholar
  12. Clarke LA, Pregibon D (1992) Tree-based models. In: Chambers JM, Hastie TJ (eds) Statistical models. Chapman and Hall, New York, pp 377–419Google Scholar
  13. De Temmerman L, Vanongeval LB, Hoenig M (2003) Heavy metal content of arable soil in northern Belgium. Water Air Soil Pollut 148:61–76CrossRefGoogle Scholar
  14. Facchinelli A, Sacchi E, Mallen L (2001) Multivariate statistical and GIS-based approach to identify heavy metal sources in soils. Environ Pollut 114:313–324CrossRefGoogle Scholar
  15. Friedl MA, Brodley CE, Strahler AH (1999) Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE T Geosci Remote Sens 37:969–977CrossRefGoogle Scholar
  16. Fuge R (2005) Anthropogenic sources. In: Selinum O (ed) Essentials of medical geology: Impacts of the natural environment on public health. Academic Press, Amsterdam, pp 43–60Google Scholar
  17. Grunwald S (2009) Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma 152:195–207CrossRefGoogle Scholar
  18. Guo G, Wu F, Xie F, Zhang R (2012) Spatial distribution and pollution assessment of heavy metals in urban soils from southwest China. J Environ Sci 24:410–418CrossRefGoogle Scholar
  19. Iden SC, Durner W (2008) Multiple batch extraction test to estimate contaminant release parameters using a Bayesian approach. J Contamin Hydrol 95:168–182CrossRefGoogle Scholar
  20. Imperato M, Adamo P, Naimo D, Arienzo M, Stanzione D, Violante P (2003) Spatial distribution of heavy metals in urban soils of Naples city (Italy). Environ Pollut 124:247–256CrossRefGoogle Scholar
  21. Lee C-S, Li X, Shi W, Cheung SC, Thorton I (2006) Metal contamination in urban, suburban, and country park soils of Hong Kong: a study based on GIS and multivariate statistics. Sci Total Environ 356:45–61CrossRefGoogle Scholar
  22. Li X, Lee SL, Wong SC, Shi W, Thornton I (2004) The study of metal contamination in urban soils of Hong Kong using a GIS-based approach. Environ Pollut 129:113–124CrossRefGoogle Scholar
  23. Li Y, Li C-K, Tao J, Wang L (2011) Study on spatial distribution of soil heavy metals in Huizhou city based on BP-ANN modeling and GIS. Environ Sci 10:1953–1960Google Scholar
  24. Lin YP, Teng TP, Chang TK (2002) Multivariate analysis of soil heavy metal pollution and landscape pattern in Changhua County in Taiwan. Landscape Urban Plan 62:19–35CrossRefGoogle Scholar
  25. Ljung K, Selinus O, Otabbong E, Berglund M (2006) Metal and arsenic distribution in soil particle sizes relevant to soil ingestion by children. App Geochem 21:1613–1624CrossRefGoogle Scholar
  26. Loh WY, Shih YS (1997) Split selection methods for classification trees. Stat Sin 7:815–840Google Scholar
  27. Lu AX, Wang JH, Qin XY, Wang KY, Han P, Zhang SZ (2012) Multivariate and geostatistical analyses of the spatial distribution and origin of heavy metals in the agricultural soils in Shunyi, Beijing, China. Sci Total Environ 425:66–74CrossRefGoogle Scholar
  28. Maas S, Scheifler R, Benslama M, Crini N, Lucot E, Brahmia Z, Benyacoub S, Giraudoux P (2010) Spatial distribution of heavy metal concentrations in urban, suburban and agricultural soils in a Mediterranean city of Algeria. Environ Pollut 158:2294–2301CrossRefGoogle Scholar
  29. Manzoor S, Munir HS, Shaheen N, Khalique A, Jaffar M (2006) Multivariate analysis of trace metals in textile effluents in relation to soil and groundwater. J Haz Mat 137:31–37CrossRefGoogle Scholar
  30. Martley E, Gulson BL, Pfeifer HR (2004) Metal concentrations in soils around the copper smelter and surrounding industrial complex of Port Kembla, NSW, Australia. Sci Total Environ 325:113–127CrossRefGoogle Scholar
  31. McKenzie NJ, Ryan PJ (1999) Spatial prediction of soil properties using environmental correlation. Geoderma 89:67–94CrossRefGoogle Scholar
  32. Ordonez A, Loredo J, de Miguel E, Charlesworth S (2003) Distribution of heavy metals in the street dusts and soils of an industrial city in northern Spain. Arch Environ Contamin Toxicol 44:160–170CrossRefGoogle Scholar
  33. Pearce F (2010) Qatar to use biofuels? What about the country’s energy consumption? The Guardian 2010:14Google Scholar
  34. Qatar National Atlas (2006) Planning council. Doha, QatarGoogle Scholar
  35. Razi MA, Athappilly K (2005) A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models. Exp Syst Appl 29:65–74CrossRefGoogle Scholar
  36. Saadia RT, Munir HS, Shaheen N, Khalique A, Manzoor S, Jaffar M (2006) Multivariate analysis of trace metal levels in tannery effluents in relation to soil and water: a case study from Peshawar, Pakistan. J Environ Manag 79:20–29CrossRefGoogle Scholar
  37. Saby N, Arrouays D, Boulonne L, Jolivet C, Pochot A (2006) Geostatistical assessment of Pb in soil around Paris, France. Sci Total Environ 367:212–221CrossRefGoogle Scholar
  38. Scull P, Franklin J, Chawick OA, McArthur D (2003) Predictive soil mapping: a review. Prog Phys Geogr 27:171–197CrossRefGoogle Scholar
  39. Shomar B, Amr M, Al-Saad K, Mohieldeen Y (2013) Natural and depleted uranium in the topsoil of Qatar: is it something to worry about? Appl Geochem 37:203–211CrossRefGoogle Scholar
  40. Sterckeman T, Douay F, Proix N, Fourrier H (2000) Vertical distribution of Cd, Pb and Zn in soils near smelters in the north of France. Environ Pollut 107:377–389CrossRefGoogle Scholar
  41. Sun C, Liu J, Wang Y, Sun L, Yu H (2013) Multivariate and geostatistical analyses of the spatial distribution and sources of heavy metals in agricultural soil in Dehui, northeast China. Chemosphere 92:517–523CrossRefGoogle Scholar
  42. Tembo BD, Sichilongo K, Cernak J (2006) Distribution of copper, lead, cadmium and zinc concentrations in soils around Kabwe town in Zambia. Chemosphere 63:497–501CrossRefGoogle Scholar
  43. The Planning Council (2012) Sustainable development in the State of Qatar. Doha, QatarGoogle Scholar
  44. Theocharopoulos S, Wagner G, Sprengart J, Mohr M, Desaules A, Muntau H, Christou M, Quevauviller P (2001) European soil sampling guidelines for soil pollution studies. Sci Total Environ 264:51–62CrossRefGoogle Scholar
  45. Tso B, Mather PM (2001) Classification methods for remotely sensed data. Taylor & Francis, London, pp 97–102CrossRefGoogle Scholar
  46. Ungaro F, Ragazzi F, Cappellin R, Giandon P (2008) Arsenic concentration in the soils of the Brenta Plain (northern Italy): mapping the probability of exceeding contamination thresholds. J Geochemic Explor 96:117–131CrossRefGoogle Scholar
  47. Vega FA, Andrade ML, Covelo EF (2010) Influence of soil properties on the sorption and retention of cadmium, copper and lead, separately and together by 20 soil horizons: comparison of linear regression and tree regression analyses. J Haz Mat 174:522–533CrossRefGoogle Scholar
  48. Venables WN, Ripley BD (1994) Modern Applied Statistics with S-PLUS. Springer, New York, p 462CrossRefGoogle Scholar
  49. Wilson JP, Gallant JC (2000) Secondary topographic attributes. In: Wilson JP, Gallant JC (eds) Terrain analysis: Principles and applications. Wiley, New York, pp 87–132Google Scholar
  50. Xie Y, Chen T-B, Lei M, Yang J, Guo Q-J, Song B, Zhou X-Y (2011) Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: accuracy and uncertainty analysis. Chemosphere 82:468–476CrossRefGoogle Scholar
  51. Zhang H, Burton S (1999) Recursive partitioning in the health sciences. Springer, New York, p 226CrossRefGoogle Scholar
  52. Zhang HH, Chen JJ, Zhu L, Li FB, Wu ZF, Yu WM, Liu JM (2011) Spatial patterns and variation of soil cadmium in Guangdong Province, China. J Geochemic Explor 109:86–91CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rania Bou Kheir
    • 1
  • Mogens Greve
    • 1
  • Mette Greve
    • 1
  • Yi Peng
    • 1
  • Basem Shomar
    • 2
    Email author
  1. 1.Faculty of Science and Technology, Department of Agroecology (DJF)Aarhus UniversityTjeleDenmark
  2. 2.Qatar Environment & Energy Research Institute (QEERI)DohaQatar

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