International Journal of Environmental Science and Development

Abstract—Air pollution is one of the key problems in urban areas and its investigation is vital both for people's health but also for the environment as a whole. In particular, ozone is a secondary air pollutant with concentrations dependent mainly on changes in the levels of other pollutants and meteorological conditions within a given region. This paper presents a multivariate analysis of hourly data on 9 air pollutants and 6 meteorological variables in the town of Dimitrovgrad, Bulgaria over a period of 7 years and 3 months. Yeo-Johnson power transformation is applied to each air pollutant variable to improve normality and symmetry. The dominant patterns in the considered data are examined with the help of Principal Component Analysis (PCA) and factor analysis. Furthermore, particular focus is given to determining the concentration levels of ozone in relation to the other air pollutants and 6 meteorological parameters using multiple linear regression. The fitting of the obtained models with coefficients of determination R2 over 78% are obtained. The results are interpreted.

Index Terms—Air pollution modeling, factor analysis, multiple linear regression, ozone concentration, principal component analysis (PCA).

S. G. Gocheva-Ilieva and A. V. Ivanov are with the Department of Applied Mathematics and Modeling, Plovdiv University ―Paisii Hilendarski‖, 24 Tsar Asen Street, 4000 Plovdiv, Bulgaria (e-mails: snow@uni-plovdiv.bg, aivanov@uni-plovdiv.bg).
I. P. Iliev is with the Physics Department, Technical University-Sofia, Branch Plovdiv, 25 Tsanko Diustabanov Street, 4000 Plovdiv, Bulgaria (e-mail: iliev55@abv.bg).

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Cite: Snezhana Georgieva Gocheva-Ilieva, Atanas Valev Ivanov, and Iliycho Petkov Iliev, "Multivariate Analysis for Modeling of Air Pollutants and Ozone Concentration in Dimitrovgrad, Bulgaria," International Journal of Environmental Science and Development vol. 7, no. 10, pp. 740-744, 2016.