Sensitivity studies of pollutant concentrations calculated by the UNI-DEM with respect to the input emissions

Ivan Dimov; Raya Georgieva; Tzvetan Ostromsky; Zahari Zlatev

Open Mathematics (2013)

  • Volume: 11, Issue: 8, page 1531-1545
  • ISSN: 2391-5455

Abstract

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The influence of emission levels on the concentrations of four important air pollutants (ammonia, ozone, ammonium sulphate and ammonium nitrate) over three European cities (Milan, Manchester, and Edinburgh) with different geographical locations is considered. Sensitivity analysis of the output of the Unified Danish Eulerian Model according to emission levels is provided. The Sobol’ variance-based approach for global sensitivity analysis has been applied to compute the corresponding sensitivity measures. To measure the influence of the variation of emission levels over the pollutants concentrations the Sobol’ global sensitivity indices are estimated using efficient techniques for small sensitivity indices to avoid the effect of loss of accuracy. Theoretical studies, as well as, practical computations are performed in order to analyze efficiency of various variance reduction techniques for computing small indices. The importance of accurate estimation of small sensitivity indices is analyzed. It is shown that the correlated sampling technique for small sensitivity indices gives reliable results for the full set of indices. Its superior efficiency is studied in details.

How to cite

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Ivan Dimov, et al. "Sensitivity studies of pollutant concentrations calculated by the UNI-DEM with respect to the input emissions." Open Mathematics 11.8 (2013): 1531-1545. <http://eudml.org/doc/269760>.

@article{IvanDimov2013,
abstract = {The influence of emission levels on the concentrations of four important air pollutants (ammonia, ozone, ammonium sulphate and ammonium nitrate) over three European cities (Milan, Manchester, and Edinburgh) with different geographical locations is considered. Sensitivity analysis of the output of the Unified Danish Eulerian Model according to emission levels is provided. The Sobol’ variance-based approach for global sensitivity analysis has been applied to compute the corresponding sensitivity measures. To measure the influence of the variation of emission levels over the pollutants concentrations the Sobol’ global sensitivity indices are estimated using efficient techniques for small sensitivity indices to avoid the effect of loss of accuracy. Theoretical studies, as well as, practical computations are performed in order to analyze efficiency of various variance reduction techniques for computing small indices. The importance of accurate estimation of small sensitivity indices is analyzed. It is shown that the correlated sampling technique for small sensitivity indices gives reliable results for the full set of indices. Its superior efficiency is studied in details.},
author = {Ivan Dimov, Raya Georgieva, Tzvetan Ostromsky, Zahari Zlatev},
journal = {Open Mathematics},
keywords = {Sobol’ global variance-based sensitivity analysis; Monte Carlo approaches for small sensitivity indices; Remote transport of air pollutants; Multidimensional numerical integration; Sobol' global variance-based sensitivity analysis; remote transport of air pollutants; multidimensional numerical integration; Unified Danish Eulerian Model (UNI-DEM)},
language = {eng},
number = {8},
pages = {1531-1545},
title = {Sensitivity studies of pollutant concentrations calculated by the UNI-DEM with respect to the input emissions},
url = {http://eudml.org/doc/269760},
volume = {11},
year = {2013},
}

TY - JOUR
AU - Ivan Dimov
AU - Raya Georgieva
AU - Tzvetan Ostromsky
AU - Zahari Zlatev
TI - Sensitivity studies of pollutant concentrations calculated by the UNI-DEM with respect to the input emissions
JO - Open Mathematics
PY - 2013
VL - 11
IS - 8
SP - 1531
EP - 1545
AB - The influence of emission levels on the concentrations of four important air pollutants (ammonia, ozone, ammonium sulphate and ammonium nitrate) over three European cities (Milan, Manchester, and Edinburgh) with different geographical locations is considered. Sensitivity analysis of the output of the Unified Danish Eulerian Model according to emission levels is provided. The Sobol’ variance-based approach for global sensitivity analysis has been applied to compute the corresponding sensitivity measures. To measure the influence of the variation of emission levels over the pollutants concentrations the Sobol’ global sensitivity indices are estimated using efficient techniques for small sensitivity indices to avoid the effect of loss of accuracy. Theoretical studies, as well as, practical computations are performed in order to analyze efficiency of various variance reduction techniques for computing small indices. The importance of accurate estimation of small sensitivity indices is analyzed. It is shown that the correlated sampling technique for small sensitivity indices gives reliable results for the full set of indices. Its superior efficiency is studied in details.
LA - eng
KW - Sobol’ global variance-based sensitivity analysis; Monte Carlo approaches for small sensitivity indices; Remote transport of air pollutants; Multidimensional numerical integration; Sobol' global variance-based sensitivity analysis; remote transport of air pollutants; multidimensional numerical integration; Unified Danish Eulerian Model (UNI-DEM)
UR - http://eudml.org/doc/269760
ER -

References

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  1. [1] Borgonovo E., A new uncertainty importance measure, Reliability Engineering & System Safety, 2007, 92(6), 771–784 http://dx.doi.org/10.1016/j.ress.2006.04.015 
  2. [2] Dimov I.T., Monte Carlo Methods for Applied Scientists, World Scientific, Hackensack, 2008 Zbl1140.65008
  3. [3] Dimov I., Faragó I., Havasi Á., Zlatev Z., Operator splitting and commutativity analysis in the Danish Eulerian Model, Math. Comput. Simulation, 2004, 67(3), 217–233 http://dx.doi.org/10.1016/j.matcom.2004.06.017 Zbl1063.92051
  4. [4] Dimov I., Georgieva R., Ivanovska S., Ostromsky Tz., Zlatev Z., Studying the sensitivity of pollutants’ concentrations caused by variations of chemical rates, J. Comput. Appl. Math., 2010, 235(2), 391–402 http://dx.doi.org/10.1016/j.cam.2010.05.041 Zbl1196.92039
  5. [5] Dimov I.T., Ostromsky Tz., Zlatev Z., Challenges in using splitting techniques for large-scale environmental modeling, In: Advances in Air Pollution Modeling for Environmental Security, Borovetz, 8–12 May, 2004, NATO Sci. Ser. IV, 54, Springer, Dordrecht, 2005, 115–132 http://dx.doi.org/10.1007/1-4020-3351-6_11 
  6. [6] Efron B., Stein C., The jackknife estimate of variance, Ann. Statist., 1981, 9(3), 586–596 http://dx.doi.org/10.1214/aos/1176345462 Zbl0481.62035
  7. [7] Helton J.C., Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal, Reliability Engineering & System Safety, 1993, 42(2–3), 327–367 http://dx.doi.org/10.1016/0951-8320(93)90097-I 
  8. [8] Hesstvedt E., Hov Ø., Isaksen I.S.A., Quasi-steady-state approximations in air pollution modeling: Comparison of two numerical schemes for oxidant prediction, International Journal of Chemical Kinetics, 1978, 10(9), 971–994 http://dx.doi.org/10.1002/kin.550100907 
  9. [9] Homma T., Saltelli A., Importance measures in global sensitivity analysis of nonlinear models, Reliability Engineering & System Safety, 1996, 52(1), 1–17 http://dx.doi.org/10.1016/0951-8320(96)00002-6 
  10. [10] Iman R.L., Hora S.C., A robust measure of uncertainty importance for use in fault tree system analysis, Risk Analysis, 1990, 10(3), 401–406, 1990 http://dx.doi.org/10.1111/j.1539-6924.1990.tb00523.x 
  11. [11] Ishigami T., Homma T., An importance quantification technique in uncertainty analysis for computer models, In: Proceedings of the First International Symposium on Uncertainty Modeling and Analysis, IEEE Computer Society Press, Los Alamitos, 1990, 398–403 
  12. [12] Marchuk G.I., Mathematical Modeling for the Problem of the Environment, Stud. Math. Appl., 16, North-Holland, Amsterdam, 1985 
  13. [13] Ostromsky Tz., Dimov I., Georgieva R., Zlatev Z., Air pollution modelling, sensitivity analysis and parallel implementation, International Journal of Environment and Pollution, 2011, 46(1–2), 83–96 http://dx.doi.org/10.1504/IJEP.2011.042610 
  14. [14] Ostromsky Tz., Dimov I., Georgieva R., Zlatev Z., Parallel computation of sensitivity analysis data for the Danish Eulerian Model, In: Large-Scale Scientific Computing, Sozopol, June 6–10, 2011, Lecture Notes in Comput. Sci., 7116, Springer, Heidelberg, 2012, 307–315 http://dx.doi.org/10.1007/978-3-642-29843-1_35 
  15. [15] Ostromsky Tz., Dimov I., Marinov P., Georgieva R., Zlatev Z., Advanced sensitivity analysis of the Danish Eulerian Model in parallel and grid environment, In: Proceedings of the Third International Conference AMiTaNS’11, Albena, June 20–25, 2011, AIP Conf. Proc., 1404, American Institute of Physics, Melville, 2011, 225–232 
  16. [16] Saltelli A., Making best use of model evaluations to compute sensitivity indices, Comput. Phys. Commun., 2002, 145(2), 280–297 http://dx.doi.org/10.1016/S0010-4655(02)00280-1 Zbl0998.65065
  17. [17] Saltelli A., Chan K., Scott E.M. (Eds.), Sensitivity Analysis, Wiley Ser. Probab. Stat., John Wiley & Sons, Chichester, 2000 Zbl0961.62091
  18. [18] Saltelli A., Ratto M., Andres T., Campolongo F., Cariboni J., Gatelli D., Saisana M., Tarantola S., Global Sensitivity Analysis. The Primer, John Wiley & Sons, Chichester, 2008 Zbl1161.00304
  19. [19] Saltelli A., Tarantola S., Campolongo F., Ratto M., Sensitivity Analysis in Practice, John Wiley & Sons, Chichester, 2004 Zbl1049.62112
  20. [20] Sobol’ I.M., Sensitivity estimates for nonlinear mathematical models, Math. Modeling Comput. Experiment, 1993, 1(4), 407–414 Zbl1039.65505
  21. [21] Sobol’ I.M., Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Math. Comput. Simulation, 2001, 55(1–3), 271–280 http://dx.doi.org/10.1016/S0378-4754(00)00270-6 Zbl1005.65004
  22. [22] Sobol’ I.M., Myshetskaya E.E., Monte Carlo estimators for small sensitivity indices, Monte Carlo Methods Appl., 2007, 13(5–6), 455–465 
  23. [23] Sobol’ I.M., Tarantola S., Gatelli D., Kucherenko S.S., Mauntz W., Estimating the approximation error when fixing unessential factors in global sensitivity analysis, Reliability Engineering & System Safety, 2007, 92(7), 957–960 http://dx.doi.org/10.1016/j.ress.2006.07.001 
  24. [24] Sudret B., Global sensitivity analysis using polynomial chaos expansions, Reliability Engineering & System Safety, 2008, 93(7), 964–979 http://dx.doi.org/10.1016/j.ress.2007.04.002 
  25. [25] Wagner H.M., Global sensitivity analysis, Oper. Res., 1995, 43(6), 948–969 http://dx.doi.org/10.1287/opre.43.6.948 Zbl0852.90122
  26. [26] Zlatev Z., Computer Treatment of Large Air Pollution Models, Environmental Science and Technology Library, 2, Springer, Berlin, 1995 
  27. [27] Zlatev Z., Dimov I., Computational and Numerical Challenges in Environmental Modelling, Stud. Comput. Math., 13, Elsevier, Amsterdam, 2006 Zbl1120.65103
  28. [28] Zlatev Z., Dimov I., Georgiev K., Three-dimensional version of the Danish-Eulerian model, Z. Angew. Math. Mech., 1996, 76(S4), 473–476 Zbl0925.65236
  29. [29] http://www.wolfram.com/mathematica/ 

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