Estimates of mass absorption cross sections of black carbon for filterbased absorption photometers in the Arctic
Sho Ohata1,2,*, Tatsuhiro Mori3,4,*, Yutaka Kondo5,*, Sangeeta Sharma6, Antti Hyvärinen7, Elisabeth
5 Andrews8,9, Peter Tunved10,11, Eija Asmi7, John Backman7, Henri Servomaa7, Daniel Veber6,
Konstantinos Eleftheriadis12, Stergios Vratolis12, Radovan Krejci10,11, Paul Zieger10,11, Makoto Koike3,
Yugo Kanaya13,14, Atsushi Yoshida5, Nobuhiro Moteki3, Yongjing Zhao15, Yutaka Tobo5,16, Junji
Matsushita5, and Naga Oshima17
10

15

20

25

30

1

Institute for Space–Earth Environmental Research, Nagoya University, Nagoya, Aichi, Japan
Institute for Advanced Research, Nagoya University, Nagoya, Aichi, Japan3
Department of Earth and Planetary Science, Graduate School of Science, The University of Tokyo, Tokyo,
Japan
4
Department of Physics, Faculty of Science Division I, Tokyo University of Science, Tokyo, Japan
5
National Institute of Polar Research, Tachikawa, Tokyo, Japan
6
Climate Chemistry Measurements Research/Climate Research Division, Environment and Climate Change
Canada/Government of Canada
7
Finnish Meteorological Institute, Helsinki, Finland
8
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO,USA
9
NOAA Global Monitoring Laboratory, 325 Broadway, Boulder, CO, USA
10
Department of Environmental Science, Stockholm University, Stockholm, Sweden
11
Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
12
Environmental Radioactivity Laboratory (ERL), Institute of Nuclear and Radiological Science & Technology,
Energy & Safety, National Centre for Scientific Research ‘‘Demokritos’’, 15310 Attiki, Greece
13
Research Institute for Global Change (RIGC), Japan Agency for Marine-Earth Science and Technology(JAMSTEC), Yokohama, Kanagawa, Japan
14
Graduate School of Maritime Sciences, Kobe University, Kobe, Japan
15
Air Quality Research Center, University of California, Davis, CA, USA
16
Department of Polar Science, School of Multidisciplinary Sciences, The Graduate University for Advanced
Studies, SOKENDAI, Tachikawa, Tokyo, Japan
17
Meteorological Research Institute, Tsukuba, Japan
2*

These authors contributed equally to this work.

35

Correspondence to: Sho Ohata (sho.ohata@isee.nagoya-u.ac.jp)
Running title: Normalization of BC measurements by COSMOS

1

40

Abstract. Long-term measurements of atmospheric mass concentrations of black carbon (BC) areneeded to investigate changes in its emission, transport, and deposition. However, depending on
instrumentation, parameters related to BC such as aerosol absorption coefficient (babs) have beenmeasured instead. Most ground-based measurements of babs in the Arctic have been made by filterbased absorption photometers, including particle soot absorption photometers (PSAP), continuous light45

absorption photometers (CLAP), Aethalometers, and multi-angle absorption photometers (MAAP). The
measured babs can be converted to mass concentrations of BC (MBC) by assuming the value of the mass
absorption cross section (MAC; MBC = babs/MAC). However, the accuracy of conversion of babs to MBC
has not been adequately assessed. Here, we introduce a systematic method for deriving MAC values
from babs measured by these instruments and independently measured MBC. In this method, MBC was50

measured with a filter-based absorption photometer with a heated inlet (COSMOS). COSMOS-derived
MBC (MBC (COSMOS)) is traceable to a rigorously calibrated single particle soot photometer (SP2) andthe absolute accuracy of MBC (COSMOS) has been demonstrated previously to be about 15 % in Asiaand the Arctic. The necessary conditions for application of this method are a high correlation of themeasured babs with independently measured MBC, and long-term stability of the regression slope, which55

is denoted as MACcor (MAC derived from the correlation). In general, babs–MBC (COSMOS)
correlations were high (r2 = 0.76–0.95 for hourly data) at Alert in Canada, Ny-Ålesund in Svalbard,
Barrow in Alaska, Pallastunturi in Finland, and Fukue in Japan, and stable for up to 10 years. We
successfully estimated MACcor values (10.8–15.1 m2 g–1 at a wavelength of 550 nm for hourly data) for
these instruments and these MACcor values can be used to obtain error-constrained estimates of MBC

60

from babs measured at these sites even in the past, when COSMOS measurements were not made.
Because the absolute values of MBC at these Arctic sites estimated by this method are consistent witheach other, they are applicable to the study of spatial and temporal variation of MBC in the Arctic and toevaluation of the performance of numerical model calculations.

2

65

1 Introduction
Black carbon (BC) aerosols strongly absorb solar radiation and thereby impact the radiation budget in
the Arctic (Bond et al., 2013; AMAP, 2015). In addition, BC deposited on snow decreases the snow
surface albedo and accelerates snowmelt (AMAP, 2015; Flanner et al., 2009). According to recent
70

climate model calculations in the sixth phase of the Coupled Model Intercomparison Project (CMIP6;
Eyring et al., 2016), BC contributes the second largest positive radiative forcing in the Arctic, after
carbon dioxide (CO2) (Oshima et al., 2020). BC is one of the short-lived climate forcers (SLCFs) andreductions of BC emissions can decrease the positive Arctic radiative forcing over much shorter
timescales than can reductions of CO2 emissions (Sand et al., 2016). Long-term measurements of mass75

concentrations of BC in the atmosphere (MBC [µg m–3]) at various locations provide fundamental data
for the detection of long-term trends in MBC in the Arctic that are associated with changes in BC
emissions. Such MBC data are also useful for validation and improvement of climate models. However,
because many long-term surface instruments measure aerosol light absorption coefficient (babs [Mm–1])
rather than MBC, there are large uncertainties in MBC estimated from the measurements of babs; these80

uncertainties have not been critically evaluated.
A continuous soot monitoring system called COSMOS (Kanomax, Osaka, Japan) has been developed tomeasure MBC (Miyazaki et al., 2008; Kondo et al., 2009, 2011). This filter-based absorption photometeris equipped with an inlet that is heated to 300°C to remove non-refractory components from the aerosol
phase. COSMOS MBC values (MBC (COSMOS)) have been compared with those measured by a single

85

particle soot photometer (SP2; Droplet Measurement Technologies, Longmont, CO, USA; MBC (SP2)),
which is based on a laser-induced incandescence technique (Schwarz et al., 2006; Moteki and Kondo,
2010); simultaneous measurements in Asia and at Ny-Ålesund in Svalbard have shown that MBC (SP2)and MBC (COSMOS) agree to within about 10 % (Kondo et al., 2009, 2011; Ohata et al., 2019).
Long-term measurements of babs at various sites have been carried out by other types of filter-based90

absorption photometers, including the particle absorption soot photometer (PSAP; Radiance Research,
Seattle, WA, USA), the continuous light absorption photometer (CLAP; NOAA, Boulder, CO, USA;
Ogren et al., 2017), the Aethalometer (Magee Scientific, Berkeley, CA, USA), and the multi-angle
absorption photometer (MAAP; Thermo Scientific, Waltham, MA, USA) (e.g., Schmeisser et al., 2018).
Measurements of light-absorption and light-scattering properties of aerosols are important for

95

constraining their interannual and seasonal variability, potential particle sources, and resulting aerosolradiation interactions in the Earth system (Schmeisser et al., 2018; Bellouin et al., 2020). However, theaccuracy and stability of conversion of babs obtained by these instruments to MBC have not yet been fully
evaluated, mainly because of a lack of simultaneous and reliable long-term MBC measurements. The
3

relationship between babs obtained by these instruments and MBC are complicated by complex
100

contributions from mixing states of BC (i.e., lensing effect by BC-coating materials; Bond et al., 2006:
Lack et al., 2008), other co-existing light-absorbing aerosols such as brown carbon and mineral dust,
and measurement artifacts by light-scattering aerosols on filters (Bond et al., 1999). Evaluations thathave been completed to date include those of Kanaya et al. (2013, 2020), who compared MBC(COSMOS) with the babs measured by MAAP (babs (MAAP)) on Fukue Island, Japan, and Sinha et al.

105(2017), who compared babs measured by PSAP (babs (PSAP)) at Barrow in Alaska and Ny-Ålesund(Zeppelin station), Svalbard. The results of these studies showed that babs (MAAP) and babs (PSAP)
were strongly correlated with MBC (COSMOS), making it possible to convert babs to MBC at these sites
with reasonable accuracy. Long-term observations of babs have been made also at Arctic: Alert in
Canada by PSAP and Aethalometer (Sharma et al., 2004, 2006, 2017), Ny-Ålesund by Aethalometer

110(Eleftheriadis et al., 2009) and MAAP, and Pallastunturi in Finland by MAAP (Hyvärinen et al., 2011;
Lihavainen et al., 2015). To investigate the possibility of converting babs to MBC at each of these sites, it
is important to simultaneously measure MBC and babs by collocating a COSMOS (or SP2) at each sitewith each of these filter-based absorption photometer instruments.
The conversion of babs obtained by these instruments to MBC can be made by assuming a reasonable

115

conversion factor, i.e, the value of mass absorption cross section (MAC [m2 g–1]; MBC = babs/MAC). The
MAC values can depend on location because the spatiotemporal variations in microphysical properties
of BC (i.e., mixing states and size distributions) and properties of co-existing light-absorbing andscattering aerosols will affect babs measurements. The plausible MAC values for conversion can also
depend on the type of instrument because each instrument uses a different wavelength or wavelengths

120

and adopts various correction methods for quantifying babs. Despite several intercomparisons and field
experiments (Asmi et al., 2020) as well as thorough assessment of these techniques (Lack et al., 2008;
Moosmüller et al., 2009) the simultaneous changes in aerosol source region, mixing state, concentrationand particle optical size are reflected in the instruments' response in a complex way and with a variablelevel of uncertainty.

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In general, the MAC of BC, here simply denoted as “MACBC” for both bare and internally-mixed BC, isa fundamental optical parameter that relates MBC with babs of BC (babs,BC) in climate models (i.e., babs,BC= MBC × MACBC). Bond and Bergstrom (2006) reported the MACBC value of 7.5 m2 g–1 at a wavelengthof 550 nm for combusted fresh BC. Cho et al., (2021) estimated MACBC values of 6–12 m2 g–1 at 550
nm in the Asian outflow using aircraft-based SP2 data and Mie theory. Yuan et al. (2021) showed that130

the MACBC values at 870 nm at a rural site in Germany clearly increased as the coating thickness of BC
increased.
However, in this paper we focus on the MAC values mainly from the viewpoint of a conversion factor
to obtain error-constrained MBC from the babs measurements by the filter-based absorption photometers4because such MBC data will be the observational base for understanding long-term trends and spatial
135

distributions of BC in the Arctic. Detailed investigations of the accuracy of the absolute values of babs
measured at each site are beyond the scope of this study.
We critically re-examine the concepts underpinning the use of filter-based instruments to estimate MBC.
We derive MAC values for PSAP/CLAP, Aethalometer, and MAAP measurements based on theircomparison with COSMOS measurements at the four above-mentioned Arctic sites (Alert, Ny-Ålesund,140

Barrow, and Pallastunturi) and one East Asian site (Fukue). The variability of the derived MAC values
and their dependencies on observation site and instrument type are analyzed. We also compare MBC
values measured by COSMOS and SP2 at Alert and Fukue to confirm their agreement under different
environmental conditions.

2 Methods
145

2.1 Observation sites
Measurements of babs by the various types of filter-based absorption photometers were compared withmeasurements of MBC by COSMOS at Arctic sites Alert in Canada (82.5º N, 62.5º W; Sharma et al.,2017), Ny-Ålesund (Zeppelin station) in Svalbard (78.9º N, 11.9º E; Sinha et al., 2017), Barrow in
Alaska (71.3º N, 156.6º W; Sinha et al., 2017), and Pallastunturi (Pallas, hereafter) in Finland (68.0º N,

150

24.0º E; Hyvärinen et al., 2011), as summarized in Table 1 and Fig. 1. Along with these sites,
comparisons were also made at a remote site on Fukue Island (32.8º N, 128.7º E; Kanaya et al., 2020) inJapan, where air masses from the Asian continent are occasionally transported to the site and properties
of aerosols should be distinctly different from those at the Arctic sites. Instruments used at each site arelisted in Table 1 and described in the following section.

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2.2 Instruments
2.2.1 SP2
In this study we used the SP2 and COSMOS as standard instruments to measure MBC. Detailed
descriptions of the SP2, including calibration methods, are given elsewhere (Schwarz et al., 2006;
Moteki and Kondo, 2010). Briefly, the SP2 uses the laser-induced incandescence technique and detects

160

BC on a single-particle basis. We used two SP2s in this study: the one installed at Fukue wasmaintained and calibrated by the University of Tokyo (UT-SP2, hereafter) and the other one at Alert
was maintained and calibrated by Environmental and Climate Change Canada (EC-SP2, hereafter). The
configuration of the UT-SP2 is identical to that described by Moteki and Kondo (2010). The model
designation of the EC-SP2 was “SP2-D” with eight channels. The UT-SP2 and EC-SP2 measured BC

165

size distributions in the mass-equivalent diameter (Dm) range 70–850 and 60–600 nm, respectively. The
void-free density of BC was assumed to be 1.8 g cm–3. These SP2s were calibrated using fullerene soot5

particles (Alfa Aeser, stock #40971, lot #FS12S011; Moteki and Kondo, 2010; Kondo et al., 2011). The
laser-induced incandescence signal intensity of the UT-SP2 for the specific mass of ambient BC
particles in Tokyo agree with that of fullerene soot particles to within about 10 % (Kondo et al., 2011).
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Laborde et al. (2012) reported similar SP2 calibration curves for fullerene soot particles, diesel exhaust,
and ambient BC particles in Switzerland. The accuracy of MBC (SP2) estimated from the uncertainty of
the calibration and operational conditions of SP2 was about 10 %. No particle-size cut was used for the
inlet of the UT-SP2, whereas a PM1 cyclone was used for the EC-SP2.
2.2.2 COSMOS

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2.2.2.1 Measurements of MBC by COSMOSThe principles of operation of the COSMOS apparatus are detailed in previous papers (Miyazaki et al.,2008; Kondo et al., 2011; Kondo, 2015; Ohata et al., 2019). Briefly, the COSMOS measures theattenuation coefficient (b0) of aerosols collected on a quartz-fiber filter at a given wavelength (λ = 565
nm). Most previous studies used filters from Pallflex (E70-2075W, Pall, Port Washington, NY, USA),180

which are no longer available. Consequently, high-efficiency particulate air (HEPA) filters (L-371M)
have been used for more recent observations (Irwin et al., 2015), including this study. An importantdifference between the COSMOS and the other types of filter-based absorption photometer is that the
inlet of the COSMOS is heated to 300°C to remove volatile light scattering particles (LSPs) andcoatings of BC from the aerosol phase. Therefore, the effect on b0 of co-existing volatile components

185

externally or internally mixed with BC particles can be ignored. The COSMOS is equipped with a PM1
cyclone to minimize the effect in coarse mode of refractory non-BC particles, such as dust and sea-salt
particles. Consequently, the absorption coefficient for the COSMOS is given as𝑏!"# (COSMOS) = 𝑓$%& 𝑏' .

(1)Here, ffil is a factor used to correct for the increase of absorption caused by multiple scattering in the
190

filter medium. It is given by
1

𝑓$%& (𝑇𝑟) = [1.0796 Tr + 0.71] B with Tr ≥ 0.7,

(2)where Tr is the filter transmission and B is a scaling factor (Bond et al. 1999; Ogren 2010; Ohata et al.,2019). The MAC for the COSMOS [m2 g–1] is operationally defined as
MAC (COSMOS, SP2) ≡195)!"# (+,-.,-)
0$% (-12),(3)

where the numerator and denominator, respectively, are simultaneous measurements of babs [Mm–1] by
COSMOS and MBC [µg m–3] by SP2 for ambient air. The MAC value for a Pallflex filter at λ = 565 nm
was previously set at 8.73 [m2 g–1] with B = 1.397 (Sinha et al., 2017). For a HEPA filter, the value of B

6

is about 6 % lower (Irwin et al., 2015). Depending on the filters used (Pallflex or HEPA), theappropriate B value was used in this study.
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Once the MAC (COSMOS, SP2) is determined, MBC (COSMOS) [g m–3] at standard temperature andpressure (0°C, 1013 hPa) can be estimated as)(+,-.,-)

𝑀3+ (COSMOS) = .4+!"#(+,-.,-,-12).

(4)

One particular purpose of the heating of sampled air to 300°C is to make the MAC (COSMOS, SP2)
stable and independent of original mixing states of BC particles. In other words, the heating treatment
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makes babs (COSMOS) more proportional to BC mass concentrations, as compared to the other filterbased absorption photometers described in Sect. 2.2.3. As a consequence, unlike the other filter-based
absorption photometers, the absorption coefficient of unheated original aerosols is not provided by
COSMOS. Thus, the COSMOS has been developed to measure MBC, not babs. In this sense, MBC(COSMOS) is different from “equivalent” BC mass concentrations estimated from the unheated babs

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measurements (Petzold et al., 2013).
We call the COSMOS that was calibrated by comparison with the SP2 in Tokyo the “standard
COSMOS”, described hereafter as Std-COSMOS. Because the MAC of the Std-COSMOS wasdetermined by comparison with SP2 (Eq. (2)), it acts as a transfer standard for the SP2. The babs
(COSMOS) of each COSMOS manufactured is compared with the Std-COSMOS by sampling ambient

215

BC particles in Osaka, Japan, typically for 1–2 weeks. The comparisons during these periods were
statistically reliable partly due to relatively high BC concentrations in Osaka. The babs (COSMOS) of 28
COSMOS instruments manufactured thus far agree with that of Std-COSMOS to within about ±7 %,indicating reliable quality control in manufacturing. The small differences originating from theuncertainty of the filter sampling spot size of each unit are corrected for in deriving MBC (COSMOS).

220

It is important to compare MBC (COSMOS) and MBC (SP2) outside Tokyo and Osaka, to confirm boththe strong correlation between MBC (COSMOS) and MBC (SP2) and the long-term stability of the MAC(COSMOS) value. Ohata et al. (2019) made these comparisons at two remote sites: at Cape Hedo
(26.9°N, 128.3°E), Japan, and at Ny-Ålesund. At each of these locations, the concentrations of BC and
LSP and the mixing states of BC were considerably different from those in Tokyo and Osaka. MBC

225(COSMOS) and MBC (SP2) agree to within about 10 % at these sites, thus demonstrating the validity ofusing the Std-COSMOS to calibrate each of the COSMOS instruments to be used for field observations.
Ohata et al. (2019) also showed that the dependencies of MAC (COSMOS) on the thickness of coatingsof BC particles, MBC, and volume concentrations of the co-existing LSPs were small. Although the
MAC (COSMOS) showed a slight dependence on the mass size distributions of BC, the sensitivity of

230

the MAC (COSMOS) to such variations in microphysical properties of BC was generally less than 10 %(Kondo et al., 2011; Ohata et al., 2019).
7

Previously estimated uncertainties of MBC (COSMOS) were about 10 % based on the range ofagreement between MBC measurements by COSMOS and UT-SP2 (Kondo et al., 2011; Ohata et al.,2019). It may be more appropriate to estimate the absolute accuracy of MBC (COSMOS) to be about
235

15 %, including the above-mentioned 10 % uncertainty of MBC (SP2). This 15 % uncertainty also covers
the range of agreement between MBC (COSMOS) and MBC (SP2) previously reported by other groups at
Ny-Ålesund (Zannata et al., 2018) and at Fukue (Miyakawa et al., 2017).
Although we used the SP2 and COSMOS as standard instruments to measure MBC in this study,
thermal-optical analysis, which quantifies elemental carbon (EC) mass concentrations (MEC), also has

240

been a traditional standard method to measure BC. Measurements of MEC can depend on thetemperature protocol and optical charring correction method used (e.g., Bond et al., 2013). Agreements
within 10 % of MBC (SP2), MBC (COSMOS), and MEC were reported by Kondo et al. (2011), whereas
systematic differences between MBC (SP2) and MEC up to a factor of 2 were found by Pileci et al. (2021).
Although the difference between MBC (COSMOS) and MEC was generally lower than 5 ng m–3 at the245

Arctic site Barrow (Sinha et al., 2017), this difference can be important for pristine summer Arctic
conditions (MBC (COSMOS) < 20 ng m–3). Considering these previously reported agreements anddiscrepancies between MBC (SP2 or COSMOS) and MEC, in some cases the MAC values determined bybabs and MBC measurements (this study) can differ from those determined by babs and MEC measurements(Zanatta et al., 2016).

250

2.2.2.2 Effect of light-absorbing FeOx particles on MBC (COSMOS)
Light-absorbing iron oxide (FeOx) aerosols such as magnetite, which the SP2 can distinguish from BC(Yoshida et al., 2016; Lamb, 2019), can affect MBC measured by filter-based absorption photometers.
FeOx aerosols are emitted from both anthropogenic sources (e.g., motor vehicle exhaust) and natural
sources (e.g., wind-blown mineral dust). Within the detectable diameter range of the UT-SP2 (Dm = 70–

255

850 nm for BC and Dm =170–2100 nm for FeOx), the mass concentration ratios of FeOx to BC weretypically ~0.4 in East Asia and ~0.2 in the Arctic; they were mainly of anthropogenic origin in the formof aggregated magnetite nanoparticles in both regions (Moteki et al., 2017; Ohata et al., 2018; Yoshida
et al., 2018, 2020). FeOx aerosols contribute at least 4–7 % of the short-wave absorbing powers of BC
in Asian continental outflows (Moteki et al., 2017) and their direct radiative forcing has been estimated260

to be 0.22 W m–2 over East Asia (Matsui et al., 2018). Here, we estimate the effect of light absorption
by FeOx on MBC measured by the COSMOS. The ratio of light absorbed by FeOx to that absorbed by
BC at a wavelength l (e(l)) is given by
0 '(𝜀(𝜆) =)*+, .4+∫0 6 '-./023*_)*+, (75 ,8) 9&:;71
7
108 '($%∫0 '-./0 .4+23*_$% (75 ,8) 9&:;71
9
1

,

(5)

8

where Dm is mass equivalent diameter of bare BC or FeOx; DL and DU are the lower and upper limits,
265

respectively, of the diameter for the integral calculus; dMBC/dlogDm and dMFeOx/dlogDm are the mass
size distributions of BC and FeOx, respectively; and MACMie_BC (Dm, l) and MACMie_FeOx (Dm, l) are
the MAC values of bare BC and FeOx, respectively, for Dm and l calculated by Mie theory.
The mass size distributions of BC and FeOx at Fukue and Ny-Ålesund (Fig. 2) were obtained by fitting
monomodal and bimodal lognormal functions to the average mass size distributions measured by the270

SP2 during each observation campaign (Yoshida et al., 2020). The measurements at Fukue were madein April 2019 and those at Ny-Ålesund in March 2017. The MACMie_BC (Dm, l) and MACMie_FeOx (Dm,l) data (Fig. 2) were calculated by Mie theory for l = 565 nm (wavelength used for COSMOS). For thiscalculation, we assumed BC and FeOx to be in the form of bare spheres with void-free densities of 1.80
g cm–3 and 5.17 g cm–3, respectively. The refractive index of BC we used was 1.99 + 0.64i, which is the275

value for BC at l = 600 nm (Bergstrom, 1972). The refractive index of FeOx we used was 2.56 + 0.57i,
which is the value for magnetite at l = 600 nm (Huffman and Stapp, 1973).
From Eq. (4), the e values at Fukue and Ny-Ålesund were calculated to be 3.6 % and 1.9 %,respectively, for (DL, DU) = (30, 1000 nm). These e values became 4.6 % and 2.6 % for (DL, DU) = (30,2500 nm). Because COSMOS is equipped with a PM1 cyclone, we estimated the effect of light280

absorption by FeOx on MBC measured by COSMOS to be < 4 % in East Asia and < 2 % in the Arctic.
Note that these estimates are upper limits of the effect of FeOx because the PM1 cyclone is designed toremove particles of > 1 µm aerodynamic diameter (Da). Due to the fractal shape and high density of
FeOx particles (Moteki et al., 2017), Dm is considerably smaller than Da for FeOx particles and thus DU
in Eq. (4) should be less than 1 µm.

285

The effect of FeOx on MBC (COSMOS) should be even smaller considering that the mass concentrationof anthropogenic FeOx is correlated with MBC, as mentioned above. Even if babs (COSMOS) is enhanced
by FeOx by a few percent, this effect is already incorporated to some extent, by operationally defining
MAC (COSMOS, SP2) by Eq. (2).
The effect of FeOx on babs may be somewhat higher for the other filter-based absorption photometers290

than for COSMOS if they are equipped with a larger particle size cut (PM2.5 or PM10). For accurate
measurements of MBC, the use of a PM1 cyclone or impactor is recommended to minimize the effects of
FeOx, as well as other refractory particles such as natural dust and sea-salt particles.

92.2.3 Filter-based absorption photometers other than COSMOS
2.2.3.1 PSAP and CLAP
295

The principle of operation of the PSAP is similar to those of COSMOS (Bond et al., 1999; Sinha et al.,2017). In this study, we also used babs data obtained with a continuous light absorption photometer(CLAP) (Ogren et al., 2017). The CLAP is conceptually similar to the PSAP but uses solenoid valves tocycle through eight sample filter spots. The PSAP and CLAP both utilize the Pallflex filters. The unitto-unit variations of the PSAP and CLAP were reported to be within 6 % (Bond et al.,1999) and 4 %300(Ogren et al., 2017), respectively. The wavelengths of the light absorption measured by either PSAP or
CLAP at Barrow, Ny-Ålesund, and Alert were about 467, 530, and 660 nm. The major difference of the
PSAP and CLAP from the COSMOS is that the sample air inlets of the PSAP and CLAP are not heatedto 300°C. Therefore, the effect of the attenuation of light by LSPs is corrected for by using the aerosol
light scattering coefficient simultaneously measured by an integrating nephelometer (Bond et al., 1999;

305

Ogren, 2010). This correction adjusts for measurement artifacts but introduces uncertainties in theestimate of babs (PSAP or CLAP). At the above three sites, light scattering coefficients measured bynephelometers at wavelengths of 450, 550, and 700 nm were used for this correction. The babs for the
PSAP or CLAP (hereafter, babs (PSAP/CLAP)) at λ = 550 nm was obtained by adjusting measured
absorption at 530 to 550 nm by using the λ–1 relationship (Sinha et al., 2017; Sharma et al., 2017).

310

Schmeisser et al. (2017) reported that the median value of the absorption Ångström exponent at Arctic
sites was 1.04, which supports our assumption of the λ–1 relationship. The accuracy of the babs measured
by PSAP ranges between 20 and 30 % (Bond et al., 2013). Note that a custom-built PSAP (Krecl et al.,2007) was used at Ny-Ålesund and commercial ones were used at Alert and Barrow.
2.2.3.2 Aethalometer315

An AE-31 Aethalometer (Hansen et al., 1984) has been used for measurements of babs at Alert without
any particle size cut (Sharma et al., 2017). This Aethalometer measures the attenuation (ATN) of lighttransmitted through particles accumulating on a quartz fiber filter at seven wavelengths (370, 470, 520,
590, 660, 880, and 950 nm). In deriving babs (Aethalometer) from ATN data, the correction factor Cf =3.45 (Backman et al., 2017) was applied. This correction factor is very close to the correction factor C0320= 3.5 recommended by the World Meteorological Organization/Global Atmosphere Watch(WMO/GAW, 2016). The uncertainty of C0 is approximately 25 % (WMO/GAW, 2016).
Another AE-31 Aethalometer has also been used at Ny-Ålesund (Zeppelin station) (Eleftheriadis et al.,2009), where the sampling inlet was equipped with a calculated PM10 size cut. Data post-processing
included flagging based on Zeppelin station logs, Ny-Ålesund harbor logs and diagnostics reported by325

the instrument (flowrate, raw attenuation, zero signal, etc). A correction factor C0 = 3.5 was used tocompensate for the multiple scattering effect.
10

The filter loading effect is not significant for Arctic aerosol, as reported by Backman et al. (2017). For
Alert, the slope of the correction factor Cf-1 to ATN is k = 0.00074, indicating a 5 % difference at an
ATN value of 80. For Zeppelin, the loading effect causes a 2 % difference in Cf at an attenuation value330

of 80. These uncertainties are considered small compared to the overall babs uncertainty, which is 20–
30 % (Bond et al., 2013). Therefore, the loading correction is not applied to the AE31 measurements.
Corrections for light scattering by using nephelometer data were also not applied. One of the
manufacturer’s suggested values of MAC (Aethalometer) is given by 14625 / (λ [nm] ×C0) [m2 g–1]
which corresponds to 7.1 m2 g–1 for λ = 590 nm and C0 = 3.5.

335

2.2.3.3 MAAP
Detailed descriptions of the MAAP are given elsewhere (Petzold et al., 2002, 2005; Petzold andSchӧnlinner, 2004; Kanaya et al., 2013). In brief, the MAAP monitors the transmittance of light through a
glass-fiber tape and measures reflectance at two angles. To remove the influence of LSPs, babs (MAAP)
from particles deposited on the filter is derived by radiative transfer calculations. The uncertainty of babs

340

(MAAP) was estimated by Petzold and Schӧnlinner (2004) to be 12 %. The unit-to-unit variation of the
MAAP was reported to be within 5 % (Müller et al., 2011). The MAC values for the MAAP (MAC
(MAAP)) for λ = 637 nm was determined by comparing babs (MAAP) and MBC measured at four sites inGermany by the German reference method VDI2465 Part 1 (GRM; Schmid et al., 2001), represented by(.441))

MAC (MAAP, GRM) ≡ 0!"# (<=.) .

(5)

$%

345

For the measurements of MBC (GRM), organic carbon was removed by solvent extraction and the residualBC particles on the filters were oxidized to CO2 and quantified by coulometric titration. The measurement
uncertainty of MBC (GRM) was about 25 % (Petzold and Schӧnlinner, 2004). The MAC of 6.6 m2 g–1 is thedefault setting by the manufacturer based on their study. In determining MAC (MAAP, GRM), an SP2 was
not used to measure MBC, and this is a potential source of discrepancy in this value of MAC, as discussed

350

in Sect. 3.4.1 and 3.5.2. A correction factor of 1.05 due to the wavelength shift from the nominal value(Müller et al., 2011) was applied in this study. Note that the measured peak wavelength of the light sourceof the MAAP at Fukue was 639 nm (Kanaya et al., 2013), which is very slightly different from thepreviously reported value (637 nm; Müller et al., 2011).

3 Results and discussion
355

3.1 Alert
3.1.1 COSMOS-SP2 comparison
Long-term measurements of BC using different model versions of SP2s have been conducted at Alert
since 2011 (Sharma et al., 2017). In this study, we used the data obtained by an EC-SP2 (model “SP2-D”
11with eight channels; see Sect. 2.2.1) from January to May 2018 for comparison with the COSMOS data.
360

The EC-SP2 and COSMOS aspired sample air from a common inlet with a PM1 size cut. Fig. 3a shows
the number and mass size distributions of BC averaged over the observation period. The mode diameterof the average mass size distribution of BC was ~210 nm in mass-equivalent diameter, which is similar
to that previously reported at Alert (Sharma et al., 2017) and to that observed by aircraft-based
measurements over Alert (Schulz et al., 2019). Because the upper limit of the detectable diameter range

365

of BC was ~600 nm for the EC-SP2, we have estimated MBC (SP2) over the range up to 1000 nm byfitting lognormal functions to the measured mass size distributions. The time series of hourly values of
MBC (COSMOS) and MBC (SP2) (Fig. 3b) were strongly correlated (r2 = 0.92; r2 is the square of thecorrelation coefficient) and the slope of the regression forced through the origin was 1.02 (Fig. 3c).
Based on the slope value of the regression for whole MBC ranges observed, the agreement between MBC

370(COSMOS) and MBC (SP2) at Alert was generally within 10 %. The degree of agreement between MBC(COSMOS) and MBC (SP2) was also examined on a logarithmic scale in Fig. S1a in the Supplement.
When MBC (SP2) is relatively low (MBC < 10 ng m–3), which corresponds to the monthly-averaged MBC
ranges in summer at Arctic sites (Sinha et al., 2017), MBC (COSMOS) tended to be higher than MBC(SP2) by about 1–2 ng m–3. This small absolute difference is consistent with the previously reported

375

difference between MBC (COSMOS) and MEC at Barrow (Sinha et al., 2017).
Although this agreement between MBC (COSMOS) and MBC (SP2) at Alert was consistent with thosereported in previous studies using UT-SP2 (Kondo et al., 2011; Ohata et. al., 2019), note that there weresome differences between MBC (SP2) measured by the EC-SP2 and that by the UT-SP2. The EC-SP2
was calibrated using Aquadag samples at Alert during the observation period and also calibrated using

380

fullerene soot samples at the Paul Scherrer Institute in Switzerland after the observation period. Because
the sensitivity of the incandescence signals of the SP2 to Aquadag is higher than that to fullerene soot,the calibration curve for Aquadag needs correction to obtain the fullerene-soot equivalent calibration
curve (Baumgardner et al., 2012). Additionally, to make this correction, assumptions of the effective
density (reff) values of Aquadag (Moteki and Kondo, 2010; Gysel et al., 2011), which depend on the385

mobility diameter of Aquadag, are needed since a differential mobility analyzer (DMA) is used for theon-site calibration at Alert instead of an aerosol particle mass analyzer (APM) or a centrifugal particle
mass analyzer. The reff values of Aquadag samples can depend on their batches (Gysel et al., 2011). In
the previous study by Sharma et al. (2017), the constant value of reff (= 0.7 g cm–3) for Aquadag was
assumed in order to derive MBC (SP2) at Alert. However, we have found that MBC (SP2) at Alert was

390

highly dependent on the assumed reff values of Aquadag used for the on-site calibration with a DMA.
Because of this, we used the calibration curve obtained by fullerene soot with an APM at the Paul
Scherrer Institute after the observation period for this study. The conditions of the EC-SP2 might havediffered slightly during and after the observation period, which may lead to additional uncertainties for
12

MBC (SP2) at Alert, although the difference between Aquadag calibrations made before and after the395

campaign was less than about 10 %. In addition, the upper limit of the detectable diameter of BC for the
EC-SP2 (Dm ~600 nm) was lower than that for the UT-SP2 (Dm ~850 nm), although the abovementioned extrapolation up to 1000 nm was made to derive MBC (SP2) at Alert. Despite thesedifferences between EC-SP2 and UT-SP2, MBC (COSMOS) and MBC (SP2) agree to within 10 % at
Alert, consistent with previous studies that reported the stability of the relationship between MBC

400(COSMOS) and MBC (SP2) at various sites (Kondo et al., 2011; Ohata et. al., 2019).
3.1.2 COSMOS-PSAP comparison
Measurements of MBC (COSMOS) at Alert began in January 2018. A PM1 cyclone was used for the
COSMOS and a PM1 impactor for the PSAP and two CLAP instruments (CLAP1, CLAP2). The timeseries of 1-h and 24-h averaged MBC (COSMOS) were strongly correlated with babs (PSAP; λ = 550 nm)

405

for 2018–2019 (r2 ~0.96; Fig. 4a–d). In this study, we define the MAC value, MACcor, as the slope ofthe least squares regression forced through the origin in the correlation plot. The values of MACcor(PSAP; λ= 550 nm) for the whole period were 13.9 m2 g–1 and 14.0 m2 g–1 for the 1-h and 24-h
averaged data, respectively, as summarized in Table 2. The results of the same analyses for otherwavelengths of the PSAP and the two CLAPs show that the strength of the correlation depended little

410

on wavelength (Table 2). The babs, and therefore the MAC, for the PSAP and the two CLAP instruments(CLAP1, CLAP2) agree to within 13 % at λ = 550 nm, indicating a small difference in the performanceof these instruments.
Along with the correlation analysis, variability of the babs (PSAP) / MBC (COSMOS) ratio was also
analyzed for the 1-h and 24-h data (Fig. 4e and f). This ratio can be interpreted as an hourly or daily

415

MAC value at each time. Because this ratio tends to be unstable when the MBC (COSMOS) values arevery low, we set a threshold MBC (COSMOS) value of 2 ng m–3 in this analysis, as shown in thesefigures. The median ratio, defined as median MAC and denoted as MACmed, was 13.5 m2 g–1 for both 1h and 24-h data, which is very close to MACcor (13.9 m2 g–1 and 14.0 m2 g–1 for the 1-h and 24-h data,
respectively) (Table 3). The difference between MACcor and MACmed was about 4 %, leading to the420same difference between the estimated MBC values if these MAC values are used for conversion of babs
(PSAP) to MBC. Based on the interquartile ranges of the babs (PSAP) / MBC (COSMOS) ratios (Fig. 4e
and f), variations of the ratios (with an MBC threshold of 2 ng m–3), denoted as VMAC, were within 19 %and 18 % of the MACmed values for the 1-h and 24-h data, respectively (Table 3). Therefore, conversionof 1-h and 24-h averaged babs (PSAP; λ = 550 nm) data to MBC by assuming a constant MACmed leads to425

uncertainty of about 19 % at Alert. We used the same method in estimating MACcor, MACmed, and VMAC
for other instruments and other locations, as summarized in Table 3. Note that this estimated uncertainty
can depend on the threshold value of MBC (COSMOS) assumed in the analysis. Fig. S2 in the
Supplement shows histograms of the babs (PSAP) / MBC (COSMOS) ratios for MBC (COSMOS) < 10 ng
13m–3. While similar MACmed values were obtained for data with MBC (COSMOS) < 10 ng m–3 and for all
430

dataset, the interquartile ranges of the babs (PSAP) / MBC (COSMOS) ratios are larger for MBC(COSMOS) < 10 ng m–3. The relative uncertainty becomes higher (lower) in summer (winter/spring)when the MBC values tend to be low (high) (Fig. 4a and b).
3.1.3 COSMOS–Aethalometer comparison
Measurements of babs at Alert were made by an Aethalometer at wavelengths of 370, 470, 520, 590, 660,

435

880, and 950 nm without any particle size cut. Time series of babs (Aethalometer; λ = 590 nm) and MBC(COSMOS) in 2018–2019 are shown in Fig. S3a and b in the Supplement. babs (Aethalometer; λ = 590nm) was highly correlated (r2 > 0.90) with MBC (COSMOS) (Fig 5a and b). The MACcor (Aethalometer;
λ = 590 nm) values were 12.5 and 12.7 m2 g–1 for the 1-h and 24-h data, respectively. The MACmedvalues of the babs (Aethalometer) / MBC (COSMOS) ratios were 13.5 m2 g–1 and 13.8 m2 g–1 for the 1-h

440

and 24-h data, respectively (Fig. 5c and d), which agree with the MACcor values to within 8 %.
Therefore, depending on the MAC values used, the estimated MBC values can differ by about 8 %.
Because the interquartile ranges of the babs (Aethalometer) / MBC (COSMOS) ratios were 11.4–16.5 m2
g–1 (1-h data) and 12.1–15.7 m2 g–1 (24-h data), the VMAC was about 22 % (with an MBC threshold of 2
ng m–3) for babs (Aethalometer; λ = 590 nm) at Alert (Table 3). The MACmed values for low MBC data

445

(MBC (COSMOS) < 10 ng m–3) agree with those for all datasets to within 10 % (Fig S3c and d in the
Supplement).
The MACcor (Aethalometer) values for each wavelength are summarized in Table 4. Note that thesewavelength-dependent MACcor values should be interpreted as the simple conversion factors to obtainaverage MBC from babs (Aethalometer), which might have been contributed to by BC and also other450

light-absorbing aerosols. In other words, these MACcor values differ from MACBC, as discussed in Sect.
1. The r2 values were generally high for all wavelengths examined. This weak dependence on
wavelength indicates that the contribution of other light-absorbing aerosols such as brown carbon (BrC)
to babs (Aethalometer) is small or the BrC/BC concentration ratio was rather stable at Alert during
2018–2019, because BrC should enhance light absorption in near ultraviolet wavelengths.

455

The MACcor (Aethalometer) and MACcor (PSAP) are compared in Table 5. They agree within 10 % at
three wavelengths, despite the different particle size cuts of the inlets for Aethalometer (total suspended
particle) and PSAP (PM1). This agreement is consistent with the results by Backman et al. (2017), whoshowed that the correction factor Cf of 3.45 for Aethalometer harmonizes babs (Aethalometer) with babs
(PSAP), babs (CLAP), and babs (MAAP) at Arctic sites.

14

460

3.2 Ny-Ålesund
3.2.1 COSMOS-PSAP comparison
Simultaneous measurements of MBC (COSMOS) for PM1 and babs (PSAP) for PM10 began at NyÅlesund in 2012 (Sinha et al., 2017; Fig. S4a and b in the Supplement). The 1-h and 24-h averaged babs
(PSAP; λ = 550 nm) were well correlated (r2 = 0.76–0.82) with MBC (COSMOS), and the MACcor

465(PSAP) value for the whole period was 14.4–15.2 m2 g–1 (Fig. 6a and b). Year-to-year variations of
MACcor (PSAP) are also shown in Fig. 7a and Table 6. The correlation between babs (PSAP) and MBC(COSMOS) during April–December 2012 was weak for unknown reasons. Excluding this period,
average MACcor (PSAP) during 2013–2016 was 15.2 ± 2.2 (1σ) and 16.6 ± 1.4 m2 g–1 for the 1-h and24-h data, respectively. Although the reason for the relatively large change in MACcor (PSAP) values

470

during 2014–2015 (Fig. 7a and Table 6) is not clear, this may be partly because babs (PSAP) data from
December 2014 to April 2015 (during an “Arctic haze” period) were not available (Fig. S4a and b in the
Supplement).
The MACmed values of the babs (PSAP) / MBC (COSMOS) ratios were 16.7 and 17.2 m2 g–1 for the 1-h
and 24-h data, respectively, when the MBC threshold of 2 ng m–3 was applied in the analysis (Fig. 6c and

475d). The MACmed values were by 16 % and 13 % higher than MACcor for 1-h and 24-h data, respectively(Table 3). Therefore, conversion of babs (PSAP; λ = 550 nm) to MBC using a constant MACcor may resultin a slightly biased MBC, especially for lower babs data. This is partly because the correlation of babs
(PSAP) with MBC (COSMOS) is not very high and scatter of the data, especially those with lower MBC
values, contributes to large variations of the babs (PSAP) / MBC (COSMOS) ratios (Fig. 6 and Fig. S4c

480

and d in the Supplement). The interquartile range of the ratios were 10.6–21.7 m2 g–1 and 11.9–21.4 m2
g–1 for the 1-h and 24-h data, respectively. Although these large variations might be partly attributed toactual variations in mixing states of BC, artifacts of babs measurements by PSAP at Ny-Ålesund may be
a contributing factor, considering the higher correlations of babs (Aethalometer) and babs (MAAP) at NyÅlesund with MBC (COSMOS) (Sect. 3.2.2 and 3.2.3). Based on the interquartile ranges of the babs

485

(PSAP) / MBC (COSMOS) ratios, VMAC was 37% and 31% for 1-h and 24-h data, respectively (Table 3).
The above-mentioned bias leads to an additional uncertainty of about 15 % for the estimates of MBC, if
the constant MACcor value is used.
3.2.2 COSMOS-Aethalometer comparison
Measurements of babs (Aethalometer; λ = 590 nm) for PM10 were compared with measurements of MBC

490(COSMOS) for PM1 during 2012–2019. The time series data of babs (Aethalometer) were highly
correlated with those for MBC (COSMOS) (Fig. S5a and b in the Supplement) (r2 = 0.90 for both the 1-h
and 24-h data; Fig. 8a and b). The MACcor (Aethalometer) values were 10.2 and 10.1 m2 g–1 for the 1-h
and 24-h data, respectively. Year-to-year variations of MACcor (Aethalometer) are also shown in Fig. 7a15and Table 7. The r2 values were generally high for each year and the average MACcor (Aethalometer)
495

during 2012–2019 was 10.2 ± 1.6 (1σ) and 10.0 ± 1.3 m2 g–1 for the 1-h and 24-h data, respectively. The
MACcor (Aethalometer) value for 2012 was 8.7 m2 g–1 at 590 nm (i.e., 9.3 m2 g–1 at 550 nm assuming
the λ–1 relationship) for 24-h data, which is consistent with the MACcor of 9.8 m2 g–1 at 550 nm inferredfrom the SP2 and Aethalometer measurements in the spring of 2012 (Zanatta et al., 2018). At NyÅlesund, the MACcor (Aethalometer) values (10.2 and 10.1 m2 g–1 for the 1-h and 24-h data,

500

respectively) were systematically lower than the MACcor (PSAP) values (14.4 m2 g–1 and 15.2 m2 g–1).
This discrepancy is different than at Alert (Sect. 3.1.3) and the reason is unclear, but could be partly due
to uncertainty in the absolute values of babs, as discussed in Sect. 1 and Sect. 2.2.3.
The MACmed values of the babs (Aethalometer) / MBC (COSMOS) ratios were 11.2 and 12.3 m2 g–1 forthe 1-h and 24-h data, respectively (Fig. 8c and d). While the MACmed for the 1-h data agree with

505

MACcor to within 10 %, there is a 22 % discrepancy for the 24-h data under the assumed thresholdsetting (2 ng m–3) of MBC (COSMOS) (Table 3). Therefore, conversion of 24-h averaged babs
(Aethalometer; λ = 590 nm) to MBC using a constant MACcor may be somewhat biased, especially forlower babs values (Fig S5c and d in the Supplement). At Ny-Ålesund, the VMAC was about 25 % for babs
(Aethalometer; λ = 590 nm). The above-mentioned bias leads to an additional uncertainty of about 20 %510

for conversion of 24-averaged low babs data to MBC, if the constant MACcor value is assumed.
3.2.3 COSMOS-MAAP comparison
Measurements of babs (MAAP; λ = 637 nm) without any particle size cut were compared withmeasurements of MBC (COSMOS) for PM1 during 2017–2020. The time series of babs (MAAP) and MBC(COSMOS) tracked each other (Fig. S6a and b in the Supplement) and were highly correlated (r2 =515

0.90 for the 1-h data and r2 = 0.83 for the 24-h data; Fig. 9a and b). The MACcor (MAAP) values were
10.6 and 10.9 m2 g–1 for the 1-h and 24-h data, respectively. These MACcor values are about 60 %higher than the manufacturer’s default setting (= 6.6 m2 g–1) of MAC (MAAP). One possible reason isthe difference of the methods of MBC measurements to determine MACcor (MAAP) values, asmentioned in Sect. 2.2.3.3. Another reason could be that the difference in microphysical properties of520

BC (mixing states and size distribution) and properties of LSPs led to the difference in the MACcor(MAAP) values.
Year-to-year variations of MACcor (MAAP) are also shown in Fig. 7a and Table 8. The r2 values weregenerally high for each year and the average MACcor (MAAP) during 2017–2020 was 11.1 ± 0.7 (1σ)
and 11.7 ± 1.1 m2 g–1 for the 1-h and 24-h data, respectively. The MACmed values of the babs (MAAP) /525

MBC (COSMOS) ratios were 10.8 and 11.2 m2 g–1 for the 1-h and 24-h data, respectively (Fig. 9c and d).
The difference between MACcor and MACmed was limited to 3 % (Table 3). As discussed for the PSAP
and Aethalometer in the previous sections, the relative uncertainty becomes higher when the MBC values
tend to be low (Fig. S6c and d in the Supplement).
16

The MACcor (MAAP) and MACcor (PSAP) at Ny-Ålesund are compared in Table 11 in Sect. 3.6 after
530

adjusting measurement wavelengths. MACcor (PSAP) values are 17 % and 20 % larger than MACcor(MAAP) values for 1-h and 24-h data, respectively. A custom-built PSAP was used at Ny-Ålesund. The
systematic difference of babs measured by the custom-built PSAP and MAAP was also observed at 3
European background sites (Zanatta et al., 2016), although the previously reported difference was muchlarger (more than 59 %) than that of our measurements at Ny-Ålesund.

535

3.3 Barrow
3.3.1 COSMOS-PSAP/CLAP comparison
Simultaneous measurements of PM1 for MBC (COSMOS) and babs (PSAP/CLAP) began at Barrow in2012 (Sinha et al., 2017). At Barrow, both the PSAP and CLAP aspired ambient air using PM1 andPM10 impactors alternately for 30 min of each hour. Here we used the data from the PSAP/CLAP

540

equipped with the PM1 impactor and data from the PSAP in 2012–2015 and the CLAP in 2016–2019(Fig. S7 in the Supplement). Because the 24-h averaged babs (PSAP) and babs (CLAP) values agreed towithin 2 % during 2012–2015 (Sinha et al., 2017) when the PSAP and CLAP overlapped, we consider
the two instruments to be equivalent. The MBC (COSMOS) data from June 2018 to May 2019 were
unavailable due to problems with the COSMOS instrument.

545

The babs (PSAP/CLAP; λ = 550 nm) data were strongly correlated with those for MBC (COSMOS) (r2 =0.88 and r2 = 0.86; Fig. 10a and b) and the MACcor (PSAP/CLAP) derived from 1-h and 24-h averaged
data for the whole period were 10.8 and 10.6 m2 g–1, respectively. Average MACcor (PSAP/CLAP)
during 2012–2018 was stable at 11.0 ± 0.9 (1σ) m2 g–1 (Fig. 7b and Table 9). Yearly MBC (COSMOS)
values did not exhibit large changes during this period (Fig. 7b). The babs (CLAP) data was weakly

550

correlated with MBC (COSMOS) data during June–December 2019 (Table 9), indicating that either the
CLAP or COSMOS results might not have been accurate during this period. Therefore, in Table 9 we
calculated the average MACcor (PSAP/CLAP) by excluding the MAC value for 2019.
The MACmed values of the babs (PSAP/CLAP) / MBC (COSMOS) ratios were 11.2 and 11.0 m2 g–1 for 1h and 24-h data (Fig. 10c and d), which are very close to the MACcor values of 10.8 and 10.6 m2 g–1,

555

respectively (Table 3). Therefore, when either MACcor or MACmed is used for conversion of babs
(PSAP/CLAP; λ = 550 nm) to MBC, the resulting MBC values differ by only about 4 %. The VMAC wasabout 25 % for babs (PSAP /CLAP; λ = 550 nm) at Barrow (Table 3). Because of scatter in the data,
especially at lower MBC values (Fig. 10a and b), the interquartile ranges of the babs (PSAP) / MBC(COSMOS) ratios are much larger when MBC (COSMOS) is less than 10 ng m–3 (Fig. S7c and d).

17

560

3.4 Pallas
3.4.1 COSMOS-MAAP comparison
Measurements of babs (MAAP; λ = 637 nm) have been made since 2007 at the Global Atmospheric
Watch (GAW) station at Pallas (Hyvärinen et al., 2011). PM10 and PM1 inlets were used for MAAP and
COSMOS, respectively. MBC (COSMOS) measurements began in July 2019; we used the data collected

565

up to July 2020 in this study. The MBC (COSMOS) data for about 3 months (February to April 2020)
were unavailable due to an air sampling problem.
The babs (MAAP) 1-h and 24-h values (Fig. S8 in the Supplement) were strongly correlated with thosefor MBC (COSMOS) with r2 = 0.93 and r2 = 0.95, respectively (Fig. 11a and b). MACcor (MAAP) was
13.0 m2 g–1 for both the 1-h and 24-h data. This MACcor value is about twice the manufacturer’s default

570

setting (= 6.6 m2 g–1) of MAC (MAAP), possibly for the same reasons discussed in Sect. 3.2.3.
The MACmed values of the babs (MAAP) / MBC (COSMOS) ratios for the 1-h and 24-h data were 12.4
and 13.1 m2 g–1, respectively (Fig. 11c and d), which are very close to that for MACcor (13.0 m2 g–1 forboth 1-h and 24-h data, Table 3). Therefore, the difference between the estimated MBC values is lessthan 5 % when these MACcor or MACmed values are used for conversion of babs (MAAP) to MBC. The

575

VMAC was about 25 % for babs (MAAP) at Pallas (Table 3). The MACmed values for low MBC data (MBC
(COSMOS) < 10 ng m–3) are very close to those for all dataset (Fig S8c and d in the Supplement).
3.5 Fukue Island
3.5.1 COSMOS-SP2 comparison
The UT-SP2 was operated at Fukue for 3 weeks in April 2019 (Yoshida et al., 2020), as mentioned in

580

Sect. 2.2.1. Fig. 12a shows the number and mass size distributions of BC measured by the UT-SP2
averaged over the observation period. In addition to the MBC (SP2) derived by integrating the mass size
distributions over the detectable diameter range (Dm = 70–850 nm), we also estimated MBC (SP2) in theDm = 30–1000 nm range by fitting a lognormal function to the data. As the two sets of MBC (SP2) values
deviated by less than 2 %, we used the former MBC (SP2) for comparison with MBC (COSMOS). The

585

time series of hourly values of MBC (COSMOS) were strongly correlated (r2 = 0.97) with MBC (SP2)(Fig. 12b) and the slope of the regression was 0.92 (Fig. 12c). This relationship agrees with thoseobserved by Ohata et al. (2019) at Tokyo, Cape Hedo, and Ny-Ålesund and those observed at Alert(Sect. 3.1.1), thus confirming the clear and consistent relationship between MBC (COSMOS) and MBC(SP2). Miyakawa et al. (2017) also reported a strong correlation (r2 = 0.92; regression slope 1.14)

590

between MBC (COSMOS) and MBC (SP2) at Fukue in spring 2015 by using an SP2 maintained andcalibrated by the Japan Agency for Marine-Earth Science and Technology.
The degree of agreement between MBC (COSMOS) and MBC (SP2) at Fukue was also examined on a18

logarithmic scale in Fig. S1b in the Supplement. When MBC (SP2) is lower than ~70 ng m–3, MBC(COSMOS) tended to be slightly higher than MBC (SP2). A similar feature was previously reported at
595

Cape Hedo in Japan (Ohata et al., 2019). The Cape Hedo site is located near the coast (i.e., the distanceof this site to the coast is ~0.2 km) and the interference of submicron sea salt particles might contributeto this feature (Ohata et al., 2019). At Fukue, when maritime air mass is transported to the site, therelative abundance of sea salt particles to BC might be also enhanced possibly affecting the COSMOS
measurements, although the distance from the site to the coast (~1.5 km) is slightly farther than for

600

Cape Hedo. This feature was not clearly observed by a previous study at Fukue (Miyakawa et al., 2017).
3.5.2 COSMOS-MAAP comparison
Kanaya et al. (2013, 2016, 2020) made simultaneous measurements of MBC (COSMOS) and babs
(MAAP; λ = 639 nm) at Fukue for about 10 years (April 2009–May 2019; Fig. S9 in the Supplement).
The air inlet for the MAAP and COSMOS was equipped with a PM1 cyclone after November 2011.

605

Before that a PM2.5 cyclone was used instead. babs (MAAP) was highly correlated (r2 = 0.94) with MBC(COSMOS) and the MACcor (MAAP) for the entire period was found to be 10.8 m2 g–1 and 10.9 m2 g–1
for the 1-h and 24-h data (Fig. 13a and b), respectively. Because the correlation of babs (MAAP) with
MBC (COSMOS) was also strong for individual years, MACcor (MAAP) for each year was also derived(Fig. 7c and Table 10). MBC (COSMOS) decreased by about 50 % during this period, owing to a large

610

decrease of BC emissions in China (Kanaya et al., 2020). However, the yearly average MACcor(MAAP) values were stable at 11.1 ± 1.0 (1σ) m2 g–1 for both the 1-h and 24-h data, despite the largechange in MBC (COSMOS). This MACcor value is about 70 % higher than the manufacturer’s default
setting (= 6.6 m2 g–1), possibly for the same reasons discussed in Sect. 3.2.3.
Because the amount of data with MBC less than 2 ng m–3 was very small at Fukue, the MACmed values

615

and the interquartile ranges of the babs (MAAP) / MBC (COSMOS) ratios were obtained for all data
without applying any MBC threshold. The MACmed was 11.4 m2 g–1 for both 1-h and 24-h data (Fig. 13cand d), which agrees well (within 6 %) with the MACcor values derived from correlation plots (10.8 and10.9 m2 g–1 for 1-h and 24-h data, respectively) (Table 3). Therefore, using either MACcor or MACmedfor conversion of babs (MAAP) to MBC affects the resulting MBC values by less than 6 %. The VMAC was620

about 15 %, which is lower than those at Arctic sites (Table 3) partly because the higher MBC(COSMOS) values at Fukue make the calculated ratios more stable. Also, aerosol properties including
mixing states of BC might be more stable at Fukue than those at the Arctic sites examined in this study.

3.6 Spatial variability of MACcor and r2
In previous sections, we showed that the MACcor values depended on instrument and observation site.
625

The values of MACcor (λ = 550 nm) and r2 are summarized in Table 11. Here, the MACcor (MAAP; λ19

~637 nm) and MACcor (Aethalometer; λ = 590 nm) values were adjusted to those at λ = 550 nm byassuming an absorption Ångstrom exponent of 1.0 (i.e., a λ–1 relationship). The unit-to-unit variations ofbabs measurements were reported to be within 5 % for MAAP (Müller et al., 2011), 6 % for PSAP (Bondet al.,1999), and 4 % for CLAP (Ogren et al., 2017), if the careful calibration of flows and filter
630

sampling spot sizes of these instruments are made for individual units. Therefore, the spatial variationsof MACcor values observed in this study likely reflects differences of aerosol properties at theobservation sites.
The values of MACcor (PSAP) at Alert and MACcor (PSAP/CLAP) at Barrow were both determinedwith a PM1 size cut and they differed by about 22 % for 1-h data. Differences in aerosol properties

635

including mixing states of BC at these sites could contribute to the different MAC values, although thiseffect cannot be assessed quantitatively with only this dataset. The correlations of babs (PSAP) with MBC(COSMOS) at Alert were somewhat higher (r2 = 0.95–0.96) than those of babs (PSAP/CLAP) at Barrow(r2 = 0.86–0.88). The stronger correlation of babs (PSAP) with MBC (COSMOS) at Alert suggests thatenvironmental conditions including LSP/BC ratios and mixing states of BC were more stable at Alert.

640

We found that, at Alert, babs (PSAP) data with loading and scattering corrections were strongly
correlated with the uncorrected babs (PSAP) data and the contribution of the loading and scatteringcorrections was about 35 %, on average. In contrast, at Barrow, the contribution of these corrections
was about 63 %. This suggests that at Alert, the LSP/BC ratio was small and stable, and the influence of
LSPs on derived babs (PSAP) was small. The greater distance from continental sources of aerosols at

645

Alert than at Barrow (Fig. 1), may contribute to these observed differences.
At Ny-Ålesund, where a PM10 inlet was used, the MACcor (PSAP) values were higher than those at
Alert and Barrow. Also, the r2 values at Ny-Ålesund (r2 = 0.76–0.82) were lower than those at Alert.
Effects of particles larger than 1 µm including dust and sea salt may partly contribute to the larger MAC
and lower r2 values at Ny-Ålesund.

650

The MACcor (PSAP) and MACcor (Aethalometer) agree to within 4 % for 1-h data at Alert, in spite ofthe different particle size cut of the inlets. However, they differed by about 24 % for 1-h data at NyÅlesund. Although the agreements were somewhat better for 2015–2016 at Ny-Ålesund (Fig. 7a), thereason for the overall discrepancy is unknown. Furthermore, while the MACcor (PSAP) at Ny-Ålesund
was higher than that at Alert, the opposite result was obtained by Aethalometers, which is not easily

655interpreted.
The values of MACcor (MAAP) determined at Ny-Ålesund and Pallas differ by about 18 %. Thisdifference may be attributed to the difference of average mixing states of BC and properties of other coexisting aerosols, which were affected by environmental conditions. Because these are the only
available MACcor (MAAP) data sets derived from MBC (COSMOS) in the Arctic, it is difficult to further

660

evaluate spatial variability.
20

We have shown that in general, babs values obtained by PSAP, CLAP, Aethalometer, and MAAP werestrongly correlated with MBC (COSMOS) at all four Arctic sites, although the strength of thecorrelations differed somewhat among the sites. Based on the analysis of babs/MBC variations among
these sites, the MACcor and MACmed were most stable for the PSAP with a PM1 inlet at Alert and most
665

variable for PSAP with a PM10 inlet at Ny-Ålesund (Table 3). The average MACcor (λ = 550 nm) values
at these four Arctic sites were 13.0 ± 1.6 (1s; 12 % of the average) and 13.1 ± 1.7 (1s; 13 %) m2 g–1 for
1-h and 24-h data, respectively (Table 11). However, these correlations and resulting MACcor values
may not hold outside the Arctic, where environmental conditions can be very different, especially themixing states of BC and amount of interference by LSPs.

670

Zanatta et al. (2016), using MEC measured by the thermal-optical transmittance method with the
EUSAAR-2 protocol instead of MBC (COSMOS), reported the average MACcor value at λ = 637 nm fornine European background sites to be 10.0 m2 g–1. From this MACcor (λ = 637 nm) value, the value of
MACcor at λ = 550 nm is calculated to be 11.6 m2 g–1 by assuming an absorption Ångstrom exponent of
1.0. Although their MACcor values were generally obtained using PM10 inlets or without particle size-

675

cuts, their average MACcor value (= 11.6 m2 g–1) is about 11 % lower than our average MACcor value(13.0–13.1 m2 g–1) at 4 Arctic sites determined in this study. This discrepancy may be partly due to thedifferent methods used to determine absolute mass concentrations of BC.
Mason et al. (2018) derived the values of MACcor (PSAP) and MACcor (CLAP) for PM1 size range inbiomass burning and agriculture fire plumes during the SEAC4RS aircraft observation campaign by680

using MBC (SP2) data. They reported the MACcor (PSAP; λ = 532 nm) and MACcor (CLAP; λ = 532 nm)
values to be 21.0 and 26.5 m2 g–1, respectively, which are about 60 % larger than the average MACcor
value (13.0–13.1 m2 g–1) determined in this study. Although the causes for their very high MACcor
values are not clear, one possible explanation given by Mason et al. (2018) is the considerable amountof additional absorbers other than BC, including tar balls, that might have existed in their samples. Also,

685

strong lensing effects by BC coatings could contribute to the high MACcor values. Thus, the MACcor
values can be highly dependent on environmental conditions and those reported in the present study are
considered to be site-specific values, although the variability (1s) of our MACcor values in the 4 Arctic
sites was within 13 % of the average MACcor value for these 4 sites.

4 Summary and conclusions
690

Long-term measurements of MBC by ground-based instruments are needed to investigate changes in theemission, transport, and deposition of BC. Various types of filter-based absorption photometers,
including the particle absorption soot photometer (PSAP), the continuous light absorption photometer(CLAP), the Aethalometer, and the multi-angle absorption photometer (MAAP) have been used in the
Arctic. To date, the accuracy of MBC estimated from absorption coefficients (babs) measured by these
21

695

instruments have not been adequately assessed, mainly because of a lack of simultaneous and reliable
MBC measurements.
In this paper, we introduced a systematic methodology to derive MBC from babs measured by these
instruments. To obtain accurate values of MBC, we used a filter-based absorption photometer with aheated inlet (COSMOS), which we calibrated to within 10 % uncertainty with an SP2 deployed in

700

Tokyo. Individual COSMOS instruments used for field observations were calibrated against thestandard COSMOS to within about 10 %. The accuracy of MBC (COSMOS) has previously been
demonstrated to be about 15 % by comparison with MBC (SP2) for sites in Asia and the Arctic. The
effect on MBC (COSMOS) of interference by light-absorbing FeOx particles was estimated to be only afew percent, owing partly to the particle-size cut off of 1 μm by the PM1 cyclone used. This effect may

705

be somewhat higher for the other filter-based absorption photometers equipped with larger particle-size
cuts. The two necessary conditions for application of our method are a high correlation of babs withindependently measured MBC and long-term stability of the slope of the regression, which represents
MACcor.
We compared babs (PSAP/CLAP) with MBC (COSMOS) at Alert (PM1) for 2 years, Ny-Ålesund (PM10)710

for 4 years, and Barrow (PM1) for 7 years. The babs (PSAP/CLAP) was highly correlated with MBC(COSMOS) at these sites. For 1-h data, the MACcor (PSAP/CLAP) at λ = 550 nm was 13.9 m2 g–1 at
Alert, 14.4 m2 g–1 at Ny-Ålesund, and 10.8 m2 g–1 at Barrow. The VMAC was 19 % at Alert, 37 % at NyÅlesund, and 22 % at Barrow (Table 3).
We also compared babs (Aethalometer) with MBC (COSMOS) at Alert (total suspended particles) for 2

715

years and at Ny-Ålesund (PM10) for 8 years. They were highly correlated and the MACcor(Aethalometer; λ = 590 nm) for 1-h data was 12.5 m2 g–1 at Alert and 10.2 m2 g–1 at Ny-Ålesund. Oneof the manufacturer’s suggested MAC (Aethalometer) values is given by 14625 / (λ ×C0) whichcorresponds to 7.1 m2 g–1 for λ = 590 nm and C0 = 3.5, and which is considerably lower than the values
obtained in our study. The VMAC was 22 % at Alert and 25 % at Ny-Ålesund (Table 3).

720

The babs (MAAP) and MBC (COSMOS) were also compared at Ny-Ålesund (total suspended particles)for 4 years, at Pallas (PM10) for about 1 year, and at Fukue (PM1) for about 10 years. babs (MAAP) was
highly correlated with MBC (COSMOS) at these sites. For 1-h data, The MACcor (MAAP) at λ = 637 nm
was 10.6 m2 g–1 at Ny-Ålesund and 13.0 m2 g–1 at Pallas. The MACmed (MAAP) at λ = 639 nm at Fukue
was stable at 11.1 ± 1.0 m2 g–1, despite a 50 % decrease in MBC (COSMOS) during this period (Fig. 7c).

725

The default setting of MAC (MAAP) by the manufacturer (6.6 m2 g–1) is about half the MACcor
obtained in this study indicating a similar overestimation of MBC if the default value is used to convert
babs (MAAP) to MBC at these sites. For 1-h data, the VMAC was 20 % at Ny-Ålesund, 27 % at Pallas, and
15 % at Fukue.

22

Our results show that Arctic MBC can be derived from babs obtained from PSAP, CLAP, Aethalometer,
730

and MAAP measurements with reasonable accuracy by using the MACcor obtained from the regression
slope of the babs–MBC correlation, especially for long data-averaging times. However, scatter in babs–
MBC (COSMOS) correlations indicate that the accuracy of this method will be somewhat lower than that
achieved by direct measurement of MBC (COSMOS). We also caution that the reliability of the use ofbabs data to derive MBC at other locations, especially those outside the Arctic, is unknown. Rigorous735

comparisons with COSMOS or SP2 data, such as those of this study, are required if use of our method
is to expand beyond the Arctic region. Moreover, long-term comparisons are desirable for accurate
determination of the MACcor. Short-term comparisons will be of limited value for understanding thevariability of MAC for each instrument and location.

Data availability
740

The

MBC

and

babs

data

set

used

in

this

publicationisavailable

online(https://ads.nipr.ac.jp/dataset/A20201120-001).

Author contributions
SO, TM, and YKo designed the study, conducted the analyses, and wrote the paper. SS and DV
contributed to the field observations and data analysis of SP2, PSAP, CLAP, and Aethalometer at Alert.
745

AH, EAs, JB, and HS contributed to the field observations and data analysis of MAAP at Pallas. EAncontributed to the field observations and data analysis of PSAP and CLAP at Barrow. PT obtained andanalyzed PSAP data at Ny-Ålesund. KE and SV obtained and analyzed Aethalometer data at NyÅlesund. RK and PZ obtained and analyzed MAAP data at Ny-Ålesund. YKa contributed to the fieldobservations and data analysis of MAAP and COSMOS at Fukue. AY and NM obtained and analyzed

750

SP2 data at Fukue. SO, TM, YKo, MK, YZ, YT, JM, and NO contributed to instrument maintenanceand data analysis of COSMOS.

Competing interests
The authors declare that they have no conflicts of interest.

Acknowledgements
755

We thank Kevin Rawlings and Melody Fraser of Environment and Climate Change (Canada) and CFS
Alert for operations and maintenance of the Alert site. We thank Bryan Thomas, Peter Detwiler, andRoss Peterson for supporting the measurements at Barrow. We thank the staff of the Norwegian Polar
Institute for supporting the measurements at Ny-Ålesund (Zeppelin). This research was performed by
23

the
760

Environment

Research

JPMEERF20152005,

andTechnology

JPMEERF20172003,

Development

Fund

JPMEERF20182003,(JPMEERF20142003,

JPMEERF20202003,

and

JPMEERF20205001) of the Environmental Restoration and Conservation Agency of Japan; theJapanese Ministry of Education, Culture, Sports, Science, and Technology; the Japan Society for the
Promotion of Science KAKENHI Grants (JP12J06736, JP1604452, JP18H04143, JP19H05699,
JP23221001, JP25220101, JP26241003, JP26701004, JP16H01770, JP17H04709, JP18H03363,
765

JP19K20437, JP19K20441, and JP20H00638); the Arctic Challenge for Sustainability (ArCS) project(JPMXD1300000000);

theArcticChallengeforSustainability

II

(ArCS

II)

project(JPMXD1420318865); and a grant for the Global Environmental Research Coordination System from
the Ministry of the Environment, Japan (MLIT1753). Pallas and Zeppelin measurements and/or analysis
were conducted under the financial support of the ACTRIS by the European Union’s Horizon 2020
770

research and innovation programme under grant agreement no. 654109, partly under the European
Union’s Horizon 2020 research and innovation program under grant agreement no. 689443 via projectiCUPE (Integrative and Comprehensive Understanding on Polar Environments) and the 16ENV02
Black Carbon project of the European Union through the European Metrology Programme forInnovation and Research (EMPIR). The research was also supported by Academy of Finland via project

775

NABCEA (grant no. 29664) and Academy of Finland Flagship funding (grant no. 337552). MAAP
measurements at Zeppelin were funded and supported by Swedish Environmental Protection Agency(Naturvårdsverket). E. Andrews contribution to this effort was supported in part by the Atmospheric
Radiation Measurement (ARM) user facility, a US Department of Energy (DOE) Office of Science user
facility managed by the Biological and Environmental Research program.

780

References
Arctic Monitoring and Assessment Programme (AMAP): AMAP Assessment 2015: Black carbon andozone as Arctic climate forcers, Oslo, Norway, pp. vii + 116, 2015.
Asmi, E., Backman, J., Servomaa, H., Virkkula, A., Gini, M., Eleftheriadis, K., Müller, T., Ohata, S.,
Kondo, Y., and Hyvärinen, A.: Characterizing the Arctic absorbing aerosol with multi-instrument

785

observations, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-400, 2020.
Backman, J., Schmeisser, L., Virkkula, A., Ogren, J. A., Asmi, E., Starkweather, S., Sharma, S.,
Eleftheriadis, K., Uttal, T., Jefferson, A., Bergin, M., Makshtas, A., Tunved, P., and Fiebig, M.:
On Aethalometer measurement uncertainties and an instrument correction factor for the Arctic,
Atmos. Meas. Tech., 10, 5039–5062, https://doi.org/10.5194/amt-10-5039-2017, 2017.

790

Baumgardner, D., Popovicheva, O., Allan, J., Bernardoni, V., Cao, J., Cavalli, F., Cozic, J., Diapouli, E.,
Eleftheriadis, K., Genberg, P. J., Gonzalez, C., Gysel, M., John, A., Kirchstetter, T. W.,
Kuhlbusch, T. A. J., Laborde, M., Lack, D., Müller, T., Niessner, R., Petzold, A., Piazzalunga, A.,24

Putaud, J. P., Schwarz, J., Sheridan, P., Subramanian, R., Swietlicki, E., Valli, G., Vecchi, R., andViana, M.: Soot reference materials for instrument calibration and intercomparisons: A workshop
795

summary

withrecommendations,

Atmos.

Meas.

Tech.,

5,

1869–1887,

https://doi.org/10.5194/amt-5-1869-2012, 2012.
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K.
S., Christensen, M., Daniau, A.-L., Dufresne, J.-L., Feinglod, G., Fiedler, S., Foster, P., Gettelman,
A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G.,
800

Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E.,
Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.: Bounding global aerosol
radiative forcing of climate change, Rev. Geophys., 58, e2019RG000660,
https://doi.org/10.1029/2019RG000660, 2020.
Bergstrom, R. W.: Predictions of the spectral absorption and extinction coefficients of an urban air

805

pollution

aerosol

model,

Atmos.

Environ.,6,

247–258,

https://doi.org/10.1016/0004-

6981(72)90083-2, 1972.
Bond, T. C. and Bergstrom, R. W.: Light absorption by carbonaceous particles: An investigative review,
Aerosol Sci. Tech., 40, 1, 27–67, https://doi.org/10.1080/02786820500421521, 2006.
Bond, T. C., Anderson, T. L., and Campbell, D.: Calibration and Intercomparison of Filter-Based810

Measurements of Visible Light Absorption by Aerosols, Aerosol Sci. Tech., 30, 6, 582–600,
https://doi.org/10.1080/027868299304435, 1999.
Bond, T. C., Habib, G., and Bergstrom, R. W.: Limitations in the enhancement of visible lightabsorption

due

to

mixingstate,J.

Geophys.

Res.

Atmos.,

111,

D20211,

https://doi.org/10.1029/2006JD007315, 2006.
815

Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., Deangelo, B. J., Flanner, M. G.,
Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M.
G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K., Hopke,
P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U., Schwarz, J. P., Shindell, D.,Storelvmo, T., Warren, S. G., and Zender, C. S.: Bounding the role of black carbon in the climate

820

system:A

scientificassessment,

J.

Geophys.

Res.

Atmos.,

118,

1–173,

https://doi.org/10.1002/jgrd.50171, 2013.
Cho, C., Schwarz, J. P., Perring, A. E., Lamb, K. D., Kondo, Y., Park, J., Park, D., Shim, K., Park, J.,
Park, R. J., Lee, M., Song, C., and Kim, S.: Science of the Total Environment Light-absorption
enhancement of black carbon in the Asian out flow inferred from airborne SP2 and in-situ
825

measurements

during

KORUS-AQ,

Sci.

https://doi.org/10.1016/j.scitotenv.2021.145531, 2021.
25

Total

Environ.,773,

145531,Eleftheriadis, K., Vratolis, S., and Nyeki, S.: Aerosol black carbon in the European Arctic:
Measurements at Zeppelin station, Ny-Ålesund, Svalbard from 1998–2007, Geophys. Res. Lett.,
36, L02809, https://doi.org/10.1029/2008GL035741, 2009.
830

Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.:
Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental designand organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016,
2016.
Flanner, M. G., Zender, C. S., Hess, P. G., Mahowald, N. M., Painter, T. H., Ramanathan, V., and835

Rasch, P. J.: Springtime warming and reduced snow cover from carbonaceous particles, Atmos.
Chem. Phys., 9, 2481–2497, https://doi.org/10.5194/acp-9-2481-2009, 2009.
Gysel, M., Laborde, M., Olfert, J. S., Subramanian, R., and Gréhn, A. J.: Effective density of Aquadag
and fullerene soot black carbon reference materials used for SP2 calibration, Atmos. Meas. Tech.,4, 2851–2858, https://doi.org/10.5194/amt-4-2851-2011, 2011.

840

Hansen, A. D. A., Rosen, H., and Novakov, T.: The aethalometer – an instrument for the real-timemeasurement of optical absorption by aerosol particles, Sci. Total Environ., 36, 191–196, 1984.
Huffman, D. R. and Stapp, J. L.: Optical Measurements on Solids of Possible Interstellar Importance,
in: Interstellar dust and related topics, edited by: Greenberg, J. M. and Van de Hulst, H. C., Reidel,
Boston, pp. 297–301, 1973.

845

Hyvärinen, A. P., Kolmonen, P., Kerminen, V. M., Virkkula, A., Leskinen, A., Komppula, M., Hatakka,
J., Burkhart, J., Stohl, A., Aalto, P., Kulmala, M., Lehtinen, K. E. J., Viisanen, Y., and Lihavainen,
H.: Aerosol black carbon at five background measurement sites over Finland, a gateway to the
Arctic, Atmos. Environ., 45, 4042–4050, https://doi.org/10.1016/j.atmosenv.2011.04.026, 2011.
Irwin, M., Kondo, Y., and Moteki, N.: An empirical correction factor for filter-based photo-absorption

850

black

carbonmeasurements,

J.

Aerosol

Sci.,

80,

86–97,

https://doi.org/10.1016/j.jaerosci.2014.11.001, 2015.
Kanaya, Y., Taketani, F., Komazaki, Y., Liu, X., Kondo, Y., Sahu, L. K., Irie, H., and Takashima, H.:
Comparison of black carbon mass concentrations observed by multi-angle absorption photometer(MAAP) and continuous soot-monitoring system (COSMOS) on Fukue Island and in Tokyo,
855Japan, Aerosol Sci. Tech., 47, 1, 1–10, https://doi.org/10.1080/02786826.2012.716551, 2013.
Kanaya, Y., Pan, X., Miyakawa, T., Komazaki, Y., Taketani, F., Uno, I., and Kondo, Y.: Long-term
observations of black carbon mass concentrations at Fukue Island, western Japan, during 2009–2015: constraining wet removal rates and emission strengths from East Asia, Atmos. Chem. Phys.,
16, 10689–10705, https://doi.org/10.5194/acp-16-10689-2016, 2016.
26

860

Kanaya, Y., Yamaji, K., Miyakawa, T., Taketani, F., Zhu, C., Choi, Y., Komazaki, Y., Ikeda, K., Kondo,Y., and Klimont, Z.: Rapid reduction in black carbon emissions from China: Evidence from 20092019 observations on Fukue Island, Japan, Atmos. Chem. Phys., 20, 6339–6356,
https://doi.org/10.5194/acp-20-6339-2020, 2020.
Kondo, Y.: Effects of Black Carbon on Climate: Advances in Measurement and Modeling, Monogr.

865

Environ. Earth Planets, 3, 1–85, https://doi.org/10.5047/meep.2015.00301.0001, 2015.
Kondo, Y., Sahu, L., Kuwata, M., Miyazaki, Y., Takegawa, N., Moteki, N., Imaru, J., Han, S.,
Nakayama, T., Oanh, N. T. K., Hu, M., Kim, Y. J., and Kita, K.: Stabilization of the mass
absorption cross section of black carbon for filter-based absorption photometry by the use of aheated inlet, Aerosol Sci. Tech., 43, 8, 741–756, https://doi.org/10.1080/02786820902889879,

870

2009.
Kondo, Y., Sahu, L., Moteki, N., Khan, F., Takegawa, N., Liu, X., Koike, M., and Miyakawa, T.:
Consistency and traceability of black carbon measurements made by laser-induced incandescence,
thermal-optical transmittance, and filter-based photo-absorption techniques, Aerosol Sci. Tech.,45, 2, 295–312, https://doi.org/10.1080/02786826.2010.533215, 2011.

875

Krecl, P., Ström, J., and Johansson, C.: Carbon content of atmospheric aerosols in a residential area
during the wood combustion season in Sweden, Atmos. Environ., 41, 6974–6985,
https://doi.org/10.1016/j.atmosenv.2007.06.025, 2007.
Laborde, M., Mertes, P., Zieger, P., Dommen, J., Baltensperger, U., and Gysel, M.: Sensitivity of theSingle Particle Soot Photometer to different black carbon types, Atmos. Meas. Tech., 5, 1031–

880

1043, https://doi.org/10.5194/amt-5-1031-2012, 2012.
Lack, D. A., Cappa, C. D., Covert, D. S., Baynard, T., Massoli, P., Sierau, B., Bates, T. S., Quinn, P. K.,
Lovejoy, E. R., and Ravishankara, A. R.: Bias in filter-based aerosol light absorption
measurements due to organic aerosol loading: Evidence from ambient measurements, Aerosol. Sci.
Tech., 42, 1033–1041, https://doi.org/10.1080/02786820802389277, 2008.

885

Lamb, K. D.: Classification of iron oxide aerosols by a single particle soot photometer using supervisedmachine learning, Atmos. Meas. Tech., 12, 3885–3906, https://doi.org/10.5194/amt-12-38852019, 2019.
Lihavainen, H., Hyvärinen, A., Asmi, E., Hatakka, J., and Viisanen, Y.: Long-term variability of aerosol
optical properties in northern Finland, Boreal Env. Res., 20, 526–541, 2015.

890

Mason, B., Wagner, N. L., Adler, G., Andrews, E., Brock, C. A., Gordon, T. D., Lack, D. A., Perring, A.
E., Richardson, M. S., Schwarz, J. P., Shook, M. A., Thornhill, K. L., Ziemba, L. D., and Murphy,
D. M.: An intercomparison of aerosol absorption measurements conducted during the SEAC4RS
27

campaign, Aerosol Sci. Tech., 52, 1012–1027, https://doi.org/10.1080/02786826.2018.1500012,
2018.
895

Matsui, H., Mahowald, N. M., Moteki, N., Hamilton, D. S., Ohata, S., Yoshida, A., Koike, M., Scanza,
R. A., and Flanner, M. G.: Anthropogenic combustion iron as a complex climate forcer, Nat.
Commun., 9, https://doi.org/10.1038/s41467-018-03997-0, 2018.
Miyakawa, T., Oshima, N., Taketani, F., Komazaki, Y., Yoshino, A., Takami, A., Kondo, Y., andKanaya, Y.: Alteration of the size distributions and mixing states of black carbon through900transport in the boundary layer in east Asia, Atmos. Chem. Phys., 17, 5851–5864,
https://doi.org/10.5194/acp-17-5851-2017, 2017.
Miyazaki, Y., Kondo, Y., Sahu, L. K., Imaru, J., Fukushima, N., and Kano, M.: Performance of a newly
designed continuous soot monitoring system (COSMOS), J. Environ. Monitor., 10, 1195–1201,
https://doi.org/10.1039/b806957c, 2008.

905

Moosmüller, H., Chakrabarty, R.K., and Arnott, W.P.: Aerosol light absorption and its measurement: A
review,J.

Quant.

Spectrosc.

Radiat.

Transf.,110,

844–878,https://doi.org/10.1016/j.jqsrt.2009.02.035, 2009.
Moteki, N. and Kondo, Y.: Dependence of laser-induced incandescence on physical properties of black
carbon aerosols: Measurements and theoretical interpretation, Aerosol Sci. Technol., 44, 8, 663–
910

675, https://doi.org/10.1080/02786826.2010.484450, 2010.
Moteki, N., Adachi, K., Ohata, S., Yoshida, A., Harigaya, T., Koike, M., and Kondo, Y.: Anthropogenic
iron oxide aerosols enhance atmospheric heating, Nat. Commun., 8,
https://doi.org/10.1038/ncomms15329, 2017.
Müller, T., Henzing, J. S., De Leeuw, G., Wiedensohler, A., Alastuey, A., Angelov, H., Bizjak, M.,

915

Collaud Coen, M., Engström, J. E., Gruening, C., Hillamo, R., Hoffer, A., Imre, K., Ivanow, P.,
Jennings, G., Sun, J. Y., Kalivitis, N., Karlsson, H., Komppula, M., Laj, P., Li, S. M., Lunder, C.,
Marinoni, A., Martins Dos Santos, S., Moerman, M., Nowak, A., Ogren, J. A., Petzold, A., Pichon,
J. M., Rodriquez, S., Sharma, S., Sheridan, P. J., Teinilä, K., Tuch, T., Viana, M., Virkkula, A.,
Weingartner, E., Wilhelm, R., and Wang, Y. Q.: Characterization and intercomparison of aerosol

920

absorption photometers: result of two intercomparison workshops, Atmos. Meas. Tech., 4, 245–
268, https://doi.org/10.5194/amt-4-245-2011, 2011.
Ogren, J. A.: Comment on “calibration and intercomparison of filter-based measurements of visible
light absorption by aerosols,” Aerosol Sci. Tech., 44, 8, 589–591,
https://doi.org/10.1080/02786826.2010.482111, 2010.

28

925

Ogren, J. A., Wendell, J., Andrews, E., and Sheridan, P. J.: Continuous light absorption photometer for
long-term studies, Atmos. Meas. Tech., 10, 4805–4818, https://doi.org/10.5194/amt-10-48052017, 2017.
Ohata, S., Yoshida, A., Moteki, N., Adachi, K., Takahashi, Y., Kurisu, M., and Koike, M.: Abundanceof Light-Absorbing Anthropogenic Iron Oxide Aerosols in the Urban Atmosphere and Their

930

Emission Sources, J. Geophys. Res. Atmos., 123, 8115–8134,
https://doi.org/10.1029/2018JD028363, 2018.
Ohata, S., Kondo, Y., Moteki, N., Mori, T., Yoshida, A., Sinha, P. R., and Koike, M.: Accuracy of
black carbon measurements by a filter-based absorption photometer with a heated inlet, Aerosol
Sci. Tech., 53, 9, 1079–1091, https://doi.org/10.1080/02786826.2019.1627283, 2019.

935

Oshima, N., Yukimoto, S., Deushi, M., Koshiro, T., Kawai, H., Tanaka, T. Y., and Yoshida, K.: Globaland Arctic Radiative Forcing of Anthropogenic Gases and Aerosols in MRI-ESM2.0, Prog. Earth
Planet. Sci., 7, 38, https://doi.org/10.1186/s40645-020-00348-w, 2020.
Petzold, A., Kramer, H., and Schönlinner, M.: Continuous measurement of atmospheric black carbon
using a multi-angle absorption photometer, Environ. Sci. Pollut. R. Special issue 4, 78–82, 2002.

940

Petzold, A. and Schönlinner, M.: Multi-angle absorption photometry – A new method for themeasurement of aerosol light absorption and atmospheric black carbon, J. Aerosol Sci., 35, 421–
441, https://doi.org/10.1016/j.jaerosci.2003.09.005, 2004.
Petzold, A., Schloesser, H., Sheridan, P. J., Arnott, W. P., Ogren, J. A., and Virkkula, A.: Evaluation ofmultiangle absorption photometry for measuring aerosol light absorption, Aerosol Sci. Tech., 39,

945

1, 40–51, https://doi.org/10.1080/027868290901945, 2005.
Petzold, A., Ogren, J. A., Fiebig, M., Laj, P., Li, S. M., Baltensperger, U., Holzer-Popp, T., Kinne, S.,
Pappalardo, G., Sugimoto, N., Wehrli, C., Wiedensohler, A., and Zhang, X.-Y.:
Recommendations for reporting black carbon measurements, Atmos. Chem. Phys., 13, 8365–8379,
https://doi.org/10.5194/acp-13-8365-2013, 2013.

950

Pileci, R. E., Modini, R. L., Bertò, M., Yuan, J., Corbin, J. C., Marinoni, A., Henzing, B., Moerman, M.
M., Putaud, J. P., Spindler, G., Wehner, B., Müller, T., Tuch, T., Trentini, A., Zanatta, M.,
Baltensperger, U., and Gysel-Beer, M.: Comparison of co-located refractory black carbon (rBC)and elemental carbon (EC) mass concentration measurements during field campaigns at several
European sites, Atmos. Meas. Tech., 14, 1379–1403, https://doi.org/10.5194/amt-14-1379-2021,

955

2021.

29

Sand, M., Berntsen, T. K., Von Salzen, K., Flanner, M. G., Langner, J., and Victor, D. G.: Response of
Arctic temperature to changes in emissions of short-lived climate forcers, Nat. Clim. Change, 6,286–289, https://doi.org/10.1038/nclimate2880, 2016.
Schmeisser, L., Andrews, E., Ogren, J. A., Sheridan, P., Jefferson, A., Sharma, S., Kim, J. E., Sherman,
960

J. P., Sorribas, M., Kalapov, I., Arsov, T., Angelov, C., Mayol-Bracero, O. L., Labuschagne, C.,
Kim, S.-W., Hoffer, A., Lin, N.-H., Chia, H.-P., Bergin, M., Sun, J., Liu, P., and Wu, H.:
Classifying aerosol type using in situ surface spectral aerosol optical properties, Atmos. Chem.
Phys., 17, 12097–12120, https://doi.org/10.5194/acp-17-12097-2017, 2017.
Schmeisser, L., Backman, J., Ogren, J. A., Andrews, E., Asmi, E., Starkweather, S., Uttal, T., Fiebig,

965

M., Sharma, S., Eleftheriadis, K., Vratolis, S, Bergin, M., Tunved, P., and Jefferson, A.:
Seasonality of aerosol optical properties in the Arctic, Atmos. Chem. Phys., 18, 11599–11622,
https://doi.org/10.5194/acp-18-11599-2018, 2018.
Schmid, H., Laskus, L., Jürgen Abraham, H., Baltensperger, U., Lavanchy, V., Bizjak, M., Burba, P.,
Cachier, H., Crow, D., Chow, J., Gnauk, T., Even, A., Ten Brink, H. M., Giesen, K. P.,

970

Hitzenberger, R., Hueglin, C., Maenhaut, W., Pio, C., Carvalho, A., Putaud, J. P., Toom-Sauntry,
D., and Puxbaum, H.: Results of the “carbon conference” international aerosol carbon round robintest stage I, Atmos. Environ., 35, 2111–2121, https://doi.org/10.1016/S1352-2310(00)00493-3,
2001.
Schulz, H., Zanatta, M., Bozem, H., Richard Leaitch, W., Herber, A. B., Burkart, J., Willis, M. D.,

975

Kunkel, D., Hoor, P. M., Abbatt, J. P. D., and Gerdes, R.: High Arctic aircraft measurementscharacterising black carbon vertical variability in spring and summer, Atmos. Chem. Phys., 19,2361–2384, https://doi.org/10.5194/acp-19-2361-2019, 2019.
Schwarz, J. P., Gao, R. S., Fahey, D. W., Thomson, D. S., Watts, L. A., Wilson, J. C., Reeves, J. M.,
Darbeheshti, M., Baumgardner, D. G., Kok, G. L., Chung, S. H., Schulz, M., Hendricks, J., Lauer,

980

A., Kärcher, B., Slowik, J. G., Rosenlof, K. H., Thompson, T. L., Langford, A. O., Loewenstein,M., and Aikin, K. C.: Single-particle measurements of midlatitude black carbon and lightscattering aerosols from the boundary layer to the lower stratosphere, J. Geophys. Res. Atmos.,
111, D16207, https://doi.org/10.1029/2006JD007076, 2006.
Sharma, S., Lavoué, D., Chachier, H., Barrie, L. A. and Gong, S. L.: Long-term trends of the black985

carbon concentrations in the Canadian Arctic, J. Geophys. Res., 109, D15203,
https://doi.org/10.1029/2003JD004331, 2004.
Sharma, S., Andrews, E., Barrie, L. A., Ogren, J. A., and Lavoué, D.: Variations and sources of theequivalent black carbon in the high Arctic revealed by long-term observations at Alert and30

Barrow: 1989–2003, J. Geophys. Res., 111, D14028, https://doi.org/10.1029/2005JD006581,
990

2006.
Sharma, S., Leaitch, W. R., Huang, L., Veber, D., Kolonjari, F., Zhang, W., Hanna, S. J., Bertram, A.
K., and Ogren, J. A.: An evaluation of three methods for measuring black carbon in Alert, Canada,
Atmos. Chem. Phys., 17, 15225–15243, https://doi.org/10.5194/acp-17-15225-2017, 2017.
Sinha, P. R., Kondo, Y., Koike, M., Ogren, J. A., Jefferson, A., Barrett, T. E., Sheesley, R. J., Ohata, S.,

995

Moteki, N., Coe, H., Liu, D., Irwin, M., Tunved, P., Quinn, P. K., and Zhao, Y.: Evaluation ofground-based black carbon measurements by filter-based photometers at two Arctic sites, J.
Geophys. Res. Atmos., 122, 3544–3572. https://doi.org/10.1002/2016JD025843, 2017.
World Meteorological Organization/Global Atmosphere Watch: WMO/GAW aerosol measurement
procedures, guidelines, and recommendations, GAW Report No. 227, 2016.

1000

Yoshida, A., Moteki, N., Ohata, S., Mori, T., Tada, R., Dagsson-Waldhauserová, P., and Kondo, Y.:
Detection of light-absorbing iron oxide particles using a modified single-particle soot photometer,
Aerosol Sci. Tech., 50, 3, i–iv, https://doi.org/10.1080/02786826.2016.1146402, 2016.
Yoshida, A., Ohata, S., Moteki, N., Adachi, K., Mori, T., Koike, M., and Takami, A.: Abundance andEmission Flux of the Anthropogenic Iron Oxide Aerosols From the East Asian Continental

1005

Outflow, J. Geophys. Res. Atmos., 123, 11, 194–11, 209, https://doi.org/10.1029/2018JD028665,
2018.
Yoshida, A., Moteki, N., Ohata, S., Mori, T., Koike, M., Kondo, Y., Matsui, H., Oshima, N., Takami,A., and Kita, K.: Abundances and Microphysical Properties of Light-Absorbing Iron Oxide andBlack Carbon Aerosols Over East Asia and the Arctic, J. Geophys. Res. Atmos., 125,1010

https://doi.org/10.1029/2019JD032301, 2020.
Yuan, J., Modini, R. L., Zanatta, M., Herber, A. B., Müller, T., Wehner, B., Poulain, L., Tuch, T.,
Baltensperger, U., and Gysel-beer, M.: Variability in the mass absorption cross section of black
carbon (BC) aerosols is driven by BC internal mixing state at a central European background site
(Melpitz, Germany) in winter, Atmos. Chem. Phys., 21, 635–655, https://doi.org/10.5194/acp-21-

1015

635-2021, 2021.
Zanatta, M., Gysel, M., Bukowiecki, N., Müller, T., Weingartner, E., Areskoug, H., Fiebig, M., Yttri, K.
E., Mihalopoulos, N., Kouvarakis, G., Beddows, D., Harrison, R. M., Cavalli, F., Putaud, J. P.,
Spindler, G., Wiedensohler, A., Alastuey, A., Pandolfi, M., Sellegri, K., Swietlicki, E., Jaffrezo, J.
L., Baltensperger, U., and Laj, P.: A European aerosol phenomenology-5: Climatology of black1020

carbon optical properties at 9 regional background sites across Europe, Atmos. Environ., 145,
346–364, https://doi.org/10.1016/j.atmosenv.2016.09.035, 2016.
31

Zanatta, M., Laj, P., Gysel, M., Baltensperger, U., Vratolis, S., Eleftheriadis, K., Kondo, Y., Dubuisson,
P., Winiarek, V., Kazadzis, S., Tunved, P., and Jacobi, H.-W.: Effects of mixing state on opticaland radiative properties of black carbon in the European Arctic, Atmos. Chem. Phys., 18, 14037–
1025

14057, https://doi.org/10.5194/acp-18-14037-2018, 2018.

32

Figures

Figure 1. Locations of the Arctic sites where MBC and babs were measured for this study.
1030

33

Figure 2. Mass size distributions of BC (black line) and FeOx (red line) and mass absorption crosssections calculated by Mie theory for bare BC (black dashed line) and bare FeOx (red dashed line) at (a)Fukue in April 2019 and (b) Ny-Ålesund in March 2017. Dm is the mass equivalent diameter of bare BC
1035

or FeOx. Assumptions for the Mie calculations are given in Sect. 2.

34

1040

Figure 3. (a) Number and mass size distributions of BC averaged over the observation period at Alertfrom January to May 2018. The dashed (solid) red line is the lognormal fit to the number (mass) size
distribution. (b) Time series (1-h data) and (c) correlation of MBC measured by COSMOS and SP2. The
solid red line in the correlation plot is the least squares regression forced through the origin.

1045

35

Figure 4. Time series of MBC (COSMOS) and babs (PSAP; λ = 550 nm) from January 2018 to December
2019 at Alert for (a) 1-h averaged and (b) 24-h averaged data. (c) and (d) Corresponding correlations of
MBC (COSMOS) and babs (PSAP). The solid red lines are the least squares regressions forced through1050

the origin. (e) and (f) Corresponding histograms of babs (PSAP) / MBC (COSMOS) ratios for all data anddata with MBC (COSMOS) > 2 ng m–3. The interquartile ranges are shown in parentheses.
36Figure 5. Correlations of MBC (COSMOS) and babs (Aethalometer; λ = 590 nm) from January 2018 to
December 2019 at Alert for (a) 1-h averaged and (b) 24-h averaged data. The solid red lines are the least1055

squares regressions forced through the origin. (c) and (d) Corresponding histograms of babs
(Aethalometer) / MBC (COSMOS) ratios for all data and data with MBC (COSMOS) > 2 ng m–3.

37

1060

Figure 6. Correlations of MBC (COSMOS) and babs (PSAP; λ = 550 nm) from April 2012 to September
2016 at Ny-Ålesund for (a) 1-h averaged and (b) 24-h averaged data. The solid red lines are the leastsquares regressions forced through the origin. (c) and (d) Corresponding histograms of babs (PSAP) /MBC (COSMOS) ratios for all data and data with MBC (COSMOS) > 2 ng m–3.
1065

38

Figure 7. (a) Time series of yearly MACcor (PSAP; λ = 550 nm), MACcor (Aethalometer; λ = 590 nm),
MACcor (MAAP; λ = 637 nm), and MBC (COSMOS) at Ny-Ålesund. (b) Time series of yearly MACcor
1070(PSAP/CLAP; λ = 550 nm) and MBC (COSMOS) at Barrow. (c) Time series of yearly MACcor (MAAP;
λ = 639 nm) and MBC (COSMOS) at Fukue. In each panel, yearly MACcor and MBC (COSMOS) arecalculated from 1-h data. The dashed lines show the averages of yearly MACcor for the entire time series.

39

1075

Figure 8. Correlations of MBC (COSMOS) and babs (Aethalometer; λ = 590 nm) from April 2012 toAugust 2019 at Ny-Ålesund for (a) 1-h averaged and (b) 24-h averaged data. The solid red lines are theleast squares regressions forced through the origin. (c) and (d) Corresponding histograms of babs
(Aethalometer) / MBC (COSMOS) ratios for all data and data with MBC (COSMOS) > 2 ng m–3.
1080

40

Figure 9. Correlations of MBC (COSMOS) and babs (MAAP; λ = 637 nm) from January 2017 to
December 2020 at Ny-Ålesund for (a) 1-h averaged and (b) 24-h averaged data. The solid red lines are1085

the least squares regressions forced through the origin. (c) and (d) Corresponding histograms of babs
(MAAP) / MBC (COSMOS) ratios for all data and data with MBC (COSMOS) > 2 ng m–3.

41

1090

Figure 10. Correlations of MBC (COSMOS) and babs (PSAP/CLAP; λ = 550 nm) from August 2012 to
December 2019 at Barrow for (a) 1-h averaged and (b) 24-h averaged data. The solid red lines are theleast squares regressions forced through the origin. (c) and (d) Corresponding histograms of babs
(PSAP/CLAP) / MBC (COSMOS) ratios for all data and data with MBC (COSMOS) > 2 ng m–3.

1095

42

Figure 11. Correlations of MBC (COSMOS) and babs (MAAP; λ = 637 nm) from July 2019 to July 2020
at Pallas for (a) 1-h averaged and (b) 24-h averaged data. The solid red lines are the least squares
regressions forced through the origin. (c) and (d) Corresponding histograms of babs (MAAP) / MBC
1100(COSMOS) ratios for all data and data with MBC (COSMOS) > 2 ng m–3.

43

Figure 12. (a) Number and mass size distributions of BC averaged over the observation period at Fukue
1105

in April 2019. The dashed (solid) red line is the lognormal fit to the number (mass) size distribution. (b)Time series (1-h data) and (c) correlation of MBC measured by COSMOS and SP2. The solid red line in
the correlation plot is the least squares regression forced through the origin.

44

1110

Figure 13. Correlations of MBC (COSMOS) and babs (MAAP; λ = 639 nm) from April 2009 to May
2019 at Fukue for (a) 1-h averaged and (b) 24-h averaged data. The solid red lines are the least squares
regression forced through the origin. (c) and (d) Corresponding histograms of babs (MAAP) / MBC(COSMOS) ratios.
1115

45

Tables
Table 1. Observation sites, periods, and instruments used in this study.
1120

Location
Alert (ALT)
Ny-Ålesund (ZEP)
1125

Barrow (BRW)
Pallas (PAL)
Fukue (Japan) (FKE)1130

PeriodJan – May 2018
Jan 2018 – Dec 2019
Apr 2012 – Sep 2016
Apr 2012 – Aug 2019
Jan 2017 – Dec 2020
Aug 2012 – Dec 2019
Jul 2019 – Jul 2020
Apr 2019
Apr 2009 – May 2019

Instruments
COSMOS, EC-SP2COSMOS, PSAP, Aethalometer (AE31)
COSMOS, PSAPCOSMOS, Aethalometer (AE31)
COSMOS, MAAP
COSMOS, PSAP, CLAPCOSMOS, MAAP
COSMOS, UT-SP2
COSMOS, MAAP

Table 2. MACcor (PSAP/CLAP; λ) values at Alert during 2018–2019. r2 is the square of the correlation
coefficient.
1135

1140

1145

Instrument
CLAP1
CLAP2
PSAP
CLAP1
CLAP2
PSAP
CLAP1
CLAP2
PSAP

λ (nm)
450
450
450
550
550
550
700
700
700

MACcor (1-h)[m2 g–1]
r2(1-h)
13.6
0.93
15.4
0.96
15.7
0.95
12.1
0.93
13.6
0.96
13.9
0.96
9.7
0.93
10.8
0.95
11.5
0.94

1150

46

MACcor (24-h)[m2 g–1]
r2 (24-h)
13.6
0.95
15.4
0.96
15.4
0.96
12.1
0.95
13.8
0.95
14.0
0.95
9.7
0.95
10.9
0.95
11.6
0.95

Table 3. MAC, r2, and variability of MAC (VMAC; interquartile range in relative terms) of MAAP,
PSAP/CLAP, and Aethalometer at observation sites in this study.
1155

Site

Instrument

λ

Inlet

Period(1-h)
MACcor[m2 g–1]

r2

MACmed[m2 g–1]

VMAC[%](24-h)
MACcor[m2 g–1]

r2

MACmed[m2 g–1]

VMAC
[%]

PM1
TSP*
PM10
PM10
TSP*
PM1
PM10
PM1**

2018–20192018–20192012–2016
2012–20192017–2020
2012–20192019–2020
2009–2019

13.9
12.5
14.4
10.2
10.6
10.8
13.0
10.8

0.95
0.90
0.76
0.90
0.90
0.88
0.93
0.95

13.5
13.5
16.7
11.2
10.8
11.2
12.4
11.4

19
22
37
25
20
22
27
15

14.0
12.7
15.2
10.1
10.9
10.6
13.0
10.9

0.96
0.94
0.82
0.90
0.83
0.86
0.95
0.94

13.5
13.8
17.2
12.3
11.2
11.0
13.1
11.4

18
22
31
28
17
26
21
15

[nm]
ALT
ALT
ZEP
ZEP
ZEP
BRW
PAL
FKE
*

PSAP
AE31
PSAP
AE31
MAAP
PSAP/CLAP
MAAP
MAAP

550
590
550
590
637
550
637
639

Total suspended particle.
A PM2.5 cyclone was used before November 2011.

**

1160

Table 4. MACcor (Aethalometer; λ) and r2 values at Alert during 2018–2019.
1165

1170

λ (nm)
370
470
520
590
660
880
950

MACcor (1-h)[m2 g–1]
r2(1-h)
18.6
0.86
15.4
0.89
13.9
0.90
12.5
0.90
11.4
0.89
8.8
0.82
8.1
0.79

MACcor (24-h)[m2 g–1]
r2 (24-h)
18.7
0.90
15.6
0.93
14.1
0.94
12.7
0.94
11.6
0.94
8.9
0.94
8.1
0.94

1175

47

1180

1185

Table 5. MACcor (PSAP) and MACcor (Aethalometer) values derived from 24-h averaged data at Alertduring 2018–2019.
λ (nm)
PSAP/Aeth
450/470550/590700/660

MACcor (PSAP) MACcor (Aeth)[m2 g–1][m2 g–1]
15.4
15.6
14.0
12.7
11.6
11.6

MACcor (Aeth)/ MACcor (PSAP)
1.01 (1.06)*
1.01 (1.03)*
1.00 (0.94)*

*

1190

1195

1200

1205

MACcor (Aethalometer) values measured at λ = 470, 590, and 660 nm were adjusted to those at λ =450, 550, and 700 nm (wavelengths used for PSAP) by assuming an absorption Ångstrom exponentof 1.0.

Table 6. Year-to-year variability of MACcor (PSAP; λ = 550 nm) and r2 at Ny-Ålesund.
Year2012 (Apr–Dec)
2013
2014
2015
2016 (Jan–Sep)
Average (2013–2016)*
All***

MACcor (1-h)[m2 g–1]
r2 (1-h)
5.7
0.30
17.0
0.81
17.4
0.80
12.0
0.84
14.5
0.90
15.2 ± 2.2
0.84 ± 0.04
14.4
0.76

MACcor (24-h)[m2 g–1]
r2 (24-h)
5.8
0.44
17.2
0.85
18.5
0.81
15.9
0.94
14.8
0.95
16.6 ± 1.4
0.89 ± 0.06
15.2
0.82

Average and standard deviation for individual years
Derived by regression slope for all data points

**

1210

Table 7. Year-to-year variability of MACcor (Aethalometer; λ = 590 nm) and r2 at Ny-Ålesund.
1215

1220

1225

Year
2012 (Apr–Dec)
2013
2014
2015
2016
2017
2018
2019 (Jan–Aug)
Average*
All***

MACcor (1-h)[m2 g–1]
r2 (1-h)
8.67
0.80
9.65
0.87
9.77
0.92
9.82
0.96
12.4
0.92
13.0
0.86
10.3
0.92
8.07
0.91
10.2 ± 1.6
0.90 ± 0.05
10.2
0.90

Average and standard deviation for individual years
Derived by regression slope for of all data points

**

1230

48

MACcor (24-h)[m2 g–1]
r2 (24-h)
8.75
0.85
8.89
0.75
10.0
0.95
9.87
0.98
12.2
0.95
11.5
0.87
10.6
0.94
8.37
0.92
10.0 ± 1.3
0.90 ± 0.07
10.1
0.90

Table 8. Year-to-year variability of MACcor (MAAP; λ = 637 nm) and r2 at Ny-Ålesund.
1235

1240

Year
2017
2018
2019
2020
Average*
All***

MACcor (1-h)[m2 g–1]
r2 (1-h)
10.3
0.85
11.9
0.74
11.6
0.92
10.4
0.92
11.1 ± 0.7
0.86 ± 0.07
10.6
0.90

MACcor (24-h)[m2 g–1]
r2 (24-h)
10.7
0.57
13.3
0.64
12.2
0.92
10.5
0.97
11.7 ± 1.1
0.78 ± 0.17
10.9
0.83

Average and standard deviation for individual years
Derived by regression slope for of all data points

**

1245

Table 9. Year-to-year variability of MACcor (PSAP/CLAP; λ = 550 nm) and r2 at Barrow.

1250

1255

Year
2012 (Aug–Dec)
2013
2014
2015
2016
2017
2018 (Jan–May)
2019 (Jun–Dec)
Average (2012–2018)*
All**

MACcor (1-h)[m2 g–1]
r2 (1-h)
9.00
0.65
10.5
0.91
11.0
0.96
11.7
0.91
11.3
0.89
11.5
0.91
12.0
0.86
4.6
0.28
11.0 ± 0.9
0.87 ± 0.09
10.8
0.88

1260
*

Average and standard deviation for individual years
Derived by regression slope for all data points**

49

MACcor (24-h)[m2 g–1]
r2 (24-h)
8.80
0.67
10.5
0.91
10.8
0.91
11.5
0.91
11.2
0.88
11.3
0.93
10.9
0.69
5.1
0.41
10.7 ± 0.8
0.84 ± 0.10
10.6
0.86

1265

Table 10. Year-to-year variability of MACcor (MAAP; λ = 639 nm) and r2 at Fukue.
MACcor (1-h)
Year[m2 g–1]
r2 (1-h)2009 (Apr–Dec) 10.4
0.98
2010
9.62
0.95
2011
11.2
0.95
2012
12.6
0.96
2013
12.8
0.94
2014
10.7
0.98
2015
10.0
0.96
2016
9.90
0.95
2017
10.9
0.93
2018
11.4
0.96
2019 (Jan–May) 12.1
0.95
Average*
11.1 ± 1.0
0.96 ± 0.01
**
All
10.8
0.95

1270

1275

1280

*

1285

MACcor (24-h)[m2 g–1]
r2 (24-h)
10.5
0.99
9.74
0.95
11.3
0.96
12.7
0.96
12.7
0.94
10.8
0.98
9.96
0.95
9.97
0.95
11.1
0.90
11.5
0.96
12.2
0.95
11.1 ± 1.0
0.95 ± 0.02
10.9
0.94

Average and standard deviation for individual years
Derived by regression slope for all data points

**

1290

Table 11. MACcor and r2 for MAAP, PSAP/CLAP, and Aethalometer at λ = 550 nm at observation sites
in this study.

Site

Instrument

Inlet

ALT
PSAP
PM1
ALT
AE31
TSP*
ZEP
PSAP
PM10
ZEP
AE31
PM10
ZEP
MAAP
TSP*
BRW
PSAP/CLAP
PM1
PAL
MAAP
PM10
FKE
MAAP
PM1**
****
Average for the 4 Arctic sites(1-h)
MACcor[m2 g–1]
13.9
13.4***
14.4
10.9***
12.3***
10.8
15.1***
12.5***
13.0±1.6

Period2018–20192018–20192013–2016
2012–20192017–2020
2012–20182019–2020
2009–2019

r2
0.95
0.89
0.76
0.90
0.90
0.88
0.93
0.95
0.89±0.06(24-h)
MACcor[m2 g–1]
14.0
13.6***
15.2
10.8***
12.6***
10.6
15.1***
12.7***
13.1±1.7

r2
0.96
0.92
0.82
0.90
0.83
0.86
0.95
0.95
0.89±0.05

1295*

Total suspended particles.
A PM2.5 cyclone was used before November 2011.
***
MACcor (MAAP; λ ~637 nm) and MACcor (Aethalometer; λ = 590 nm) values were adjusted to λ = 550 nm byassuming an absorption Ångstrom exponent of 1.0.
****
Average and standard deviation values were calculated excluding MAAP data at Fukue.
**

1300

1305

50