IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i13p2919-d1182696.html
   My bibliography  Save this article

Optimizing Air Pollution Modeling with a Highly-Convergent Quasi-Monte Carlo Method: A Case Study on the UNI-DEM Framework

Author

Listed:
  • Venelin Todorov

    (Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 8, 1113 Sofia, Bulgaria
    Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 25A, 1113 Sofia, Bulgaria)

  • Slavi Georgiev

    (Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 8, 1113 Sofia, Bulgaria
    Department of Applied Mathematics and Statistics, University of Ruse, 8 Studentska Str., 7004 Ruse, Bulgaria)

  • Ivan Georgiev

    (Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 8, 1113 Sofia, Bulgaria
    Department of Applied Mathematics and Statistics, University of Ruse, 8 Studentska Str., 7004 Ruse, Bulgaria)

  • Snezhinka Zaharieva

    (Department of Electronics, University of Ruse, 8 Studentska Str., 7004 Ruse, Bulgaria)

  • Ivan Dimov

    (Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 25A, 1113 Sofia, Bulgaria)

Abstract

In this study, we present the development of an advanced air pollution modeling approach, which incorporates cutting-edge stochastic techniques for large-scale simulations of long-range air pollutant transportation. The Unified Danish Eulerian Model (UNI-DEM) serves as a crucial mathematical framework with numerous applications in studies concerning the detrimental effects of heightened air pollution levels. We employ the UNI-DEM model in our research to obtain trustworthy insights into critical questions pertaining to environmental preservation. Our proposed methodology is a highly convergent quasi-Monte Carlo technique that relies on a unique symmetrization lattice rule. By fusing the concepts of special functions and optimal generating vectors, we create a novel algorithm grounded in the component-by-component construction method, which has been recently introduced. This amalgamation yields particularly impressive outcomes for lower-dimensional cases, substantially enhancing the performance of the most advanced existing methods for calculating the Sobol sensitivity indices of the UNI-DEM model. This improvement is vital, as these indices form an essential component of the digital ecosystem for environmental analysis.

Suggested Citation

  • Venelin Todorov & Slavi Georgiev & Ivan Georgiev & Snezhinka Zaharieva & Ivan Dimov, 2023. "Optimizing Air Pollution Modeling with a Highly-Convergent Quasi-Monte Carlo Method: A Case Study on the UNI-DEM Framework," Mathematics, MDPI, vol. 11(13), pages 1-14, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2919-:d:1182696
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/13/2919/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/13/2919/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stefka Fidanova & Petar Zhivkov & Olympia Roeva, 2022. "InterCriteria Analysis Applied on Air Pollution Influence on Morbidity," Mathematics, MDPI, vol. 10(7), pages 1-8, April.
    2. Iooss, Bertrand & Van Dorpe, François & Devictor, Nicolas, 2006. "Response surfaces and sensitivity analyses for an environmental model of dose calculations," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1241-1251.
    3. Jacques, Julien & Lavergne, Christian & Devictor, Nicolas, 2006. "Sensitivity analysis in presence of model uncertainty and correlated inputs," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1126-1134.
    Full references (including those not matched with items on IDEAS)

    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. Venelin Todorov & Ivan Dimov, 2022. "Innovative Digital Stochastic Methods for Multidimensional Sensitivity Analysis in Air Pollution Modelling," Mathematics, MDPI, vol. 10(12), pages 1-14, June.
    2. Labopin-Richard T. & Gamboa F. & Garivier A. & Iooss B., 2016. "Bregman superquantiles. Estimation methods and applications," Dependence Modeling, De Gruyter, vol. 4(1), pages 1-33, March.
    3. Iooss, Bertrand & Ribatet, Mathieu, 2009. "Global sensitivity analysis of computer models with functional inputs," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1194-1204.
    4. Auder, Benjamin & De Crecy, Agnès & Iooss, Bertrand & Marquès, Michel, 2012. "Screening and metamodeling of computer experiments with functional outputs. Application to thermal–hydraulic computations," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 122-131.
    5. Broto, Baptiste & Bachoc, François & Depecker, Marine & Martinez, Jean-Marc, 2019. "Sensitivity indices for independent groups of variables," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 163(C), pages 19-31.
    6. López-Benito, Alfredo & Bolado-Lavín, Ricardo, 2017. "A case study on global sensitivity analysis with dependent inputs: The natural gas transmission model," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 11-21.
    7. Dimov, I. & Georgieva, R., 2010. "Monte Carlo algorithms for evaluating Sobol’ sensitivity indices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(3), pages 506-514.
    8. Mara, Thierry A. & Tarantola, Stefano, 2012. "Variance-based sensitivity indices for models with dependent inputs," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 115-121.
    9. Deman, G. & Kerrou, J. & Benabderrahmane, H. & Perrochet, P., 2015. "Sensitivity analysis of groundwater lifetime expectancy to hydro-dispersive parameters: The case of ANDRA Meuse/Haute-Marne site," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 276-286.
    10. Blagojević, Nikola & Didier, Max & Stojadinović, Božidar, 2022. "Quantifying component importance for disaster resilience of communities with interdependent civil infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    11. Rogeau, A. & Girard, R. & Kariniotakis, G., 2017. "A generic GIS-based method for small Pumped Hydro Energy Storage (PHES) potential evaluation at large scale," Applied Energy, Elsevier, vol. 197(C), pages 241-253.
    12. Salacinska, K. & El Serafy, G.Y. & Los, F.J. & Blauw, A., 2010. "Sensitivity analysis of the two dimensional application of the Generic Ecological Model (GEM) to algal bloom prediction in the North Sea," Ecological Modelling, Elsevier, vol. 221(2), pages 178-190.
    13. Zio, E. & Pedroni, N., 2009. "Functional failure analysis of a thermal–hydraulic passive system by means of Line Sampling," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1764-1781.
    14. Marrel, Amandine & Iooss, Bertrand & Van Dorpe, François & Volkova, Elena, 2008. "An efficient methodology for modeling complex computer codes with Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4731-4744, June.
    15. Haro Sandoval, Eduardo & Anstett-Collin, Floriane & Basset, Michel, 2012. "Sensitivity study of dynamic systems using polynomial chaos," Reliability Engineering and System Safety, Elsevier, vol. 104(C), pages 15-26.
    16. Marrel, Amandine & Iooss, Bertrand & Laurent, Béatrice & Roustant, Olivier, 2009. "Calculations of Sobol indices for the Gaussian process metamodel," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 742-751.
    17. Lin, Yan-Hui & Yam, Richard C.M., 2017. "Uncertainty importance measures of dependent transition rates for transient and steady state probabilities," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 402-409.
    18. Marie Hermant & Olivier Blanchard & Guillaume Perouel & Guillaume Boulanger & Mathilde Merlo & Virginie Desvignes, 2018. "Environmental Exposure of the Adult French Population to Permethrin," Risk Analysis, John Wiley & Sons, vol. 38(4), pages 853-865, April.
    19. Mirko Ginocchi & Ferdinanda Ponci & Antonello Monti, 2021. "Sensitivity Analysis and Power Systems: Can We Bridge the Gap? A Review and a Guide to Getting Started," Energies, MDPI, vol. 14(24), pages 1-59, December.
    20. Hübler, Clemens, 2020. "Global sensitivity analysis for medium-dimensional structural engineering problems using stochastic collocation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).

    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:gam:jmathe:v:11:y:2023:i:13:p:2919-:d:1182696. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.