Analysis of Air Pollution in Bayburt Province with Statistical Methods.

OpenAlex İngilizce

In this study, the temporal changes of air pollutants belonging to the city center of Bayburt, their interactions with local meteorological parameters, and trends were analyzed using statistical methods. In this context, hourly measured PM10, SO2, NO, NO2, NOX, O3 and hourly temperature (t), wind speed (ws), humidity (rh), pressure (p) data for the 2017-2019 period were analyzed. The average value of PM10 concentrations from primary pollutants was 40.5 µg/m3, and the average value of SO2 concentrations was 6.85 µg/m3. According to the results of the Mann-Whitney U test, it was found that the averages concentrations of the cold season pollutants significantly differed statistically from the averages concentrations of the hot season (p=0.000<0.05). According to the Kruskal Wallis test, a statistically significant difference was found between the averages of the pollutant concentrations by years (p=0.000<0.05). It was determined by Post-Hoc/Tamhane’s T2 analysis which years there was a differentiation between. Spearman's rho correlation analysis results reveal a statistically significant relationship between air pollutants and meteorological parameters (p=0.000<0.05). Accordingly, it was determined that the relationship between PM10, and relative humidity is negative, the relationship between SO2 and air pressure is positive, the relationship between NO, NO2, and NOx, wind speed and temperature is negative, the relationship between O3 and temperature with wind speed is positive. According to the innovative trend analysis method (ITA) results, PM10 levels tend to decrease and other pollutants tend to increase. Considering the time interval of the data used, although it is not observed that the pollutant averages exceed the limit values, the increasing trend of pollutants reveals that more efforts should be made to maintain positive air quality. Statistical data analysis in the study was carried out with SPSS 22 software.