Current Journal of Applied Science and Technology

Air pollution is one of the key problems in urban areas and its investigation is vital both for people's health and for the environment as a whole. In particular, ground 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 statistical study based on 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 of the time series. 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 for determining the concentration levels of ozone in relation to the other air pollutants and/or 6 meteorological time series using principal component regression (PCR). The good fitting of the obtained models with coefficients of determination R2 over 78% is obtained. An example of using the model to forecast the concentrations of ozone for 24 hours ahead is given. The obtained results could be used as an assessment in all analyses of the air quality of the town Dimitrovgrad, including the official reports of the Environmental Agency and also as an independent alternative to the official alerting systems.