AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-2837-2017Comparison of different Aethalometer correction schemes and a reference
multi-wavelength absorption technique for ambient aerosol dataSaturnoJorgej.saturno@mpic.dehttps://orcid.org/0000-0002-3761-3957PöhlkerChristopherhttps://orcid.org/0000-0001-6958-425XMassabòDarioBritoJoelhttps://orcid.org/0000-0002-4420-9442CarboneSamaraChengYafanghttps://orcid.org/0000-0003-4912-9879ChiXuguangDitasFlorianhttps://orcid.org/0000-0003-3824-9373Hrabě de AngelisIsabellaMorán-ZuloagaDanielPöhlkerMira L.RizzoLuciana V.https://orcid.org/0000-0002-1748-6997WalterDavidhttps://orcid.org/0000-0001-6807-5007WangQiaoqiaoArtaxoPaulohttps://orcid.org/0000-0001-7754-3036PratiPaolohttps://orcid.org/0000-0002-8097-9460AndreaeMeinrat O.https://orcid.org/0000-0003-1968-7925Biogeochemistry and Multiphase
Chemistry Departments, Max Planck Institute for Chemistry, P.O. Box 3060, 55020 Mainz, GermanyDepartment of Physics & INFN, University of Genoa, via Dodecaneso
33, 16146, Genoa, ItalyLaboratory for Meteorological Physics, University Blaise Pascal,
Clermont-Ferrand, FranceInstitute of Agrarian Sciences, Federal University of Uberlândia,
Uberlândia, Minas Gerais, BrazilInstitute for Climate and Global Change and School of Atmospheric
Sciences, Nanjing University, Nanjing, ChinaDepartment of Earth and Exact Sciences, Institute of Environmental,
Chemical and Pharmaceutics Sciences, Federal University of São Paulo,
São Paulo, BrazilDepartment of Applied Physics, Institute of Physics, University of
São Paulo, Rua do Matão, Travessa R, 187, CEP 05508-900, São
Paulo, SP, BrazilScripps Institution of Oceanography, University of California San
Diego, La Jolla, CA 92098, USAGeology and Geophysics Department, King Saud University, Riyadh, Saudi
ArabiaJorge Saturno (j.saturno@mpic.de)9August20171082837285031October201613December201619February201712July2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/10/2837/2017/amt-10-2837-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/2837/2017/amt-10-2837-2017.pdf
Deriving absorption coefficients from Aethalometer attenuation data
requires different corrections to compensate for artifacts related to
filter-loading effects, scattering by filter fibers, and scattering by
aerosol particles. In this study, two different correction schemes were
applied to seven-wavelength Aethalometer data, using multi-angle absorption
photometer (MAAP) data as a reference absorption measurement at 637 nm. The
compensation algorithms were compared to five-wavelength offline absorption
measurements obtained with a multi-wavelength absorbance analyzer (MWAA),
which serves as a multiple-wavelength reference measurement. The online
measurements took place in the Amazon rainforest, from the wet-to-dry
transition season to the dry season (June–September 2014). The mean
absorption coefficient (at 637 nm) during this period was
1.8 ± 2.1 Mm-1, with a maximum of 15.9 Mm-1. Under these
conditions, the filter-loading compensation was negligible. One of the
correction schemes was found to artificially increase the short-wavelength
absorption coefficients. It was found that accounting for the aerosol optical
properties in the scattering compensation significantly affects the
absorption Ångström exponent (åABS) retrievals.
Proper Aethalometer data compensation schemes are crucial to retrieve the
correct åABS, which is commonly implemented in brown carbon
contribution calculations. Additionally, we found that the wavelength
dependence of uncompensated Aethalometer attenuation data significantly
correlates with the åABS retrieved from offline MWAA
measurements.
Introduction
Aerosol particles scatter and absorb solar radiation in the atmosphere and
thus have an important impact on the Earth's radiative budget and climate
(Andreae and Ramanathan, 2013; IPCC, 2013; Penner et al., 1992; Yu et al.,
2006). Light absorption by atmospheric aerosols is dominated by black carbon
(BC), an aerosol species that is emitted by incomplete combustion of biomass
or fossil fuels (Bond and Bergstrom, 2006). Black carbon absorbs radiation
from infrared to near-UV wavelengths and leads to positive radiative forcing
(IPCC, 2013). Other light-absorbing aerosols include a class of organics
called brown carbon (BrC) (Andreae and Gelencsér, 2006), mineral
dust (Myhre and Stordal, 2001), and primary biological aerosol particles (PBAPs) (Després et al., 2012). High uncertainties still remain
regarding the aerosol interactions with solar radiation (Andreae and
Ramanathan, 2013; Bond et al., 2013), especially because ambient aerosol
absorption is often measured over a limited wavelength range or at only one
wavelength.
The wavelength dependence of aerosol light absorption is expressed by the
absorption Ångström exponent (åABS)
(Ångström, 1929). The åABS of fresh
fossil-fuel-derived BC is typically around 1.0 – i.e., the absorption changes as a
function of λ-1 (Bergstrom et al., 2002). However, when BC
particle size is larger than 50 nm or becomes coated by non-absorbing
materials, the åABS can decrease below 1.0 (Lack and
Langridge, 2013). Moreover, the bulk aerosol wavelength dependence can
significantly increase in the presence of other light absorbers, such as BrC
(Andreae and Gelencsér, 2006; Kirchstetter et al., 2004), reaching high
values between 3.5 and 7.0. Assuming a fixed spectral dependence of 1 for BC,
several studies have estimated the BrC contribution as a function of
åABS (Favez et al., 2010; Sandradewi et al., 2008).
However, given the uncertainties associated with the åABS of
BC, these methods could potentially provide erroneous BrC estimations (Garg
et al., 2016; Schuster et al., 2016a, b; Wang et al., 2016).
Absorption coefficients and BC mass concentrations are related by the mass
absorption cross section (MAC) (Bond et al., 2013). Ground-based continuous
measurements of BC mass concentrations and absorption coefficients are
required to retrieve the appropriate ambient aerosol MAC values, since this
relationship and its wavelength dependence are affected by the mixing
state and physical and chemical conditions of the aerosol particles (Flowers
et al., 2010; Lack and Cappa, 2010; Moosmüller et al., 2011). Moreover,
retrieving the wavelength dependence of ambient aerosol optical properties
requires absorption measurements at two or more wavelengths.
Only a few commercially available techniques offer multi-wavelength absorption
measurements. The most commonly used methods are filter-based techniques,
including a modified version of the particle soot absorption photometer
(PSAP) (Virkkula, 2010; Virkkula et al., 2005), which measures at three
different wavelengths in the visible spectral region, and the Aethalometer
(Hansen et al., 1984), which measures the attenuation of light at two or
seven different wavelengths (2λ vs. 7λ instrument). The
above-mentioned instruments are filter-based techniques that determine
attenuation and suffer from various artifacts (detailed discussion below),
converting attenuation coefficients to absorption coefficients requires
several corrections (Arnott et al., 2005; Collaud Coen et al., 2010; Schmid
et al., 2006; Virkkula et al., 2007; Weingartner et al., 2003) that generally
need concomitant scattering and additional absorption measurements. The
correction process of filter-based measurement artifacts introduces
uncertainties in the åABS that are difficult to determine
(Collaud Coen et al., 2010).
Two well-known artifacts affect filter-based absorption measurements by
enhancing or reducing the effective optical path length. One of them is
related to the multiple-scattering effects, which induces a positive bias of
light attenuation. The multiple-scattering effects are caused by the
scattering by the filter fibers and the scattering by aerosol particles on the filter. The scattering by aerosol particles depends
on the optical properties and size distribution of the measured aerosol
particles. On the other hand, the second effect is related to the
shadowing that deposited aerosol particles cause on each other.
This effect, called filter-loading effect, reduces the optical path
length in the filter and depends on the amount and optical properties of the
deposited particles.
The bias related to multiple-scattering effects can be reduced by measuring
the radiation reflected by the filter at different angles and simulating the
radiation transfer. This principle was incorporated in the design of the
multi-angle absorption photometry (MAAP) technique (Petzold and
Schönlinner, 2004). The design consists of a single-wavelength instrument
(637 nm LED light source) that measures the transmitted radiation through a
glass-fiber filter and the reflectance at two different angles (130∘
and 165∘). Using this configuration and a radiation transfer model,
the instrument is able to account for the mentioned artifacts related to
multiple scattering and provides approximately “corrected” absorption
coefficients (Petzold et al., 2005).
For accurate estimation of absorption coefficients and their spectral
dependency, Aethalometer measurements rely on a number of correction
procedures; a compilation of different correction schemes can be found in
Collaud Coen et al. (2010). The first systematic correction scheme that deals
with the different artifacts affecting Aethalometer measurements was proposed
by Weingartner et al. (2003). This correction scheme uses a comparison with
an indirect light absorption measurement (extinction minus scattering) to
estimate a multiple-scattering compensation. In addition, a filter-loading
correction was estimated by empirically calculating a “shadowing factor”.
This correction consists of the following empirical equation that converts
attenuation coefficients, σATN, into absorption
coefficients, σap,
σap=σATNC⋅RATN,
where C accounts for multiple-scattering effects on the filter, due to
(a) scattering by the filter fibers and (b) scattering by aerosol particles
embedded on the filter. The factor R(ATN) accounts for the filter-loading
effect.
Later, Virkkula et al. (2007) proposed another filter-loading correction
through calculating the average attenuation before and after a filter change.
This correction applied a compensation factor in the form of
(1 +k× ATN), where k
is calculated for each filter change and ATN corresponds to the attenuation.
A similar approach was used to design the dual-spot technology Aethalometer
(model AE33) that intrinsically compensates for filter-loading effects using
a two beam system with different flow rates (Drinovec et al., 2015).
In a detailed study, Arnott et al. (2005) introduced a scattering correction
factor that accounts for the aerosol particle scattering artifact. In a
similar way, Schmid et al. (2006) proposed a correction algorithm that
included a parameterization of the scattering by filter fibers and scattering
by aerosol particles as a function of åABS and an iteration
procedure to obtain corrected absorption coefficients. Both correction
schemes used photoacoustic spectroscopy (PAS) measurements at 532 nm as a
reference absorption measurement. Later, by using MAAP absorption
measurements as a reference, Collaud Coen et al. (2010) evaluated the
above-mentioned correction algorithms and proposed two new ones based on the
Schmid and Arnott corrections. Their algorithms, among several changes to the
previous ones, included a new scattering correction parameterization that
uses measured optical properties of the aerosol particles instead of the
“standard” ones implemented in Schmid and Arnott correction algorithms. The
comparison made by Collaud Coen et al. (2010) resulted in a good agreement
between MAAP and Aethalometer BC measurements when using the “Schmid-like”
correction algorithm. On the other hand, the “Arnott-like” algorithm lead
to many negative σap values, especially under low absorption
conditions (Collaud Coen et al., 2010).
Previous studies on Aethalometer compensation schemes have evaluated
corrected absorption coefficients in comparison to reference absorption
measurements (PAS or MAAP), which were done at only one wavelength. In this
study, we use a multi-wavelength absorbance analyzer (MWAA), introduced by
Massabò et al. (2013, 2015), to conduct a systematic multi-wavelength
evaluation of ambient data. This way we can estimate the impact of the most
common and reliable Aethalometer correction schemes on the
åABS uncertainties. We used collected MAAP filter samples
from long-term aerosol measurements in central Amazonia to perform offline
multi-wavelength absorption measurements using the MWAA. The results
presented here are relevant for the study of valuable multi-wavelength data
provided by the widely used Aethalometers.
Materials and methodsSampling site and selected data period
Field measurements were carried out at the Amazon Tall Tower Observatory
(ATTO) (08.602′02∘ S, 00.033′59∘ W), located in the Uatumã Sustainable Development Reserve,
Amazonas State, Brazil, in the central Amazon Basin. The site is located 150
km NE of the city of Manaus, upwind of the urban plume. A detailed
description of the site can be found in Andreae et al. (2015).
The atmospheric aerosol was collected by using a 60 m long, 1 in. diameter
stainless steel inlet tube without size cut-off, installed on a triangular
mast since early 2014. The laminar flow rate in the inlet was constant at
30 L min-1. The aerosol stream relative humidity was decreased down to
30–40 % by using diffusion driers. In this study, we corrected the data
for standard temperature and pressure (273.15 K and 1013.25 hPa) and did
not apply any correction to compensate for particle losses. The sampling
period analyzed here comprises the wet-to-dry transition time
(June–July 2014) and part of the dry season (August–September 2014). In
the beginning of the measurement period (beginning of June), aerosol particle
number concentrations were very low, in the order of 100–400 cm-3,
measured by a condensation particle counter (CPC) (Andreae et al., 2015).
These typical wet-season conditions slightly changed during the transition
season until the end of August, when particle number concentrations increased
to around 500–2000 cm-3 (Andreae et al., 2015). The selected
measurement period was a good opportunity to evaluate the Aethalometer
performance under different conditions. During this period, the aerosol
absorption coefficients increased from near detection limit values to the
highest values measured at the ATTO site during the dry season.
Instrumentation
A 7λ Aethalometer (model AE31, Magee Scientific Company, Berkeley,
USA; nominal wavelengths: 370, 470, 520, 590, 660, 880, and 950 nm) was
used to measure attenuation coefficients σATN, which are
reported by the instrument as equivalent black carbon (BCe) mass
concentrations. Details about the measurement principle and the different
corrections to the data are explained in the next section.
Scattering coefficients, σsp, were measured by a 3λ nephelometer (model Aurora 3000, Ecotech Pty Ltd., Knoxfield, Australia;
nominal wavelengths: 450, 525, and 635 nm). The instrument was manually
calibrated using CO2 as span gas. Zero tests and spans were conducted
periodically. The scattering coefficients measured by the instrument were
corrected for truncation errors following the method proposed by Müller
et al. (2011), using the sub-micrometer correction factors as function of the
scattering Ångström exponents. The detection limits, calculated as
3 standard deviations of 1 min resolution particle-free air
measurements, were 1.1, 0.9, and 0.7 Mm-1 at 450, 525, and 635 nm,
respectively. Due to a malfunction of the 635 nm channel, we excluded those
data from our calculations.
A multi-angle absorption photometer, MAAP (model 5012, Thermo Electron Group,
Waltham, USA) was used to measure the absorption coefficient at 637 nm. The
instrument uses a glass-fiber filter tape, where the aerosol particles are
collected on a sample spot. Light transmission (at 0∘) and
reflectance at two different angles (130∘ and 165∘) are
measured every 5 min (Petzold et al., 2005). A radiative model calculation
provides the light absorption coefficient derived from the absorbance
measurements and accounts for the light scattering by filter fibers and
aerosol particles deposited on the filter. The instrument reports BCe
mass concentrations calculated by assuming a mass absorption cross section
(MAC) of 6.6 m2 g-1, based on Bond et al. (2006). A measurement
bias after every filter change can occur if the absorption coefficients
exceed ∼ 20 Mm-1 (Hyvärinen et al., 2013), which was not the
case during the period of this study. The instrument sampled at a flow rate
of 500 L h-1 in series with the nephelometer and was configured to
trigger a filter change when transmission reached a minimum of 60 % or
after 24 h. Therefore, more samples were collected during the dry season,
when the aerosol particle concentration was higher and the transmission
threshold was reached quickly. All data obtained from the online measurements
(nephelometer, Aethalometer, and MAAP) were aggregated to 30 min means. MAAP
data below the detection limit (0.132 Mm-1 with 30 min resolution)
were excluded from the analysis.
The MWAA was used to measure the light absorption coefficients on
MAAP-collected filter samples. This instrument was developed by Massabò
et al. (2013) and measures the light transmitted through a filter sample
(forward hemisphere) and the light reflected at two different angles
(backward hemisphere) in a similar configuration to the MAAP. By using a
radiative transfer model, the light absorption coefficients can be
calculated. The instrument design offers the advantage of accounting for the
multiple-scattering effects and is able to measure absorption coefficients at
three different wavelengths, as initially introduced, and was later upgraded
to measure at five different wavelengths (375, 407, 532, 635, and 850 nm)
(Massabò et al., 2015). The MAAP aerosol-laden filter tape was collected
at the ATTO site and analyzed by MWAA at the University of Genoa, Genoa,
Italy. During transport of the samples, they could be affected by aging of
the organic aerosol, microbial processes, and/or loss of semi-volatile
material (Laskin et al., 2015; Saleh et al., 2014). In order to avoid these
issues, the samples were collected everyday directly from the MAAP and kept
frozen (-4 ∘C) during the campaign time and transported in a cool
bag with blue ice (∼ 72 h) to the laboratory in Genoa. We reanalyzed
some samples after they were stored at room temperature during 3 days to
investigate the potential aging of carbonaceous material collected on the
filters and found no significant differences in the absorbance results
measured by the MWAA.
Aethalometer measurements and corrections
The Aethalometer continuously measures light attenuation on an aerosol-laden
filter. The attenuation is defined, according to the Lambert–Beer law, as
ATN=-100⋅lnII0,
where I and I0 are the light intensity transmitted through an aerosol
loaded and an original area of the filter tape, respectively. A list of
symbols and abbreviations is provided in Table 1. The instrument is programmed to
calculate the BCe mass concentration by assuming that a change in
attenuation (ΔATN) is caused by an increase in the BC mass deposited
on the filter substrate during an interval Δt (min), as follows:
BCngm-3=A⋅ΔATNαATN⋅Q⋅Δt,
where A is the filter area (1.67 cm2), αATN is the
λ-dependent BC mass attenuation cross section
(14 625 nm m2 g-1λ-1), and Q is the volumetric flow
rate in L min-1. By using the αATN recommended by the
manufacturer, we reversed the calculation to convert reported mass
concentrations back to attenuation coefficients, as
σATN=BCngm-3⋅αATN.
Two different correction schemes were applied to our dataset, including the
Schmid et al. (2006) and the Collaud Coen et al. (2010) algorithms. These two
correction schemes were chosen because both of them compensate for the three
artifacts that affect Aethalometer measurements. The Arnott and Collaud
Coen's Arnott-like corrections were excluded due to their limitations when
dealing with low-absorption data. Both the Collaud Coen and the Schmid correction
require concomitant scattering measurements and a reference absorption
measurement, which in our case was the MAAP. Moreover, we present and discuss
a comparison of corrected Aethalometer data to the multi-wavelength light
absorption measurement obtained from the MWAA.
Schmid correction algorithm
The Schmid correction consists of an iterative procedure, which is applied
to each measured attenuation spectrum. The compensated absorption
coefficients, σap, are calculated from attenuation
coefficients, σATN, by accounting for the different artifacts,
σap=σATNCref+Csca⋅R=σATNCref+msω01-ω01f-1lnATN-ln10ln50-ln10+1,
where Cref compensates for the scattering effects in comparison
with a reference absorption measurement, Csca accounts for the
scattering effect of non-absorbing aerosol particles and R, for the
filter-loading effect. The Schmid formulation uses the scattering factor
ms and ω0 to calculate Csca and the filter
loading correction proposed by Weingartner et al. (2003), which takes
ATN = 10 % as a reference point and includes the shadowing factor
parameter, f, which describes the slope between σATN and
ln(ATN).
As a first step, Cref is calculated for attenuation coefficients
corresponding to attenuation values lower than 10 %, when the
filter-loading correction is considered negligible (ATN < 10 %;
R≈ 1). By using MAAP absorption coefficient measurements, it is
possible to obtain Cref as follows:
Cref=σATN,10σMAAP,
where σMAAP is the absorption coefficient measured by the
MAAP at 637 nm and σATN,10 is the attenuation
coefficient at 637 nm when ATN < 10 %.
Attenuation coefficients at 590 nm were interpolated to 637 nm assuming a
power-law relationship as,
σATN637nm=σATN590nm⋅637nm590nm-åATN.
The attenuation Ångström exponent åATN used in this step was
calculated by applying a log–log fit to σATN vs. λ ,
where åATN was obtained from the slope as follows:
lnσATN=-åATNlnλ+lnconstant.
Absorption Ångström exponents (åABS) were obtained in a similar
way in further calculations.
The multiple-scattering correction factor, Cref, obtained from
Eq. (6) was averaged over the sampling period to calculate the measured
filter-loading correction factor, Rmeas, as
Rmeas=σATNσMAAP⋅C‾ref.
Weingartner et al. (2003) found that the linear relationship between
σATN and ln(ATN) can be used to parameterize the
filter-loading effect. The slope of this relationship was given by the
shadowing factor parameter, f. By applying a linear fit to the
Rmeas values obtained from Eq. (9) and the attenuation data, as
shown in Eq. (10), the term (1/f-1) can be obtained from the slope.
R=1f-1lnATN-ln10ln50-ln10+1
Assuming f is wavelength-independent, the averaged f is then used to calculate
R at different wavelengths.
In the next step, C, understood as the overall multiple-scattering
correction factor (Cref+Csca), is parameterized as a
function of λ. The single-scattering albedo, ω0, at
637 nm is used in the following equation to calculate C as
C=C∗+msω01-ω0,
where C∗ corresponds to the multiple-scattering effect by filter
fibers and ms to the aerosol scattering factor found by Arnott
et al. (2005) (see Table S1). The implemented approach is useful to examine
any wavelength dependence on C. The values of ω0 were
interpolated to the different Aethalometer wavelengths by using the Eq. (12),
assuming that absorption and scattering coefficients follow a power-law
wavelength dependence described by åABS and
åSCA, respectively.
ω0λ=σspσsp+σap=ω0,refλλref-åSCAω0,refλλref-åSCA+1-ω0,refλλref-åABS
Different åABS values (1; 1.25; 1.5; 1.75; 2) are then used
to generate different correlation factors between ln(C) vs. ln(λ).
The coefficients resulting from a quadratic fit are used to parameterize C
as a function of åABS (see Fig. 4 in Schmid et al., 2006).
An iteration procedure is used to force the convergence of
åABS. In our calculations, the data converged after seven
iterations.
Collaud Coen correction algorithm
In this study we implemented the Collaud Coen correction algorithm that
resembles the Schmid correction (see Eq. 14b in Collaud Coen et al., 2010).
This algorithm is different from the original Schmid algorithm in the
calculations of the filter-loading effect and the multiple-scattering
correction factor. As shown in Eq. (6), the Schmid algorithm filters the data
for ATN < 10 % in order to account only for the scattering by
filter fibers in the Cref calculation. On the other hand, the Collaud
Coen algorithm applies a prior filter-loading correction and then, by
dividing the reference absorption data (MAAP) by the Aethalometer attenuation
coefficients, Cref is obtained, which accounts for both
scattering by filter fibers and scattering by embedded aerosol particles.
Regarding the filter-loading effect, Collaud Coen et al. used the linear
dependency of the shadowing factor, f, on the single-scattering albedo,
ω0, expressed by Eq. (12), to calculate f using measured
ω0 and assuming m was constant (m=0.74).
f=m⋅1-ω0+1
Additionally, they found statistically better results by correlating
σATN vs. ATN, instead of the logarithmic correlation
proposed by Weingartner et al. (2003), which was implemented by Schmid et
al. (2010). Considering no filter-loading artifact for ATN = 0, they
proposed the following equation, which replaces Eq. (10):
R=1m1-ω‾0+1-1ATN50+1.
In this case, ω0 was averaged for every filter spot period (from
one filter spot change to the next) and this average was used for calculating
every measurement included in the corresponding filter spot period. The
ω0 values at different wavelengths were calculated by using
Eq. (12) but including attenuation Ångström exponents
(åATN) because åABS is not known yet.
Filter-loading corrected data are then divided by the MAAP absorption
coefficients to obtain an average Cref. Regarding the embedded
aerosol scattering effects, the Collaud Coen correction includes a change in
the aerosol scattering effect parameter expressed as ms in
Eq. (11). The constant ms values used by Schmid et al. (2010)
correspond to ammonium sulfate. Collaud Coen substituted them by the measured
aerosol scattering properties by using the following equation
ms=βSCAd-1⋅c⋅λ-åSCAd-1;d=0.564;c=0.00032910(σspinMm-1units),
where βSCA is the scattering
proportionality constant and c and d are constants corresponding to the
power-law relation between σATN and σsp,
previously reported by Arnott et al. (2005).
Finally, the corrected absorption coefficients are calculated in a similar
way to Eq. (5) but using ms from Eq. (15) and averaging
Cref, ms, ω0 and R over a filter spot
period, i.e., from a filter change time to the subsequent one.
Time series (June–September 2014) of (a) scattering by
aerosol particles measured by the nephelometer and (b) Aethalometer
attenuation and MAAP absorption coefficient measurements at 637 nm during
the sampling period.
Results and discussion
The beginning of the sampling period is characterized by low scattering
coefficients compared to the second half of the period when scattering
increases significantly. Several scattering peaks can be observed after the
beginning of August (see Fig. 1a). Occasionally, local or regional biomass
burning plumes reach the site during the dry season and scattering by aerosol
particles increases significantly due to enhanced concentration of fine mode
aerosol particles, which are more efficient in scattering light in the
visible range. The major effect of multiple-scattering artifacts is evident
when comparing MAAP measured absorption coefficients and Aethalometer
measured attenuation coefficients (see Fig. 1b). The absorption coefficients
averaged 1.8 ± 2.1 Mm-1, with the minimum values occurring in the
beginning of the sampling period, whereas a maximum of absorption (up to
15.9 Mm-1, measured by MAAP) took place between 18 and 23 August 2014.
Calculated back-trajectories using the HYSPLIT model (Draxler and Hess, 1998)
confirmed that air masses on the days of maximum absorption and scattering
were coming from south and southeast, an area with intense fire activity;
see Fig. S1 in the Supplement. Levoglucosan
measurements further confirmed the predominance of biomass-burning-originated
aerosol particles (not shown). From 1 June to 1 August 2014, the attenuation
coefficient at 637 nm had a median of 5.1 Mm-1 (3.2–7.9,
interquartile range, IQR). Then, during the first days of August, it
increased slightly until the biomass burning event took place on 18–23
August 2014. The maximum attenuation coefficient during this event reached
115 Mm-1. Details about this event, regarding chemical composition and
CCN activity, are presented in Pöhlker et al. (2016, 2017). The observed
absorption and attenuation coefficients represent typical conditions at the
ATTO site for the wet, transition and dry periods. In the next sections, we
present data compensated to account for the different filter artifacts and
study the influence of the applied compensation algorithms on the
åABS. The artifacts that affect the åABS
retrieval from filter-based multi-wavelength absorption measurements could be
avoided by using PAS methods that have been successfully implemented to
measure light absorption by suspended aerosol particles (e.g., Ajtai et al.,
2010). However, PAS measurements have high detection limits and have only
been implemented at near-source measurement sites (Cappa et al., 2012; Cheng
et al., 2016; Lewis et al., 2008) and not in clean environments like central
Amazonia.
Multiple scattering correction calculated by using MAAP
absorption coefficients as reference (λ=637 nm). Light-gray
points represent all calculated Cref values. The black line and shaded
area represent a conditional non-parametric mean estimation and its
confidence limits, respectively.
Aethalometer corrections
Immediately after every Aethalometer filter change, aerosol particles are
collected on a clean new spot. Under these conditions, the filter-loading
effect is considered to be negligible because there is not enough aerosol on
the filter to “darken” the substrate (Virkkula et al., 2007). Therefore,
the only bias to the Aethalometer response is given by the scattering effects
by filter fibers. The scattering by filter fibers, expressed as
Cref, was calculated by using Eq. (6), assuming R≈1 for
data corresponding to ATN < 10 %. The Cref time
series is shown in Fig. 2. We observed that Cref decreased
somewhat from June–July to August–September, when the average ± 1SD values were 6.3 ± 1.5 and 4.9 ± 1.1,
respectively. Additionally, we observed a larger Cref variability
during the transition period, which may increase the uncertainty of the
corrected absorption coefficients. This seasonal effect on the multiple-scattering compensation parameter could be related to the condensation or
adsorption of semi-volatile organic compounds or liquid organic aerosol
particles on the filter fibers, inducing a change in the filter matrix
optical properties (Collaud Coen et al., 2010; Subramanian et al., 2007;
Weingartner et al., 2003). The Schmid algorithm uses an average
Cref for further calculation of the filter-loading correction
factor, R. We found that using an overall average Cref
significantly affects the calculation of the shadowing factor (f).
Therefore, two different averages of Cref were implemented in
this work for the two above-mentioned periods, transition (June–July) and
dry season (August–September). Subsequent multiple-scattering correction
calculations were conducted using real-time Cref values.
The measured filter-loading calibration factor (Rmeas) was
obtained by using Eq. (9). Then, by following the Schmid algorithm, the
shadowing factor was calculated by applying a fit to Eqs. (9) and (10) (Rizzo
et al., 2011; Schmid et al., 2006). The calculated average shadowing factors
were 1.10 ± 0.10 and 1.04 ± 0.08 for June–July and
August–September, respectively. These values were lower compared to those
obtained for darker aerosols (f=1.23–1.89) (Weingartner et al., 2003)
and for biomass burning aerosol (f=1.2) (Schmid et al., 2006). At
660 nm, the Aethalometer wavelength that is closer to the MAAP measurement
wavelength, the filter-loading correction calculation resulted in R
correction factors of 0.98 ± 0.02 and 1.01 ± 0.01 for June–July
and August–September, respectively. A slight wavelength dependence was
observed; the R values were up to 4 % higher at 370 nm compared to
those calculated at 950 nm during the cleanest period of this study
(June–July). A similar behavior was observed during August–September. As
explained by Schmid et al. (2006), this wavelength dependency is related to
the fact that R depends on ATN, which increases with decreasing wavelength.
The obtained R correction factors were very close to 1 – i.e., the filter
loading effect barely affected the conversion from attenuation to absorption
coefficients, even during the most polluted period, August–September. A
filter-loading correction factor close to 1 was expected since the average
ω0 measured during the campaign was 0.88 ± 0.04 at 637 nm. A
high ω0 is related to the predominance of scattering aerosol
particles, which diminishes the shadowing effect of dark aerosol particles
embedded in the filter matrix (Weingartner et al., 2003).
To compare both correction schemes in terms of the filter-loading correction,
Cref was recalculated after compensating all the data for filter
loading by (1) following the Schmid et al. correction and (2) the Collaud
Coen et al. correction, which includes ω0 in the shadowing factor
calculation and the relationship σATN vs. ATN. We found no
statistical difference between the two correction algorithms in terms of the
filter-loading compensation because this effect was generally low over the
sampling period. More information about the effect of increasing attenuation
on the calculated Cref after applying the filter-loading
correction can be found in the Supplement (Fig. S2).
As already mentioned, the multiple-scattering effects significantly affect
the correction of Aethalometer data by a factor of 5 to 7. According to
previous studies, the multiple-scattering correction is the most important
one in ambient aerosol with a high ω0 (Collaud Coen et al., 2010;
Rizzo et al., 2011; Schmid et al., 2006; Segura et al., 2014). The seasonal
variability of C can be explained by the different scattering properties of
the aerosol particles in the different seasons (Collaud Coen et al., 2010).
In order to compare the different scattering contributions to C, we
calculated Cref and Csca by using the Collaud Coen
algorithm. Csca was calculated using Eq. (5) in this case. We
observed that a lower ω0 during the biomass burning period was
related to a decrease in the scattering correction factor, Csca.
The relative contribution of Csca was examined and it was found
that the relative contribution from the scattering correction decreases with
decreasing ω0 and increasing βsca; see Fig. 3. No
correlation was found between Csca and åSCA
since the scattering Ångström exponent was quite stable during the
sampling period with the exception of the few days influenced by regional
biomass burning (see Fig. S3). In other words, the Csca relative
contribution was only affected by variations on βsca and
ω0. Given that R is almost negligible in our dataset, the
comparison between both algorithms was done in terms of their different ways
to treat the multiple-scattering effects.
Filter cycle averaged data corresponding to (a)
scattering proportionality constant, (b) single-scattering albedo at 660 nm,
and (c) relative contribution of Csca to the total multiple-scattering
compensation (Cref+Csca) at 660 nm. Vertical bars in (a) and (b) correspond to 1 standard deviation.
Scatter plot of (a) Collaud Coen and (b) Schmid
corrections results vs. MAAP absorption coefficients (all data at 637 nm).
The fit was obtained by applying a standardized major axis regression.
A scatter plot of both corrections' outputs vs. MAAP measurements is shown in
Fig. 4. We found that corrected AE data fitted very well the MAAP
measurements for both correction algorithms. The slopes were 1.05
(1.04–1.06) and 1.03 (1.02–1.03) for the Schmid and Collaud Coen
corrections, respectively, with significant correlation factors. The slight
difference between both correction schemes in terms of the comparison to MAAP
measurements can be related to the parameterization of C applied by Schmid
et al., which is not implemented by Collaud Coen et al., and the way Collaud
Coen et al. estimate Cref.
Absorption Ångström exponent
The MWAA was used as a reference multi-wavelength measurement since it
accounts for multiple-scattering effects by means of a similar configuration
to the MAAP. Light absorption coefficients obtained from the MWAA (at 635 nm)
and from the MAAP (at 637 nm) were compared by applying an linear regression
to both datasets after integrating the MAAP data over the filter total
sampling times, as shown in Fig. 5. The fit resulted in a MWAA
underestimation of 14 to 18 % when fitting the whole dataset. In general,
all values measured by the MWAA at 635 nm were below the MAAP measurements
at 637 nm with a decreasing offset towards lower absorption coefficients.
This could be associated with a significant volatilization of the absorbing
aerosol collected during the polluted period. The comparison of Aethalometer
and MWAA at different wavelengths was based on the assumption that these losses
are wavelength-independent.
Scatter plot and linear regression of MWAA and MAAP
absorption coefficient data. The 1:1 relationship is represented by a dashed
line. The fit was obtained by applying a standardized major axis regression.
Wavelength dependence retrieved from MWAA absorption data
and its standard error (purple points and shaded area), raw attenuation data
(gray line), and Schmid (orange points) and Collaud Coen (green points)
corrected absorption data, all averaged over MWAA sample intervals. Error
lines were removed to improve visualization.
MWAA data measured at five different wavelengths was used to retrieve
åABS by applying a log–log fit as expressed in Eq. (8).
Figure 6 shows the MWAA Ångström exponents and their uncertainty
intervals, together with the values obtained from the two different
Aethalometer corrections and the original åATN. The MWAA
åABS retrieved from each filter were not all statistically
optimal; 30 out of 175 had a R2 < 0.85. All the values below
this R2 limit were excluded from the results shown in Fig. 6. Absorption
Ångström exponents obtained using the Schmid correction were mostly
higher than the MWAA results. On the other hand, the Collaud Coen correction
resulted in a better approach to reproduce the MWAA data, with most of the
results in the MWAA uncertainty range. During the biomass burning period,
from 18 to 23 August 2014, the BrC contribution became more important and
caused an increase in the åABS and both algorithms' results
became similar to each other and slightly higher than the MWAA
åABS. After the biomass burning episode, when the
scattering and absorption coefficients fell down to background levels, the
offset between both algorithms, in terms of åABS, widened
again. In this regard, the Collaud Coen algorithm, which includes a modified
scattering correction, seems to be more appropriate to retrieve the
åABS for a broader range of absorption coefficients.
Scatter plot of AAE values obtained by Aethalometer
corrections vs. AAE obtained from MWAA measurements. The dark-colored lines
correspond to the standardized major axis linear fits and light-colored
lines correspond to 1 standard deviation of the retrieved AAE data. The
dashed gray line represents a 1:1 relationship.
A scatter plot of the åABS data, including the
corresponding linear fits, is shown in Fig. 7. The data analyzed in this
comparison include only filters that had a σap vs. λ log–log fit with R2 > 0.85. Although both algorithms
overestimate the åABS retrieved from the MWAA measurements,
the Collaud Coen algorithm produces a lower offset and a better linear fit,
with a R2= 0.72. On the other hand, the Schmid algorithm seems to
be artificially enhancing the absorption at lower wavelengths. When applying
linear regressions forced through the origin, the overall tendency showed a
statistically significant åABS overestimation by the Schmid
algorithm and a better fit for the Collaud Coen algorithm (not shown). The
original attenuation Ångström exponent (åATN,
without applying any compensation) was also found to fit the
MWAA-retrieved åABS quite well (slope IQR: 0.89–1.10 with
R2= 0.75, not shown). This finding is in accordance with Ajtai et
al. (2011), who found a good agreement between 4λ PAS measurements
and the Aethalometer raw wavelength dependence at a suburban site.
Overestimation of Aethalometer corrected absorption
coefficients relative to MWAA at 370 nm. Values above zero are related to an
overestimation of σap and, below zero, to an underestimation of
σap at this given wavelength.
Overestimation of near-UV absorption by AE corrections
The unexpectedly high åABS, especially that obtained by
applying the Schmid algorithm, is probably caused by an artificial
enhancement of the near-UV absorption. Figure 8 shows the relative
enhancement of the absorption coefficients at 370 nm, compared to the MWAA
absorption at 375 nm. No interpolation was applied to match both wavelengths
since they are close enough that the differences were negligible
(∼ 3 % for a åABS of 2.0). It is clear that the
Schmid algorithm almost always overestimated the absorption at 370 nm. Only
a few filters showed a difference close to or below zero. On average, the
Schmid algorithm overestimation relative to MWAA was a factor of
0.46 ± 0.31. In the case of the Collaud Coen algorithm, the average
difference was slightly positive, being a factor of 0.19 ± 0.32, and
reaching an average of 0.12 ± 0.12 for
σap > 5 Mm-1, during the biomass burning
event. A near-UV over- or underestimation of the data will substantially
affect brown carbon calculations if apportionment algorithms based on the
wavelength dependence of absorption are used. More details on the effects of
inaccurate åABS on the BrC / BC apportionment are
discussed in Garg et al. (2016), Schuster et al. (2016a, b), Wang et
al. (2016), and references therein. A BrC estimation is beyond the scope of
this paper.
Conclusions
We applied two different correction algorithms to compensate for the various
Aethalometer absorption measurement artifacts. The compensated data were
compared to an offline multi-wavelength reference absorption measurement
technique. This comparison allowed studying the effects of the correction
schemes on the absorption at lower wavelengths and showed how this affects
the åABS retrieval. We found that both analyzed algorithms
efficiently reproduce the reference MAAP absorption coefficients from
Aethalometer data. However, the Schmid algorithm overestimates the
åABS compared to that obtained by the multiple wavelength
measurement (MWAA). On the other hand, the Collaud Coen algorithm as well as
the “raw” Aethalometer attenuation spectral dependence reproduced the åABS values obtained from MWAA measurements quite
well. The
under- or overestimation of short-wavelength absorption coefficients by
compensation algorithms is a factor that has to be considered when using
corrected Aethalometer data to apportion the black and brown carbon
contributions to total absorption. When comparing the absorption coefficients
obtained from the different correction algorithms to the reference
measurement at 370 nm, we found that the Collaud Coen algorithm is more
appropriate to achieve the best comparison at this wavelength, especially for
data with σap > 5 Mm-1. The Schmid
algorithm resulted in high enhancements of the absorption coefficients at
370 nm over the sampling period.
The data presented in this paper can be accessed via e-mail request to Jorge Saturno (j.saturno@mpic.de).
The Supplement related to this article is available online at https://doi.org/10.5194/amt-10-2837-2017-supplement.
The authors declare that they have no conflict of interest.
Acknowledgements
This work has been supported by the Max Planck Society (MPG) and the Max
Planck Graduate School (MPGS). For the operation of the ATTO site, we
acknowledge the support by the German Federal Ministry of Education and
Research (BMBF contract 01LB1001A) and the Brazilian Ministério da
Ciência, Tecnologia e Inovação (MCTI/FINEP contract
01.11.01248.00) as well as the Amazon State University (UEA), FAPEAM,
LBA/INPA and SDS/CEUC/RDS-Uatumã. Paulo Artaxo acknowledges support from
FAPESP – Fundação de Amparo à Pesquisa do Estado de São
Paulo. Jorge Saturno is grateful for a PhD scholarship from the Fundación
Gran Mariscal de Ayacucho (Fundayacucho). We acknowledge Paola Fermo, Raquel
Gonzalez and Lorenza Corbella for the levoglucosan analysis. This paper
contains results of research conducted under the Technical/Scientific
Cooperation Agreement between the National Institute for Amazonian Research,
the State University of Amazonas, and the Max-Planck-Gesellschaft e.V.; the
opinions expressed are the entire responsibility of the authors and not of
the participating institutions. We highly acknowledge the support by the
Instituto Nacional de Pesquisas da Amazônia (INPA). We would like to
especially thank all the people involved in the technical, logistical, and
scientific support of the ATTO project, in particular Reiner Ditz, Jürgen
Kesselmeier, Niro Higuchi, Matthias Sörgel, Stefan Wolff, Thomas Disper,
Andrew Crozier, Uwe Schulz, Steffen Schmidt, Antonio Ocimar Manzi, Alcides
Camargo Ribeiro, Hermes Braga Xavier, Elton Mendes da Silva, Nagib Alberto de
Castro Souza, Adi Vasconcelos Brandão, Amaury Rodrigues Pereira, Antonio
Huxley Melo Nascimento, Thiago de Lima Xavier, Josué Ferreira de Souza,
Roberta Pereira de Souza, Bruno Takeshi, Ana María Yáñez-Serrano
and Wallace Rabelo Costa. Moreover, we thank Thorsten Hoffmann, Ulrich
Pöschl, Arthur Sedlacek, Jeannine Ditas, Su Hang, Jian Wang, Sachin
Gunthe, Jan-David Förster, Ming Jing, Tobias Könemann, Maria
Praß, Andrea Arangio and Bruna Amorim Holanda for support and stimulating
discussions.
The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL)
for the provision of the HYSPLIT transport and dispersion model and READY
website (http://www.ready.noaa.gov) used in this publication.
The article processing charges for this open-access publication were covered by the Max Planck Society.
Edited by: Paolo Laj Reviewed
by: two anonymous referees
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