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Separation of uncertainty and interindividual variability in human exposure modeling

Journal of Exposure Science & Environmental Epidemiology volume 19, pages 201–212 (2009)Cite this article

Abstract

The NORMTOX model predicts the lifetime-averaged exposure to contaminants through multiple environmental media, that is, food, air, soil, drinking and surface water. The model was developed to test the coherence of Dutch environmental quality objectives (EQOs). A set of EQOs is called coherent if simultaneous exposure to different environmental media that are all polluted up to their respective EQOs does not result in exceeding the acceptable or tolerable daily intake (ADI or TDI). Aim of the present study is to separate the impact of uncertainty and interindividual variability in coherence predictions with the NORMTOX model. The method is illustrated in a case study for chlorfenvinphos, mercury and nitrate. First, ANOVA was used to calculate interindividual variability in input parameters. Second, nested Monte Carlo simulation was used to propagate uncertainty and interindividual variability separately. Lifetime-averaged exposure to chlorfenvinphos, mercury and nitrate was modeled for the Dutch population. Output distributions specified the population fraction at risk, due to a particular exposure, and the reliability of this risk. From the case study, it was obtained that at lifelong exposure to all media polluted up to their standard, 100% of the Dutch population exceeds the ADI for chlorfenvinphos, 15% for mercury and 0% for nitrate. Variance in exposure to chlorfenvinphos, mercury and nitrate is mostly caused by interindividual variability instead of true uncertainty. It is concluded that the likelihood that ADIs of chlorfenvinphos and mercury will be exceeded should be further explored. If exceeding is likely, decision makers should focus on identification of high-risk subpopulations, rather than on additional research to obtain more accurate estimates for particular parameters.

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References

  • Berthouex P.M., and Brown L.C. Statistics for Environmental Engineers. CRC Press LLC, Florida, 1994.

    Google Scholar 

  • BIBRA. A Critical Review of the Methodology for the Estimation of Dietary Intake of Food Chemicals in the UK. MAFF Report 1A005, UK, 1995.

  • Bogen K.T., and Spear R.C. Integrating uncertainty and interindividual variability in environmental risk assessment. Risk Anal 1987: 7 (4): 427–436.

    Article  CAS  PubMed  Google Scholar 

  • Breuninger M.I., Schachtel G.A., Lührmann P.M., Hartmann B., and Neuhäuser-Berthold M. Complex correlation structure in consumption rates of major food groups: implications for the assessment of radiation exposure. Public Health Nutr 2006: 6 (1): 87–94.

    Article  Google Scholar 

  • Buck R.J., Hammerstrom K.A., and Ryan P.B. Bias in population estimates of long-term exposure from short-term measurements of individual exposure. Risk Anal 1997: 17 (4): 455–466.

    Article  Google Scholar 

  • Buck R.J., Ozkaynak H., Xue J.P., Zartarian V.G., and Hammerstrom K. Modeled estimates of chlorpyrifos exposure and dose for the Minnesota and Arizona NHEXAS populations. J Expo Sci Environ Epidemiol 2001: 11 (3): 253–268.

    Article  CAS  Google Scholar 

  • Bukowski J., Korn L., and Wartenberg D. Correlated inputs in quantitative risk assessment: The effects of distributional shape. Risk Anal 1995: 15 (2): 215–219.

    Article  Google Scholar 

  • Burmaster D.E., and Wilson A.M. An introduction to second-order random variables in human health risk assessments. Human Ecol Risk Assess 1996: 2 (4): 892–919.

    Article  Google Scholar 

  • Chavarri M.J., Herrera A., and Arino A. The decrease in pesticides in fruit and vegetables during commercial processing. Int J Food Sci Technol 2005: 40 (2): 205–211.

    Article  CAS  Google Scholar 

  • Cohen J.T., Lampson M.A., and Bowers T.S. The use of two-stage Monte Carlo simulation techniques to characterize variability and uncertainty in risk analysis. Human Ecol Risk Assess 1996: 2 (4): 939–971.

    Article  Google Scholar 

  • Cullen A. Comparison of measured and predicted environmental PCB concentrations using simple compartmental models. Environ Sci Technol 2002: 36 (9): 2033–2038.

    Article  CAS  PubMed  Google Scholar 

  • Cullen A.C., and Frey H.C. Probabilistic Techniques in Exposure Assessment, A Handbook for Dealing with Variability and Uncertainty in Models and Inputs. Society for Risk Analysis, Plenum Press, New York, 1999.

    Google Scholar 

  • Decisioneering Inc.. Crystal Ball 2000, User Manual. Colarado, USA, 2000.

  • DEFRA (Department for Environment, Food and Rural Affairs). Contaminated Land Exposure Assessment (CLEA). Available at: www.environment-agency.gov.uk/subjects/landquality/ accessed November 2 2006.

  • Dejonckheere W., Steurbaut W., Drieghe S., Verstraeten R., and Braeckman H. Pesticide residue concentrations in the Belgian total diet, 1991–1993. J AOAC Int 1996: 79 (2): 520–528.

    CAS  PubMed  Google Scholar 

  • Ferson S. What Monte Carlo methods cannot do. Human Ecol Risk Assess 1996: 2 (4): 990–1007.

    Article  Google Scholar 

  • Frey H.C. Quantitative Analysis of Uncertainty and Variability in Environmental Policy Making. AAAS/EPA, Research Associate Center for Energy and Environmental Studies, Pittsburgh, USA, 1992.

    Google Scholar 

  • Frey H.C., and Burmaster D.E. Methods for characterizing variability and uncertainty: comparison of bootstrap simulation and likelihood-based approaches. Risk Anal 1999: 19 (1): 109–130.

    Google Scholar 

  • Fryer M., Collins C.D., Ferrier H., Colvile R.N., and Nieuwenhuijsen M.J. Human exposure modeling for chemical risk assessment: a review of current approaches and research and policy implications. Environ Sci Policy 2006: 9: 261–274.

    Article  Google Scholar 

  • Gaddum J.H. Lognormal distributions. Nature 1945: 156: 463–466.

    Article  Google Scholar 

  • Haas C.N. On modeling correlated random variables in risk assessment. Risk Anal 1999: 19 (6): 1205–1214.

    CAS  PubMed  Google Scholar 

  • Hammersley J.S., and Handscomb D.C. Monte Carlo Methods. John Wiley and Sons Inc., New York, 1964.

    Book  Google Scholar 

  • Hoffman F.O., and Hammonds J.S. Propagation of uncertainty in risk assessments: the need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability. Risk Anal 1994: 14: 707–712.

    Article  CAS  PubMed  Google Scholar 

  • Hulshof K.F.A.M., Kistemaker C., and Bouman M. De consumptie van groepen voedingsmiddelen door Nederlandse bevolkingsgroepen, Voedselconsumptiepeiling 1997–1998. TNO-report v98.804, Netherlands Organisation for Applied Scientific Research TNO, Zeist, The Netherlands (in Dutch), 1998.

    Google Scholar 

  • Kelly E.J., and Campbell K. Separating variability and uncertainty in environmental risk assessment—Making choices. Human Ecol Risk Assess 2000: 6 (1): 1–13.

    Article  Google Scholar 

  • Kim H., and Weisel C.P. Dermal absorption of dichloro- and trichloroacetic acids from chlorinated water. J Expo Sci Environ Epidemiol 1998: 8 (4): 555–575.

    CAS  Google Scholar 

  • Lessmann K., Beyer A., Klasmeier J., and Matthies M. Influence of distributional shape of substance parameters on exposure model output. Risk Anal 2005: 25 (5): 1137–1145.

    Article  PubMed  Google Scholar 

  • MAFF (Ministry of Agriculture, Fisheries and Food). Consumer exposure Model: User Manual. Pesticide Safety Directorate, UK, 1996.

  • McKone T.E., and Ryan P.B. Human exposures to chemicals through food chains: an uncertainty analysis. Environ Sci Technol 1989: 23: 1154–1163.

    Article  CAS  Google Scholar 

  • Mokhtari A., and Frey H.C. Sensitivity analysis of a two-dimensional probabilistic risk assessment model using analysis of variance. Risk Anal 2005a: 25 (6): 1511–1529.

    Article  PubMed  Google Scholar 

  • Mokhtari A., and Frey H.C. Recommended practice regarding selection of sensitivity analysis methods applied to microbial food safety process risk models. Human Ecol Risk Assess 2005b: 11 (3): 591–605.

    Article  Google Scholar 

  • Mokhtari A., and Frey H.C. Evaluation of sampling-based methods for sensitivity analysis: case study for the E. Coli food safety process risk model. Human Ecol Risk Assess 2006: 12 (6): 1128–1152.

    Article  Google Scholar 

  • Morgan M.G., and Henrion M. Uncertainty: a Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. Cambridge University Press, UK, 1990.

    Book  Google Scholar 

  • Nauta M.J. Separation of uncertainty and variability in quantitative microbial risk assessment models. Int J Food Microbiol 2000: 57: 9–18.

    Article  Google Scholar 

  • NRC (National Research Council). Reactor Safety: an Assessment of Accident Risks in the U.S. Commercial Nuclear Power Plants. Nuclear Regulatory Commission, NUREG-75-014 (WASH-1400), Washington, DC, USA, 1975.

  • NRC (National Research Council). Science and Judgment in Risk Assessment. National Academy Press, Washington, DC, USA, 1994.

  • OSACDTSC (Office of Scientific Affairs, California Department of Toxic Substances Control). Caltox, a Multimedia Total Exposure Model for Hazardous-Waste Sites. Part I. Executive Summary. California Environmental Protection Agency, Sacramento, USA, 1993.

  • Price P.S., Su S., Harrington J., and Keenan R. Uncertainty and variation in indirect exposure assessment: an analysis of exposure to tetrachlorodibenzo-p-dioxin from a beef consumption pathway. Risk Anal 1996: 16: 263–277.

    Article  CAS  PubMed  Google Scholar 

  • Price P.S., Young J.S., and Chaisson C.F. Assessing aggregate and cumulative pesticide risks using a probabilistic model. Ann Occup Hyg 2001: 45: S131–S142.

    Article  CAS  PubMed  Google Scholar 

  • PSD (Pesticides Safety Directorate). Operator Exposure Models on the PSD Website, Available: http://www.pesticides.gov.uk/, accessed at 2 November 2006, 2003.

  • Ragas A.M.J., and Huijbregts M.A.J. Evaluating the coherence between environmental quality objectives and the acceptable or tolerable daily intake. Regul Toxicol Pharmacol 1998: 27: 251–264.

    Article  CAS  PubMed  Google Scholar 

  • RATSC (Risk Assessment and Toxicology Steering Committee). Risk Assessment Approaches Used by UK Government for Evaluating Human Health Effects of Chemicals. Institute for Environment and Health, University of Leicester, UK, 1999.

  • Roseberry A.M., and Burmaster D.E. Lognormal distributions for water intake by children and adults. Risk Anal 1992: 12 (1): 99–104.

    Article  CAS  PubMed  Google Scholar 

  • Ruffle B., Burmaster D.E., Anderson P.D., and Gordon H.D. Lognormal distributions for fish consumption by the general U.S. population. Risk Anal 1994: 14 (4): 395–404.

    Article  Google Scholar 

  • Saltelli A., Tarantola S., Campolongo F., and Ratto M. Sensitivity Analysis in Practice. A Guide to Assessing Scientific Models. John Wiley & Sons publishers, Chichester, UK, 2004.

    Google Scholar 

  • Slob W. Modeling long-term exposure of the whole population to chemicals in food. Risk Anal 1993: 13: 525–530.

    Article  CAS  PubMed  Google Scholar 

  • Slob W. Probabilistic dietary exposure assessment taking into account variability in both amount and frequency of consumption. Food Chem Toxicol 2006: 44: 933–951.

    Article  CAS  PubMed  Google Scholar 

  • Slob W., and Bakker M.I. Probabilistische berekening van inname van stoffen via incidenteel geconsumeerde voedingsproducten. Report 320103003/2004. National Institute of Public Health and the Environment, Bilthoven, The Netherlands (in Dutch), 2004.

    Google Scholar 

  • Smith A.E., Ryan P.B., and Evans J.S. The effect of neglecting correlations when propagating uncertainty and estimating the population distribution of risk. Risk Anal 1992: 12: 467–474.

    Article  CAS  PubMed  Google Scholar 

  • Stanek E.J., Calabrese E.J., and Xu L. A caution for Monte Carlo risk assessment of long term exposures based on short term exposure study data. Human Ecol Risk Assess 1998: 4 (2): 409–422.

    Article  CAS  Google Scholar 

  • U.S. EPA. Exposure Factors Handbook, Volume III, Activity Factors. EPA/600/p-95/002fc. U.S. EPA, Office of Research and Development, Washington, DC, 1997.

  • Van Veen M.P. CONSEXPO: a program to estimate Consumer Product Exposure and Uptake. Report 612810 002, National Institute of Public Health and the Environment, Bilthoven, The Netherlands, 1995.

    Google Scholar 

  • Von Stackelberg K.E., Burmistrov D., Vorhees D.J., Bridges T.S., and Linkov I. Importance of uncertainty and variability to predicted risks from trophic transfer of PCBs in dredged sediments. Risk Anal 2002: 22 (3): 499–512.

    Article  PubMed  Google Scholar 

  • Wallace L., and Williams R. Validation of a method for estimating long-term exposures based on short-term measurements. Risk Anal 2005: 25 (3): 687–694.

    Article  PubMed  Google Scholar 

  • WHO (World Health Organization). Human Exposure Assessment. Environmental Health Criteria 214. International Program on Chemical Safety (IPCS), Geneva, Switzerland, 2000.

  • Wilson N.D., Shear N.M., Paustenbach D.J., and Price P.S. The effect of cooking practices on the concentration of DDT and PCB compounds in the edible tissue of fish. J Expo Sci Environ Epidemiol 1998: 8 (3): 423–440.

    CAS  Google Scholar 

  • Wu F.C., and Tsang Y.P. Second-order Monte Carlo uncertainty/variability analysis using correlated model parameters: application to salmonid embryo survival risk assessment. Ecol Modell 2004: 177: 393–414.

    Article  Google Scholar 

  • Yokota F., Gray G., Hammitt J.K., and Thompson K.M. Tiered chemical testing: a value of information approach. Risk Anal 2004: 24 (6): 1625–1639.

    Article  PubMed  Google Scholar 

  • Zar J.H. Biostatistical Analysis. 4th edn. Prentice-Hall, Upper Saddle River, NJ, 1999.

    Google Scholar 

  • Zatarian V.G., Ozkaynak H., Burke J.M., Zufall M.J., Rigas M.L., and Furtaw Jr E.J. A modeling framework for estimating children's residential exposure and dose to chlorpyrifos via dermal residue contact and nondietary ingestion. Environ Health Perspect 2000: 108 (6): 505–514.

    Article  Google Scholar 

  • Zheng J., and Frey H.C. Quantitative analysis of variability and uncertainty with known measurement error: methodology and case study. Risk Anal 2005: 25 (3): 663–675.

    Article  PubMed  Google Scholar 

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Acknowledgements

This research was financially supported by the European Union (European Commission, FP6 project NoMiracle, Contract No. 003956). We thank the two anonymous reviewers for their valuable suggestions, which significantly improved the paper.

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Authors and Affiliations

  1. Department of Environmental Science, Radboud University Nijmegen, Nijmegen, The Netherlands

    Ad M J Ragas, Femke P E Brouwer, Frederike L Büchner & Mark A J Huijbregts

  2. Department of Mathematics, Radboud University Nijmegen, Nijmegen, The Netherlands

    Harrie W M Hendriks

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  1. Ad M J Ragas
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Correspondence to Ad M J Ragas.

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Supplementary information accompanies the paper on the Journal of Exposure Analysis and Environmental Epidemiology website (http://www.nature.com/jes)

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Ragas, A., Brouwer, F., Büchner, F. et al. Separation of uncertainty and interindividual variability in human exposure modeling. J Expo Sci Environ Epidemiol 19, 201–212 (2009). https://doi.org/10.1038/jes.2008.13

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