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The use of sensory perception indicators for improving the characterization and modelling of total petroleum hydrocarbon (TPH) grade in soils

Environmental Monitoring and Assessment volume 188, Article number: 129 (2016) Cite this article

Abstract

This paper proposes a multistep approach for creating a 3D stochastic model of total petroleum hydrocarbon (TPH) grade in potentially polluted soils of a deactivated oil storage site by using chemical analysis results as primary or hard data and classes of sensory perception variables as secondary or soft data. First, the statistical relationship between the sensory perception variables (e.g. colour, odour and oil–water reaction) and TPH grade is analysed, after which the sensory perception variable exhibiting the highest correlation is selected (oil–water reaction in this case study). The probabilities of cells belonging to classes of oil–water reaction are then estimated for the entire soil volume using indicator kriging. Next, local histograms of TPH grade for each grid cell are computed, combining the probabilities of belonging to a specific sensory perception indicator class and conditional to the simulated values of TPH grade. Finally, simulated images of TPH grade are generated by using the P-field simulation algorithm, utilising the local histograms of TPH grade for each grid cell. The set of simulated TPH values allows several calculations to be performed, such as average values, local uncertainties and the probability of the TPH grade of the soil exceeding a specific threshold value.

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References

  • Almeida, J. A. (2010). Modelling of cement raw material compositional indices with direct sequential cosimulation. Engineering Geology, 114(1–2), 26–33.

    Article  Google Scholar 

  • Almeida, J., & Lopes, M. (2005). Stochastic simulation of rainfall using a space-time geostatistical algorithm. In P. Renard, H. Demougeot-Renard, & R. Froidevaux (Eds.), Geostatistics for environmental applications (pp. 455–466). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Arbia, G., Lafratta, G., & Simeoni, C. (2007). Spatial sampling plans to monitor the 3-D spatial distribution of extremes in soil pollution surveys. Computational Statistics and Data Analysis, 51, 4069–4082.

    Article  Google Scholar 

  • Brito, M. G. (2005). Metodologia para a avaliação e remediação da contaminação por metais pesados em áreas industriais degradadas. PhD Thesis. Universidade Nova de Lisboa, Lisbon (in Portuguese).

  • Brito, M. G., Costa, C. N., Almeida, J. A., Vendas, D., & Verdial, P. H. (2006). Characterization of maximum infiltration areas using GIS tools. Engineering Geology, 85(1), 14–18.

    Article  Google Scholar 

  • Chakraborty, S., Weindorf, D. C., Zhu, Y., Lin, B., Morgan, C. L. S., Ge, Y., & Galbraith, J. (2012). Assessing spatial variability of soil petroleum contamination using visible near-infrared diffuse reflectance spectroscopy. Journal of Environmental Monitoring, 14, 2886–2892.

    CAS  Article  Google Scholar 

  • Charifo, G., Almeida, J. A., & Ferreira, A. (2013). Managing borehole samples of unequal lengths to construct a high-resolution mining model of mineral grades zoned by geological units. Journal of Geochemical Exploration, 132, 209–223.

    CAS  Article  Google Scholar 

  • D’Or, D., Bogaert, P., & Christakos, G. (2001). Applications of BME to soil texture mapping. Stochastic Environmental Research and Risk Assessment, 15(1), 87–100.

    Article  Google Scholar 

  • D’Or, D., Demougeot-Renard, H., & Garcia, M. (2009). An integrated geostatistical approach for contaminated site and soil characterisation. Mathematical Geosciences, 41(3), 307–322.

    Article  Google Scholar 

  • Demougeot-Renard, H., & De-Fouquet, C. (2004). Geostatistical approach for assessing soil volumes requiring remediation: validation using lead-polluted soils underlying a former smelting works. Environmental Science and Technology, 38(19), 5120–5126.

    CAS  Article  Google Scholar 

  • Deutsch, C. V., & Journel, A. G. (1992). GSLIB, Geostatistical software library and user’s guide. New York: Oxford University Press.

    Google Scholar 

  • Froidevaux, R. (1993). Probability field simulation. In A. Soares (Ed.), Geostatistics Troia’92 (Vol. 1, pp. 73–84). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Garcia, M., & Froidevaux, R. (1997). Application of geostatistics to 3D modelling of contaminated sites: a case study. In A. Soares, J. Gomez-Hernandez, & R. Froidevaux (Eds.), Geostatistics for environmental applications geoENV I (pp. 309–325). Dordrecht: Kluwer Academic Publishers.

    Chapter  Google Scholar 

  • Goovaerts, P. (1997). Geostatistics for natural resource evaluation. New York: Oxford University.

    Google Scholar 

  • Goovaerts, P. (2001). Geostatistical modeling of uncertainty in soil science. Geoderma, 103, 3–26.

    Article  Google Scholar 

  • Guerreiro, L., Almeida, J. A. & Soares, A. (1998). Permeability simulation in poor sampling conditions. Abu Dhabi International Petroleum Exhibition and Conference (SPE paper 49451), Society of Petroleum Engineers.

  • Hendriks, L. A. M., Leummens, H., Stein, A., & De-Bruijn, P. J. (1998). Use of soft data in a GIS to improve estimation of the volume of contaminated soil. Water, Air, & Soil Pollution, 101(1–4), 217–234.

    CAS  Article  Google Scholar 

  • Horta, A., Malone, B., Stockmann, U., Minasny, B., Bishop, T. F. A., McBratney, A. B., Pallasser, R., & Pozza, L. (2015). Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: a prospective review. Geoderma, 241–242, 180–209.

    Article  Google Scholar 

  • Hosseini, A. H., Deutsch, C. V., Biggar, K. W., & Mendoza, C. A. (2010). Probabilistic data integration for characterization of spatial distribution of residual LNAPL. Stochastic Environmental Research and Risk Assessment, 24, 735–749.

    Article  Google Scholar 

  • INETI (2008). Map 34B – Loures geological map of Portugal scale 1/50 000. Dep. Geol. Instituto Nacional de Engenharia, Tecnologia e Inovação, Lisboa (in Portuguese).

  • Iturbe, R., Flores-Serrano, R. M., Castro, A., Flores, C., & Torres, L. G. (2010). Subsoil TPH in two pipeline pumping stations and one pipeline right-of-way in north Mexico. Journal of Environmental Management, 11, 2396–2402.

    Article  Google Scholar 

  • Journel, A. G., & Alabert, F. G. (1989). Non Gaussian data expansion in the earth sciences. Terra Nova, 1, 123–134.

    Article  Google Scholar 

  • Journel, A. G., & Huijbregts, C. J. (1978). Mining geostatistics (p. 600). London: Academic.

    Google Scholar 

  • Journel, A. G., & Isaaks, E. H. (1984). Conditional indicator simulation: application to a Saskatchewan uranium deposit. Mathematical Geology, 16(7), 685–718.

    CAS  Article  Google Scholar 

  • Li, J., Zhang, J., Lu, Y., Chen, Y., Dong, S., & Shim, H. (2012). Determination of total petroleum hydrocarbons (TPH) in agricultural soils near a petrochemical complex in Guangzhou, China. Environmental Monitoring and Assessment, 184(1), 281–287.

    CAS  Article  Google Scholar 

  • Mata-Lima, H., Silva, F., Alvino-Borba, A., & Almeida, J. A. (2014). Environmental management in organizations: is ISO 14001 implementation growing fast enough to improve environmental conditions in the metropolitan areas of developing countries? Environmental Quality Management, 24(2), 61–77.

    Article  Google Scholar 

  • Milillo, T. M., Sinha, G., & Gardella, J. A. (2012). Use of geostatistics for remediation planning to transcend urban political boundaries. Environmental Pollution, 170, 52–62.

    CAS  Article  Google Scholar 

  • Modis, C., Papantonopoulos, G., Komnitsas, K., & Papaodysseus, K. (2008). Mapping optimization based on sampling size in earth related and environmental phenomena. Stochastic Environmental Research and Risk Assessment, 22(1), 83–93.

    Article  Google Scholar 

  • Morakinyo, J. A., & Mackay, R. (2005). Geostatistical modelling of ground conditions to support the assessment of site contamination. Stochastic Environmental Research and Risk Assessment, 20, 106–118.

    Article  Google Scholar 

  • Mulligan, C. N., Yong, R. N., & Gibbs, B. F. (2001). Remediation technologies for metal contaminated soils and groundwater: an evaluation. Engineering Geology, 60(1–4), 193–207.

    Article  Google Scholar 

  • Nunes, R., & Almeida, J. A. (2010). Parallelization of sequential Gaussian, indicator and direct simulation algorithms. Computers & Geosciences, 36(8), 1042–1052.

    Article  Google Scholar 

  • Pereira, M. J., Almeida, J., Costa, C., & Soares, A. (2001). Accounting for soft information in mapping soil contamination with TPH at an oil storage site. In P. Monastiez, D. Allard, & R. Froidevaux (Eds.), Geostatistics for environmental applications geoENV III (pp. 475–486). Dordrecht: Kluwer.

    Chapter  Google Scholar 

  • Pinedoa, J., Ibáñeza, R., Lijzenb, J. P. A., & Irabiena, Á. (2013). Assessment of soil pollution based on total petroleum hydrocarbons and individual oil substances. Journal of Environmental Management, 130, 72–79.

    Article  Google Scholar 

  • Qin, X. S., Huang, G. H., & He, L. (2009). Simulation and optimization technologies for petroleum waste management and remediation process control. Journal of Environmental Management, 90(1), 54–76.

    CAS  Article  Google Scholar 

  • Qingcheng, H. (2006). Mathematical models of the interaction between the geological and ecological environment. In: I. S. Zektser, B. Marker, J. Ridgway, L. Rogachevskaya & G. Vartanyan (Eds.), Geology and ecosystems (pp. 251–263), Berlin: Springer.

  • Quental, P., Almeida, J. A., & Simões, M. (2012). Construction of high-resolution stochastic geological models and optimal upscaling to a simplified layer-type hydrogeological model. Advances in Water Resources, 39, 18–32.

    Article  Google Scholar 

  • Schnabel, U., & Tietje, O. (2003). Explorative data analysis of heavy metal contaminated soil using multidimensional spatial regression. Environmental Geology, 44(8), 893–904.

    CAS  Article  Google Scholar 

  • Soares, A. (1992). Geostatistical estimation of multi-phase structures. Mathematical Geology, 24(2), 149–160.

    Article  Google Scholar 

  • Soares, A. (2001). Direct sequential simulation and cosimulation. Mathematical Geology, 33(8), 911–926.

    Article  Google Scholar 

  • Soares, A., Almeida, J. A. & Guerreiro, L. (2006). Incorporating secondary information using direct sequential cosimulation. In: T. C. Coburn, J. M. Yarus & R. L. Chambers (Eds.), Stochastic modeling and geostatistics: Principles, methods, and case studies, volume II (pp. 35–43). AAPG Computer Applications in Geology 5.

  • Srivastava, R. M. (1992) Reservoir characterisation with probability field simulation. SPE Annual Conference and Exhibition paper #24753 (pp. 927–938), Washington DC.

  • Verscheuren, I. K., & Kranendijk, I. J. S. M. (1986). Sensory perception in soil pollution studies. In J. W. Assink & W. J. Van den Brink (Eds.), Contaminated soil. Dordrecht: Martinus Nijhoff Publishers.

    Google Scholar 

  • VROM (2012). VROM (Dutch Ministry of Housing, Spatial Planning and the Environment) Soil Remediation Circular 2009 Staatscourant 3 April 2012, Nr. 6563 Ministry of Housing, Spatial Planning and the Environment, The Hague.

  • Yun, J., Lee, J. Y., Khim, J., & Ji, W. H. (2013). Assessing soil and groundwater contamination in a metropolitan redevelopment project. Environmental Monitoring and Assessment, 185(8), 6855–6865.

    CAS  Article  Google Scholar 

Download references

Acknowledgments

This work is a contribution to the CRUDE Project PTDC/CTE-GEX/72959/2006 and Project UID/GEO/04035/2013, both funded by FCT—Fundação para a Ciência e a Tecnologia, Portugal.

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Affiliations

  1. Departamento de Ciências da Terra (GeoBioTec), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal

    Sónia Roxo, José António de Almeida, Filipa Vieira Matias & Sofia Barbosa

  2. ILATTI – Universidade Federal da Integração Latino Americana, Foz do Iguaçu, Brazil

    Herlander Mata-Lima

  3. CERENA—Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    Herlander Mata-Lima

Authors
  1. Sónia Roxo
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  2. José António de Almeida
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  3. Filipa Vieira Matias
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  4. Herlander Mata-Lima
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  5. Sofia Barbosa
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Correspondence to José António de Almeida.

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Roxo, S., de Almeida, J.A., Matias, F.V. et al. The use of sensory perception indicators for improving the characterization and modelling of total petroleum hydrocarbon (TPH) grade in soils. Environ Monit Assess 188, 129 (2016). https://doi.org/10.1007/s10661-016-5135-4

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  • DOI: https://doi.org/10.1007/s10661-016-5135-4

Keywords

  • Soil contamination
  • Sensory perception indicators
  • Total petroleum hydrocarbons (TPHs)
  • Combining hard data and soft data
  • Stochastic simulation
  • Mapping of uncertainty