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Grundwasser

, Volume 12, Issue 1, pp 3–14 | Cite as

Einfluss von Heterogenität und Messfehler auf die Bestimmung von Abbauraten erster Ordnung – eine Virtueller-Aquifer-Szenarioanalyse

  • S. BauerEmail author
  • C. Beyer
  • O. Kolditz
Fachbeiträge

Kurzfassung

Die grundlegende Idee des virtuellen Aquifers ist, durch numerische Modellierung von typischen Schadensfällen Erkundungsstrategien zu simulieren und zu bewerten. In diesem Beitrag wird die Bestimmung von Abbauraten erster Ordnung untersucht. Eine Schadensquelle wird dabei in einen virtuellen Aquifer eingebracht und die stationäre Schadstofffahne unter der Annahme einer Abbaukinetik erster Ordnung simuliert. Diese Fahne wird dann durch Beobachtungspegel entlang der Zentrallinie der Fahne untersucht. Anhand von vier typischen Methoden werden Abbauraten erster Ordnung berechnet und mit dem vorgegebenen Wert verglichen. Dieser Vergleich wird für unterschiedlich stark ausgeprägte hydraulische Heterogenitäten durchgeführt. Dabei zeigt sich, dass mit zunehmender Heterogenität die ermittelten Abbauraten die tatsächliche Abbaurate um Größenordnungen überschätzen können und sie somit sehr unsicher sind. Bei der Untersuchung der Messfehler wurden Abweichungen bei der Bestimmung der Piezometerhöhe und der Konzentration angenommen. Hierbei ergibt sich, dass Messfehler ebenfalls zu einer hohen Unsicherheit der Ratenkonstante führen können, wobei Messfehler der Piezometerhöhe einen stärkeren Einfluss haben.

Influence of heterogeneity and measurement error on the determination of first order degradation rates – a virtual aquifer scenario analysis

Abstract

The principal idea of the Virtual Aquifer is to simulate and evaluate monitoring strategies and remediation options for contaminated sites by modelling typical contamination scenarios. Here the determination of first order degradation rates is studied. A virtual reality is generated by simulating the spreading of a plume, originating from a defined source and subject to first order degradation. This plume is investigated using monitoring wells placed along the plume center line. From the information thus obtained, first order degradation rates are calculated by typically used methods and are then compared to the predefined value. This comparison is conducted for different degrees of heterogeneity. It is found that with increasing heterogeneity, the calculated degradation rates can overestimate the real degradation rate by several orders of magnitude and show a high uncertainty. Measurement errors are then introduced for piezometric head and concentration measurements. It is found that up to several orders of magnitude deviations can occur between the estimated first order rate constant and the true rate, with errors of the piezometric head measurement causing the dominant uncertainty.

Keywords

natural attenuation heterogeneity first-order degradation rate virtual aquifer scenario analysis 

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Literatur

  1. Bauer, S., Beyer, C., Kolditz, O.: Assessing measurement uncertainty of first order degradation rates in heterogeneous aquifers. Water Resour. Res. 42, W01420, doi:10.1029/2004WR003878. (2006)Google Scholar
  2. Bauer, S., Beyer, C., Kolditz, O.: Assessing measurements of first order degradation rates by using the Virtual Aquifer approach.- IAHS Publication 297: 274–281. (2005)Google Scholar
  3. Bauer, S., Kolditz, O.: Uncertainty assessment of integral pumping tests in heterogeneous aquifers. Calibration and Reliability in Groundwater Modelling: From Uncertainty to Decision Making (Proceedings of ModelCARE’2005, The Hague, The Netherlands, June 2005). IAHS Publ. 304, pp.123–129. (2006)Google Scholar
  4. Beyer, C., Bauer S., Kolditz, O.: Uncertainty assessment of degradation rate measurements in heterogeneous media using the virtual aquifer approach.- In: Kolditz, O., Bauer, S., Gronewold, J. (Hrsg.): Proceedings of the 5th Workshop „Porous Media“, Blaubeuren, Dezember 2004; Tübingen. (2005a)Google Scholar
  5. Beyer, C., Bauer, S., Kolditz, O.: Uncertainty assessment of contaminant plume length estimates in heterogeneous aquifers. Journal of Contaminant Hydrology, Volume 87, Issues 1–2, 10 September 2006, Pages 73–95. (2006)Google Scholar
  6. Bockelmann, A., Zamfirescu, D., Ptak, T., Grathwohl, P., Teutsch, G.: Quantification of mass fluxes and natural attenuation rates at an industrial site with a limited monitoring network: A case study.- J. Contam. Hydrol. 60: 97–121. (2003)Google Scholar
  7. Buscheck, T.E., Alcantar, C.M.: Regression techniques and analytical solutions to demonstrate intrinsic bioremediation.- In: Proceedings of the 1995 Battelle International Conference on In-Situ and On Site Bioreclamation, Batelle, CA, April 1995: 109–116. (1995)Google Scholar
  8. Chapelle, F. H., Bradley, P.M., Lovley, D.R., Vroblesky, D.A.: Measuring rates of biodegradation in a contaminated aquifer using field and laboratory methods.- Ground Water 34 (4): 691–698. (1996)CrossRefGoogle Scholar
  9. Herfort, M.: Reactive transport of organic compounds within a heterogeneous porous aquifer.- Tübinger Geowissenschaftliche Arbeiten 54.- 76 S.; Universität Tübingen, Tübingen. (2000)Google Scholar
  10. Kolditz, O., de Jonge, J., Beinhorn, M., Xie, M., Kalbacher, M., Wang, W., Bauer, S., McDermott, C., Chen, C., Beyer, C., Gronewold, J., Kemmler, D., Manabe, T., Legeida D., Adamidis, P.: GeoSys – Theory and users manual, release 4.2. GeoHydrology/HydroInformatics, Center for Applied Geoscience, Universität Tübingen, Tübingen. (2005)Google Scholar
  11. McNab Jr, W.W., Dooher, B.P.: A critique of a steady-state analytical method for estimating contaminant degradation rates.- Ground Water 36 (6): 983–987. (1998)CrossRefGoogle Scholar
  12. Pebesma, E.J., Wesseling, C.G.: Gstat: A program for geostatistical modeling, prediction and simulation.- Computers & Geosciences 24 (1): 17–31, doi:10.1016/S0098-3004(97)00082-4. (1998)CrossRefGoogle Scholar
  13. Rehfeldt, K.R., Boggs, J.M., Gelhar, L.W.: Field study of dispersion in a heterogeneous aquifer, 3, geostatistical analysis of hydraulic conductivity.- Water Resour. Res. 28 (12): 3309–3324. (1992)CrossRefGoogle Scholar
  14. Rubin,Y.: Applied stochastic hydrogeology.- 416 S.; New York, NY. (2003)Google Scholar
  15. Schäfer, D., Dahmke, A., Kolditz, O., Teutsch, G.: „Virtual Aquifers”: A concept for evaluation of exploration, remediation and monitoring strategies.- In: Kovar, K., Hrkal, Z.: Calibration and reliability in groundwater modelling: A few steps closer to reality (Proceedings ModelCARE 2002, Konferenz in Prag, Juni 2002.- IAHS Publication 277: 52–59. (2002)Google Scholar
  16. Schäfer, D., Schlenz B., Dahmke, A.: Evaluation of exploration and monitoring methods for verification of natural attenuation using the virtual aquifer approach.- Biodegradation Journal 15 (6): 453–465. (2004)CrossRefGoogle Scholar
  17. Schäfer, D., Hornbruch, G., Schlenz B., Dahmke, A.: Schadstoffausbreitung unter Annahme verschiedener kinetischer Ansätze zur Modellierung mikrobiellen Abbaus.- Grundwasser 12 (1). (2007)Google Scholar
  18. Suarez, M.P., Rifai, H.S.: Evaluation of BTEX remediation by natural attenuation at a coastal facility.- Ground Water Monit. Remed. 22 (1): 62–77. (2002)CrossRefGoogle Scholar
  19. Stenback, G.A., Ong, S.K., Rogers, S.W., Kjartonson, B.H.: Impact of transverse and longitudinal dispersion on first-order degradation rate constant estimation.- J. Cont. Hydrol. 73: 3–14. (2004)Google Scholar
  20. Sudicky, E.A.: A natural gradient experiment on solute transport in a sand aquifer: Spatial variability of hydraulic conductivity and its role in the dispersion process.- Water Resour. Res. 22 (13): 2069–2082. (1986)CrossRefGoogle Scholar
  21. U.S. Environmental Protection Agency: Use of monitoring natural attenuation at Superfund, RCRA Corrective Action, and Underground Storage Tank Sites.- Office of Solid Waste and Emergency Response Directive 9200: 4–17; Washington, D.C. (1999)Google Scholar
  22. Wiedemeier, T.H., Swanson, M.A., Wilson, J.T., Kampbell, D.H., Miller, R.N., Hansen, J.E.: Approximation of biodegradation rate constants for monoaromatic hydrocarbons (BTEX) in ground water.- Ground Water Monit. Remed. 16 (3): 186–194. (1996)CrossRefGoogle Scholar
  23. Wiedemeier, T.H., Rifai, H.S., Wilson, J.T., Newell, C.: Natural attenuation of fuels and chlorinated solvents in the subsurface.- 617 S.; New York, NY. (1999)Google Scholar
  24. Wilson, J.T., Pfeffer, F.M., Weaver, J.W., Kampbell, D.H., Wiedemeier, T.H., Hansen, J.E., Miller, R.N.: Intrinsic bioremediation of JP-4 Jet Fuel.- In: Symposium on Intrinsic Bioremediation of Ground Water, Denver, Colorado.- US-EPA: 60–72. (1994)Google Scholar
  25. Wilson, R.D., Thornton, S.F. Mackay, D.M.: Challenges in monitoring the natural attenuation of spatially variable plumes.- Biodegradation Journal 15 (6): 459–469. (2004)Google Scholar
  26. Zamfirescu, D., Grathwohl, P.: Occurrence and attenuation of specific organic compounds in the groundwater plume at a former gasworks site.- J. Contam. Hydrol. 53: 407–427. (2001)Google Scholar
  27. Zhang, Y.-K., Heathcote, R.C.: An improved method for estimation of biodegradation rate with field data.- Ground Water Monit. Remed. 23 (3): 112–116. (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Zentrum für Angewandte GeowissenschaftenUniversität TübingenTübingenDeutschland

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