Assessment of the Environmental Consequences Caused by Endogenous and Anthropogenic Hazards for the Azov Recreation Area
Abstract
The Russian sector of the Azov recreational area is under the influence of numerous pollution sources of anthropogenic or natural origin. To ensure a comfortable recreation and recovery of the population, it is necessary to apply methods of integrated system monitoring, which makes it possible to identify patterns of the pollution formation in heterogeneous areas of the territory and places of increased environmental risk. Mathematical and simulation modelling should be an integral part of such systems, as a tool for scenario analysis, forecasting and warning. The paper proposes cellular automaton modelling methods, which have the advantages of expressivity, which allows for expansion of the possibilities for studying complex physicochemical processes and phenomena associated with the migration and transformation of reagents in the atmosphere and in the aquatic environment. The method is implemented in the form of a software application. We also present the results of the simulation of diffusion process and two substances - products of combustion in a fire with a point localization of epicentres; independent dispersion and interaction of light fractions of emission products upon activation of a land volcano and heavy fractions of mud volcano breccias of an underwater mud volcano.
References
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[31] Toffolli, T., and Margolus, N. 1987. Cellular Automata Machines. MIT Press.
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[35] Zhang, X., Zhou, L., and Zheng, Q. 2019. Prediction of landscape pattern changes in a coastal river basin in south-eastern China. International Journal of Environmental Science and Technology, 16(10): 6367–6376. DOI:http://dx.doi.org/10.1007/s13762-018-2170-4
[2] Aloyan, A.E., Ermakov, A.N., Arutyunyan, V.O., and Zagainov, V.A. 2010. Dynamics of trace gases and aerosols in the atmosphere with consideration for heterogeneous processes. Izvestiya - Atmospheric and Ocean Physics, 46(5): 608–622. DOI:http://doi.org/10.1134/S0001433810050063
[3] Aloyan, A.E., Yermakov, A.N., and Arutyunyan, V.O. 2016. The role of sulfate aerosol in the formation of cloudiness over the sea. Izvestiya - Atmospheric and Ocean Physics, 52(4): 353–364. DOI:http://dx.doi.org/10.1134/S0001433816040022
[4] Babeshko, V.A., et al. 2015. Convergent properties of block elements. Doklady Physics, 60(11): 515–518. DOI:http://doi.org/10.1134/S1028335815110099
[5] Bandman, O. 1999. Comparative Study of Cellular automata Diffusion Models. Lecture Notes in Computer Science, 1662: 395–399. DOI:http://doi.org/10.1007/3-540-48387-X_41
[6] Bandman, O. 2006. Parallel Simulation of Asynchronous Cellular Automata Evolution. Proceedings of 7th International Conference on Cellular Automata, for Research and Industry (ACRI 2006) 4173: 41–47.
[7] Bandman, O.L. 2010. Cellular automata composition techniques for spatial dynamics simulation. Understanding Complex Systems, 2010: 81–115. DOI:http://doi.org/10.1007/978-3-642-12203-3-5
[8] Bandman, O.L., and Kireeva, A.E. 2015. Stochastic cellular automata simulation of oscillations and autowaves in reaction-diffusion systems. Numerical Analysis and Applications, 8(3): 208–222. DOI:http://doi.org/10.1134/S1995423915030027
[9] Barovik, D.V., and Taranchuk, V.B. 2010. Mathematical Modelling of Running Crown Forest Fires. Mathematical Modelling and Analysis, 15(2): 161–174. DOI:http://doi.org/10.3846/1392-6292.2010.15.161-174
[10] Boccara, N. 2004. Reaction-Diffusion complex systems. Springer.
[11] Burks, A.W. 1966. Theory of Self-Reproducing Automata. University of Illinois Press.
[12] Fletcher, R., and Fortin, M. 2018. Spatial ecology and conservation modeling: Applications with R. Springer Nature Switzerland AG. Part of Springer Nature. DOI:http://doi.org/10.1007/978-3-030-01989-1
[13] Frisch, U., Hasslacher, B., and Pomeau Y. 1986. Lattice-Gas automata for Navier–Stokes equations. Phys. Rev. Lett, 56: 1505.
[14] Gloaguen, P., Etienne, M.P., and Le Corff, S. 2018. Stochastic differential equation based on a multimodal potential to model movement data in ecology. Journal of the Royal Statistical Society Series C-Applied Statistics, 67(3): 599–619. DOI:http://doi.org/10.1111/rssc.12251
[15] Hertwig, D., et al. 2018. Evaluation of fast atmospheric dispersion models in a regular street network. Environmental Fluid Mechanics, 18 (4): 1007–1044. DOI:http://doi.org/10.1007/s10652-018-9587-7
[16] Jorgensen, S.E., Nielsen, S.N., and Fath, B.D. 2015. Recent progress in systems ecology. Ecological Modelling, 319: 112–118. DOI:http://doi.org/10.1016/j.ecolmodel.2015.08.007
[17] Kalgin, K.V. 2012. Parallel implementation of asynchronous cellular automata on a 32-core computer. Numerical Analysis and Applications, 5(1): 45–53. DOI:http://doi.org/10.1134/S1995423912010053
[18] Lazaridis, M. 2011. Atmospheric Dispersion: Gaussian Models. Environmental Pollution Series, 19: 201–232. DOI:http://doi.org/10.1007/978-94-007-0162-5_6
[19] Malinetskii, G.G., and Stepantsov, М.Е. 1998. Simulation of diffusion processes by means of cellular automata with Margolus neighborhood. Computational Mathematics and Mathematical Physics, 38(6): 973–975.
[20] Medvedev, Yu.G. 2009. Multiparticle cellular automaton model of fluid flow FHP-MP. Tomsk State University Bulletin, 1 (6): 33–40.
[21] Morvan, D., and Dupuy, J.L. 2004. Modeling of propogation of a wildfire through a Mediterraen shrub using a multiphase foundation. Combustion and Flame, 138(3): 199–210. DOI:http://dx.doi.org/10.1016/j.combustflame.2004.05.001
[22] Penenko, V., Baklanov, A., Tsvetova, E., and Mahura, A. 2012. Direct and inverse problems in a variational concept of environmental modeling. Pure and Applied Geophysics, 169(3): 447–465. DOI:http://doi.org/10.1007/s00024-011-0380-5
[23] Penenko, V.V. Variational methods for targeted monitoring of atmospheric quality by specified cost criteria. 2018. IOP Conference Series: Earth and Environmental Science, 211(1): 012048. DOI:http://doi.org/10.1088/1755-1315/211/1/012048
[24] Ratner, S., and Zaretskaya, M. 2018. Forecasting the Ecology Effects of Electric Cars Deployment in Krasnodar Region (Russia): Learning Curves Approach. Journal of Environmental Management and Tourism, IX(1): 82–94. DOI:http://dx.doi.org/10.14505/jemt.v9.1(25).11
[25] Rubtsov, S.E., Pavlova, A.V., and Sunozov, A.A. 2012. To the cellular automaton modeling of the process of diffusion and interaction of substances. Environmental Protection in the Oil and Gas Complex, (2): 30–34.
[26] Shahbazian, Z., Faramarzi, M., Rostami, N., and Mahdizadeh, H. 2019. Integrating logistic regression and cellular automata–Markov models with the experts’ perceptions for detecting and simulating land use changes and their driving forces. Environmental Monitoring and Assessment, 191(7): 422. DOI:http://dx.doi.org/10.1007/s10661-019-7555-4
[27] Sofiev, M., et al. 2006. A dispersion modelling system SILAM and its evaluation against ETEX data. Atmospheric Environment, 40(4): 674–685. DOI:http://doi.org/10.1016/j.atmosenv.2005.09.069
[28] Temerdashev, Z.A., et al. 2018. Gas Chromatography–Mass Spectrometry Determination of Polycyclic Aromatic Hydrocarbons in Surface Water. Journal of Analytical Chemistry, 73(12): 1154–1161. DOI:http://doi.org/10.1134/S1061934818120109
[29] Temerdashev, Z.A., Pavlenko, L.F., Korpakova. I.G., and Ermakova, Y.S. 2018. Analytical Aspects of the Determination of the Total Concentration and Differentiation of Anthropogenic and Biogenic Hydrocarbons in Aquatic Ecosystems. Journal of Analytical Chemistry, 73(12): 1137–1145. DOI:http://doi.org/10.1134/S1061934818120092
[30] Toffoli, Т. 1984. Cellular Automata as an Alternative to rather than approximation of Differential Equations in Modeling Physics. Physica D, 10: 117–127.
[31] Toffolli, T., and Margolus, N. 1987. Cellular Automata Machines. MIT Press.
[32] Weimar, J. 1997. Cellular Automata for Reaction-Diffusion Systems. Parallel Computing, 23(11): 1699–1715.
[33] Zannetti, P. 1993. Numerical simulation modelling of air pollution: an overview. Air Pollution: Computational Mechanics Publications, 3–14.
[34] Zaretskaya, M.V., and Lozovоу, V.V. 2019. Development of methods for assessing the quality of water in the licensed area of an oil and gas complex enterprise. Environmental protection in the oil and gas complex, (1): 33–38. DOI:http://dx.doi.org/10.33285/2411-7013-2019-1(286)-33-38
[35] Zhang, X., Zhou, L., and Zheng, Q. 2019. Prediction of landscape pattern changes in a coastal river basin in south-eastern China. International Journal of Environmental Science and Technology, 16(10): 6367–6376. DOI:http://dx.doi.org/10.1007/s13762-018-2170-4
Published
2020-01-25
How to Cite
PAVLOVA, Alla et al.
Assessment of the Environmental Consequences Caused by Endogenous and Anthropogenic Hazards for the Azov Recreation Area.
Journal of Environmental Management and Tourism, [S.l.], v. 10, n. 7, p. 1445-1457, jan. 2020.
ISSN 2068-7729.
Available at: <https://journals.aserspublishing.eu/jemt/article/view/4266>. Date accessed: 04 nov. 2024.
doi: https://doi.org/10.14505//jemt.v10.7(39).02.
Section
Article
Copyright© 2024 The Author(s). Published by ASERS Publishing 2024. This is an open access article distributed under the terms of CC-BY 4.0 license.