Skip to main content
SpringerLink
Log in

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

  • Published:
Water, Air, & Soil Pollution Aims and scope Submit manuscript

Abstract

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Albergaria, J. T., Delerue-Matos, C. M., & Alvim-Ferraz, C. M. (2006). Remediation efficiency of vapour extraction of sandy soils contaminated with cyclohexane: Influence of air flow rate and of water and natural organic matter contents. Environmental Pollution, 143(1), 146–152.

    Article  CAS  Google Scholar 

  • Albergaria, J. T., Alvim-Ferraz, M. C. M., & Delerue-Matos, C. (2008). Soil vapor extraction in sandy soils: Influence of airflow rate. Chemosphere, 73(9), 1557–1561.

    Article  CAS  Google Scholar 

  • Albergaria, J. T., Alvim-Ferraz, M. D. M., & Delerue-Matos, C. (2012). Remediation of sandy soils contaminated with hydrocarbons and halogenated hydrocarbons by soil vapour extraction. Journal of Environmental Management, 104, 195–201.

    Article  CAS  Google Scholar 

  • Alvim-Ferraz, M. C. M., Albergaria, J. T., & Delerue-Matos, C. (2006). Soil remediation time to achieve clean-up goals I: Influence of soil water content. Chemosphere, 62(5), 853–860.

    Article  CAS  Google Scholar 

  • Baehr, A. L., Hoag, G. E., & Marley, M. C. (1989). Removing volatile contaminants from the unsaturated zone by inducing advective air-phase transport. Journal of Contaminant Hydrology, 4(1), 1–26.

    Article  CAS  Google Scholar 

  • Barnes, D. L. (2003). Estimation of operation time for soil vapor extraction systems. Journal of Environmental Engineering-Asce, 129(9), 873–878.

    Article  CAS  Google Scholar 

  • Barron, A. R. (1991). Universal approximation bonds for superpositions of a sigmoidal function. Technical report No. 58, Department of Statistics, University of Illinois, Urbana Champaign.

  • Chaloulakou, A., Saisana, M., & Spyrellis, N. (2003). Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. Science of the Total Environment, 313(1–3), 1–13.

    Article  CAS  Google Scholar 

  • De la Torre-Sanchez, R., Baruch, I., & Barrera-Cortes, J. (2006). Neural prediction of hydrocarbon degradation profiles developed in a biopile. Expert Systems with Applications, 31(2), 383–389.

    Article  Google Scholar 

  • Falta, R. W., Javandel, I., Pruess, K., & Witherspoon, P. A. (1989). Density driven flow of gas in the unsaturated zone due to the evaporation of volatile organic compounds. Water Resources Research, 25(10), 2159–2169.

    Article  CAS  Google Scholar 

  • Fass, S., Vogel, T. M., Vaudrey, H., Baud-Grasset, F., & Block, J. C. (1999). Prediction of chemicals biodegradation in soils: a tentative of modeling. Physics and Chemistry of the Earth Part B-Hydrology Oceans and Atmosphere, 24(6), 495–499.

    Google Scholar 

  • Gardner, M. W., & Dorling, S. R. (2000). Statistical surface ozone models: an improved methodology to account for non-linear behaviour. Atmospheric Environment, 34(1), 21–34.

    Article  CAS  Google Scholar 

  • Goudarzi, N., Goodarzi, M., Araujo, M. C. U., & Galvao, R. K. (2009). QSPR modeling of soil sorption coefficients (K-OC) of pesticides using SPA-ANN and SPA-MLR. Journal of Agricultural and Food Chemistry, 57(15), 7153–7158.

    Article  CAS  Google Scholar 

  • Grasso, D. (1993). Hazardous waste site remediation, source control. Connecticut: Lewis Publisher Inc.

    Google Scholar 

  • Kaleris, V., & Croise, J. (1997). Estimation of cleanup time for continuous and pulsed soil vapor extraction. Journal of Hydrology, 194(1–4), 330–356.

    Article  CAS  Google Scholar 

  • Kemper, T., & Sommer, S. (2002). Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environmental Science & Technology, 36(12), 2742–2747.

    Article  CAS  Google Scholar 

  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.

    Article  Google Scholar 

  • Poznyak, T., Garcia, A., Chairez, I., Gomez, M., & Poznyak, A. (2007). Application of the differential neural network observer to the kinetic parameters identification of the anthracene degradation in contaminated model soil. Journal of Hazardous Materials, 146(3), 661–667.

    Article  CAS  Google Scholar 

  • Sawyer, C. S., & Kamakoti, M. (1998). Optimal flow rates and well locations for soil vapor extraction design. Journal of Contaminant Hydrology, 32(1–2), 63–76.

    Article  CAS  Google Scholar 

  • Sleep, B. E., & Sykes, J. F. (1989). Modeling the transport of volatile organics in variably saturated soils. Water Resources Research, 25(1), 81–92.

    Article  CAS  Google Scholar 

  • Soares, A. A., Albergaria, J. T., Domingues, V. F., Alvim-Ferraz, M. C. M., & Delerue-Matos, C. (2010). Remediation of soils combining soil vapor extraction and bioremediation: Benzene. Chemosphere, 80(8), 823–828.

    Article  CAS  Google Scholar 

  • Sousa, S. I. V., Martins, F. G., Pereira, M. C., & Alvim-Ferraz, M. C. M. (2006). Prediction of ozone concentrations in Oporto city with statistical approaches. Chemosphere, 64(7), 1141–1149.

    Article  CAS  Google Scholar 

  • Sousa, S. I. V., Martins, F. G., Alvim-Ferraz, M. C. M., & Pereira, M. C. (2007). Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environmental Modelling & Software, 22(1), 97–103.

    Article  Google Scholar 

  • USEPA (2007). Treatment Technologies For Site Cleanup: Annual Status Report, 12th Ed., Retrieved January 19, 2014 from http://www.clu-in.org/download/remed/asr/12/asr12_main_body.pdf.

  • Yoon, H., Werth, C. J., Valocchi, A. J., & Oostrom, M. (2008). Impact of nonaqueous phase liquid (NAPL) source zone architecture on mass removal mechanisms in strongly layered heterogeneous porous media during soil vapor extraction. Journal of Contaminant Hydrology, 100(1–2), 58–71.

    Article  CAS  Google Scholar 

  • Zornoza, R., Mataix-Solera, J., Guerrero, C., Arcenegui, V., Garcia-Orenes, F., Mataix-Beneyto, J., et al. (2007). Evaluation of soil quality using multiple lineal regression based on physical, chemical and biochemical properties. Science of the Total Environment, 378(1–2), 233–237.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This work received financial support from the European Union (FEDER funds through COMPETE) and National Funds (FCT, Fundação para a Ciência e Tecnologia) through project Pest-C/EQB/LA0006/2013.

Author information

Authors and Affiliations

  1. Requimte, Instituto Superior de Engenharia, Instituto Politécnico do Porto, Rua Dr. António Bernardino de Almeida, 431, 4200-072, Porto, Portugal

    José Tomás Albergaria & C. Delerue-Matos

  2. LEPABE, Departamento de Engenharia Química, Faculdade de Engenharia da Universidade do Porto, R. Dr. Roberto Frias, 4200-465, Porto, Portugal

    F. G. Martins & M. C. M. Alvim-Ferraz

Authors
  1. José Tomás Albergaria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. G. Martins
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. C. M. Alvim-Ferraz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C. Delerue-Matos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José Tomás Albergaria.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Albergaria, J.T., Martins, F.G., Alvim-Ferraz, M.C.M. et al. Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction. Water Air Soil Pollut 225, 2058 (2014). https://doi.org/10.1007/s11270-014-2058-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11270-014-2058-y

Keywords

Search

Navigation