BasinBox: a generic multimedia fate model for predicting the fate of chemicals in river catchments

  • A. Hollander
  • M. A. J. Huijbregts
  • A. M. J. Ragas
  • D. van de Meent
Part of the Developments in Hydrobiology book series (DIHY, volume 187)


Multimedia fate models have proven to be very useful tools in chemical risk assessment and management. This paper presents BasinBox, a newly developed steady-state generic multimedia fate model for evaluating risks of new and existing chemicals in river basins. The model concepts, as well as the intermedia processes quantified in the model, are outlined, and an overview of the required input parameters is given. To test the BasinBox model, calculations were carried out for predicting the fate of chemicals in the river Rhine basin. This was done for a set of 3175 hypothetical chemicals and three emission scenarios to air, river water and cropland soils. For each of these hypothetical chemicals and emission scenarios the concentration ratio between the downstream area and the upstream area was calculated for all compartments. From these calculations it appeared that BasinBox predicts significant concentration differences between upstream and downstream areas of the Rhine river basin for certain types of chemicals and emission scenarios. There is a clear trend of increasing chemical concentrations in downstream direction of the river basin. The calculations show that taking into account spatial variability between upstream, midstream and downstream areas of large river basins can be useful in the predictions of environmental concentrations by multimedia fate models.

Key words

multimedia fate model river catchment Rhine risk assessment 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Asselman, N. E. M., 1997. Suspended Sediment in the River Rhine. The Impact of Climate Change on Erosion, Transport and Deposition. PhD-thesis, Department of Physical Geography, Utrecht University, Utrecht.Google Scholar
  2. Baughman, G. L. & R. R. Lassiter, 1978. Prediction of environmental pollutant concentration. In Cairns, J., D. L. Dickson & A.W. Maki (eds), Estimating the Hazard of Chemical Substances to Aquatic Life. American Society for Testing Materials (ASTM) 657: 34–54.Google Scholar
  3. Boorman, D. B., 2003. LOIS in-stream water quality modelling. Part 1: catchments and methods. Science of the Total Environment 314: 379–395.PubMedCrossRefGoogle Scholar
  4. Brandes, L. J., H. den Hollander & D. van de Meent, 1996. SimpleBox 2.0: A Nested Multimedia Fate Model for Evaluating the Environmental Fate of Chemicals. RIVM, Bilthoven.Google Scholar
  5. Briggs, C. G., R. H. Bromilov & A. A. Evans, 1982. Relationships between lipophilicity and root uptake and translocation of non-ionised chemicals by barley. Pesticide Science 13: 495–504.CrossRefGoogle Scholar
  6. Cahill, T. M. & D. Mackay, 2003. A high-resolution model for estimating the environmental fate of multi-species chemicals: application to malathion and pentachlorophenol. Chemosphere 53: 571–581.PubMedCrossRefGoogle Scholar
  7. Chamberlain, A.C., 1967. Transport of lycopodium spores and other small particles to rough surfaces. Proceedings of the Royal Society of London A296: 45–70.CrossRefGoogle Scholar
  8. Centrum voor Landbouw en Milieu (CLM), 2004. Werken aan duurzame landbouw en een aantrekkelijk platteland. (in Dutch).
  9. Chemicalland21, 2005.
  10. Commonwealth Scientific and Industrial Research Organization (CSIRO), 1994. CSIRO sustainable ecosystems-Software and resources.
  11. Coulibaly, L., M. E. Labib & R. Hazen, 2004. A GIS-based multimedia watershed model: development and application. Chemosphere 55: 1067–1080.PubMedCrossRefGoogle Scholar
  12. Cousins, I. T. & D. Mackay, 2001. Strategies for including vegetation compartments in multimedia models. Chemosphere 44: 643–654.PubMedCrossRefGoogle Scholar
  13. De Nooij, R. J. W., W. C. E. P. Verberk, H. J. R. Lenders, R. S. E. W. Leuven & P. H. Nienhuis, 2006. The importance of hydrodynamics for protected and endangered biodiversity of lowland rivers. Hydrobiologia 565: 153–162.CrossRefGoogle Scholar
  14. De Wit, M. J. M., 1999. Nutrient fluxes in the Rhine and Elbe basins. Ph.D. thesis. Department of Physical Geography, Utrecht University, Utrecht.Google Scholar
  15. Den Hollander, H. & D. van de Meent, 2004. SimpleBox 3.0: A Multimedia Fate Model for Evaluating Environmental Behaviour of Chemicals. RIVM, Bilthoven.Google Scholar
  16. Diamond, M. L., D. A. Priemer & N. L. Law, 2001. Developing a multimedia model of chemical dynamics in an urban area. Chemosphere 44: 1655–1667.PubMedCrossRefGoogle Scholar
  17. DiToro, D. M., C. S. Zarba, D. J. Hansen, W. J. Berry, R. C. Swartz, C. E. Cowan, S. P. Pavlou, H. E. Allen, N. A. Thomas & P. R. Paquin, 1991. Technical basis for establishing sediment quality criteria for nonionic organicchemicals using equilibrium partitioning. Environmental Toxicology and Chemistry 10: 1541–1583.CrossRefGoogle Scholar
  18. Deutscher Wetterdienst (DWD), 2004. EC, 2000. Water Framework Directive. European Commission, Brussels.Google Scholar
  19. ECB, 2003. Technical Guidance Document on Risk Assessment. JRC-Ispra, Italy.Google Scholar
  20. ECNC, 2004. European Centre for Nature Conservation.
  21. FAO, 2001. Food and Agriculture Organization of the United Nations. Scholar
  22. Feijtel, T., G. Boeije, M. Matthies, A. Young, G. Morris, C. Gandolfi, B. Hansen, K. Fox, M. Holt, V. Koch, R. Schroder, G. Cassani, D. Schowanek, J. Rosenblom & H. Niessen, 1997. Development of a geography-referenced regional exposure assessment tool for European rivers-GREAT-ER contribution to GREAT-ER #1. Chemosphere 34: 2351–2373.CrossRefGoogle Scholar
  23. Fenner, K., M. Scheringer & K. Hungerbühler, 2000. Persistence of parent compounds and transformation products in a level IV multimedia model. Environmental Science and Technology 34: 3809–3817.CrossRefGoogle Scholar
  24. Fenner, K., M. Scheringer, M. MacLeod, M. Matthies, T. McKone, M. Stroebe, A. Beyer, M. Bonnell, A. C. Le Gall, J. Klasmeier, D. Mackay, D. van de Meent, D. Pennington, B. Scharenberg, N. Suzuki & F. Wania, 2005. Comparing estimates of persistence and long-range transport potential among multimedia models. Environmental Science and Technology 39: 1932–1942.PubMedCrossRefGoogle Scholar
  25. Hofstee, C. & H. Leenaers, 2002. Actief beheer van de waterbodem in landelijk perspectief. TNO-NITG, Utrecht (in Dutch).Google Scholar
  26. Hollander, A., L. K. Hessels, P. de Voogt & D. van de Meent, 2004. Implementation of depth-dependent soil concentrations in multimedia mass balance models. SAR and QSAR in Environmental Research 15: 457–468.PubMedCrossRefGoogle Scholar
  27. Horstmann, M. & M. S. McLachlan, 1998. Atmospheric deposition of semivolatile organic compounds to two forest canopies. Atmospheric Environment 32: 1799–1809.CrossRefGoogle Scholar
  28. Jackson, R., 1996. A global analysis of root distributions for terrestrial biomes. Oecologia 108: 389–411.CrossRefGoogle Scholar
  29. Junge, C. E., 1977. Basic considerations about trace constituent in the atmosphere related to the fate of global pollutants. In Suffet, I. H. (ed.), Fate of Pollutants in the Air and Water Environment. Wiley-Interscience: 7–25.Google Scholar
  30. Koninklijk Nederlands Meteorologisch Instituut (KNMI), 2004. Klimaat en klimaatverandering: klimatologische informatie. (in Dutch).
  31. Mackay, D., 1991. Multimedia Environmental Models. Lewis, Chelsea.Google Scholar
  32. Mackay, D. & S. Paterson, 1981. Calculating fugacity. Environmental Science and Technology 15: 1006–1014.CrossRefGoogle Scholar
  33. Mackay, D., S. Paterson & M. Joy, 1983. Application of fugacity models to the estimation of chemical-distribution and persistence in the environment. ACS Symposium Series 225: 175–196.CrossRefGoogle Scholar
  34. McKone, T. E., 1993. CalTOX, A Multimedia Total-exposure Model for Hazardous-wastes Sites. Part 1: Executive Summary. Lawrence Livermore National Laboratory, Livermore.Google Scholar
  35. McKone, T. E. & D. H. Bennett, 2003. Chemical-specific representation of air-soil exchange and soil penetration in regional multimedia models. Environmental Science and Technology 37: 3123–3132.PubMedCrossRefGoogle Scholar
  36. McKone, T. E., A. B. Bodnar & E. G. Hertwich, 2001. Development and Evaluation of State-specific Landscape Data Sets for Multimedia Source-to-dose Models. School of Public Health, University of California, Berkeley.Google Scholar
  37. McLachlan, M. S., G. Czub & F. Wania, 2002. The influence of vertical sorbed phase transport on the fate of organic chemicals in surface soils. Environmental Science and Technology 36: 4860–4867.PubMedCrossRefGoogle Scholar
  38. MeteoSchweiz, 2004.
  39. Meybeck, M., L. Laroche, H. H. Durr & J. P. M. Syvitski, 2003. Global variability of daily total suspended solids and their fluxes in rivers. Global and Planetary Change 39: 65–93.CrossRefGoogle Scholar
  40. Nationmaster, 2005.
  41. Nienhuis, P. H., A. D. Buijse, R. S. E. W. Leuven, A. J. M. Smits, R. J. W. de Nooij & E. M. Samborska, 2002. Ecological rehabilitation of the lowland basin of the river Rhine (NW Europe). Hydrobiologia 478: 53–72.CrossRefGoogle Scholar
  42. Paterson, S. & D. Mackay, 1994. Interpreting chemical partitioning in a soil-plant-air system with a fugacity model. In Trapp, S. & C. McFarlane (eds), Plant Contamination, Modeling and Simulation of Organic Chemical Processes. Lewis Publishers/CRC Press: 191–214.Google Scholar
  43. Prevedouros, K., K. C. Jones & A. J. Sweetman, 2004. European-scale modeling of concentrations and distribution of polybrominated diphenyl ethers in the pentabromodiphenyl ether product. Environmental Science and Technology 38: 5993–6001.PubMedCrossRefGoogle Scholar
  44. Scheringer, M., F. Wegmann, K. Fenner & K. Hungerbuhler, 2000. Investigation of the cold condensation of persistent organic pollutants with a global multimedia fate model. Environmental Science and Technology 34: 1842–1850.CrossRefGoogle Scholar
  45. Schumm, S. A., 1977. The Fluvial System. Wiley-Interscience, New York.Google Scholar
  46. Schwarzenbach, R. P., P. M. Gschwend & D. M. Imboden, 1993. Environmental Organic Chemistry. John Wiley & Sons, New York.Google Scholar
  47. Scorecard, 2005. The pollution information site.
  48. Scurlock, J. M. O., G. P. Asner & S. T. Gower, 2001. Worldwide Historical Estimates of Leaf Area Index, 1932–2000. Oak Ridge National Laboratory, Oak Ridge.Google Scholar
  49. Severinsen, M. & T. Jager, 1998. Modelling the influence of terrestrial vegetation on the environmental fate of xenobiotics. Chemosphere 37: 41–62.CrossRefGoogle Scholar
  50. Stroebe, M., M. Scheringer & K. Hungerbühler, 2004. Measures of overall persistence and the temporal remote state. Environmental Science and Technology 38: 5665–5673.PubMedCrossRefGoogle Scholar
  51. Suzuki, N., K. Murasawa, T. Sakurai, K. Nansai, K. Matsuhashi, Y. Moriguchi, K. Tanabe, O. Nakasugi & M. Morita, 2004. Geo-referenced multimedia environmental fate model (G-CIEMS): model formulation and comparison to the generic model and monitoring approaches. Environmental Science and Technology 38: 5682–5693.PubMedCrossRefGoogle Scholar
  52. Tiktak, A., D. de Nie, T. van der Linden & R. Kruijne, 2002. Modelling the leaching and drainage of pesticides in the Netherlands: theGeoPEARLmodel.Agronomie 22: 373–387.CrossRefGoogle Scholar
  53. Toose, L., D. G. Woodfine, M. MacLeod, D. Mackay & J. Gouin, 2004. BETR-World: a geographically explicit model of chemical fate: application to transport of alpha-HCH to the Arctic. Environmental Pollution 128: 223–240.PubMedCrossRefGoogle Scholar
  54. Trapp, S., 1996. Querprofile, WQ-, QW-, WB-und Wu-Regressionen, Einleiterstandorte fü r den Rhein. Universitä t Osnabrü ck, Institut fü r Umweltsystemforschung, Osnabrü ck.Google Scholar
  55. Trapp, S. & M. Matthies, 1996. Generic one compartment model for uptake of organic chemicals by foliar vegetation. Environmental Science and Technology 30: 360.Google Scholar
  56. US-EPA., 2002. TRIM.FaTE Technical Support Document. Volume 1: Description of Module. US-Environmental Protection Agency, North Carolina.Google Scholar
  57. Vermeire, T. G., D. T. Jager, B. Bussian, J. Devillers, K. den Haan, B. Hansen, I. Lundberg, H. Niessen, S. Robertson, H. Tyle & P. T. J. van der Zandt, 1997. European Union System for the Evaluation of Substances (EUSES). Principles and structure. Chemosphere 34: 1823–1836.PubMedCrossRefGoogle Scholar
  58. Vermeire, T., M. Rikken, L. Attias, P. Boccardi, G. Boeije, D. Brooke, J. de Bruijn, M. Comber, B. Dolan, S. Fischer, G. Heinemeyer, V. Koch, J. Lijzen, B. Müller, R. Murray-Smith & J. Tadeo, 2005. European union system for the evaluation of substances: the second version. Chemosphere 59: 473–485.PubMedCrossRefGoogle Scholar
  59. Webster, E., D. Mackay, A. Di Guardo, D. Kane & D. Woodfine, 2004. Regional differences in chemical fate model outcome. Chemosphere 55: 1361–1376.PubMedCrossRefGoogle Scholar
  60. Woodfine, D. G., M. MacLeod, D. Mackay & J. R. Brimacombe, 2001. Development of continental scale multimedia contaminant fate models: integrating GIS. Environmental Science and Pollution Research 8: 164–172.CrossRefPubMedGoogle Scholar
  61. Zeng, X. B., R. E. Dickinson, A. Walker, M. Shaikh, R. S. DeFries & J. G. Qi, 2000. Derivation and evaluation of global 1-km fractional vegetation cover data for land modeling. Journal of Applied Meteorology 39: 826–839.CrossRefGoogle Scholar
  62. Zhang, Q. O., J. C. Crittenden, D. Shonnard & J. R. Mihelcic, 2003. Development and evaluation of an environmental multimedia fate model CHEMGL for the Great Lakes region. Chemosphere 50: 1377–1397PubMedCrossRefGoogle Scholar

Copyright information

© Springer2006 2006

Authors and Affiliations

  • A. Hollander
    • 1
  • M. A. J. Huijbregts
    • 1
  • A. M. J. Ragas
    • 1
  • D. van de Meent
    • 1
    • 2
  1. 1.Department of Environmental Science, Institute for Wetland and Water ResearchRadboud University NijmegenNijmegenThe Netherlands
  2. 2.National Institute of Public Health and the EnvironmentLaboratory for Ecological Risk AssessmentBilthovenThe Netherlands

Personalised recommendations