ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-12733-2016Deriving brown carbon from multiwavelength absorption measurements: method
and application to AERONET and Aethalometer observationsWangXuanxuanw12@mit.eduhttps://orcid.org/0000-0002-8532-5773HealdColette L.https://orcid.org/0000-0003-2894-5738SedlacekArthur J.https://orcid.org/0000-0001-9595-3653de SáSuzane S.MartinScot T.AlexanderM. LizabethWatsonThomas B.AikenAllison C.https://orcid.org/0000-0001-5749-7626SpringstonStephen R.https://orcid.org/0000-0003-0159-4931ArtaxoPaulohttps://orcid.org/0000-0001-7754-3036Department of Civil and Environmental Engineering, Massachusetts
Institute of Technology, Cambridge, MA, USADepartment of Earth, Atmospheric and Planetary Sciences, Massachusetts
Institute of Technology, Cambridge, MA, USAEnvironmental and Climate Sciences Department, Brookhaven National
Laboratory, Upton, NY, USASchool of Engineering and Applied Science, Harvard University,
Cambridge, MA, USAPacific Northwest National Laboratory, Richard, WA, USAEarth and Environmental Sciences Division, Los Alamos National
Laboratory, Los Alamos, NM, USAInstitute of Physics, University of São Paulo, São Paulo,
BrazilXuan Wang (xuanw12@mit.edu)13October20161619127331275217March201629March201621September201623September2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/12733/2016/acp-16-12733-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/12733/2016/acp-16-12733-2016.pdf
The radiative impact of organic aerosols (OA) is a large
source of uncertainty in estimating the global direct radiative effect (DRE)
of aerosols. This radiative impact includes not only light scattering but
also light absorption from a subclass of OA referred to as brown carbon
(BrC). However, the absorption properties of BrC are poorly understood, leading
to large uncertainties in modeling studies. To obtain observational
constraints from measurements, a simple absorption Ångström exponent
(AAE) method is often used to separate the contribution of BrC absorption
from that of black carbon (BC). However, this attribution method is based on
assumptions regarding the spectral dependence of BC that are often violated
in the ambient atmosphere. Here we develop a new AAE method which improves
upon previous approaches by using the information from the wavelength-dependent measurements themselves and by allowing for an
atmospherically relevant range of BC properties, rather than fixing these at
a single assumed value. We note that constraints on BC optical properties and
mixing state would help further improve this method. We apply this method to
multiwavelength absorption aerosol optical depth (AAOD) measurements at
AERONET sites worldwide and surface aerosol absorption measurements at
multiple ambient sites. We estimate that BrC globally contributes up to
40 % of the seasonally averaged absorption at 440 nm. We find that the
mass absorption coefficient of OA (OA-MAC) is positively correlated with the
BC / OA mass ratio. Based on the variability in BC properties and
BC / OA emission ratio, we estimate a range of
0.05–1.5 m2 g-1 for OA-MAC at 440 nm. Using the combination of
AERONET and OMI UV absorption observations we estimate that the
AAE388/440nm for BrC is generally ∼ 4 worldwide, with a
smaller value in Europe (< 2). Our analyses of observations at two
surface sites (Cape Cod, to the southeast of Boston, and the GoAmazon2014/5
T3 site, to the west of Manaus, Brazil) reveal no significant relationship
between BrC absorptivity and photochemical aging in urban-influenced
conditions. However, the absorption of BrC measured during the biomass
burning season near Manaus is found to decrease with photochemical aging with
a lifetime of ∼ 1 day. This lifetime is comparable to previous
observations within a biomass burning plume but much slower than estimated
from laboratory studies. Given the large uncertainties associated with
AERONET retrievals of AAOD, the most challenging aspect of our analysis is
that an accurate, globally distributed, multiple-wavelength aerosol absorption
measurement dataset is unavailable at present. Thus, achieving a better
understanding of the properties, evolution, and impacts of global BrC will
rely on the future deployment of accurate multiple-wavelength absorption
measurements to which AAE methods, such as the approach developed here, can
be applied.
Introduction
The radiative impacts of carbonaceous aerosols, which encompass both black
carbon (BC) and organic carbon (OC), remain highly uncertain. Aerosol
absorption is dominated by BC, which is estimated to be the second largest
warming agent contributing to climate change in the last Intergovernmental
Panel on Climate Change (IPCC) report (IPCC, 2013). However, the uncertainty
associated with the BC radiative forcing is as large as a factor of 2 (Bond
et al., 2014; Myhre et al., 2013),
and recent work shows that the IPCC estimate is likely biased high (Wang et
al., 2014). In contrast, OC is typically treated as a purely scattering
agent. However, recent studies show that some OC can also absorb light,
primarily at UV wavelengths (Arola et al., 2011; Hecobian et al., 2010;
Chakrabarty et al., 2010; Kirchstetter et al., 2004; Chen and Bond, 2010).
This absorbing OC, so-called brown carbon (BrC), is mainly produced from
biomass burning or biofuel combustion (Washenfelder et al., 2015; Ramanathan
et al., 2007) but can also be generated from secondary sources involving the
photooxidation of anthropogenic and biogenic volatile organic compounds
(VOCs) or aqueous-phase chemistry in cloud droplets (Graber et al., 2006;
Ervens et al., 2011).
Modeling studies estimate that BrC contributes 20 to 40 % of total
carbonaceous aerosol absorption and that its absorption direct radiative
effect (DRE) ranges from +0.1 to +0.6 Wm-2 (Feng et al., 2013; Lin
et al., 2014; Wang et al., 2014; Saleh et al., 2015; Jo et al., 2016).
However, all of these studies suffer from substantial uncertainties given
that our knowledge of the sources, optical properties, and chemical
transformations of BrC is poorly understood. Although we know that BrC is
associated with biofuel and biomass burning combustion, the role of fuel
source and burning conditions in determining BrC absorption is not well
known. Saleh et al. (2014) and Martinsson et al. (2015) suggest that the
absorption of BrC generated from biomass burning is correlated with the
BC / OC emission ratio. Various secondary organic aerosol (SOA)
precursors are also thought to be a source of BrC, including monoterpenes,
isoprene, and nitroaromatic compounds (Laskin et al., 2015). However, the
formation and abundance of these particles in the atmosphere is poorly
constrained. In addition to sources, the uncertainty in estimates of BrC
absorption is also driven by uncertainty in optical properties. For example, comparison has shown measured refractive index (RI) and absorption Ångström
exponent (AAE) to differ significantly between studies (Wang
et al., 2014; Laskin et al., 2015). Finally, chemical transformations may
also alter the optical properties of BrC in the atmosphere. Some studies
suggest that absorption increases during the formation and chemical aging of
certain types of biogenic and aromatic SOA (Flores et al., 2014; Laskin et
al., 2015), while other studies indicate that the absorption of BrC may
decrease during photolysis (Zhong and Jiang, 2011; Lee et al., 2014;
Martinsson et al., 2015). Most of these results are from laboratory
experiments and require confirmation from field observations. Forrister et
al. (2015) use airborne observations of two fire events to show that the
absorption associated with BrC decreases following emission, estimating a
half-life for biomass burning BrC absorption of 9–15 h. However, this rate
is much slower than that suggested by laboratory studies (5 min to 3 h)
(Zhong and Jiang, 2011; Lee et al., 2014; Zhao et al., 2015), though none of
these has explored how the absorption of primary BrC from biomass burning
evolves under oxidizing conditions. Given the above uncertainties, field
measurements of BrC are vital not only for constraining models but also for
understanding the properties and transformations of this aerosol and its
radiative impacts.
To date, the only method for directly measuring BrC absorption involves
extraction of filter samples in water, acetone, or methanol. This approach
is offline and requires detailed laboratory analysis. This is therefore not
a viable approach for obtaining global, continuous measurements; direct
observations of BrC absorption from field campaigns are also limited. As a
result, more indirect methods based on calculating the difference between
total absorption and that of BC have been developed. However, it is clear
that the uncertainty associated with separating BrC absorption from BC
absorption is larger than the uncertainty associated with the organic
extraction method, because the absorption of BC itself can be highly
uncertain (Koch et al., 2009; Bond et al., 2013; Wang et al., 2014). To
separate BrC absorption from total absorption from satellite or ground-based
measurements, one can use complex model retrievals to determine particle
type and refractive index (Tesche et al., 2011; Arola et al., 2011). The
uncertainty in this approach can be very large and is hard to quantify given
that it relies on multiple assumptions regarding aerosol composition and
size distributions (Li et al., 2009). Alternatively, because BrC primarily
absorbs light in the near UV, its AAE differs from BC, and therefore a
simple AAE method can also be used to estimate BrC absorption:
AAE=-lnabsλ1absλ2lnλ1λ2.
Here λ1 and λ2 are two reference wavelengths;
abs(λ) is the absorption (or absorption coefficient, or AAOD) at the
corresponding wavelength. We can consider the case where we have absorption
measurements at three wavelengths, one in the near UV or a short wavelength
including BrC absorption and the other two in the visible spectrum without
BrC absorption (for example, 440, 675, and 870 nm) to try to separate the
absorption of BC and BrC. If there is no dust present, the absorption at the
two longer wavelengths (675 and 870 nm) is solely from BC, and the
absorption at the shortest wavelength (440 nm) includes contributions from
both BC and BrC. As a result, if the AAE of BC is known, the absorption of BC
at 440 nm can be calculated using the longer wavelengths measurements. Then
the absorption from BrC can be simply derived by removing this BC
contribution to the 440 nm measurements. The retrieval of continuous
measurements of total aerosol absorption provided by the Aerosol Robotic
Network (AERONET) of ground-based sun photometers since 1992 is an attractive
resource for development of such indirect methods. Several studies have
applied this idea, using empirically estimated BC-AAE to derive the BrC
absorption (Russell et al., 2010; Chung et al., 2012; Bahadur et al., 2012).
In light of the critical need for observational constraints on BrC, in this
study we build on previous AAE-based efforts to estimate BrC. We describe a
new AAE method to separate BrC and BC absorption and then apply this method
to derive BrC AAOD from AERONET observations as well as from a suite of
Aethalometer field observations of absorption. In doing so, we aim to
improve our understanding of BrC emissions and optical properties, as well
as provide a new observational constraint for BrC modeling studies.
Method for deriving BrC absorption from observations
Many studies have applied the simple AAE-based approach to laboratory and
field measurements. These studies typically assume AAE = 1 for BC to
derive the BrC absorption (Clarke et al., 2007; Herich et al., 2011;
Sandradewi et al., 2008; Yang et al., 2009; J. Liu et al., 2015; Olson et
al., 2015; and references therein) However, the AAE = 1 assumption may
not be representative of ambient BC. Lack and Langridge (2013) summarize a
series of field measurements and find that AAE of BC (for 467 and 660 nm)
typically ranges from 0.8 to 1.4. Furthermore, the assumption that BC
AAE = 1 under all conditions is also theoretically incorrect. Figure 1
summarizes a series of Mie calculations for single BC particles of varying
size and coating. An AAE of 1 is reasonable when the diameter of the BC
particle is smaller than 10 nm. However, BC associated with biomass burning
and biofuel sources is typically larger than 70 nm (Bond and Bergstrom,
2006). For these larger BC particles, the AAE is highly sensitive to the
size. In addition, coating of BC by other materials, as is commonly observed
in the atmosphere (Bond et al., 2013), also modulates the AAE. Finally, the
AAE of particles > 20 nm is sensitive to the reference
wavelengths chosen. Taken together, the assumption of AAE = 1 for ambient
BC is clearly not supported by either theory or field observations, and
estimates of BrC absorption based on this underlying assumption are subject
to large errors.
The Ångström absorption exponent (AAE) for BC estimated
using Mie calculations as a function of size and for a series of coating
states.
Several previous studies have gone beyond the AAE-BC = 1 assumption and
used the AAE to separate the BC (or BrC) contribution from total absorption.
These analyses typically rely on empirical information from previous
observations. For example, Bahadur et al. (2012) and Chung et al. (2012)
apply the same approach where they group AERONET sites by regions and
possible source types, and by analyzing these groups, they estimate the
possible AAE (or SSA or EAE) and the corresponding range for pure BC or pure
BrC. They then apply these empirical constraints to estimate the BC or BrC
contributions at other sites. Bahadur et al. (2012) used this method and
found that BrC contributes 28 % of the total aerosol absorption at 440 nm in
North America. Chung et al. (2012) concluded that ∼ 20 % of the
absorption DRE estimated in previous global studies for BC should be
attributed to BrC. This approach assumes that the AAE is an intrinsic
property by composition; however, it is clear from Fig. 1 that the AAE is
strongly size-dependent, and may therefore vary geographically with
combustion conditions. These methods can also only be applied within a given
dataset, such as AERONET.
Here we develop a novel method to derive BrC absorption using
multiple-wavelength absorption measurements. This AAE-based method does not
rely on assumed or empirically estimated BC and BrC AAE values as in previous
studies; rather, it combines multiple-wavelength absorption measurements with
theoretical Mie calculations for BC. As shown in Fig. 1, the AAE of BC is
different when using different reference wavelength pairs. We characterize
the WDA (wavelength dependence of absorption Ångström exponent) to describe
this difference. This WDA can be seen as the wavelength dependence of the
wavelength dependence of absorption, which provides additional information on
the aerosol properties that has not been exploited in previous studies.
Assuming that we have absorption measurements at 440, 675, and 870 nm, then
WDA=AAE440/870-AAE675/870,
and in the absence of BrC, the AAE440/870 and AAE675/870 are the
AAE of BC calculated using the 440 nm/870 nm and
675 nm/870 nm wavelength pairs. Note that the assumption of
wavelength-independent AAE = 1 for BC would lead to a WDA of 0. For a
given population of BC particles, we can use Mie theory to calculate a WDA
value by assuming that the particles are spherical. The observed size
distribution of BC is typically lognormal, with geometric median diameter
(GMD) ranging from 20 to 300 nm and standard deviation (δ) ranging
from 1.4 to 2.2 (Akagi et al., 2012; Schwarz et al., 2008; Lack et al., 2012;
Dubovik et al., 2002; Shamjad et al., 2012; Moffet and Prather, 2009; Knox et
al., 2009; Lewis et al., 2009). We perform Mie calculations using these size
distributions and a refractive index of 1.95–0.79i, as suggested by Bond and
Bergstrom (2006). We also perform an additional set of calculations for
coated BC. The refractive index for coated material is assumed to be
1.55–0.001i, which is the typical value for non-absorbing organic and
inorganic (Kopke et al., 1997). We first assume the coating thickness is
10–100 % of the BC core radius and then only select the calculations
with absorption enhancement smaller than a factor of 2. This is supported by
field measurements and most laboratory experiments (Schwarz et al., 2008;
Lack et al., 2012; Moffet and Prather, 2009; Cappa et al., 2012; Bueno et
al., 2011; Shiraiwa et al., 2010; Shamjad et al., 2012; Knox et al., 2009;
S. Liu et al., 2015). Figure 2 shows the range (shaded region) of calculated WDA
of BC versus AAE675/870 that we estimate based on the above assumptions.
The range in the estimated wavelength dependence of AAE
(WDA) for BC (shaded region) based on Mie calculations (see Sect. 2 for
size and coating assumptions). The black line is the median WDA. Red crosses
show the total absorption from 2005–2014 10-year seasonal average AAOD
measurements at three wavelengths from the AERONET network in north hemisphere
winter (December, January, and February). Observations which lie above the
shaded region include detectable contributions of BrC absorption.
The black line is the median value of the WDA of BC as a function of
AAE675/870. For a given set of multiwavelength absorption measurements,
if the calculated WDA falls above the shaded region, this suggests that there
are components other than BC in the sample which absorb light more strongly
at 440 nm than at longer wavelengths, supporting the presence of BrC. To
illustrate this we take the AAOD measured at all the sites of the global
AERONET network as an example. AERONET is a global ground-based aerosol
observation network of radiometers (Dubovik and King, 2000; Holben et al.,
2001). AERONET AAOD can be calculated at four wavelengths (440, 675, 870, and
1020 nm) based on aerosol optical depth (AOD) and single-scattering albedo
(SSA), which are retrieved by measuring the sky radiance in a wide angular
range. The latest version 2 AERONET product includes two levels of data: 1.5
(cloud-screened) and 2 (cloud-screened and finally quality-assured). The
level-2 AERONET SSA data are only available under high AOD conditions (AOD
> 0.4 at 440 nm) (Dubovik and King, 2000; Dubovik et al.,
2002); this subset is only 20 % of the
level-1.5 measurements, which makes the level-2 AAOD biased towards
high-aerosol loading conditions. As we want to estimate BrC absorption for a
wider range of conditions in the atmosphere, we use the level-2 AOD and SSA
in addition to recovering the missing SSA from level-1.5 in the following analysis.
The uncertainty in partially using level-1.5 SSA is hard to estimate, but it
could be small for our BrC contribution analysis if such uncertainties are
similar at all wavelengths.
While AERONET provides global observations of the column-integrated AOD, few
of these sites actually have continuous measurements of AAOD throughout the
year because the SSA is not always retrieved. For example, more than half of
the AERONET sites measured AAOD for only 1 month in 2014. As a result, we use
the data from the past decade (2005–2014) to enhance our sampling. To reduce
the influence of sporadic events in the analysis, when showing the 10-year
seasonal average value, only sites with data for more than 6 years within a
given season are selected. The AAOD from AERONET reflects not only the
absorption from BC and BrC but also that from dust. We use two thresholds to
exclude the data possibly affected by dust. First, we use the coarse-mode AOD
contribution (at 440 nm) provided by AERONET. We assume that dust controls
the total extinction of particles larger than 1 µm diameter
(coarse-mode), and therefore remove data with a coarse-mode AOD contribution
> 10 % from our analysis. Second, we apply the strict
filtering of AERONET observations proposed by Russell et al. (2010) and Chung
et al. (2012), excluding data with extinction Ångström exponent (EAE)
< 1, as well as Bahadur et al. (2012), excluding data exhibiting
scattering Ångström exponent (SAE) < 1.2 and
AAE675/870/ AAE440/675 < 0.8. Bahadur et al. (2012)
refer to data filtered by this criterion as “dust-free”. However, we note
that AERONET observations are not a direct measurement of absorption but instead a
retrieved quantity, and though we have attempted to minimize dust
contamination in this dataset, retrieval assumptions may also impact our
analysis. This is discussed in further detail in Sect. 3, but here we apply
our methodology to AERONET observations primarily for illustration purposes.
The red crosses in Fig. 2 show the calculated WDA using the seasonal average
observed AAOD from the global AERONET network in northern hemispheric winter
(December, January, and February, same sites in Fig. 3a). Many points fall
within the shaded region, suggesting that the absorption for these sites at
440 nm is primarily from BC. We cannot preclude the presence of BrC in these
samples, but the contribution is likely small and cannot be estimated using
our method without additional information about the size and coating state of
BC particles. BrC is clearly present (and contributing to the absorption at
440 nm) for the remaining sites which lie above the shaded region. We
calculate the highest and lowest possible BrC absorption at 440 nm based on
the lowest and highest WDA (WDA1 and WDA2) as follows:
BCAAE440/870=AAE675/870+WDA,BrCabs440=abs440-BCabs440.
The BrC absorption at 440 nm is calculated as the median of these highest
and lowest possible absorptions. For those points that fall within the shaded
region, the BrC absorption is determined as the median of the highest
possible absorption and 0. The methodological uncertainty varies as a function
of the relative amount of BrC and the measured wavelengths. For example, with
measurements of absorption at 440, 675, and 870 nm wavelengths, BrC must
contribute at least 4 % of the total absorption at 440 nm to be detected
by this approach. This implies a “detection limit” to this approach, where
contributions of 4 % or less of BrC to total absorption cannot be
identified. This detection limit varies with AAE, and is highest when the AAE
of BC is in the range of 1.1 to 1.3. We also estimate the methodological
uncertainty range for BrC absorption at 440 nm by repeating this calculation
using the lowest and highest WDA value of the shaded region (WDA1 and
WDA2). For conditions with AAE675/870 > 1, the
methodological uncertainty of derived BrC absorption at 440 nm using the
above wavelengths is smaller than 28 % when the BrC absorption
contribution is larger than 30 %, but could be as large as 110 % when
the BrC contributes around 10 % of the total absorption. For conditions
where AAE675/870 < 1, this uncertainty is only 8 % when
the real BrC absorption contribution is larger than 30 and 35 % when the
contribution is 10 %. Given the modest range in the calculated WDA for BC
(< 25 %), this method decreases the uncertainty in estimated BrC
compared to the traditional BC AAE = 1 method. Lack and Langridge (2013)
show that the bias in the traditional BC AAE = 1 method is also
associated with the BrC / BC ratio. The bias from that method is smaller than
33 % when BrC contributes 23–41 % of total absorption but much
larger (more than 100 %) for other BrC contributions. In contrast, for
the annual mean observations from the global AERONET network in 2014 that lie
above the shaded BC region shown in Fig. 2, the uncertainty of BrC absorption
derived using our method is smaller than 25 %. The spherical assumption
in the Mie calculations could lead to additional uncertainties, as previous
work suggests that the shape of BC can affect both the SSA and the absorption
enhancement from coating (Adachi et al., 2010; Kahnert and Devasthale, 2011).
However, this uncertainty is hard to estimate since it is difficult to
quantify how particle shape influences AAE and WDA. Our estimated BrC
absorption is the externally mixed BrC absorption, which does not include the
influence of BrC coated on BC. This is consistent with BrC measurements as
the absorption of coated BrC is included in the absorption of BC and cannot
be measured separately.
In contrast to previous AAE-based methods, our approach uses the theoretical
relationship between AAE and WDA for BC shown in Fig. 2 in combination with
the observed total AAE, and does not rely on any other data. This also makes
our method “wavelength-flexible”. Although we use 440/675/870 nm to
describe our method, any three wavelengths with one in the near UV and two at
longer wavelengths in the visible spectrum can be used.
As the absorption from primary OA (Br-POA) from biofuel and biomass burning
typically dominates that of absorbing SOA (Br-SOA) (Martinsson et al., 2015;
Laskin et al., 2015), the absorption of Br-SOA is much more challenging to
detect than Br-POA in most field measurements. We therefore focus our
analysis on the primary sources of BrC.
AERONET network and data analysisGlobal BrC-AAOD from AERONET
Figure 3 shows the derived AERONET BrC-AAOD at 440 nm in different seasons.
Our BrC-AAOD calculation is based on the daily data from AERONET. This is
different from the data points in Fig. 2 (10-year seasonally averaged data, only for
illustration). The AERONET observations of wavelength-dependent absorption
are retrieved from the direct and diffuse radiation measured by sun/sky
radiometers, but they do not include any aerosol assumptions such as those used in
the AERONET retrieval of refractive index and size distribution. AERONET AAOD
is widely used to investigate the sources, compositions, and properties of
aerosols (Russell et al., 2010; Bond et al., 2014; Sayer et al., 2014).
However, we show that the retrieval is an indirect measure of aerosol
absorption and that uncertainties and assumptions in the retrieval scheme may
impact the reported multiwavelength absorption and introduce subtle
inconsistencies with our assumed population of particles. Given the paucity
of direct measurements of multiwavelength absorption (see datasets described
in Sect. 4), we apply our methodology to the AERONET observations to provide
a first-look constraint on global BrC AAOD.
Derived seasonal mean BrC-AAOD at 440 nm from AERONET observations
(2005–2014, 10-year average) in northern hemispheric (a) winter,
(b) spring, (c) summer, and (d) fall. The color
bar is saturated at 0.010 to emphasize regional variations, but maximum
values reach 0.056.
The accuracy of specific numerical values presented below is challenging to
estimate, and we provide these values for completeness in the text; however, we focus
our conclusions on the qualitative spatial and seasonal differences in
estimated BrC, which are likely more robust.
For the data points below the methodology detection limit, we calculate the
BrC-AAOD as the mean of 0 and the associated detection limit; the mean
BrC-AAOD for these points is 0.0034 ± 0.05. The fraction of the data
below the detection limit is 22 % globally and is regionally consistent.
In general, the seasonal average BrC-AAOD is smaller than 0.005 at most sites
but larger in Asia. The BrC-AAOD can be as large as 0.056 in the winter at
the site near Beijing. The average BrC-AAOD derived at AERONET sites is
0.0031 globally, 0.0018 in North America, 0.0026 in Europe, 0.0119 in East
Asia, and 0.004 in South America. The mean BrC-AAOD in the major biomass
burning season in Southeast Asia (spring, 0.006) is ∼ 60 % higher
than non-biomass-burning seasons (0.038). In contrast, no significant
seasonal variations are found in other regions (data in South America are
only available during biomass burning seasons due to the data filtering). The
sites in Africa exhibit low BrC-AAOD even during biomass burning seasons.
This is because nearly all the data with high AAOD in Africa are excluded
from the analysis due to the influence of dust.
Figure 4 shows the contribution of BrC-AAOD to total AAOD at 440 nm at each
AERONET site. The annual mean BrC AAOD contribution falls below 30 % at
80 % of the AERONET sites. Generally, East Asia and Europe in northern
hemispheric winter have higher BrC AAOD contributions (28 and 21 %) than
other regions/seasons (average 10 %). Our estimate in the California region
(14 % in the north and 11 % in the south) is comparable with that of Bahadur
et al. (2012) (15 % in the north and 9 % in the south) during winter
and spring, but smaller during summer and fall (∼ 12 % vs.
∼ 30 %). The OC in both East Asia and Europe has large
contributions from residential heating using biofuel, which suggests such
biofuel emissions of OC may be more absorbing than other sources that
dominate in other seasons. In contrast, in Southeast Asia, the contributions
of BrC to total AAOD are relatively aseasonal (< 5 % seasonal
differences). As the absolute value of BrC-AAOD is larger in the fire season
but the contribution of BrC to total absorption is not, the BrC absorption
associated with biomass burning from large scale fires may not be very
different from the other sources that dominate in other seasons.
Derived seasonal mean BrC-AAOD contributions to total AAOD at
440 nm from AERONET observations (2005–2014, 10-year average) in northern
hemispheric (a) winter, (b) spring, (c) summer,
and (d) fall. The color bar is saturated at 40 % to capture
regional variation, but values up to 45 % are estimated at specific
sites.
The uncertainty in our derived BrC-AAOD (described in Sect. 2) is different
at each site. More than 90 % of all the daily data in these 10 years have
a methodological uncertainty smaller than 30 %. The methodological
uncertainty at a given site at a given hour can be as high as 100 %.
However, very low BrC-AAOD values are derived for these highly uncertain
data, having little impact on the 10-year-averaged BrC-AAOD. Since AAE
< 1 is frequently (60 % of the observations for the wavelength
pair of 675/870 nm) observed at most AERONET sites, substantial BrC
absorption would be misattributed to BC using the simple BC-AAE = 1
method. By assuming BC-AAE = 1, the global mean BrC-AAOD at 440 nm would
be estimated as only 0.001, 40 % lower than our estimate.
The relationship between monthly mean derived AERONET BrC-AAOD at
440 nm and BC-AAOD at 675 nm at AERONET stations in (a) North
America, (b) East Asia, (c) Europe, and (d)
Southern Hemisphere for the years 2005–2014.
One challenge of this analysis is the well-known uncertainties associated
with the AERONET observations. The measurement uncertainty is ±0.01 for
AOD and ±0.03 for SSA when AOD > 0.2, and it could be as large as
±0.07 for SSA when AOD < 0.2. The uncertainty of AAOD depends
on the corresponding AOD value; for example, this uncertainty is ±0.015
with AOD = 0.4 (Dubovik et al., 2002). Because our method is sensitive to
the AAE not the AAOD, the uncertainty could be small for our BrC contribution
analysis if such uncertainties from AERONET are similar at all wavelengths.
If the AERONET AAOD uncertainties vary substantially with wavelength, the
influence on our analysis could be large and hard to quantify. In addition,
the AERONET uncertainties suggest AAOD < 0.01 is certainly below
the observed detection limit. In Fig. 3 and in the above discussion, most
sites exhibit derived BrC-AAOD smaller than 0.01. However, all of these
values are seasonal means over 10 years and include both non-BrC detected
(BrC-AAOD ∼ 0) data and BrC detected data. If instead we replace the
BrC-AAOD < 0.01 data points by BrC-AAOD = 0.005 (the median of
0 and 0.01), the results are very similar.
In our method, we assume that the BrC absorption contribution is negligible
at 675 and 870 nm. This is supported by the laboratory measurements (Chen
and Bond, 2010; Zhang et al., 2013; Yang et al., 2009; Kirchstetter et al.,
2004). However, Alexander et al. (2008) find the BrC absorption may be
significant at 675 nm by examining an electron energy-loss spectrum from a
transmission electron microscope. If BrC absorbs significantly at 675 nm,
our estimate of BrC absorption at 440 nm would be underestimated.
Relationship between BrC-AAOD and BC-AAOD
Figure 5 compares the derived BrC-AAOD at 440 nm and BC-AAOD at 675 nm at
AERONET sites for 2005–2014. As BrC absorbs little light at wavelengths
longer than 600 nm, BC-AAOD is effectively equivalent to total AAOD at
675 nm. Globally, the BrC-AAOD and BC-AAOD are moderately well correlated
(R2∼ 0.6, not shown in Fig. 5). To identify whether the
correlation is different under different conditions, we further disaggregate
the data by emission type and region. We use the anthropogenic emission
inventory of Bond et al. (2007) and biomass burning emission inventory from
GFED4 (Giglio et al., 2013) to identify the dominant emission type for each
data point. For a given month, if the biofuel or biomass burning emissions of
both BC and OC contribute more than 60 % of the total emissions in a
2∘× 2∘ area around a given AERONET site, we
identify the corresponding data points as dominated by that source (70 %
of data points that do not meet this criterion and are thus excluded). The
results are summarized in Figure 5. After the data are separated by dominant
source, the correlation between BrC-AAOD and BC-AAOD increases in all regions
except the biomass-burning-dominated European sites. The correlation slope of
BrC-AAOD/BC-AAOD varies by region but is similar for different sources in the
same region. Although we select the data using emissions in the surrounding
2∘× 2∘ area to denote the regional influence, this
separation may be inaccurate if long-range transport is a significant source
of carbonaceous aerosol at a given site. In addition, this data separation is
not able to account for the variability in combustion fuel and conditions. In
East Asia, the correlation slope (m) between BrC-AAOD and BC-AAOD is
∼ 0.9. It decreases to ∼ 0.5 in other regions.
BrCAAOD=m×BCAAOD
Equation (5) provides a
simple method to estimate the absorption of BrC by measuring BC absorption.
On average, globally, BrC-AAOD at 440 nm is roughly 50 %
(m∼ 0.5) of the BC-AAOD at 675 nm based on global AERONET data.
Frequency distributions of the AAOD measured by OMI at 388 nm
(black) and AERONET at 440 nm (red), as well as AERONET observations
adjusted to 388 nm using a range of assumed AAE for BrC (dashed lines) for
(a) the whole world, (b) North America,
(c) Europe,
and (d) East Asia. Details can be found in Sect. 3.4.
The AAOD is the total column absorption of aerosols, which can be written as
the product of column aerosol mass and the mass absorption coefficient (MAC)
of aerosols. Based on the linear relationship between BrC-AAOD and BC-AAOD,
we are able to connect the MAC of OC (identical to the MAC of BrC when
assuming all OC are BrC) with the mass ratio of BC / OC:
MACOC=m×MACBC×MassBCMassOC.
The MAC of OC is related
to the properties of OC such as size distribution, mixing state, hygroscopic
growth, and refractive index (RI). Generally, there is a positive correlation
between the MAC and the imaginary part of the RI (i) although the
relationship is not linear (Bond and Bergstrom, 2006). This tells us that the
i of BrC is likely to be positively correlated with the mass ratio of
BC / OC in a certain environment, as shown by Saleh et al. (2014). The
observed relationships shown in Fig. 5 confirm that the
absorption properties of BrC are likely related to the emission ratio of
BC / OC, which further connects to fuel types and combustion conditions.
The absorptivity of OC emitted from sources with higher BC / OC is likely
to be higher.
Both the BC optical properties and the BC / OC ratio may vary under
different conditions. It is therefore challenging to estimate the MAC of OC
accurately based on Eq. (6). However, we can estimate typical values of
regional average MAC of OC given that the regional BC / OC emission
ratios do not vary substantially in emission inventories, and assuming that
the emission ratio of BC / OC is a reasonable proxy for the mass
concentration ratio (i.e. relative differences in losses and sources are
negligible). Based on the biofuel emissions of Bond et al. (2007), the
BC / OC emission ratio is 0.18 ± 0.03 in North America and Europe
and 0.24 ± 0.06 in other regions. The BC / OC emission ratio for
biomass burning in the GFED4 inventory is 0.12 ± 0.06 for different
source types. Considering the typical size distributions and coating
thickness for BC from biofuel and biomass burning sources, the MAC of BC from
these sources is calculated to be 7.8 ± 5 m2 g-1 by Mie
theory (Wang et al., 2014). We therefore estimate the average MAC of OC to be
0.7 m2 g-1 from biofuel in North America/Europe,
0.94 m2 g-1 from biofuel in other regions, and
0.47 m2 g-1 from biomass burning at 440 nm. Taking the mass
ratio of total organic aerosol (OA) and OC to be 2.1 (Turpin and Lim, 2001;
Aiken et al., 2008), the corresponding MAC for OA are 0.33, 0.45, and
0.22 m2 g-1. Considering the variability in the correlation
slopes in Fig. 5, the BC / OC emission ratios, the size distribution, and
the mixing state of BC, we estimate a range of MAC of
0.1–3.1 m2 g-1 for OC and 0.05–1.5 m2 g-1 for OA at
440 nm. The upper limit is comparable to the highest MAC of
acetone/methanol-soluble OA found in laboratory experiments (Kirchstetter et
al., 2014; Yang et al., 2009). We note that these estimates of OC-MAC (or
OA-MAC) are subject to uncertainties associated with the AERONET retrieval of
absorption. It should be mentioned that the MAC of OA reflects both the
BrC-MAC and the BrC contribution to the total OA. Since we cannot isolate the
BrC contribution to OA, we use the term OA-MAC instead of BrC-MAC to
characterize the absorption of OA in this and following analysis.
BrC-AAE from combining AERONET and OMAERUV data
From AERONET data, we can derive BrC-AAOD at one wavelength only (440 nm).
However, this is insufficient for estimating the full radiative impacts of
BrC. To estimate the AAE of BrC, AAOD with at least one more near-UV wavelength is
needed. Here we use the OMAERUV (Ozone Monitoring Instrument near-UV aerosol
algorithm) (Torres et al., 2007; Ahn et al., 2014) product together with AERONET to calculate the AAE of BrC.
OMI is a nadir-viewing spectrometer aboard NASA's Earth Observing System's
(EOS) Aura satellite. The Aura polar-orbiting satellite orbits with a 16-day
repeat cycle and a local Equator-crossing time of 13:45 ± 15 min. OMI
measures near-UV radiance at 354 and 388 nm and reports AOD, SSA, and AAOD at
354, 388, and 500 nm with a spatial resolution of 13 km × 24 km.
The AAOD at 388 nm is directly retrieved from radiance absorption, while the
other two wavelengths are derived from 388 nm data. In the analyses below,
we use the AAOD at 388 nm from the level-3 OMAERUVd gridded product and only
select the highest-quality data by filtering out retrievals affected by large
solar zenith angle (> 70∘), out-of-bounds AOD
(> 6 at 500 nm) or SSA (> 1), low terrain pressure
(< 628.7 hPa), cloud contamination, and cross-track anomaly. The
root mean square error of the AAOD is estimated to be ∼ 0.01 (Torres et
al., 2007).
Given that the measurement methods of AERONET and OMAERUV are very different
and the temporal coverage of both are relatively poor, we do not combine the
OMAERUV AAOD at 388 nm with the AERONET AAOD at other wavelengths to
calculate BrC-AAOD at 388 nm for each AERONET site. Instead, we
statistically compare AAOD observations in different regions between these
two products. We assume that the distribution of AAOD from a large group of
data points should be similar between AERONET and OMAERUV despite differences
in the measurement approach. In Fig. 6, the frequency distributions for AAOD
of OMAERUV (388 nm) and AERONET (440 nm) are plotted as solid black and red
lines. To compare the observations at the same wavelength, we transfer the
AERONET AAOD to 388 nm by fixing the AAE388/440nm of BC and
BrC. When assuming AAE388/440nm=1 for BC, the 388 nm AERONET
AAOD distribution with different BrC-AAE388/440nm is shown as
dashed lines in Fig. 6. Assuming that a different BC-AAE388/440nm in
the range of 0.5 to 1.5 only slightly alters the dashed lines with
BrC-AAE388/440nm=2, other dashed lines are largely
unaffected. This arises due to the dominance of BrC on total AAE when
BrC-AAE388/440nm > 2.
In Fig. 6a, it is clear that the 388 nm AERONET AAOD distribution with
BrC-AAE388/440nm= 4 (orange dashed line) is the best match to
the OMAERUV AAOD (black solid line). This suggests the global mean AAE of BrC
should be ∼ 4. Similar results are found in North America (Fig. 6b),
East Asia (Fig. 6c), and the rest of the world except Europe. In Europe,
regardless of our choice of BrC-AAE388/440nm, the 388 nm
AERONET AAOD distribution does not match the OMAERUV AAOD. This suggests that
both BC and BrC may have smaller AAE in Europe. As previously mentioned, when
BrC-AAE388/440nm is smaller than 2, the 388 nm AERONET AAOD
distribution is sensitive to not only BrC-AAE388/440nm but also BC-AAE. Thus, by combining OMAERUV and AERONET AAOD data, we find that
BrC-AAE388/440nm is typically ∼ 4 globally but smaller
(< 2) in Europe. These values are smaller than laboratory
measurements of fresh emission from pyrolyzing wood by Chen and Bond, 2010
(AAE380/460nm > 7) and biomass burning smoke by
Kirchsteter et al. (2004) (AAE350/450nm= 4.8). It should be
mentioned that these AAE values are based on the 388–440 nm pair and may change
for other wavelength pairs.
Many studies have evaluated OMAERUV AOD by comparing them with ground-based
measurements. The correlation between OMAERUV and AERONET AOD is usually
found to be high (R> 0.8) (Jethva and Torres, 2011; Ahn et
al., 2014). Jethva et al. (2014) also compare the SSA between OMAERUV and
AERONET and find 69 % of the data agree within the absolute difference of
±0.05 for all aerosol types. Significant differences between the two
datasets are most shown at dust-dominated sites. These dust-influenced sites
are not included in our analysis. Furthermore, Jethva et al. (2014) compare
these products at 440 nm. The OMAERUV SSA estimated at this wavelength
relies on a number of assumptions and is more uncertain than that reported at
388 nm that we use in our analysis. It is not possible to directly compare
the SSA/AAOD at 388 nm since AERONET does not make measurements at this
wavelength. Therefore, we believe that the comparison between AERONET and
OMAERUV is still valid. However, if the OMAERUV SSA is higher or lower than
AERONET at 388 nm, our estimate of the BrC-AAE388/440nm would
be biased low or high.
Surface multiple-wavelength absorption measurementsSites and instruments
In addition to the retrieved column AAOD from AERONET, we also use our method
to derive the BrC absorption from direct measurements of absorption at a
series of surface sites. Among the measurement methods for aerosol
absorption, only the filter-based method using an Aethalometer (AE, Magee
Scientific, http://www.mageesci.com) can be used to derive BrC
absorption with our method. Aethalometers are designed to measure BC mass
concentrations and can measure aerosol absorption at 7 wavelengths ranging
from 370 to 950 nm (version AE-31). These seven wavelengths include more than two
wavelengths at both UV and long wavelengths (> 600 nm). As
described in Sect. 2, our method requires absorption measurements in at least
one wavelength in the near UV and another two measurements at wavelengths
> 600 nm. None of the other commercial instruments currently
available meet this requirement.
As a filter-based method, Aethalometer measurements are known to exhibit
artifacts from filter loading, filter scattering, and aerosol multiple
scattering (Liousse et al., 1993; Collaud Coen et al., 2010). It is commonly
thought that the absorption measurements from Aethalometers are biased
towards much higher values (Arnott et al., 2005; Schmid et al., 2006).
Although several correction algorithms have been published, many of these
require additional information. Different correction methods may even lead to
very different corrected results for the same original data (Schmid et al.,
2006; Arnott et al., 2005; Collaud Coen et al., 2010; Weingartner et al.,
2003). In our analyses of surface measurements, we will focus on the BrC
absorption contribution and BrC-AAE, which are both ratios of AAOD. Following
Sect. 2, the measurement bias on the absolute absorption will have minimal
impact on the ratio unless there is a wavelength-dependent bias in the
uncorrected data. By analyzing a series of different correction algorithms,
Collaud Coen et al. (2010) conclude that it is not possible to precisely
estimate the expected bias in AAE but that the corrected AAE is most likely
to remain the same or increase slightly. In our BrC derivation method, a
small change in AAE will not significantly impact the estimated WDA or the
BrC contribution. Thus, we can apply our method to derive the BrC absorption
contributions from the uncorrected multiple-wavelength absorption measured by
Aethalometer. We also use these measurements to analyze the variation in BrC
absorption at a single site, though the absolute values are likely biased
high. The Aethalometer data are uncorrected in the following analyses except
where noted.
Figure 7 shows the locations of the 10 surface sites we use in this analysis.
These are Zeppelin Mountain in the Arctic, Barrow in Alaska, Tiksi in northern
Siberia (on the shore of the Laptev Sea), Cool (near Sacramento in northern
California) and Cape Cod (to the southeast of Boston) in the United States,
Ispra in northern Italy, Preila in eastern Lithuania, SIRTA southwest of
Paris in France, Finokalia in Greece (on the northeastern coast of the island
of Crete), and the T3 site of the Observations and Modeling of the Green Ocean
Amazon campaign (GoAmazon2014/5) to the west of Manaus in Brazil. Detailed
information on these sites including measurement periods and references is
given in Table 1. The absorption data from the GoAmazon-T3 site are provided
with correction for filter loading and multiple-scattering effects using the
methods outlined by Rizzo et al. (2011) and Schmid et al. (2006).
The locations of a series of field sites used in this study where
multiple-wavelength absorption was measured using an Aethalometer. Each site
is colored according to the mean aerosol absorption measured at 370 nm. Values from
the GoAmazon-T3 site are corrected, while others are uncorrected (see
Sect. 4.1).
Summary of surface sites measuring aerosol absorption by
Aethalometer.
SiteLocationTimeSiteMean aerosolCampaignReferences(latitude,periodpropertyabsorption atlongitude)370 nm, Mm-1Zeppelin Mountain(78.9∘ N, 11.9∘ E)2010background0.63EMEPahttp://www.emep.intBarrow(71.3∘ N, 156.6∘ W)2010–2014background1.31NOAA/ESRL sitehttp://www.arm.govTiksi(71.6∘ N, 128.9∘ E)2012–2014urban3.8NOAA/ESRL sitehttp://www.arm.govCool(38.9∘ N, 121∘ W)Jul 2010rural7.5CARESbZaveri et al. (2012)Finokalia(35.3∘ N, 25.7∘ E)2014background13.54EMEPhttp://www.emep.intIspra(45.8∘ N, 8.6∘ E)2010–2011urban121.6EMEPhttp://www.emep.intPrelia(51.4∘ N, 21∘ E)2010urban29.29EMEPhttp://www.emep.intSIRTA(48.7∘ N, 2.16∘ E)Sep 2010–Mar 2011urban55.95EMEPhttp://www.emep.intGoAmazon-T3c(3.2∘ S, 60.6∘ W)2014–2015urban/rural5.59GoAmazon2014/5Martin et al. (2016)Cape Cod(42.3∘ N, 70.1∘ W)Jul 2012; Feb–Mar 2013urban/background9.22TCAPdBerg et al. (2016)
a EMEP: the European Monitoring and Evaluation
Programme. b CARES: Carbonaceous Aerosols and Radiative Effects
Study. c T3 site in GoAmazon2014/5 (Observations and Modeling of
the Green Ocean Amazon) campaign. d TCAP: Two-Column Aerosol
Project.
Monthly variation in the derived BrC contribution to total
absorption at 370 nm (black) and BrC-AAE (red) at a series of surface sites
(see Table 1, Fig. 7). The BrC-AAE in (g) GoAmazon-T3 site and
(h) Cape Cod is calculated using the 370–430 nm wavelength pair,
while that at other sites is based on the 370–470 nm wavelength pair. Error
bars indicate the standard deviation for values averaged in each month.
Estimated BrC absorption and AAE
At Zeppelin Mountain and Cool, we detect little to no BrC absorption with our
method. The Zeppelin Mountain site is very clean and aerosol absorption here
is generally associated with long-range transport. The Cool site is located
east of Sacramento, and as there were negligible biofuel or biomass burning
emissions in July 2010, no major BrC sources are likely to impact this site.
Br-SOA may contribute to the absorption at Cool, but it is below the
detection limits of our method.
Figure 8 shows the monthly variation in the contribution of BrC to total
aerosol absorption at 370 nm at the eight other sites. Since these eight sites are
located in different environments from rural to urban, the chemical
composition of OC is likely to be very different among these sites. However,
the monthly mean contributions of BrC absorption at 370 nm occupy a
relatively small range from 7 to 35 %. These numbers are smaller than the
near-surface values (around 50 % at 365 nm) estimated for a fire plume
based on filter extracts from airborne measurements during the Deep
Convective Clouds and Chemistry (DC3) campaign (J. Liu et al., 2015). It is
possible that the BrC contribution is higher in such a concentrated plume
compared to the well-mixed air represented by our monthly averages. In
addition, while BrC absorption was measured directly during DC3,
J. Liu et al. (2015) estimate of the contribution of BC to total absorption was based on the
(possibly inaccurate) assumption that BC-AAE = 1. The annual average
total aerosol absorption at the urban site Ispra is nearly 120 times larger
than the background site of Barrow in Alaska; however, BrC contributes
comparable amounts to the total absorption at these sites. In Sect. 3.1, we
showed similar BrC contributions of < 30 % (at 440 nm) at
80 % of AERONET sites; the similar results obtained here from direct
measurements of absorption support the analysis of the AERONET data despite
concerns about data quality and retrieval assumptions. It is likely that the
proportion of BC absorption and BrC absorption does not differ substantially
among regions even though the emission sources are very different. This is
consistent with our speculation in Sect. 3.2 that the MAC of OC is related to
the combustion properties and positively correlated with BC / OC emission
ratio. Because higher OC-MAC is usually associated with higher BC / OC
mass ratio, the absorption ratio of BC / OC may be roughly constant if
BC-MAC is relatively constant. This ensures that the proportion of BC to BrC
absorption does not vary much among different sources. Small seasonal
variations in the fractional contribution of BrC absorption are seen at the
sites of Finokalia and SIRTA (Fig. 8b and c). At these two sites, BrC
contributes more absorption in winter than in summer. This is also similar to
the analysis of the AERONET observations; however, these variations are not
large and the monthly mean BrC absorption contributions remain in the
10–30 % range. This winter shift is likely to be associated with the
residential heating from biofuel combustion. The BC / OC ratio for
biofuel emissions from residential heating is typically lower than other
sources (Streets et
al., 2004). However, given the variability in BC-MAC, it is not clear whether
this lower BC / OC mass ratio is the dominant reason for the winter
shift.
The contribution of BrC absorption changes with wavelength. Considering the
influence of the AAE of both BC and BrC, the contribution of BrC at 370 nm
should be a little larger than at 440 nm. For example, with BrC-AAE = 4,
a 20 % BrC absorption contribution at 370 nm will decrease to 13 %
at 440 nm and 7 % at 550 nm. By comparing the results from these eight
sites with the nearest AERONET sites, we find that the BrC absorption
contributions at 370 nm at the surface are similar to the column BrC-AAOD
contributions at 440 nm from AERONET. This may suggest that the BrC
absorption contribution increases with altitude. This vertical difference was
also identified in analysis of the DC3 airborne observations (J. Liu et al.,
2015). Alternatively, it may suggest a high bias in the BrC estimated from
AERONET observations.
Measurements from the TCAP campaign in Cape Cod in February 2013.
The relationship between uncorrected OA-MAC measured at 370 nm and
(a) the observed BC / OA ratio, as well as (b) the measured
photochemical clock. All values are hourly means. The colors in (b)
indicate different wind directions discussed in Sect. 4.3.
Figure 8 also shows the estimated AAE370/430nm of BrC from
Aethalometer observations. Generally, the monthly mean
BrC-AAE370/430nm at these eight sites ranges from 2 to 4. Low
BrC-AAE (< 2) is frequently observed at the three European sites of
SIRTA, Prelia, and Ispra (Fig. 8c, d, and e), consistent with the analysis of
AERONET and OMI measurements over Europe. At most of the sites (Fig. 8a, b,
c, f, and h), the monthly variations in BrC-AAE370/430nm are
similar to the variation in the BrC absorption contributions. As described
above, an increase in BrC absorption contribution may be due to either an
increase in OC-MAC or a decrease in the BC / OC mass ratio. This suggests
the BrC-AAE370/430nm is either positively correlated with
OC-MAC or negatively correlated with BC / OC emission ratio. The latter
case was observed in the laboratory study of Saleh et al. (2014). However,
the correlations between BrC-AAE and BrC absorption contributions are only
slight to moderate (R2< 0.3) at these sites.
Estimating the evolution of OA-MAC
To better understand the absorption properties of BrC and eliminate the
influence of aerosol mass, analysis of both BrC absorption and OA mass are
necessary. Additional measurements of OA mass are available at two of our
eight sites (Cape Cod and GoAmazon-T3).
At the Cape Cod site during the TCAP campaign, the refractory black carbon
(rBC) and OA mass were measured using a single-particle soot photometer (SP2;
Schwarz et al., 2008) and a high resolution time-of-flight aerosol mass
spectrometer (HR-ToF-AMS; Canagaratna et al., 2007) in February 2013. With
both derived BrC absorption and measured OA mass, OA-MAC can be directly
estimated. As discussed in Sect. 4.1, the OA-MAC derived from uncorrected
Aethalometer data are biased high, but the relative variation can be used in
the analysis. These data are used to examine the influence of emission
properties and chemical processing on the absorption properties of BrC. To
identify the impact of emission properties on OA-MAC, we compare OA-MAC with
the co-measured BC / OC mass ratio (Fig. 9a). These are highly correlated
(R2= 0.75), which further confirms the previously discussed
relationship between OA-MAC and BC / OC ratio.
Measurements from the GoAmazon2014/5 campaign at the T3 site, to the
west of Manaus, Brazil. The relationship between hourly mean BrC-MAC measured
at 370 nm and (a) the observed BC / OA ratio during
15 August–15 October 2014 (IOP2), as well as (b) the measured photochemical clock
during January–March 2015 with selected Manaus plumes and (c) IOP2,
excluding Manaus plumes. In (b) and (c), the red points are
individual hourly average measurements, whereas the box-and-whisker plots
show the binned mean (black points), median (black lines inside boxes), lower
and upper quartile (boxes), and 5th and 95th percentile (whiskers).
The observations at Cape Cod also provide the opportunity to identify whether
there is any relationship between BrC absorption and chemical processing
during transport. In laboratory studies, both increases in OA-MAC from new
BrC-OA generation and decreases in OA-MAC from BrC-OA photolysis (bleaching)
have been observed (Laskin et al., 2015; Lee et al., 2014; Flores et al.,
2014). The only previous field
observations of chemical processing of BrC by Forrister et al. (2015) found
that photolysis decreases the MAC of biomass-burning-sourced BrC, but the
rate of change is much slower than previously estimated from laboratory
experiments. Here we use the quantity of -log(NOx/ NOy) as a
photochemical clock. Assuming the photochemical rate of converting NOx
(NO + NO2) to total reactive nitrogen (NOy) is equal to the
reaction between NO2 and OH, this photochemical clock can be seen as a
measure of chemical processing since emission (Kleinman et al., 2008). The
OA-MAC is plotted versus this photochemical clock in Fig. 9b, colored according to wind
direction. The data points from TCAP in February can be divided into two groups according to wind direction: the first group includes those with winds from the northwest (red points in Fig. 9b),
which are mostly affected by transported urban air from Boston; the remaining
data which represent background air masses are shown as a second group (blue
points in Fig. 9b). The OA-MAC, as well as the mass concentrations of both BC
and OA, for the data from the northwest is generally larger than the other
group due to the influence of fresher urban emissions. We can see from
Fig. 9b that this source difference dominates the variation in OA-MAC. For
either individual group, we find no significant trend with photochemical
clock. Our analysis shows that the OA-MAC neither increases nor decreases
with increasing aging time in background or urban air masses. This suggests either
that the generation and photolysis of BrC counteract each other or
that the influence from chemical processing is much smaller than emissions
and transport for these observations. However, we note that these wintertime
measurements are not optimal for identifying any photochemical processing;
additional measurements in multiple seasons are required.
We repeat the analysis applied at the Cape Cod site to the observations from
the GoAmazon-T3 site in the Amazon. Mass concentrations of BC and OA were
measured at this site using an SP2 and an HR-ToF-AMS (de Sá et al.,
personal communication, 2016) during two intensive operating periods (IOPs). IOP1 took place in
the wet season, from 1 February to 31 March, and IOP2 occurred during the
dry season, from 15 August to 15 October, both in 2014. Unfortunately, data
availability for BC and NOx concentrations are poor during IOP1. We
therefore supplement our analysis with Aerosol Chemical Speciation Monitoring
(ACSM) measurements of OA from January to March 2015. During this time period
the sources of OA at the T3 site are generally from the city of Manaus and
the forested region around it, with very low incidence of fires. IOP2 occurs
during the biomass burning season and the T3 site is highly influenced by
fires during this period (Martin et al., 2015). Figure 10a shows that the
measured OA-MAC and the BC / OC ratio during IOP2 are correlated (R2= 0.5) at this site as well.
Figure 10b and c compare the measured OA-MAC with the photochemical clock
-log(NOx/ NOy) at the GoAmazon-T3 site under low biomass
burning (January–March 2015) and high biomass burning (IOP2) influence.
During the low biomass burning season, we select the daytime data from Manaus
only (based on wind direction from the east) to eliminate the potential
contamination of the photochemical clock from biogenic NOx emissions
around the site. In contrast, we exclude the Manaus plume-related data points
during the high biomass burning season (IOP2) to ensure that the analysis
reflects only the near-field (fresh) and far-field (aged) fires. The results
presented here are not strongly sensitive to this data filtering. However, we
acknowledge that recirculation in the basin surrounding the T3 site may
complicate the quantitative interpretation of the photochemical clock.
Figure 10b demonstrates that, under urban influence, there is little evidence
for a decrease in BrC absorption due to photochemical processing, similar to
our results from Cape Cod. However, during the biomass burning season we
observe a clear decrease in OA-MAC with increasing aging time (Fig. 10c).
This suggests that the absorption of OA emitted from biomass burning
decreases due to photolysis or oxidation of BrC. We note that the total mass
of OA does not increase or decrease significantly with increasing aging time;
therefore, while we cannot definitively rule out the formation of SOA during
transit leading to a “whitening” of total OA, the observations do not
suggest that this is a major effect. The binned median OA-MAC values in
Fig. 10c can be fitted exponentially with -log(NOx/ NOy)
(R2= 0.95). By assuming the oxidation rate of NO2 to be
1.05 × 10-11 cm3 mol-1 s-1 at 01.00 atm and
300 K (Sander et al., 2011) and typical daytime OH concentration of
5 × 105 mol cm-3 we estimate an e-folding time and
half-life for the OA absorption to be 22 daytime hours and 14 daytime hours,
respectively (or 45 and 30 h, assuming zero nighttime OH extending 13 h of the day
during IOP2). Since the biomass burning emissions during IOP2 are frequent
and disperse, it is not clear how much of the transport of biomass burning
plumes from source to the T3 site occurs at night. These calculated lifetimes
neglect other losses of NOx and are highly sensitive to the assumed OH
concentration; at extremely high (∼ 107 mol cm-3) or lower
(∼ 105 mol cm-3) OH concentrations, the half-life in
sunlight would be calculated to be 1 h or 3.5 days. If the OH concentration
in the measurement period is, as assumed here, similar to typical global mean
values (Stone et al., 2012), the half-life is comparable to the biomass
burning plume observed by Forrister et al. (2015) (9–15 h). However, this
half-life would be significantly longer than estimated in laboratory studies
which have focused on SOA aging (5 min to 3 h), suggesting that additional
laboratory studies are necessary to examine the aging of BrC from biomass
burning. We also observe that the BrC absorption does not appear to decrease
to 0 with continued aging (> 15 h) in our analysis. This is also
consistent with Forrister et al. (2015), who suggest that sunlight shows no
effect on BrC absorption after about 12 h of continuous exposure. This may
results in a persistent BrC background from fire emissions, even after aging.
Discussion
By using a new method to derive BrC absorption we identify consistent BrC
characteristics from both the global AERONET sun photometer network and 8
surface sites. At most sites, the BrC absorption contribution in the UV
ranges from 10 to 30 %. This range of BrC absorption contribution can be
used to constrain model simulations or provide a rough estimate of BrC based
on measured BC.
The relatively consistent contribution of BrC to total absorption can be
explained by the correlation between OC-MAC and the BC / OC mass ratio,
which is observed at both AERONET and surface measurement sites. As our
analysis shows that higher OC-MAC is found to be associated with lower OC
mass contribution in the atmosphere, the BrC absorption contribution lies
within a narrow range globally despite differences in the emission. Based on
this correlation and BC / OC emission ratios, we estimate a range of MAC:
0.1–3.1 m2 g-1 for OC and 0.05–1.5 m2 g-1 for OA at
440 nm from AERONET observations when assuming OA / OC = 2.1. This
correlation also suggests the BC / OC emission ratio could provide
important information for building an emission inventory for BrC. However, our
analysis is based on ambient measurements of BC / OC absorption ratios,
and extrapolation of these results to emissions requires further investigation
of how the BC / OC ratio changes (via chemistry, transport, and removal)
from source to ambient measurement. Further laboratory studies which include
both BrC and BC measurements are required to examine how BrC absorption
varies with emission properties.
The BrC-AAE388/440nm that we estimate from AERONET and OMI
measurements is also very similar to that which we derive from surface in
situ measurements. Both analyses suggest that the global mean
BrC-AAE388/440nm is likely to be ∼ 4. However, lower
AAE388/440nm (< 2) are found in Europe from both
surface measurements and AERONET sites, suggesting that the BrC in Europe may
exhibit different optical properties. These BrC-AAE values are within the
range of 1.9 to 9 measured in laboratory experiments (Laskin et al., 2015).
However, these results are based on different wavelength pairs and are
therefore not directly comparable. In addition, many of the laboratory
reported values typically rely on one long wavelength in the visible spectrum
(e.g. AAE370/660nm or AAE330/600nm). Since BrC
absorption at these long wavelengths is too small to be detected accurately,
these BrC-AAE are likely quite uncertain.
For well-mixed air masses exposed to urban emissions, our analysis of the
observations at Cape Cod in February and at GoAmazon-T3 site in the
non-biomass burning season do not provide any evidence for evolving BrC
optical properties associated with photolysis or oxidation. It may be either that
the chemistry impact is not as significant as emissions/transport or
that the photolysis and generation of BrC counteract each other. In contrast,
the observations at GoAmazon-T3 site during the biomass burning season
exhibit a ∼ 1-day photochemical lifetime (in sunlight) for BrC
absorption. This decrease in absorption is qualitatively consistent with
previous field observations (Forrister et al., 2015) and may suggest that the
absorption of BrC from fire emissions is geographically limited to the
near field. This may also explain the somewhat counterintuitive lack of
strong BrC signature in biomass burning regions/seasons in our analysis of
AERONET observations (Fig. 3). The majority of studies which have
investigated the “browning” or “whitening” of BrC have focused on
laboratory experiments at extreme conditions; however, the chemical processes
in the real atmosphere may be very different from these controlled
environments. Additional laboratory and field studies of how the optical
properties of primary BrC may evolve due to photooxidation are required.
Using BrC-AAE =4 as suggested by our analysis of AERONET and OMI
satellite observations, the BrC column absorption contribution is 0–40 %
at 440 nm and less than 20 % at 550 nm. This suggests that the previous
model estimated BrC absorption DRE contributions (20–40 %) are likely to
be biased slightly high (Feng et al., 2013; Lin et al., 2014). Including
photochemical “whitening” of BrC from fires in these models may resolve
these discrepancies.
By applying a new AAE method that we describe in this paper, we have
obtained global observational constraints on BrC absorption. However, these
results are subject to uncertainties associated both with the methodology
and with the dataset to which it is applied. The core issue for all methods
that use the AAE to estimate the absorption from BrC is the uncertainty
associated with the absorption of BC. Our method improves upon previous
studies using this approach by using the information from the wavelength-dependent measurements themselves and by allowing for an
atmospherically relevant range of BC properties, rather than fixing these at
a single assumed value. Additional constraints on BC optical properties and
mixing state would help further improve the method.
Given the large uncertainties associated with AERONET retrievals of AAOD,
the most challenging aspect of our analysis is that an accurate, globally
distributed, multiple-wavelength aerosol absorption measurement dataset is
unavailable at present. Thus, while our study provides qualitative global
constraints and insight into BrC aging processes from the Aethalometer
observations, achieving a better understanding of the properties, evolution,
and impacts of global BrC will rely on the future deployment of accurate
multiple-wavelength absorption measurements to which AAE methods, such as
the approach developed here, can be applied.
Data availability
The AERONET data are freely available online at
http://aeronet.gsfc.nasa.gov/ (NASA, 2016). The OMAERUV product is available online
on the Goddard Earth Sciences Data and Information Services Center (GES DISC, 2016) web page:
http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/OMI/. The data from
the GoAmazon2014/5 campaign, CARES campaign and TCAP campaign are available
online at ARM's Data Discovery (ARM, 2016) browser:
http://www.archive.arm.gov/discovery/#v/home/s/. The data from the EMEP
and NOAA/ESRL sites are accessible online on the World Data Centre for
Aerosols web page: http://ebas.nilu.no/ (EBAS, 2016).
Acknowledgements
This work was supported by the EPA-STAR program.
Although the research described in this article was funded in part by
the US EPA through grant/cooperative agreement RD-83503301, it has not been
subjected to the EPA's required peer and policy review and therefore does
not necessarily reflect the views of the EPA and no official endorsement
should be inferred. The GoAmazon2014/5 and TCAP data were obtained from the
Atmospheric Radiation Measurement (ARM) Climate Research Facility, a US
Department of Energy Office of Science user facility sponsored by the Office
of Biological and Environmental Research. For the GoAmazon2014/5 data, we
also acknowledge the support from the Central Office of the Large Scale
Biosphere Atmosphere Experiment in Amazonia (LBA), the Instituto Nacional de
Pesquisas da Amazônia (INPA), and the Universidade do Estado do Amazonia
(UEA). The work of GoAmazon2014/5 campaign was conducted under 001030/2012-4
of the Brazilian National Council for Scientific and Technological
Development (CNPq). The AMS measurement at GoAmazon2014/5-T3 site was
performed using EMSL, a DOE Office of Science User Facility sponsored by the
Office of Biological and Environmental Research and located at Pacific
Northwest National Laboratory. We thank Jesse Kroll, Eleanor Browne, Kelsey Boulanger, Anthony Carasquillo, and Kelly Daumit for AMS measurement during
TCAP. We also thank the Norwegian Institute for Air Research (NILU) and the
NOAA Earth System Research Laboratory for providing the EMEP and NOAA/ESRL
background site measurements, as well as the AERONET staff for establishing and
maintaining the sun photometer network used in this study.
Edited by: U. Pöschl
Reviewed by: four anonymous referees
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