## Appendix B HyspLIT Analysis of Sand Episode Originating from Aralkum Desert  
During the episode [28], air mass trajectory came from the northeast, passed through Romania, Ukraine, Russia, Kazakhstan and Uzbekistan (Figure A2b). Finally, extremely high concentrations of PM\({}_{10}\) were probably, in large part, due to the dust coming from the Aralkum Desert located at the Kazakhstan and Uzbekistan border. In the period before and after PM pollution episodes, back trajectories showed less capability for long-range transport as evident from Figure 19,c (note the differences in zoom level for same 72 h trajectory duration). Appendix C Correlation of PM\({}_{2.5}\) Concentration in the City of Novi Sad between Spatially Distributed Measuring Sites  
In order to better understand spatial variability of PM pollution in the city of Novi Sad, data for PM\({}_{2.5}\) fine particulate matter at additional sampling sites, which was measured simultaneously using low-cost sensors and reference pumps set to 48 h averaging, were correlated with the NN1 SEPA station. Pearson's correlation coefficient between 48 h concentrations measured at the site that belongs to the national network of air quality monitoring NN1 collected with equivalent monitor GRIMM Aerosol EDM 180 [29] and concentrations measured using reference gravimetric pumps LVS3 Sven Leckel [19] at MS1, MS2, MS3 and MS5 site vary between 0.85 and 0.99 (Figure 20). The highest Pearson's correlation coefficient was identified, not surprisingly, between sites situated along the same street, Rumenacka (Figure 20), at the distance of about 2.7 km. High correlation despite the relatively large distance between the sites indicates that sources of pollution ovary, most probably due to traffic consisting of mainly light and heavy trucks and buses, since the roundabout represents the main way that leads to the highway. Pearson's correlation coefficient was also high for the other three sites,approximately 0.86 for the MS1 site and 0.85 for the other two sites located on the other side of the Danube river at Petrovaradin, a location where there are currently no monitoring sites from national, regional or local monitoring network. Pearson's correlation coefficient between 1 h concentrations measured at the site that belongs to the national network of air quality monitoring NN1 and concentrations measured using low-cost sensors at MS1, MS2, MS3 and MS5 shown in Figure A4 vary between 0.61 and 0.97. The highest Pearson's correlation coefficient can be noticed between the sites at the same street, Rumenacka (Figure A4b), at a distance of about 2.7 km, r = 0.97. Pearson's coefficient correlation of 0.89 is identified between NN1 measured with equivalent instrument GRIMM Aerosol EDM 180 and Dunavnet _ekoNET_ device equipped with PMS7003 low-cost sensor located at MS1 (Figure A4a) and MS3 (Figure A4c). A Pearson's correlation coefficient that is lowest is identified between the equivalent device at NN1 and low-cost device at MS5 (Figure A4d). The possible reason for the lower coefficient correlation between NN1 and MS5 is probably due to differences in particulate matter levels and probably due to divergent content as well as local meteorological conditions at two locations. This illustrates the need for higher spatial resolution of PM monitoring efforts and, to some extent, locations where there is a need to deploy additional sensors (locations with lower correlation to reference stations). Appendix D Changes in Diurnal Variation of Gaseous Pollutants at NN2 before and after Measures of the State Emergency  
In Figure A5, diurnal variation of the main gaseous pollutants at Liman SEPA, NN2 automatic monitoring station, before and after the state emergency measures, are shown:  
* Daily profiles of SO2, the main pollutant, depicted in the form of a boxplot (Figure A5a), show that, on average, there is a slight change a week before and 10 days during the emergency state.