Abstract
The air quality data for the city of Sofia were retrieved for the period 2009 to 2018 and includes daily observations from four automatic monitoring stations (Hypodruma, Pavlovo, Nadejda, Drujba) for the following variables: PM10, NO, NO2, SO2, O3, air temperature, humidity, wind and solar radiation. The multivariate statistical modelling of pre-treated data set was performed by the use of Principal component analysis (PCA). The PCA reveals three principal components, which are responsible for urban air quality in all monitoring stations explaining between 80 and 84% of total data variance. The first principal component (PC1), explaining between 45 and 51% from data variance is positively associated with PM10, NOx and SO2, and negatively correlated with ozone. PC1 describes the elevated concentrations of the primary pollutants during winter and elevated ozone levels during summer. Together with the expressed seasonality, factor scores of PC1 reveal a decreasing trend of all associated pollutants during the investigated period. The PC2 (19 to 21% of explained data variance) reflects the high concentrations of PM10 and NOx during calm summer days. The PC3 (10 to 13% of explained data variance) reflects the elevated concentrations of SO2 during windy days. A significant seasonality and decrease trend for PM10, NOx and SO2 is observed at all monitoring stations. There is no significant difference in urban air quality between working days and weekends.
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Acknowledgements
This work has been carried out in the framework of the National Science Program “Environmental Protection and Reduction of Risks of Adverse Events and Natural Disasters”, approved by the Resolution of the Council of Ministers №577/17.08.2018 and supported by the Ministry of Education and Science (MES) of Bulgaria (Agreement № Д01-279/03.12.2021).
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Tsakovski, S., Danchovski, V., Dimitrova, R., Mukhtarov, P. (2023). Multivariate Statistical Modelling of Urban Air-Quality. In: Dobrinkova, N., Nikolov, O. (eds) Environmental Protection and Disaster Risks. EnviroRISKs 2022. Lecture Notes in Networks and Systems, vol 638. Springer, Cham. https://doi.org/10.1007/978-3-031-26754-3_19
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