Principal Component Regression Modeling and Analysis of PM10 and Meteorological Parameters in Sarajevo with and without Temperature Inversion

Abstract

The specific geographic location of Sarajevo, which is located in a valley surrounded by mountains, provides the opportunity to analyze the relation between the concentration of (Formula presented.) and meteorological parameters with and without temperature inversion. The main aim of this paper was to develop forecasting models of the hourly average of (Formula presented.) values in the Sarajevo urban area based on meteorological parameters measured in Sarajevo and on the Bjelasnica mountain with and without temperature inversion by using principal component regression (PCR). Also, this research explored and analyzed the differences in the values of the meteorological parameters and (Formula presented.) in Sarajevo with and without temperature inversion, and the difference in temperatures between Sarajevo and Bjelasnica with temperature inversion using statistical hypothesis testing with a total of 240 hypothesis tests performed. The measurements of meteorological parameters were taken from 2020 to 2022 for both Sarajevo (630 m) and the Bjelasnica mountain (2067 m), which allowed for the identification of time periods with and without temperature inversion, while measurements of (Formula presented.) were taken only in Sarajevo. Data were collected during the heating season (November, December, January, February and March). Since analyses have shown that only January and November had time periods with and without temperature inversion during each hour of the day, a total of seven cases were identified: two cases with and five cases without temperature inversion. For each case, three PCR models were developed using all principal components, backward elimination and eigenvalue principal component elimination criteria ((Formula presented.)). A total of 21 models were developed. The performance of the models were evaluated based on the coefficient of determination (Formula presented.) and the standard error (Formula presented.). The backward elimination models were shown to have high performances with the highest value of (Formula presented.) 97.19 and the lowest value of (Formula presented.). The study showed that some principal components with eigenvalues (Formula presented.) were significantly related to the independent variable (Formula presented.) and thus were retained in the PCR models. In the study, it was shown that backward elimination PCR was an adequate tool to develop (Formula presented.) forecasting models with high performances and that it could be useful for authorities for early warnings or other action to protect citizens from very harmful pollution. Hypothesis tests showed different relations of meteorological parameters and (Formula presented.) with and without temperature inversion.