In early October 2020, northern Europe experienced an episode with poor air
quality due to high concentrations of particulate matter (PM). At several
sites in Norway, recorded weekly values exceeded historical maximum
PM10 concentrations from the past 4 to 10 years. Daily mean PM10
values at Norwegian sites were up to 97 µgm-3 and had a
median value of 59 µgm-3. We analysed this severe pollution episode caused by long-range atmospheric transport based on surface and
remote sensing observations and transport model simulations to understand
its causes. Samples from three sites in mainland Norway and the Arctic
remote station Zeppelin (Svalbard) showed strong contributions from mineral
dust to PM10 (23 %–36 % as a minimum and 31 %–45 % as a maximum) and biomass burning (8 %–16 % to 19 %–21 %). Atmospheric transport simulations indicate that Central Asia was the main source region for mineral dust
observed in this episode. The biomass burning fraction can be attributed to
forest fires in Ukraine and southern Russia, but we cannot exclude other
sources contributing, like fires elsewhere, because the model underestimates observed concentrations. The combined use of remote sensing, surface
measurements, and transport modelling proved effective in describing the
episode and distinguishing its causes.
Introduction
In early October 2020, atmospheric concentrations of PM10 and PM2.5, i.e. particulate matter (PM) with an equivalent aerodynamic diameter of ≤10 and ≤2.5µm, were elevated at many stations in northern Europe (Fig. 1), exceeding international and national air quality guidelines (AQGs) (WHO, 2006). On 2 and/or 3 October, daily mean EU-limit
values for PM10 of 50 µgm-3 were exceeded at 45
of the European Environment Agency's (EEA) air quality urban and regional
background sites (Fig. 1). In Norway, daily averaged PM10 values
exceeded previous maximum values recorded at several sites. Visibility was
strongly reduced, and questions concerning the source of these pollutants
and possible influence on health arose (e.g. Aftenposten, 2020).
Mean PM2.5(a, c) and PM10(b, d) values (EEA, 2020) on 2 (a, b) and 3 (c, d) October 2020 (24 h mean) at all EEA air quality background sites (includes urban as well as rural, but sites close to roads are excluded). Triangles indicate three Norwegian rural background sites with extensive PM speciation (Sect. 2.1.1). For comparison, the WHO air quality guidelines for maximum daily averaged PM10 and PM2.5 are 50 and 25 µgm-3, respectively, and the EU-limit value for PM10 is 50 µgm-3.
PM levels are usually increased in winter and spring compared to summer in
urban areas in northern Europe, which is largely explained by increased emissions
from residential wood burning and resuspension of road dust generated by use
of studded tires, contributing to the fine and the coarse fraction of
PM10, respectively (Kukkonen et al., 2020; Laupsa et al., 2009; Yttri
et al., 2009, 2005). However, in this case the widespread
exceedance of PM10 values, not only in Norway, pointed to long-range
transport (LRT) of PM. Violation of limit values caused by LRT of PM in
Norway rarely occurs. Historically, high levels of LRT of PM have been
associated with secondary inorganic aerosol species, SO42-,
NO3-, and NH4+, formed by atmospheric oxidation of
European SO2, NH3, and NOx emissions (e.g. Tarrasón et al., 2019; Yttri et al., 2021). A fast-track analysis showed that the source of
the increased concentrations could have been biomass burning in eastern
Europe. However, the fact that concentrations of coarse-fraction PM10
were also enhanced suggested that sources such as dust, ash, and sea salt
aerosol could also be important. Out of these, mineral dust (from
agricultural or natural sources) is the most likely aerosol based on the
region with enhanced PM levels (Fig. 1).
Events of LRT of mineral dust to Scandinavia are rare. Mineral dust
outbreaks from the Sahara can reach the surface in Scandinavia (e.g. Ansmann
et al., 2003), although the dust plumes likely reside at higher altitudes.
An aerosol transport event with combined contributions from biomass burning
from the Iberian Peninsula and dust emissions from North Africa, affecting
several regions in Europe, was described by Akritidis et al. (2020). Also,
transport events of mineral dust from Kazakhstan have been shown to affect
Scandinavia (e.g. Hongisto and Sofiev, 2004). Besides affecting surface
concentrations of PM and air quality, airborne dust can strongly influence
the radiation budget of the atmosphere, both directly and indirectly (e.g.
Kylling et al., 2018; Myhre et al., 2013).
LRT events of aerosol particles from biomass burning have been frequently
observed in Scandinavia (e.g. Yttri et al., 2007b; Saarikoski et al., 2007),
including the European Arctic (Stohl et al., 2007), and can be related to
wildfires and agricultural waste burning in the Baltic countries, western
Russia, Belarus, and Ukraine. Fires release various particulate and gaseous
substances such as organic and black carbon (OC and BC), CO2, CH4,
and polycyclic aromatic hydrocarbons (PAHs) (Hao et al., 2016; Hao and Ward,
1993; Shi et al., 2015), some of which have an adverse effect on human
health, such as benzo(a)pyrene, a group 1 carcinogen (IARC, 2021).
To quantify biomass burning contributions to PM we analyse filter samples
for carbonaceous aerosol as well as levoglucosan (a biomass burning tracer).
LRT modelling of the biomass burning aerosol in this study is based on BC,
which is formed by the incomplete combustion of, for example, fossil fuels,
biofuels, and biomass (Bond et al., 2013). BC not only affects human health
(Lelieveld et al., 2015), but also climate (Myhre et al., 2013).
Here, we show how LRT of mineral dust from Central Asia and biomass burning
aerosol, mainly from Ukraine, caused elevated PM levels in Norway. Our
results are based on a combination of in situ observations, satellite images,
and model simulations of long-range atmospheric transport.
Data and methodsSurface observationsSites and instrumentation
There are 48 (urban categorized) sites in Norwegian cities and towns
measuring both PM10 and PM2.5 mass concentration as part of
their air quality programme (Hak, 2015). Rural background stations include
the Birkenes Observatory (58∘23′ N, 8∘15′ E; 219 m above sea level, a.s.l.), Hurdal (60∘22 N, 11∘4′ E; 300 m a.s.l.),
and Kårvatn (62∘47′ N, 8∘53′ E; 210 m a.s.l.). These are all EMEP (European Monitoring and Evaluation Programme) sites located in southern Norway (Fig. 1). The Birkenes Observatory is situated close to the Skagerrak coast (∼20 km), whereas Hurdal and Kårvatn are further inland. Forests dominate the land use at all three sites, with the
remainder being meadows, low-intensity agricultural areas, and freshwater
lakes. All three sites are thus considered rural background sites for air
quality measurements. The Zeppelin Observatory (78∘54′ N
11∘52′ E; 472 m a.s.l.) is located on the Zeppelin Mountain 2 km south of the Ny-Ålesund settlement at the west coast of Svalbard
(Norway) and is considered an Arctic remote/global background site. PM mass
concentration (PM10 and PM2.5), organic and elemental carbon
(OC/EC) (in PM10 and PM2.5), organic tracers (levoglucosan,
mannosan, galactosan, arabitol, mannitol, glucose, trehalose,
2-methylerythritol, and 2-methylthreitol) (in PM10), and crustal
elements (Al, Fe, Mn, and Ti) (in PM10) were obtained from low-volume
samplers (flow rate of 38 L min-1) with a sampling time of 1 week at
the rural background sites. Prefired (850 ∘C; 3 h) quartz fibre
filters (Whatman QM-A; 47 mm in diameter) were conditioned (20±1∘C; 50±5 % RH (relative humidity); 48 h) before and
after exposure. Filters were stored at 4 ∘C after weighing and
before OC/EC analysis and at -18∘C prior to organic tracer
analysis. Please find further details on the analysis methods in Appendix A.
At the Zeppelin Observatory, OC/EC, organic tracers, and crustal elements were obtained from a high-volume sampler with a PM10 inlet (flow rate of 667 L min-1) and with a sampling time of 1 week. Quartz fibre filters (Pallflex Tissuquartz 2500QAT-UP; 150 mm in diameter) were prefired (850 ∘C; 3 h) but not conditioned, and they were stored at -18∘C.
Major inorganic anions (SO42-, NO3-, Cl-) and
cations (Ca2+, Mg2+, K+, Na+, NH4+) were
collected using a NILU stacked filter unit (SFU) collecting aerosol
particles on a Teflon filter (2 µm pore, 47 mm Zefluor Teflon, Gelman Sciences) with a sampling time of 24 h. The SFU does not have a
pre-impactor but has a downward-facing inlet that effectively reduces the
sampling efficiency for aerosol particles with an equivalent aerodynamic
diameter larger than 10 µm.
PM10 mass concentration was measured using a TEOM 1400a (Tapered
Element Oscillating Monitor) (Patashnick and Rupprecht, 1991) instrument operating at a 1 min time resolution at the Birkenes Observatory. The aggregated weekly mean PM10 concentration obtained by the TEOM instrument is proven equivalent to the weekly time resolved, filter-based PM10 measurements at Birkenes, which is determined according
to the reference method EN 12341 (CEN, 2014).
Absorption coefficients (BAbs) were measured using a seven-wavelength (λ= 370, 470, 520, 590, 660, 880, and 950 nm) Aethalometer (AE33,
Magee Scientific) operating downstream of a PM10 inlet and calculated
according to Drinovec et al. (2015) at the Birkenes and Zeppelin
observatories. BAbs was converted to equivalent black carbon (eBC) using co-located EC
measurements.
Mineral dust mass estimation
We assumed observed levels of Al, Fe, Mn, and Ti to be associated with
mineral dust exclusively and that Al was present as Al2O3, Fe as
Fe2O3, Mn as MnO, and Ti as TiO2 (Alastuey et al., 2016). Not
all mineral dust elements were measured, such as Si, nor is the mineralogy of
the mineral dust collected on the filter samples known; thus, SiO2 was estimated based on an empirical factor (Eq. 1) (Alastuey et al., 2016). We also made assumptions regarding the speciation of CO32-, assuming 70 % was present as CaCO3, 20 % as MgCO3, and 10 % as K2CO3. The exact CO32- speciation is, however, of minor importance, as the CO32- fraction increases the lower mineral dust estimate (Eq. 2) by only 6.3 % (assuming all is MgCO3) to 10 % (assuming all is K2CO3), using data for Zeppelin as an example. Ca2+ and Mg2+, assumed to be associated with CO32-, were not considered part of the water-soluble fraction determined by ion chromatography (see Appendix A for a description of the ion chromatography method). Excess Ca2+, Mg2+,
and K+ (i.e. not part of sea salt aerosol or K2CO3) were
assumed to be present as oxides (CaO, MgO, and K2O), which together
with the carbonates and metal oxides provided the upper mineral dust
estimate (Eq. 3). We consider this an approximation, as chemical reactions
likely take place during atmospheric transport. Both NO3- and
SO42- were enhanced during the episode and could indicate the
presence of, for example, Mg(NO3)2 and MgSO4 formed from reactions
between MgCO3 and acids such as HNO3 or H2SO4 (Laskin
et al., 2005). Accounting for such reactions would increase the upper
mineral dust estimate (Eq. 3) by 12 %–21 %. However, it is not apparent that nitrates and sulfates formed this way should be apportioned to the mineral dust fraction. Finally, ion equivalent calculations suggest that all SO42- and NO3- were associated with NH4+; thus,
we do not include these potential reactions in the upper estimate of mineral
dust.
CO32- can also be part of wildfire emissions and is a dominant
species of ash produced at around 500 ∘C, with CaCO3 being
the most abundant species followed by MgCO3 and K2CO3 (Bodí et al., 2014). Hence, it is questionable whether CO32- should be apportioned as mineral dust or biomass burning when such emissions are mixed in the atmosphere. At temperatures >580∘C, carbonates dissociate to oxides (Bodí et al., 2014), which are partially soluble in water; thus,
as for CO32-, it is not clear if CaO, MgO, and K2O should be
part of mineral dust or biomass burning particles. Consequently, any attempt
to reconstruct the mineral dust mass concentration should be considered semi-quantitative. We thus calculate a lower (Eq. 2) and an upper (Eq. 3) estimate of the mineral dust mass concentration including no or all CO32- and oxides, respectively. With
CO32- and oxides apportioned to mineral dust (Eq. 3), an upper
estimate of BB (Eq. 8) would not be possible and vice versa.
1[SiO2]=2.5×[Al2O3]2[Mineral dustmass]lower=[SiO2]+[Al2O3]+[Fe2O3]+[MnO]+[TiO2]3[Mineral dustmass]upper=[SiO2]+[Al2O3]+[Fe2O3]+[MnO]+[TiO2]+[(n)CO32-]+[(n)O](n=Ca2+,Mg2+,2Na+,2K+)
Biomass burning mass estimation
Levoglucosan is formed from the thermal degradation of cellulose and is a
proven tracer of biomass burning (BB) emissions (Locker, 1988; Simoneit et
al., 1999) as demonstrated in numerous papers (e.g. Zdráhal et al., 2002; Puxbaum et al., 2007; Szidat et al., 2009; Yttri et al., 2019). Emission ratios of levoglucosan from wildfires are likely to vary widely, reflecting combustion conditions and vegetation, as well as source region. Here we apply emission ratios for total carbon and organic carbon (Yttri et al., 2014) to calculate OCBB and ECBB, which are derived from ambient sampling of wildfires emissions in eastern Europe, including Ukraine (Saarikoski et al., 2007). Calculated concentrations of OCBB and ECBB should be considered semi-quantitative given the uncertainty of the emission ratios and the potential atmospheric degradation of levoglucosan.
Although BB aerosol from wildfires is dominated by carbonaceous aerosol, carbonates and oxides can result from wildfires, as well as being a part of mineral dust. Hence, we calculated a lower estimate of the biomass
burning aerosol accounting for the carbonaceous fraction (TCBB=OCBB+ECBB) (Eqs. 4–7) and an upper estimate accounting for carbonates and oxides, in addition to the carbonaceous fraction (Eq. 8). For Eqs. (4), (5), and (10), notations in parentheses are emissions ratios.
4[TCBB]=[Levoglucosan]×(TC/levoglucosan)BB,5[OCBB]=[TCBB]×(OC/TC)BB,6[ECBB]=[TCBB]-[OCBB],7[BBmass]Lower=[OCBB×2.2]+[ECBB×1.1],8[BBmass]Upper=[OCBB2.2]+[ECBB×1.1]+[(n)CO32-]+[(n)O](n=Ca2+,Mg2+,2Na+,2K+),
where BBmass is the mass concentration (µgm-3) of the biomass burning aerosol.
For aerosol particles dominated by biomass burning, a factor of 2.2–2.6 to
convert OCBB (µgCm-3) to OMBB (organic matter; µgm-3) is recommended, whereas 1.9–2.2 is suggested for aged aerosol particles (Turpin and Lim, 2001). Here we used a factor of 2.2 both for OCBB and OC, as OC at rural background and remote sites largely is long-range transported and thus aged. Similarly, a factor of 1.1 was used
for both ECBB and EC (Kiss et al., 2002).
We estimated levels of primary biological aerosol particles (PBAPs), biogenic
secondary organic aerosol (BSOA), and fossil fuel sources (FF), as these are
complementary to BB and yield a better constraint on the biomass burning
source. For PBAPs, we used observed levels of arabitol, mannitol, glucose,
and trehalose (ΣPBAP-Tracers) (Table 1); an OC-to-ΣPBAP-Tracers ratio of 14.6±2.1 derived from Yttri et al. (2021); and an OM:OC conversion factor of 1.75 (Yttri et al., 2011) to calculate OMPBAP (unit: µgm-3). OMBSOA was estimated based on observed levels of 2-methyltetrols (ΣBSOA-tracers= 2-methylerythritol and 2-methylthreitol) (Table 2), an OC-to-ΣBSOA-tracers ratio of 165±18 derived from Yttri et al. (2021), and an OM:OC conversion factor of 2.2 (Turpin and Lim, 2001). Yttri et al. (2021) found that 2-methyltetrols traced local BSOA, which was 30 % of total identified BSOA constituents in PM10; hence total OMBSOA was adjusted accordingly. EC from fossil fuel sources (ECFF) was calculated according to Eq. (9):
[ECFF]=[EC]-[ECBB],
where ECBB is obtained from Eq. (6).
Mean concentrations of PM and associated species observed during
the episode compared to the long-term weekly mean (±SD) for
September–November (2010/11–2019) for Birkenes, Hurdal, Kårvatn, and Zeppelin, except crustal elements at Birkenes (2014–2019) and Zeppelin (2018–2019), as well as EC/OC and organic tracers at Zeppelin (2017–2019).
OC from fossil fuel sources (OCFF) was calculated according to Eq. (10):
[OCFF]=[ECFF]×(OC/EC)FF,
where the (OC/EC)FF ratio (2.0±0.25) derived from Yttri et al. (2021) includes both primary and secondary OC. An OM:OC conversion factor of 1.2 was applied for OCFF and 1.1 for ECFF. As for BBmass we
used the index “mass” in PBAPmass, BSOAmass, and FFmass to impress that the unit is µgm-3.
Source apportionment of the absorption coefficient into eBCBB and eBCFF
We used positive matrix factorization with a multilinear engine (PMF-ME2)
(Canonaco et al., 2013) and SoFi Pro software (Canonaco et al., 2021) to
apportion eBCBB and eBCFF based on observations of the absorption coefficient. PMF yields factor profiles (here the wavelength-dependent
absorption; see Yttri et al., 2021) and time series of the emission sources.
Two-factor solutions from repeat bootstrapped PMF runs were mapped via
Ångström exponents (AAEs, calculated from the factor profiles) using
factor 1 = lowest AAE and factor 2 = highest AAE. An averaged two-factor solution for each site was then determined, with AAE factor 1/factor 2 of 0.94/2.01 at Birkenes and 0.8/1.7 at Zeppelin. Noting that traffic
emissions may be partly biofuel derived and that residential coal
combustion may be partly responsible for the high AAE factor, we identify
factor 1 as fossil/liquid fuel eBC and factor 2 as biomass burning/solid
fuel eBC consistent with literature AAEs for these sources (Sandradewi et
al., 2008; Zotter et al., 2017).
Satellite observations
The Ocean and Land Colour Instrument (OLCI) on board the Sentinel-3
satellites (Donlon et al., 2012) measures the solar radiation reflected by
the Earth's surface and atmosphere in 21 spectral bands from the visible to
the near infrared. Over land and within 300 km of charted land, the ground
spatial resolution is 300 m. The primary objective of OLCI products “is to screen the ocean and land surface to harvest information related to biology. OLCI also provides information on the atmosphere and contributes to climate study (Sentinel-3 OLCI, 2021)”.
We use OLCI measurements to visualize the aerosol when passing over central
Norway and Sweden and to investigate the effect of the aerosol on the
measured OLCI radiances by comparison to radiative transfer simulations.
Furthermore, we use observations from the Cloud-Aerosol Lidar with
Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and
Infrared Pathfinder Satellite Observations (CALIPSO) platform. CALIPSO was
launched April 2006 (Winker et al., 2009). CALIOP is a two-wavelength (1064
and 532 nm), polarization-sensitive (at 532 nm) elastic backscatter lidar,
which provides global daytime and night-time profiles of aerosol backscatter,
extinction (with an extinction-to-backscatter a priori), and linear particle
depolarization with altitude resolution between 30 and 300 m, below 8.3 km,
and between 30.1 and 40.0 km, respectively. CALIOP has a small horizontal
footprint of 335 m and a revisit time of ∼16 d. Here, we
utilize the level 2 data products (version 4.21) of the aerosol extinction
at 532 nm to evaluate the representation of the dust and BC (from BB) plume
in our atmospheric transport simulations. It is given at a spatial
resolution of 60 m vertically and 5 km horizontally. The V4 level 2
cloud–aerosol discrimination (CAD) algorithm distinguishes between
following tropospheric aerosol subtypes: clean marine, polluted
continental/smoke, clean continental, polluted dust, elevated smoke, and
dusty marine (Kim et al., 2018). The data were downloaded from the ICARE
Data and Services Center (http://www.icare.univ-lille1.fr/, last access: 19 January 2021).
Radiative transfer model
To understand the processes influencing the OLCI radiances, radiative
transfer simulations were made using the DISORT model (Stamnes et al., 1988;
Buras et al., 2011) within the libRadtran framework (Emde et al., 2016). The
subarctic summer atmosphere (Anderson et al., 1986) was adopted as the
ambient atmosphere. Visible Infrared Imaging Radiometer Suite (VIIRS)
measurements indicate cloud top height between about 2–4 km and cloud
optical depth between 50–100 (https://worldview.earthdata.nasa.gov/, last access: 11 June 2021). Hence, for the cloudy simulations, a cloud layer of optical depth 70 was included between 2 and 3 km. For simulations over water, the Cox and Munk (1954a, b) ocean bidirectional reflectance distribution function (BRDF) was adopted. Aerosols were included using a profile based on FLEXPART simulation results over Norway (Sect. 3.1). The aerosol optical properties were prepared for input to libRadtran with the MOPSMAP tool (Gasteiger and Wiegner, 2018).
Atmospheric transport models and emission data sets
With the Lagrangian FLEXible PARTicle dispersion model FLEXPART version 10.4 (Pisso et al., 2019) we model mineral dust in forward mode and BC in both forward and backward mode. FLEXPART calculates trajectories of particles to describe transport and diffusion of tracers in the atmosphere. Particles are assumed to be spherical and influenced by gravitational settling, dry deposition, and in-cloud and below-cloud scavenging (Grythe et al., 2017). The model is widely applied for LRT modelling of fire emissions (Evangeliou et al., 2016; 2019) and dust sources (e.g. Sodemann et al., 2015).
Emissions from BB were adopted from the Copernicus Atmosphere Monitoring
Services (CAMS) Global Fire Assimilation System (GFAS). CAMS GFAS
assimilates fire radiative power (FRP) observations from satellite-based
sensors converting the energy released during fire combustion into gases and
aerosol daily fluxes (Di Giuseppe et al., 2016; Kaiser et al., 2012). Data are available globally on a regular grid with horizontal resolution of 0.1∘ from 2003 to present. FRP observations assimilated
in GFAS are the NASA Terra MODIS and Aqua MODIS active fire products
(https://modis-fire.umd.edu/, last access: 11 June 2021; Kaufman et al., 2003). FRP measures
the heat power emitted by fires, as a result of the combustion process, and
is directly related to the total biomass combusted (Wooster et al., 2005). Using land-use-dependent conversion factors, GFAS converts FRP into emission estimates of 44 smoke constituents, such as CO, CO2, CH4, and black carbon and organic matter components of the aerosol (Kaiser et al., 2012). Here, we used emissions of BC that were subsequently ingested into FLEXPART, which simulated it forward to track atmospheric LRT. The simulations were driven by operational meteorological analysis data from the European Centre for Medium-range Weather Forecast (ECMWF) of 1∘ spatial resolution and 3-hourly temporal resolution. The spatial resolution of the output was set to 0.5∘ and the temporal resolution to daily. The simulations accounted for wet and dry deposition, assuming a particle density of 1500 kg m-3, a logarithmic size distribution with an aerodynamic mean diameter of 0.25 µm, and a standard deviation of 0.3 (Hu et al., 2018; Long et al., 2013).
Besides forward simulations of BC, we also performed backward simulations
based on 3-hourly releases from the rural background and remote stations to
obtain the emission sensitivity and distinguish sources contributing to BC
concentrations at these locations. We thereby assumed the same properties
for BC as in the forward simulation. Emission sensitivity in the bottom 500 m was linked to fields of BC emissions from biomass burning based on GFAS and BC emissions from fossil fuels retrieved from the Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants (ECLIPSE) emission data set (Stohl et al., 2015; Klimont et al., 2017) on 0.5∘ resolution.
Emissions of mineral dust are calculated with the FLEXDUST module (Groot
Zwaaftink et al., 2016). This module describes dust mobilization and
emission as a function of (threshold) friction velocity following the
approach introduced by Marticorena and Bergametti (1995).
Modelled threshold friction velocity is influenced by soil moisture (Fécan et al., 1999), and sediment regions were identified
based on large-scale topography (Ginoux et al., 2001).
Emissions are calculated at 0.25∘ resolution and 3-hourly interval.
The forward simulations include 10 size bins for dust smaller than 20 µm (diameter). For comparison to measurements based on PM10, we will here only consider the size bins with dust up to 10 µm. Based on FLEXDUST emissions, we run two forward simulations of atmospheric transport of dust with FLEXPART. The main simulation included global dust emissions, while the additional simulation included only dust from the Central Asian desert region, here defined as a square region extending from 42 to 82∘ E and 35 to 50∘ N.
We chose this setup rather than backwards simulations (like we did for BC)
because backwards simulations for mineral dust would have required separate
simulations for each size bin and station, and this approach was thus more
efficient. FLEXPART and FLEXDUST simulations of mineral dust were driven
with the same meteorological forcing data as for biomass burning FLEXPART
simulations.
For comparison, we also include estimates from an operational air quality
forecast system. The CAMS regional ensemble forecast (Marécal et al., 2015) is composed of nine air quality models run over a European domain
(30∘ W to 75∘ E and 25 to 75∘ N). Forecasts are produced daily and run for 96 h. The ensemble is taken as the median of the nine models, which has higher skill than any of individual nine models. The dust product is the median of the prognostic simulations of mineral dust from each model. Mineral dust is represented in each model by differing size bins and physics, so the dust concentration represents PM of all sizes associated with mineral dust, which may include size bins up to PM20. While each model uses its own schemes to
represent the dust emissions, they all use the dust from the CAMS global
model to represent initial and boundary conditions (Collin, 2021). The CAMS
global model is run using IFS (Rémy et al., 2019), which simulates dust
emissions, uses three size bins (0.03–0.55, 0.55–0.9, and 0.9–20 µm), and performs assimilation of satellite aerosol optical depth to update
aerosol concentrations. The CAMS model data were downloaded from the
Copernicus Atmospheric Data Store (https://ads.atmosphere.copernicus.eu/,
last access: 18 March 2021).
Results and discussions
Although the episode was initially mostly detected based on strong impacts
on air quality at the surface, the aerosols were not only present near the
surface. We will first use satellite observations to assess the vertical
extent and origin of the plumes. Figure 2 presents an RGB (red, green, blue)
composite of OLCI observations for 2 October 2020. First, the image clearly shows the presence of aerosols above the cloud layer over Norway/Sweden.
Aerosols were thus present, in considerable amounts, at an altitude of at
least 2–3 km. We will further use the OLCI measurements in combination with
radiative transfer simulations to determine the absorption and scattering
properties of the aerosol. Four points are marked in Fig. 2, indicating
cloud only (Cloud), aerosol over cloud (Aerosol A), aerosol over water
(Aerosol B), and water without visible cloud and aerosol (Water). For these
four cases OLCI top-of-the-atmosphere radiance spectra were averaged over
9×9 km2. The spatially averaged spectra and their standard
deviations are shown as solid lines in Fig. 3.
RGB from OLCI bands 3, 6, and 8 centred at 442.5, 560, and 665 nm.
Data from 2 October 2020, 09:33 UTC.
The OLCI radiances (solid lines) at the four locations marked in
Fig. 2. The error bars are standard deviation of the OLCI radiances within
the square markers in Fig. 2. Note that for Aerosol B and Water the error
bars are small and thus not shown. The dotted lines are radiative transfer
model simulations of the OLCI measurements. See Sect. 2.3 for further
details.
The aerosol reduces the radiance by about a factor of 2 when above the cloud
layer due to increased absorption by the aerosol compared to the
non-absorbing cloud (compare Aerosol A and Cloud in Fig. 3), while above
water the aerosol increases the radiance due to increased backscattering
(compare Aerosol B and Water in Fig. 3).
To elucidate the aerosol type(s) that may reproduce the OLCI radiances,
radiative transfer simulations were made. These simulations required aerosol
optical depth, single scattering albedo (SSA), and phase function as input.
Aerosol optical depth is available from several sensors for the episode (see
for example https://worldview.earthdata.nasa.gov/, last access: 11 June 2021). However, information about aerosol type and absorption and scattering properties is generally not available. Thus, simulations were made with a combination of highly scattering (resembling mineral dust) and highly absorbing aerosols (soot). The microphysical properties of the aerosol were taken from Hess et al. (2018) and the optical properties calculated by the MOPSMAP tool (Gasteiger and Wiegner, 2018). The scattering aerosols have a SSA of 0.98 at 400 nm, and it decreases to about 0.93 at 1000 nm. The absorbing aerosols have a SSA of 0.28 at 400 nm, and it decreases to about 0.1 at 1000 nm. No single aerosol
type was able to reproduce the measurements; rather, various combinations of
the highly scattering and highly absorbing aerosols were needed to match the
measurements. The amounts of scattering and absorbing aerosols were
determined by scaling the scattering and absorption aerosol optical depths
to get a best match between the OLCI radiances and the simulations, solid,
and dotted lines in Fig. 3 respectively. For the water case (blue lines in
Fig. 3), a highly scattering optical depth (OD) of 0.45 and highly absorbing
OD of 0.2, both at 550 nm, were used. For Aerosol B (clearly visible aerosol
over water, red lines in Fig. 3), highly scattering OD =2.5 and highly
absorbing OD =0.7, while for Aerosol A (aerosol over cloud, black lines in Fig. 3) highly scattering OD =2.1 and highly absorbing OD =0.7. Thus, the reproduction of the satellite measurements indicates that the aerosol had an
absorbing component.
The episode was clearly visible from a satellite perspective, and the impact
on the near-surface air quality was considerable. The 24 h mean PM10
concentrations on 2 and 3 October ranged from 8 to 97 µgm-3 at
Norwegian urban sites, with a median value of 59 µgm-3. The
episode thereby caused violation of the 24 h PM10 EU limit of 50 µgm-3 at 39 out of 48 Norwegian sites on 2 and/or 3 October, corresponding to 70 exceedance days in total. In comparison to years 2018 and 2019, for a selection of 30 sites with measurements in those years, this means that the number of exceedance days during the episode corresponded to 18 % of the average total exceedance days for 2018 and 2019. The number of exceedance days is used to determine whether measures to improve air quality need to be taken, and it does not distinguish whether the cause of poor air quality is due to LRT or local pollution.
During this episode, it appears that local sources were of minor importance,
since 24 h mean concentrations observed at the rural background site
Birkenes, of 66 and 61 µgm-3, were in the same range as the urban sites. Weekly mean concentrations at the three rural background sites ranged from 18.6–22.2 µgm-3 for PM10 and
from 4.1–16.1 µgm-3 for PM2.5, with new maximum values for September–November 2010/11–2019 for all but PM2.5 at Birkenes (Fig. 4; Table 1). Reconstructed PM10 mass concentration at Zeppelin amounted to 6.3 µgm-3. A prevailing coarse fraction (PM10-2.5) of PM10 was seen at Birkenes (78 % of
PM10) and Hurdal (61 %), whereas it was minor at Kårvatn
(23 %). The spatial variability is not easily explained but was seen at
Norwegian urban sites during the episode as well (27 %–78 % in
PM10-2.5, Table 1). A pronounced and even dominating coarse fraction
can occur when sources such as sea salt aerosol and mineral dust are
prominent.
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 (5th, 25th, 50th, 75th, and
95th percentiles and outliers) at the rural background sites Birkenes, Hurdal,
and Kårvatn as well as the remote site Zeppelin of weekly data in the period
September–November for (a) PM10 mass concentration (2010–2019); (b) PM2.5 mass concentration (2010–2019); (c) organic carbon (OC) in PM10
(2010–2019 for Birkenes, 2011–2019 for Hurdal and Kårvatn, and 2017–2019 for Zeppelin); (d) OC in PM2.5 (similar to panel c but for Zeppelin with no measurements); (e) elemental carbon (EC) in PM10 (similar to panel c); (f) EC in PM2.5 (similar to panel d); (g) crustal elements in PM10
(2014–2019); (h) levoglucosan (2010–2019), mannitol, and 2-methylerythritol in PM10 (2016–2018); (i) crustal elements in PM10 at Zeppelin (2018–2019); and (j) levoglucosan, mannitol, and 2-methylerythritol in PM10 at Zeppelin (2017–2019). In panels (g) and (h) the box plots are for Birkenes only.
We thus see indications of influences from mineral dust and BB aerosols,
which can be confirmed by chemical speciation of PM, available at the three
rural background sites and the Zeppelin Observatory (Fig. 4;
Table 1). For a selection of species, we also show long-term measurements for comparison. With the chemical analysis performed (Table 1), with assumptions made regarding
mineral dust composition (lower estimate) (Sect. 2.1.2), and with OC and EC
conversion factors (Sect. 2.1.3), we were able to explain 92 % (Birkenes), 74 % (Hurdal), and 75 % (Kårvatn) of the PM10 mass at the rural sites. Note that secondary inorganic aerosol constituents and inorganic anions and cations were not available for the Kårvatn site. The range of
mass closure obtained for the sites is comparable to previous studies (e.g.
Putaud et al., 2010; Yttri et al., 2021; Aas et al., 2021) but should be
considered a conservative estimate, using the lower estimate of the mineral
dust fraction.
Mineral dust
Mineral dust elements (Al, Fe, Mn, and Ti) were all highly elevated during
the episode. At Birkenes, the Al and Fe levels were over 30 times higher
than the long-term seasonal mean (2014–2019), whereas the corresponding
factors for Ti and Mn were respectively 27 and 16 (Table 1; Fig. 4). Maximum-ever concentrations were observed for all mineral dust elements at Birkenes by a fair margin, suggesting that an event of this magnitude is rare. Note that long-term time series of mineral dust are available only for Birkenes. Kårvatn experienced the highest concentration for all the mineral dust elements during the episode, followed by Birkenes and Hurdal, although with a minor difference. The relative composition of the mineral dust elements was
indistinguishable between the three rural background sites and the Zeppelin
Observatory. Al was most abundant at all sites, and concentrations of other
elements relative to Al are similar at all sites: Al:Fe:Ti:Mn=1:(1.5-1.6):(26-38):(56-65). This similar relative composition points at a common origin of the observed mineral dust. The Al:Fe ratio points to Eurasian mineral dust sources (Crocchianti et al., 2021), which is supported
by our FLEXPART/FLEXDUST model simulations and CAMS regional mineral dust
product, discussed below. Observed elements at Zeppelin were of similar
composition, yet observed masses were 2.4–3.7 times lower than the mean at
the rural background sites, which is not surprising given the larger
distance to the source region.
We find a lower estimate of the mineral dust concentration at the rural
background sites ranging from 5.6–7.6 µgm-3 (Eq. 2) and an upper estimate ranging from 7.2–8.6 µgm-3 (Eq. 3), whereas 1.9–2.6 µgm-3 was attributed to mineral dust at the remote site. Only CO32- was included in the upper estimate for Kårvatn, as data needed to calculate the oxides were missing. The oxides increased the upper estimate at Birkenes and Hurdal by 14 %. If we assume a similar increase at Kårvatn, the upper estimate increases from 8.6 to
9.8 µgm-3. The lower estimate provides a 25 %–36 % mass contribution to PM10 at the rural background sites, whereas the upper estimate ranges from 32 %–47 %. Similarly, at Zeppelin, we found that 31 %–41 % of PM10 was mineral dust. Note, though, that we do not have measurements of PM10 concentrations at Zeppelin. We therefore assumed that our PM reconstruction from different constituents (Sect. 2.1.2 and 2.1.3) explains as much of the total PM10 as it did for the background sites Birkenes and Hurdal, which is on average 83 %.
Simulations with FLEXPART, based on dust emissions from FLEXDUST, help us to
further demonstrate and understand the LRT of mineral dust during this
episode. Figure 5 shows the modelled surface concentrations on 27 September
2020, during the storm in Central Asia that caused the large mineral dust
emissions, and on 2 October 2020. These time steps were chosen based on the
availability of CALIOP overpasses that captured the dust (or BC) plume,
which will be discussed later. In addition, we provide a video of
modelled dust surface concentrations between 25 September and 8 October 2020 in the Supplement. FLEXPART results show a distinct dust plume transporting dust from the regions east of the Caspian Sea towards north-west Europe. The dust plume partly overlaps with a wildfire plume starting in Ukraine, as is illustrated by the black lines in the right-hand-side figures. The contour lines of 500 hPa geopotential height illustrate how
transport of mineral dust originating from Central Asia is forced on a front
between a high-pressure region over Russia and low pressure in Europe. We
see enhanced mineral dust concentrations over North Africa and a plume of
dust transported over eastern Europe. Dust originating from North Africa is
partly mixed into the dust plume from Central Asia yet contributes little to
the increased surface concentrations of mineral dust observed in Norway
during this episode, as will be further discussed below. Natural dust
sources in Central Asia include the Karakum and Aralkum deserts in
Turkmenistan and Kazakhstan. These deserts are part of what is sometimes
referred to as the dust belt, extending from the west coast of Africa to
China (Prospero et al., 2002). There is a variety of deserts in Central Asia with different characteristics. Most of these dust sources are active between March and October (Shen et al., 2016; Indoitu et al., 2012). According to our FLEXDUST simulations, a dust storm occurred in Central Asia in the end of September. From 25 September 2020 total dust
emissions in this region started increasing, reaching maximum values on 27 September 2020, and slowly decreasing again until the 3 October 2020. Dust
emissions were up to 2 g m-2 h-1, and total emissions from this
region amounted to 5.1 Tg in 8 d.
Modelled surface concentrations of BC from biomass burning (a, c, e) and mineral dust (b, d, f) on 27 September and 2 October 2020 (midnight and noon). Blue contours: ECMWF 500 hPa geopotential height (m). In the right column black contour lines indicate the regions of the modelled wildfire plume (BC >0.03µgm-3) as shown in panels (a), (c), and (e). BC simulations started only after 27 September 2020 because no relevant emissions were observed for the episode in Norway. The red lines indicate the location of CALIOP overpasses shown in Fig. 6.
Figure 6 shows the curtain of aerosol extinction profiles at 532 nm (colour
coded; the overpass is plotted in Fig. 5) with FLEXPART mineral dust and BC
concentration contour lines overlaid. The top panel shows CALIOP aerosol
extinction from 26 September 2020, time of first record 23:48:09, between
36 and 45∘ E, with dominating aerosol subtype (not shown here) dust and polluted dust, at the surface around 40.5 and 43∘ E, elsewhere and at elevated levels. The FLEXPART simulation captured both the elevated concentrations near the surface and
the vertical spread of the dust plume at the time of emissions and near the
source region of the dust (white contours). The dust plumes reached Norway
on 2 October 2020. Figure 6 shows CALIOP aerosol extinction from that day,
at around midnight (middle panel, 13.5–29∘ E) and around noon
(lower panel, 0–9∘ E). The modelled dust plumes partly coincide
with the satellite images. The night-time CALIOP curtain shows an area with
enhanced aerosol loading extending to above 5 km of polluted
continental/dust west of 16∘ E and a lower region with
dust/polluted dust east of 20∘ E. Also note that the lack of
absorbing aerosols around 20∘ E in the satellite images is locally
due to obstructions by clouds. Twelve hours later the CALIOP overpasses the
lowermost tip of Norway, showing mineral dust reaches over 4 km height
(around 5∘ E). A video of dust RGB (red, green, blue)
composite images, which are based on infrared channel data from the Spinning
Enhanced Visible and Infrared Imager (SEVIRI), is provided in the Supplement (EUMETSAT Image Gallery, 2022).
Aerosol extinction at 532 nm [units: km-1] during three overpasses (see Fig. 5 for location) is shown as colour plot (a: CALIOP overpass from 26 September 2020, time of first record 23:48; b: 2 October 2020, time of first record 01:18; and c: 2 October 2020, time of first record 11:55). The titles of panels (a)–(c) indicate the FLEXPART
3-hourly time step corresponding to the overpass. White contours show
FLEXPART mineral dust concentrations (thick and thin lines; a: 40 and 6 µgm-3; b: 50 and 10 µgm-3; c: 15 and 3 µgm-3). Magenta contours show FLEXPART BC concentrations (thick and thin lines; a: none; b: 50 and 5 ng m-3; c: 40 and 10 ng m-3).
In Fig. 7 we compare time series of the FLEXPART modelled surface
concentrations of mineral dust to the observed PM10 concentrations at
Birkenes. (Time series of PM10 are not available at the other stations
included in the model results.) We further show surface concentrations from
the CAMS regional model product over Europe, which includes the three
stations in Norway but not the remote station in the Arctic. Both FLEXPART
and CAMS regional show increased surface concentrations of mineral dust at
all three stations between 28 and 30 September 2020 and between 1 and 4 October
2020. While the first peak is not seen clearly in the observation, the second
peak is visible in both models for all stations. The timing of the second
peak value appears similar, yet continuation of the event is different.
Moreover, the estimated values of surface concentrations are larger in
FLEXPART than in CAMS regional. This is even more obvious for the weekly
averages shown in the bottom panel of Fig. 7, where FLEXPART modelled
concentration are a factor of 1.7 to 3.4 higher than CAMS. The lower
estimates of mineral dust by CAMS are probably a result of various factors.
One issue was apparent when we examined the dust concentrations from each of
the nine ensemble members. At Birkenes all models and at Kårvatn all
except for one of the models underestimated the dust concentration. We also
found that the European Air Pollution Dispersion model (EURAD, Memmesheimer et al., 2004) and MOdèle de Chimie Atmosphérique à Grande Echelle model (MOCAGE, Martet et al., 2009) tended to greatly underestimate the
dust concentrations (values ranging from 0.1–3.1 µgm-3),
which will contribute to an overall lowering of the median. For the MOCAGE
model, this has been linked to the deposition of desert dust being too high
and an incorrect treatment of the boundary conditions from the CAMS global
model in a previous MOCAGE model cycle, and both issues have been
subsequently corrected (Mathieu Joly, personal communication, 2021). Furthermore, likely causes of differences between the models are for instance included dust
sources, emission, and scavenging parameterizations. The dust plume from
Central Asia did not reach Zeppelin until 4 October, and peak surface
concentrations are lower than for the rural stations, complying with the
observations. Comparison to average dust concentrations based on 8–10 d
sample measurements (Fig. 7, bottom) indicates a nice agreement between
the model and observations, although modelled dust concentrations are
overestimated at Zeppelin. From an additional simulation that only included
mineral dust emissions from Central Asia, we find that desert regions in
Central Asia are major contributors to the dust episode, accounting for
88 % of surface dust concentrations at the three rural background sites.
The modelled relative contribution of mineral dust from Central Asia is
similar at all sites, pointing at common sources. This agrees with the
observed relative composition of the mineral dust elements between the three
sites being indistinguishable. At Zeppelin, the model finds a relatively
larger contribution from other, most likely local, sources. No indication of
this was seen in the observations, suggesting that local mineral dust
emissions were overestimated by the model.
PM10 concentrations observed at Birkenes (a). Mineral dust surface concentrations as simulated with FLEXPART (b) and CAMS regional (c) at three stations in Norway and the remote station (outside CAMS domain). (d) Mean concentrations from simulations compared to observations from weekly samples for Birkenes (30 September–7 October 2020), Hurdal and
Kårvatn (28 September–5 October 2020), and Zeppelin (2–12 October
2020). For FLEXPART estimates in panel (d), light green colours indicate dust from global sources and dark green colours indicate the contribution of dust from Central Asia only.
Biomass burning
We calculated that carbonaceous aerosol, which generally is the major
fraction of biomass burning aerosol, made a 26 %–36 % contribution to PM10, 34 %–44 % to PM2.5, and 21 %–43 % to PM10-2.5 (Table 1) when converting OC and EC to account for other elements than just carbon. Adding CO32- increased the contribution only to a minor extent (1 %–4 %). The carbonaceous aerosol made a similar contribution to reconstructed PM10 at Zeppelin (29 %) as for the rural background sites, as did CO32- (1 %).
BB aerosol mainly consist of EC and OC. Observed EC levels at the rural
background sites were either a record high or top six, considering both the
annual (not shown) and the September–November 2010/11–2019 time series (Table 1). OC (in PM10) levels were also highly elevated, i.e. within the 95th–99th percentile. The OC level was noticeably higher at Kårvatn and Hurdal compared to Birkenes, whereas EC (in PM10) was highest at Birkenes, although by a short margin (Fig. 2). The OC and EC levels observed at Zeppelin were the highest reported since regular measurements started in
2017 but still 3 times lower compared to the major wildfire episode
influencing the site in April/May 2006 (Stohl et al., 2007).
The split between the fine and coarse fraction of PM10 varied
substantially between EC and OC at the rural background sites and between
sites. EC results from incomplete combustion of biomass and fossil fuel and
is almost exclusively associated with fine aerosol particles. Hence, one
would expect rather high EC values for the fine fraction of PM10. The
50:50 % split between the fine and coarse fraction of PM10 seen at Birkenes therefore is a rare finding, whereas the 79:21 % and 84:16 % splits for the two other rural background sites are closer to the long-term mean (97:3 %). OC was even more skewed towards the coarse fraction than EC, with a fine–coarse split ranging from 34:66 % at Birkenes to 72:28 % at Kårvatn. The reason for such an atypically high coarse
fraction could be due to condensation, agglomeration, and heterogenic
chemical reactions influencing the size distribution of carbonaceous
aerosol, as well as, for OC, contribution of PBAP. Another possibility is that pyroconvection entrains large, partly combusted particles from the ground to
such high altitudes that they can be subject to LRT. Further, Dusek et al. (2017) and Yttri et al. (2021) pointed to charring of coarse-fraction PBAP
during thermal-optical analysis (TOA), forming pyrolytic carbon that is
erroneously interpreted as coarse EC. In fact, the episode coincided with
the time of the year when PBAPs peak (early fall) (Yttri et al., 2007a, 2021), and the PBAP tracer levels were highly elevated but still
comparable to levels previously seen at this time of the year (Fig. 4, Table 1).
A lower estimate of 12 %–16 % (Eq. 7) was calculated for BBmass to PM10 at the rural background sites, whereas the upper estimate ranged from 17 %–21 %. The lower estimate apportioned only 7.6±1.8 % of PM10 to BBmass at Zeppelin, which likely experienced a more pronounced degradation of levoglucosan due to its remote location. The upper estimate of 17 % is in line with that observed at the rural background sites.
Estimates of the major carbonaceous aerosol (OC and EC) sources (BB, BSOA,
FF, and PBAP) can be derived from the source-specific organic tracers listed
in Table 1 (see Sect. 2.1.3 for details). Here we estimated the
PBAPmass, BSOAmass, and FFmass contribution to PM10 for a better constraint and understanding of the BB source, finding that their joint contribution made an equally large contribution to PM10 as
the lower estimate of BB at both the rural background sites (13 %–17 %) and
at the remote site (6.4 %), underlining the importance of the BB source.
Combined, the contribution of BBmass (lower estimate), PBAPmass,
BSOAmass, and FFmass ought to match the observed level of the carbonaceous aerosol (here, sum of OM and EC ×1.1). For the rural background sites this matched quite well (99±21 %), whereas it only amounted to 48 % at the remote Arctic site. The most likely explanation of
this discrepancy is failure to account for degradation of organic tracers,
and particularly levoglucosan. There are indications that 2-methyltetrols
have short atmospheric lifetimes as well (Yttri et al., 2021), whereas the
low levels of PBAP tracers at Zeppelin likely reflect the scarce vegetation
of the Arctic biome. Further, the ER ratios used for PBAP and BSOA (Sect. 2.1.4) are derived from measurements in the boreo-nemoral biome, thus their suitability in the Arctic biome is a matter of discussion. Consequently, the apportionment of BBmass, PBAPmass, BSOAmass, and FFmass is associated with greater uncertainty than for the rural background sites.
High-time-resolution measurements of eBC at Birkenes were attributed to a
biomass burning fraction (eBCBB) and a fossil fuel combustion fraction (eBCFF), apportioning 43 % to eBCBB and 57 % to eBCFF for the episode in question (midnight 1–2 October to midnight 3–4 October 2020). Extending the period to match that of the weekly sample (30 September–7 October 2020) reduced the eBCBB fraction to 35 %, whereas the eBCFF fraction increased to 65 %. The eBCBB/eBCFF split is thus comparable to the levoglucosan approach, which apportioned equally large shares to ECBB and ECFF for 30 September–7 October, but note
that the range (50±20 %) of the levoglucosan approach is very wide.
eBCBB (R2=0.82) correlated higher with the high-time-resolution measurements of PM10 at Birkenes than eBCFF
(R2=0.67), suggesting that biomass burning emissions were more
important for the evolution of PM than fossil fuel sources. eBCFF
explained 60 % of eBC at Zeppelin and eBCBB 40 %, considering both
the episode (2–7 October 2020) and the longer period covered by the filter
sample (2–12 October 2020). As for Birkenes, this corresponds with the
levoglucosan estimate for EC, which apportions an equally large fraction to
ECFF and ECBB.
Time series of eBC at Birkenes and Zeppelin are shown in Figs. 8 and 9, they
indicate that eBC values from both biomass burning and fossil fuel were of
similar magnitude, and both reached peak values on 3 October 2020 at Birkenes
and on 5 October 2020 at Zeppelin. Also, modelled black carbon from biomass
burning peaks on 3 October 2020 at Birkenes, yet simulated values are
underestimated by roughly a factor 10 compared to observations. BC
concentrations at the other background stations are of similar magnitude,
while there is a delay in peak concentration at Kårvatn compared to the
other stations. The high correlation (R2=0.92) between eBC and the
major OM fraction at Zeppelin (Fig. 9), obtained from the collocated aerosol
chemical speciation monitor time-of-flight (ACSM-ToF) instrument, points to
combustion sources as the origin of OM. The correlation was more pronounced
for eBCFF (R2=0.83) than for eBCBB (R2=0.66). The episode of enhanced BC from fossil fuel combustion is not reproduced in our model results for the rural background stations and is strongly underestimated at Zeppelin. Explanations can be found in the use of monthly mean fossil fuel BC emissions that could average out effects of short-term emissions.
eBCBB(a) and eBCFF(b) concentrations retrieved with PMF from observations at Birkenes (black) and simulated with FLEXPART at Birkenes, Hurdal, and Kårvatn.
eBCBB(a) and eBCFF(b) concentrations retrieved with PMF from observations (black) and simulated with FLEXPART (blue) and observed OM concentrations (green) at Zeppelin.
We further investigate the LRT of BC from biomass burning based on maps and
cross sections coinciding with CALIOP observations, like for mineral dust
(Figs. 5 and 6). In addition, we provide an animation of modelled BC
from biomass burning surface concentrations between 27 September and 8 October 2020 in the Supplement. Although BC from biomass burning is strongly underestimated at Birkenes, the model results do show elevated BC
concentrations over southern Norway on 2 October 2020. In the CALIOP
profiles, modelled BC plumes (magenta contour lines) coincide with regions
of very strong aerosol extinction, though being mainly categorized as
dust/polluted (22–28∘ E). While not explicitly shown here, we
would like to note that also the TROPOspheric Monitoring Instrument
(TROPOMI) on board the Copernicus Sentinel-5 Precursor satellite detects
enhanced aerosol index values and increased CO total column over the Baltic
countries on 1–2 October, which confirms the presence of UV-absorbing
aerosols, including biomass burning aerosols, in this region. In the region
at approximately 20∘ E on 2 October 2020 at 00:00 a clear BC signal
is modelled yet absent in the CALIOP profile due to clouds.
We use backward modelling to characterize the source regions for the
observed air pollution. The source–receptor relationship (SRR) for BC at
Birkenes during this episode is shown in Fig. 10 (top panel). The SRR
indicates how sensitive the concentrations at the receptor are to emissions
in different source regions. As for the dust plume, LRT is strongly
influenced by a low- and high-pressure system (Fig. 5). The strong winds
from the east confine the SRR mostly to a region extending between Birkenes
and Central Asia. Wildfires contributing to the BC concentrations at
Birkenes are thus mostly restricted to this region, and the largest
contributions are seen in Ukraine and southern Russia, although some
contributions from fires in North America and northern Russia are seen as
well. Due to the combination of a strong underestimation of modelled BC from
biomass burning and good representation of dust concentrations at our
stations, we expect that the BC emissions are a larger cause of error than
the actual atmospheric transport modelling. It is thus likely that we are
missing some sources contributing to the plume (Fig. 10, top panel) or that locations are correct, yet total emissions are underestimated. Fossil fuel emissions are more widespread, and mostly emissions in southern Sweden and from the Baltic States down to Ukraine contribute to the BC concentrations at Birkenes (Fig. 10, bottom) in this event. Similar results
were seen at the stations Kårvatn and Hurdal (not shown). At Zeppelin,
there was an enhanced influence of fossil fuel emissions in northern Europe
on BC levels.
BC source–receptor relationship at Birkenes (2 October 2020 at 00:00 to 4 October 2020 at 00:00) modelled with FLEXPART in backwards mode (a) and BC fossil fuel emissions (b). Markers in the top panel indicate locations with BC emissions from biomass burning that contribute >0.1 ng m-3 to BC concentrations at Birkenes. Only emissions that contribute to
the modelled concentrations at Birkenes are included in the lower panel. The
source–receptor relationship includes air masses up to 100 m above ground
level.
Conclusions
An exceptional episode of elevated PM10 concentrations at several measurement sites, with levels exceeding those on any other occasion in the last 10 years, was observed on 2 and 3 October 2020 in Norway and elsewhere in northern Europe. We have analysed this episode based on surface
observations, satellite observations, and atmospheric transport modelling.
LRT of PM was recorded with satellite observations, showing aerosols above
the cloud layer. Radiative transport simulations with concentration profiles
of different aerosol types were performed to gain an understanding of the
processes influencing radiances observed by the OLCI (Sentinel-3). These
indicated that there was a contribution from both absorbing and scattering
particles for this episode. The rather larger contribution of coarse
fraction relative to the fine fraction of PM10, however, pointed more
in the direction of mineral dust. Chemical analysis of surface samples was
thus necessary to determine the sources. In our surface observations, a
clear influence of biomass burning was revealed (12 %–21 %), as well as mineral dust (25 %–47 %).
Surface concentrations of crustal elements Al, Fe, Ti, and Mn all strongly
exceeded previous maximum recordings. At Birkenes, crustal element levels
were 16 to 30 times higher than long-term mean values. Contributions of
mineral dust were estimated to be 25 %–36 % of PM10 as a minimum and 31 %–47 % as a maximum. The ratios between crustal element levels were similar at all stations and pointed to a common dust source. Based on atmospheric transport simulations, we concluded that Central Asia, including
the Karakum desert, was the main source of mineral dust observed in Norway
during this episode, contributing roughly 88 % to surface dust
concentrations.
Coincidentally, biomass burning emissions in the same transport pathway as
the mineral dust plume caused simultaneous peaks in PM10.
Contributions of biomass burning to PM10 were estimated to be 12 %–16 % up to 17 %–21 % at the rural background stations and 8 %–17 % at the remote site. Fires in Ukraine were a source of LRT to the background stations, as shown with backwards transport modelling. The model results, however,
underestimated the retrieved BC levels at Birkenes and Zeppelin based on
surface eBC observations. It could thus be that emissions in this region
were underestimated or that additional sources were relevant. A qualitative
comparison between CALIOP observations and FLEXPART model output, although
limited due to cloud coverage, suggested that the model does capture the
location of the BC plume.
Our analysis reveals how unrelated emission sources can combine during long-range transport to cause extreme adverse air quality events in Norway.
Considering the 24 h PM10 EU limit of 50 µgm-3, a total of 70 exceedance days at 39 stations were observed in this single event. It
thereby corresponds to 18 % of the annual mean number of exceedance days
in Norway. This shows the large impact LRT episodes may have on air quality
regulations. The combined use of remote sensing, high-quality measurements
and transport modelling proved effective in describing the episode and
distinguishing its causes.
Further research is needed to assess whether there are reasons to assume
that this kind of episodes may occur more frequently in future. Emissions
from wildfires and mineral dust sources are sensitive to changes in climate,
land use, and human activities, and different temporal and spatial scales
should be considered. Also, changes in atmospheric transport patterns could
affect the occurrence of LRT episodes in Europe. Future studies should thus
include earth system model simulations to give us a better understanding of
the occurrence of such episodes in the future, although especially the
complexity due to human component will make it difficult to draw
conclusions.
Sample analysis
OC/EC was obtained by thermal-optical analysis (TOA), using the Lab OC-EC aerosol analyser (Sunset Laboratory Inc) and according to
the EUSAAR-2 protocol (Cavalli et al., 2010). The sample content
of CO32--carbon was determined as for OC and EC but after
subjecting a punch of the filter sample to thermal-oxidative pre-treatment
(Jankowski et al., 2008; Evangeliou et al., 2016). The samples' OC and EC
content were corrected with respect to the CO32--carbon.
Organic tracers (levoglucosan, mannosan, galactosan, arabitol, mannitol,
glucose, trehalose, 2-methylerythritol, and
2-methylthreitol) were analysed using Waters Acquity ultra-performance
liquid chromatography (UPLC) in combination with Waters Premier XE
high-resolution time-of-flight mass spectrometry (HR-ToF-MS) operated in the
negative electrospray ionization (ESI) mode. The analytical methodology is
based on that of Dye and Yttri (2005) but deviates by choice of column (two
2.1 mm × 150 mm HSS T3, 1.8 µm, Waters Inc.). All species were
identified by retention time and mass spectra of authentic standards, and isotope-labelled standards of levoglucosan, galactosan, mannitol, arabitol, trehalose, and glucose were used as recovery standard (see Yttri et al., 2021).
Al, Fe, Mn, and Ti were analysed by ICP-MS (inductively coupled plasma mass
spectrometry) (Agilent 7700x). Prior to analysis, aerosol filter samples
were extracted (diluted supra pure HNO3), digested (75 min; max temp of
250 ∘C for 15 min) using an ultraCLAVE microwave system
(Milestone, Italy), and diluted to 10 mL (ion exchanged H2O). External
calibration was applied and calibration standards made of HNO3 (supra
pure) (10 % v/v) to adapt to the sample matrix. Indium was used as
recovery standard and applied to all samples, standards, blank filters, and
reference materials.
Prior to ion chromatography analysis, filter samples were soaked in Milli-Q
water (10 mL) and subjected to ultrasonic agitation (30 min). Extracts were
analysed with respect to Ca2+, K+, Mg2+, Na+, and
NH4+ on a Dionex Integrion ion chromatograph, using a Dionex cation exchange CS16 column (3 mm × 250 mm), and a conductivity
detector. Samples were eluted using methane sulfonic acid (34 mM) at a flow
rate of 0.5 mL min-1. Cl-, NO3-, and
SO42- were analysed on a Dionex Integrion ion chromatograph, using
a Dionex anion exchange AS9-SC column (4 mm × 250 mm) and a conductivity detector. Samples were eluted using carbonate (K2CO3, 2.0 mM; HCO3-, 0.75 mM) at a flow rate of 2 mL min-1.
Data availability
All in situ data are reported to the EMEP monitoring programme (Tørseth
et al., 2012, 10.5194/acp-12-5447-2012) and are available from the database infrastructure EBAS
(http://ebas.nilu.no/, last access: 11 June 2021) hosted at NILU. PM measurements are available from EEA (2020; https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm, last access: 24 January 2022). FLEXPART simulation results are available from the authors upon request.
The videos show an animation of dust RGB (red, green, blue) composite images, which are based on infrared channel data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Images, with 15 min temporal resolution, were downloaded from EUMETSAT (https://eumetview.eumetsat.int/mapviewer/?product=EO:EUM:DAT:MSG:DUST, last access: 17 January 2022; EUMETSAT Image Gallery, 2022). The regions with bright pink colours visualize the dust transport over several days (30 September–3 October). Furthermore, the supplement includes two animations of FLEXPART modelled surface concentrations of mineral dust and BC from biomass burning from 25 September until 7 October 2020, like also partly presented in Fig. 5. The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-3789-2022-supplement.
Author contributions
KEY, WA, and MJ provided analysis and interpretation of surface observations.
SMP provided eBC PMF analysis. HU provided (historical) elemental data. AK
prepared analysis of the OLCI data and radiative transfer modelling. KS and
SE prepared visualization and interpretation of CALIOP data. PH contributed
to retrieval and interpretation of CAMS regional dust product. NE, SE, and
CDGZ performed FLEXDUST and FLEXPART simulations and prepared analysis and
visualization of model results. CDGZ and KEY wrote the original draft of the
paper. All authors contributed with critical review, commentary, and editing
of the manuscript.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
The paper contains modified Copernicus Atmosphere Monitoring Service Information 2020; neither the European Commission nor ECMWF is responsible for any use that may be made of the information it contains.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank NASA and CNES engineers and scientists for making CALIOP data
available. The lidar data were downloaded from the ICARE Data and Service
Center. We thank ESA for making OLCI data available through the Copernicus
programme. Furthermore, we would like to acknowledge EUMETSAT for making
dust RGBs available for download. The research leading to these results has
benefited from Aerosols, Clouds, and Trace gases Research InfraStructure
(ACTRIS), funding from the European Union Seventh Framework Programme
(FP7/2007–2013) under ACTRIS-2 and the grant agreement no. 262254, and the
COST Action CA16109, Chemical On-Line cOmpoSition and Source Apportionment
of fine aerosoL (COLOSSAL).
Financial support
The in situ data used are part of the Norwegian national monitoring programme (Aas et al., 2021) funded by the Norwegian Environment Agency (grant no. 21087006), except the tracer analysis at Zeppelin, which is funded by the Ministry of Climate and Environment, and the tracer analysis at Birkenes, which is funded by NILU.
Review statement
This paper was edited by Frank Dentener and reviewed by two anonymous referees.
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