At the beginning of October 2020, an exceptional air pollution episode was observed in Norway, and elsewhere in northern Europe. Visibility was obscured and reasons for this were discussed in the media. The episode was also clearly visible in records of PM10 and PM2.5. The EU health limit value of a daily averaged PM10 concentration of 50 µg m-3 was exceeded at 38 out of 47 urban Norwegian sites and at the one rural background site (Birkenes) performing high time resolution PM10
measurements. Daily averaged PM10 values reached up to 97 µg m-3 and the median value for all sites was 59 µg m-3 on 2 and 3 October 2020. The spatial extent of the region with elevated PM concentrations and the similar PM values at background and urban sites indicate that this was an episode of long-range transported air pollution. Indeed, satellite images show that aerosols were not only present near the surface, but also at higher elevations above the cloud layer (Figure 7.1). Also at the Zeppelin observatory (Svalbard), where there are no regular PM2.5 or PM10 measurements, elevated levels of several pollutants were observed in this period, (with a slight delay compared to the Norwegian mainland). Further analysis of this episode based on surface observations in Norway and at Svalbard and atmospheric transport modelling reveals likely sources of the pollution.
Figure 7.1: RGB (Red, Green, Blue) composite from the Ocean and Land Colour instrument (OLCI) on board the Sentinel-3 satellite. Data from 2 Oct 2020, 09:33 UTC (left) and a picture from the middle parts of Norway, showing the visual effect of the episode 2 Oct 2020, 16:55 UTC (right).
Chemical speciation of weekly samples from rural background stations Birkenes Observatory, Hurdal and Kårvatn and the remote Arctic Zeppelin Observatory, were compared to long-term records for the period September to November of the respective stations. This is visualised in Figure 7.2, where the weekly samples from this episode reach maximum values for PM10 and crustal elements and are above 75 percentiles of long-term observations for organic and elemental carbon. The high values of levoglucosan point to a strong contribution of some type of biomass burning, like wildfires. The very high values of crustal elements, which exceed 16 to 30 times the long-term mean of the respective elements, point to the presence of mineral dust.
Figure 7.2: Panels show concentrations observed in the October 2020 episode (red diamonds: Birkenes, orange circles: Hurdal, purple triangles: Kårvatn, blue diamonds: Zeppelin) along with box plots (5, 25, 50, 75, 95 percentiles and outliers) at the rural background sites Birkenes, Hurdal and Kårvatn and remote site Zeppelin of weekly data in the period Sept.-Nov. for a) PM10 mass concentration (2010-2019); b) Elemental Carbon (EC) in PM10 (2010-2019 for Birkenes, 2011-2019 for Hurdal and Kårvatn, 2017-2019 for Zeppelin); c) Organic Carbon (OC) in PM10 (similar as for b);
d) Levoglucosan in PM10 (2010-2019 for Birkenes, 2017-2019 for Zeppelin); Crustal elements in PM10 (Long term means for 2014-2019 for Birkenes only); f) Crustal elements in PM10 at Zeppelin (2018-2019).
We here aim to quantify the contribution of mineral dust and biomass burning aerosol to the PM10
mass concentration. We assume that Al, Fe, Mn and Ti are exclusively associated with mineral dust and that these elements are present as Al2O3, Fe2O3, MnO and TiO2 (Alastuey et al., 2016). Besides these, we estimate contributions of SiO2 based on an empirical factor relative to Al2O3 (Alastuey et al., 2016), since Si is not included in the measurements. Finally, CO32- is present in different forms and can be associated with both mineral dust and wildfire emissions. We thus calculate both lower (excluding CO32-) and upper estimates (including CO32-) of mineral dust. With this method, we found that weekly average mineral dust concentrations in PM10 ranged from 5.6 to 9.4 µg m-3 at rural background sites and from 1.9 to 2.5 µg m-3 at the remote Arctic site. This means that mineral dust contributed 25 to 45% to PM10 at background sites and 32 to 41% to the reconstructed PM10 at the remote site. The elemental ratio could not clearly reveal a specific source region, but was quite similar at all background stations, indicating a common source. We therefore complement the observations with atmospheric transport simulations, for which we used the Lagrangian particle dispersion model FLEXPART (Pisso et al., 2019) in combination with the FLEXDUST emission module (Groot Zwaaftink et al., 2016). These simulations show that a dust plume originating in Central Asia, during a dust storm between 25 September and 3 October, was forced towards northern Europe in between a high-pressure system over Russia and a low-pressure system over Europe (Figure 7.3).
There are several natural dust sources in Central Asia that are part of the global dust belt, including the Karakum and Aralkum deserts in Turkmenistan and Kazahkstan, and most of these sources are active between March and October. Transport events of dust from Central Asia to Norway appear to be rarely described.
The FLEXPART modelled surface concentrations of mineral dust at Birkenes were in good agreement with the observations from weekly samples. By comparing a simulation with global dust emissions and a simulation that only included dust emissions in Central Asia, we could estimate that roughly 88% of the surface dust concentrations were due to emissions in Central Asia. This was similar at all rural background stations.
Besides the mineral dust, we quantified the influence of biomass burning on the surface PM10
concentrations. This was done based on observations of levoglucosan, a biomass burning tracer, and we estimate that 8-21% of weekly PM10
concentrations around 2 October 2020 originated from biomass burning contributions. The lowest estimate was calculated for the remote site and was explained by a more pronounced degradation of levoglucosan. At the Birkenes and Zeppelin observatories, we used high time resolution absorption coefficient measurements to derive the fraction of equivalent black carbon explained by biomass burning, seeing values exceeding 150 ng m
-3 at Zeppelin (Figure 4.1) and close to 700 ng m-3 at Birkenes. With backwards simulations of the FLEXPART model at Zeppelin Observatory we can trace back air masses to analyse possible sources.
A so-called footprint (Figure 7.4) shows where air masses may have picked up BC emissions before reaching Zeppelin. In combination with estimates of BC emissions from wildfires in the Copernicus Atmosphere Monitoring Services (CAMS) Global Fire Assimilation System (GFAS) inventory,
we could identify relevant sources in the region of Ukraine and southern Russia, where several wildfires were reported during this period. Similar results were obtained for the Birkenes Observatory.
Since our modelled BC concentrations at Zeppelin are somewhat lower than the observations, we cannot exclude other sources that are not included in the
Figure 7.4: BC source-receptor relationship at Zeppelin (2-8 October 2020) as modelled with FLEXPART in backwards mode. The black dots indicate regions with considerable BC Figure 7.3: Modelled surface concentrations of
mineral dust on 27 September (top) and 2 October ( bottom). Blue contours: ECMWF 500 hPa geopotential height (m).