Use of uncertainty function for identification of hazardous states of atmospheric pollution vector

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.200140

Keywords:

atmospheric pollution, pollution concentration, uncertainty function, radial velocity, state vector

Abstract

The use of estimation of the values of the uncertainty function to identify hazardous states of an arbitrary atmospheric pollution vector is considered. At the same time, it is proposed to estimate the uncertainty function in a fixed-width window moving along the trajectory of the state vector. This allows not only identifying the occurrence of hazardous states of atmospheric pollution, but also determining their radial velocity relative to the monitoring post. Zero radial velocity of hazardous states of atmospheric pollution allows identifying current states of no pollution dispersion in the atmosphere. These states turn out to be especially dangerous, since they lead to the accumulation of pollution and an increase in their concentration in the atmosphere. Verification of the possibility of using the uncertainty function to identify hazardous states of the atmospheric pollution vector was carried out using experimental data. At the same time, formaldehyde, ammonia and carbon monoxide were considered as hazardous components of the state vector of atmospheric pollution. The verification results generally indicate the possibility of using the uncertainty function to identify hazardous states of the atmospheric pollution vector. The use of uncertainty function is found to be invariant with respect to the irregularity of recording of atmospheric pollution at stationary monitoring posts. It is shown that the use of uncertainty function enables the identification of hazardous states characterized not only by exceeding the maximum permissible concentrations, but also by the zero radial velocity relative to the monitoring point. It is experimentally found that in order to identify hazardous states of atmospheric pollution, the window length should be from 4 to 8 readings

Author Biographies

Boris Pospelov, V. N. Karazin Kharkiv National University Svobody sq., 4, Kharkiv, Ukraine, 61022

Doctor of Technical Sciences, Professor

Department of Ecological Safety and Environmental Education

Evgeniy Rybka, National University of Civil Defence of UkraineChernyshevska str., 94, Kharkiv, Ukraine, 61023

Doctor of Technical Sciences, Senior Researcher

Research Center

Ruslan Meleshchenko, National University of Civil Defence of UkraineChernyshevska str., 94, Kharkiv, Ukraine, 61023

PhD, Associate Professor

Department of Fire and Rescue Training

Olekcii Krainiukov, V. N. Karazin Kharkiv National University Svobody sq., 4, Kharkiv, Ukraine, 61022

Doctor of Geographical Sciences, Associate Professor

Department of Ecological Safety and Environmental Education

Serhii Harbuz, National University of Civil Defence of UkraineChernyshevska str., 94, Kharkiv, Ukraine, 61023

PhD

Department of Fire and Technological Safety of Facilities and Technologies

Yuliia Bezuhla, National University of Civil Defence of UkraineChernyshevska str., 94, Kharkiv, Ukraine, 61023

PhD

Department of Management and Organization in the Field of Civil Protection

Ihor Morozov, National Academy of the National Guard of UkraineZakhysnykiv Ukrainy sq., 3, Kharkіv, Ukraine, 61001

PhD, Senior Researcher

Department of Research and Organization

Anna Kuruch, National Academy of the National Guard of UkraineZakhysnykiv Ukrainy sq., 3, Kharkіv, Ukraine, 61001

PhD

Department of Research and Organization

Olena Saliyenko, National Academy of the National Guard of UkraineZakhysnykiv Ukrainy sq., 3, Kharkіv, Ukraine, 61001

PhD

Department of Research and Organization

Ruslan Vasylchenko, National Academy of the National Guard of UkraineZakhysnykiv Ukrainy sq., 3, Kharkіv, Ukraine, 61001

PhD

Department of Research and Organization

References

  1. Kondratenko, O. M., Vambol, S. O., Strokov, O. P., Avramenko, A. M. (2015). Mathematical model of the efficiency of diesel particulate matter filter. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 6, 55–61.
  2. Vasiliev, M. I., Movchan, I. O., Koval, O. M. (2014). Diminishing of ecological risk via optimization of fire-extinguishing system projects in timber-yards. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 5, 106–113.
  3. Dubinin, D., Korytchenko, K., Lisnyak, A., Hrytsyna, I., Trigub, V. (2017). Numerical simulation of the creation of a fire fighting barrier using an explosion of a combustible charge. Eastern-European Journal of Enterprise Technologies, 6 (10 (90)), 11–16. doi: https://doi.org/10.15587/1729-4061.2017.114504
  4. Semko, A., Rusanova, O., Kazak, O., Beskrovnaya, M., Vinogradov, S., Gricina, I. (2015). The use of pulsed high-speed liquid jet for putting out gas blow-out. The International Journal of Multiphysics, 9 (1), 9–20. doi: https://doi.org/10.1260/1750-9548.9.1.9
  5. Kustov, M. V., Kalugin, V. D., Tutunik, V. V., Tarakhno, E. V. (2019). Physicochemical principles of the technology of modified pyrotechnic compositions to reduce the chemical pollution of the atmosphere. Voprosy khimii i khimicheskoi tekhnologii, 1, 92–99. doi: https://doi.org/10.32434/0321-4095-2019-122-1-92-99
  6. Vasyukov, A., Loboichenko, V., Bushtec, S. (2016). Identification of bottled natural waters by using direct conductometry. Ecology Environment and Conservation, 22 (3), 1171–1176.
  7. Pascual, M., Ellner, S. P. (2000). Linking ecological patterns to environmental forcing via nonlinear time series models. Ecology, 81 (10), 2767–2780. doi: https://doi.org/10.1890/0012-9658(2000)081[2767:leptef]2.0.co;2
  8. Parrott, L. (2004). Analysis of simulated long-term ecosystem dynamics using visual recurrence analysis. Ecological Complexity, 1 (2), 111–125. doi: https://doi.org/10.1016/j.ecocom.2004.01.002
  9. Proulx, R. (2007). Ecological complexity for unifying ecological theory across scales: A field ecologist's perspective. Ecological Complexity, 4 (3), 85–92. doi: https://doi.org/10.1016/j.ecocom.2007.03.003
  10. Marwan, N., Kurths, J. (2002). Nonlinear analysis of bivariate data with cross recurrence plots. Physics Letters A, 302 (5-6), 299–307. doi: https://doi.org/10.1016/s0375-9601(02)01170-2
  11. Kantz, H., Schreiber, T. (2003). Nonlinear Time Series Analysis. Cambridge University Press. doi: https://doi.org/10.1017/cbo9780511755798
  12. Eckmann, J.-P., Kamphorst, S. O., Ruelle, D. (1987). Recurrence Plots of Dynamical Systems. Europhysics Letters (EPL), 4 (9), 973–977. doi: https://doi.org/10.1209/0295-5075/4/9/004
  13. Webber, C. L., Zbilut, J. P.; Riley, M. A., Van Orden, G. (Eds.) (2004). Chapter 2. Recurrence quantification analysis of nonlinear dynamical systems. Tutorials in Contemporary Nonlinear Methods for the Behavioral Sciences. Available at: https://www.nsf.gov/pubs/2005/nsf05057/nmbs/nmbs.pdf
  14. Webber, C. L., Zbilut, J. P. (1994). Dynamical assessment of physiological systems and states using recurrence plot strategies. Journal of Applied Physiology, 76 (2), 965–973. doi: https://doi.org/10.1152/jappl.1994.76.2.965
  15. Marwan, N., Trauth, M. H., Vuille, M., Kurths, J. (2003). Comparing modern and Pleistocene ENSO-like influences in NW Argentina using nonlinear time series analysis methods. Climate Dynamics, 21 (3-4), 317–326. doi: https://doi.org/10.1007/s00382-003-0335-3
  16. Pospelov, B., Andronov, V., Rybka, E., Meleshchenko, R., Borodych, P. (2018). Studying the recurrent diagrams of carbon monoxide concentration at early ignitions in premises. Eastern-European Journal of Enterprise Technologies, 3 (9 (93)), 34–40. doi: https://doi.org/10.15587/1729-4061.2018.133127
  17. Turcotte, D. L. (1997). Fractals and chaos in geology and geophysics. Cambridge University Press. doi: https://doi.org/10.1017/CBO9781139174695
  18. Poulsen, A., Jomaas, G. (2011). Experimental Study on the Burning Behavior of Pool Fires in Rooms with Different Wall Linings. Fire Technology, 48 (2), 419–439. doi: https://doi.org/10.1007/s10694-011-0230-0
  19. Zhang, D., Xue, W. (2010). Effect of heat radiation on combustion heat release rate of larch. Journal of West China Forestry Science, 39, 148.
  20. Andronov, V., Pospelov, B., Rybka, E. (2017). Development of a method to improve the performance speed of maximal fire detectors. Eastern-European Journal of Enterprise Technologies, 2 (9 (86)), 32–37. doi: https://doi.org/10.15587/1729-4061.2017.96694
  21. Pospelov, B., Andronov, V., Rybka, E., Skliarov, S. (2017). Design of fire detectors capable of self-adjusting by ignition. Eastern-European Journal of Enterprise Technologies, 4 (9 (88)), 53–59. doi: https://doi.org/10.15587/1729-4061.2017.108448
  22. Pospelov, B., Andronov, V., Rybka, E., Skliarov, S. (2017). Research into dynamics of setting the threshold and a probability of ignition detection by self­adjusting fire detectors. Eastern-European Journal of Enterprise Technologies, 5 (9 (89)), 43–48. doi: https://doi.org/10.15587/1729-4061.2017.110092
  23. Pospelov, B., Andronov, V., Rybka, E., Meleshchenko, R., Gornostal, S. (2018). Analysis of correlation dimensionality of the state of a gas medium at early ignition of materials. Eastern-European Journal of Enterprise Technologies, 5 (10 (95)), 25–30. doi: https://doi.org/10.15587/1729-4061.2018.142995
  24. Pospelov, B., Rybka, E., Meleshchenko, R., Gornostal, S., Shcherbak, S. (2017). Results of experimental research into correlations between hazardous factors of ignition of materials in premises. Eastern-European Journal of Enterprise Technologies, 6 (10 (90)), 50–56. doi: https://doi.org/10.15587/1729-4061.2017.117789
  25. Bendat, J. S., Piersol, A. G. (2010). Random data: analysis and measurement procedures. John Wiley & Sons, 640.
  26. Shafi, I., Ahmad, J., Shah, S. I., Kashif, F. M. (2009). Techniques to Obtain Good Resolution and Concentrated Time-Frequency Distributions: A Review. EURASIP Journal on Advances in Signal Processing, 2009 (1). doi: https://doi.org/10.1155/2009/673539
  27. Pospelov, B., Rybka, E., Meleshchenko, R., Borodych, P., Gornostal, S. (2019). Development of the method for rapid detection of hazardous atmospheric pollution of cities with the help of recurrence measures. Eastern-European Journal of Enterprise Technologies, 1 (10 (97)), 29–35. doi: https://doi.org/10.15587/1729-4061.2019.155027
  28. Pospelov, B., Rybka, E., Togobytska, V., Meleshchenko, R., Danchenko, Y., Butenko, T. et. al. (2019). Construction of the method for semi-adaptive threshold scaling transformation when computing recurrent plots. Eastern-European Journal of Enterprise Technologies, 4 (10 (100)), 22–29. doi: https://doi.org/10.15587/1729-4061.2019.176579
  29. Singh, P. (2016). Time-frequency analysis via the fourier representation. HAL. Available at: https://hal.archives-ouvertes.fr/hal-01303330/document
  30. Pospelov, B., Andronov, V., Rybka, E., Popov, V., Romin, A. (2018). Experimental study of the fluctuations of gas medium parameters as early signs of fire. Eastern-European Journal of Enterprise Technologies, 1 (10 (91)), 50–55. doi: https://doi.org/10.15587/1729-4061.2018.122419
  31. Stankovic, L., Dakovic, M., Thayaparan, T. (2014). Time-frequency signal analysis. Kindle edition, Amazon, 655.
  32. Avargel, Y., Cohen, I. (2010). Modeling and Identification of Nonlinear Systems in the Short-Time Fourier Transform Domain. IEEE Transactions on Signal Processing, 58 (1), 291–304. doi: https://doi.org/10.1109/tsp.2009.2028978
  33. Giv, H. H. (2013). Directional short-time Fourier transform. Journal of Mathematical Analysis and Applications, 399 (1), 100–107. doi: https://doi.org/10.1016/j.jmaa.2012.09.053
  34. Pospelov, B., Andronov, V., Rybka, E., Popov, V., Semkiv, O. (2018). Development of the method of frequency­temporal representation of fluctuations of gaseous medium parameters at fire. Eastern-European Journal of Enterprise Technologies, 2 (10 (92)), 44–49. doi: https://doi.org/10.15587/1729-4061.2018.125926
  35. Akhtimankina, A. V. (2015). Lecturer Investigation of dynamics of concentration of harmful substances in atmosphere of shelekhov city. Izvestiya Irkutskogo gosudarstvennogo universiteta. Seriya «Nauki o Zemle», 13, 42–57.

Downloads

Published

2020-04-30

How to Cite

Pospelov, B., Rybka, E., Meleshchenko, R., Krainiukov, O., Harbuz, S., Bezuhla, Y., Morozov, I., Kuruch, A., Saliyenko, O., & Vasylchenko, R. (2020). Use of uncertainty function for identification of hazardous states of atmospheric pollution vector. Eastern-European Journal of Enterprise Technologies, 2(10 (104), 6–12. https://doi.org/10.15587/1729-4061.2020.200140