A Multi-Country Statistical Analysis Covering Turkey, Slovakia, and Romania in an Educational Framework

This paper uses hierarchical regression analysis, a statistically robust method, to explore the correlations between two meteorological parameters and three particulate matter concentrations. The dataset is provided by six sensors located in three cities from three countries, and the measurements we...

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Bibliographic Details
Published in:Sustainability 2023-12, Vol.15 (24), p.16735
Main Authors: Pekdogan, Tugce, Udriștioiu, Mihaela Tinca, Puiu, Silvia, Yildizhan, Hasan, Hruška, Martin
Format: Article
Language:English
Subjects:
Air pollution
COVID-19
Outdoor air quality
Sensors
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Summary:This paper uses hierarchical regression analysis, a statistically robust method, to explore the correlations between two meteorological parameters and three particulate matter concentrations. The dataset is provided by six sensors located in three cities from three countries, and the measurements were taken simultaneously for three months at each minute. Analyses and calculations were performed with the Statistical Package for the Social Sciences (SPSS). The results underscore that the complexity of air pollution dynamics is affected by the location even when the same type of sensors is used, and emphasize that a one-size-fits-all approach cannot effectively address air pollution. The findings are helpful from three perspectives: for education, to show how to handle and communicate a solution for local communities’ issues about air pollution; for research, to understand how easy a university can generate and analyze open-source data; and for policymakers, to design targeted interventions addressing each country’s challenges.
ISSN:2071-1050
2071-1050
DOI:10.3390/su152416735