- About
- Editorial board
- Articles
- Special issues
- Highlight articles
- Manuscript tracking
- Subscribe to alerts
- Peer review
- For authors
- For reviewers
- EGU publications

Journal cover
Journal topic
**Atmospheric Chemistry and Physics**
An interactive open-access journal of the European Geosciences Union

Journal topic

- About
- Editorial board
- Articles
- Special issues
- Highlight articles
- Manuscript tracking
- Subscribe to alerts
- Peer review
- For authors
- For reviewers
- EGU publications

**Research article**
14 Dec 2018

**Research article** | 14 Dec 2018

Production of particulate brown carbon during atmospheric aging of residential wood-burning emissions

^{1}Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen, Switzerland^{2}Department of Physics & INFN, University of Genoa, via Dodecaneso 33, 16146, Genova, Italy^{3}Aerosol d.o.o, Kamniška 41, 1000 Ljubljana, Slovenia^{4}Condensed Matter Physics, Jožef Stefan Institute, 1000 Ljubljana, Slovenia^{a}now at: Metrology Research Centre, National Research Council Canada, Ottawa, Canada

^{1}Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen, Switzerland^{2}Department of Physics & INFN, University of Genoa, via Dodecaneso 33, 16146, Genova, Italy^{3}Aerosol d.o.o, Kamniška 41, 1000 Ljubljana, Slovenia^{4}Condensed Matter Physics, Jožef Stefan Institute, 1000 Ljubljana, Slovenia^{a}now at: Metrology Research Centre, National Research Council Canada, Ottawa, Canada

**Correspondence**: Imad El-Haddad (imad.el-haddad@psi.ch) and André S. H. Prévôt
(andre.prevot@psi.ch)

**Correspondence**: Imad El-Haddad (imad.el-haddad@psi.ch) and André S. H. Prévôt
(andre.prevot@psi.ch)

Abstract

Back to toptop
We investigate the optical properties of light-absorbing organic carbon
(brown carbon) from domestic wood combustion as a function of simulated
atmospheric aging. At shorter wavelengths (370–470 nm), light absorption by
brown carbon from primary organic aerosol (POA) and secondary organic
aerosol (SOA) formed during aging was around 10 % and 20 %,
respectively, of the total aerosol absorption (brown carbon plus black
carbon). The mass absorption cross section (MAC) determined for black carbon
(BC, 13.7 m^{2} g^{−1} at 370 nm, with geometric standard deviation GSD =1.1) was consistent with that
recommended by Bond et al. (2006). The corresponding MAC of POA
(5.5 m^{2} g^{−1}; GSD =1.2) was higher than that of SOA
(2.4 m^{2} g^{−1}; GSD =1.3) at 370 nm. However, SOA presents a
substantial mass fraction, with a measured average SOA ∕ POA mass ratio
after aging of ∼5 and therefore contributes significantly to the
overall light absorption, highlighting the importance of wood-combustion SOA
as a source of atmospheric brown carbon. The wavelength dependence of POA and
SOA light absorption between 370 and 660 nm is well described with
absorption Ångström exponents of 4.6 and 5.6, respectively.
UV-visible absorbance measurements of water and methanol-extracted OA were
also performed, showing that the majority of the light-absorbing OA is water
insoluble even after aging.

How to cite

Back to top
top
How to cite.

Kumar, N. K., Corbin, J. C., Bruns, E. A., Massabó, D., Slowik, J. G., Drinovec, L., Močnik, G., Prati, P., Vlachou, A., Baltensperger, U., Gysel, M., El-Haddad, I., and Prévôt, A. S. H.: Production of particulate brown carbon during atmospheric aging of residential wood-burning emissions, Atmos. Chem. Phys., 18, 17843–17861, https://doi.org/10.5194/acp-18-17843-2018, 2018.

1 Introduction

Back to toptop
Atmospheric aerosols contribute to radiative forcing either directly by absorbing and scattering light or indirectly by acting as cloud condensation and ice nuclei. While black carbon (BC) from combustion processes is the most efficient light-absorbing aerosol component, organic aerosol (OA) may also absorb solar radiation (Alexander et al., 2008; Chen and Bond, 2010; Kirchstetter et al., 2004). This light-absorbing OA, denoted as brown carbon (BrC), absorbs most strongly at shorter UV-visible wavelengths (Hoffer et al., 2006; Andreae and Gelencsér, 2006). Global chemical-transport model estimates indicate that the BrC contribution to the positive radiative forcing of climate by anthropogenic aerosols may not be negligible (Feng et al., 2013; Lin et al., 2014; Wang et al., 2014; Jo et al., 2016).

Unlike BC, whose light absorption properties are relatively constant across sources (Bond et al., 2013), BrC is composed of a wide range of largely unknown compounds, which exhibit highly variable spectral dependence and absorption efficiencies. For example, reported imaginary indices of refraction for different organic species, which describe the absorption of these compounds, span 2 orders of magnitude (Lu et al., 2015). Because it is impractical to experimentally separate BrC from nonabsorbing OA, optical properties are typically determined for the bulk OA of a given source. The large variability of BrC fraction in combustion aerosol may contribute to the wide variation in reported properties of BrC containing OA.

Biomass burning OA, which contributes two-thirds of the global budget of directly emitted primary OA (POA), is expected to be a considerable source of BrC (Chakrabarty et al., 2010; Hecobian et al., 2010; Lack and Langridge, 2013; Liu et al., 2014). The variability in reported light absorption properties of biomass burning OA with fuel type and burn conditions remains a major obstacle complicating its treatment in climate models (Saleh et al., 2013; Lu et al., 2015). Residential biomass burning is typically characterized by a more efficient combustion, than open burning. Residential wood burning represents a substantial contribution to anthropogenic combustion emissions (Bond et al., 2013), especially in urban atmospheres, and is considered the largest source of OA in Europe during winter (Denier Van Der Gon et al., 2015).

Upon photo-oxidation, biomass-burning emissions produce secondary organic aerosol (SOA) at concentrations similar to or exceeding the POA (Grieshop et al., 2009; Bruns et al., 2015, 2016; Corbin et al., 2015a; Bertrand et al., 2017). There is a growing body of evidence that light absorption by OA changes with OH exposure (aging) owing to the production of secondary BrC or to the transformation of primary BrC (Heringa et al., 2011; Lee et al., 2014; Forrister et al., 2015; Zhao et al., 2015). However, these effects have not yet been systematically investigated and must be quantified to assess the climate effects of primary and aged biomass burning OA.

Here, we show that both POA and SOA from residential biomass burning emissions aged in controlled smog chamber experiments contain BrC. Wavelength-dependent, mass-normalized absorption cross sections (MACs) of POA and SOA are presented from online aerosol measurements as a function of aging for the first time. Complementary measurements of filter-extract absorbance (conducted in different solvents) are used to obtain the imaginary refractive index and to investigate the solubility of BrC in fresh and aged OA. While results presented here are related to flaming residential wood combustion emissions and cannot therefore be generalized, the approach used can be extrapolated for the characterization and quantification of the contribution of BrC in other primary and aged emissions.

2 Methods

Back to toptop
Laboratory measurements were conducted in an 8 m^{3} Teflon smog chamber
(Platt et al., 2013; Bruns et al., 2015) installed within a
temperature-controlled housing. Conditions in the chamber were maintained to
represent winter time in Europe, i.e., relative humidity ranging between 50 % and 90 %, at 263 K (Bruns et al., 2015, 2016). Beech wood was combusted in
a residential wood stove. Primary emissions were sampled through heated
lines at 413 K, diluted by a factor of ∼14 using an ejector
diluter (DI-1000, Dekati Ltd.), then sampled into the chamber, which
provided an additional 10-fold dilution. The overall dilution was a factor
of 100 to 200. As we aimed to sample only flaming-phase emissions into the
chamber, samples were taken when the modified combustion efficiency (ratio
of CO_{2} to the sum of CO and CO_{2}) was > 0.90. Despite
maintaining the same combustion conditions, the resulting organic fraction
to the total carbonaceous aerosols in the different samples was highly
variable, indicating that these samples are representative of a mixture of
preignition and flaming emissions (with varying contributions of each
combustion stage). Finally, the resulting NO_{x} ∕ NMOG ratios, which
dramatically influence SOA formation through influencing the fate of peroxy
radicals, RO_{2}, were estimated to be between 0.035 and 0.35 ppm (ppm C)^{−1} (Bruns et al., 2016). These conditions can be considered as high
NO_{x} representative of urban or sub-urban conditions, where most of the
RO_{2} radicals react with NO, rather than RO_{2} ∕ HO_{2}.

After injection of the primary emissions and stabilization of the
concentrations, nitrous acid (HONO) was continuously added, which
dissociates upon irradiation (*λ* < 400 nm) and forms the
hydroxyl radical (OH). Then, a 9-times deuterated butanol sample (butanol- D9,
98 %, Cambridge Isotope Laboratories) was subsequently injected into the
chamber. The decay of butanol-D9 was used to infer the time-resolved OH
exposure of the sampled aerosol (Barmet et al., 2012).
The chamber was exposed to UV lights for ∼3.5 h.

Particles were collected onto filters (47 mm Tissuquartz, Pall
Corporation, 26 L min^{−1} for 30–32 min) for offline optical measurements
and the determination of elemental carbon (EC) mass. Three filters were
collected during each experiment, namely (i) a primary aerosol filter sample
(“primary”); (ii) a slightly aged aerosol (“Aged1”, OH exposure
$\sim \mathrm{1}\times {\mathrm{10}}^{\mathrm{7}}$ molecules cm^{−3} h), collected 30 min
after the UV lights were switched on; and (iii) an aged aerosol (“Aged2”,
OH exposure $\sim \mathrm{4}\times {\mathrm{10}}^{\mathrm{7}}$ molecules cm^{−3} h), collected at
the end of the experiment (see Fig. S1 in the Supplement for the sampling periods). A charcoal
denuder was installed upstream of the filter sampler to remove organic
gases. Filters were stored at 253 K until analysis.

In addition to the characterization of the particle optical properties
detailed in the next section, a set of online and offline techniques were
used for the characterization of the gaseous and particulate emissions before
and after aging. The nonrefractory particle size-segregated chemical
composition was measured with a high-resolution (HR) time-of-flight aerosol mass
spectrometer (AMS) (DeCarlo et al., 2006). Uncertainties related to particle
collection efficiency in the AMS are considered negligible for the relatively
large particles sampled here, which in terms of volume are within the size
range transmitted efficiently by the AMS aerodynamic lens (Liu et al., 2007).
The collection efficiency of wood-combustion OA is expected to be unity
(Corbin et al., 2015b). Details related to the AMS data analysis and
calibration can be found elsewhere (Bruns et al., 2015, 2016). A scanning
mobility particle sizer was used to measure the size distribution of the
evolving aerosol. Organic gases were monitored by a proton transfer reaction
time-of-flight mass spectrometer (PTR-MS, [H_{3}O^{+}] reagent ion,
Ionicon Analytik GmbH) (Bruns et al., 2017), following the same procedure as
in Klein et al. (2016). Additionally, elemental carbon (EC) mass
concentration was measured offline using a sunset thermo-optical analyzer,
following the EUSAAR2 protocol (Cavalli et al., 2010).

A dual-spot Aethalometer (Magee
Scientific Aethalometer AE33, Aerosol d.o.o.) was used for real-time aerosol
light attenuation measurements at seven wavelengths (*λ*=370, 470,
520, 590, 660, 880 and 950 nm) (Drinovec et al., 2015). The instrument
measures the attenuation coefficient (*b*_{ATN}) of a light beam
transmitted through a filter tape loaded with aerosol samples. The use of the
sampling flow (here, 2 L min^{−1}), integration time for the measurement
(here, 1 min) and automated dual-spot loading compensation to obtain
*b*_{ATN} has been described by Drinovec et al. (2015).

The loading-compensated *b*_{ATN} was used to infer the aerosol absorption
coefficient, *b*_{abs}, using a constant wavelength-independent correction
factor *C*, which accounts for multiple scattering within the filter matrix
(Weingartner et al., 2003):

$$\begin{array}{}\text{(1)}& {b}_{\mathrm{abs}}\left(\mathit{\lambda}\right)={b}_{\mathrm{ATN}}\left(\mathit{\lambda}\right)/C.\end{array}$$

As discussed in detail by Corbin et al. (2018), the wavelength dependence of
*C* can be expected to be negligible. The loading-compensated *b*_{ATN} at
880 nm from the AE33 is further used to infer the equivalent-BC mass
concentration, *M*_{eBC}:

$$\begin{array}{}\text{(2)}& {M}_{\mathrm{eBC}}={\displaystyle \frac{{b}_{\mathrm{ATN}}\left(\mathrm{880}\phantom{\rule{0.125em}{0ex}}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right)}{{\mathit{\sigma}}_{\mathrm{ATN}}\left(\mathrm{880}\phantom{\rule{0.125em}{0ex}}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right)}},\end{array}$$

where *σ*_{ATN} is the mass attenuation cross section of BC
deposited on the filter of the AE33. *M*_{eBC} inferred from
Eq. (2) only equals the true BC mass concentration,
*M*_{BC}, if the applied *σ*_{ATN} is identical to the
true attenuation cross section of BC, *σ*_{ATN,BC}, and if
light attenuation at 880 nm is exclusively due to BC.
*σ*_{ATN,BC}(880 nm) can be inferred from the true MAC of BC,
MAC_{BC} and the true *C* value,

$$\begin{array}{}\text{(3)}& {\mathit{\sigma}}_{\mathrm{ATN},\mathrm{BC}}\left(\mathrm{880}\phantom{\rule{0.125em}{0ex}}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right)=\phantom{\rule{0.125em}{0ex}}{\mathrm{MAC}}_{\mathrm{BC}}\left(\mathrm{880}\phantom{\rule{0.125em}{0ex}}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right)\cdot C\end{array}$$

with MAC_{BC} being defined as follows:

$$\begin{array}{}\text{(4)}& {\mathrm{MAC}}_{\mathrm{BC}}\left(\mathit{\lambda}\right)={\displaystyle \frac{{b}_{\mathrm{abs},\mathrm{BC}}\left(\mathit{\lambda}\right)}{{M}_{\mathrm{BC}}}},\end{array}$$

where *b*_{abs,BC} is the absorption coefficient due to BC.

The manufacturer default values are 1.57 for *C* (TFE-coated glass fiber
filters) and 12.2 m^{2} g^{−1} for *σ*_{ATN} at 880 nm, which corresponds to a MAC_{BC}(880 nm) of 7.77 m^{2} g^{−1}
(Gundel et al., 1984; Drinovec et al., 2015). However,
these three parameters depend on aerosol properties. Here, we have
determined the *C* value by applying Eq. (1) to *b*_{ATN} measured by the
Aethalometer and the absorption coefficient, ${b}_{\mathrm{abs}{}_{\mathrm{MWAA}}}$, measured by a multiwavelength
absorbance analyzer (MWAA; Massabò et al., 2013, 2015). The MAC_{BC}(880 nm)
was determined using Eq. (4) to compare ${b}_{\mathrm{abs}{}_{\mathrm{MWAA}}}$
from the MWAA measurements with EC mass from the Sunset thermo-optical
analyzer (see Fig. 1a, b and Sect. 4.1 for detailed discussion).
Following this procedure, the MWAA and Sunset analyzer will be defined as
reference methods for absorption coefficient and EC mass concentration,
respectively. Note that data from these reference methods were only
available with low time resolution and for a subset of all samples. Thus,
the Aethalometer anchored against these reference methods was used to
obtain the wavelength-dependent absorption coefficients and the eBC mass
concentrations with high time resolution using Eqs. (1) and (2),
respectively. Processing the loading-compensated AE33 attenuation
coefficients with *C* value and MAC_{BC}, determined with independent MWAA
and Sunset analyzer measurements, ensures that the inferred
*b*_{abs}(λ) (Eq. 1) and *M*_{eBC} (Eq. 2)
have minimal bias compared to respective true values.

The MWAA (Massabò et al., 2013, 2015) was used as reference method for
the aerosol absorption coefficient. It measures the absorption coefficient
${b}_{\mathrm{abs}{}_{\mathrm{MWAA}}}\left(\mathit{\lambda}\right)$ of particles deposited on
standard filter samples. It is composed of five light sources (laser diodes with *λ*=375, 407, 532, 635 and 850 nm) placed above the filter, an automated sample changer, and three low-noise UV-enhanced
photodiodes. The first
photodiode is placed behind the filter for transmittance measurements
(0^{∘} relative to the incident light, 1.5 cm from the sample), while
the other two photodiodes are positioned at 125 and 165 ^{∘} (11 cm
from the sample) to collect the back-scattered light. These transmittance and
reflectance measurements are used together with a radiative transfer model
(Hänel et al., 1987), which takes into account multiple scattering within
the particle and/or filter layer, to
retrieve both the total optical thickness and the particle-filter-layer
single scattering albedo, providing the absorption coefficient
${b}_{\mathrm{abs}{}_{\mathrm{MWAA}}}\left(\mathit{\lambda}\right)$ values. These calculations
largely follow the approach implemented in the multi-angle absorption
photometer (MAAP, Petzold and Schönlinner, 2004).

Filter samples were extracted for UV-visible absorbance measurements in
10 mL ultrapure water or methanol in an ultrasonic bath for 20 min at
30 ^{∘}C. Samples were subsequently briefly vortexed (1 min) and
filtered with 0.45 µm nylon membrane syringe filters following the
procedure described in Daellenbach et al. (2016). Absorption spectra were
measured from 280 to 500 nm using a UV-visible spectrophotometer (Ocean
Optics) coupled to a 50 cm long-path detection cell (Krapf et al., 2016).
Light attenuation by the OA in solution, ATN_{OA−sol}, at a given
wavelength was recorded as the logarithm of the ratio of signal intensities
of the reference (solvent) (*I*_{0}) and the sample (*I*), both corrected for
background signals with the light source off. From ATN_{OA−sol}, the
absorption coefficient of OA in solution,
${b}_{\mathrm{abs},\mathrm{OA}-\mathrm{sol}}\left(\mathit{\lambda}\right)$, can be quantified as
follows:

$$\begin{array}{}\text{(5)}& {b}_{\mathrm{abs},\mathrm{OA}-\mathrm{sol}}\left(\mathit{\lambda}\right)={\displaystyle \frac{{\mathrm{ATN}}_{\mathrm{OA}-\mathrm{sol}}\left(\mathit{\lambda}\right)}{l}},\end{array}$$

where *l* is the optical path length.

The absorbance measurements are aimed at inferring the imaginary part of the refractive index. For this, ${b}_{\mathrm{abs},\mathrm{OA}-\mathrm{sol}}\left(\mathit{\lambda}\right)$ is transformed to the absorption coefficient of the bulk OA in the pure form, ${b}_{\mathrm{abs},\mathrm{OA}-\mathrm{bulk}}$ (Sun et al., 2007):

$$\begin{array}{}\text{(6)}& {b}_{\mathrm{abs},\mathrm{OA}-\mathrm{bulk}}\left(\mathit{\lambda}\right)={\displaystyle \frac{{b}_{\mathrm{abs},\mathrm{OA}-\mathrm{sol}}\left(\mathit{\lambda}\right){\mathit{\rho}}_{\mathrm{OA}}}{\frac{{m}_{\mathrm{OA}}}{{V}_{\mathrm{solvent}}}}},\end{array}$$

where *ρ*_{OA} is the bulk density of OA (assumed to be
1.5 g cm^{−3}, typical of wood-burning OA; Sun et al., 2007;
Moosmüller et al., 2009; Corbin et al., 2015a), *m*_{OA} is the
extracted OA mass, and *V*_{solvent} is the solvent volume. The bulk
absorption coefficient directly leads to the imaginary part of the OA
refractive index, *k*_{OA}, in pure form (Moosmüller et al.,
2009):

$$\begin{array}{}\text{(7)}& {k}_{\mathrm{OA}}\left(\mathit{\lambda}\right)={b}_{\mathrm{abs},\mathrm{OA}-\mathrm{bulk}}\left(\mathit{\lambda}\right){\displaystyle \frac{\mathit{\lambda}}{\mathrm{4}\mathit{\pi}}}.\end{array}$$

Inserting Eq. (6) into Eq. (7) eventually provides the following (P. F. Liu et al., 2015):

$$\begin{array}{}\text{(8)}& {k}_{\mathrm{OA}}\left(\mathit{\lambda}\right)={\displaystyle \frac{\mathit{\lambda}{\mathit{\rho}}_{\mathrm{OA}}{V}_{\mathrm{solvent}}}{\mathrm{4}\mathit{\pi}{m}_{\mathrm{OA}}}}{b}_{\mathrm{abs},\mathrm{OA}-\mathrm{sol}}\left(\mathit{\lambda}\right).\end{array}$$

The mass of organics dissolved in the solution could not be quantified.
Therefore, we use an upper limit value for *m*_{OA}, approximated
as the integral of AMS-measured OA mass concentration times sample flow rate
over the filter-sampling period. Accordingly, the resulting
*k*_{OA} values represent lower limits for the true values, as the
OA extraction efficiency was not accounted for. If the OA extraction
efficiency was less than unity, then the absorption (or MAC) predicted from
our solvent-extraction measurements would be less than that measured (or
calculated) using our real-time measurements (MWAA-calibrated Aethalometer).

It is important to draw a clear distinction between uncertainties related to measurement precision and accuracy and those related with experimental variability. In this section we discuss the quantifiable and unquantifiable uncertainties related with the different measurements. In the result section, we will present our confidence levels on the average parameters determined based on the experimental variability, which we judge to be the main source of variance in the data.

The estimated uncertainty in the AMS-derived OA mass concentrations is ∼25 %, which includes both potential biases and precision. This estimate
is based on the variation in the AMS calibration factors and estimated
uncertainties in the SMPS (scanning mobility particle sizer) used for the AMS
calibration (Bruns et al., 2015, 2016). Uncertainties related to particle
transmission efficiency in the AMS are considered negligible for the
particles sampled here (Liu et al., 2007), whose volume size distribution
falls within the range transmitted efficiently by the AMS aerodynamic lens
(see Fig. S4). The bounce-related collection efficiency (CE) of the AMS was
concluded to be unity for wood-burning OA in the literature reviewed by
Corbin et al. (2015b; in their Sect. S1.2). For the present data, the
comparison between the SMPS mass (predicted from fitted volume distributions
using a density of 1.5 g cm^{−3}) and the total PM predicted as
AMS-OA+eBC suggests a CE value between 0.7 and 1.0 (19 % relative
uncertainty), consistent with average literature values and the uncertainty
estimates. The uncertainty in EC mass concentration, estimated from
measurement repeats based on the EUSAAR2 protocol only, is within 7 % in
our case. The precision uncertainty in the Aethalometer attenuation
measurements was estimated as 15 Mm^{−1} based on the standard deviation
of its signals prior to aerosol being injected into the smog chamber. The
MWAA data have an estimated noise level and precision of 12 Mm^{−1} and
10 % respectively, and these uncertainties have been added in quadrature
to provide the overall uncertainties shown, for example, as error bars in
Fig. 1 below. To compare the MWAA and Aethalometer measurements, we
determined ${b}_{\mathrm{abs},\phantom{\rule{0.125em}{0ex}}\mathrm{MWAA},\phantom{\rule{0.125em}{0ex}}\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}}$ by extrapolating the absorption
coefficients measured at 850 to 880 nm using an *α* value determined
from the ratio between the absorption coefficients at 850 and 635nm. The
uncertainty associated with this extrapolation is considered negligible
relative to the overall MWAA uncertainty.

There are significant uncertainties in the measurement of aerosol absorption
using filter-based techniques (e.g., Collaud Coen et al., 2010). Here, we
have used MWAA measurements as a reference to scale the Aethalometer data,
using a single *C* value. The correction factor *C*, which accounts for
scattering effects within the filter matrix (Drinovec et al., 2015), may
depend on the aerosol sample (Collaud Coen et al., 2010). In this study, we
evaluated the variability in this factor for our primary and aged samples, by
directly comparing the Aethalometer to MWAA measurements, as discussed below.
The MWAA has been previously validated against a polar nephelometer and a
MAAP (Massabo et al., 2013), which, in turn, has been validated against
numerous in situ methods (e.g., Slowik et al., 2007). The excellent
correlation between MWAA and EC in our study (discussed below) supports the
high confidence in the MWAA filter-based absorption measurements conducted
here. Another significant source of uncertainty in filter-based absorption
measurements is the possible sorption (or evaporation) of volatile organics
on (or from) the filter material. This may lead to an overestimation (or
underestimation) of OA absorption. However, we have minimized sorption
artefacts by utilizing a charcoal denuder. We have obtained an excellent
correlation between OA absorption measurements derived from the
MWAA-calibrated Aethalometer and from quartz filter samples (see discussion
below, Fig. 6 in the main text and Fig. S13 in the Supplement). Although both
of these techniques involved filter sampling, their sampling timescale is an
order of magnitude different, and a difference is therefore expected if
sorption (or evaporation) caused a substantial bias in our results. We
therefore conclude that it is unlikely that artifacts associated with filter
sampling have biased the absorption measurements. Finally, uncertainties
related to pyrolysis during thermo-optical analysis may bias EC measurements.
Such uncertainties arise from unstable organic compounds, and can be
significant for biomass-burning samples, leading to biases on the order of
20 % for EC (e.g., Schauer et al., 2003; Yang and Yu, 2002). To minimize
these biases we applied the EUSAAR2 protocol. The optical properties of such
organics are generally different from BC; therefore, the excellent
correlation between MWAA and EC data in Fig. 1a suggests that pyrolysis
effects were not a major source of uncertainty in our data set.

3 Optical properties analysis

Back to toptop
In this section we describe the methodology adapted for the determination of the MACs for the different aerosol material from the Sunset, MWAA and Aethalometer measurements. The assumptions and limitations underlying these calculations are clearly stated. We also explain the relationship between the MACs and the wavelength dependence of the overall absorption.

The wavelength dependence of the overall absorption due to both BC and BrC has often been described assuming a power law:

$$\begin{array}{}\text{(9)}& {b}_{\mathrm{abs}}\left(\mathit{\lambda}\right)\propto {\mathit{\lambda}}^{-\mathit{\alpha}},\end{array}$$

where *α* is the Ångström absorption exponent, often
determined by fitting the absorption coefficient measurements across the
entire wavelength range. Equation. (9) is an empirical simplification, which
breaks down when different components with different spectral dependences
contribute to the absorption, e.g., a mix of BrC and black carbon (e.g.,
Moosmüller et al., 2011). In practice,
different values of *α* would be obtained for different choices of
*λ* ranges, and therefore we alternatively calculated two-wavelength
absorption exponents according to

$$\begin{array}{}\text{(10)}& \mathit{\alpha}(\mathit{\lambda},{\mathit{\lambda}}_{\mathrm{ref}})=-{\displaystyle \frac{\mathrm{ln}\left(\frac{{b}_{\mathrm{abs}}\left(\mathit{\lambda}\right)}{{b}_{\mathrm{abs}}\left({\mathit{\lambda}}_{\mathrm{ref}}\right)}\right)}{\mathrm{ln}\left(\frac{\mathit{\lambda}}{{\mathit{\lambda}}_{\mathrm{ref}}}\right)}},\end{array}$$

where *λ* is a wavelength of interest (in nm) and *λ*_{ref} is
the reference wavelength, here 880 nm. This reference wavelength was chosen
because BC is expected to fully dominate light absorption in this range
(Laskin et al., 2015).

Black carbon is known to have an *α* between 0.9 and 1.1 (Kirchstetter
et al., 2004; Bond et al., 2013; S. Liu et al., 2015), whereas BrC, which
preferentially absorbs at shorter wavelengths, has a higher *α* (Saleh
et al., 2013; Laskin et al., 2015). Thus, we interpret an increase in *α*(*λ*,*λ*_{ref}) of the total aerosol as being due to an
increased contribution of BrC to the total absorption. Values of *α*(*λ*,*λ*_{ref}) can potentially change due to other effects
such as a wavelength-dependent lensing effect on absorption by BC (e.g., Lack
and Langridge, 2013) or the restructuring of BC aggregates during aging. The
former effect was negligible under our conditions, as elaborated on below.
The latter, if it occurs during aging, would be attributed to SOA absorption
in our approach. However, this is not an issue if our values are accordingly
applied in, for example, model simulations, following the same assumption as in our approach. This means that
the potential restructuring effects must implicitly be considered within the
MAC(*λ*) of SOA, while the MAC(*λ*) of BC must be kept fixed.

In a mixture of *n* absorbing species, the total absorption at any wavelength
may be written as the sum of the absorbance of each of the species.
Accordingly, Eq. (10) can be expressed for a multicomponent system

$$\begin{array}{ll}\text{(11)}& {\displaystyle}& {\displaystyle}\mathit{\alpha}\left(\mathit{\lambda},{\mathit{\lambda}}_{\mathrm{ref}}\right)={\displaystyle \frac{\mathrm{1}}{\mathrm{ln}\left({\mathit{\lambda}}_{\mathrm{ref}}/\mathit{\lambda}\right)}}\mathrm{ln}\left({\displaystyle \frac{\sum _{i=\mathrm{1}}^{n}{b}_{\mathrm{abs},\mathrm{i}}\left(\mathit{\lambda}\right)}{\sum _{i=\mathrm{1}}^{n}{b}_{\mathrm{abs},\mathrm{i}}\left({\mathit{\lambda}}_{\mathrm{ref}}\right)}}\right){\displaystyle}& {\displaystyle}\phantom{\rule{1em}{0ex}}={\displaystyle \frac{\mathrm{1}}{\mathrm{ln}\left({\mathit{\lambda}}_{\mathrm{ref}}/\mathit{\lambda}\right)}}\mathrm{ln}\left({\displaystyle \frac{\sum _{i=\mathrm{1}}^{n}{M}_{i}{\mathrm{MAC}}_{\mathrm{i}}\left(\mathit{\lambda}\right)}{\sum _{i=\mathrm{1}}^{n}{M}_{i}{\mathrm{MAC}}_{\mathrm{i}}\left({\mathit{\lambda}}_{\mathrm{ref}}\right)}}\right),\end{array}$$

where the right-hand side follows the general definition of MAC along the
lines of Eq. (4). *M*_{i} and MAC_{i} are the mass concentration and MAC,
respectively, of the *i*th species, with *n* absorbing species in total. By
considering that the light absorption at *λ*_{ref}=880 nm is
exclusively due to BC, and by defining BC to be the *n*th species, Eq. (11) can be written as

$$\begin{array}{ll}\text{(12)}& {\displaystyle}& {\displaystyle}\mathit{\alpha}\left(\mathit{\lambda},\phantom{\rule{0.125em}{0ex}}\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right)=\phantom{\rule{0.125em}{0ex}}\phantom{\rule{0.125em}{0ex}}{\displaystyle \frac{\mathrm{1}}{\mathrm{ln}\left(\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}/\mathit{\lambda}\right)}}\mathrm{ln}\left({\displaystyle \frac{{\mathrm{MAC}}_{\mathrm{BC}}\left(\mathit{\lambda}\right)}{{\mathrm{MAC}}_{\mathrm{BC}}\left(\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right)}}\right.{\displaystyle}& {\displaystyle}\phantom{\rule{1em}{0ex}}\left.+\sum _{i=\mathrm{1}}^{n-\mathrm{1}}{\displaystyle \frac{{M}_{i}{\mathrm{MAC}}_{i}\left(\mathit{\lambda}\right)}{{b}_{\mathrm{abs}}\left(\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right)}}\right).\end{array}$$

In Eq. (12), the summation now only goes over the *n*−1 organic
species, which contribute to light absorption.

The fresh combustion aerosol exclusively contains BC and POA as absorbing
species. For the data at time *t*_{0} before the start of photo-oxidative
aging, Eq. (12) simplifies to

$$\begin{array}{ll}\text{(13)}& {\displaystyle}& {\displaystyle}\mathit{\alpha}\left({t}_{\mathrm{0}},\mathit{\lambda},\phantom{\rule{0.125em}{0ex}}\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right)={\mathit{\alpha}}_{\mathrm{BC}+\mathrm{POA}}\left({t}_{\mathrm{0}},\mathit{\lambda},\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right){\displaystyle}& {\displaystyle}\phantom{\rule{1em}{0ex}}={\displaystyle \frac{\mathrm{1}}{\mathrm{ln}\left(\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}/\mathit{\lambda}\right)}}\mathrm{ln}\left({\displaystyle \frac{{\mathrm{MAC}}_{\mathrm{BC}}({t}_{\mathrm{0}},\phantom{\rule{0.125em}{0ex}}\phantom{\rule{0.125em}{0ex}}\mathit{\lambda})}{{\mathrm{MAC}}_{\mathrm{BC}}({t}_{\mathrm{0}},\phantom{\rule{0.125em}{0ex}}\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm})}}\right.\\ {\displaystyle}& {\displaystyle}\phantom{\rule{1em}{0ex}}+\left.{\displaystyle \frac{{M}_{\mathrm{OA}}\left({t}_{\mathrm{0}}\right){\mathrm{MAC}}_{\mathrm{POA}}\left({t}_{\mathrm{0}},\mathit{\lambda}\right)}{{b}_{\mathrm{abs}}\left({t}_{\mathrm{0}},\phantom{\rule{0.125em}{0ex}}\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right)}}\right).\end{array}$$

In Eq. (13), *M*_{OA}(*t*_{0}) is the mass concentration of
primary organic aerosol measured by the AMS at *t*_{0}.
MAC_{BC}(*t*_{0}, 880 nm) was inferred from the MWAA and Sunset
thermo-optical analysis and shown to be independent of the experimental
conditions (Sect. 4.1; Fig. 1a). Absorption coefficients
*b*_{abs}(*t*_{0}*λ*) are obtained from the high time resolution
attenuation measurements by the Aethalometer referenced to the MWAA
absorption measurements as described above. *α*(*t*_{0}*λ*, 880 nm) is derived from *b*_{abs}(*t*_{0},*λ*) and *b*_{abs}(*t*_{0}, 880 nm) using Eq. (10). We have intentionally formulated
Eq. (13) to highlight that the retrieved MAC_{OA}(t,*λ*) depends mainly on the input *M*_{OA}. Correspondingly, the
retrieved MAC_{OA}(*t*,*λ*) is mainly sensitive to potential
AMS calibration biases. This leaves only two free parameters in
Eq. (13), MAC_{BC}(*t*_{0}, *λ*) and
MAC_{POA}(*t*_{0}, *λ*). These were determined by fitting
Eq. (13) to *α*(*t*_{0}*λ*, 880 nm),
*M*_{OA}(*t*_{0}), MAC_{BC}(*t*_{0}, 880 nm) and
*b*_{abs}(*t*_{0}, 880 nm) data
measured in all experiments for fresh emissions at *t*_{0}. This approach
contains the implicit assumption that the two MAC values are also independent
of experimental conditions, and therefore these MACs should be considered as
average values. The accuracy of these MAC values obviously depends on the
accuracy of the absorption and mass measurements. First, a systematic bias in
the *C* value potentially caused by a systematic bias in the MWAA
measurements propagates to an identical bias in both MAC_{BC}(*t*_{0},
*λ*) and MAC_{POA}(*t*_{0}, *λ*). Second, a systematic
bias in the Sunset EC mass measurements yields a corresponding inverse bias
in MAC_{BC}(*t*_{0}, *λ*), while MAC_{POA}(*t*_{0},
*λ*) remains unaffected. Third, a systematic bias in the AMS POA mass
yields a corresponding inverse bias in MAC_{POA}(*t*_{0}, *λ*),
while MAC_{BC}(*t*_{0}, *λ*) remains unaffected.
Eq. (13) shows that *α* of the primary aerosol at a certain
wavelength is largely driven by MAC_{POA}(*t*_{0},*λ*), i.e., the optical properties of POA, and by
the ratio $\frac{{M}_{\mathrm{OA}}\left({t}_{\mathrm{0}}\right)}{{b}_{\mathrm{abs}}\left({t}_{\mathrm{0}},\phantom{\rule{0.125em}{0ex}}\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right)}$,
which reflects the relative contributions of POA and BC to total primary
aerosol mass.

The MAC of SOA, MAC_{SOA}, can be generally defined
as follows:

$$\begin{array}{}\text{(14)}& {\mathrm{MAC}}_{\mathrm{SOA}}={\displaystyle \frac{{b}_{\mathrm{abs},\mathrm{SOA}}}{{M}_{\mathrm{SOA}}}},\end{array}$$

where *b*_{abs,SOA} and *M*_{SOA} are the absorption
coefficient and mass concentration of SOA, respectively. In the aged
aerosol, which contains the absorbing species BC, POA and SOA,
*b*_{abs,SOA} is the difference of the total absorption minus the
absorption by POA and BC:

$$\begin{array}{}\text{(15)}& {b}_{\mathrm{abs},\mathrm{SOA}}\left(t,\mathit{\lambda}\right)={b}_{\mathrm{abs}}\left(t,\mathit{\lambda}\right)-{b}_{\mathrm{abs},\mathrm{POA}+\mathrm{BC}}\left(t,\mathit{\lambda}\right).\end{array}$$

The absorption by POA and BC in the aged aerosol is a priori unknown, but can
be calculated under certain assumptions. The first assumption is that SOA
does not contribute to absorption at 880 nm: ${b}_{\mathrm{abs},\mathrm{POA}+\mathrm{BC}}\left(t,\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right)\equiv {b}_{\mathrm{abs}}\left(t,\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right)$. The second assumption is that the *α* values defined by
Eq. (12) for primary emissions do not change during aging, such that
${\mathit{\alpha}}_{\mathrm{POA}+\mathrm{BC}}\equiv {\mathit{\alpha}}_{\mathrm{POA}+\mathrm{BC}}$.
The latter approximation is based on the underlying assumptions that the MAC
of POA is not altered by aging and that the proportions of POA and BC mass
lost to the wall are identical. Under these assumptions
${b}_{\mathrm{abs},\mathrm{POA}+\mathrm{BC}}$ becomes

$$\begin{array}{ll}\text{(16)}& {\displaystyle}& {\displaystyle}{b}_{\mathrm{abs},\mathrm{POA}+\mathrm{BC}}\left(t,\mathit{\lambda}\right)={\displaystyle}& {\displaystyle}\phantom{\rule{1em}{0ex}}{b}_{\mathrm{abs}}\left(t,\phantom{\rule{0.125em}{0ex}}\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right){\left({\displaystyle \frac{\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}}{\mathit{\lambda}}}\right)}^{{\mathit{\alpha}}_{\mathrm{POA}+\mathrm{BC}}\left({t}_{\mathrm{0}},\mathit{\lambda},\phantom{\rule{0.125em}{0ex}}\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right)}.\end{array}$$

Note that inferring ${b}_{\mathrm{abs},\mathrm{POA}+\mathrm{BC}}\left(t,\mathit{\lambda}\right)$ from
*b*_{abs}(*t*, 880 nm) implicitly accounts for the
decrease in the BC and POA absorption due to wall losses.

*M*_{SOA} was obtained as total organic minus POA mass concentration:

$$\begin{array}{}\text{(17)}& {M}_{\mathrm{SOA}}\left(t\right)={M}_{\mathrm{OA}}\left(t\right)-{M}_{\mathrm{POA}}\left(t\right)\end{array}$$

The POA mass concentration in the aged aerosol can be inferred from the
initial OA mass concentration in the fresh emissions by accounting for the
wall losses using Eq. (S1) in the Supplement and the wall loss time constant
*τ* (see Sect. S1 in the Supplement):

$$\begin{array}{}\text{(18)}& {M}_{\mathrm{POA}}\left(t\right)={M}_{\mathrm{OA}}\left({t}_{\mathrm{0}}\right)\mathrm{exp}\left({\mathit{\tau}}^{-\mathrm{1}}t\right).\end{array}$$

Inserting Eqs. (15)–(18) into Eq. (14) provides the final equation for
inferring MAC_{SOA}.

$$\begin{array}{ll}\text{(19)}& {\displaystyle}& {\displaystyle}{\mathrm{MAC}}_{\mathrm{SOA}}\left(t,\mathit{\lambda}\right)={\displaystyle}& {\displaystyle \frac{{b}_{\mathrm{abs}}\left(t,\mathit{\lambda}\right)-{b}_{\mathrm{abs}}\left(t,\phantom{\rule{0.125em}{0ex}}\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right){\left(\frac{\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}}{\mathit{\lambda}}\right)}^{{\mathit{\alpha}}_{\mathrm{POA}+\mathrm{BC}}\left({t}_{\mathrm{0}},\mathit{\lambda},\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}\right)}}{{M}_{\mathrm{OA}}\left(t\right)-{M}_{\mathrm{OA}}\left({t}_{\mathrm{0}}\right)\mathrm{exp}\left({\mathit{\tau}}^{-\mathrm{1}}t\right)}}\end{array}$$

MAC_{SOA} can be calculated for every data point in time
and for all Aethalometer wavelengths from 370 to 660 nm
(MAC_{SOA} defined to be zero at *λ*≥ 880 nm), as all quantities on the right-hand side of Eq. (19) are available from
either the Aethalometer or AMS measurements or are otherwise known. It can
be seen from Eq. (19) that the mass concentrations used to calculate
MAC_{SOA} solely originate from AMS data, thus being
consistent with the calculation of MAC_{POA} (see
above). Eq. (19) is based on the assumption that POA is “chemically
inert”, i.e., no chemically induced changes of *M*_{POA} and
MAC_{POA} occur. Such chemically induced changes of
absorption coefficient by POA, through a change of *M*_{POA} or
MAC_{POA}, if they occur, are assigned to the absorption by SOA, thus
resulting in a corresponding adjustment of the inferred MAC_{SOA}.

The imaginary part of the refractive index of an aerosol component is an
intensive material property. However, the MAC of such an aerosol component
additionally depends on the size and morphology of the aerosol (except for
the Rayleigh regime). The online aerosol absorption measurements provide
estimates for MAC values, while the UV-visible absorbance measurements of
filter extracts provide the imaginary part of the refractive index. We used
Mie calculations in order to compare the two quantities. The
*k*_{OA}(λ) obtained from the filter extracts is
converted to a MAC_{OA,bulk} by assuming that all OA
is present in homogeneous spherical particles with a diameter distribution
identical to the mobility diameter distribution measured by the SMPS. In
this manner, MAC_{OA,bulk} becomes equal to the
mass-weighted average (= volume-weighted average) of the diameter-dependent
MAC:

$$\begin{array}{ll}\text{(20)}& {\displaystyle}& {\displaystyle}{\mathrm{MAC}}_{\mathrm{OA},\mathrm{bulk}}\left(\mathit{\lambda}{n}_{\mathrm{OA}}{k}_{\mathrm{OA}}\phantom{\rule{0.125em}{0ex}}{\mathit{\rho}}_{\mathrm{OA}}\right)={\displaystyle}& {\displaystyle}\phantom{\rule{1em}{0ex}}{\displaystyle \frac{{\sum}_{i}{N}_{i}{d}_{i}^{\mathrm{3}}{\mathrm{MAC}}_{i}^{\mathrm{Mie}}(\mathit{\lambda},{n}_{\mathrm{OA}},{k}_{\mathrm{OA}},{\mathit{\rho}}_{\mathrm{OA}}\phantom{\rule{0.125em}{0ex}})}{{\sum}_{i}{N}_{i}{d}_{i}^{\mathrm{3}}}}.\end{array}$$

Here, *N*_{i} and *d*_{i} are the number of particles and particle diameter,
respectively, in the *i*th size bin, and *n*_{OA} is the real
part of the refractive index of the OA (which is assumed to be
*n*_{OA} =1.5 typical for organic material; Lu et al., 2015).
The MAC of particles with diameter *d*_{i},
MAC${}_{i}^{\mathrm{Mie}}$, was calculated using the Mie code by
Peña and Pal (2009) (incorporated into Igor Pro 6.3, WaveMetrics, OR,
USA, by Taylor et al., 2015). MAC${}_{i}^{\mathrm{Mie}}$ also
depends on the density of OA, for which we assume a value of *ρ*_{OA}=1.5 g cm^{−3} (see
Sect. 2.2), as the volume-specific absorption cross section obtained from
Mie theory needs to be converted to a mass-specific absorption
cross section. We note that as we have used the same value of *ρ*_{OA} in the calculation of both
MAC${}_{i}^{\mathrm{Mie}}$ and *k*_{OA}(λ), MAC_{OA,bulk} becomes independent of the
assumed *ρ*_{OA} value.

Assuming spherical particles and neglecting the presence of BC in these
particles may seem inappropriate. However, calculations considering BC and
assuming core-shell morphology revealed (1) limited sensitivity of the
resulting MAC_{OA} to this assumption and (2) a higher
than measured lensing effect. Therefore, a substantial fraction of the OA
seems to be externally mixed and to dominate the measured size distribution
(see also Sect. 4.1).

4 Results and discussion

Back to toptop
We have independently determined the MAC_{BC}(880 nm) and the
Aethalometer C values under our conditions, as follows. We determined
MAC_{BC}(880 nm) from the regression between the absorption
coefficients at 880 nm obtained from the MWAA and the EC mass measured by
the Sunset analyzer (Fig. 1a). The slope of this regression may be used to
estimate the MAC_{BC}(880 nm), which we retrieved as 4.7±0.3 m^{2} g^{−1} by an uncertainty-weighted linear least-squares fit.
The corresponding intercept was not significantly different from zero ($-\mathrm{3}\pm \mathrm{3}$ Mm^{−1}). Our MAC_{BC}(880 nm) is not statistically
significantly different from the value recommended by Bond et al. (2006) for
externally mixed BC (extrapolating their MAC_{BC}(550 nm) to
880 nm by assuming *α*=1 provides MAC_{BC}(880 nm) $=\mathrm{4.7}\phantom{\rule{0.25em}{0ex}}\pm \mathrm{0.7}$ m^{2} g^{−1}). The strong correlation
between ${b}_{\mathrm{abs},\phantom{\rule{0.125em}{0ex}}\mathrm{MWAA},\phantom{\rule{0.125em}{0ex}}\mathrm{880}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}}$ and EC in Fig. 1a shows that
MAC_{BC}(880 nm) did not vary with aging during our study (see also
Fig. S2a). It also indicates that measurement artefacts for both instruments
were negligible, as the fundamental differences between the two techniques
mean that any artefacts are unlikely to be similar between them (charring for
EC vs. adsorption artefacts for MWAA). Our absorption coefficient
measurements also provide insights into particle mixing state in this study.
Since a single MAC adequately described our samples at all levels of aging
(Figs. 1a and S2a), in spite of a factor of 3.3 average increase in the
aerosol mass, our samples cannot be adequately described by a core-shell Mie
model. Such a core-shell model would predict an absorption enhancement by a
factor of ∼1.8 (Bond et al., 2006) for the observed OA mass increase
with aging, which was not observed in our case. This observation is also
supported by the time-resolved attenuation measurements at 880 nm using the
Aethalometer (Fig. S3), suggesting little (< 10 %) to no
increase in the attenuation coefficients upon SOA formation. We emphasize
that this conclusion does not indicate that no internal mixing occurred, but
rather that the simplified concept of negligible mixing better describes our
data than the equally simplified concept of a core-shell description of
coatings that completely envelop the central BC core. This may be due to the
complex morphology of internally mixed BC, which has been previously observed
for wood burning particles (e.g., China et al., 2013; S. Liu et al., 2015;
Liu et al., 2017). It may also be related to the fact that OA and BC are
emitted during separate phases of combustion. OA-rich particles are emitted
during the pre-flaming pyrolysis stage of combustion, whereas most BC is
emitted during flaming combustion (Heringa et al., 2011; Corbin et al.,
2015a, b; Haslett et al., 2018). These two stages of combustion may coexist
in different regions of the stove, particularly during simulated real-world
usage. As lensing effect was negligible in our case; we have assumed that the
aerosol optically behaves as an external mix between BC and BrC during Mie
calculation (see Sect. 3.4). We note that while this assumption is important
for estimating the BC absorption, the conclusions drawn about the BrC
absorption are not very sensitive to the assumed morphology.

We determined time-resolved wavelength-dependent absorption coefficients as
follows. We used the Aethalometer to obtain filter attenuation coefficients
with high time resolution, which were then calibrated to obtain absorption
coefficients by deriving the factor *C* (Eq. 1) using the MWAA
measurements of filter samples. *C* was obtained from an uncertainty-weighted
linear least-squares fit as 3.0±0.2 (Fig. 1b); the intercept of the fit
was not significantly different from zero, within 2 standard deviations ($-\mathrm{17}\phantom{\rule{0.25em}{0ex}}\pm \mathrm{14}$). A very strong
correlation could be observed between MWAA and Aethalometer (Fig. 1b),
implying that *C* is independent of the type of the aerosol sampled (see also
Fig. S2b). Therefore, we used a single *C* value to obtain time-resolved
wavelength-dependent absorption coefficients from the Aethalometer
attenuation measurements at the different wavelengths for primary and aged
aerosols.

Note that the manufacturer's default values, which were not applied in our
case, are 1.57 for *C* (using TFE-coated glass fiber filters) and 12.2 m^{2} g^{−1} for *σ*_{ATN} at 880 nm (Gundel et al., 1984; Drinovec et al.,
2015). The *C* value determined here is larger than the manufacturer-default
value for the AE33, resulting in smaller absorption coefficients. However,
the calculated *σ*_{ATN} at 880 nm (13.8 m^{2} g^{−1}), which
can be retrieved as the product of the *C* value and MAC_{BC}(880 nm) (Eq. 3), is similar to the factory-default *σ*_{ATN}. Therefore, our
calibrated M_{eBC}, calculated from the attenuation coefficients using
*σ*_{ATN} (Eq. 2), are similar to the factory-default M_{eBC}.
We note that M_{eBC} has not been used for MAC_{OA} calculations, and is
only used for the calculation of the mass fractions of BC and OA for display
purposes (Figs. 2, 3, 7 and 8).

In this section we derive the wavelength-dependent mass absorption
cross sections for BC, POA and SOA. In Fig. 2, we display the evolution of
*α* (370, 880 nm) as a function of OH exposure. Figure 3 shows the
relationship between *α*(*λ*, 880 nm) and *f*_{OA} for
primary and aged aerosols.

The *α* (370, 880 nm) values computed for the primary aerosol
(OH exposure =0 molecules cm^{−3} h) ranged between 1.3 and
1.7 (Fig. S5), which is within the range reported previously for
biomass-burning emissions (Kirchstetter et al., 2004; Lewis et al., 2008;
Zotter et al., 2017). The *α*(*λ*, 880 nm) is slightly higher than
that of pure BC (∼1.2; Bond et al., 2013; Zotter et al., 2017) for
small *f*_{POA}, while increasing *f*_{POA} corresponded to a
distinct increase in *α*(*λ*, 880 nm). This increase provides
clear evidence for the contribution of primary BrC to the absorption at lower
wavelengths (shown explicitly in Eq. (13). The *f*_{POA}
ranges from 0.12 to 0.63, which is lower than *f*_{POA} reported for
open burning emissions (e.g., *f*_{POA}∼0.75, Ulevicius et
al., 2016), because our wood-stove emissions feature a more efficient
combustion. As illustrated in Fig. S5, the observed absorption spectra have
steeper gradients with decreasing wavelength compared to the lines of
constant *α*. Such a systematic increase in *α*(*λ*, 880 nm)
with decreasing *λ* reflects the more-efficient light absorption by BrC
at shorter wavelengths (Moosmüller et al., 2011) and shows that the power
law wavelength dependence is an inaccurate oversimplification for this mixed
aerosol.

Figure 3b shows that upon aging, the OA
fraction rapidly increased (a typical time series of raw data is shown in
Fig. S1), reaching an average value of 0.81 (full range for aged OA: 0.74 < *f*_{OA} < 0.89) at high OH exposures (> 2×10^{7} molecules cm^{−3} h) and resulting in a
corresponding increase in ${\mathit{\alpha}}_{\mathrm{BC}+\mathrm{POA}+\mathrm{SOA}}$(370, 880 nm). The increases in ${\mathit{\alpha}}_{\mathrm{BC}+\mathrm{POA}+\mathrm{SOA}}$(370, 880 nm) and *f*_{OA} were
always correlated and plateaued at OH exposures beyond $\sim \mathrm{2}\times {\mathrm{10}}^{\mathrm{7}}$ molecules cm^{−3} h,
as seen in Fig 2. Also, note in
Fig. 2 that at the highest OH exposures, the highest values of ${\mathit{\alpha}}_{\mathrm{BC}+\mathrm{POA}+\mathrm{SOA}}$(370, 880 nm)
were reached, on average 1.8, during experiments where the *f*_{OA}
was highest. Such strong correlation between SOA formation and
${\mathit{\alpha}}_{\mathrm{BC}+\mathrm{POA}+\mathrm{SOA}}$(370, 880 nm) suggests the production of
substantial amounts of brown SOA. A similar relationship is observed between
${\mathit{\alpha}}_{\mathrm{BC}+\mathrm{POA}+\mathrm{SOA}}(\mathit{\lambda}$, 880 nm) and *f*_{OA} for
higher wavelengths, as shown in Fig. S6. Similar to the case of POA, a
systematic decrease in *α*(*λ*, 880 nm) with increasing *λ* is observed, reflecting the preferential absorption of BrC SOA at shorter
wavelengths. We note that ${\mathit{\alpha}}_{\mathrm{BC}+\mathrm{POA}+\mathrm{SOA}}$(370, 880 nm) as
a function of *f*_{OA} for all experiments lies below the overall trend for
the primary aerosol (dashed line in Fig. 3b), implying that
MAC_{SOA}(370 nm) was smaller than MAC_{POA}(370 nm).

We determined best-fit values
for MAC_{BC}(*λ*) and MAC_{POA}(*λ*) from the data
shown in Fig. 3a. Figure 3a includes least-squares fits of Eq. (13) to the
data, with MAC_{BC}(*λ*) and MAC_{POA}(*λ*) as fit
parameters. The fit results are shown in Table 1. The obtained fit value of
MAC_{BC}(370 nm) was 13.7 m^{2} g^{−1} (GSD 1.1, 1*σ* uncertainty
12.4–15.1 m^{2} g^{−1}), higher but not statistically significantly different
from the range estimated based on Bond et al. (2013), considering the
uncertainties of both the *α*_{BC} values and the MAC_{BC}(520 nm).
Meanwhile, the mean MAC_{POA}(370 nm) value, equal to 5.5 m^{2} g^{−1},
obtained under our conditions for domestic wood burning is ∼2.4 times higher than that obtained by Saleh et al. (2014) for open biomass
burning primary emissions, suggesting the presence of more-strongly
absorbing organic material under our conditions (this comparison is
continued in Sect. 4.3).

The MAC_{SOA}(*λ*) values, determined using Eq. (19),
are shown in Fig. 4 and Table 1. MAC_{SOA}(370 nm) was
2.2 m^{2} g^{−1} (GSD 1.39), a factor of 2.5 smaller than
MAC_{POA}(370 nm), but approximately an order of magnitude higher
than values reported for ambient oxygenated aerosols or laboratory SOA from
biogenic and traditional anthropogenic precursors such as terpenes and
methyl-benzenes (Clarke et al., 2007; Lambe et al., 2013; Romonosky et al.,
2015; Liu et al., 2016). The predominant SOA precursors identified in wood
smoke comprise (methyl)naphthalene(s) and phenol derivatives from lignin
pyrolysis (Bruns et al., 2016; Ciarelli et al., 2017), the oxidation products
of which are expected to be highly light-absorbing due to the presence of
aromatic moieties in the SOA (Laskin et al., 2015; Bruns et al., 2016). In
this regard, it is not surprising that the MAC_{SOA}(370 nm) values
obtained here are similarly high to those obtained from methanol-extracted
SOA from guaiacol and naphthalene oxidation (0.5–3.0 m^{2} g^{−1},
Romonosky et al., 2015).

Table 1 shows the fitting errors related with MAC_{BC}(*λ*),
MAC_{POA}(*λ*) and MAC_{SOA}(*λ*), arising from our
measurement precision and experimental variability. These fitting errors are
greater than our estimated uncertainties in the absorption coefficients
measured by MWAA (10 %), and comparable to our estimated uncertainty in OA
mass measured by AMS (30 %). The residuals in the fitted
MAC_{BC}(*λ*) are relatively low (< 10%), increasing
with decreasing *λ*. By contrast, the uncertainties in the fitted
MAC_{POA}(*λ*) are much higher (GSD = 1.2–1.5) and increase with
increasing *λ*. The relative residuals between the measured and
fitted *α*(*λ*, 880 nm) for primary emissions showed a mean bias and RMSE
of 0.07 and 0.13, respectively (Fig. S7), indicating that our fitted MAC
results provide a good description of the data set. MAC_{SOA}(*λ*)
values determined were highly variable between experiments with a GSD =1.39 and 2.42 for *λ*=370 and 660 nm, respectively. In Fig. S10, we show
the distribution of MAC_{SOA}(*λ*) values as boxes and whiskers
against OH exposure, showing no particular dependence of these values on
aging, as will be discussed below. Therefore, we expect the fitting errors
in MAC_{SOA} and of MAC_{POA} to be mainly related to true changes in
the organic aerosol chemical composition between different burns, since the
variability of MAC_{BC}(*λ*) was relatively small. In Sect. 4.3,
we discuss this variability further using the results of an additional and
independent analysis.

The relationships between the MAC_{SOA}(*λ*),
MAC_{POA}(*λ*) and MAC_{BC}(*λ*) and wavelength
appear to fall on three unique lines in the range 660 to 370 nm when plotted
in log–log space, as shown in Fig. 4 (Fig. S8 shows the same data plotted on
a linear scale). This indicates that a power-law approximation provides a
good description of the behavior of individual components within this
wavelength range from 370 to 660 nm. Accordingly we fitted the power law
coefficients to the data shown in Fig. 4 ($\mathrm{ln}\left({\mathrm{MAC}}_{i}\right)=\mathrm{ln}\left({A}_{i}\right)+{\mathit{\alpha}}_{i}\mathrm{ln}\left(\mathit{\lambda}\right)$,
with *i*= BC, POA or SOA) and fitting parameters are shown as multivariate
probability density functions in Fig. S9. This yielded *α*_{BC}=1.2, *α*_{POA}=4.6 and *α*_{SOA}=5.6,
with corresponding uncertainties of approximately 20 % (complete details
of the uncertainties are provided in Table S1 in the Supplement). Note that
*α*_{BC} in the range 660 to 370 nm obtained from this fit is
very similar to *α*_{BC} values that can be inferred by
extrapolating the data shown in Fig. 3a to *f*_{OA}=0. The high
*α* values obtained for the organic fractions are consistent with
previous measurements for BrC-containing POA (e.g., Chakrabarty et al., 2010,
2013).

In Fig. 5, we examine whether the
absorption profile of SOA evolved with aging. A change in MAC_{SOA}(370 nm) or *α*_{SOA}
with increasing OH exposure may indicate either a change in the
mass-specific absorption of the condensing SOA species with time, or a
change (e.g., “bleaching”) in the MAC of pre-existing POA. Figure 5 indicates
that neither of these scenarios was the case. Both MAC_{SOA}(370 nm) and
*α*_{SOA} were statistically independent of the OH exposure,
for exposures up to 40 molec. OH cm^{−3} h. This signifies that under our
conditions and within our measurement uncertainties the optical properties
of the additional organic mass formed was constant with aging, under the
assumption that the light-absorption properties of POA were negligibly
influenced by aging. Most of the variability in MAC_{SOA}(*λ*)
discussed above is therefore related to experiment-to-experiment differences
rather than to the extent of OH exposure, as is also shown below.

Figure 6 shows the MAC_{OA}(370 nm) determined from the water and methanol
extracts against the MAC_{OA}(370 nm) determined from the online
measurements. The MAC_{OA}(370 nm) from online measurements was estimated
by subtracting the contribution of BC, assuming a constant MAC_{BC}(370 nm) =13.7 m^{2} g^{−1}
as obtained in this work (Table 1). We performed
all the calculations and comparisons at *λ*=370 nm, as the signal-to-noise ratio of the absorption coefficients measured by UV-visible
spectroscopy and the contribution of BrC to the total carbonaceous
absorption is highest at this wavelength. The MAC of the extracts was
computed from the *k*_{OA} through Mie calculations. Repetition of both water
and methanol extracts yielded results that were consistent within 10 %
(Fig. S11). Average raw absorption spectra are shown in Fig. S12.

Figure 6b shows excellent correlation between the MAC_{OA}(370 nm)
values obtained from the *k*_{OA} of the solvent-extracted OA
with the in situ method described above. The Pearson correlation coefficient
was 0.8, for both solvents. This correlation suggests that none of the
assumptions employed in either method led to substantial errors in precision,
providing direct support for our results. A similar relationship was observed
between *k*_{OA} and the MAC_{OA}(370 nm) determined from
the online measurements (Fig. S13), showing that this relationship is not
sensitive to assumptions underlying the Mie calculations. It further suggests
that the wide variability observed in the MAC_{OA} values of
different burns, as seen Fig. 6, most likely reflects real variability in the
optical properties of POA and SOA rather than random noise or experimental
errors in the retrieved quantities. MAC_{OA} retrieved based on the
*k*_{OA} of the water-soluble OA show substantially more scatter than
observed in Fig. 6b (for both primary and aged data), suggesting a variable
extraction efficiency in the case of water, which we also attribute to
variability in the OA composition.

The data in Fig. 6b show that the methanol extracts correspond to a MAC about 50 % smaller than that of the online data. The scatter in the data is significantly reduced for the aged data (note that, in this analysis, aged OA refers to the sum of POA and SOA, since the reported values represent all OA after aging). This reduced scatter is expected, considering that aging is likely to result in more-spherical particles. We have assumed particle sphericity when interpreting the SMPS data and performing the Mie analysis. While the propagation of quantifiable uncertainties leads to an error estimate of ∼25 %, considering the simplifications that were necessary for the Mie analysis, we consider a 50 % closure to be an adequate agreement. Despite this, we cannot exclude additional methanol-insoluble brown carbon. Conversely, the fit in Fig. 6a indicates that the apparent MAC of water-soluble species was one-quarter of the respective methanol MAC, according to the slope of only 12±3 %. Only the aged data have been fit to illustrate this point. This strong disagreement shows that the BrC in our samples was hardly water soluble, even for the most aged samples. As we expect that the majority of OA in our samples formed by wood pyrolysis (Shafizadeh, 1984; Di Blasi, 2008; Corbin et al., 2015b), we can compare our results directly to those of Chen and Bond (2010), who also found that primary wood-pyrolysis BrC was water insoluble. Moreover, the poor water solubility of the light-absorbing components of SOA (Zhang et al., 2011) is in line with the results by Bruns et al. (2016), who showed that SOA precursors during these experiments were predominantly aromatic compounds.

The results above highlight the variability in the OA absorption properties.
In this section, we discuss potential reasons for this variability and
compare our results to the literature. Figure 7 shows the imaginary refractive
index of methanol-extracted OA at 370 nm, *k*_{OA,methanol}(370 nm) (Eq. 8),
as a function of *M*_{BC} ∕ *M*_{OA} and aging. The data are plotted against
*M*_{BC} ∕ *M*_{OA} instead of *f*_{OA} to allow for a direct
comparison with the literature (see Fig. S14 for a plot against
*f*_{OA}). An approximately linear trend of
*k*_{OA,methanol}(370 nm) with *M*_{BC} ∕ *M*_{OA} is seen in log space. This
aging-independent relationship may be useful in, for example, atmospheric
scenarios where wood-burning OA is a dominant aerosol component but its
exact degree of aging is unknown. The decrease in *M*_{BC} ∕ *M*_{OA} caused by
formation of SOA during aging results in a concurrent decrease in
*k*_{OA,methanol}(370 nm), implying that *k*_{SOA} < *k*_{POA} . This result is consistent with the smaller MAC of SOA
compared to POA obtained from online measurements (Table 1) and with recent
results reported by Sumlin et al. (2017). We emphasize that the derived
quantity here is the imaginary refractive index *k* of the total aged OA, not
the SOA.

The increase in *k*_{OA,methanol}(370 nm) with increasing
*M*_{BC} ∕ *M*_{OA} indicates that the OA compounds present at higher
*M*_{BC} ∕ *M*_{OA} absorbed more efficiently than at low *M*_{BC} ∕ *M*_{OA}. If
the variability in *M*_{BC} ∕ *M*_{OA} was driven partly by OA partitioning,
then this implies that lower-volatility compounds were more absorbing than
high-volatility compounds, consistent with the results by Saleh et al. (2014), who investigated the relation between OA absorption and volatility
using thermodesorber measurements. A correlation between *k*_{OA} and
*M*_{BC} ∕ *M*_{OA} has also been reported by Lu et al. (2015). The
parameterizations reported by these authors are included in Fig. 7, where
the wavelength dependence reported by those authors has been used to adjust
their parameterizations to 370 nm. Despite these differences, our results
confirm the generality of the correlation proposed by Saleh et al. (2014),
but using a method that is independent of potential biases related to
internal mixing effects, filter-based absorption measurements or Mie
calculations. Indeed, we emphasize that the *k*_{OA} obtained here is a
lower limit: as our approach does not account for the OA extraction
efficiency; *k*_{OA,methanol}(370 nm) may be underestimated by up to a factor
of ∼2, based on Fig. 6b.

5 Atmospheric implications

Back to toptop
In this section, we seek to estimate the relative importance of OA
absorption at different wavelengths relative to that of the total
carbonaceous aerosol as a function of aging. For these calculations, the
MAC(*λ*) values for the different components and their relative mass
abundance are required. We used the power law parameters reported above to
generate continuous MAC_{BC}(*λ*), MAC_{POA}(*λ*) and MAC_{SOA}(*λ*) functions together with their
associated uncertainties (Fig. 8a), which allow the extrapolation of these
parameters in the range [280; 880 nm].

The contributions of the different components as a function of OH exposure were calculated by assuming that SOA production follows the first-order decay of its precursors, i.e., the reaction with OH. Under this assumption, the time-dependent mass concentration of SOA compared to POA can be expressed as

$$\begin{array}{ll}\text{(21)}& {\displaystyle}& {\displaystyle}{M}_{\mathrm{SOA},\mathrm{WLC}}\left(t\right)/{M}_{\mathrm{POA},\mathrm{WLC}}\left(t\right)={\displaystyle}& {\displaystyle}{M}_{\mathrm{SOAP},\mathrm{WLC}}/{M}_{\mathrm{POA},\mathrm{WLC}}\times \left(\mathrm{1}-\mathrm{exp}\left(-{k}_{\mathrm{OH}}{\mathrm{OH}}_{\mathrm{exp}}\right)\right).\end{array}$$

In this equation, *M*_{SOA,WLC}(t),
*M*_{POA,WLC}(t) and *M*_{SOAP,WLC} are the
wall-loss-corrected mass concentrations of SOA, POA and the SOA potential
(the maximum SOA formed upon the consumption of all
precursors). *k*_{OH} represents an estimation of the reaction rate of
SOA precursors towards OH based on SOA production rates. By fitting the
observed ${M}_{\mathrm{SOA},\mathrm{WLC}}\left(t\right)/{M}_{\mathrm{POA},\mathrm{WLC}}\left(t\right)$ against the OH exposure, *k*_{OH} and ${M}_{\mathrm{SOAP},\mathrm{WLC}}/{M}_{\mathrm{POA},\mathrm{WLC}}$ can be estimated.
For these calculations, we have estimated the wall losses using two
approaches, as described in the Supplement.

The ${M}_{\mathrm{SOAP},\mathrm{WLC}}/{M}_{\mathrm{POA},\mathrm{WLC}}$ was on average equal to 7.8
(GSD =1.4) and *k*_{OH} was estimated as $\mathrm{2.7}\times {\mathrm{10}}^{-\mathrm{11}}$ molecule^{−1} cm^{3} (GSD =1.4), consistent with chemical nature of the SOA
precursors as measured
(e.g., PAH and phenol derivatives) by a PTR-MS (Bruns et al., 2016, 2017).
These high rates and enhancement ratios indicate the rapid production of SOA.

Based on the bulk gas phase measurements of SOA precursors (Bruns et al.,
2016), the obtained enhancements are consistent with high bulk SOA yields of
∼50 %. These high values are not surprising, considering
the nature of these gases (e.g., PAH and phenol derivatives), the low
temperatures (263 K) and the relatively high concentrations (aged OA
∼100 µg m^{−3}) at which the experiments have been
conducted (Bruns et al., 2016).

Combining these calculated enhancements with the average contributions of POA
in primary emissions, the evolution of *f*_{OA} with aging was
determined and is shown in Fig. 8b. The uncertainties in Fig. 8b (dotted
lines) represent 1 standard deviation on *f*_{OA} obtained by a Monte
Carlo propagation of uncertainties due to experiment-to-experiment
variability, fitting errors and wall loss correction errors (see Supplement).
While this calculation represents a simplification of the SOA production
mechanisms (the dependence of SOA yields on OH exposures and/or multigeneration
chemistry and OA mass concentrations was neglected), it results in
residuals much smaller than the experiment-to-experiment variability. We
therefore used these calculations to assess the relative contribution of OA
to the total carbonaceous absorption. We show in Fig. 8c that below 400 nm
and upon aging, the absorption coefficient of the total organics was at least
as high as the one of BC.

Using the MAC values of the different components (in m^{2} g^{−1}),
their abundance (in g m^{−3}) and the solar irradiance data (*S*, in
W m^{−2} nm^{−1}) (Gueymard et al., 2002) calculated at sea level for a
cloudless day, the fractional energy transfer due to the BrC light absorption
relative to that due to the total carbonaceous aerosol absorption,
*W*_{OA}(OH_{exp}), in air masses
dominated by residential burning emissions can be determined as

$$\begin{array}{ll}\text{(22)}& {\displaystyle}& {\displaystyle}{W}_{\mathrm{OA}}\left({\mathrm{OH}}_{\mathrm{exp}}\right)={\displaystyle \frac{{\mathrm{RET}}_{\mathrm{OA}}\left({\mathrm{OH}}_{\mathrm{exp}}\right)}{{\mathrm{RET}}_{\mathrm{tot}}\left({\mathrm{OH}}_{\mathrm{exp}}\right)}}{\displaystyle}& {\displaystyle}={\displaystyle \frac{{\int}_{\mathrm{300}}^{\mathrm{880}}\left\{{\displaystyle \begin{array}{c}{M}_{\mathrm{POA}}\left({\mathrm{OH}}_{\mathrm{exp}}\right)\cdot {\mathrm{MAC}}_{\mathrm{POA}}\left(\mathit{\lambda}\right)\\ +{M}_{\mathrm{SOA}}\left({\mathrm{OH}}_{\mathrm{exp}}\right)\cdot {\mathrm{MAC}}_{\mathrm{SOA}}\left(\mathit{\lambda}\right)\end{array}}\right\}\cdot S\left(\mathit{\lambda}\right)\cdot \mathrm{d}\mathit{\lambda}}{{\int}_{\mathrm{300}}^{\mathrm{880}}\left\{{\scriptscriptstyle \begin{array}{c}{M}_{\mathrm{BC}}\left({\mathrm{OH}}_{\mathrm{exp}}\right)\cdot {\mathrm{MAC}}_{\mathrm{BC}}\left(\mathit{\lambda}\right)+{M}_{\mathrm{POA}}\left({\mathrm{OH}}_{\mathrm{exp}}\right)\cdot {\mathrm{MAC}}_{\mathrm{POA}}\left(\mathit{\lambda}\right)\\ +{M}_{\mathrm{SOA}}\left({\mathrm{OH}}_{\mathrm{exp}}\right)\cdot {\mathrm{MAC}}_{\mathrm{SOA}}\left(\mathit{\lambda}\right)\end{array}}\right\}\cdot S\left(\mathit{\lambda}\right)\cdot \mathrm{d}\mathit{\lambda}}}.\end{array}$$

Here, RET_{OA}(OH_{exp}) and
RET_{tot}(OH_{exp}) denote the rate
of energy transfer per volume (in W m^{−3}) to the air mass in question
due to light absorption by OA and the total carbonaceous aerosol,
respectively. We note that while RET_{OA}(OH_{exp}) and RET_{tot}(OH_{exp}) are extensive properties,
*W*_{OA}(OH_{exp}) does not depend on
the loading, scattering and/or lensing, provided that scattering/lensing
similarly affects BC and OA present in the same air mass (e.g., BC and OA have a similar size distribution).

We also note that *W*_{OA}(OH_{exp})
depends on the photon flux, *S*(*λ*), but we consider this dependence to
be trivial compared to the variability in the aerosol emissions and their
light-absorbing properties (error bars considering these variabilities are
shown in Fig. 8d). Errors in *W*_{OA} were propagated by Monte Carlo
simulations using the uncertainties from the estimated MAC values of BC and
OA fractions and the variability in *f*_{OA}. Our sensitivity analysis
suggests that the major part of the variance in predicting *W*_{OA} for
primary emissions stems from the variability in the POA mass fraction. In
contrast, the SOA mass absorption cross sections at lower wavelengths are
the most critical factor for assessing the relative importance of BrC
absorptivity in aged emissions.

Figure 8d shows that the fractional energy transfer to the air mass,
*W*_{OA}, due to the absorption by the primary organic aerosol
was around 10 % of that of the total carbonaceous aerosol for our samples.
This percentage is comparable to that observed by Fu et al. (2012), in
spite of *f*_{OA} in their samples being much higher, because of the high OA
MACs in our samples (Table 1). Moreover, with aging, the fraction of OA is
enhanced, resulting in a sizeable increase in *W*_{OA}, from ∼0.1
to ∼0.3 (Fig. 8d), highlighting that SOA formation in biomass
burning plumes is an atmospherically relevant source of BrC. We note that
our data are more representative of flaming conditions. More data are needed
on the chemical nature of primary particulate emissions and of the
contributing SOA precursors as well as the absorptivity of these primary and
secondary products, for better constraining the influence of biomass-burning-related BrC on the Earth's climate.

6 Conclusions

Back to toptop
We determined wavelength-dependent MAC values of BC, POA and SOA, as well as
*k*_{OA}for methanol and water extracts of fresh and aged OA, for
wood-burning emissions through smog-chamber experiments. To our knowledge,
this is the first determination of these properties for wood-burning OA. We
showed that the MAC_{OA}(370 nm) values calculated based on *k*_{OA} through
Mie analysis correlated well with those estimated from online filter-based
measurements. This correlation between independent MAC measurements supports
the quality of both methods. While MAC_{OA}(370 nm) values computed based
on *k*_{OA,methanol} were two times lower than those estimated from online
filter-based measurements, calculations based on *k*_{OA,water} could only
explain 12 % of the measured absorption, suggesting that BrC species in
POA and SOA are mostly water insoluble. The MAC_{OA} was found to vary by
more than 1 order of magnitude. Similar to previous reports, this
variability could be related to the variability in the ratio of the mass
concentrations of BC and OA (*M*_{BC} ∕ *M*_{OA}) due to very different
mechanisms of oxidative aging and burn-to-burn variability.

The MAC_{POA} and MAC_{SOA} determined for wavelengths between 370 and
660 nm followed a power-law dependence on *λ* with an absorption
Ångström exponent of 4.6 and 5.6 for POA and SOA, respectively. In
addition to following this power law, the MACs of POA and SOA appeared to be
constant for OH exposures up to 40×10^{6} molecules cm^{−3} h.

The mean MAC_{POA}(370 nm) obtained under our conditions was 5.5 m^{2} g^{−1}, considerably higher than previously reported values for
open biomass burning. The mean MAC_{SOA}(370 nm) was 2.2 m^{2} g^{−1}
(1*σ* variability: 1.6–3.1 m^{2} g^{−1} according to a GSD =1.39) under our experimental conditions, 2.3 times lower than the mean
MAC_{POA}(370 nm) but approximately an order of magnitude higher than MAC
values estimated for ambient oxygenated aerosols or reported for SOA from
biogenic and traditional anthropogenic precursors. We propose that the
important role of oxidized phenols and aromatics in forming wood-burning SOA
(Bruns et al., 2016) is the cause of this observation. This hypothesis is
supported by our observed reaction rates with OH, and by the
water-insolubility of the BrC in aged OA.

Overall, the absorption by organic aerosols was estimated to contribute 10 %–30 % of the total solar absorption of wood-combustion aerosols, where 10 % represents the primary OA and 30 % the aged OA. SOA formation in biomass burning plumes is therefore an atmospherically relevant source of BrC.

Data availability

Back to toptop
Data availability.

Data are available at https://doi.org/10.5281/zenodo.2164456 (Kumar et al., 2018).

Supplement

Back to toptop
Supplement.

The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-17843-2018-supplement.

Author contributions

Back to toptop
Author contributions.

IEH, ASHP, MG and JCC conceptualized the study. NKK, EAB, IEH, JGS and AV performed the experiments. Formal analysis was carried out by NKK, JCC, IEH, DM, LD and GM. IEH, ASHP, UB, MG, JGS and PP supervised the study. NKK, JCC, IEH and MG wrote the paper.

Competing interests

Back to toptop
Competing interests.

Luka Drinovec and Griša Močnik were employed by Aerosol d.o.o when the experiments were conducted.

Acknowledgements

Back to toptop
Acknowledgements.

The research leading to these results has received funding from the European
Research Council grant (ERC-CoG 615922-BLACARAT) and by the Competence
Centre Energy and Mobility (CCEM) project 807.

Edited by: Yafang Cheng

Reviewed by: two anonymous referees

References

Back to toptop
Alexander, D. T. L., Crozier, P. A., and Anderson, J. R.: Brown carbon spheres in East Asian outflow and their optical properties., Science, 321, 833–8336, https://doi.org/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, https://doi.org/10.5194/acp-6-3131-2006, 2006.

Barmet, P., Dommen, J., DeCarlo, P. F., Tritscher, T., Praplan, A. P., Platt, S. M., Prévôt, A. S. H., Donahue, N. M., and Baltensperger, U.: OH clock determination by proton transfer reaction mass spectrometry at an environmental chamber, Atmos. Meas. Tech., 5, 647–656, https://doi.org/10.5194/amt-5-647-2012, 2012.

Bertrand, A., Stefenelli, G., Bruns, E. A., Pieber, S. M., Prévôt, A. S. H., Wortham, H., Temime-roussel, B., Slowik, J. G., EL Haddad, I., and Marchand, N.: Primary emissions and secondary aerosol production potential from woodstoves for residential heating?: influence of the stove technology and combustion efficiency, Atmos. Environ., 169, 65–79, https://doi.org/10.1016/j.atmosenv.2017.09.005, 2017.

Bond, T. C., Habib, G., and Bergstrom, R. W.: Limitations in the enhancement of visible light absorption due to mixing state, J. Geophys. Res.-Atmos., 111, 1–13, https://doi.org/10.1029/2006JD007315, 2006.

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.-Atmos., 118, 5380–5552, https://doi.org/10.1002/jgrd.50171, 2013.

Bruns, E. A., Krapf, M., Orasche, J., Huang, Y., Zimmermann, R., Drinovec, L., Mocnik, G., El-Haddad, I., Slowik, J. G., Dommen, J., Baltensperger, U., and Prévôt, A. S. H.: Characterization of primary and secondary wood combustion products generated under different burner loads, Atmos. Chem. Phys., 15, 2825–2841, https://doi.org/10.5194/acp-15-2825-2015, 2015.

Bruns, E. A., El Haddad, I., Slowik, J. G., Kilic, D., Klein, F., Baltensperger, U., and Prévôt, A. S. H.: Identification of significant precursor gases of secondary organic aerosols from residential wood combustion., Sci. Rep., 6, 27881, https://doi.org/10.1038/srep27881, 2016.

Bruns, E. A., Slowik, J. G., El Haddad, I., Kilic, D., Klein, F., Dommen, J., Temime-Roussel, B., Marchand, N., Baltensperger, U., and Prévôt, A. S. H.: Characterization of gas-phase organics using proton transfer reaction time-of-flight mass spectrometry: fresh and aged residential wood combustion emissions, Atmos. Chem. Phys., 17, 705–720, https://doi.org/10.5194/acp-17-705-2017, 2017.

Cavalli, F., Viana, M., Yttri, K. E., Genberg, J., and Putaud, J.-P.: Toward a standardised thermal-optical protocol for measuring atmospheric organic and elemental carbon: the EUSAAR protocol, Atmos. Meas. Tech., 3, 79–89, https://doi.org/10.5194/amt-3-79-2010, 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, https://doi.org/10.5194/acp-10-6363-2010, 2010.

Chakrabarty, R. K., Arnold, I. J., Francisco, D. M., Hatchett, B., Hosseinpour, F., Loria, M., Pokharel, A., and Woody, B. M.: Black and brown carbon fractal aggregates from combustion of two fuels widely used in asian rituals, J. Quant. Spectrosc. Ra., 122, 25–30, https://doi.org/10.1016/j.jqsrt.2012.12.011, 2013.

Chen, Y. and Bond, T. C.: Light absorption by organic carbon from wood combustion, Atmos. Chem. Phys., 10, 1773–1787, https://doi.org/10.5194/acp-10-1773-2010, 2010.

China, S., Mazzoleni, C., Gorkowski, K., Aiken, A. C., and Dubey, M. K.: Morphology and mixing state of individual freshly emitted wildfire carbonaceous particles, Nat. Commun., 4, 2122, https://doi.org/10.1038/ncomms3122, 2013.

Ciarelli, G., El Haddad, I., Bruns, E., Aksoyoglu, S., Möhler, O., Baltensperger, U., and Prévôt, A. S. H.: Constraining a hybrid volatility basis-set model for aging of wood-burning emissions using smog chamber experiments: a box-model study based on the VBS scheme of the CAMx model (v5.40), Geosci. Model Dev., 10, 2303–2320, https://doi.org/10.5194/gmd-10-2303-2017, 2017.

Clarke, A., McNaughton, C., Kapustin, V., Shinozuka, Y., Howell, S., Dibb, J., Zhou, J., Anderson, B. E., Brekhovskikh, V., Turner, H., and Pinkerton, M.: Biomass burning and pollution aerosol over North America: organic components and their influence on spectral optical properties and humidification response, J. Geophys. Res.-Atmos., 112, 1–13, https://doi.org/10.1029/2006JD007777, 2007.

Collaud Coen, M., Weingartner, E., Apituley, A., Ceburnis, D., Fierz-Schmidhauser, R., Flentje, H., Henzing, J. S., Jennings, S. G., Moerman, M., Petzold, A., Schmid, O., and Baltensperger, U.: Minimizing light absorption measurement artifacts of the Aethalometer: evaluation of five correction algorithms, Atmos. Meas. Tech., 3, 457–474, https://doi.org/10.5194/amt-3-457-2010, 2010.

Corbin, J. C., Lohmann, U., Sierau, B., Keller, A., Burtscher, H., and Mensah, A. A.: Black carbon surface oxidation and organic composition of beech-wood soot aerosols, Atmos. Chem. Phys., 15, 11885–11907, https://doi.org/10.5194/acp-15-11885-2015, 2015a.

Corbin, J. C., Keller, A., Lohmann, U., Burtscher, H., Sierau, B., and Mensah, A. A.: Organic emissions from a wood stove and a pellet stove before and after simulated atmospheric aging, Aerosol Sci. Technol., 49, 1037–1050, https://doi.org/10.1080/02786826.2015.1079586, 2015b.

Corbin, J. C.; Pieber, S. M.; Czech, H.; Zanatta, M.; Jakobi, G.; Massabò, D.; Orasche, J.; El Haddad, I.; Mensah, A. A.; Stengel, B.; Drinovec, L.; Mocnik, G.; Zimmermann, R.; Prévôt, A. S. H., and Gysel, M.: Brown and black carbon emitted by a marine engine operated on heavy fuel oil and distillate fuels: optical properties, size distributions and emission factors, J. Geophys. Res.-Atmos., 123, 6175–6195, https://doi.org/10.1029/2017JD027818, 2018.

DeCarlo, P. F., Kimmel, J. R., Trimborn, A., Northway, M. J., Jayne, J. T., Aiken, A. C., Gonin, M., Fuhrer, K., Horvath, T., Docherty, K. S., Worsnop, D. R., and Jimenez, J. L.: Field deployable, high resolution, time-of-flight aerosol mass spectrometer, Anal. Chem., 78, 8281–8289, doi:8410.1029/2001JD001213, 2006.

Denier van der Gon, H. A. C., Bergström, R., Fountoukis, C., Johansson, C., Pandis, S. N., Simpson, D., and Visschedijk, A. J. H.: Particulate emissions from residential wood combustion in Europe – revised estimates and an evaluation, Atmos. Chem. Phys., 15, 6503–6519, https://doi.org/10.5194/acp-15-6503-2015, 2015.

Di Blasi, C.: Modeling chemical and physical processes of wood and biomass pyrolysis, Prog. Energ. Combust., 34, 47–90, https://doi.org/10.1016/j.pecs.2006.12.001, 2008.

Drinovec, L., Mocnik, G., Zotter, P., Prévôt, A. S. H., Ruckstuhl, C., Coz, E., Rupakheti, M., Sciare, J., Müller, T., Wiedensohler, A., and Hansen, A. D. A.: The “dual-spot” Aethalometer: an improved measurement of aerosol black carbon with real-time loading compensation, Atmos. Meas. Tech., 8, 1965–1979, https://doi.org/10.5194/amt-8-1965-2015, 2015.

Feng, Y., Ramanathan, V., and Kotamarthi, V. R.: Brown carbon: a significant atmospheric absorber of solar radiation?, Atmos. Chem. Phys., 13, 8607–8621, https://doi.org/10.5194/acp-13-8607-2013, 2013.

Forrister, H., Liu, J., Scheuer, E., Dibb, J., Ziemba, L., Thornhill, L. K., Anderson, B., Diskin, G., Perring, A. E., Schwarz, J. P., Campuzan-Jost, P., Day, D. A., Palm, B. B., Jimenez, J. L., Nenes, A., and Weber, R. J.: Evolution of brown carbon in wildfire plumes, Gephys. Res. Lett., 42, 4623–4630, https://doi.org/10.1002/2015GL063897, 2015.

Fu, J. S., Hsu, N. C., Gao, Y., Huang, K., Li, C., Lin, N.-H., and Tsay, S.-C.: Evaluating the influences of biomass burning during 2006 BASE-ASIA: a regional chemical transport modeling, Atmos. Chem. Phys., 12, 3837–3855, https://doi.org/10.5194/acp-12-3837-2012, 2012.

Grieshop, A. P., Donahue, N. M., and Robinson, A. L.: Laboratory investigation of photochemical oxidation of organic aerosol from wood fires 2: analysis of aerosol mass spectrometer data, Atmos. Chem. Phys., 9, 2227–2240, https://doi.org/10.5194/acp-9-2227-2009, 2009.

Gueymard, C., Myers, D., and Emery, K.: Proposed Reference Irradiance Spectra for Solar Energy Systems Testing, Sol. Energy, 73, 6, 443–467, 2002.

Gundel, L. A., Dod, R. L., Rosen, H., and Novakov.: The relationship between optical attenuation and black carbon concentration for ambient and source particles, Sci. Total Environ., 36, 197–202, 1984.

Haslett, S. L., Thomas, J. C., Morgan, W. T., Hadden, R., Liu, D., Allan, J. D., Williams, P. I., Keita, S., Liousse, C., and Coe, H.: Highly controlled, reproducible measurements of aerosol emissions from combustion of a common African biofuel source, Atmos. Chem. Phys., 18, 385–403, https://doi.org/10.5194/acp-18-385-2018, 2018.

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, https://doi.org/10.5194/acp-10-5965-2010, 2010.

Heringa, M. F., DeCarlo, P. F., Chirico, R., Tritscher, T., Dommen, J., Weingartner, E., Richter, R., Wehrle, G., Prévôt, A. S. H., and Baltensperger, U.: Investigations of primary and secondary particulate matter of different wood combustion appliances with a high-resolution time-of-flight aerosol mass spectrometer, Atmos. Chem. Phys., 11, 5945–5957, https://doi.org/10.5194/acp-11-5945-2011, 2011.

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, https://doi.org/10.5194/acp-6-3563-2006, 2006.

Jo, D. S., Park, R. J., Lee, S., Kim, S.-W., and Zhang, X.: A global simulation of brown carbon: implications for photochemistry and direct radiative effect, Atmos. Chem. Phys., 16, 3413–3432, https://doi.org/10.5194/acp-16-3413-2016, 2016.

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.-Atmos., 109, 1–12, https://doi.org/10.1029/2004JD004999, 2004.

Krapf, M., Haddad, I. El, Bruns, E. A., Haddad, I. El, Molteni, U., Daellenbach, K. R., Prévôt, A. S. H., Baltensperger, U., and Dommen, J.: Labile peroxides in secondary organic aerosol labile peroxides in secondary organic aerosol, Chem 1, 603–616, https://doi.org/10.1016/j.chempr.2016.09.007, 2016.

Kumar, N. K., Corbin, J. C., Bruns, E. A., Massabó, D., Slowik, J. G., Drinovec, L., Mocnik, G., Prati, P., Vlachou, A., Baltensperger, U., Gysel, M., El-Haddad, I., and Prévôt, A. S. H.: Production of particulate brown carbon during atmospheric aging of residential wood-burning emissions, https://doi.org/10.5281/zenodo.2164456, 2018.

Lack, D. A. and Langridge, J. M.: On the attribution of black and brown carbon light absorption using the Ångström exponent, Atmos. Chem. Phys., 13, 10535–10543, https://doi.org/10.5194/acp-13-10535-2013, 2013.

Lambe, A. T., Cappa, C. D., Massoli, P., Onasch, T. B., Forestieri, S. D., Martin, A. T., Cummings, M. J., Croasdale, D. R., Brune, W. H., Worsnop, D. R., and Davidovits, P.: Relationship between oxidation level and optical properties of secondary organic aerosol, Environ. Sci. Technol., 47, 6349–6357, https://doi.org/10.1021/es401043j, 2013.

Laskin, A., Laskin, J., and Nizkorodov, S. A.: Chemistry of atmospheric brown carbon, Chem. Rev., 115, 4335–4382, https://doi.org/10.1021/cr5006167, 2015.

Lee, H. J., Aiona, P. K., Laskin, A., Laskin, J., and Nizkorodov, S. A.: Effect of solar radiation on the optical properties and molecular composition of laboratory proxies of atmospheric brown carbon, Environ. Sci. Technol., 48, 10217–10226, https://doi.org/10.1021/es502515r, 2014.

Lewis, K., Arnott, W. P., Moosmüller, H., and Wold, C. E.: Strong spectral variation of biomass smoke light absorption and single scattering albedo observed with a novel dual-wavelength photoacoustic instrument, J. Geophys. Res.-Atmos., 113, 1–14, https://doi.org/10.1029/2007JD009699, 2008.

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: effect of SOA and brown carbon, J. Geophys. Res.-Atmos., 119, 7453–7476, https://doi.org/10.1002/2013JD021186, 2014.

Liu, J., Scheuer, E., Dibb, J., Ziemba, L. D., Thornhill, K. L., Anderson, B. E., Wisthaler, A., Mikoviny, T., Devi, J. J., Bergin, M., and Weber, R. J.: Brown carbon in the continental troposphere, Geophys. Res. Lett., 41, 2191–2195, https://doi.org/10.1002/2013GL058976, 2014.

Liu, D. T., Whitehead, J., Alfarra, M. R., Reyes-Villegas, E., Spracklen, D. V., Reddington, C. L., Kong, S. F., Williams, P. I., Ting, Y. C., Haslett, S., Taylor, J. W., Flynn, M. J., Morgan, W. T., McFiggans, G., Coe, H., and Allan, J. D.: Black-carbon absorption enhancement in the atmosphere determined by particle mixing state, Nat. Geosci., 10, 184–188, https://doi.org/10.1038/ngeo2901, 2017.

Liu, J., Lin, P., Laskin, A., Laskin, J., Kathmann, S. M., Wise, M., Caylor, R., Imholt, F., Selimovic, V., and Shilling, J. E.: Optical properties and aging of light-absorbing secondary organic aerosol, Atmos. Chem. Phys., 16, 12815–12827, https://doi.org/10.5194/acp-16-12815-2016, 2016.

Liu, P. F., Abdelmalki, N., Hung, H.-M., Wang, Y., Brune, W. H., and Martin,
S. T.: Ultraviolet and visible complex refractive indices of secondary
organic material produced by photooxidation of the aromatic compounds toluene
and *m*-xylene, Atmos. Chem. Phys., 15, 1435–1446,
https://doi.org/10.5194/acp-15-1435-2015, 2015.

Liu, P. S. K., Deng, R., Smith, K. A., Jayne, J. T., Williams, L. R., Canagaratna, M. R., Moore, K., Onasch, T. B., Worsnop, D. R., and Deshler, T.: Transmission efficiency of an aerodynamic focusing lens system: comparison of model calculations and laboratory measurements for the aerodyne aerosol mass spectrometer, Aerosol Sci. Tech., 41, 721–733, 2007.

Liu, S., Aiken, A. C., Gorkowski, K., Dubey, M. K., Cappa, C. D., Williams, L. R., Herndon, S. C., Massoli, P., Fortner, E. C., Chhabra, P. S., Brooks, W. A., Onasch, T. B., Jayne, J. T., Worsnop, D. R., China, S., Sharma, N., Mazzoleni, C., Xu, L., Ng, N. L., Liu, D., Allan, J. D., Lee, J. D., Fleming, Z. L., Mohr, C., Zotter, P., Szidat, S., and Prévôt, A. S. H.: Enhanced light absorption by mixed source black and brown carbon particles in UK winter, Nat. Commun., 6, 8435, https://doi.org/10.1038/ncomms9435, 2015.

Lu, Z., Streets, D. G., Winijkul, E., Yan, F., Chen, Y., Bond, T. C., Feng, Y., Dubey, M. K., Liu, S., Pinto, J. P., and Carmichael, G. R.: Light absorption properties and radiative effects of primary organic aerosol emissions, Environ. Sci. Technol., 49, 4868–4877, https://doi.org/10.1021/acs.est.5b00211, 2015.

Massabò, D., Bernardoni, V., Bove, M. C., Brunengo, A., Cuccia, E., Piazzalunga, A., Prati, P., Valli, G., and Vecchi, R.: A multi-wavelength optical set-up for the characterization of carbonaceous particulate matter, J. Aerosol Sci., 60, 34–46, https://doi.org/10.1016/j.jaerosci.2013.02.006, 2013.

Massabò, D., Caponi, L., Bernardoni, V., Bove, M. C., Brotto, P., Calzolai, G., Cassola, F., Chiari, M., Fedi, M. E., Fermo, P., Giannoni, M., Lucarelli, F., Nava, S., Piazzalunga, A., Valli, G., Vecchi, R., and Prati, P.: Multi-wavelength optical determination of black and brown carbon in atmospheric aerosols, Atmos. Environ., 108, 1–12, https://doi.org/10.1016/j.atmosenv.2015.02.058, 2015.

Moosmüller, H., Chakrabarty, R. K., and Arnott, W. P.: Aerosol light absorption and its measurement: A review, J. Quant. Spectrosc. Ra., 110, 844–878, https://doi.org/10.1016/j.jqsrt.2009.02.035, 2009.

Moosmüller, H., Chakrabarty, R. K., Ehlers, K. M., and Arnott, W. P.: Absorption Ångström coefficient, brown carbon, and aerosols: basic concepts, bulk matter, and spherical particles, Atmos. Chem. Phys., 11, 1217–1225, https://doi.org/10.5194/acp-11-1217-2011, 2011.

Petzold, A. and Schönlinner, M.: Multi-angle absorption photometr – A new method for the measurement of aerosol light absorption and atmospheric black carbon, J. Aerosol Sci., 35, 421–441, https://doi.org/10.1016/j.jaerosci.2003.09.005, 2004.

Platt, S. M., El Haddad, I., Zardini, A. A., Clairotte, M., Astorga, C., Wolf, R., Slowik, J. G., Temime-Roussel, B., Marchand, N., Ježek, I., Drinovec, L., Močnik, G., Möhler, O., Richter, R., Barmet, P., Bianchi, F., Baltensperger, U., and Prévôt, A. S. H.: Secondary organic aerosol formation from gasoline vehicle emissions in a new mobile environmental reaction chamber, Atmos. Chem. Phys., 13, 9141–9158, https://doi.org/10.5194/acp-13-9141-2013, 2013.

Romonosky, D. E., Laskin, A., Laskin, J., and Nizkorodov, S. A.: High-resolution mass spectrometry and molecular characterization of aqueous photochemistry products of common types of secondary organic aerosols, J. Phys. Chem. A, 119, 2594–2606, https://doi.org/10.1021/jp509476r, 2015.

Saleh, R., Hennigan, C. J., McMeeking, G. R., Chuang, W. K., Robinson, E. S., Coe, H., Donahue, N. M., and Robinson, A. L.: Absorptivity of brown carbon in fresh and photo-chemically aged biomass-burning emissions, Atmos. Chem. Phys., 13, 7683–7693, https://doi.org/10.5194/acp-13-7683-2013, 2013.

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., Yokelson, R. J., Donahue, N. M., and Robinson, A. L.: Brownness of organics in aerosols from biomass burning linked to their black carbon content, Nat. Geosci., 7, 2–5, https://doi.org/10.1038/ngeo2220, 2014.

Schauer, J. J., Mader, B. T., Deminter, J. T., Heidemann, G., Bae, M. S., Seinfeld, J. H., Flagan, R. C., Cary, R. A., Smith, D., Huebert, B. J., Bertram, T., Howell, S., Kline, J. T., Quinn, P., Bates, T., Turpin, B., Lim, H. J., Yu, J. Z., Yang, H., and Keywood, M. D.: ACE-Asia intercomparison of a thermaloptical method for the determination of particle-phase organic and elemental carbon, Environ. Sci. Technol., 37, 993–1001, 2003.

Shafizadeh, F.: The chemistry of pyrolysis and combustion. The chemistry of solid Wood, ACS Symp. Ser., 207, 489–529, https://doi.org/10.1021/ba-1984-0207, 1984.

Slowik, J. G., Cross, E. S., Han, J.-H., Davidovits, P., Onasch, T. B., Jayne, J. T., Williams, L. R., Canagaratna, M. R., Worsnop, D. R., Chakrabarty, R. K., Moosmüller, H., Arnott, W. P., Schwarz, J. P., Gao, R. S., Fahey, D. W., Kok, G. L., and Petzold, A.: An inter-comparison of instruments measuring black carbon content of soot particles, Aerosol Sci. Technol., 41, 295–314, 2007.

Sumlin, B. J., Pandey, A., Walker, M. J., Pattison, R. S., Williams, B. J., and Chakrabarty, R. K.: Atmospheric photooxidation diminishes light absorption by primary brown carbon aerosol from biomass burning, Environ. Sci. Tech. Lett., 4, 540–545, https://doi.org/10.1021/acs.estlett.7b00393, 2017.

Sun, H., Biedermann, L., and Bond, T. C.: Color of brown carbon: A model for ultraviolet and visible light absorption by organic carbon aerosol, Geophys. Res. Lett., 34, 1–5, https://doi.org/10.1029/2007GL029797, 2007.

Ulevicius, V., Byčenkien$\dot{\mathrm{e}}$, S., Bozzetti, C., Vlachou, A., Plauškait$\dot{\mathrm{e}}$, K., Mordas, G., Dudoitis, V., Abbaszade, G., Remeikis, V., Garbaras, A., Masalaite, A., Blees, J., Fröhlich, R., Dällenbach, K. R., Canonaco, F., Slowik, J. G., Dommen, J., Zimmermann, R., Schnelle-Kreis, J., Salazar, G. A., Agrios, K., Szidat, S., El Haddad, I., and Prévôt, A. S. H.: Fossil and non-fossil source contributions to atmospheric carbonaceous aerosols during extreme spring grassland fires in Eastern Europe, Atmos. Chem. Phys., 16, 5513–5529, https://doi.org/10.5194/acp-16-5513-2016, 2016.

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, https://doi.org/10.5194/acp-14-10989-2014, 2014.

Weingartner, E., Saathoff, H., Schnaiter, M., Streit, N., Bitnar, B., and Baltensperger, U.: Absorption of light by soot particles: determination of the absorption coefficient by means of aethalometers, J. Aerosol Sci., 34, 1445–1463, https://doi.org/10.1016/S0021-8502(03)00359-8, 2003.

Yang, H. and Yu, J. Z.: Uncertainties in charring correction in the analysis of elemental and organic carbon in atmospheric particles by thermal/optical methods, Environ. Sci. Technol., 36, 5199–5204, 2002.

Zhang, X., Lin, Y.-H., Surratt, J. D., Zotter, P., and Prévôt, A. S. H.: Light-absorbing soluble organic aerosol in Los-Angeles and Atlanta: A contrast in secondary organic aerosol, Geophys. Res. Lett, 38, L21810, https://doi.org/10.1029/2011GL049385, 2011.

Zhao, R., Lee, A. K. Y., Huang, L., Li, X., Yang, F., and Abbatt, J. P. D.: Photochemical processing of aqueous atmospheric brown carbon, Atmos. Chem. Phys., 15, 6087–6100, https://doi.org/10.5194/acp-15-6087-2015, 2015.

Zotter, P., Herich, H., Gysel, M., El-Haddad, I., Zhang, Y., Močnik, G., Hüglin, C., Baltensperger, U., Szidat, S., and Prévôt, A. S. H.: Evaluation of the absorption Ångström exponents for traffic and wood burning in the Aethalometer-based source apportionment using radiocarbon measurements of ambient aerosol, Atmos. Chem. Phys., 17, 4229–4249, https://doi.org/10.5194/acp-17-4229-2017, 2017.

Short summary

It is clear that considerable uncertainties still exist in understanding the magnitude of aerosol absorption on a global scale and its contribution to global warming. This manuscript provides a comprehensive assessment of the optical absorption by organic aerosols (brown carbon) from residential wood combustion as a function of atmospheric aging.

It is clear that considerable uncertainties still exist in understanding the magnitude of...

Atmospheric Chemistry and Physics

An interactive open-access journal of the European Geosciences Union