Construction of a method for detecting arbitrary hazard pollutants in the atmospheric air based on the structural function of the current pollutant concentrations

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.218714

Keywords:

air pollution, structural function, detection of hazardous pollution, pollution inhomogeneity scale

Abstract

This paper reports the construction of a method for calculating the structural function within a moving window of the fixed size, based on measuring the vector of current concentrations of arbitrary air pollutants. The use of a moving window makes it possible to reveal the current moments of the emergence of inhomogeneities in the polluted atmosphere. In this case, the time shift of the structural function reveals the corresponding time scale of this heterogeneity. It has been shown that, in contrast to the known method, the proposed method makes it possible to reveal the dynamics of the levels and scales of local inhomogeneities of the polluted air using only the current measurements of concentration for an arbitrary number of pollutants. It is noted that the method does not use information about the current meteorological conditions of the atmosphere and the features of urban infrastructure near a pollution control point. Therefore, the method is universal; it could be applied to arbitrary control points of atmospheric pollution across various territories of states. The efficiency of the proposed method was tested using the example of actual measurements of the concentrations of urban air pollutants involving formaldehyde, ammonia, and nitrogen dioxide. The reported results generally indicate the applicability of the proposed method. It has been experimentally established that the method makes it possible to identify, in real time, the areas of local inhomogeneities characteristic of hazardous air pollution associated with the absence of dispersion and accumulation of pollutants in the air. In addition, the method makes it possible to detect in real time both the levels and the scale of inhomogeneities in the polluted atmosphere. It has been experimentally established that before the occurrence of the tested reliable emergency in a polluted atmosphere, the level of local heterogeneity was 0.015 units at its time scale corresponding to 8 counts. Next, by the time of the emergency, the level of heterogeneity decreased to 0.0025 units at the time scale corresponding to 2 counts. It has been experimentally established that for this case the forecast time of the occurrence of an emergency was 4 counts or 24 hours

Author Biographies

Volodymyr Sadkovyi, National University of Civil Defence of Ukraine Chernyshevska str., 94, Kharkiv, Ukraine, 61023

Doctor of Public Administration Sciences, Professor, Rector

Boris Pospelov, Scientific-Methodical Center of Educational Institutions in the Sphere of Civil Defence Chernyshevska str., 94, Kharkiv, Ukraine, 61023

Doctor of Technical Sciences, Professor

Department of Organization and Coordination of Research Activities

Vladimir Andronov, National University of Civil Defence of Ukraine Chernyshevska str., 94, Kharkiv, Ukraine, 61023

Doctor of Technical Sciences, Professor

Research Center

Evgenіy Rybka, National University of Civil Defence of Ukraine Chernyshevska str., 94, Kharkiv, Ukraine, 61023

Doctor of Technical Sciences, Senior Researcher

Research Center

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

Doctor of Geographical Sciences, Associate Professor

Department of Environmental Safety and Environmental Education

Anatoliy Rud, State Agrarian and Engineering University in Podilia Shevchenka str., 13, Kamianets-Podilsky, Ukraine, 32300

PhD, Professor

Department of Agroengineering and System Technology

Kostiantyn Karpets, V. N. Karazin Kharkiv National University Svobody sq., 4, Kharkiv, Ukraine, 61022

PhD, Associate Professor

Department of Ecology and Neoecology

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

PhD, Associate Professor

Department of Prevention Activities and Monitoring

References

  1. Egondi, T., Kyobutungi, C., Ng, N., Muindi, K., Oti, S., Vijver, S. et. al. (2013). Community Perceptions of Air Pollution and Related Health Risks in Nairobi Slums. International Journal of Environmental Research and Public Health, 10(10), 4851–4868. doi: https://doi.org/10.3390/ijerph10104851
  2. Pospelov, B., Andronov, V., Rybka, E., Krainiukov, O., Maksymenko, N., Meleshchenko, R. et. al. (2020). Mathematical model of determining a risk to the human health along with the detection of hazardous states of urban atmosphere pollution based on measuring the current concentrations of pollutants. Eastern-European Journal of Enterprise Technologies, 4 (10 (106)), 37–44. doi: https://doi.org/10.15587/1729-4061.2020.210059
  3. McCarron, G. (2018). Air Pollution and human health hazards: a compilation of air toxins acknowledged by the gas industry in Queensland’s Darling Downs. International Journal of Environmental Studies, 75 (1), 171–185. doi: https://doi.org/10.1080/00207233.2017.1413221
  4. Ten threats to global health in 2019. World Health Organization.
  5. Social Costs of Morbidity Impacts of Air Pollution (2016). OECD Environment Working Papers. doi: https://doi.org/10.1787/5jm55j7cq0lv-en
  6. Burnett, R., Chen, H., Szyszkowicz, M., Fann, N., Hubbell, B., Pope, C. A. et. al. (2018). Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proceedings of the National Academy of Sciences, 115 (38), 9592–9597. doi: https://doi.org/10.1073/pnas.1803222115
  7. Kemp, A. C., Horton, B. P., Donnelly, J. P., Mann, M. E., Vermeer, M., Rahmstorf, S. (2011). Climate related sea-level variations over the past two millennia. Proceedings of the National Academy of Sciences, 108 (27), 11017–11022. doi: https://doi.org/10.1073/pnas.1015619108
  8. Pope, C. A., Cropper, M., Coggins, J., Cohen, A. (2014). Health benefits of air pollution abatement policy: Role of the shape of the concentration–response function. Journal of the Air & Waste Management Association, 65 (5), 516–522. doi: https://doi.org/10.1080/10962247.2014.993004
  9. Xie, Y., Bowe, B., Yan, Y., Xian, H., Li, T., Al-Aly, Z. (2019). Estimates of all cause mortality and cause specific mortality associated with proton pump inhibitors among US veterans: cohort study. BMJ, l1580. doi: https://doi.org/10.1136/bmj.l1580
  10. Bowe, B., Xie, Y., Xian, H., Balasubramanian, S., Zayed, M. A., Al-Aly, Z. (2016). High Density Lipoprotein Cholesterol and the Risk of All-Cause Mortality among U.S. Veterans. Clinical Journal of the American Society of Nephrology, 11 (10), 1784–1793. doi: https://doi.org/10.2215/cjn.00730116
  11. About Underlying Cause of Death, 1999-2018. Available at: https://wonder.cdc.gov/ucd-icd10.html
  12. Vitolo, C., Scutari, M., Ghalaieny, M., Tucker, A., Russell, A. (2018). Modeling Air Pollution, Climate, and Health Data Using Bayesian Networks: A Case Study of the English Regions. Earth and Space Science, 5 (4), 76–88. doi: https://doi.org/10.1002/2017ea000326
  13. Bai, L., Wang, J., Ma, X., Lu, H. (2018). Air Pollution Forecasts: An Overview. International Journal of Environmental Research and Public Health, 15 (4), 780. doi: https://doi.org/10.3390/ijerph15040780
  14. Tong, Y., Wan, B. (2001). Methods of forecasting air pollution and their development at home and abroad. Proceedings of the Sixth National Academic Conference on Environmental Monitoring.
  15. Rahman, N. H. A., Lee, M. H., Suhartono, Latif, M. T. (2014). Artificial neural networks and fuzzy time series forecasting: an application to air quality. Quality & Quantity, 49 (6), 2633–2647. doi: https://doi.org/10.1007/s11135-014-0132-6
  16. Luo, X., Cao, H. (2012). Evaluation of air quality using the CMAQ modeling system. Procedia Environmental Sciences, 12, 159–165. doi: https://doi.org/10.1016/j.proenv.2012.01.261
  17. Sharma, N., Agarwal, A. K., Eastwood, P., Gupta, T., Singh, A. P. (2017). Introduction to Air Pollution and Its Control. Air Pollution and Control, 3–7. doi: https://doi.org/10.1007/978-981-10-7185-0_1
  18. Marwan, N. (2011). How to avoid potential pitfalls in recurrence plot based data analysis. International Journal of Bifurcation and Chaos, 21 (04), 1003–1017. doi: https://doi.org/10.1142/s0218127411029008
  19. Marwan, N., Webber, C. L., Macau, E. E. N., Viana, R. L. (2018). Introduction to focus issue: Recurrence quantification analysis for understanding complex systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 28 (8), 085601. doi: https://doi.org/10.1063/1.5050929
  20. 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
  21. 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
  22. Adeniji, A. E., Olusola, O. I., Njah, A. N. (2018). Comparative study of chaotic features in hourly wind speed using recurrence quantification analysis. AIP Advances, 8 (2), 025102. doi: https://doi.org/10.1063/1.4998674
  23. Wendi, D., Marwan, N., Merz, B. (2018). In Search of Determinism-Sensitive Region to Avoid Artefacts in Recurrence Plots. International Journal of Bifurcation and Chaos, 28 (01), 1850007. doi: https://doi.org/10.1142/s0218127418500074
  24. Donner, R. V., Balasis, G., Stolbova, V., Georgiou, M., Wiedermann, M., Kurths, J. (2019). Recurrence‐Based Quantification of Dynamical Complexity in the Earth's Magnetosphere at Geospace Storm Timescales. Journal of Geophysical Research: Space Physics, 124 (1), 90–108. doi: https://doi.org/10.1029/2018ja025318
  25. Garcia-Ceja, E., Uddin, M. Z., Torresen, J. (2018). Classification of Recurrence Plots’ Distance Matrices with a Convolutional Neural Network for Activity Recognition. Procedia Computer Science, 130, 157–163. doi: https://doi.org/10.1016/j.procs.2018.04.025
  26. Neves, F. M., Viana, R. L., Pie, M. R. (2017). Recurrence analysis of ant activity patterns. PLOS ONE, 12 (10), e0185968. doi: https://doi.org/10.1371/journal.pone.0185968
  27. Ozken, I., Eroglu, D., Breitenbach, S. F. M., Marwan, N., Tan, L., Tirnakli, U., Kurths, J. (2018). Recurrence plot analysis of irregularly sampled data. Physical Review E, 98 (5). doi: https://doi.org/10.1103/physreve.98.052215
  28. Thiel, M., Romano, M. C., Kurths, J., Meucci, R., Allaria, E., Arecchi, F. T. (2002). Influence of observational noise on the recurrence quantification analysis. Physica D: Nonlinear Phenomena, 171 (3), 138–152. doi: https://doi.org/10.1016/s0167-2789(02)00586-9
  29. Schinkel, S., Dimigen, O., Marwan, N. (2008). Selection of recurrence threshold for signal detection. The European Physical Journal Special Topics, 164 (1), 45–53. doi: https://doi.org/10.1140/epjst/e2008-00833-5
  30. Eroglu, D., Marwan, N., Stebich, M., Kurths, J. (2018). Multiplex recurrence networks. Physical Review E, 97 (1). doi: https://doi.org/10.1103/physreve.97.012312
  31. Webber,, C. L., Ioana, C., Marwan, N. (Eds.) (2016). Recurrence Plots and Their Quantifications: Expanding Horizons. Springer Proceedings in Physics. doi: https://doi.org/10.1007/978-3-319-29922-8
  32. 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
  33. 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
  34. 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
  35. Pospelov, B., Andronov, V., Rybka, E., Krainiukov, O., Karpets, K., Pirohov, O. et. al. (2019). Development of the correlation method for operative detection of recurrent states. Eastern-European Journal of Enterprise Technologies, 6 (4 (102)), 39–46. doi: https://doi.org/10.15587/1729-4061.2019.187252
  36. 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
  37. Stankovic, L., Dakovic, M., Thayaparan, T. (2014). Time-frequency signal analysis. Kindle edition, 655.
  38. 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
  39. 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
  40. 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
  41. Pospelov, B., Rybka, E., Meleshchenko, R., Krainiukov, O., Harbuz, S., Bezuhla, Y. et. al. (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. doi: https://doi.org/10.15587/1729-4061.2020.200140
  42. Pospelov, B., Kovrehin, V., Rybka, E., Krainiukov, O., Petukhova, O., Butenko, T. et. al. (2020). Development of a method for detecting dangerous states of polluted atmospheric air based on the current recurrence of the combined risk. Eastern-European Journal of Enterprise Technologies, 5 (9 (107)), 49–56. doi: https://doi.org/10.15587/1729-4061.2020.213892
  43. Kaladze, V. A. (2012). Mathematical models of casual processes with stationary increments and the non-uniform information dynamic processing. Lorman, 136.
  44. Tatarskiy, V. I. (1967). Rasprostranenie voln v turbulentnoy atmosfere. Moscow: Nauka.
  45. Prohorov, S. A., Grafkin, V. V. (2010). Strukturno-spektral'niy analiz sluchaynyh protsessov. Samara: SNTS RAN, 128.
  46. 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.
  47. Otrosh, Y., Semkiv, O., Rybka, E., Kovalov, A. (2019). About need of calculations for the steel framework building in temperature influences conditions. IOP Conference Series: Materials Science and Engineering, 708, 012065. doi: https://doi.org/10.1088/1757-899x/708/1/012065
  48. Semko, A. N., Beskrovnaya, M. V., Yagudina, N. I., Vinogradov, S. A., Hritsina, I. N. (2014). The usage of high speed impulse liquid jets for putting out gas blowouts. Journal of Theoretical and Applied Mechanics, 52 (3), 655–664.
  49. 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
  50. Loboichenko, V. M., Vasyukov, A. E., Tishakova, T. S. (2017). Investigations of Mineralization of Water Bodies on the Example of River Waters of Ukraine. Asian Journal of Water, Environment and Pollution, 14 (4), 37–41. doi: https://doi.org/10.3233/ajw-170035
  51. 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

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Published

2020-12-31

How to Cite

Sadkovyi, V., Pospelov, B., Andronov, V., Rybka, E., Krainiukov, O., Rud, A., Karpets, K., & Bezuhla, Y. (2020). Construction of a method for detecting arbitrary hazard pollutants in the atmospheric air based on the structural function of the current pollutant concentrations. Eastern-European Journal of Enterprise Technologies, 6(10 (108), 14–22. https://doi.org/10.15587/1729-4061.2020.218714

Issue

Vol. 6 No. 10 (108) (2020): Ecology

Section

Ecology

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Copyright (c) 2020 Volodymyr Sadkovyi, Boris Pospelov, Vladimir Andronov, Evgenіy Rybka, Olekcii Krainiukov, Anatoliy Rud, Kostiantyn Karpets, Yuliia Bezuhla

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