# Examining the Amount of Particulate Matter (PM) Emissions in Urban Areas
## 6 Conclusions  
When monitoring PM concentration at the bus station, only the number of buses was recorded, and there were no other vehicles nearby. At the railway station, we recorded all vehicles that passed through the measuring area. In 12 h, 10,290 passenger vehicles and 21 buses passed through. However, we detected greater concentrations of PM at the bus station than at the railway crossing, despite the fact that the number of vehicles at the railway crossing was much higher. We hypothesize that the concentration of particulate matter emissions is higher in connection with bus transport in this specific city. In future research, we must focus on the vehicle fleet of specific carriers and analyze the emission standards of buses used in the given location.  
We continuously monitored the number of vehicles that passed the measuring areas, and recorded the highest number of vehicles near the railway crossing. During the monitored period, 10,296 passenger vehicles, 24 trucks, and 21 buses passed through the measuring area. The number of other means of transport is negligible due to the considerable preponderance of the number of passenger vehicles. At elementary schools, we only monitored the number of vehicles in the morning hours when the intensity of driving and the concentration of PM are the highest. We recorded 391 passenger vehicles in this area. Only buses travelled through the bus station, and during the observed period, we recorded 169.  
We used a correlation between the monitored values to monitor the dependence between the number of vehicles and the detected levels of pollution. From our results, we observed a strong dependence between the intensity of traffic and the level of air pollution, and the values of the correlation coefficient were >0.7 at each point. We observed deteriorated air quality during our measurements, especially at the railway crossing, where the concentration values of PM\({}_{10}\) emissions ranged from 47 to 67 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\). This pollution was detected in peak intervals. At this time, an increased number of vehicles moves on the measuring section. We assessed the air quality levels based on assessments provided the Slovak Hydrometeorological Institute.  
However, we did not consider other meteorological influences, such as wind speed or direction, during our measurements. In future projects, we want to assign data on air temperature and humidity, and wind speed, to the factors influencing the degree of correlation between traffic and air quality. In further research, it would be possible to expand the methodology of the work by processing data indicating air quality. It is possible to assume that the results of such measurements would be similar. The results of research confirmed that road transport considerably contributes to air quality, especially near roads.  
Conceptualization, M.L. and V.H.; methodology, B.S. and M.L.; formal analysis, J.G. and V.H.; investigation, B.S. and J.G.; resources, M.L. and V.H.; data curation J.G., B.S. and M.L.; writing--original draft preparation, J.G. and V.H.; writing--review and editing, B.S., V.H. and M.L.; visualization M.L. and V.H.; supervision, J.G. and B.S.; project administration, J.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project of institutional research of the Faculty of Operation and Economics of Transport and Communications, University of Zilina, no. 2/KCMD/2021, \"Research on the impact of urban logistics on the environment\".  
Not applicable.  
Data available in a publicly accessible repository. The data presented in this study are openly available in FigShare.  
The authors declare no conflict of interest.