Environmental Exposure Assessment

  • D. Van De Meent
  • J.H.M. De Bruijn

Organisms, man included, are exposed to chemicals through environmental media. Assessment of exposure concentrations can be done by measurement or by other means of estimation, e.g. model-based computation. For the risk assessment of existing situations, both measurement and modelling can be used; to assess the risks posed by new chemicals or new situations, modelling is the only option. Although it may seem natural to assume that measurement yields more certainty, this is not necessarily so. Chemical analyses are usually carried out on samples, taken at specific locations and times.

Keywords

Clay Heat Content Ozone Sedimentation Sewage 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lassiter RR. 1982. Testing models of the fate of chemicals in aquatic environments. In: Dickson KL, Maki AW, Cairns J Jr, eds., Modelling the Fate of Chemicals in the Aquatic Environment. Ann Arbor Science, Ann Arbor, MI, pp. 397-407.Google Scholar
  2. 2.
    Commission of the European Communities. 1994. Guidance document for the risk assessment of existing chemicals in the context of EC regulation 793/93. Commission of the European Communities, Directorate General of the Environment, Nuclear Safety and Civil Protection, Brussels, Belgium.Google Scholar
  3. 3.
    Dickson KL, Maki AW, Cairns J Jr. 1982. Modelling the Fate of Chemicals in the Aquatic Environment. Ann Arbor Science. Ann Arbor, MI.Google Scholar
  4. 4.
    Neely WB, Blau GE. 1985. Environmental Exposure from Chemicals, Vol. I and II. CRC Press, Boca Raton, FL.Google Scholar
  5. 5.
    Trapp S, Matthies M. 1998. Chemodynamics and Environmental Modeling. An Introduction. Springer, Heidelberg, Germany.Google Scholar
  6. 6.
    Mackay D. 2001. Multimedia Environmental Models. Second Edition. CRC Press LLC, Boca Raton, FL.Google Scholar
  7. 7.
    Organization for Economic Co-operation and Development. 1989. Compendium of environmental exposure assessment methods for chemicals. Environment Monographs 27. OECD, Paris, France.Google Scholar
  8. 8.
    European Centre for Ecotoxicology and Toxicology of Chemicals. 1992. Estimating environmental concentrations of chemicals using fate and exposure models. Technical Report 50. ECETOC, Brussels, Belgium.Google Scholar
  9. 9.
    Gifford FA. 1961. Use of routine meteorological observations for estimating the atmospheric dispersion. Nucl Safety2:47-57.Google Scholar
  10. 10.
    Turner DB. 1970. Workbook of atmospheric dispersions estimates. EPA Ref. AP-26 (NTIS PB 191-482). US Environmental Protection Research, Triangle Park, NC.Google Scholar
  11. 11.
    Green AE, Singhal RP, Venkateswar R. 1980. Analytical extensions of the Gaussian plume model. J Air Pollut Control Assoc30:773-776.Google Scholar
  12. 12.
    Seinfeld H. 1986. Atmospheric Chemistry and Physics of Air Pollution. Wiley, New York, NY.Google Scholar
  13. 13.
    Werkgroep Verspreiding Luchtverontreiniging. 1984. Parameters in het lange-termijn model verspreiding luchtverontreiniging; Nieuwe aanbevelingen. Toegepast Natuurwetenschappelijk Onderzoek (TNO), Delft, The Netherlands [in Dutch].Google Scholar
  14. 14.
    Van Jaarsveld JA. 1990. An operational atmospheric transport model for priority substances; Specification and instructions for use. RIVM Report 222501002. National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.Google Scholar
  15. 15.
    Van Den Hout KD, Van Dop H. 1985. Interregional modelling. In: Zwerver S, Van Ham J, eds., Interregional Air Pollution Modelling. Plenum Press, New York, NY.Google Scholar
  16. 16.
    Szepesi DJ. 1989. Compendium of Regulatory Air Quality Simulation Models. Akademiai Kiado, Budapest, Hungary.Google Scholar
  17. 17.
    Toet C, De Leeuw FAAM. 1992. Risk assessment system for new chemical substances: Implementation of atmospheric transport of organic compounds. RIVM Report 679102008. National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.Google Scholar
  18. 18.
    European Commission. 2003. Technical guidance document in support of Commission Directive 93/67/ EEC on risk assessment for new notified substances, Commission Regulation (EC) no. 1488/94 on Risk Assessment for existing substances and Directive 98/8/ EC of the European Parliament and of the Council concerning the placing of biocidal products on the market. European Chemicals Bureau, Joint Research Centre, Ispra (VA), Italy.Google Scholar
  19. 19.
    US Environmental Protection Agency. 1987. PDM3 Documentation. Exposure Evaluation Division, Office of Toxic Substances, Washington, DC.Google Scholar
  20. 20.
    Rapaport RA. 1988. Prediction of consumer product chemical concentrations as a function of publicly owned treatment type and riverine dilution. Environ Toxicol Chem7:107-115.CrossRefGoogle Scholar
  21. 21.
    De Nijs T, De Greef J. 1992. Ecotoxicological risk evaluation of the cationic fabric softener DTDMAC II. Exposure modelling. Chemosphere24:611-627.CrossRefGoogle Scholar
  22. 22.
    Versteeg DJ, Feijtel TCJ, Cowan CE, Ward TE, Rapaport RA. 1992. An environmental risk assessment for DTDMAC in The Netherlands. Chemosphere24:641- 662.CrossRefGoogle Scholar
  23. 23.
    European Commission. 2004. European Union System for the Evaluation of Substances 2.0 (EUSES 2.0). Prepared for the European Chemicals Bureau by the National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands (RIVM Report 601900005). Available via the European Chemicals Bureau, Ispra, Italy. http://ecb.jrc.it.Google Scholar
  24. 24.
    European Centre for Ecotoxicology and Toxicology of Chemicals. 1994. HAZCHEM, A mathematical model for use in risk assessment of substances. Special report No.8. ECETOC, Brussels, Belgium.Google Scholar
  25. 25.
    Rapaport RJ, Caprara RJ. 1988. PG ROUT: A national surface water quality model. Presented at the 61st Annual Conference of the Water Pollution Control Federation, 2- 6 October 1988, Dallas, TX.Google Scholar
  26. 26.
    Caprara RJ, Rapaport RA. 1991. PG ROUT: A steadystate national water quality model. Proc. Nat. Conf. on Integrated Water Information Management. 4-9 August 1991, Atlantic City, NY, pp. 134-141.Google Scholar
  27. 27.
    Feijtel TCJ, Boeije G, Matthies M, Young A, Morris G, Gandolfi C, Hansen B, Fox K, Holt M, Koch V, Schröder R, Cassani G, Schowanek D, Rosenblom J, Niessen H. 1997. Development of a Geography-referenced Regional Exposure Assessment Tool for European Rivers – GREAT-ER. Chemosphere34:2351-2374.CrossRefGoogle Scholar
  28. 28.
    Matthies M, Berlekamp J, Koormann F, Wagner JO. 2001. Georeferenced regional simulation and aquatic exposure assessment. Wat Sci Technol43(7):231-238.Google Scholar
  29. 29.
    European Centre for Ecotoxicology and Toxicology of Chemicals. 1994. Environmental exposure assessment. Technical Report. 61. ECETOC, Brussels, Belgium.Google Scholar
  30. 30.
    Brock Neely W. 1982. The definition and use of mixing zone. Environ Sci Technol16:519A-5121A.Google Scholar
  31. 31.
    Csanady GT 1973. Turbulent Diffusion in the Environment. Geophysics and Astrophysics Monographs, Vol. 3. D. Reidel Publ. Co., Dordrecht, The Netherlands.Google Scholar
  32. 32.
    Fischer HB, Imberger J, List EJ, Koh RCY, Brooks RH. 1979. Mixing in inland and coastal waters. Academic Press, New York, NY.Google Scholar
  33. 33.
    Noppeney RM. 1988. Gevoeligheidsonderzoek alarmmodel Rijn: De invloedslengte van samenvloeiingen bij dispersie. Mededeling 20. Delft Technical University, 190 Environmental exposure assessment Faculty of Civil Engineering, Delft, The Netherlands [in Dutch].Google Scholar
  34. 34.
    Doneker RL, Jirka GH. 1988. CORMIX1. An expert system for mixing zone analysis of toxic and conventional single port aquatic discharges. DeFrees Hydraulics Laboratory, Dep. Env. Eng., Cornell University, Ithaca, New York, NY.Google Scholar
  35. 35.
    De Greef J, Van De Meent D. 1989. Beoordelingssysteem nieuwe stoffen: Transportroutines, een receptuur voor het schatten van de snelheid van het transport in oppervlaktewater. RIVM Report 958701001. National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands [in Dutch].Google Scholar
  36. 36.
    Jørgensen SE. 1984. Modelling the Fate and Effect of Toxic Substances in the Environment. Developments in Environmental Modelling, 6. Elsevier Sci. Publ., Amsterdam, The Netherlands.Google Scholar
  37. 37.
    Jørgensen SE, Gromiec MJ. 1989. Mathematical Submodels in Water Quality Systems. Developments in Environmental Modelling, 14. Elsevier Sci. Publ., Amsterdam, The Netherlands.Google Scholar
  38. 38.
    Burns LA, Cline DM, Lassiter RR. 1982. Exposure analysis modelling system (EXAMS): User manual and systems documentation. US Environmental Protection Agency, Athens, GA.Google Scholar
  39. 39.
    Burns LA, Cline DM. 1985. Exposure analysis modelling system, Reference manual for EXAMS II. EPA-600/3- 85-038. US Environmental Protection Agency, Athens, GA.Google Scholar
  40. 40.
    Ambrose RB, Wool TA, Connolly JP, Schanz RW. 1988. WASP 4, A hydrodynamic and Water Quality Model. Model Theory, User’s Manual and Programmer’s Guide. EPA/600/3-87/039. U.S. Environmental Protection Agency, Athens, GA.Google Scholar
  41. 41.
    Delft Hydraulics Laboratory. 1990. DELWAQ Version 3.0 User Manual. Delft Hydraulics Laboratory, Delft, The Netherlands.Google Scholar
  42. 42.
    Beck A, Scheringer M, Hungerbühler K. 2000. Fate Modelling within LCA: The Case of Textile Chemicals, Intern J LCA5:335-344.CrossRefGoogle Scholar
  43. 43.
    Struijs J, Stoltenkamp J, Van De Meent D. 1991. A spreadsheet-based model to predict the fate of xenobiotics in a municipal wastewater treatment plant. Wat Res 25:891-900.CrossRefGoogle Scholar
  44. 44.
    Struijs J, Van Den Berg T. 1992. Degradation rates in the environment: Extrapolation of standardized tests. RIVM Report 679102012. National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.Google Scholar
  45. 45.
    International Institute for Applied System Analysis. 1991. Chemical time bombs: Definitions, concepts and examples; Basis document 1. In: Stigliani WM, ed., ER- 91-16. IIASA, Laxenburg, Austria.Google Scholar
  46. 46.
    Stigliani WM, Doelman P, Salomons W, Schulin R, Schmidt GRB, Van Der Zee S. 1991. Chemical time bombs: predicting the unpredictable. Environment 33:4- 9, 26-30.Google Scholar
  47. 47.
    Carsel RF, Smith CN, Mulkey LA, Dean JD, Jowise P. 1984. Users manual for the pesticide root zone model (PRZM). EPA-600/3-84-109. US Environmental Protection Agency, Athens, GA.Google Scholar
  48. 48.
    Carsel RF, Nixon WB, Ballantine LB. 1986. Comparison of pesticide root zone model predictions with observed concentrations for the tobacco pesticide Metalaxyl in unsaturated zone soils. Environ Toxicol Chem5:345-353.CrossRefGoogle Scholar
  49. 49.
    Smith CN, Parrish RS, Brown DS. 1990. Conducting field studies for testing pesticides leaching models. Intern J Environ Anal Chem39:3-21.CrossRefGoogle Scholar
  50. 50.
    Klein M. 1991. Application and validation of pesticide leaching models. Pestic Sci 31:389-398.CrossRefGoogle Scholar
  51. 51.
    Bonazountas M, Wagner J. 1984. SESOIL: A seasonal soil compartment model. Office of Toxic Substances, US Environmental Protection Agency, Washington, DC.Google Scholar
  52. 52.
    Hettrick DM, Travis CC, Leonard SK, Kinerson RS. 1988. Qualitative validation of pollutant transport components of an unsaturated soil zone model. ORNLTM- 10672. Oak Ridge National Laboratory, Oak Ridge, TN.Google Scholar
  53. 53.
    Matthies M, Behrendt H. 1991. Pesticide transport modelling in soil for risk assessment of groundwater contamination. Toxicol Environ Chem31-32:357-365.Google Scholar
  54. 54.
    Van Der Linden AMA, Boesten JJTI. 1989. Berekening van de mate van uitspoeling en accumulatie van bestrijdingsmiddelen als functie van hun sorptiecoëfficiënt en omzettingsnelheid in bouwvoormateriaal. RIVM Report 72800003. National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands [in Dutch].Google Scholar
  55. 55.
    Swartjes FA, Van Der Linden AMA, Van Den Berg R. 1993. Dutch risk assessment system for new chemicals: Soil-groundwater module. RIVM Report 679102015. National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.Google Scholar
  56. 56.
    Mackay D. 1979. Finding fugacity feasible. Environ Sci Technol13:1218-1223.CrossRefGoogle Scholar
  57. 57.
    Mackay D, Paterson S. 1981. Calculating fugacity. Environ Sci Technol15:1006-1014.CrossRefGoogle Scholar
  58. 58.
    Mackay D, Paterson S, Cheung B, Brock Neely W. 1985. Evaluating the environmental behaviour of chemicals with a Level III fugacity model. Chemosphere14:335- 374.CrossRefGoogle Scholar
  59. 59.
    Mackay D, Paterson S, Shiu WY. 1992. Generic models for evaluating the regional fate of chemicals. Chemosphere24:695-717.CrossRefGoogle Scholar
  60. 60.
    Frische R, Klöpffer W, Rippen G, Günther K-L. 1984. The environmental segment approach for estimating potential environmental concentrations. I. The model. Ecotoxicol Environ Saf 8:352-362.CrossRefGoogle Scholar
  61. 61.
    Van De Meent D. 1993. SIMPLEBOX, a generic multimedia fate evaluation Model. RIVM Report 672720001. National Institute for Public Health and the Environment, Bilthoven, The Netherlands.Google Scholar
  62. 62.
    Scheil S, Baumgarten G, Reiter B, Schwartz S, Wagner JO, Matthies M, Trapp S. 1994. CEMO-S: Eine objectorientierte Software zur Expositionsmodellierung. In: Totsche K, Matthies M, ed., Eco-Informa ‘94, Vol. 7. Wien, Austria, pp. 391-404 [in German].Google Scholar
  63. 63.
    McKone TE, Enoch KG. 2002. CalTOXTM , A Multimedia Total Exposure Model. Spreadsheet User’s Guide Version 4.0. Report LBNL-47399. Lawrence Berkeley National Laboratory, Berkeley, CA. http://eetd.lbl.gov/ied/era/.Google Scholar
  64. 64.
    Brandes LJ, den Hollander H, Van de Meent D. 1996. SimpleBox 2.0: a nested multimedia fate model for evaluating the environmental fate of chemicals. RIVM Report 719101029. National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands. http://www/rivm.nl.Google Scholar
  65. 65.
    Den Hollander H A, Van Eijkeren JCH, Van de Meent D. 2004. SimpleBox 3.0: Multimedia Mass Balance Model for Evaluating the Fate of Chemical in the Environment. RIVM Report 601200003/2004. National Institute for Public Health and the Environment (RIVM). Bilthoven, The Netherlands. http://www/rivm.nl, http://ecb.jrc.it.Google Scholar
  66. 66.
    Webster E, Mackay D, Di Guardo A, Kane D, Woodfine D. 2004. Regional differences in chemical fate model outcome. Chemosphere55:1361-1376.CrossRefGoogle Scholar
  67. 67.
    Mackay D, Di Guardo A, Paterson S, Cowan CE. 1996. Evaluating the environmental fate of a variety of types of chemicals using the EQC model. Environ Toxicol Chem 15:1627-1637.CrossRefGoogle Scholar
  68. 68.
    Scheringer M. 1996. Persistence and spatial range as endpoints of an exposure-based assessment of organic chemicals. Environ Sci Technol 30:1652-1659CrossRefGoogle Scholar
  69. 69.
    Beyer A, Matthies M. 2002. Criteria for Atmospheric Long-Range Transport Potential and Persistence of Pesticides and Industrial Chemicals. Umweltbundesamt, ed.. Erich-Schmidt-Verlag, Berlin, Germany.Google Scholar
  70. 70.
    Wania F, Mackay D. 2000. The Global Distribution Model. A Non-Steady-State Multi-Compartmental Mass Balance Model of the Fate of Persistent Organic Pollutants in the Global Environment. Technical Report and Computer Program on CD-ROM. 64.Google Scholar
  71. 71.
    Wegmann F, Möller M, Scheringer M, Hungerbühler K. 2004. Influence of Vegetation on the Environmental Partitioning of DDT in Two Global Multimedia Models. Environ Sci Technol38:1505-1512.CrossRefGoogle Scholar
  72. 72.
    MacLeod M, Woodfine DG, Mackay D, McKone TE, Bennett DH, Maddalena R. 2001. BETRNorth America: A regionally segmented multimedia contaminant fate model for North America. Environ Sci Pollut Res8:156- 163.CrossRefGoogle Scholar
  73. 73.
    Toose L, Woodfine DG, MacLeod M, Mackay D, Gouin J. 2004. BETR-World: a geographically explicit model of chemical fate: application to transport of a-HCH to the Arctic. Environ Pollut 128:223–240.CrossRefGoogle Scholar
  74. 74.
    Pennington DW, Margni M, Ammann C, Jolliet O. 2005. Multimedia fate and human intake modeling: spatial versus nonspatial insights for chemical emissions in Western Europe. Environ Sci Technol 39:1119 -1128.CrossRefGoogle Scholar
  75. 75.
    Gusev A, Mantseva E, Shatalov V, Strukov B. 2005. Regional Multicompartment Model MSCE-POP. EMEP/ MSC-E Technical Report 5/2005. Meteorological Synthesizing Centre - East, Moscow, Russia. www. msceast.org.Google Scholar
  76. 76.
    Mackay D, Diamond M. 1989. Application of the QWASI (quantitative water air sediment interaction) fugacity model to the dynamics of organic and inorganic chemicals in lakes. Chemosphere 18:1343-1365.CrossRefGoogle Scholar
  77. 77.
    Cowan CE, Mackay D, Feijtel TCJ, Van De Meent D, Di Guardo A, Davies J, Mackay N. 1995. The multimedia fate model: A vital tool for predicting the fate of chemicals. SETAC Press, Pensacola, FL.Google Scholar
  78. 78.
    Berding V, Matthies M. 2002. European scenarios for EUSES regional distribution model. Environ Sci Pollut Res 9(3):193-198.CrossRefGoogle Scholar
  79. 79.
    Scheringer M. 2002. Persistence and spatial range of environmental chemicals. Wiley-VCH Verlag, Weinheim, Germany.CrossRefGoogle Scholar
  80. 80.
    UNEP. 2001. Stockholm Convention on Persistent Organic Pollutants; Geneva, Switzerland.Google Scholar
  81. 81.
    United Nations Economic Commission for Europe. 1979. Convention on Long-range Transboundary Air Pollution and its 1998 Protocols on Persistent Organic Pollutants and Heavy Metals. ECE/EB.AIR/66, ISBN 92-1-116724- UNECE, Geneva, Switzerland.Google Scholar
  82. 82.
    Webster E, Mackay D, Wania F. 1998. Evaluating environmental persistence. Environ Toxicol Chem 17:2148-2158.CrossRefGoogle Scholar
  83. 83.
    Van de Meent D, McKone TE, Parkerton T, Matthies M, Scheringer M, Wania F, Purdy R, Bennett D. 2000. Persistence and transport potentials of chemicals in a multi-media environment. In: Klecka G et al, eds. Persistence and long-range transport of chemicals in the environment,pp. 169-204. SETAC Press, Pensacola, FL.Google Scholar
  84. 84.
    Organization for Economic Co-operation and Development. 2004. Guidance document on the use of multimedia models for estimating overall environmental persistence and long-range transport. OECD Environment, Health and Safety Publications, Series on Testing and Assessment 45. OECD, Paris, France.Google Scholar
  85. 85.
    Fenner K, Scheringer M, MacLeod MJ, Matthies M, McKone TE, Stroebe M, Beyer A, Bonnell M, Le Gall AC, Klasmeier J, Mackay D, Van de Meent D, Pennington D, Scharenberg B, Suzuki N, Wania F. 2005. Comparing estimates of persistence and long-range transport potential among multimedia models. Environ Sci Technol 39:1932-1942.CrossRefGoogle Scholar
  86. 86.
    Klasmeier J, Matthies M, MacLeod M, Fenner K, Scheringer M, Stroebe M, LeGall AC, McKone T, Van de Meent D, Wania F. 2006. Application of multimedia models for screening assessment of long-range transport potential and overall persistence. Environ Sci Technol 40:53-60.CrossRefGoogle Scholar
  87. 87.
    Organization for Economic Co-operation and Development. 2007. (Q)SAR Application Toolbox. OECD, Paris, France. www.oecd.org.Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • D. Van De Meent
  • J.H.M. De Bruijn

There are no affiliations available

Personalised recommendations