IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v120y2013icp98-105.html
   My bibliography  Save this article

Uncertainty propagation in a model for the estimation of the ground level concentration of dioxin/furans emitted from a waste gasification plant

Author

Listed:
  • Ripamonti, G.
  • Lonati, G.
  • Baraldi, P.
  • Cadini, F.
  • Zio, E.

Abstract

In this paper we compare two approaches for uncertainty propagation in a model for Environmental Impact Assessment (EIA). A purely Probabilistic (PMC) and a Hybrid probabilistic–possibilistic Monte Carlo (HMC) method are considered in their application for the estimation of the ground level concentration of dioxin/furans emitted from a waste gasification plant. Under the condition of insufficient information for calibrating the estimation model parameters, HMC is shown to be a valid way for properly propagating parameters uncertainty to the model output, without adopting arbitrary and subjective assumptions on the input probability distribution functions. In this sense, HMC could improve the transparency of the EIA procedures with positive effects on the communicability and credibility of its findings.

Suggested Citation

  • Ripamonti, G. & Lonati, G. & Baraldi, P. & Cadini, F. & Zio, E., 2013. "Uncertainty propagation in a model for the estimation of the ground level concentration of dioxin/furans emitted from a waste gasification plant," Reliability Engineering and System Safety, Elsevier, vol. 120(C), pages 98-105.
    • Handle: RePEc:eee:reensy:v:120:y:2013:i:c:p:98-105
    DOI: 10.1016/j.ress.2013.05.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832013001439
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2013.05.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dubois, Didier, 2006. "Possibility theory and statistical reasoning," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 47-69, November.
    2. Baudrit, C. & Dubois, D., 2006. "Practical representations of incomplete probabilistic knowledge," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 86-108, November.
    3. Aven, Terje & Zio, Enrico, 2011. "Some considerations on the treatment of uncertainties in risk assessment for practical decision making," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 64-74.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Molin Sun & Zhongyi Zheng & Longhui Gang, 2018. "Uncertainty Analysis of the Estimated Risk in Formal Safety Assessment," Sustainability, MDPI, vol. 10(2), pages 1-16, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aven, T., 2011. "Interpretations of alternative uncertainty representations in a reliability and risk analysis context," Reliability Engineering and System Safety, Elsevier, vol. 96(3), pages 353-360.
    2. Li, Yanfu & Zio, Enrico, 2012. "Uncertainty analysis of the adequacy assessment model of a distributed generation system," Renewable Energy, Elsevier, vol. 41(C), pages 235-244.
    3. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    4. Nicola Pedroni & Enrico Zio, 2013. "Uncertainty Analysis in Fault Tree Models with Dependent Basic Events," Risk Analysis, John Wiley & Sons, vol. 33(6), pages 1146-1173, June.
    5. Yu, Xuchao & Liang, Wei & Zhang, Laibin & Reniers, Genserik & Lu, Linlin, 2018. "Risk assessment of the maintenance process for onshore oil and gas transmission pipelines under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 50-67.
    6. Coppi, Renato & Gil, Maria A. & Kiers, Henk A.L., 2006. "The fuzzy approach to statistical analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 1-14, November.
    7. Aven, Terje, 2012. "The risk concept—historical and recent development trends," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 33-44.
    8. Milazzo, Maria Francesca & Aven, Terje, 2012. "An extended risk assessment approach for chemical plants applied to a study related to pipe ruptures," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 183-192.
    9. Luciano Stefanini & Maria Letizia Guerra, 2016. "On Possibilistic Representations of Fuzzy Intervals," Working Papers 1602, University of Urbino Carlo Bo, Department of Economics, Society & Politics - Scientific Committee - L. Stefanini & G. Travaglini, revised 2016.
    10. Hu, Lunhu & Kang, Rui & Pan, Xing & Zuo, Dujun, 2020. "Risk assessment of uncertain random system—Level-1 and level-2 joint propagation of uncertainty and probability in fault tree analysis," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    11. Nicola Pedroni & Enrico Zio & Alberto Pasanisi & Mathieu Couplet, 2017. "A critical discussion and practical recommendations on some issues relevant to the non-probabilistic treatment of uncertainty in engineering risk assessment," Post-Print hal-01652230, HAL.
    12. Montes, Ignacio & Miranda, Enrique & Montes, Susana, 2014. "Stochastic dominance with imprecise information," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 868-886.
    13. Nicola Pedroni & Enrico Zio & Alberto Pasanisi & Mathieu Couplet, 2017. "A Critical Discussion and Practical Recommendations on Some Issues Relevant to the Nonprobabilistic Treatment of Uncertainty in Engineering Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 37(7), pages 1315-1340, July.
    14. Aven, Terje & Guikema, Seth, 2011. "Whose uncertainty assessments (probability distributions) does a risk assessment report: the analysts' or the experts'?," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1257-1262.
    15. Francis, Royce & Bekera, Behailu, 2014. "A metric and frameworks for resilience analysis of engineered and infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 90-103.
    16. Bjørnsen, Kjartan & Selvik, Jon Tømmerås & Aven, Terje, 2019. "A semi-quantitative assessment process for improved use of the expected value of information measure in safety management," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 494-502.
    17. Ibsen Chivatá Cárdenas & Saad S.H. Al‐Jibouri & Johannes I.M. Halman & Frits A. van Tol, 2014. "Modeling Risk‐Related Knowledge in Tunneling Projects," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 323-339, February.
    18. Gia Sirbiladze & Irina Khutsishvili & Otar Badagadze & Mikheil Kapanadze, 2016. "More Precise Decision-Making Methodology in the Temporalized Body of Evidence. Application in the Information Technology Management," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1469-1502, November.
    19. Bing Wu & Huibin Tian & Xinping Yan & C. Guedes Soares, 2020. "A probabilistic consequence estimation model for collision accidents in the downstream of Yangtze River using Bayesian Networks," Journal of Risk and Reliability, , vol. 234(2), pages 422-436, April.
    20. Baraldi, Piero & Podofillini, Luca & Mkrtchyan, Lusine & Zio, Enrico & Dang, Vinh N., 2015. "Comparing the treatment of uncertainty in Bayesian networks and fuzzy expert systems used for a human reliability analysis application," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 176-193.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:120:y:2013:i:c:p:98-105. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.