ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-3413-2016A global simulation of brown carbon: implications for photochemistry and
direct radiative effectJoDuseong S.ParkRokjin J.rjpark@snu.ac.krhttps://orcid.org/0000-0001-8922-0234LeeSeungunKimSang-WooZhangXiaoluSchool of Earth and Environmental Science, Seoul National
University, Seoul, 151-747, Republic of KoreaDepartment of Civil and Environmental Engineering,
University of California, Davis, CA, USARokjin J. Park (rjpark@snu.ac.kr)16March20161653413343220July201515October20153March20163March2016This 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/16/3413/2016/acp-16-3413-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/3413/2016/acp-16-3413-2016.pdf
Recent observations suggest that a certain fraction of organic carbon (OC)
aerosol effectively absorbs solar radiation, which is also known as brown
carbon (BrC) aerosol. Despite much observational evidence of its presence,
very few global modelling studies have been conducted because of poor
understanding of global BrC emissions. Here we present an explicit global
simulation of BrC in a global 3-D chemical transport model (GEOS-Chem),
including global BrC emission estimates from primary (3.9 ± 1.7 and
3.0 ± 1.3 TgC yr-1 from biomass burning and biofuel) and
secondary (5.7 TgC yr-1 from aromatic oxidation) sources. We evaluate
the model by comparing the results with observed absorption by water-soluble
OC in surface air in the United States, and with single scattering albedo
observations at Aerosol Robotic Network (AERONET) sites all over the globe. The model successfully
reproduces the seasonal variations of observed light absorption by
water-soluble OC, but underestimates the magnitudes, especially in regions
with high secondary source contributions. Our global simulations show that
BrC accounts for 21 % of the global mean surface OC concentration, which
is typically assumed to be scattering. We find that the global direct
radiative effect of BrC is nearly zero at the top of the atmosphere, and
consequently decreases the direct radiative cooling effect of OC by 16 %.
In addition, the BrC absorption leads to a general reduction of NO2
photolysis rates, whose maximum decreases occur in Asia up to -8 %
(-17 %) on an annual (spring) mean basis. The resulting decreases of
annual (spring) mean surface ozone concentrations are up to -6 %
(-13 %) in Asia, indicating a non-negligible effect of BrC on
photochemistry in this region.
Introduction
Carbonaceous aerosols (CAs) are one of the most poorly understood aerosols
(Goldstein and Galbally, 2007; Park et al., 2003) and are divided into black
carbon (BC) and organic carbon (OC) aerosols. These two types of CAs are
emitted together mainly by combustion processes (except for secondary
organic carbon, SOC). In the literature, BC is considered as light-absorbing
and OC as light-scattering aerosols until recently. Therefore, the climatic
effect of CAs depends on the relative contributions of BC to CAs. For
example, the net direct radiative forcing (DRF) of biomass burning is
estimated to be negligible, whereas diesel use causes climate warming,
although the first source is larger than the latter with regard to CAs (Forster et al.,
2007).
Many field observations and chamber studies recently showed that a certain
fraction of OC could absorb solar radiation, especially for ultra-violet
wavelengths (< 400 nm) (Alexander et al., 2008; Hecobian et al., 2010;
Kirchstetter and Thatcher, 2012; Kirchstetter et al., 2004; Yang et al.,
2009). This light-absorbing OC fraction is referred to as brown carbon (BrC)
aerosol (Andreae and Gelencser, 2006; Laskin et al., 2015). If BrC is
prevalent, and its DRF is significant, then previous estimates of the DRF of
CAs need to be revised.
Recent studies showed that the solar absorption of BrC is not negligible, and
is even comparable to that of BC (Alexander et al., 2008; Chung et al., 2012;
Kirchstetter and Thatcher, 2012). Using residential wood smoke samples,
Kirchstetter and Thatcher (2012) calculated that BrC absorption accounts for
14 % of total solar absorption by CA, and even contributes 49 % of
solar absorption of CA at wavelengths below 400 nm. Chung et al. (2012)
found that OC contributes about 45 % of CA absorption at 520 nm by
analyzing observations at the Gosan site in South Korea. Using aerosol
optical property observations at Aerosol Robotic Network (AERONET) sites,
Bahadur et al. (2012) estimated that BrC absorption at 440 nm is about
40 % of BC absorption at the same wavelength, whereas at 675 nm it is
less than 10 % of BC absorption.
Several efforts have also been made to examine the chemical and physical
properties of BrC. Some studies showed that humic-like substances (HULIS)
were related to BrC (Hoffer et al., 2006; Kim and Paulson, 2013; Lukács
et al., 2007) based on the high absorption Ångström exponent (AAE) of
HULIS in the range of 6–7, indicating that the specific absorption increases
substantially towards the shorter wavelengths (Hoffer et al., 2006), although
the sources and the dominating chromophores of HULIS have not clearly been
revealed yet (Moise et al., 2015; Graber and Rudich, 2006). Alexander et
al. (2008) observed individual BrC spheres in East Asian outflows, and showed
that the characteristics of BrC spheres (AAE of 1.5) were different from
those of HULIS and also strongly absorbing. On the other hand, several
classes of compounds have been identified as potential contributions to BrC
– nitroaromatic compounds, such as nitrophenols, imidazole-based and other
N-heterocyclic compounds, and quinones (Laskin et al., 2015). Furthermore,
SOC produced from aromatic species has been found to absorb solar radiation,
especially in high-NOx conditions (Jaoui et al., 2008; Laskin et al.,
2015; Lin et al., 2015; Liu et al., 2012; Nakayama et al., 2010, 2013; Yu et
al., 2014; Zhong and Jang, 2011).
Even though the chemical composition of BrC is not clearly understood yet,
observations strongly indicate possible important sources of BrC (Laskin et
al., 2015). Using the positive matrix factorisation analysis of absorption at
365 nm over the southeastern United States in 2007, Hecobian et al. (2010)
showed that biomass burning was the most dominant source of BrC (55 %),
followed by SOC (26–34 %). Many other studies have also suggested
biomass burning as the most important BrC source (Chakrabarty et al., 2010;
Clarke et al., 2007; Favez et al., 2009; Hoffer et al., 2006; Kirchstetter
and Thatcher, 2012; Kirchstetter et al., 2004; McMeeking, 2008; Saleh et al.,
2014). Several studies recently proposed SOC as an additional BrC source,
especially when it is aged in the atmosphere (Bones et al., 2010; Flores et
al., 2014; Hawkins et al., 2014; Jaoui et al., 2008; Laskin et al., 2014,
2010; Liu et al., 2014; Nakayama et al., 2010, 2013; Nguyen et al., 2012;
Updyke et al., 2012; Zhang et al., 2011; Zhong and Jang, 2011).
Despite the ample observational studies, very few modelling studies have been
conducted to simulate global and regional distributions of BrC and to further
quantify its radiative effect (Feng et al., 2013; Jacobson, 2001; Lin et al.,
2014; Park et al., 2010; Wang et al., 2014). Jacobson (2001) first assumed
10 % of OC as a solar-absorbing aerosol in a model, and this assumption
resulted in an increase of the global DRF by 0.03–0.05 W m-2. Park et
al. (2010) estimated BrC concentrations in East Asia using the mass ratio of
BrC to BC, and the resulting annual clear-sky DRF of BrC over East Asia was
0.05 W m-2. Feng et al. (2013) simulated global BrC concentrations by
considering 92 % of OC from biomass burning and biofuel use as BrC, and
estimated 0.09 W m-2 for the global clear-sky DRF of BrC. Lin et
al. (2014) calculated the DRF of OC by assuming that all of the biomass
burning and the biofuel OC is BrC, and all of the SOC (as a high-absorbing
case) as BrC. They estimated the global clear-sky DRF of OC as
-0.20 W m-2.
In this study, we estimate global primary BrC emissions from open burning
and biofuel use based on a reported relationship between AAE and modified
combustion efficiency (MCE) (McMeeking, 2008). In addition to the primary
source above, we also consider SOC produced from aromatic oxidation as a
secondary source of BrC (Hecobian et al., 2010; Jaoui et al., 2008; Lin et
al., 2015; Nakayama et al., 2010; Nakayama et al., 2013; Zhong and Jang,
2011). Based on these sources, a global distribution of BrC concentrations
is explicitly simulated for the entire year of 2007 using a global 3-D
chemical transport model (GEOS-Chem). We evaluate the model by comparing its
results with observations in the United States and all over the globe. Using
the best estimate of annual mean BrC concentrations, we examine the global
direct radiative effect (DRE) of BrC and its effect on photochemistry.
BrC emissions
In this section, we discuss our method to estimate primary and secondary
sources of BrC, and provide explicit global BrC emissions. The primary and
secondary sources include biomass burning and biofuel use, and the
production from aromatic volatile organic compounds (VOCs), respectively.
Estimated global emissions are used as input for GEOS-Chem below to
explicitly simulate spatial and temporal distributions of BrC
concentrations.
Primary sources
Biomass burning is the largest source of CAs globally (Bond et al.,
2004). OC is primarily emitted during the smoldering (low-temperature
burning) phase of combustion (Chakrabarty et al., 2010, 2014; Schnaiter et
al., 2006), whereas BC is preferentially emitted from the flaming
(high-temperature burning) phase. Therefore, BrC is also emitted largely
during the smoldering phase of burning. Here we use the relationship between
the burning efficiency and the observed aerosol light absorption to estimate
the BrC emission from biomass burning.
Previous studies have suggested MCE defined in Eq. (1) below to provide
quantitative information of burning efficiencies that can be categorised into
flaming versus smoldering combustion (Kaufman et al., 1998; Ward et al.,
1992; Ward and Hao, 1991). For example, Reid et al. (2005) used a MCE value
of 0.9 to differentiate between flaming (MCE > 0.9) and smoldering
combustion (MCE < 0.9).
MCE=ΔCCO2ΔCCO2+ΔCCO,
where ΔC is the change in species concentration in fire off-gas
relative to clean air [molecules m-3].
McMeeking (2008) further found a linear relationship between the observed
attenuation Ångström exponents and the calculated MCE values from a
number of biomass burning samples, as shown in Eq. (2).
Å=-17.34×MCE+18.20,
where Å is the AAE of biomass burning samples.
Emission factors (EFs) and calculated parameters used for primary
BrC emission estimates. Biomass burning emission is classified for six
vegetation types based on the FINN inventory. Here BrC / OC is the mass
ratio of BrC to OC emitted from biomass burning and biofuel use.
Source typeCO2EF [g kg-1]CO EF [g kg-1]MCEOC EF [g kg-1]BC EF [g kg-1]BrC / OC Biomass burningcase1case2case3Boreal forest15141180.8917.80.200.1350.0930.057Cropland15371110.8983.30.690.9460.6520.400Savanna/grassland1692590.9482.60.370.1890.1230.067Temperate forest16301020.9109.20.560.2110.1450.088Tropical forest1643920.9194.70.520.3120.2130.128Woody savannah/shrubland1716680.9416.60.500.1230.0810.046Biofuel*0.6630.4520.271
* Detailed information is given in
Table 2.
The coefficient of determination (R2) of the relationship in Eq. (2) is
0.39, so the associated uncertainty appears to be significant. However, the
negative relationship between AAE and MCE in Eq. (2) is robust as identified
by previous studies (Saleh et al., 2014; Kirchstetter and Thatcher, 2012).
For example, absorption of aerosols from biomass burning can be contributed
by either BC or BrC, or both (Moise et al., 2015). As discussed above, the
absorption of carbonaceous aerosols is mainly caused by BC at high-MCE
conditions (> 0.9); in contrast, the BC / CA ratio is almost zero at low-MCE conditions (< 0.8) (McMeeking, 2008). Using Eq. (2), we calculate
AAE values of 0.86 and 4.3 at MCE values of 1.0 and 0.8, respectively, and
each calculated AAE is in good agreement with the observed BC (0.86) and BrC
AAE (5.0) from biomass burning samples measured by Kirchstetter and
Thatcher (2012). Saleh et al. (2014) also showed that the BC to OC ratio
(proportional to MCE) has a negative relationship with AAE.
In addition, we are able to obtain the BrC / BC absorption ratio using
AAE. In Appendix A, we present a detailed description of our method for
estimating the relationship between the BrC / BC absorption ratio and
AAE. Our method assumes external mixing, and this assumption can cause
uncertainties when particles are internally mixed (such as the coating effect).
For uncertainty analysis, we calculate three BrC / BC absorption cases as
shown in Fig. 1, which shows the estimated BrC / BC absorption ratio at
550 nm as a function of MCE. Different lines indicate different AAEs of BC
and BrC according to the Table 1 of Kirchstetter and Thatcher (2012). They
calculated BC AAE and BrC AAE using 115 wood smoke samples. For the
calculation of BrC AAE, BC AAE had to be decided, and they assumed three
different BC AAEs (0.86, 1.00, 1.15) based on their smoke samples and
previous studies. Resulting BrC AAEs were 5.00, 5.48, and 6.19. We conduct
three simulations according to the Fig. 1, as described later in this
section. For high-MCE conditions (> 0.95), the BrC contribution to the CA
absorption is negligible, whereas it becomes significant for low-MCE
conditions (< 0.85).
Estimated absorption ratios of BrC to BC at 550 nm as a function of
MCE. We assume that the CA absorption is only contributed by BC and BrC
absorption. Black solid line indicates case 1, red dashed line represents
case 2, and blue dotted line shows case 3.
We calculate the MCE of biomass burning based on the Fire Inventory from
NCAR (National Center for Atmospheric Research)
(FINN) (Wiedinmyer et al., 2011) with vegetation-dependent emission factors
of CO2 and CO using Eq. (3) as follows:
MCE=ΔCCO2ΔCCO2+ΔCCO=EFCO2/MWCO2EFCO2/MWCO2+EFCO/MWCO,
where EF is the emission factor [g-species kg-dry matter-1] and MW is
the molecular weight [g-species mole-1].
Finally, mass absorption efficiency (MAE), which is used for converting light
absorption to mass concentration, is needed to obtain the BrC / BC mass
ratio from the BrC / BC absorption ratio. For the fresh BC MAE at
550 nm, we use the value of 7.5 m2 g-1 recommended by Bond and
Bergstrom (2006) (Nakayama et al., 2013; Park et al., 2010). For BrC, a large
range of MAE values (0.09–4.1 m2 g-1 at 550 ± 30 nm) has
been reported (Alexander et al., 2008; Cheng et al., 2011; Chung et al.,
2012; Clarke et al., 2007; Favez et al., 2009; Hecobian et al., 2010; Hoffer
et al., 2006; Kirchstetter et al., 2004; McMeeking, 2008; Yang et al., 2009).
The highest MAE (3.6–4.1 m2 g-1 at 550 nm) was observed by
Alexander et al. (2008), who used transmission electron microscopy to
identify the optical properties of individual BrC particles in the
atmosphere. Generally, low MAEs were reported when analyzing water-soluble
organic carbon (WSOC) from water extracts (Cheng et al., 2011; Hecobian et
al., 2010; Srinivas and Sarin, 2014), indicating that WSOC may include both
BrC and colourless OC. Intermediate MAEs mostly came from optical measurements
(Chung et al., 2012; Favez et al., 2009; Yang et al., 2009). For the primary
BrC MAE, we use 1.0 m2 g-1 at 550 nm based on McMeeking (2008),
who conducted a number of MAE measurements of biomass burning samples
(∼ 30 unique fuels tested in ∼ 230 burns) using both filter-based
and optical-based methods. In brief, we use the MAE values of 7.5 and
1.0 m2 g-1 at 550 nm for BC and primary BrC, respectively. But
at a shorter wavelength, a higher MAE value was used for primary BrC (e.g.
5.3 m2 g-1 at 365 nm as discussed in Sect. 4).
Global biofuel consumption estimates, EFs of OC, and OC biofuel
emission estimates for each biofuel category. Base year is 2000.
a From Fernandes et al. (2007).
b From Bond et al. (2004).
c Global mean value is estimated from Bond et al. (2004).
d From GEOS-Chem biofuel OC inventory (carbon_200909) by Bond
et al. (2007).
Using the results in Fig. 1 with Eq. (3), we calculate the EF (mass) ratio of
BrC to OC as summarised in Table 1. The EF ratio of BrC to OC differs for
each vegetation type and assumed BC AAE (0.86–1.15). Among different
vegetation types, cropland burning shows the highest BrC to OC mass ratio,
driven by the low MCE and the highest ratio of BC to OC EF. Because we
calculate the BrC to OC EF ratio by multiplying the BrC to BC EF ratio by the
BC to OC ratio, the high BC to OC ratio can lead to a high BrC to OC ratio.
Although Table 1 shows the highest BrC / OC ratio from cropland burning,
its contribution to the global BrC emission is small because the OC emission
from the cropland is the lowest (Wiedinmyer et al., 2011). Instead, the
tropical forest burning is the highest, and the resulting total BrC emission
from biomass burning is 3.9 ± 1.7 TgC yr-1, which contributes
about 17 ± 7 % of total OC emission from biomass burning
(22.7 TgC yr-1) (Wiedinmyer et al., 2011).
Our method of estimating BrC emissions from biofuel use is similar to that of
estimating emissions from biomass burning. We estimate BrC / OC ratio
using the MCE and BC to OC ratio in the same way as the biomass burning
estimates. The only difference is that the biofuel emission of each sector is
not known (the biomass burning emission is known for each vegetation type).
Therefore, we first estimate OC biofuel emissions from each biofuel category
with the information given by previous studies (Bond et al., 2007, 2004;
Fernandes et al., 2007). Because there is no clear evidence that BrC is
emitted by dung, charcoal, and the industrial sector, here we consider only
fuelwood and agricultural residue as BrC sources. Fuelwood burning is the
largest contributor to biofuel BrC emission. Our estimate of BrC / OC
mass ratio is 0.271–0.663 from biofuel use. Overall results are summarised
in Table 2. Note that base year of Table 2 is 2000 because previous studies
reported their values based on 2000. We scale up the emission for 2007 as
described in Sect. 3.2. Resulting BrC emission from biofuel use is
3.0 ± 1.3 TgC yr-1, which is comparable to BrC emission from
biomass burning.
Secondary source
We consider SOC as a source of BrC in the model based on the observed optical
characteristic of SOC, depending on its chemical formation, as follows:
(1) anthropogenic (aromatic) SOCs tend to absorb solar radiation more
efficiently than biogenic SOCs (Jacobson, 1999; Nakayama et al., 2010; Zhong
and Jang, 2011; Zhong et al., 2012); (2) the solar absorption efficiency
increases as SOCs undergo atmospheric aging processes (Bones et al., 2010;
Lambe et al., 2013; Laskin et al., 2015, 2010; Updyke et al.,
2012); (3) SOCs formed in inorganic seeds have a darker colour than others
(Jaoui et al., 2008; Nakayama et al., 2013; Zhong and Jang, 2011; Zhong et
al., 2012); moreover, SOCs become darker when they undergo aging in the
presence of nitrogen-containing inorganic gases and aerosols (Bones et al.,
2010; Laskin et al., 2010; Liu et al., 2012).
Among those factors, the first two are more important than the last. For
example, the absorbance of aged biogenic SOCs produced in inorganic seeds is
much lower than that of fresh anthropogenic SOCs under no-seed conditions
(Zhong and Jang, 2011). Furthermore, Lambe et al. (2013) suggested that the
effect of NOx on SOC light absorption is small under typical ranges of
VOC / NOx. Therefore, here we consider the first two factors for BrC
simulations in the model. We assume anthropogenic (aromatic) SOCs with high
atmospheric aging as BrC in the model. Atmospheric aging is calculated using
the volatility basis set (VBS) approach with six bins in the model (Jo et
al., 2013), where SOC concentrations of the first two bins are considered as
BrC. However, we note that some brown SOCs can be bleached when they undergo
photodissociation (Zhong and Jang, 2011; Sareen et al., 2013). Furthermore,
browning reactions can be accelerated by cloud and fog processing of aerosols
(Moise et al., 2015), which are not considered in this study. More detailed
treatments of the chemical aging of BrC are needed in future BrC models.
BrC from anthropogenic SOC has different optical properties (i.e. MAE,
imaginary refractive index) compared with BrC from wood burning. Therefore,
we apply different optical parameters for the model evaluation (Sect. 4) such
as 5.3 m2 g-1 (McMeeking, 2008) for primary BrC and
1.5 m2 g-1 (Nakayama et al., 2010) for secondary BrC at 365 nm
(note that the MAE of primary BrC at 550 nm is 1.0 m2 g-1 as
discussed in Sect. 2.1). The estimated annual source of secondary BrC is
5.7 TgC yr-1, which contributes 45 % of total BrC sources.
Model descriptionGeneral
We use the GEOS-Chem (version 9.1.2) global 3-D chemical transport model (Bey
et al., 2001) to simulate BrC for 2007. The model is driven by Modern Era
Retrospective-analysis for Research and Applications (MERRA) assimilated
meteorological data from the Global Modelling and Assimilation Office Goddard
Earth Observing System (Rienecker et al., 2011). The data include winds,
precipitation, temperature, boundary layer height, and other meteorological
variables at 0.5∘× 0.667∘ horizontal resolutions,
but are degraded to 2∘× 2.5∘ for computational
efficiency.
We conduct a fully coupled oxidant–aerosol simulation, including
SO42-–NO3-–NH4+, soil dust, and sea salt aerosols.
The simulation of carbonaceous aerosols in the GEOS-Chem is based on Park et
al. (2003, 2006). The model carries BC and POC (primary organic carbon), with a hydrophobic and
hydrophilic fraction for each. We assume that 80 % of BC and 50 % of
POC are emitted as hydrophobic (the rest is hydrophilic), then hydrophobic
aerosols become hydrophilic with an e-folding time of 1.15 days (Cooke et
al., 1999). For the SOC simulation, we use the VBS approach based on Jo et
al. (2013). All SOC is considered as hydrophilic, and more details are
described in previous SOC studies (Chung and Seinfeld, 2002; Henze and
Seinfeld, 2006; Henze et al., 2008; Jo et al., 2013; Liao et al., 2007). Note
that we consider only the carbon mass of OC including BrC as discussed below,
to avoid uncertainties involved in converting organic carbon to organic
matter concentrations, which is typically done by multiplying a constant
ratio (e.g. 1.4–2.1) (Aiken et al., 2008; Turpin and Lim, 2001).
Emissions
We use fossil fuel and biofuel emissions of CAs for 2000 with no monthly
variations from Bond et al. (2007). However, domestic wood burning for
heating has strong seasonal dependency, so we additionally use the Monitoring
Atmospheric Composition and Climate/City Zen (MACCity) emission inventory
(Diehl et al., 2012; Granier et al., 2011) to obtain seasonal variations of
global biofuel emissions and to scale up for 2007. For this, we divide the
whole globe into regions with similar seasonality according to the
Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)
(Lamarque et al., 2010), which is the basis of the MACCity inventory. We
apply the annual trend of each ACCMIP region to the emissions from Bond et
al. (2007). The emissions for each region and trends are listed in Tables S1
and S2 in the Supplement.
We use biomass burning emissions from FINN version 1.0 (Wiedinmyer et al.,
2011), which provides global daily estimates of trace gases and aerosols at
1 km horizontal resolution for 2002–2012
(http://bai.acd.ucar.edu/Data/fire/). However, the FINN version 1.0
inventory does not include aromatic VOCs (benzene, toluene, and xylene), the
emissions of which are estimated by multiplying dry burned matter by emission
factors from Akagi et al. (2011) and Andreae and Merlet (2001).
Model evaluation
We conduct a model evaluation using the observed light absorption of WSOC
measured by Hecobian et al. (2010) and Zhang et al. (2011, 2013) in the
United States. The model evaluation allows us not only to validate simulated
BrC concentrations but also to examine each source contribution to BrC in the
United States. We also use the global single scattering albedo (SSA)
observations from the AERONET to evaluate the effect of including BrC on
light absorption by aerosols over the globe.
United States
Prior to evaluating BrC simulations, we first focus on BC and OC aerosols in
the model to examine the general model performance in simulating carbonaceous
aerosol concentrations in the United States. We use BC and OC observations
from the Interagency Monitoring of Protected Visual Environments (IMPROVE)
network for 2007 (Malm et al., 1994). Most sites were situated in rural
regions, measuring background concentrations of BC and OC. The data were
available every 3 days; more than 20 000 samples were used for our
comparison. For comparison with the model results, we computed the observed
monthly mean concentrations of BC and OC averaged on the
2∘× 2.5∘ model grid.
Figure 2 shows scatter plot comparisons of the observed and simulated monthly
mean BC and OC concentrations in the United States. The model slightly
underestimates both BC and OC over the United States, consistent with similar
comparisons in Huang et al. (2013). We calculate the annual mean
concentrations of the model using the simulated values of model grid boxes
corresponding to the IMPROVE network sites. The simulated annual mean BC
concentration is 0.22 µgC m-3, which is 12 % lower than
the observed mean value (0.25 µgC m-3). However, the bias in
the model is larger for OC by 30 % (1.16 and 0.81 µgC m-3
for observed and simulated OC concentrations, respectively), which is
additionally due to the underestimation of SOC in the model (Jo et al.,
2013). This low bias for SOC can be reflected in the simulated BrC
concentrations, which is discussed later in this section.
Scatter plot of simulated versus observed BC concentrations (left)
and OC concentrations (right). Unit is µgC m-3. Values are
monthly means for 2007. Regression equations and correlations are shown
inset. Regression is computed with reduced major axis (RMA) method.
We use the light absorption observations of WSOC measured using a UV–Vis
spectrophotometer and long-path absorption cell by Hecobian et al. (2010),
and compare them with the light absorption by BrC in the model. Absorption
coefficients of WSOC at 365 nm were measured at 15 sites in the southeastern
United States in 2007. Among them, eight sites are in urban areas, and the
others are in rural regions. Detailed descriptions of the measurements are
available in Hecobian et al. (2010).
Because light absorption observations are measured only for water-soluble
fractions of OC, and do not include water-insoluble components, we separate
BrC in the model into water-soluble and water-insoluble components. The model
divides OC (or BrC) into hydrophilic and hydrophobic components. For the
comparison, we do not use the simulated hydrophilic fraction, but instead use
an observed WSOC / OC ratio because the assumed division of hydrophobic
and hydrophilic fractions of OC and their conversion can be applicable in a
global sense, but in a regional sense it may cause a significant
discrepancy. For example, the observed water-soluble fraction of the total OC
is generally low (of the order of 25 %) in the Los Angeles basin (Zhang
et al., 2013); on the other hand, the model simulates a high water-soluble
fraction of the total OC (63–74 %) in this region. For this reason, we
decided to use the observed WSOC / OC ratio for the evaluations. In the
southeastern United States, the observed WSOC / OC ratio is about 0.58
(Weber et al., 2007; Zhang et al., 2013), which is also used to estimate the
water-soluble BrC concentrations from the total BrC concentrations in the
model.
Because the model simulates a mass concentration of BrC, a conversion from
the mass concentration to light absorption is carried out by multiplying MAE
values. For BrC from wood burning, we use the MAE value of
5.3 m2 g-1 at 365 nm measured by McMeeking (2008) in order to
retain the consistency between our emission estimates and the evaluation. For
BrC from SOC, we select the MAE of 1.5 m2 g-1 at 365 nm
calculated by Nakayama et al. (2010) (see Fig. 4 in their paper).
Figure 3 shows monthly mean simulated and observed light absorption
coefficients of BrC at 365 nm averaged over all sites in the southeastern
United States for 2007. Black circles and coloured bars indicate the observed
and simulated BrC absorption at 365 nm, respectively, and different colours
in the bar show contributions from different sources. Each panel represents a
different model simulation with each case of AAE selections as shown in
Fig. 1 and Table 1.
Simulated versus observed monthly mean light absorption at 365 nm
by water-soluble BrC over the southeastern United States in 2007. Unit is Mm-1.
Black circles denote observations, and bar graphs indicate model results for
each source: biomass burning (red), biofuel (green), and SOC (blue). Each
panel shows the comparisons with different emission estimate cases –
(a) case 1, (b) case 2, and (c) case 3.
In winter months (November through March), the observed light absorptions
were generally high and reached a peak in March. These high absorptions were
highly correlated with levoglucosan, which is a marker for biomass burning
(Hecobian et al., 2010). During the summer, the observed light absorptions
decreased substantially.
The model generally captures the observed seasonal variation with high
absorption in the winter, having a peak in March and low absorption in the
summer (R of 0.93). On an annual mean basis, we find that the model is too
high at 46 % for case 1, and is too low at -31 % for case 3,
relative to the observations. The model for case 2 is in
best agreement with the observations (4 %) on an annual mean basis.
The BrC source contribution in the model is similar to the observed source
contribution. Hecobian et al. (2010) showed that biomass burning was the main
contributor for the winter season, whereas the SOC contribution increased
during the summer season. The simulated seasonal variation is consistent with
the observation, as shown in Fig. 3. The annual mass contribution of SOC to
BrC is 38 % (in case 2), which is in good agreement with the observed
contribution of 32 % (Hecobian et al., 2010). Based on the results in
Fig. 3, the model for the case 2 yields best estimates of BrC emissions.
In addition to the observation by Hecobian et al. (2010), we use the light
absorption observations by Zhang et al. (2011, 2013). Measurements were
carried out in Atlanta, GA (33.778427∘ N, 84.396181∘ W),
Pasadena, CA (34.140528∘ N, 118.122455∘ W), and Riverside,
CA (33.97185∘ N, 117.32266∘ W) for a month or less. As
discussed above, we apply the observed WSOC / OC ratio to the model BrC
concentrations: 26 % for the Los Angeles basin (Pasadena and Riverside)
(Zhang et al., 2013) and 58 % for Atlanta (Weber et al., 2007; Zhang et
al., 2013).
Figure 4 shows the daily mean observed and simulated light absorption
coefficients from the best model (case 2) for Atlanta, Pasadena, and
Riverside for 2010. The upper panel shows the comparison of the observed
versus simulated light absorption for Atlanta. The highest observed daily
absorption occurred on 24 August, but the model fails to reproduce it.
Furthermore, the model generally overestimates the observed absorption by
44 %, and the large discrepancies mainly occur in September. This large
discrepancy in September is similar to the result shown in Fig. 3b for 2007.
Simulated versus observed daily mean light absorption at 365 nm by
water-soluble BrC over the United States in 2010; unit is Mm-1. Black circles
denote observations and bar graphs indicate model results for each source –
biomass burning (red), biofuel (green), and SOC (blue).
The middle and lower panels show the comparisons at the Los Angeles basin
sites in May and June. The observed mean light absorptions at these sites
(0.81 and 0.98 Mm-1 for Pasadena and Riverside, respectively) are
higher than the observed mean light absorption (0.56 Mm-1) for Atlanta.
However, the model underestimates the observations by 38 % (Pasadena) and
48 % (Riverside). Zhang et al. (2013) showed that the main sources of BrC
at these sites were SOC from anthropogenic emissions. The model also shows a
high contribution (85 %) of the secondary source to the total BrC mass
concentrations, but the magnitudes are generally lower than the observations,
and this low bias is likely related to the underestimation of the simulated SOC
concentrations using the 1-D VBS (Jo et al., 2013).
We find from the model evaluation over the United States that the model
generally captures the observed mean absorption and its seasonal variability
in the region where primary sources are dominant. On the other hand, the
model underestimates the observed mean absorption in the region with the
dominant secondary sources. The low bias is partly explained by the SOC
underestimation in the model. However, the underestimations of BrC from SOC
(38–48 %) are higher than those of SOC (18 %), indicating the
importance of additional secondary BrC sources that we did not include in the
model.
A MAE value for secondary BrC could be another possible reason for the bias
in the model. Although chamber studies suggested weak absorbing
characteristics of BrC from SOC (Nakayama et al., 2010,
2013; Zhong and Jang, 2011), some field observations speculated the existence
of strongly absorbing BrC from SOC (Alexander et al., 2008; Chung et al.,
2012). For example, applying the MAE value of 3.5 m2 g-1 at
365 nm (a half of the MAE at 365 nm from Alexander et al., 2008) for
secondary BrC yields a similar mean absorption value to the observation over
LA basin. Extensive observations of optical characteristics of BrC depending
on the formation mechanisms would be needed to reduce the associated
uncertainties and to improve the model.
Evaluation against global AERONET observations
No global observation of BrC is available yet. Here we use the observed SSA
at AERONET sites to evaluate the model by focusing on the effect of BrC on
the simulated aerosol absorption. We also use observed aerosol optical depth
(AOD) to evaluate the model capability to simulate aerosol mass
concentrations.
For comparisons of AOD and SSA between the model and observations, we use
FlexAOD (http://pumpkin.aquila.infn.it/flexaod/), which calculates AOD
and SSA using simulated aerosol mass concentrations from GEOS-Chem with the
Mie algorithm (Mischenko et al., 2002; Curci et al., 2015).
For optical properties of BrC, we use imaginary refractive indices of BrC
from McMeeking (2008) for wood burning sources, and from Nakayama et al. (2010)
for SOC sources. Detailed description of the values used in AOD and
SSA calculation are provided in Sect. 6, where we discuss the DRE of BrC.
Figure 5 shows comparisons of monthly mean simulated versus observed AOD at
500 nm and SSA at 440 nm. We find that the model captures the observed AOD
quite well with a regression slope of 0.86 and a R of 0.88. However, the
model tends to overestimate the observed SSA, implying that the simulated
aerosol concentrations appear to have too large a fraction of scattering
aerosols. We find that the inclusion of BrC in the model reduces the high
bias of simulated SSA by 33 and 23 % (lower left and lower right panel of
Fig. 5), indicating a considerable contribution of BrC to aerosol absorption.
Although the statistics suggest a greater improvement with case 1 in
terms of the bias, simulated SSA values at sites in Africa with high BrC
concentrations, are too low apart from the regression line
(discrepancy > 0.1). This result also supports our selection of
case 2 as the best model for BrC emission estimates.
Scatter plots of simulated versus observed AOD at 500 nm (upper
left), SSA at 440 nm without BrC (upper right), SSA at 440 nm with BrC of
case 1 (lower left), and SSA at 440 nm with BrC of case 2 (lower right) for
2007. Reduced major axis regression is shown along with the regression
equation and R. Each point indicates monthly averaged AOD or SSA when the
number of observation is greater than 10 days.
Despite a decrease of simulated SSA with BrC, the model is still too high
relative to the observations. The overestimation might be partly caused by
the underestimation of BC emissions from biomass burning (Bond et al., 2013).
This is also supported by the fact that the discrepancy gets larger for
biomass burning regions, where a difference between the model and AERONET SSA
is 40 % higher than that in regions with high anthropogenic emissions.
Emission factors of BC used in this study are 0.2–0.69 g kg-1
(Wiedinmyer et al., 2011), which are lower than the value of 1 g kg-1
used by Chin et al. (2009), who found no significant bias in their model
compared with the AERONET SSA. Lin et al. (2014) also reported a small bias
in their model compared with AERONET SSA using 4.7 Tg yr-1 of global
annual biomass burning BC emissions, which is about 2 times higher than
2.2 Tg yr-1 of this study.
Annual surface map of total BrC (top left) and BrC from three
source categories: biomass burning (top right), biofuel (bottom left), and
SOC (bottom right). Mean values are presented in the upper right corner of
each panel. Unit is µgC m-3.
In addition to the biomass burning emission of BC, the anthropogenic emission
of BC could also contribute to the simulated SSA bias. Cohen and Wang (2014)
showed that a global top-down emission of BC is twice as large as the
bottom-up estimates of BC based on the Kalman filter approach. They suggested
that BC emissions in East Asia, Southeast Asia, and eastern Europe are
significantly underestimated in current bottom-up emission inventories. This
issue is critically important, and possibly has an important implication for
climate. However, an investigation of BC emissions for the SSA discrepancy
above is beyond the scope of our work, and will be conducted in future
studies.
Light absorption enhancement of aged BC could also be one of the reasons for
the SSA overestimation in the model. Here we use the same optical parameters
for all BC in the model. However, Bond et al. (2006) suggested that the
absorption of aged BC is about 1.5 times greater than that of fresh BC. BC
aging occurs as it is mixed internally with other aerosols. If we assume
hydrophilic BC as aged BC in the model and its absorption enhancement by a
factor of 1.5 relative to hydrophobic BC, the high bias of simulated SSA is
additionally reduced by about 20 % (not shown).
We further compare the model against AERONET AAE as shown in Fig. S1 in the Supplement. We
find that the model overestimates the observed AAE after including BrC, in
part, because the model underestimates BC emissions as discussed above.
However, the simulated AAE will be decreased if we increase BC emissions as
suggested by the top-down estimate (Cohen and Wang, 2014). For example, for
regions (North America, Central America, South America, Southeast Asia, and
Australia) where the difference between our BC emission and the top-down
estimate is within a factor of 2, we find that the model with BrC shows a
better agreement with the observed AAE (Fig. S2) and with the observed SSA
(Fig. S3).
Considering all these uncertainties, our evaluation above indicates that the
model for case 2 results in the best estimates of simulated BrC
concentrations, which will be used for examining BrC effects on climate and
photochemistry below together with two other cases considered as the upper and lower
limits of our estimates.
Global tropospheric budgets of BrC compared to those of OC and BC.
Uncertainties are indicated in the parentheses.
Figure 6 shows our best estimates of annual mean concentrations of BrC and
each source contribution in surface air for 2007. Values are high in regions
where biomass burning (Southeast Asia and South America) and biofuel (East
Asia and northeast India) sources are dominant. These primary sources account
for 77 % of BrC concentrations in surface air. On the other hand,
secondary sources are relatively minor in the surface, but their contribution
increases in the free troposphere, as discussed in Sect. 5.2.
Figure 7 shows BrC to BC and OC ratios in surface air in the model. The BrC
to BC ratio is highest over the eastern North Pacific and the North
Atlantic. This high ratio over the ocean reflects a secondary chemical
production, which contributes to BrC but not to BC. Over the continents, the
ratio is generally higher in heavy biomass burning regions (South America
and Africa) than in industrialised regions (East Asia, Europe, and the
eastern United States) because more BrC than BC is emitted from biomass
burning.
Annual mean ratios of BrC to BC (left) and OC (right) in surface
air. Global mean values are presented in the upper right corner of each
panel.
Similarly, the BrC to OC ratio is also high over the oceans because of
secondary BrC, the concentrations of which increase with atmospheric aging.
Over the continents, the ratio is smaller reflecting relatively fresh
emissions of OC from anthropogenic sources that do not directly contribute to
BrC. We find that the BrC to OC ratio is relatively high in regions with
large biofuel use (northern India and central Asia). Although China is one of
the largest emission source regions for BrC (Fig. 6), both BrC to BC and BrC
to OC ratios are relatively low because of high concentrations of BC and OC.
Our global mean BrC to BC and BrC to OC ratios at the surface are 1.24 and
0.21, respectively, and are lower than the ratio (3.4 of BrC to BC ratio and
0.43 of BrC to OC ratio in terms of burden) of Feng et al. (2013), but higher
than the ratio (1.0 of BrC to BC ratio) used in Park et al. (2010).
DRE of BrC at the top of the atmosphere. Upper panels are for
radiative effect of BrC from primary sources (a) and from secondary sources (b).
The DRE increase of OC owing to the absorption of BrC is shown
in (c) (i.e. the DRE of OC with absorbing BrC minus the DRE of OC including BrC as
scattering OC, which is typically assumed in previous studies). Radiative
effect of total OC (BrC is assumed to be scattering OC) is represented in (d).
The 70∘ S–70∘ N averages are shown in the upper
right corner of each panel.
Tropospheric budget of BrC
Table 3 summarises our best estimates of the global tropospheric budgets of
BrC, along with BC and OC. The global BrC source is
12.5 ± 3.0 TgC yr-1, which accounts for 27 % of OC sources.
Although the biofuel emission (6.5 TgC yr-1) is 3 times lower than
the biomass burning emission (22.7 TgC yr-1) for OC, the biofuel
emission (3.0 ± 1.3 TgC yr-1) becomes significant for BrC,
contributing about 43 % of primary sources. The secondary source of BrC
is 5.7 TgC yr-1, and is comparable to the primary sources
(6.8 ± 3.0 TgC yr-1).
Wet deposition is the main removal process for BrC, and accounts for 86 %
of total removal processes. The remaining loss is due to dry deposition. The
contribution of wet deposition to total deposition of BrC is similar to that
of OC (82 %), because we treat BrC scavenging similarly to that of OC.
Because secondary BrC is produced all over the troposphere (not only at the
surface) and is hydrophilic, most secondary BrC is removed through wet deposition
processes (92 %).
The global burden of BrC shows the highest contribution from secondary BrC
(50 %) compared to primary contributions from biomass burning (30 %)
and biofuel (20 %). This result is opposite to the source contributions
in surface air shown in Fig. 6. The contribution of secondary BrC to the
atmospheric burden is twice as high as the contribution of secondary BrC to
the surface concentration (23 %), reflecting a relatively large
production of BrC in the free troposphere as well as limited export of
primary BrC from the surface to the free troposphere.
Our BrC lifetime is 5.8 days, which is lower than that of OC (7.9 days)
because of different contributions of the secondary sources for BrC and OC.
The latter species includes a larger fraction of secondary species
(52 %), the lifetime of which is usually longer than that of POC
especially for not aged biogenic SOC (Jo et al., 2013). No significant
difference between the lifetimes of BrC and BC exists because BrC, which is
more hydrophilic than BC, is more prone to wet scavenging than BC.
Direct radiative effect of BrC
We use imaginary refractive indices of BrC as a function of wavelength for
radiative transfer calculations to account for the wavelength dependency of
the BrC absorption. Imaginary refractive indices in the literature have a
wide range of values, even from the same sources, such as wood burning
(Chakrabarty et al., 2010; Kirchstetter et al., 2004; McMeeking, 2008). In
order to maintain the consistency with BrC emission estimates from primary
sources, we use the imaginary refractive indices reported by
McMeeking (2008), which are 0.18, 0.14, and 0.10 at 370, 405, and 532 nm,
respectively. The values are interpolated with the AAE at every 50 nm
wavelength interval for the radiative transfer calculations. For secondary
BrC, values from Nakayama et al. (2010) are used with 0.047 and 0.007 at 355
and 532 nm, respectively, based on the measurements for SOC from toluene.
We calculate AOD, SSA, and asymmetry parameter using FlexAOD, which is
described in Sect. 4.2. Note that we calculate DRE rather than DRF. DRE is
the instantaneous radiative impact of all atmospheric particles on the
Earth's energy balance, and DRF is the change in DRE from pre-industrial to
present day (Heald et al., 2014). We use the rapid radiative transfer model
for GCMs (general circulation models) (RRTMGs) (Iacono et al., 2008) for DRE calculations. Wavelengths used
for the calculation are 300, 304, 393, 533, 702, 1010, 1270, 1462, 1784,
2046, 2325, 2788, 3462, and 8021 nm. MERRA reanalysis data are used for
albedo and other meteorological variables.
Extinction efficiencies and SSAs of selected aerosols at 0.4 µm used for calculating photolysis rates in GEOS-Chem. SNA indicates
inorganic salt comprised of sulfate, nitrate and ammonium aerosols.
Figure 8a and b show the clear-sky DRE values of primary and secondary BrC
concentrations. Because the imaginary refractive indices of BrC are between
those of strongly absorbing BC and scattering OC, the global mean DRE of BrC
is close to zero, as shown in a and b.
Although the DRE of BrC at the top of the atmosphere is nearly zero, the
increased DRE of OC after considering BrC absorption (usually considered as
scattering OC) is 0.11 W m-2, as shown in Fig. 8c. The DRE of OC
without BrC absorption is -0.69 W m-2 (Fig. 8d), and this value is
increased to -0.58 W m-2 after considering BrC absorption.
Consequently, the cooling effect of OC is reduced by 16 %.
Despite the negligible effect of BrC on DRE or DRF, its significance
manifests for OC DRF estimates, which have been conducted based on the
assumption of scattering OC. For example, AeroCom phase II simulations
calculated -0.03 W m-2 as the global mean DRF of POC from fossil
fuel and biofuel, and -0.06 W m-2 for that of SOC (Myhre et al.,
2013). Because the biofuel emission is about twice as large as the fossil
fuel emission (Bond et al., 2007), and one-half of OC from biofuel is BrC,
one-third of the POC from fossil fuel and biofuel is BrC. Therefore,
one-third of DRF (-0.01 W m-2) of POC in AeroCom is related to BrC,
whose DRF is close to zero. For SOC, because the pre-industrial biogenic SOC
concentration is similar to present-day conditions, almost all DRF of SOC is
from anthropogenic SOC. Based on previous SOC studies (Henze et al., 2008; Jo
et al., 2013; Murphy and Pandis, 2010), approximately one-third of
anthropogenic SOC is highly aged, and can thus be assumed to be BrC in this
simple estimation. As a result, one-third of DRF (-0.02 W m-2) of
SOC in AeroCom is related to BrC. The total DRF of BrC that was assumed to be
scattering OC in the AeroCom study is -0.03 W m-2. Because DRF of
BrC is almost negligible, the negative DRF of OC (-0.09 W m-2) in
AeroCom could likely be overestimated by 50 %. We think, however, the
warming effect of BrC on the negative DRF or DRE of OC would be a low-end
value because our best model likely underestimates BrC concentrations
especially from the secondary source.
Effect on ozone photochemistry
BrC absorption, particularly at UV wavelengths, has an important implication
for ozone photochemistry. Here we examine the effect of BrC absorption on
photochemistry by updating photolysis rate calculations in GEOS-Chem
following Martin et al. (2003). Table 4 shows the calculated extinction
efficiency and SSA of important aerosols at 0.4 µm, which affect UV
extinction, and thus photolysis rate calculations, in the model. Values of
OC, BC, and inorganic aerosols are from GEOS-Chem, in which we update aerosol
optical properties by adding those of BrC. We include optical properties of
primary and secondary BrC separately because they differ substantially. For
example, SSA values of primary BrC are smaller than those of secondary BrC,
and thus have a greater impact on UV radiation. Compared with other aerosols,
SSA values of BrC are generally lower than those of OC and inorganic
aerosols, but higher than those of BC.
Changes in annual NO2 photolysis rate (a, b) and O3
concentration (c, d) at the surface due to BrC absorption.
Martin et al. (2003) showed that the effects of aerosols on photolysis rates
increased CO by 5–15 ppbv in the remote Northern Hemisphere (annual mean
concentrations less than 140 ppbv). This increase resulted in an improved
model agreement with observations, but there was a still gap between the
model and the observations. In our simulation with BrC, CO concentration is
further increased by 0.2–1.9 ppbv in remote Northern Hemisphere regions
(annual mean concentrations less than 140 ppbv in the model). On the other
hand, OH concentrations are decreased by 0–10 % in the boundary layer
over the Northern Hemisphere (maximum decreases occur in regions with high
BrC concentrations, shown in Fig. 6). The change of OH owing to BrC is about
one-third of the OH change according to the overall aerosol effects from
Martin et al. (2003). Therefore, the inclusion of BrC significantly affects
tropospheric chemistry, especially for regions with heavy biomass burning and
biofuel emissions.
Finally, we quantify the effects of BrC on global NO2 photolysis rates
and ozone concentrations at the surface. Figure 9 shows changes in annual
NO2 photolysis rates and O3 concentrations in surface air owing to
BrC absorption. Although BrC absorption is included, there are no significant
changes (less than 1 %) of the global mean NO2 photolysis rate and
O3 concentration in surface air. However, the effect of BrC appears to
be important for regions with high BrC concentrations. We find a maximum
decrease of the annual mean NO2 photolysis rate by 8 % in surface
air over Asia where the resulting reduction of O3 concentration is up to
-2 ppbv (6 % of annual mean surface O3 concentration). We also
find that the BrC effect has a strong seasonal variation such that it is
maximised in the spring when surface O3 concentration is decreased up to
-13 % in Asia because of high BrC concentration
(55 µgC m-3). This maximum O3 decrease by BrC
(-13 %) is similar to the O3 decrease (15 %) by fire aerosols
in Jiang et al. (2012).
Conclusion
OC has been considered to be a scattering aerosol, but emerging evidence has
shown that some OC can efficiently absorb solar radiation. This absorbing OC
is called BrC. With increasing recognition of its importance, especially for
solar absorption at UV and short visible wavelengths, quantification of its
spatial and temporal distribution is much needed for the study of climate
and air quality issues. Here we conducted an explicit global BrC simulation
for the full year of 2007 using a global 3-D chemical transport model
(GEOS-Chem), and examined its implication for climate and O3
photochemistry.
We first estimated primary BrC emissions from biomass burning and biofuel use
based on the relationship between AAE and MCE. Our estimates of primary BrC
emissions are 3.9 ± 1.7 and 3.0 ± 1.3 TgC yr-1 from
biomass burning and biofuel use, respectively. The secondary BrC source is
estimated to be 5.7 TgC yr-1 from the aromatic oxidation.
With explicit BrC emissions, a coupled oxidant–aerosol simulation was
conducted for 2007 to obtain the spatial and temporal distributions of BrC
concentrations. We first evaluated the model by comparing the simulated
versus observed BrC absorption in the United States and found that the model
successfully reproduced the observed seasonal variation of light absorption
by WSOC in the southeastern United States, whereas the model significantly
underestimated secondary BrC over the Los Angeles basin.
Our budget analysis showed that BrC from primary sources are dominant
(77 %) in surface air, but BrC from secondary sources becomes important
with increasing altitudes. For example, BrC from secondary sources accounts
for the 50 % of the tropospheric BrC burden, which is higher than its
23 % contribution to surface BrC concentrations. Our global mean value of
the BrC to BC ratio is 1.83 for the whole atmosphere, and 1.24 for the
surface, which significantly differs from the values used in previous
studies.
Using our best results, we estimated the DRE of BrC to be close to zero at
the top of the atmosphere because the imaginary refractive indices of BrC are
in the midpoint between those of BC and OC. Despite a negligible contribution
to DRE, the inclusion of BrC absorption in the model offsets the negative
radiative effect of OC by 0.11 W m-2 (16 %).
Finally, we included BrC absorption in photolysis rate calculations in the
model. We found that the NO2 photolysis rate is decreased up to 8 %,
especially for Asia, where BrC concentration is high. Resulting annual
surface O3 concentrations are decreased up to -2 ppbv (6 %). This
effect is more important in the spring, when a typical O3 maximum occurs
in Asia, where the effect of BrC decreases the surface O3 concentration
by up to -13 %.
Many chemical transport models and air quality models have included the
effect of aerosols on photolysis rate calculations, but have not considered
the BrC effect. Based on our analysis, BrC absorption could have a
significant direct impact on regional air quality by being involved in
O3 photochemical formation. Its significance, however, can be expanded
to the globe by its effect on the atmospheric oxidation capacity, which has
an indirect but important implication for global air quality and climate.
Relationship between BrC / BC absorption ratio and AAE
In this section we describe a procedure for obtaining the relationship
between the BrC / BC absorption ratio and AAE. Assuming no internal
mixing and dust influence, total absorption at a certain wavelength (λ) can be expressed as
αλ,CA=αλ,BrC+αλ,BC.
Rewriting Eq. (A1) using AAE,
αλ0,CAλλ0-ÅCA=αλ0,BrCλλ0-ÅBrC+αλ0,BCλλ0-ÅBC.
Dividing each side of Eq. (A2) by αλ0,BC:
(1+F)λλ0-ÅCA=Fλλ0-ÅBrC+λλ0-ÅBC,
where F is the BrC / BC absorption ratio at λ0.
We can solve Eq. (A3) analytically, and the procedure is described in
Appendix A2. We do not use the analytical relationship because it uses only
three wavelengths for the calculations. The Ångström relationship is
based on empirical fitting. AAE varies in different wavelength regions, even
if we use the same samples. For example, Chung et al. (2012) showed that CA
AAE is about 1.2 when the first four wavelengths (370, 470, 520, 590 nm) are
used, while the CA AAE is 1.35 with the last four wavelengths (590, 660, 880,
950 nm). This discrepancy is much increased in the case of BrC AAE. Liu et
al. (2014) showed that BrC AAE varies by approximately 20 %, depending on
wavelength pairs. Furthermore, if we calculate AAE of BrC using the MAE of
Kirchstetter et al. (2004), AAE of BrC in all wavelengths (from 350 to
650 nm, 7 values) is fitted to 5.9 with a R2 of 0.96. However, the AAE
of BrC using just two wavelengths is 4.1 for the 350–440 nm and 8.0 for the
550–600 nm.
Therefore, we calculate the relationship between MCE and F by regression
using multiple wavelengths: [300, 350, 400, 450, 500, 550, 600, 650, 700,
750, 800, 850, 900 nm]. If we rewrite Eq. (A3) for the regression form,
ÅCAlog(λ)+C=-logFλλ0-ÅBrC+λλ0-ÅBC,
where the residual term C is
C=-ÅCAlog(λ0)-log(1+F).
The left side of Eq. (A4) has the shape of Ax+B. Therefore, by linear
regression analysis, we can obtain ÅCA (the slope of the
regression) as varying F on the right side. For example, Fig. S4 shows the
linear regression case for F=4.0. In this case, R2 is 0.99 and
Ångström exponent of CA is 4.44. Y intercept of the numerical fitting is
-29.81, which is consistent with Y intercept (-29.64) from Eq. (A5).
The difference between two Y intercept values are always within 1 %,
which shows the numerical fitting with Eq. (A4) satisfies both the slope
(A) and the intercept (B) at the same time within 1 % error. We
choose an ÅBrC values of 5.0, 5.48,
6.19 and an ÅBC values of 0.86,
1.00, 1.15, following Kirchstetter and Thatcher (2012), who
estimated mean ÅBrC using several wood samples (87 samples)
over the 360 to 700 nm spectrum range. We assign a λ0 value of
550 nm. The coefficient of determination (R2) is greater than 0.98 in
all the regression analyses. The calculated relationship between MCE and F is
plotted in Fig. 1. As expected, emissions of BrC are increased when MCE is
decreased.
Analytical derivation of Eq. (A3)
Here we describe the procedure to obtain the analytical relationship between
MCE and F. First, substituting λ1 and λ2 in Eq. (A3),
(1+F)λ1λ0-ÅCA=Fλ1λ0-ÅBrC+λ1λ0-ÅBC,(1+F)λ2λ0-ÅCA=Fλ2λ0-ÅBrC+λ2λ0-ÅBC.
Assuming AAE between λ0 and λ1 is equal to AAE between
λ0 and λ2, divide Eq. (A6) by Eq. (A7), and rearrange
terms:
λ1λ2-ÅCA=Fλ1λ0-ÅBrC+λ1λ0-ÅBCFλ2λ0-ÅBrC+λ2λ0-ÅBC.
Taking the logarithm of both sides:
ÅCA=-logFλ1λ0-ÅBrC+λ1λ0-ÅBCFλ2λ0-ÅBrC+λ2λ0-ÅBC/logλ1λ2.
Substituting Eq. (2) into Eq. (A9) gives
MCE=18.2+logFλ1λ0-ÅBrC+λ1λ0-ÅBCFλ2λ0-ÅBrC+λ2λ0-ÅBC/logλ1λ2/17.34.
After assigning ÅBrC, ÅBC, and the corresponding
three wavelengths (λ0, λ1 and λ2) in
Eq. (A10), we obtain the relationship between MCE and F analytically.
The Supplement related to this article is available online at doi:10.5194/acp-16-3413-2016-supplement.
Acknowledgements
We thank anonymous reviewers for their helpful comments on the manuscript. We
thank the principal investigators and their staff for establishing and
maintaining the AERONET sites used in this study. This study was supported by
the Eco Innovation Program of KEITI (ARQ201204015) and by Korea Ministry of
Environment as the “Climate Change Correspondence Program”. Edited by: K. Tsigaridis
ReferencesAiken, A. C., DeCarlo, P. F., Kroll, J. H., Worsnop, D. R., Huffman, J. A.,
Docherty, K. S., Ulbrich, I. M., Mohr, C., Kimmel, J. R., and Sueper, D.:
O / C and OM / OC ratios of primary, secondary, and ambient organic
aerosols with high-resolution time-of-flight aerosol mass spectrometry,
Environ. Sci. Technol., 42, 4478–4485, 2008.Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S.,
Karl, T., Crounse, J. D., and Wennberg, P. O.: Emission factors for open and
domestic biomass burning for use in atmospheric models, Atmos. Chem. Phys.,
11, 4039–4072, 10.5194/acp-11-4039-2011, 2011.Alexander, D. T. L., Crozier, P. A., and Anderson, J. R.: Brown carbon
spheres in East Asian outflow and their optical properties, Science, 321,
833–836, 10.1126/science.1155296, 2008.Andreae, M. O. and Gelencsér, A.: Black carbon or brown carbon? The
nature of light-absorbing carbonaceous aerosols, Atmos. Chem. Phys., 6,
3131–3148, 10.5194/acp-6-3131-2006, 2006.
Andreae, M. O. and Merlet, P.: Emission of trace gases and aerosols from
biomass burning, Global Biogeochem. Cy., 15, 955–966, 2001.
Bahadur, R., Praveen, P. S., Xu, Y., and Ramanathan, V.: Solar absorption by
elemental and brown carbon determined from spectral observations, P. Natl.
Acad. Sci., 109, 17366–17371, 2012.
Bey, I., Jacob, D. J., Yantosca, R. M., and Logan, J. A.: Global modeling of
tropospheric chemistry with assimilated meteorology – Model description and
evaluation, J. Geophys. Res., 106, 23073–23095, 2001.
Bond, T. C. and Bergstrom, R. W.: Light absorption by carbonaceous particles:
An investigative review, Aerosol Sci. Tech., 40, 27–67, 2006.Bond, T. C., Streets, D. G., Yarber, K. F., Nelson, S. M., Woo, J. H., and
Klimont, Z.: A technology-based global inventory of black and organic carbon
emissions from combustion, J. Geophys. Res., 109, D14203,
10.1029/2003JD003697, 2004.Bond, T. C., Habib, G., and Bergstrom, R. W.: Limitations in the enhancement
of visible light absorption due to mixing state, J. Geophys. Res, 111,
D20211, 10.1029/2006JD007315, 2006.Bond, T. C., Bhardwaj, E., Dong, R., Jogani, R., Jung, S., Roden, C.,
Streets, D. G., and Trautmann, N. M.: Historical emissions of black and
organic carbon aerosol from energy-related combustion, 1850–2000, Global
Biogeochem. Cy., 21, GB2018, 10.1029/2006GB002840, 2007.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 system: A scientific
assessment, J. Geophys. Res., 118, 5380–5552, 10.1002/jgrd.50171, 2013.Bones, D. L., Henricksen, D. K., Mang, S. A., Gonsior, M., Bateman, A. P.,
Nguyen, T. B., Cooper, W. J., and Nizkorodov, S. A.: Appearance of strong
absorbers and fluorophores in limonene-O3 secondary organic aerosol due to
NH4+-mediated chemical aging over long time scales, J. Geophys. Res.,
115, D05203, 10.1029/2009JD012864, 2010.Chakrabarty, R. K., Moosmüller, H., Chen, L.-W. A., Lewis, K., Arnott, W.
P., Mazzoleni, C., Dubey, M. K., Wold, C. E., Hao, W. M., and Kreidenweis, S.
M.: Brown carbon in tar balls from smoldering biomass combustion, Atmos.
Chem. Phys., 10, 6363–6370, 10.5194/acp-10-6363-2010, 2010.
Chakrabarty, R. K., Pervez, S., Chow, J. C., Watson, J. G., Dewangan, S.,
Robles, J., and Tian, G.: Funeral pyres in South Asia: Brown carbon aerosol
emissions and climate impacts, Environ. Sci. Tech. Lett., 1, 44–48, 2014.Cheng, Y., He, K.-B., Zheng, M., Duan, F.-K., Du, Z.-Y., Ma, Y.-L., Tan,
J.-H., Yang, F.-M., Liu, J.-M., Zhang, X.-L., Weber, R. J., Bergin, M. H.,
and Russell, A. G.: Mass absorption efficiency of elemental carbon and
water-soluble organic carbon in Beijing, China, Atmos. Chem. Phys., 11,
11497–11510, 10.5194/acp-11-11497-2011, 2011.Chin, M., Diehl, T., Dubovik, O., Eck, T. F., Holben, B. N., Sinyuk, A., and
Streets, D. G.: Light absorption by pollution, dust, and biomass burning
aerosols: a global model study and evaluation with AERONET measurements, Ann.
Geophys., 27, 3439–3464, 10.5194/angeo-27-3439-2009, 2009.Chung, C. E., Kim, S.-W., Lee, M., Yoon, S.-C., and Lee, S.: Carbonaceous
aerosol AAE inferred from in-situ aerosol measurements at the Gosan ABC super
site, and the implications for brown carbon aerosol, Atmos. Chem. Phys., 12,
6173–6184, 10.5194/acp-12-6173-2012, 2012.Chung, S. and Seinfeld, J.: Global distribution and climate forcing of
carbonaceous aerosols, J. Geophys. Res., 107, 4407,
10.1029/2001JD001397, 2002.Clarke, A., McNaughton, C., Kapustin, V., Shinozuka, Y., Howell, S., Dibb,
J., Zhou, J., Anderson, B., Brekhovskikh, V., and Turner, H.: Biomass burning
and pollution aerosol over North America: Organic components and their
influence on spectral optical properties and humidification response, J.
Geophys. Res., 112, D12S18, 10.1029/2006JD007777, 2007.
Cohen, J. B. and Wang, C.: Estimating global black carbon emissions using a
top-down Kalman Filter approach, J. Geophys. Res.-Atmos., 119, 307–323,
2014.Cooke, W., Liousse, C., Cachier, H., and Feichter, J.: Construction of a
1 × 1 fossil fuel emission data set for carbonaceous aerosol and
implementation and radiative impact in the ECHAM4 model, J. Geophys. Res.,
104, 22137–22162, 1999.
Curci, G., Hogrefe, C., Bianconi, R., Im, U., Balzarini, A., Baró, R.,
Brunner, D., Forkel, R., Giordano, L., Hirtl, M., Honzak, L.,
Jiménez-Guerrero, P., Knote, C., Langer, M., Makar, P. A., Pirovano, G.,
Pérez, J. L., San José, R., Syrakov, D., Tuccella, P., Werhahn, J.,
Wolke, R., Žabkar, R., Zhang, J., and Galmarini, S.: Uncertainties of
simulated aerosol optical properties induced by assumptions on aerosol
physical and chemical properties: An AQMEII-2 perspective, Atmos. Environ.,
115, 541–552, 2015.Diehl, T., Heil, A., Chin, M., Pan, X., Streets, D., Schultz, M., and Kinne,
S.: Anthropogenic, biomass burning, and volcanic emissions of black carbon,
organic carbon, and SO2 from 1980 to 2010 for hindcast model experiments,
Atmos. Chem. Phys. Discuss., 12, 24895–24954,
10.5194/acpd-12-24895-2012, 2012.
Favez, O., Alfaro, S. C., Sciare, J., Cachier, H., and Abdelwahab, M. M.:
Ambient measurements of light-absorption by agricultural waste burning
organic aerosols, J. Aerosol Sci., 40, 613–620, 2009.Feng, Y., Ramanathan, V., and Kotamarthi, V. R.: Brown carbon: a significant
atmospheric absorber of solar radiation?, Atmos. Chem. Phys., 13, 8607–8621,
10.5194/acp-13-8607-2013, 2013.
Fernandes, S. D., Trautmann, N. M., Streets, D. G., Roden, C. A., and Bond,
T. C.: Global biofuel use, 1850–2000, Global Biogeochem. Cy., 21, GB2019,
2007.
Flores, J. M., Washenfelder, R., Adler, G., Lee, H., Segev, L., Laskin, J.,
Laskin, A., Nizkorodov, S., Brown, S., and Rudich, Y.: Complex refractive
indices in the near-ultraviolet spectral region of biogenic secondary organic
aerosol aged with ammonia, Phys. Chem. Chem. Phys., 16, 10629–10642, 2014.
Forster, P. V., Ramaswamy, P., Artaxo, T., Berntsen, R., Betts, D. W., Fahey,
J., Haywood, J., Lean, D. C., Lowe, G., Myhre, J., Nganga, R., Prinn, G.,
Raga, M. S., and Dorland, R. V.: Changes in Atmospheric Constituents and in
Radiative Forcing., Cambridge University Press, United Kingdom and New York,
NY, USA, 2007.
Goldstein, A. H. and Galbally, I. E.: Known and unexplored organic
constituents in the earth's atmosphere, Environ. Sci. Technol., 41,
1514–1521, 2007.Graber, E. R. and Rudich, Y.: Atmospheric HULIS: How humic-like are they? A
comprehensive and critical review, Atmos. Chem. Phys., 6, 729–753,
10.5194/acp-6-729-2006, 2006.
Granier, C., Bessagnet, B., Bond, T., D'Angiola, A., Denier van der Gon, H.,
Frost, G. J., Heil, A., Kaiser, J. W., Kinne, S., and Klimont, Z.: Evolution
of anthropogenic and biomass burning emissions of air pollutants at global
and regional scales during the 1980–2010 period, Climatic Change, 109,
163–190, 2011.
Hawkins, L. N., Baril, M. J., Sedehi, N., Galloway, M. M., De Haan, D. O.,
Schill, G. P., and Tolbert, M. A.: Formation of Semisolid, Oligomerized
Aqueous SOA: Lab Simulations of Cloud Processing, Environ. Sci. Technol., 48,
2273–2280, 2014.Heald, C. L., Ridley, D. A., Kroll, J. H., Barrett, S. R. H., Cady-Pereira,
K. E., Alvarado, M. J., and Holmes, C. D.: Contrasting the direct radiative
effect and direct radiative forcing of aerosols, Atmos. Chem. Phys., 14,
5513–5527, 10.5194/acp-14-5513-2014, 2014.Hecobian, A., Zhang, X., Zheng, M., Frank, N., Edgerton, E. S., and Weber, R.
J.: Water-Soluble Organic Aerosol material and the light-absorption
characteristics of aqueous extracts measured over the Southeastern United
States, Atmos. Chem. Phys., 10, 5965–5977, 10.5194/acp-10-5965-2010,
2010.Henze, D. K. and Seinfeld, J. H.: Global secondary organic aerosol from
isoprene oxidation, Geophys. Res. Lett., 33, L09812,
10.1029/2006GL025976, 2006.Henze, D. K., Seinfeld, J. H., Ng, N. L., Kroll, J. H., Fu, T.-M., Jacob, D.
J., and Heald, C. L.: Global modeling of secondary organic aerosol formation
from aromatic hydrocarbons: high- vs. low-yield pathways, Atmos. Chem. Phys.,
8, 2405–2420, 10.5194/acp-8-2405-2008, 2008.Hoffer, A., Gelencsér, A., Guyon, P., Kiss, G., Schmid, O., Frank, G. P.,
Artaxo, P., and Andreae, M. O.: Optical properties of humic-like substances
(HULIS) in biomass-burning aerosols, Atmos. Chem. Phys., 6, 3563–3570,
10.5194/acp-6-3563-2006, 2006.Huang, Y., Wu, S., Dubey, M. K., and French, N. H. F.: Impact of aging
mechanism on model simulated carbonaceous aerosols, Atmos. Chem. Phys., 13,
6329–6343, 10.5194/acp-13-6329-2013, 2013.Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S.
A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res., 113,
D13103, 10.1029/2008JD009944, 2008.
Jacobson, M. Z.: Isolating nitrated and aromatic aerosols and nitrated
aromatic gases as sources of ultraviolet light absorption, J. Geophys. Res.,
104, 3527–3542, 1999.
Jacobson, M. Z.: Global direct radiative forcing due to multicomponent
anthropogenic and natural aerosols, J. Geophys. Res., 106, 1551–1568, 2001.Jaoui, M., Edney, E. O., Kleindienst, T. E., Lewandowski, M., Offenberg, J.
H., Surratt, J. D., and Seinfeld, J. H.: Formation of secondary organic
aerosol from irradiated alpha-pinene/toluene/NOx mixtures and the effect
of isoprene and sulfur dioxide, J. Geophys. Res., 113, D09303,
10.1029/2007JD009426, 2008.
Jiang, X., Wiedinmyer, C., and Carlton, A. G.: Aerosols from fires: An
examination of the effects on ozone photochemistry in the Western United
States, Environ. Sci. Technol., 46, 11878–11886, 2012.
Jo, D., Park, R., Kim, M., and Spracklen, D.: Effects of chemical aging on
global secondary organic aerosol using the volatility basis set approach,
Atmos. Environ., 81, 230–244, 2013.
Kaufman, Y. J., Justice, C. O., Flynn, L. P., Kendall, J. D., Prins, E. M.,
Giglio, L., Ward, D. E., Menzel, W. P., and Setzer, A. W.: Potential global
fire monitoring from EOS-MODIS, J. Geophys. Res., 103, 32215–32238, 1998.Kim, H. and Paulson, S. E.: Real refractive indices and volatility of
secondary organic aerosol generated from photooxidation and ozonolysis of
limonene, α-pinene and toluene, Atmos. Chem. Phys., 13, 7711–7723,
10.5194/acp-13-7711-2013, 2013.Kirchstetter, T. W. and Thatcher, T. L.: Contribution of organic carbon to
wood smoke particulate matter absorption of solar radiation, Atmos. Chem.
Phys., 12, 6067–6072, 10.5194/acp-12-6067-2012, 2012.Kirchstetter, T. W., Novakov, T., and Hobbs, P. V.: Evidence that the
spectral dependence of light absorption by aerosols is affected by organic
carbon, J. Geophys. Res., 109, D21208, 10.1029/2004JD004999, 2004.Lamarque, J.-F., Bond, T. C., Eyring, V., Granier, C., Heil, A., Klimont, Z.,
Lee, D., Liousse, C., Mieville, A., Owen, B., Schultz, M. G., Shindell, D.,
Smith, S. J., Stehfest, E., Van Aardenne, J., Cooper, O. R., Kainuma, M.,
Mahowald, N., McConnell, J. R., Naik, V., Riahi, K., and van Vuuren, D. P.:
Historical (1850–2000) gridded anthropogenic and biomass burning emissions
of reactive gases and aerosols: methodology and application, Atmos. Chem.
Phys., 10, 7017–7039, 10.5194/acp-10-7017-2010, 2010.
Lambe, A. T., Cappa, C. D., Massoli, P., Onasch, T., Forestieri, S. D.,
Martin, A. T., Cummings, M. J., Croasdale, D. R., Brune, B., and Worsnop, D.
R.: Relationship between oxidation level and optical properties of secondary
organic aerosol, Environ. Sci. Technol., 47, 6349–6357, 2013.
Laskin, A., Laskin, J., and Nizkorodov, S. A.: Chemistry of Atmospheric Brown
Carbon, Chem. Rev., 115, 4335–4382, 2015.
Laskin, J., Laskin, A., Roach, P. J., Slysz, G. W., Anderson, G. A.,
Nizkorodov, S. A., Bones, D. L., and Nguyen, L. Q.: High-resolution
desorption electrospray ionization mass spectrometry for chemical
characterization of organic aerosols, Anal. Chem., 82, 2048–2058, 2010.
Laskin, J., Laskin, A., Nizkorodov, S. A., Roach, P., Eckert, P., Gilles, M.
K., Wang, B., Lee, H. J., and Hu, Q.: Molecular Selectivity of Brown Carbon
Chromophores, Environ. Sci. Technol., 48, 12047–12055, 2014.Liao, H., Henze, D., Seinfeld, J., Wu, S., and Mickley, L.: Biogenic
secondary organic aerosol over the United States: Comparison of
climatological simulations with observations, J. Geophys. Res., 112, D06201,
10.1029/2006JD007813, 2007.
Lin, G., Penner, J. E., Flanner, M. G., Sillman, S., Xu, L., and Zhou, C.:
Radiative forcing of organic aerosol in the atmosphere and on snow: Effects
of SOA and brown carbon, J. Geophys. Res.-Atmos., 119, 7453–7476, 2014.Lin, P., Liu, J., Shilling, J. E., Kathmann, S. M., Laskin, J., and Laskin,
A.: Molecular characterization of brown carbon (BrC) chromophores in
secondary organic aerosol generated from photo-oxidation of toluene, Phys.
Chem. Chem. Phys., 17, 23312–23325, 10.1039/c5cp02563j, 2015.
Liu, J., Scheuer, E., Dibb, J., Ziemba, L. D., Thornhill, K., Anderson, B.
E., Wisthaler, A., Mikoviny, T., Devi, J. J., and Bergin, M.: Brown carbon in
the continental troposphere, Geophys. Res. Lett., 41, 2191–2195, 2014.
Liu, S., Shilling, J. E., Song, C., Hiranuma, N., Zaveri, R. A., and Russell,
L. M.: Hydrolysis of organonitrate functional groups in aerosol particles,
Aerosol Sci. Technol. 46, 1359–1369, 2012.Lukács, H., Gelencsér, A., Hammer, S., Puxbaum, H., Pio, C., Legrand,
M., Kasper-Giebl, A., Handler, M., Limbeck, A., and Simpson, D.: Seasonal
trends and possible sources of brown carbon based on 2-year aerosol
measurements at six sites in Europe, J. Geophys. Res., 112, D23S18,
10.1029/2006JD008151, 2007.
Malm, W. C., Sisler, J. F., Huffman, D., Eldred, R. A., and Cahill, T. A.:
Spatial and seasonal trends in particle concentration and optical extinction
in the United States, J. Geophys. Res., 99, 1347–1370, 1994.Martin, R. V., Jacob, D. J., Yantosca, R. M., Chin, M., and Ginoux, P.:
Global and regional decreases in tropospheric oxidants from photochemical
effects of aerosols, J. Geophys. Res., 108, 4097, 10.1029/2002JD002622,
2003.McMeeking, G. R.: The Optical, Chemical, And Physical Properties Of Aerosols
And Gases Emitted By The Laboratory Combustion Of Wildland Fuels,
Dissertation, Department of Atmospheric Science, Colorado State University,
Fort Collins, Colorado
Fall 2008, available at: http://chem.atmos.colostate.edu/Thesis/McMeeking%20dissertation.pdf
(last access: 14 March 2016), 2008.
Mischenko, M. I., Travis, L. D., and Lacis, A. A.: Scattering, Absorption,
and Emission of Light by Small Particles, Cambridge University Press, UK,
2002.
Moise, T., Flores, J. M., and Rudich, Y.: Optical Properties of Secondary
Organic Aerosols and Their Changes by Chemical Processes, Chem. Rev., 115,
4400–4439, 2015.Murphy, B. N. and Pandis, S. N.: Exploring summertime organic aerosol
formation in the eastern United States using a regional-scale budget approach
and ambient measurements, J. Geophys. Res., 115, D24216,
10.1029/2010JD014418, 2010.Myhre, G., Samset, B. H., Schulz, M., Balkanski, Y., Bauer, S., Berntsen, T.
K., Bian, H., Bellouin, N., Chin, M., Diehl, T., Easter, R. C., Feichter, J.,
Ghan, S. J., Hauglustaine, D., Iversen, T., Kinne, S., Kirkevåg, A.,
Lamarque, J.-F., Lin, G., Liu, X., Lund, M. T., Luo, G., Ma, X., van Noije,
T., Penner, J. E., Rasch, P. J., Ruiz, A., Seland, Ø., Skeie, R. B.,
Stier, P., Takemura, T., Tsigaridis, K., Wang, P., Wang, Z., Xu, L., Yu, H.,
Yu, F., Yoon, J.-H., Zhang, K., Zhang, H., and Zhou, C.: Radiative forcing of
the direct aerosol effect from AeroCom Phase II simulations, Atmos. Chem.
Phys., 13, 1853–1877, 10.5194/acp-13-1853-2013, 2013.Nakayama, T., Matsumi, Y., Sato, K., Imamura, T., Yamazaki, A., and Uchiyama,
A.: Laboratory studies on optical properties of secondary organic aerosols
generated during the photooxidation of toluene and the ozonolysis of
α-pinene, J. Geophys. Res., 115, D24204, 10.1029/2010JD014387,
2010.Nakayama, T., Sato, K., Matsumi, Y., Imamura, T., Yamazaki, A., and Uchiyama,
A.: Wavelength and NOx dependent complex refractive index of SOAs
generated from the photooxidation of toluene, Atmos. Chem. Phys., 13,
531–545, 10.5194/acp-13-531-2013, 2013.Nguyen, T. B., Lee, P. B., Updyke, K. M., Bones, D. L., Laskin, J., Laskin,
A., and Nizkorodov, S. A.: Formation of nitrogen-and sulfur-containing
light-absorbing compounds accelerated by evaporation of water from secondary
organic aerosols, J. Geophys. Res., 117, D01207, 10.1029/2011JD016944,
2012.Park, R. J., Jacob, D. J., Chin, M., and Martin, R. V.: Sources of
carbonaceous aerosols over the United States and implications for natural
visibility, J. Geophys. Res., 108, 4355, 10.1029/2002JD003190, 2003.
Park, R. J., Jacob, D. J., Kumar, N., and Yantosca, R. M.: Regional
visibility statistics in the United States: Natural and transboundary
pollution influences, and implications for the Regional Haze Rule, Atmos.
Environ., 40, 5405–5423, 2006.
Park, R. J., Kim, M. J., Jeong, J. I., Youn, D., and Kim, S.: A contribution
of brown carbon aerosol to the aerosol light absorption and its radiative
forcing in East Asia, Atmos. Environ., 44, 1414–1421, 2010.Reid, J. S., Koppmann, R., Eck, T. F., and Eleuterio, D. P.: A review of
biomass burning emissions part II: intensive physical properties of biomass
burning particles, Atmos. Chem. Phys., 5, 799–825,
10.5194/acp-5-799-2005, 2005.
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J.,
Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., and Kim, G.-K.:
MERRA: NASA's modern-era retrospective analysis for research and
applications, J. Climate, 24, 3624–3648, 2011.
Saleh, R., Robinson, E. S., Tkacik, D. S., Ahern, A. T., Liu, S., Aiken, A.
C., Sullivan, R. C., Presto, A. A., Dubey, M. K., and Yokelson, R. J.:
Brownness of organics in aerosols from biomass burning linked to their black
carbon content, Nat. Geosci., 7, 647–650, 2014.
Sareen, N., Moussa, S. G., and McNeill, V. F.: Photochemical Aging of
Light-Absorbing Secondary Organic Aerosol Material, J. Phys. Chem. A, 117,
2987–2996, 2013.Schnaiter, M., Gimmler, M., Llamas, I., Linke, C., Jäger, C., and Mutschke,
H.: Strong spectral dependence of light absorption by organic carbon
particles formed by propane combustion, Atmos. Chem. Phys., 6, 2981–2990,
10.5194/acp-6-2981-2006, 2006.
Srinivas, B. and Sarin, M.: Brown carbon in atmospheric outflow from the
Indo-Gangetic Plain: Mass absorption efficiency and temporal variability,
Atmos. Environ., 89, 835–843, 2014.Turpin, B. J. and Lim, H. J.: Species contributions to PM2.5 mass
concentrations: Revisiting common assumptions for estimating organic mass,
Aerosol Sci. Tech., 35, 602–610, 2001.
Updyke, K. M., Nguyen, T. B., and Nizkorodov, S. A.: Formation of Brown
Carbon via Reactions of Ammonia with Secondary Organic Aerosols from Biogenic
and Anthropogenic Precursors, Atmos. Environ., 63, 22–31, 2012.Wang, X., Heald, C. L., Ridley, D. A., Schwarz, J. P., Spackman, J. R.,
Perring, A. E., Coe, H., Liu, D., and Clarke, A. D.: Exploiting simultaneous
observational constraints on mass and absorption to estimate the global
direct radiative forcing of black carbon and brown carbon, Atmos. Chem.
Phys., 14, 10989–11010, 10.5194/acp-14-10989-2014, 2014.
Ward, D., Susott, R., Kauffman, J., Babbitt, R., Cummings, D., Dias, B.,
Holben, B., Kaufman, Y., Rasmussen, R., and Setzer, A.: Smoke and Fire
Characteristics for Cerrado and Deforestation Burns in Brazil: BASE-B
Experiment, Journal of Geophysical Research, 97, 14601-14619, 1992.Ward, D. E. and Hao, W.: Projections of Emissions from Burning of Biomass
Foruse in Studies of Global Climate and Atmospheric Chemistry, Air and Waste
Management Association, Vancouver, British Colombia, Canada, 1991.
Weber, R. J., Sullivan, A. P., Peltier, R. E., Russell, A., Yan, B., Zheng,
M., de Gouw, J., Warneke, C., Brock, C., and Holloway, J. S.: A study of
secondary organic aerosol formation in the anthropogenic-influenced
southeastern United States, J. Geophys. Res., 112, D13302,
10.1029/2007JD008408, 2007.Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J.
A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a
high resolution global model to estimate the emissions from open burning,
Geosci. Model Dev., 4, 625–641, 10.5194/gmd-4-625-2011, 2011.Yang, M., Howell, S. G., Zhuang, J., and Huebert, B. J.: Attribution of
aerosol light absorption to black carbon, brown carbon, and dust in China –
interpretations of atmospheric measurements during EAST-AIRE, Atmos. Chem.
Phys., 9, 2035–2050, 10.5194/acp-9-2035-2009, 2009.Yu, L., Smith, J., Laskin, A., Anastasio, C., Laskin, J., and Zhang, Q.:
Chemical characterization of SOA formed from aqueous-phase reactions of
phenols with the triplet excited state of carbonyl and hydroxyl radical,
Atmos. Chem. Phys., 14, 13801–13816, 10.5194/acp-14-13801-2014, 2014.Zhang, X., Lin, Y. H., Surratt, J. D., Zotter, P., Prévôt, A. S. H.,
and Weber, R. J.: Light-absorbing soluble organic aerosol in Los Angeles and
Atlanta: A contrast in secondary organic aerosol, Geophys. Res. Lett., 38,
L21810, 10.1029/2011GL049385, 2011.Zhang, X., Lin, Y.-H., Surratt, J. D., and Weber, R. J.: Sources, Composition
and Absorption Ångström Exponent of Light-absorbing Organic
Components in Aerosol Extracts from the Los Angeles Basin, Environ. Sci.
Technol., 47, 3685–3693, 10.1021/es305047b, 2013.
Zhong, M. and Jang, M.: Light absorption coefficient measurement of SOA using
a UV-Visible spectrometer connected with an integrating sphere, Atmos.
Environ., 45, 4263–4271, 2011.
Zhong, M., Jang, M., Oliferenko, A., Pillai, G. G., and Katritzky, A. R.: The
SOA Formation Model Combined with Semiempirical Quantum Chemistry to Predict
UV-Vis Absorption of Secondary Organic Aerosols, Phys. Chem. Chem. Phys., 14,
9058–9066, 2012.