## 5 Conclusions  
The present study gives an overview of the changes in air pollution by PM\({}_{10}\) in the three most polluted cities in Romania: Bucharest, Brasov, and Iasi. The PM\({}_{10}\) mass concentrations analyzed span five years of data, from 2017 to 2022. Outputs can be summarized as follows:  
* The enhancement of the air quality can be achieved by reducing the impact of traffic. * Despite the significant reduction, the results indicated that there is a potential non-carcinogenic chronic health risk in these cities due to the exposure to PM\({}_{10}\), as indicated by the hazard quotient method, especially during cold months
* The approach involved the non-parametric Kruskal-Wallis test and the two corrections (absolute and relative) in order to minimize the confounding factors influencing the rejecting/accepting null hypothesis. This approach can be applied to other urban areas facing high levels of air pollution, where the traffic is responsible for elevated contribution in the investigated regions. The approach is practical and easy to implement in situations where limited data are available. While this study offers valuable insights for decision-makers regarding PM\({}_{10}\) concentration behavior under reduced traffic conditions in three major cities, it has limitations related to the limited number of pollutants considered. A broader assessment (e.g., CA, CCA, PMF) that includes multiple pollutants and meteorological parameters (e.g., NO\({}_{2}\), SO\({}_{2}\), wind speed, wind direction) would provide a more comprehensive understanding of overall pollutant concentrations, sources, and associated health risks. Another limitation of the present study is related to the lack of data regarding traffic volume, as used in other studies [59; 60]. In the present paper, we also show that Google Mobility Data might serve as a good indicator of the reduction in human activities, particularly vehicular traffic. However, the association between mobility and PM\({}_{10}\) concentrations is not straightforward due to the non-linear and multifactorial nature of air pollution and human behavior. The decrease in traffic, as suggested by mobility data, plays a significant role, but the overall effect on PM\({}_{10}\) levels can be modulated by other sources and atmospheric processes. Therefore, while the Google Mobility Data does not fully explain the variations in PM\({}_{10}\) concentrations, it is essential to contextualize the observed changes and understand the partial contribution of reduced traffic to the overall air quality. This highlights the importance of considering multiple factors when analyzing the impact of human behavior on air pollution. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app1481869/s1](https://www.mdpi.com/article/10.3390/app1481869/s1)]([https://www.mdpi.com/article/10.3390/app1481869/s1](https://www.mdpi.com/article/10.3390/app1481869/s1)), Figure S1: Anthropogenic activity percent changes from business as usual in 2020 for Bucharest (a,b), Brasov (c,d) and Iasi (e,f) for different COVID-19 restrictions; Figure S2: Hazard quotient (HQ1) in (a) Bucharest, (b) Brasov, (a) Iasi EUregulations; Figure S3: Surface dust concentration from Southeast European Virtual Climate Change Center based on DREAS-assim model valid at 27 March 0900 UTC. [PERSON] and [PERSON] designed this research. [PERSON]-[PERSON] and [PERSON] performed the data analysis. [PERSON] and [PERSON] contributed to the data analysis and interpretation. The written article was prepared by [PERSON]-[PERSON] and [PERSON], with input from all other co-authors. All authors have read and agreed to the published version of the manuscript. [PERSON] was supported by the University of Bucharest PhD research grant. [PERSON] acknowledges the support from NO Grants 2014-2021, under Project EEA-RO-NO-2019-0423, contract no 31/01.09.2020. [PERSON] acknowledges the support from Romanian National Core Program Contract FORCLIMSOC Programme Contract No.