Principal Component Regression Modeling and Analysis of PM 10 and Meteorological Parameters in Sarajevo with and without Temperature Inversion

Mirza Pasic (), Halima Hadziahmetovic, Ismira Ahmovic and Mugdim Pasic
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Mirza Pasic: Faculty of Mechanical Engineering, University of Sarajevo, Vilsonovo setaliste 9, 71000 Sarajevo, Bosnia and Herzegovina
Halima Hadziahmetovic: Faculty of Mechanical Engineering, University of Sarajevo, Vilsonovo setaliste 9, 71000 Sarajevo, Bosnia and Herzegovina
Ismira Ahmovic: Federal Hydrometeorological Institute of Bosnia and Herzegovina, Bardakcije 12, 71000 Sarajevo, Bosnia and Herzegovina
Mugdim Pasic: Faculty of Mechanical Engineering, University of Sarajevo, Vilsonovo setaliste 9, 71000 Sarajevo, Bosnia and Herzegovina

Sustainability, 2023, vol. 15, issue 14, 1-22

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 P M 10 and meteorological parameters with and without temperature inversion. The main aim of this paper was to develop forecasting models of the hourly average of P M 10 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 P M 10 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 P M 10 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 ( λ < 1 ). A total of 21 models were developed. The performance of the models were evaluated based on the coefficient of determination R 2 and the standard error S E . The backward elimination models were shown to have high performances with the highest value of R 2 = 97.19 and the lowest value of S E = 1.32 . The study showed that some principal components with eigenvalues λ < 1 were significantly related to the independent variable P M 10 and thus were retained in the PCR models. In the study, it was shown that backward elimination PCR was an adequate tool to develop P M 10 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 P M 10 with and without temperature inversion.

Keywords: principal component regression; environmental and spatiotemporal statistics; temperature inversion; air quality and pollution; PM 10; meteorological parameters (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:14:p:11230-:d:1197149

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