Abstract
Air pollution modelling is a key step for air pollution policy and management. In order to assess the air pollution variability in vertical profile over the industrial agglomeration, two approaches were used. PM distribution changes with elevation and also strongly depends on meteorological conditions, the relationships were assessed using correlation analysis and then the Self-Organizing Maps (SOM) were used to find association between resulting correlations – especially between PM concentrations, elevation, selected meteorological variables, and GPS coordinates of locations measured. The calculations have been carried out using R and MatLab software.
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References
Nel, A.: Atmosphere. Air pollution-related illness: effects of particles. Science 308, 804–806 (2005)
Taiwo, A.M., Beddows, D.C.S., Shi, Z., Harrison, R.M.: Mass and number size distributions of particulate matter components: comparison of an industrial site and an urban background site. Sci. Total Environ. 475, 29–38 (2014)
Kim, K.-H., Kabir, E., Kabir, S.: A review on the human health impact of airborne particulate matter. Environ. Int. 74, 136–143 (2015)
D’Amato, G., Vitale, C., De Martino, A., Viegi, G., Lanza, M., Molino, A., Sanduzzi, A., Vatrella, A., Annesi-Maesano, I., D’Amato, M.: Effects on asthma and respiratory allergy of Climate change and air pollution. Multidiscip. Respir. Med. 10, 39 (2015)
Hu, J., Wu, L., Zheng, B., Zhang, Q., He, K., Chang, Q., Li, X., Yang, F., Ying, Q., Zhang, H.: Source contributions and regional transport of primary particulate matter in China. Environ. Pollut. 207, 31–42 (2015)
Štrbová, K., Raclavská, H., Bílek, J.: Balloon study of fugitive emissions within lower troposphere over the industrial agglomeration in Moravian-Silesian Region, Czech Republic. In: Proceedings of the 2nd South East European Conference on Sustainable Development of Energy, Water and Environment Systems, SDEWES.SEE 2016, p. 16 (2016)
Husson, F., Josse, J., Le, S., Mazet, J.: Package “ FactoMineR” (2015)
Ibarra-Berastegi, G., Sáenz, J., Ezcurra, A., Ganzedo, U., de Argandoña, J.D., Errasti, I., Fernandez-Ferrero, A., Polanco-Martínez, J.: Assessing spatial variability of SO2 field as detected by an air quality network using Self-Organizing Maps, cluster, and principal component analysis. Atmos. Environ. 43, 3829–3836 (2009)
Lu, H.C., Chang, C.L., Hsieh, J.C.: Classification of PM10 distributions in Taiwan. Atmos. Environ. 40, 1452–1463 (2006)
Gulson, B., Korsch, M., Dickson, B., Cohen, D., Mizon, K., Michael Davis, J.: Comparison of lead isotopes with source apportionment models, including SOM, for air particulates. Sci. Total Environ. 381, 169–179 (2007)
Moosavi, V., Aschwanden, G., Velasco, E.: Finding candidate locations for aerosol pollution monitoring at street level using a data-driven methodology. Atmos. Meas. Tech. 8, 3563–3575 (2015)
Vossler, T., Černikovský, L., Novák, J., Williams, R.: Source apportionment with uncertainty estimates of fine particulate matter in Ostrava, Czech Republic using positive matrix factorization. Atmos. Pollut. Res. (2016)
Pokorna, P., Hovorka, J., Klan, M., Hopke, P.K.: Source apportionment of size resolved particulate matter at a European air pollution hot spot. Sci Total Env. 502, 172–183 (2015)
Vossler, T., Cernikovsky, L., Novak, J., Placha, H., Krejci, B., Nikolova, I., Chalupnickova, E., Williams, R.: An investigation of local and regional sources of fine particulate matter in Ostrava, the Czech Republic. Atmos. Pollut. Res. 6, 454–463 (2015)
R Core Team: R Development Core Team. R A Lang. Environ. Stat. Comput. 55, 275–286 (2015)
Carslaw, D.C., Ropkins, K.: Openair—an R package for air quality data analysis. Environ. Model Softw. 27–28, 52–61 (2012)
Kaski, S., Kohonen, T.: Exploratory data analysis by the self-organizing map: structures of welfare and poverty in the world. In: Neural Networks in Financial Engineering: Proceedings of the Third International Conference on Neural Networks in the Capital Markets, pp. 498–507. World Scientific (1996)
Correia Lourenço, F.: Exploratory geospatial data analysis using self-organizing maps, case Study of Portuguese Mainland Regions (2005)
Kohonen, T.: MATLAB implementations and applications of the self-organizing map (2014)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (2001)
Kotsiantis, S.B., Kanellopoulos, D., Pintelas, P.E.: Data preprocessing for supervised leaning. Int. J. Comput. Sci. 1, 111–117 (2006)
David, C., Karl, R.: Tools for the analysis of air pollution data: package “openair” (2016). http://www.openair-project.org/
Acknowledgement
This paper was supported by research project of the Ministry of Education, Youth and Sport of the Czech Republic: The National Programme for Sustainability LO1404 – TUCENET, SP2016/17 - Research in selected fields of “Smart Energy” of the 21st Century.
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Štrbová, K., Štrba, R., Raclavská, H., Bílek, J. (2018). Analysis of Air Pollution in Vertical Profile Using Self-Organizing Maps. In: Abraham, A., Haqiq, A., Ella Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Third International Afro-European Conference for Industrial Advancement — AECIA 2016. AECIA 2016. Advances in Intelligent Systems and Computing, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-319-60834-1_17
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