KRIVAKOVSKAYA R.V., NOCHVAI V.I.
ABSTRACT
The paper describes investigation of the influence of the accuracy of air pollution monitoring data on the solution accuracy of the inverse problem for power identification of point emission sources. The inverse problem is solved using the Bayesian inference and multilinear regression model.
KEYWORDS
inverse problem, Bayes' theorem, accuracy, environmental monitoring.
REFERENCES
1. Enting, I.G. (2002), Inverse Problems in Atmospheric Constituent Transport, Cambridge University Press, NY, USA.
2. Sax, T. and Isakov, V. (2003), “A case study for assessing uncertainty in local-scale regulatory air quality modeling applications”, Atmospheric Environment, no. 37, pp. 3481-3489.
3. Hanna, S.R., Chang, J.C. and Fernau, M.E. (1998), “Monte Carlo estimates of uncertainties in predictions by a photochemical grid model(UAM-IV) due to uncertainties in input variables”, Ibid., no. 32, pp. 3619-3628.
4. Semenchin, E.A. and Kuzyakina, M.V. (2012), Stokhasticheskie metody resheniya obratnykh zadach v matematicheskoy modeli atmosfernoy diffuzii [Stochastic methods for solving inverse problems in mathematical models of atmospheric diffusion], Fizmatlit, Moscow, Russia.
5. Chow, F.K. (2008), “Source Inversion for Contaminant Plume Dispersion in Urban Environments Using Building-Resolving Simulations”, J. Of Applied Meteorology and Climatology, Vol. 47, pp. 1553-1572.
6. Keats, A., Yee, E. and Lien, F.-S. (2007), “Bayesian inference for source determination with applications to a complex urban environment”, Atmospheric Environment, no. 41, pp. 465-479.
7. Jorquera, H. and Castro, J. (2010), “Analysis of urban pollution episodes by inverse modelling”, Ibid., no. 44, pp. 42-54.
8. Gilliland, A.B., Wyat, A.K., Pinder, R.W. and Dennis, R.L. (2006), “Seasonal NH 3 emissions for the continental United States: Inverse model estimation and evaluation”, Ibid., no. 40, pp. 4986-4998.
9. Kozhevnikova, M.F., Levenets, V.V. and Rolik, I.L. (2011), “Identification of pollution sources: computational methods”, Voprosy atomnoy nauki i tekhniki. Seriya: Vakuum, chistye materialy, sverkhprovodniki (19), no. 6, pp. 149-156.
10. Selvaraju, N. and Pushpavanam S. (2010), “Refining emission rate estimates using a coupled receptordispersion modeling approach”, Atmospheric Environment, no. 44, pp. 3935-3941.
11. Chubatov, A.A. and Karmazin V.N. (2009), “Robust estimation of the intensity of atmospheric pollution source on the basis of serial functional approximation method”, Kompyuternye issledovaniya i modelirovanie, no. 4, pp. 391-403.
12. Panasenko, E.A. and Starchenko, A.V. (2008), “Numerical solution of some inverse problems with different types of sources of air pollution”, Vestn. Tomskogo gosuniversiteta. Seriya «Matematika i mekhanika», Vol. 3, no. 2, pp. 47-55.
13. Kovalets, I.V., Tsiouri, V., Andronopoulos, S. and Bartris, J.G. (2009), “Improvement of source and wind field input of atmospheric dispersion model by assimilation of concentration measurements: method and applications in idealized settings”, Applied Mathematical Modelling, Vol. 33, no. 8, pp. 3511-3521.
14. Regionalna dopovid pro stan navkolyshnogo pryrodnogo seredovyshcha v m Kyevi u 2008 r. (2008), [A regional report on the state of the environment in Kiev in 2008], Ministerstvo okhorony navkolyshnogo pryrodnogo seredovyshcha Ukrayiny, Kiev, Ukraine.
15. Bolstad, W.M. (2010), Understanding Computational Bayesian Statistics, John Wiley & Sons.
16. Popov, O.O. (2010), “Mathematical and computer modeling of man-made pressures on the city atmosphere from stationary point sources of pollution”, Abstract of Cand. Sci. (Tech.) dissertation, Kiev, Ukraine.
17. Nochvay, V.I. (2009), “Optimization model for analyzing emission scenarios in the problems of research and forecasting processes of atmospheric pollution urban area”, Abstract of Cand. Sci. (Tech.) dissertation, Kiev, Ukraine.
18. Patil, A., Huard, D. and Fonnesbeck C.J. (2010), “PyMC: Bayesian Stochastic Modeling in Python”, J. of Statistical Software, Vol. 35, Iss. 4, pp.
19. Geweke, J. (1991), “Evaluating the accuracy of sampling-based approaches to calculating posterior moments”, Bayesian Statistics, Vol. 4, pp. 169-194.
20. Markov Chain Monte Carlo in Practical (1995), Edit. Spiegelhalter, D.J., Gilks, W.R. and Richardson, S., Chapman and Hall, London, UK.
21. Marchuk, G.I. (1992), Sopryazhennye uravneniya i analiz slozhnykh system [Adjoint equations and analysis of complex system], Nauka, Moscow, Russia.
22. Available at: http://rp5.ua/