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Principles of Good Practice for the Use of Monte Carlo Techniques in Human Health and Ecological Risk Assessments

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  • David E. Burmaster
  • Paul D. Anderson

Abstract

We propose 14 principles of good practice to assist people in performing and reviewing probabilistic or Monte Carlo risk assessments, especially in the context of the federal and state statutes concerning chemicals in the environment. Monte Carlo risk assessments for hazardous waste sites that follow these principles will be easier to understand, will explicitly distinguish assumptions from data, and will consider and quantify effects that could otherwise lead to misinterpretation of the results. The proposed principles are neither mutually exclusive nor collectively exhaustive. We think and hope that these principles will evolve as new ideas arise and come into practice.

Suggested Citation

  • David E. Burmaster & Paul D. Anderson, 1994. "Principles of Good Practice for the Use of Monte Carlo Techniques in Human Health and Ecological Risk Assessments," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 477-481, August.
    • Handle: RePEc:wly:riskan:v:14:y:1994:i:4:p:477-481
    DOI: 10.1111/j.1539-6924.1994.tb00265.x
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    1. Andrew E. Smith & P. Barry Ryan & John S. Evans, 1992. "The Effect of Neglecting Correlations When Propagating Uncertainty and Estimating the Population Distribution of Risk," Risk Analysis, John Wiley & Sons, vol. 12(4), pages 467-474, December.
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    3. Olivier Chanel, 2022. "Impact of COVID‑19 Activity Restrictions on Air Pollution: Methodological Considerations in the Economic Valuation of the Long‑Term Effects on Mortality [Impact sur la pollution de l’air des restri," Working Papers hal-03778336, HAL.
    4. Charles N. Haas, 1997. "Importance of Distributional Form in Characterizing Inputs to Monte Carlo Risk Assessments," Risk Analysis, John Wiley & Sons, vol. 17(1), pages 107-113, February.
    5. Michael Greenberg & Charles Haas & Anthony Cox & Karen Lowrie & Katherine McComas & Warner North, 2012. "Ten Most Important Accomplishments in Risk Analysis, 1980–2010," Risk Analysis, John Wiley & Sons, vol. 32(5), pages 771-781, May.
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    7. Martí Nadal & Vikas Kumar & Marta Schuhmacher & José L. Domingo, 2008. "Applicability of a Neuroprobabilistic Integral Risk Index for the Environmental Management of Polluted Areas: A Case Study," Risk Analysis, John Wiley & Sons, vol. 28(2), pages 271-286, April.
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    9. Janssen, Hans, 2013. "Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 123-132.
    10. Olivier Chanel, 2022. "Impact of COVID-19 Activity Restrictions on Air Pollution: Methodological Considerations in the Economic Valuation of the Long-Term Effects on Mortality," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 534-35, pages 103-118.
    11. Myung-Hun Kim & Eul-Bum Lee, 2019. "A Forecast Model for the Level of Engineering Maturity Impact on Contractor’s Procurement and Construction Costs for Offshore EPC Megaprojects," Energies, MDPI, vol. 12(12), pages 1-18, June.
    12. Robert T. Clemen & Terence Reilly, 1999. "Correlations and Copulas for Decision and Risk Analysis," Management Science, INFORMS, vol. 45(2), pages 208-224, February.
    13. Timothy M. Barry, 1996. "Recommendations on the Testing and Use of Pseudo‐Random Number Generators Used in Monte Carlo Analysis for Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 16(1), pages 93-105, February.
    14. Ning Qin & Ayibota Tuerxunbieke & Qin Wang & Xing Chen & Rong Hou & Xiangyu Xu & Yunwei Liu & Dongqun Xu & Shu Tao & Xiaoli Duan, 2021. "Key Factors for Improving the Carcinogenic Risk Assessment of PAH Inhalation Exposure by Monte Carlo Simulation," IJERPH, MDPI, vol. 18(21), pages 1-14, October.
    15. Maria F. Poças & Jorge C. Oliveira & Rainer Brandsch & Timothy Hogg, 2010. "Feasibility Study on the Use of Probabilistic Migration Modeling in Support of Exposure Assessment from Food Contact Materials," Risk Analysis, John Wiley & Sons, vol. 30(7), pages 1052-1061, July.
    16. Harry M. Marks & Margaret E. Coleman & C.‐T. Jordan Lin & Tanya Roberts, 1998. "Topics in Microbial Risk Assessment: Dynamic Flow Tree Process," Risk Analysis, John Wiley & Sons, vol. 18(3), pages 309-328, June.
    17. Myung-Hun Kim & Eul-Bum Lee & Han-Suk Choi, 2019. "A Forecast and Mitigation Model of Construction Performance by Assessing Detailed Engineering Maturity at Key Milestones for Offshore EPC Mega-Projects," Sustainability, MDPI, vol. 11(5), pages 1-21, February.
    18. K. D. M. Pintar & A. Fazil & F. Pollari & D. F. Charron & D. Waltner‐Toews & S. A. McEwen, 2010. "A Risk Assessment Model to Evaluate the Role of Fecal Contamination in Recreational Water on the Incidence of Cryptosporidiosis at the Community Level in Ontario," Risk Analysis, John Wiley & Sons, vol. 30(1), pages 49-64, January.
    19. Charles N. Haas, 1999. "On Modeling Correlated Random Variables in Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 19(6), pages 1205-1214, December.

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