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Uncertainty and sensitive analysis of environmental model for risk assessments: An industrial case study

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  • Carlos García-Díaz, J.
  • Gozalvez-Zafrilla, J.M.

Abstract

The objectives of this paper are the application of uncertainty and sensitivity analysis methods in atmospheric dispersion modeling to study the prediction of the dispersion of pollutants in the atmosphere. The Gaussian Plume Model is used to study the impact of meteorology on the dispersion of the emissions from an industrial source complex. The determination of ground-level concentration and maximum ground-level concentration is useful for the prediction of violations of air quality regulations. The Industrial Source Complex Short-Term (ISCST-3) air pollution model was adopted to predict the ground-level concentration of sulfur dioxide (SO2) emitted by a power plant located in an industrial region site in Spain. Quantitative uncertainty analysis has become a common component of risk assessments. Uncertainties were defined a priori for each of the following variables: wind speed, wind direction, and pollutant emission rate. In order to obtain information about the uncertainty of computer code results, a number of code runs was performed using the nonparametric tolerance limits method. The Monte Carlo method was used to propagate uncertainty across codes. The Spearman rank correlation coefficient was used as a sensitivity measure.

Suggested Citation

  • Carlos García-Díaz, J. & Gozalvez-Zafrilla, J.M., 2012. "Uncertainty and sensitive analysis of environmental model for risk assessments: An industrial case study," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 16-22.
    • Handle: RePEc:eee:reensy:v:107:y:2012:i:c:p:16-22
    DOI: 10.1016/j.ress.2011.04.004
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    References listed on IDEAS

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    1. Martorell, S. & Sanchez, A. & Carlos, S., 2007. "A tolerance interval based approach to address uncertainty for RAMS+C optimization," Reliability Engineering and System Safety, Elsevier, vol. 92(4), pages 408-422.
    2. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
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    Cited by:

    1. XiaoFei, Lu & Min, Liu, 2014. "Hazard rate function in dynamic environment," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 50-60.

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