ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-12817-2018Black and brown carbon over central Amazonia: long-term
aerosol measurements at the ATTO siteBlack and brown carbon over central AmazoniaSaturnoJorgej.saturno@mpic.dehttps://orcid.org/0000-0002-3761-3957HolandaBruna A.PöhlkerChristopherc.pohlker@mpic.dehttps://orcid.org/0000-0001-6958-425XDitasFlorianhttps://orcid.org/0000-0003-3824-9373WangQiaoqiaoMoran-ZuloagaDanielBritoJoelhttps://orcid.org/0000-0002-4420-9442CarboneSamaraChengYafanghttps://orcid.org/0000-0003-4912-9879ChiXuguangDitasJeannineHoffmannThorstenHrabe de AngelisIsabellaKönemannTobiashttps://orcid.org/0000-0003-3959-3491LavričJošt V.https://orcid.org/0000-0003-3610-9078MaNanMingJinghttps://orcid.org/0000-0001-5527-3768PaulsenHaukePöhlkerMira L.RizzoLuciana V.https://orcid.org/0000-0002-1748-6997SchlagPatrickhttps://orcid.org/0000-0002-0206-8987SuHanghttps://orcid.org/0000-0003-4889-1669WalterDavidhttps://orcid.org/0000-0001-6807-5007WolffStefanZhangYuxuanArtaxoPaulohttps://orcid.org/0000-0001-7754-3036PöschlUlrichhttps://orcid.org/0000-0003-1412-3557AndreaeMeinrat O.https://orcid.org/0000-0003-1968-7925Multiphase Chemistry & Biogeochemistry Departments, Max Planck Institute for Chemistry,
55128 Mainz, GermanyJinan University Institute for Environmental and Climate Research,
Guangzhou, 510630, ChinaInstitute of Physics, University of São Paulo, São Paulo, 05508-900, BrazilLaboratory for Meteorological Physics, Université Clermont
Auvergne, 63000 Clermont-Ferrand, FranceInstitute of Agrarian Sciences,
Federal University of Uberlândia, Uberlândia, Minas Gerais, 38408-100, BrazilInstitute for Climate and Global Change Research & School of
Atmospheric Sciences, Nanjing University, Nanjing, 210093,
ChinaDepartment of Chemistry, Johannes Gutenberg University, 55128
Mainz, GermanyBiogeochemical Systems & Biogeochemical
Processes Departments, Max Planck Institute for Biogeochemistry,
07701 Jena, GermanyInstitute of General Botany,
Johannes Gutenberg University, 55128 Mainz, GermanyDepartamento de
Ciencias Ambientais, Universidade Federal de Sao Paulo, Diadema, SP, BrazilScripps Institution of Oceanography, University of California San
Diego, La Jolla, CA 92098, USAnow at: Physikalisch-Technische Bundesanstalt, Bundesallee 100, 38116 Braunschweig, GermanyJorge Saturno (j.saturno@mpic.de) and Christopher Pöhlker
(c.pohlker@mpic.de)6September20181817128171284324November201712December20172June201810August2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/12817/2018/acp-18-12817-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/12817/2018/acp-18-12817-2018.pdf
The Amazon rainforest is a sensitive ecosystem experiencing the combined
pressures of progressing deforestation and climate change. Its atmospheric
conditions oscillate between biogenic and biomass burning (BB) dominated
states. The Amazon further represents one of the few remaining continental
places where the atmosphere approaches pristine conditions during occasional
wet season episodes. The Amazon Tall Tower Observatory (ATTO) has been
established in central Amazonia to investigate the complex interactions
between the rainforest ecosystem and the atmosphere. Physical and chemical
aerosol properties have been analyzed continuously since 2012. This paper
provides an in-depth analysis of the aerosol's optical properties at ATTO
based on data from 2012 to 2017. The following key results have been
obtained.
The aerosol scattering and absorption coefficients at 637 nm,
σsp,637 and σap,637, show a pronounced
seasonality with lowest values in the clean wet season (mean ± SD:
σsp,637=7.5±9.3 M m-1; σap,637=0.68±0.91 M m-1) and highest values in the BB-polluted dry season
(σsp,637=33±25 M m-1; σap,637=4.0±2.2 M m-1). The single scattering albedo at 637 nm,
ω0, is lowest during the dry season (ω0=0.87±0.03) and
highest during the wet season (ω0=0.93±0.04).
The retrieved BC mass absorption cross sections, αabs, are
substantially higher than values widely used in the literature (i.e.,
6.6 m2 g-1 at 637 nm wavelength), likely related to thick organic
or inorganic coatings on the BC cores. Wet season values of
αabs=11.4±1.2 m2 g-1 (637 nm) and dry season
values of αabs=12.3±1.3 m2 g-1 (637 nm) were
obtained.
The BB aerosol during the dry season is a mixture of rather fresh smoke from
local fires, somewhat aged smoke from regional fires, and strongly aged smoke
from African fires. The African influence appears to be substantial, with its
maximum from August to October. The interplay of African vs. South American
BB emissions determines the aerosol optical properties (e.g., the fractions
of black vs. brown carbon, BC vs. BrC).
By analyzing the diel cycles, it was found that particles from elevated
aerosol-rich layers are mixed down to the canopy level in the early morning
and particle number concentrations decrease towards the end of the day. Brown
carbon absorption at 370 nm, σap,BrC,370, was found to
decrease earlier in the day, likely due to photo-oxidative processes.
BC-to-CO enhancement ratios, ERBC, reflect the variability of burnt
fuels, combustion phases, and atmospheric removal processes. A wide range of
ERBC between 4 and 15 ng m-3 ppb-1 was observed with
higher values during the dry season, corresponding to the lowest ω0
levels (0.86–0.93).
The influence of the 2009/2010 and 2015/2016 El Niño periods and the
associated increased fire activity on aerosol optical properties was analyzed
by means of 9-year σsp and σap time series
(combination of ATTO and ZF2 data). Significant El Niño-related
enhancements were observed: in the dry season, σsp,637
increased from 24±18 to 48±33 M m-1 and σap,
637 from 3.8±2.8 to 5.3±2.5 M m-1.
The absorption Ångström exponent, åabs,
representing the aerosol absorption wavelength dependence, was mostly
<1.0 with episodic increases upon smoke advection. A
parameterization of åabs as a function of the BC-to-OA
mass ratio for Amazonian aerosol ambient measurements is presented. The brown
carbon (BrC) contribution to σap at 370 nm was obtained by
calculating the theoretical BC åabs, resulting in BrC
contributions of 17 %–29 % (25th and 75th percentiles) to
σap 370 for the entire measurement period. The BrC
contribution increased to 27 %–47 % during fire events under El
Niño-related drought conditions from September to November
2015.
The results presented here may serve as a basis to understand Amazonian
atmospheric aerosols in terms of their interactions with solar radiation and
the physical and chemical-aging processes that they undergo during transport.
Additionally, the analyzed aerosol properties during the last two El Niño
periods in 2009/2010 and 2015/2016 offer insights that could help to assess
the climate change-related potential for forest-dieback feedbacks under
warmer and drier conditions.
Introduction
Atmospheric aerosol particles affect the Earth's climate through different
mechanisms. Direct mechanisms include the aerosol particle interactions with
radiation by scattering and absorption. The balance between scattering and
absorption can lead to warming or cooling of the atmosphere (IPCC,
2013).
Moreover, indirect mechanisms, like aerosol–cloud interactions during cloud
formation and cloud microphysical modifications, are accompanied by high
uncertainties, especially due to the lack of knowledge on pre-industrial
levels of cloud condensation nuclei (CCN) (Carslaw et al., 2013) and aerosol
spatial distribution in the atmosphere (Andreae, 2007).
Continuous aerosol measurements at remote continental locations are crucial
to understand atmospheric conditions prior to industrialization and reduce
the uncertainties in climate models (Seinfeld et al., 2016). The Amazon Basin
is one of the few continental areas in the world where the atmosphere
approximates pristine conditions during some periods of the year (Andreae et
al., 2015; M. L. Pöhlker et al., 2018). However, anthropogenic pollution
is rather persistent and, thus, reaches almost every place on the planet
(Andreae, 2007; Chi et al., 2013; Hamilton et al., 2014). The Amazon
rainforest has been impacted substantially by intensified agriculture and the
associated deforestation and infrastructural development in the last 50 years
(Artaxo et al., 2013; Davidson et al., 2012). Given these circumstances, only
when air masses travel over clean marine areas and rain-related scavenging is
significant do the observations approach near-pristine to pristine levels
(Andreae et al., 2012, 2015; M. L. Pöhlker et al., 2018).
Biogenic primary and secondary organic aerosol particles over the Amazon
rainforest are ubiquitous throughout the year (Martin et al., 2010b). During
the dry season (August–November), when fires are frequent in the forest and
its peripheries, the background biogenic aerosol is overwhelmed by BB smoke
(Andreae et al., 1988; Artaxo et al., 2002; Fuzzi et al., 2007; Guyon et al.,
2003a; Roberts et al., 2003). Despite the rare occurrence of natural tropical
forest fires (Cochrane, 2003; Nepstad et al., 2008), most of the fire
episodes in the Amazon rainforest peripheries occur due to human activity,
including land use change, brush clearing for agricultural activities,
burning of agricultural waste (Andreae, 1991; Crutzen and Andreae, 1990), and
cooperative burning of savannas by indigenous communities, which is done to
prevent larger wildfires (Bilbao et al., 2010). Starting in August, the dry
season is characterized by aerosol number concentrations of
1000–3000 cm-3 (Andreae et al., 2015). Another characteristic of the
dry season is the occurrence of abundant black carbon (BC) in the atmosphere.
This type of aerosol particles is primarily emitted by flaming and smoldering
fires together with large amounts of organic aerosols (OA) (Andreae and
Merlet, 2001) and is considered an important short-lived climate forcing
agent (Andreae, 2001; Bond et al., 2004, 2013). The light absorbing fraction
of OA, which is co-emitted with BC, is called brown carbon (BrC)
(Andreae and Gelencsér, 2006). The BC + BrC aerosol fraction is
commonly defined as light-absorbing carbonaceous (LAC) matter
(Petzold et al., 2013). A list of frequently used acronyms and symbols can be
found in Table A1.
During combustion, aerosol particles are co-emitted with carbon monoxide
(CO). The ratio between aerosol mass or number concentrations and CO has been
used to trace the origin and age of air masses (Guyon et al., 2005;
Janhäll et al., 2010). Enhancement ratios (ERBC) for open
biomass burning measured for boreal forest smoldering fires have an average
ERBC of 1.7 ng m-3 ppb-1 (Kondo et al., 2011). In
contrast, agricultural fires exhibit higher ERBC compared to forest
fires, with reported values varying between 2.2 and
30 ng m-3 ppb-1 (Mikhailov et al., 2017, and references
therein).
Biomass burning plumes are usually dominated by accumulation mode aerosol
particles, which are efficient to scatter radiation in the UV-visible range
and are also rich in BC. In the absence of BB aerosol particles, the
biological coarse mode particles become dominant in terms of mass and the
aerosol optical properties are affected (Moran-Zuloaga et al., 2018).
Therefore, clear seasonal trends in scattering and absorption have been
observed by long-term measurements in the Amazon region (Rizzo et al., 2013).
The light absorption of BC has a wavelength dependence that depends on the BC
mixing state, its size distribution, and the composition of co-emitted
particles (Andreae and Gelencsér, 2006; Kirchstetter et al., 2004; Lack
et al., 2013; Schuster et al., 2016). The wavelength dependence is described
by the absorption Ångström exponent (åabs)
(Ångström, 1929). It varies from low values
(åabs=1.0±0.1, weak spectral dependence),
usually associated with fossil fuel emitted BC (Bond and Bergstrom, 2006), up
to high values (åabs=6–7, strong spectral
dependence) for organic-rich aerosol, e.g., humic-like substances (Hoffer et
al., 2006). Measurements at an Amazonian forest site during the dry season
resulted in åabs average values below 1.0 for
absorption coefficients lower than 15 M m-1 at 450 nm (Rizzo et al.,
2011). For BB aerosol particles, the åabs is usually
higher than 1.0. However, it depends on the burning conditions, the BC-to-OA
ratio (Saleh et al., 2014), and the BC–BrC size distributions and
morphologies (Kirchstetter et al., 2004; Womack et al., 2017). Several
studies have used the absorption spectral dependence to apportion the fossil
fuel and BB contributions to total absorption (Favez et al., 2010;
Massabò et al., 2015; Sandradewi et al., 2008). However, the
åabs values do not always reflect the combustion type,
and using it as a source apportionment parameter can lead to erroneous
results (Garg et al., 2016; Lack and Langridge, 2013; Lewis et al., 2008;
Wang et al., 2016b). Several studies assume a BC åabs
of 1.0, but models show that pure BC could exhibit a broader range of
åabs values (Moosmüller et al., 2011). In order to
retrieve the ambient BC wavelength dependence, Wang et al. (2016b) proposed
the use of the wavelength dependence of åabs instead
of åabs itself. The so-called wavelength dependence of åabs (WDA) is calculated as the difference of
two wavelength pairs: one for short to long wavelengths (e.g., 440–870 nm)
and another for medium to long wavelengths (e.g., 675–880 nm).
Precise BC mass measurements are required to retrieve the correct
relationship between absorptivity and BC mass, defined as the mass absorption
cross section (MAC or αabs). The BC mass concentration has
traditionally been measured by using thermal or thermal–optical techniques
(Cachier et al., 1989; Chow et al., 2007). However, these methods suffer from
several biases, like organic carbon charring that increases the apparent BC
concentration, especially when high organic fractions are present (Andreae
and Gelencsér, 2006). More recently, laser-induced incandescence (LII)
techniques have been introduced (Snelling et al., 2005). These techniques
measure the volume-equivalent mass of refractory black carbon (rBC) that
vaporizes at temperatures of 2800–4000 K. The MAC is used by atmospheric
radiative transfer models to obtain absorption coefficients from mass
concentration data. The MAC of BC varies between 4 and 11 m2 g-1 at
550 nm, with an average of 6.5 m2 g-1 at 637 nm for fresh soot
(Bond and Bergstrom, 2006). In case of condensation of non-BC material on the
BC particles, the MAC can be enhanced due to the well-known “lensing
effect” (Fuller et al., 1999). This commonly happens when BC is emitted by
BB, since it is co-emitted with large amounts of organic vapors that can
condense on BC particles (Saleh et al., 2014). In the central Amazon, black
carbon particles have been shown to be coated by organic and inorganic matter
(Pöhlker et al., 2014; Pöschl et al., 2010). It has been found that
the coating mass significantly affects the absorption enhancement of BC
particles, but no significant changes are caused by variations in the
coating's oxygen-to-carbon ratio (Tasoglou et al., 2017). A wide range of MAC
values can be found in the literature for different fire conditions
(smoldering and flaming).
Commonly, the absorption properties of an aerosol population are reported as
the single scattering albedo (SSA, ω0), which is defined as total
scattering divided by total extinction (absorption + scattering).
Therefore, a lower ω0 is associated with a stronger absorption.
Tropical Amazonian forest fires have moderately high ω0 values
(0.93±0.02 at 670 nm), given the high amount of scattering aerosols
which are co-emitted with LAC, compared to African savanna fires that have
lower ω0 values (0.84±0.015 at 670 nm) (Reid et al., 2005).
In the Amazon rainforest, long-term measurements by Rizzo et al. (2013) have
found similar values for ω0 during the dry and wet seasons, 0.87±0.06 and 0.86±0.09, respectively. The low ω0 in the wet
season is attributed to long-range transported aerosols that include mineral
dust and aged BB aerosol particles. Aged BB aerosol is proven to have
increased MAC, and therefore lower ω0 (Reid et al., 2005).
Moreover, the biogenic part of the aerosol can contribute up to 35 % of
total light absorption (Guyon et al., 2004).
When present in large amounts in the atmosphere, mineral dust can
significantly absorb light, with a MAC of 0.02–0.1 m2 g-1 at
550 nm (Clarke and Charlson, 1985). It is mobilized from soils and suspended
in the atmosphere by windstorms in areas like the Saharan desert in Africa.
Dust aerosol particles in the atmosphere efficiently scatter visible
radiation and are able to absorb infrared radiation (Andreae, 1996), having a
åabs≫1.0 (Caponi et al., 2017; Denjean et al.,
2016). Mineral dust plumes travel over the Atlantic Ocean and are able to
reach the American continent. Depending on the circulation patterns over the
tropical Atlantic, the African dust plumes will be transported to South
America or to the Caribbean Sea and central America (Prospero et al., 1981).
The average transport time from emission to deposition in the Amazon Basin
during winter is ∼10 days (Gläser et al., 2015). Ground measurements
of aerosol physical and chemical properties have confirmed that between
January and April mineral dust plumes from Africa episodically dominate the
aerosol load over large parts of the Amazon rainforest (Formenti et al.,
2001; Guyon et al., 2004; Moran-Zuloaga et al., 2018; Talbot et al., 1990;
Wang et al., 2016a). Moreover, the dust-enriched aerosol usually arrives
together with BB aerosol emitted by fires in sub-Sahelian western Africa and
also aerosol particles emitted by industrial activities in Morocco and the
western Saharan coast (Moran-Zuloaga et al., 2018; Salvador et al., 2016). In
spite of anthropogenic disturbance of soils in Africa that could enhance the
flux of mineral dust to the atmosphere (Andreae, 1991), a decreasing trend in
mineral dust emissions since the 1980s has been observed and is mainly caused
by a reduction of surface winds in the Sahel region (Ridley et al., 2014).
This study provides a comprehensive and in-depth analysis of the aerosol
optical properties in the Amazonian atmosphere. A continuous long-term data
set (2012–2017) of different optical properties is provided. We particularly
focus on the impact of BB emissions from long-range transport and from
regional/local open fires during the dry season. By using data from another
central Amazonian remote sampling site, we extend our time series back to
2008 and provide the longest data set on optical properties measured in the
Amazon rainforest so far. By these means, we are able to study the
perturbations caused by the El Niño–Southern Oscillation (ENSO), which
has been reported to cause droughts in the Amazon Basin (see Fig. S1 in the
Supplement), with increasing fire activity and forest degradation (Aragão
et al., 2007; Cochrane, 2003; Davidson et al., 2012; Lewis et al., 2011).
Materials and methodsSampling site and measurement period
Aerosol particles and trace gases have been measured at the Amazon Tall Tower
Observatory (ATTO) site, located in the Uatumã Sustainable Development
Reserve, Amazonas State, Brazil, in central Amazonia since 2011 (Andreae et
al., 2015). The large-scale meteorological conditions of the site are
determined by the seasonal migration of the inter-tropical convergence zone
(ITCZ) (C. Pöhlker et al., 2018). From August to November, during the
dry season, the ITCZ is located in the north of South America, and
mostly Southern Hemisphere air masses reach the ATTO site, bringing BB
emissions from deforestation hotspots in southeastern Brazil (i.e., the
so-called arc of deforestation) as well as transcontinental emissions from
southern Africa. During the wet season, from February to May, when
the ITCZ shifts to southern latitudes, the air masses generally come from the
Northern Hemisphere, following a path over the Atlantic Ocean from the
African continent and then over mostly untouched forest areas upwind of the
ATTO site. The transition seasons, dry to wet and wet to dry, occur in December–January and June–July, respectively.
At the ATTO site, systematic aerosol measurements were started in March 2012,
being continuously extended and intensified since then. In the course of this
process, the aerosol inlet system was modified and upgraded step-wise. A
detailed list of the different inlet configurations and characteristics can
be found in Table S1. On 4 May 2014, a PM1 cyclone was installed in the
common inlet line for the aerosol optical measurements. The rest of the
instrumentation kept sampling total suspended particles (TSP). The sample air
was dried by diffusion driers filled with silica gel to guarantee a relative
humidity around 40 % or below. An automatic regenerating adsorption
aerosol dryer (Tuch et al., 2009) was installed in January 2015.
Another sampling site, the ZF2/TT34 tower, located ∼60 km NNW of Manaus
and ∼140 km WSW of ATTO (Fig. S2), has been the location of long-term
aerosol observations and intensive measurement campaigns (Martin et al.,
2010a; Rizzo et al., 2013). Given that most of the air masses that reach the
ZF2 site are similar to those transported over the ATTO site (C. Pöhlker
et al., 2018), the ZF2 data are usually comparable to the ATTO data and the
time series presented in this study can complement previous ZF2 time series
already reported for the period 2008–2011 (Rizzo et al., 2013).
Additionally, two intensive observation periods (IOP) and long-term
measurements of the GoAmazon2014/5 experiment took place at several
measurement sites in the Amazon Basin, including the ATTO site. More details
can be found in Martin et al. (2016, 2017).
Scattering coefficients at ATTO were measured using different nephelometers.
Figure S3 shows the measurement periods of the different instruments. The
first one was a three-wavelength integrating nephelometer (model 3563, TSI,
St. Paul, USA) (14 August 2012 to 24 November 2013). The instrument measures
aerosol scattering (σsp) and backscattering
(σbsp) at 450, 550, and 700 nm (Anderson et al., 1996).
Calibrations were periodically done by using CO2 as a span gas. Given
the optical configuration of the instrument, the truncation of forward
scattered radiation constitutes the largest source of error and was corrected
by following the procedure described by Anderson et al. (1996). The estimated
error of the nephelometer measurements is 8 % for scattering coefficients
on the order of 10 M m-1 (Rizzo et al., 2013). Using an averaging time
of 30 min, the detection limit at 550 nm was 0.14 M m-1 (Rizzo et
al., 2013).
Later, in February 2014, the TSI nephelometer was replaced by an Aurora 3000
(Ecotech Pty Ltd., Knoxfield, Australia), which measures at 450, 525, and
635 nm wavelength. Over the measurement period studied in this work, we used
two different Aurora instruments, with and without backscattering
measurement. The Aurora nephelometer was set up to work with an integration
time of 1 min. Similar to the TSI nephelometer, CO2 calibrations were
periodically performed. The data were corrected for truncation according to
Müller et al. (2011b). Uncertainty in scattering measurements by the
Aurora nephelometers was estimated to be 5 % (Müller et al., 2011b).
Aerosol light attenuation and absorption measurements
Light absorption coefficients at 637 nm wavelength, σap,637, were measured by a multi-angle absorption photometer, (MAAP, model
5012, Thermo Electron Group, Waltham, USA). This instrument measures the
transmission of light through a glass-fiber filter on which aerosol particles
are collected. Additionally to the forward hemisphere transmission
measurement, the MAAP measures the light back scattering at 130 and
165∘. By using a radiative transfer model (Petzold and
Schönlinner, 2004), the instrument is able to provide absorption
coefficients. The instrument was set up to provide data at 1 min resolution.
By averaging the data at 30 min intervals, the MAAP detection limit is
0.13 M m-1, which corresponds to a BCe mass concentration of
20 ng m-3 (calculated with a MAC of 6.6 m2 g-1). The MAAP
was generally operated at a flow rate of 10 L min-1, but for some
periods the flow rate was reduced to 8.3 L min-1. According to
Müller et al. (2011a), the MAAP measures at a wavelength of 637±1 nm, instead of the 670 nm reported in the instrument's manual. In our
calculations, we use 637 nm as the default MAAP wavelength and do not apply
any interpolation factor to scale up the data from 670 to 637 nm since it
would be within the instrument's ∼5 % uncertainty range. The total
uncertainty of the MAAP absorption measurements is of the order of 10 %
for 30 min average times (Rizzo et al., 2013).
An aethalometer was used to measure attenuation of light by aerosol particles
at different wavelengths. This instrument uses an LED light source to
irradiate an aerosol-laden quartz-fiber filter and a detector, located in the
forward hemisphere, to measure the light transmission (Hansen et al., 1984).
The measured transmission is compared to a blank measurement in order to
obtain a change in light transmission (i.e., attenuation). This attenuation
is then converted to BC mass concentration by using a mass attenuation cross
section that depends on the instrument model (14 625 and
6837.6 m2 g-1λ-1 for the AE31 and AE33 aethalometer
models, respectively).
Aethalometer measurements started at the ATTO site in April 2012 using model
AE31 (Magee Scientific, Berkeley, USA). The instrument was operated at
different flow rates during the measurement period (varying from 2.0 to
3.7 L min-1) and measured attenuation every 15 min. In January 2015,
a new aethalometer, model AE33 (Aerosol d.o.o., Ljubljana, Slovenia), was
installed. The overlapping measurement time of the AE31 and AE33 models
(27 November to 15 December 2014) enabled the comparison of both data sets.
We found good agreement between both models (difference < 10 %) for
measurements at 470, 520, 590, and 660 nm. However, the wavelength
dependence did not fit very well during this intercomparison period. Similar
deviations in the wavelength dependence of AE31 and AE33 have been reported
previously (ACTRIS, 2014). Nevertheless, it is still not clear whether the
higher wavelength dependence of the AE33 compared to the AE31 is the result
of an artifact of the instrument. An independent multi-wavelength absorption
measurement can help to clarify the aforementioned AE31–AE33 deviation in
åabs (Saturno et al., 2017). The comparison between
compensated AE31 and AE33 data was used to correct the AE33 wavelength
dependence deviation by applying intercomparison factors to AE33 data. The
obtained AE31–AE33 intercomparison fits are shown in Fig. S4.
Aethalometer data require several corrections to account for different
artifacts related to multiple scattering by the filter fibers, scattering by
embedded aerosol particles, and filter-loading effects. The correction
applied in this study has been described in a previous article (Saturno et
al., 2017). The compensation algorithm is based on the correction scheme
proposed by Collaud Coen et al. (2010). It uses MAAP data as a reference
absorption measurement, which could introduce uncertainties related to the
modification that aerosol particles can suffer by being deposited on a filter
matrix. We retrieved the åabs from applying a log–log
fit to aethalometer data corrected for filter-loading and multiple scattering
effects. In the case of aethalometer AE33, the measurements do not require a
filter-loading correction because this model uses the dual-spot technology
which accounts for this artifact (Drinovec et al., 2015). The AE33 internal
algorithm applies a multiple scattering correction using the correction
factor reported by Weingartner et al. (2003). In this study, this
compensation was reverted and the multiple scattering correction was
calculated according to a comparison with MAAP measurements, in a similar
fashion to the one applied to AE31 data mentioned above.
rBC mass measurements and MAC calculations
Refractory black carbon (rBC) was measured using a single particle soot
photometer (SP2) revision C (Droplet Measurement Technologies, Longmont,
USA). Initially, the measurements were done with a four-channel SP2 and the
instrument was upgraded on 19 January 2015 to the eight-channel
configuration. Figure S3 shows the different measurement periods of this
instrument. The SP2 uses a high-intensity Nd:YAG laser beam
(1 MW cm-2, λ=1064 nm) to irradiate aerosol particles that
are provided by an air jet at 90∘, with a flow rate of
0.12 L min-1. All particles scatter the light from the laser beam and
some of them, which are able to absorb radiation at the given wavelength
(e.g., rBC), will incandesce and vaporize at high temperatures (Moteki and
Kondo, 2008; Stephens et al., 2003). Four avalanche photo-diode (APD)
detectors are installed in the instrument to measure (a) scattering,
(b) broadband incandescence (350–800 nm), (c) narrowband incandescence
(630–880 nm), and (d) scattering with a split detector. Time-dependent data
are recorded from each particle as it passes through the laser beam. The
ratio between broadband and narrowband signals can provide information on the
particle's composition since it is related to the boiling point temperature
of the sampled particles (Schwarz et al., 2006). The instrument was
periodically calibrated using fullerene soot (Alfa Aesar Inc.) as the rBC
reference material. A quadratic fit was applied to the recorded incandescence
peak heights vs. the mass of mobility size-selected fullerene particles. The
fullerene effective densities were taken from Gysel et al. (2011). The
scattering detector was calibrated using polystyrene latex (PSL)
spheres by relating the scattering
signal to the PSL scattering cross section. The SP2 rBC dynamic ranges were
80–280 and 80–450 nm for the four-channel and eight-channel
configurations, respectively.
The narrow dynamic range of the four-channel SP2 was preventing us from
measuring rBC mass concentration values comparable to MAAP measurements. In a
comparison with another eight-channel instrument during the GoAmazon2014/5
experiment we found that the four-channel instrument was underestimating the
rBC mass concentration by a factor of 40 %. This factor was stable during
the wet season 2014, but we could not measure its stability during the
following dry season. Due to instability of this factor over the sampling
period, a proper data correction was not possible. Therefore, in this paper
we use only the eight-channel instrument's data, which were available from
9 February 2015 until 31 July 2016 with some interruptions due to hardware
failures. The eight-channel SP2 rBC size-dependent counting efficiency was
obtained by comparing the counts of fullerene particles measured by the SP2
and a condensation particle counter (CPC). This way, an underestimation
factor of 5 % was found to affect SP2 rBC mass measurements and a scaling
factor of 1.05 was applied to the data to account for this systematic error.
Similar underestimation factors have been previously reported (Liu et al.,
2017; Wang et al., 2014). The cumulative uncertainty of the SP2 measurements
associated with the counting efficiency and mass calibration of the
instrument has been estimated to be around 25 % (Wang et al., 2014).
The BC mass absorption cross section, αabs, was calculated by
running daily fits of 30 min averaged MAAP σap,637 vs. SP2
rBC mass concentration data, using a standardized major-axis estimation (as
explained in Sect. 2.6). Fits with R2<0.9 were filtered out, resulting in
a total of 106 out of 220 days included in the final result. Given the
mentioned SP2 and MAAP uncertainties, the αabs values
presented here have uncertainties around ±40 %. The obtained
αabs values (shown in Sect. 3.1) were used to convert MAAP
absorption measurements into BCe mass concentrations.
Complementary measurements
Online chemical composition of aerosol particles has been measured since
August 2014 using an aerosol chemical speciation monitor (ACSM) (Aerodyne
Research Inc., Billerica, USA). Initial results on non-refractory aerosol
chemical composition at the ATTO site have already been reported by Andreae
et al. (2015) and a detailed paper on the long-term ACSM observations is
being prepared by Carbone et al. (2018). This online mass spectrometry
technique detects organics, nitrate, sulfate, ammonium, and chloride in the
sub-micron (<1µm) aerosol size range (Ng et al., 2011).
A Picarro cavity ring-down spectrometer G1302 analyzer (Picarro Inc., Santa
Clara, USA) measured CO2 and CO at the ATTO site. Three calibration
tanks were used to calibrate the instrument every 100 h. A
Nafion™ dryer was installed in front of the
instrument in order to reduce the noise in the CO measurements, which are
affected by the high relative humidity of the tropical forest air.
Calibration and performance checks will be reported in an upcoming paper. The
instrument samples at five different heights, but we restrict our analysis to
the data measured at 79 m. All CO measurements have been conducted on the
walk-up tower. The measurement setup is largely inspired by a setup
operational at another location since 2009 (see Winderlich et al., 2010). In
order to calculate the BC enhancement ratios with respect to CO
(ERBC), we used a major-axis estimation fit that was applied to the
bivariate data (Falster et al., 2006) where the slope represents the
enhancement ratio. The 5th percentiles were used as background values.
Condensation nuclei (CN) number concentrations, NCN, and size
distributions from 10 nm to 10 µm, were continuously measured
using several instruments including mobility and optical particle sizers
(more details can be found in Andreae et al., 2015). In this study, we used
coarse mode (>1µm) number and mass concentrations obtained by
means of an optical particle sizer (OPS), model 3330 (TSI Inc., Shoreview,
USA), to identify mineral dust transport events. A detailed analysis of the
Saharan dust plume arrivals at the ATTO site can be found in Moran-Zuloaga et
al. (2018). Aerosol particle size distributions (10–430 nm diameter) were
measured with scanning mobility particle sizer (SMPS) models 3080 and 3081
(TSI Inc., Shoreview, USA) using a CPC, model 3772 (TSI Inc., Shoreview,
USA).
Wavelength dependence and BrC contribution calculations
Light scattering and absorption wavelength dependence are represented by the
Ångström exponents, åsca and
åabs, respectively. The Ångström exponent can
be retrieved when measurements at two or more different wavelengths are
available; for example, the åabs can be calculated as
åabs=-lnσapλ1σapλ2lnλ1λ2,
where σap is the absorption coefficient at two different
wavelengths, λ1 and λ2.
When measurements at more than two wavelengths are available, a linear fit
can be used to retrieve the Ångström exponent from the logarithm of
the absorption (or scattering) coefficients vs. the logarithm of the
wavelength, as follows:
lnσap=-åabslnλ+lnconstant.
Black carbon is commonly taken to be wavelength-independent with
åabs=1. However, this assumption is theoretically
wrong and the BC-related åabs is very sensitive to the
size of the particles (Moosmüller et al., 2011). Wang et al. (2016b)
proposed a method to calculate the wavelength dependence of the Ångström exponent (WDA) in order to estimate the BrC contribution to
total light absorption by aerosol particles. They use the difference between
two åabs calculated for two different wavelength pairs
(440–870 and 675–880 nm) using aerosol robotic network (AERONET) and
aethalometer data. We use a similar approach to retrieve WDA using
aethalometer data from the ATTO site. In this study the WDA is calculated as
follows:
WDA=åabs,370-950-åabs,660-950,
where åabs,370-950 and åabs,660-950 correspond to the absorption Ångström exponents calculated
for the 370–950 and 660–950 nm wavelength pairs, respectively. This way, a
theoretical BC WDA was calculated from the modeled
åabs for BC (BC
WDA =åabs,370-950BC-åabs,660-950BC).
Theoretical WDA values were calculated following conceptual Mie theory models
for (i) polydisperse BC particles (Mishchenko et al., 1999), and
(ii) core-shell internally mixed monodisperse BC (Bohren and Huffman, 1983).
Characteristic BC core size distributions measured by the SP2 during the wet
and dry seasons were used in the polydisperse BC-only model to retrieve
extinction efficiency and single scattering albedo. The refractive indices
used were 1.95–0.79i for BC (Bond and Bergstrom, 2006) and 1.55–0.001i for
the coating material (Liu et al., 2015). The latter value was only used for
the internally mixed BC case. The BC core diameters used in the internally
mixed case were 100, 125, 150, 175, 200, 225, and 250 nm, with a coating
thickness to core size ratio from 0.1 to 1. These values are in accordance
with rBC mass size distributions observed at the ATTO site; see Fig. S5.
Black carbon density was set to 1.8 g cm-3 (Schkolnik et al., 2007).
Calculated BC WDA thresholds (25th and 75th percentiles), shown in Fig. S6,
were compared to the ambient data in order to identify BrC influenced
periods. For a general analysis, data with WDA lower than the 75th percentile
were considered to be in the BC-only regime. The presence of BrC, in
addition to BC, occurred when the modeled BC absorption at 370 nm was
exceeded. A sensitivity study of this model was done by changing the
refractive indices and the core size of the model input. These results are
presented in Table S2 as relative overestimation of the BrC contribution to
σap,370. The calculated BC absorption Ångström
exponents åabsBC for the two
wavelength pairs mentioned in Eq. (3) were used to calculate BrC absorption
at 370 nm, as follows:
σap,370BC=σap,950×370950-åabs,370-950BC,σap,370BrC=σap,370-σap,370BC,
where åabs,370-950BC is obtained from the
Mie model calculations. The uncertainties of the BrC contribution to total
absorption at 370 nm were calculated using the theoretical minimum and
maximum BC WDA values. They were below 37 % overall, and decreased to
below 19 % when the BrC contribution was higher than 30 % at 370 nm.
The relative overestimation of the BrC contribution obtained by using
different BC core sizes and different refractive indices in the Mie model
calculations can be found in Table S2.
(a) Map of the northeastern Amazon Basin including averaged
backward trajectory clusters and the region of interest (ROI)
(59–54∘ W; 3.5∘ S–2.4∘ N), as a black rectangle,
used to retrieve precipitation in the ATTO area. (b) Time series of
the frequency of occurrence of each BT cluster during the sampling period.
Adapted from C. Pöhlker et al. (2018).
HYSPLIT backward trajectories and clustering
The systematic backward trajectory (BT) analysis used here is described in
C. Pöhlker et al. (2018). Briefly summarized: three-day backward
trajectories were calculated by running the NOAA hybrid single-particle
Lagrangian integrated trajectory (HYSPLIT) model (Draxler and Hess, 1998)
using 1∘ resolution meteorological data from the global data
assimilation system (GDAS1). The trajectories were calculated for 1000 m
above ground level at 1 h intervals for the period January 2008 to
June 2016. The entire trajectory ensemble was classified into 15 BT clusters
using a k-means cluster analysis. The clusters represent different air mass
transport tracks and velocities. The different cluster average trajectories
and their frequency of occurrence are shown in Fig. 1a and b, respectively.
The clusters are classified as northeasterly (“NE1”, “NE2”, and “NE3”),
east-northeasterly (“ENE1”, “ENE2”, “ENE3”, and “ENE4”), easterly
(“E1”, “E2”, “E3”, and “E4), southeasterly (“ESE1”, “ESE2”, and
“ESE3”), and southwesterly (“SW1”) trajectory clusters. In some parts of
the analysis presented here the trajectory clusters are grouped by main
directions (NE, ENE, E, and ESE).
South American fire count data were retrieved from the satellite observations
database available online by the Instituto Nacional de Pesquisas Espaciais
(INPE), Brazil, at https://prodwww-queimadas.dgi.inpe.br/bdqueimadas/,
last access: 4 April 2017. The fire data covered the same period as the
HYSPLIT clustering analysis period, January 2008 to June 2016. Fire counts
were classified at hourly resolution according to the corresponding BT
cluster where they occurred. The fire counts reported in this study were
weighted according to the trajectory density as (trajectory
counts) / 100 km2. Since the fire count number depends on the
amount of satellite data available, we use these data with caution and only
as a qualitative reference. For an extended discussion on fire geographical
locations and land cover types, see C. Pöhlker et al. (2018).
Aerosol optical depth (550 nm) observations in two different
domains of interest shown in (a) DOI1 (boundaries: 30∘ W;
20∘ S; 10∘ W; 0∘ S) and DOI2 (boundaries:
58∘ W; 14∘ S; 40∘ W; 8∘ S). Time series
of area-averaged AOD are shown in (b) for DOI1 (dark blue) and DOI2
(red). As a reference, the ATTO region of interest (ROI ATTO) is shown as a
black rectangle in (a).
Satellite data
The aerosol optical depth (AOD) at 550 nm, measured by the moderate
resolution imaging spectroradiometer (MODIS) onboard the satellites Terra and
Aqua, was retrieved for two domains of interest (see Fig. 2a).
DOI1: over the Atlantic Ocean. Used to monitor the westward transport of
BB aerosol particles from southern Africa, which are mostly emitted during
the Amazon dry season, especially between August and September (Das et al.,
2017). There is no guarantee that the observed aerosol over this area will
necessarily reach the ATTO site, but it is used as an indication of LRT
events from southern Africa that will likely reach the Amazon Basin.
Boundaries: 30∘ W; 20∘ S; 10∘ W;
0∘ S.
DOI2: over the southern Amazon. Used to monitor BB in
this region where fire activity is related to deforestation and
agriculture-related activities. Boundaries: 58∘ W;
14∘ S; 40∘ W; 8∘ S.
The MODIS products can be found online at the Goddard Earth Science Data and
Information Services Center at
https://giovanni.gsfc.nasa.gov/giovanni/, last access: 17 July 2017,
(GES-DISC, 2017).
Terra and Aqua data were averaged over the two different domains. The
averaged AOD at 550 nm time series corresponding to DOI1 and DOI2 can be
found in Fig. 2b. The seasonality observed for both data sets is similar, but
the AOD for DOI1 (Atlantic Ocean) generally increased in August and decreased
after the end of September, with some peaks in January–February, especially
in 2016. On the other hand, high AOD values in DOI2 (southern Amazon)
increased sharply in the beginning of September and decreased continuously
until the middle of December, with the exception of the dry season 2015, when
high AOD was observed until February 2016.
Data treatment
The analyzed data were averaged to 30 min intervals and corrected to
standard temperature and pressure (STP, 273.15 K and 1013.25 hPa).
Furthermore, the scattering data were interpolated to 637 nm to compare
directly to the absorption data obtained by the MAAP, in order to avoid the
uncertainty associated with the absorption spectral dependence calculation.
The time periods of major and medium dust influence were taken from a study
by Moran-Zuloaga et al. (2018). During the dry season, BB pulses were
segregated by using the 75th percentile of σap 637 as a
threshold. When examining correlations between independent measurements, we
applied standardized major-axis estimations (SMA) by using the SMATR package
(Falster et al., 2006) in the R statistical software environment
(R Development Core Team, 2009). This method minimizes the error on the x
and y axes, and not only at the y axis, like a linear regression does.
Therefore, it provides unbiased estimates of the slope (Warton et al., 2006).
Overview of aerosol optical properties during the measurement
period. (a) Scattering coefficient, (b) absorption
coefficient at 637 nm, (c) single scattering albedo at 637 nm,
(d) scattering Ångström exponent, and
(e) absorption Ångström exponent. All data were averaged on
24 h intervals and standard errors are presented as vertical gray bars. Green
and gray shaded areas correspond to the wet and dry seasons, respectively.
First and third quartiles are represented as horizontal dashed lines, and
medians as horizontal solid lines.
Descriptive statistics (mean ± standard deviation and
interquartile range, IQR) of daily averaged aerosol optical properties over
the Amazon rainforest during the different seasons and the entire measurement
period.
1 Calculated by applying a log–log linear fit to measurements
at all available wavelengths. 2 Calculated by fitting eight-channel SP2
and MAAP data. 3 Including data from July 2015/16 (wet-to-dry transition
season).
Results and discussionOverview of aerosol optical properties (2012–2017)
This section summarizes the aerosol optical properties from 5 years of
continuous measurements at the ATTO site. The corresponding time series are
shown in Fig. 3 and descriptive statistics can be found in Table 1. The wet
and dry season statistics were calculated excluding the transition periods.
The scattering coefficients, σsp, are shown in Fig. 3a,
averaging 7.5±9.3 and 33±25 M m-1 at 550 nm during the
wet and dry seasons, respectively (see Table 1). These values agree well with
previously reported results at ZF2 of 8.1±7.2 and 36±48 M m-1 at 550 nm during the wet and dry seasons, respectively
(Rizzo et al., 2013). Good agreement was also found for our results at 450
and 700 nm and the corresponding data from Rizzo et al. (2003). The
proximity of both sites, ATTO and ZF2, frequently allows probing of
comparable air masses of similar origin and atmospheric history. The
long-term measurements also show a pronounced year-to-year variability in
σsp (compare, e.g., 2014 and 2015 in Fig. 3a). The largest
observed deviations from the dry-season average were found during the dry
season 2015, with an average increase of 38 % in σsp at
550 nm. Similar increases were observed in σsp at 450 and
637 nm. These increases can be directly related to the higher occurrence of
fire episodes during the strong ENSO period 2015/2016 with its negative
precipitation anomaly, as discussed in more detail in Sects. 3.5 and 3.6.
The absorption coefficients, σap, at 637 nm (MAAP) are shown
in Fig. 3b, and averaged 0.68±0.91 and 4.0±2.2 M m-1
during the wet and dry seasons, respectively. Also for this parameter,
comparable values were measured at the ZF2 site, with averages of 1.0±1.4 and 3.9±3.6 M m-1 at 637 nm during the wet and dry
seasons, respectively (Rizzo et al., 2013). The higher increase in the
absorption coefficient (factor of 5.9) from wet to dry season compared to the
increase in scattering (factor of 4.4) affected the ω0 (see
Fig. 3c). Lower values were observed during the dry season (0.87±0.03 at
637 nm, 0.81±0.08 at 550 nm) compared to the averages observed in the
wet season (0.93±0.04 at 637 nm, 0.88±0.08 at 550 nm). At the ZF2
site, Rizzo et al. (2013) have found small differences between ω0
values during the dry and wet seasons (0.87±0.06 and 0.86±0.09 at
637 nm, respectively) for over 2 years (2008–2011) of measurements.
However, measurements during the wet season in 1998 at a sampling site closer
to ATTO (Balbina, 60 km NW of ATTO and 140 km NE of Manaus) showed higher
ω0 values: 0.92–0.95 at 550 nm (Formenti et al., 2001). These
values are within our measurement range for the same season (0.88±0.08
at 550 nm). Single scattering albedo retrieved from multi-year ground-based
radiometer measurements in the Amazonian forest had an average of 0.93±0.02 (Dubovik et al., 2002). Given that we sampled dried aerosol particles,
our average ω0 are expected to be lower than these ambient-humidity
values during the entire measurement period and the dry season. Measurements
close to BB sources in Brazil have shown a wide range of ω0; e.g.,
Chand et al. (2006) found an ω0 of 0.92±0.02 (550 nm) for
dried aerosol over Rondônia, whereas Guyon et al. (2003a) calculated
lower ω0 values during BB events at the end of the LBA-EUSTACH 1
campaign in Rondônia, reaching 0.85±0.02 at 550 nm. Freshly emitted
smoke has an even lower ω0, of 0.79±0.05 at 550 nm (Reid et
al., 1998).
The scattering Ångström exponent, åsca, is a
function of the aerosol particle size distribution. However, some studies
have found that this relationship is only evident for surface and volume mean
diameters and was not clearly valid between åsca and
count mean diameters (Rizzo et al., 2013; Virkkula et al., 2011). We obtained
higher åsca values during the dry season (1.71±0.24) compared to the wet season (1.29±0.50) as shown in Fig. 3d. This
is an indication of the dominance of fine mode aerosol (mostly BB-related)
during the dry season over the coarse mode aerosols that become more
important in the wet season (i.e., PBAP, Saharan dust and sea salt), as
previously observed at the ATTO site (Andreae et al., 2015; Moran-Zuloaga et
al., 2018). A similar seasonal trend has been observed at the ZF2 site, where
åsca was 1.70±1.41 and 1.48±1.12 (30 min
averages) for the dry and wet seasons, respectively (Rizzo et al., 2013). A
detailed analysis of the coarse mode aerosol abundance and properties
measured at the ATTO site is presented elsewhere (Moran-Zuloaga et al.,
2018).
Regarding the absorption Ångström exponent,
åabs, the overall average during the whole sampling
period was 0.93±0.16 (see Fig. 3e). Although no significant difference
was found between dry and wet season averaged values, the averaged
åabs was slightly higher during the dry season,
reaching 0.94±0.16 compared to 0.91±0.19 during the wet season. It
was found that the åabs only increased significantly
during hours or days-long episodes, typically caused by nearby burning during
the dry season. Details on the absorption wavelength dependence are discussed
in Sect. 3.7. The aethalometer compensation calculation could potentially
affect the retrieved åabs values. It has been shown
that the raw attenuation Ångström exponent can represent a good
approximation to the real åabs (Saturno et al., 2017).
High absorption and scattering coefficients coincide with ESE and E
trajectories, which are mostly dominant, but not exclusively, during the dry
season; see Fig. 1. On the other hand, during the “cleanest” periods in the wet
season, when light absorption reaches its minimum and ω0 its
maximum, the dominant trajectories are ENE and NE.
Black carbon mass absorption cross section
Accurate MAC values are required to retrieve BC mass concentrations from
absorption measurements. During the entire measurement period, the calculated
MAC was 11.9±1.4 m2 g-1 (mean ± standard deviation) at
λ=637 nm. Daily calculated MAC values in the wet season were
slightly lower on average compared to the dry season values (11.4±1.2
and 12.3±1.3 m2 g-1, respectively; see Table 1). As an
illustration of the different MAC values obtained in the wet and dry seasons,
σap,637 vs. MrBC scatter plots are presented in the
Supplement as Fig. S7. Lower MAC values measured in the wet season 2016 could
be associated with less coated BC compared to more aged particles in the dry
season, which could have thicker coatings. Nevertheless, both values are much
higher than the 6.6 m2 g-1 suggested by Bond and Bergstrom (2006),
especially considering that mineral dust and BrC do not strongly absorb at
this wavelength and would therefore have little influence on the apparent
MAC. However, they are in agreement with a modeled absorption enhancement of 1.6
calculated for open biomass burning in Brazil (Liu et al., 2017). In any
case, there are large discrepancies that make it difficult to compare
different MAC values obtained from ambient measurements due to systematic
analytical uncertainties that dominate over the natural variability (Zanatta
et al., 2016). These uncertainties are introduced by filter-based absorption
measurement biases and BC mass overestimation or underestimation when
thermal–optical methods are used. In the case of the SP2, the rBC mass
measurements are free of the different biases that affect thermal–optical
techniques and are a wavelength-independent measurement. In the case of
absorption measurements, a positive bias is introduced when organic aerosol
deposits on the filter, enhancing the scattering by the filter fibers and the
absorption by previously deposited BC particles when coating them. This
artifact can be between 12 % and 70 % for particle soot absorption
photometer (PSAP) measurements and will depend on the OA-to-BC ratio and the
aging state of the organic aerosol particles (Lack et al., 2008). We expect a
lower artifact for the MAAP since the scattering by filter fibers is
accounted for by the reflectance measurements, but using our instrumentation
we are not able to estimate the artifact coming from embedded BC absorption
being modified by organic aerosol deposition. There are only a few field
studies that present comparisons of rBC measurements and light absorption
measurements, like MAAP, photoacoustic spectrometry (PAS), or aethalometer,
and especially long-term measurements are scarce. Raatikainen et al. (2015)
reported SP2 (eight-channel) and MAAP measurements in the Finnish Arctic and
found that SP2 rBC mass concentrations were 5 times lower than MAAP
BCe mass concentration measurements, which is equivalent to MAC
values of ∼30 m2 g-1 at 637 nm. Some other studies have found
values in closer agreement with our ATTO MAC results. For example, Laborde et
al. (2013) found that air masses over Paris had an average MAC of 11.9 and
10.8 m2 g-1 (interpolated to 637 nm), for aged and fresh BB
aerosol, respectively. Additionally, Liu et al. (2010) calculated a median
MAC of 10.2±3.2 m2 g-1 during a measurement campaign at the
Jungfraujoch research station in Switzerland. Another study in Mexico City,
using PSAP for absorption measurements at λ=660 nm, found a MAC of 11.2 m2 g-1 (interpolated
to 637 nm) (Subramanian et al., 2010).
Multi-year (2012–2017) dry season weekly averages of (a)
frequency of occurrence of BT clusters, f, (b) absorption
coefficients at 637 nm, σap,637, shown as circles with
increasing diameters, the color scale corresponding to the relative BrC
contribution to σap,370, (c) single scattering albedo
at 637 nm, ω0637, and (d) aerosol optical depth at
550 nm (AOD) for the different domains of interest, DOI1 and DOI2, which
cover regions of the South Atlantic Ocean and the southern Amazon,
respectively. Boxplots in (c) and (d) represent the median
(white segment) and the 25th and 75th percentiles (lower and upper box edges,
respectively).
Variability of optical properties during the dry season
The Amazonian dry season is generally impacted by BB aerosol particles that
cause an increase in scattering and absorption coefficients (see Fig. 3a–b).
However, the aerosol optical properties vary with the burning material (and
region), as well as the aging process prior to reaching the ATTO site. In
order to study the dry season variability of BB aerosol particles, multi-year
(2012–2017) weekly averages were analyzed. The air mass trajectories,
presented as BT clusters in Fig. 4a, show a decreasing dominance of ESE winds
from August to November, whereas from October to November there is an
increasing influence of ENE winds, indicating the south-to-north air mass
trajectory shift that occurs during the transition from the dry to wet
seasons. It is important to note that southerly and easterly winds are most
likely to bring BB aerosol to the ATTO site during the dry season, given that
very active open fire areas during this period are located in the southern
Amazon and the Cerrado region (Andreae et al., 2012; Guyon et al., 2005) and,
more remotely, in southern Africa (Andreae et al., 1994; Barbosa et al.,
1999; Das et al., 2017). Aerosol optical depth at 550 nm is used in this
study as a parameter to study the seasonal pattern of BB emission transport
from both areas. In Sect. 2.5, we defined two domains of interest to study
the aerosol seasonal patterns in these two areas: DOI1 for the LRT of South
African smoke over the Atlantic Ocean, and DOI2 for the fires occurring in
the southern Amazon. For the southern African fires (DOI1), the seasonal
pattern shows an important influence during August–October, slightly
decreasing towards the end of the Amazonian dry season (see Fig. 4d). For the
southern Amazon region (DOI2), the typical fire seasonality during the dry
season is observed in the AOD over this area (Fig. 4d), with the highest
values observed in September and October. It is important to note that August
seems to be the period when African LRT is a more important source than
regional emissions and could be considered the main contributor of BB aerosol
to the ATTO site during this time. For the rest of the dry season, it is
likely that the aerosol properties are defined by South American BB
emissions. In fact, the shift in air mass trajectories and variation of BB
sources drive the BrC contribution to σap,370, as shown in
Fig. 4b. The BrC contribution (associated with high
åabs) is more important at the end of the dry season
and is lower during August, when the aerosol particles likely arrive from
Zambian woodland savanna fires (Barbosa et al., 1999), which burn more
efficiently and emit aerosol particles with lower ω0, 0.84±0.015 at 670 nm on average (Dubovik et al., 2002). Additionally, on
average, high σap,637 events (see the increasing circle size
in Fig. 4b) are more likely to bring high BrC containing aerosol, which is
another indication that closer fires have a higher probability of providing
BrC-rich aerosol particles to the ATTO site. The absorption wavelength
dependence and BrC contribution are discussed in detail in Sect. 3.6. The
differences between both identified BB sources in terms of BrC can be
explained by two reasons: (i) the BrC photochemical oxidation and destruction
of chromophores during transport (Sumlin et al., 2017) that would strongly
affect LRT aerosol, and (ii) a lower rain scavenging rate for BC during
transport, which would lead to an increased BC fraction in the aerosol
population. The increase in the single scattering albedo (ω0,
Fig. 4c) towards the end of the dry season confirms that the aerosol
particles during this time are scattering more radiation, not only due to
higher BrC presence, but also due to other light-scattering aerosol
particles.
Diel variation of (a, b) median of the accumulation mode
particle number concentration, Nacc, (c, d) median of the
absorption coefficient at 637 nm, (e, f) median of the BrC
absorption coefficient at 370 nm, (g, h) precipitation rate, and
(i, j) median of the equivalent potential temperature. Gray and
white backgrounds correspond to the night and day times, respectively. Error
bars correspond to the standard error. Please note the different y axis
scales.
Diel cycles
Figure 5 presents the diel cycles observed during the dry and the wet seasons
for the following selected aerosol properties and meteorological parameters:
Accumulation mode particle number concentration (Nacc), absorption
coefficient at 637 nm (σap,637), BrC absorption coefficient
at 370 nm (σap,BrC,370), precipitation rate
(PATTO), and equivalent potential temperature (θe).
In order to study the typical diel cycles in each season, extreme events like
mineral dust transport in the wet season and nearby BB during El Niño in
2015–2016 have been excluded by using data within the 90 % confidence
interval. The diel cycle of the equivalent potential temperature
(Fig. 5i–j), calculated according to Stull (1988), reflects the evolution of
the planetary boundary layer (PBL). Shortly before sunrise
(∼ 10:00 UTC), θe exhibits its minimum and increases
afterwards reaching its maximum values in the early afternoon hours. The
pronounced increase in θe in the early morning hours reflects
the onset of solar warming and the initiation of vertical mixing, leading to
the evolution of the convective boundary layer. After sunset, a stable
nocturnal boundary layer is formed close to the forest canopy. A detailed
analysis of the planetary boundary layer of the Amazon can be found in Fisch
et al. (2004). Figures 5a–b show diel cycles of accumulation mode
(100–430 nm) particle number concentration, Nacc. The diel
patterns are similar during both seasons, with a minimum at sunrise, and an
increase that starts in the morning at 12:00 UTC (08:00 LT) and maximum
concentrations between 17:00 and 18:00 UTC (13:00–14:00 LT). This diel
pattern observed in Nacc is driven by the diurnal evolution of the
planetary boundary layer. On the one hand, the stable nocturnal layer leads
to a concentration of particles and gases close to the canopy. On the other
hand, the canopy acts as an effective particle sink, resulting in a
concentration decrease towards the early morning (Ahlm et al., 2009). After
sunrise, vertical mixing breaks up the stable nocturnal boundary layer. While
the subsequent increase in Nacc is likely due to entrainment of
particles from the elevated aerosol-rich layers, the decrease in the
afternoon hours can be attributed to effective deposition in the forest
canopy, as also discussed in Ahlm et al. (2009). The absorption coefficient
at 637 nm, σap,637, which is mostly related to BC, follows a
diel pattern (Fig. 5c–d) similar to the Nacc trend for both
seasons. Since BC is usually not emitted by nearby sources and it is
generally transported in the accumulation mode, the similarities with
Nacc diel patterns were expected. However, the wet season diel
cycle of σap,637 exhibits a decreasing tendency that starts
two hours earlier than the decrease in Nacc. This difference can be
explained by the fact that σap,637 and Nacc are mass
and number-related measurements, respectively. Therefore, a size-dependent
deposition would affect mass and number-related aerosol properties
differently. This difference was more evident in the wet season when BC
concentrations were not as dominant as in the dry season. The diel pattern of
BrC contribution during the dry season is significantly different from the
σap,637 (BC) pattern. Brown carbon absorption at 370 nm,
σap,BrC,370, shows its highest values between 12:00 and
14:00 UTC (08:00–10:00 LT) in the dry season and starts decreasing at
14:00 UTC (10:00 LT), earlier than σap,637 and
Nacc (Fig. 5e). This observation implies that the BrC aerosol
particles measured at the ATTO site are mixed down into the boundary layer in
the early morning and are then quickly photo-degraded during the day
(Forrister et al., 2015; Laskin et al., 2015; Rincón et al., 2010; Wang
et al., 2016b; Wong et al., 2017). This pattern is not observed during the
wet season, when σap,BrC,370 exhibits no significant diel
variability.
Other remote site observations have found no significant diel variation of
the absorption coefficient, due to efficient mixing of the PBL and low
anthropogenic emissions (Chi et al., 2013). At ATTO, the high convectivity
and related entrainment of high-altitude air masses, containing regional
and/or LRT aerosols, result in a pronounced diel cycle in σap.
This is in good agreement with previous dry season results from another
Amazonian site (Brito et al., 2014).
BC-to-CO enhancement ratio
Agricultural clearing fires, like savanna fires, are dominated by the flaming
combustion phase, in contrast to deforestation fires, where less than
50 % of the biomass is burned in the flaming phase (Dubovik et al.,
2002). An important part of forest fires occurs in the form of smoldering
combustion due to higher fuel moisture and larger fuel diameters (Guyon et
al., 2005). Under smoldering fire conditions, when the combustion is less
efficient and, thus, tends to emit more CO, observations tend to show lower
ERBC and higher single scattering albedo, ω0, as well as
a higher organic carbon (OC) enhancement ratio, EROC. On the other
hand, flaming fires, which produce abundant BC aerosol particles, tend to
exhibit lower ω0 and higher ERBC (Akagi et al., 2011).
The smoke that arrives at the ATTO site during the dry season is a mixture of
smoldering and flaming emissions with varying relative fractions. The air
mass origin (i.e., the backward trajectories) largely defines whether
emissions are advected from regions with predominantly smoldering or flaming
fires (C. Pöhlker et al., 2018).
The ERBC and ω0 values allow us to distinguish between
flaming and smoldering-derived smoke and help locate the different sources.
Figure 6 shows the ERBC and ω0 values at ATTO, being
classified by grouped BT clusters. Mainly, the ESE and E trajectory clusters
have ERBC higher than 8 ng m-3 ppb-1. From the two
predominant BT cluster groups in the dry season (ESE and E), the ESE
trajectories seem to be more influenced by flaming fires since the
measurements are more shifted to high ERBC and lower ω0.
In fact, the ESE clusters are dominated by the 0.80–0.90 ω0 range,
which means they are highly loaded with light-absorbing aerosol. This is
consistent with the land cover information, which indicates that agricultural
lands account for 6 %–20 % of the ESE clusters' total land cover,
3 %–5 % of the E clusters, and <1 % of the ENE and NE clusters
(C. Pöhlker et al., 2018). The eastern clusters (E) are more equally
distributed in the ω0 range and tend to be lower in terms of
ERBC compared to the ESE clusters. Therefore, we expect E
trajectories to be more influenced by smoldering fires during the dry season
compared to the ESE trajectories, even though, as already mentioned, the
arrival of African savanna fire smoke from easterly trajectories in
August–September provides BB aerosol particles that have lower ω0
and higher ERBC.
During the wet season, when ENE and NE BT clusters are dominant, we observed
a trend towards lower ERBC and higher ω0, since the
frequency of regional fires is much lower than in the dry season. Actually,
when including data from the beginning of 2016, under the influence of ENSO,
we observed a shift towards higher ERBC in the NE directions due to
the occurrence of fires in the Guyanas area. These atypical data were
excluded from Fig. 6 to improve the contrast between the different air mass
trajectory clusters. The NE and ENE trajectories were very similar in terms
of ω0 and ERBC. Occasional dust transport events from the
Sahara, mixed with BB aerosol from the Sahel region, brought aerosol
particles with lower ω0 compared to the wet season average.
The lower ERBC observed in the wet season was likely due to
aerosol scavenging during the transatlantic advection (Moran-Zuloaga et al.,
2018), while CO is not affected by wet deposition (Liu et al., 2010). Note
that ERBC decreased more steeply with increasing ω0, and
their correlation was closer during the dry season (E and ESE BT clusters) in
comparison to the wet season. This feature might be related to the age of the
aerosol particles, because aging would make the BC become less hydrophobic
(M. L. Pöhlker et al., 2018), so that it can be more efficiently removed
by wet scavenging.
Scatter plot of 2012–2017 daily average of the BC-to-CO enhancement
ratio, ERBC vs. single scattering albedo at 637 nm,
ω0637, with marginal probability density plots (normalized counts
in log scale) for data corresponding to grouped BT clusters.
Scattering (a) and absorption (b) coefficient
(637 nm) time series measured at the ZF2 and the ATTO sites from 2008 to
2016 (24 h averaged data). Increased scattering and absorption coefficients
were observed under the influence of El Niño. (c) High ONI
indicates active ENSO periods, shown as red shaded areas.
El Niño impact on aerosol optical properties
The aerosol optical properties measured at ATTO changed during the El
Niño period at the end of 2015 and the beginning of 2016 (Fig. 3). To
have a broader view of the relationships between El Niño-related drought
conditions, increased fire abundance, and the Amazonian aerosol properties,
we added scattering and absorption data from the ZF2 site published in Rizzo
et al. (2013) and extended with recent data to the current ATTO time series
in Fig. 7a–b. Overlapping data in 2013 (Fig. 7a and b) are statistically
equivalent, with only a few days affected by probable near-site sources.
Positive Oceanic Niño Index (ONI) values (Fig. 7c) were found to be
related to higher scattering and absorption coefficients in the dry season.
However, the ENSO is not the only cause of precipitation anomalies in the
Amazon Basin. The Atlantic Multi-Decadal Oscillation (AMO) has also been
found to be causing droughts (Aragão et al., 2008). The non-ENSO daily
mean average (ZF2 and ATTO) scattering coefficient at 637 nm during the dry
seasons was 24±18 M m-1. This average increased up to 48±33 M m-1 during the ENSO periods (2009 and 2015 dry seasons). The wet
season scattering coefficient average was also affected during El Niño,
increasing from a non-ENSO average of 7±7 to 10±11 M m-1
during the wet season 2016. A similar pattern was observed for
σap,637, which increased from a non-ENSO average in the dry
seasons of 3.8±2.8 M m-1 to an ENSO average of 5.3±2.5 M m-1 (2009 and 2015 dry seasons' average). It is remarkable that
high absorption coefficients were also measured during the dry season 2010
(5.6±4.7 M m-1), a year with a mostly negative ONI. However, it
has been shown that an increased sea surface temperature in the Atlantic
Ocean (not ENSO-related) in 2010 caused a special drought period in the
Amazon rainforest (Lewis et al., 2011).
Absorption wavelength dependence and BrC contribution
Open biomass burning emits a mixture of BC and OA with high absorption
wavelength dependence (Andreae and Gelencsér, 2006; Hoffer et al., 2006;
Kirchstetter et al., 2004). However, our observations show that sometimes LAC
measured at the ATTO site can fall in the BC-only regime, with
åabs≈1. To understand this pattern, we have
analyzed the relationship between the WDA and other parameters, like the
OA-to-sulfate ratio and ω0. In Fig. 8a, WDA is presented as a
function of the OA-to-sulfate mass ratio. According to the result of an
orthogonal fit (not shown), there is a significant correlation between these
variables (R2=0.61), and the aerosol light absorption is in the BC-only
regime (shaded area in Fig. 8a) when the OA-to-sulfate ratio is lower than
∼6.5, which occurred 15 % of the time in the high-absorption
periods (σap,637 higher than the 75th percentile). On the
other hand, higher OA-to-sulfate ratios correspond to likely BrC-rich aerosol
masses, which were the majority of the cases. The ω0 at 637 nm of
the BC-only regime (inter-quartile range, IQR: 0.82–0.86) was clearly lower
than the one corresponding to the BrC-rich regime (IQR: 0.85–0.90).
In Fig. 8b, the BC-only regime data have been segregated by BT clusters. The
air masses that are more likely to bring wavelength-independent LAC to the
site are those with the faster wind speed: E3, E4, and ESE3. These emissions
could be related to ship traffic in the Atlantic Ocean, BB in southern
Africa, or power plant emissions from the western African coast. Low
OA-to-sulfate ratios with high ω0 occurred a few times and could be
explained by high sulfate input from volcanic emissions in the Congo
(Fioletov et al., 2016; Saturno et al., 2018a), rather than fossil fuel
emissions, which are typically rich in BC.
(a) Absorption wavelength dependence (WDA) as a function of
the OA-to-sulfate mass ratio during high-absorption periods in the dry
season. The color scale indicates the ω0 at 637 nm. Gray shaded
areas correspond to theoretical WDA for internally mixed BC (light gray) and
externally mixed BC (dark gray). The data inside the dashed rectangle
in (a) are used in (b) to identify the BT clusters that are
more likely to bring wavelength-independent LAC to the ATTO site.
Wavelength dependence of åabs (WDA) for
different trajectories in the dry season presented as box and whisker plots
(left). The light and dark gray shaded areas correspond to the pure BC and
internally mixed BC regimes, respectively. Notches correspond to 1.58 IQR
n-1/2. If notch ranges do not overlap, the medians are statistically
different (95 % confidence). The trajectory weighted fire counts for each
BT cluster are shown as circles on the right side. The data presented here
correspond to 1 h averages.
In an effort to identify the BrC-rich trajectories, the WDA was studied for
the different BT clusters that are mostly active during the dry season. Box
plots corresponding to each trajectory cluster, together with the average
fire counts in the geographical cluster area, are presented in Fig. 9. From
the group of ESE trajectory clusters (ESE1, ESE2, and ESE3), the ESE1
trajectories exhibit the highest WDA values, with a decreasing tendency
towards faster trajectories, ESE3 being the one with the lowest WDA values.
Even though ESE3 is the trajectory cluster with more fire counts, the fact
that those fires occur farther from the ATTO site compared to the ones in the
slowest trajectory, ESE1, could be related to a decrease in absorption
wavelength dependence during transport. A similar pattern is observed for the
easterly trajectory clusters (E1, E2, E3, and E4), where the slowest air mass
trajectories comprised of the E1 cluster exhibit the highest WDA median
compared to the rest of the E clusters. In the case of E4, the WDA 25th
percentile is lower than the rest of the E trajectories, but it shows an
increased median that can not be explained by the occurrence of fire events,
which is lower than the observations for the other clusters (E2, E3, and E4).
The E4-weighted fire counts are anyhow on the same order of magnitude as E2
and E3 and the wavelength dependence differences could be related to
different fuel types or combustion phases. Actually, the long E clusters (E3
and E4) cover more southern areas than the shorter ones (E1 and E2) and have
some overlap with ESE3. By comparing grouped E and ESE clusters, it can be
observed that WDA in the E clusters has higher variability compared to the
ESE ones. This pattern could be associated with a wider range of sources in
the E trajectories compared to ESE. The E trajectories travel over the Amazon
River, where ship traffic is quite significant. In fact, as can be observed
in Fig. 9, for the E3 and E4 trajectories, there is a significant number
(> 25th percentile) of measurements that fall in the BC-only regime.
Something similar is only observed for the ESE3 trajectories among the ESE
group. Most of the agricultural land is located along the southern margins of
the Amazon rainforest (C. Pöhlker et al., 2018). This area is within the
ESE clusters footprint. The narrower range of WDA values measured for the ESE
trajectories compared to the E ones indicates that sources in the ESE
footprint are more homogeneous compared to the sources located in the E
footprint. These WDA tendencies could be useful for understanding the BrC
emissions and atmospheric transformations in the context of the Amazon
rainforest and its surroundings.
Total absorption at 370 nm (12 h average data) segregated by BC
only (gray points) and BrC + BC (brown points). Error bars are equivalent
to ±1 standard error. Long-range transport dust events have been
excluded from the analysis.
Absorption Ångström exponent (åabs)
as a function of the BC / OA mass ratio for selected dust events in the
wet season. The black line corresponds to a non-linear least squares fit
applied to the data (y=x-0.199×0.632). The light blue lines
correspond to the standard error of the fit.
Using the calculated BC-only WDA thresholds, we were able to estimate the BrC
contribution to total absorption during the measurement period (2012–2017)
(Fig. 10). We found that BrC contributes 24 % (IQR: 17 %–29 %)
of total light absorption at 370 nm wavelength. A slight seasonal
variability was observed for the BrC relative contribution, with the medians
and IQR during the wet and dry season being 27 % (19–34) and 22 %
(16–27), respectively. However, most of the wet season data had to be
excluded, because they were from air masses rich in mineral dust, which
introduces large uncertainties into the WDA method. During El Niño, at
the end of 2015, open fire events were more frequent (with weighted fire
counts of 1756 km-2 compared to the 2008–2016 average of
1076 km-2), and the CO 95th percentile was exceeded several times. In
this period, the BrC contribution had a median of 37 % (IQR: 27–47) and
showed a significant correlation with CO (R2=0.47). This significant
increase in the BrC contribution could be related to the relatively short
distance between the fire spots and the ATTO site. It can be observed in
Fig. 10 that the El Niño influence continued during the dry season 2016,
but not as strongly as in 2015. Previous observations have shown that the
atmospheric lifetime of BB-emitted BrC is ∼1 day due to photolysis and
oxidation, which destroy the chromophores (Forrister et al., 2015; Wang et
al., 2016b; Wong et al., 2017). Therefore, BrC emitted from fires in the
southern borders of the Amazon rainforest, which require ∼3 days to be
transported to the ATTO site, is likely to be significantly photodegraded and
to contribute only weakly to total aerosol light absorption after atmospheric
processing.
The BC-to-OA mass ratio during the sampling time had a median of 0.06 (IQR:
0.04–0.10). The ratio BC to OA has been used before to parameterize
åabs and ω0 (Pokhrel et al., 2016; Saleh et
al., 2014), but little is known about this relationship for tropical forest
emissions. A broader range of the BC-to-OA mass ratio between 2014 and 2016
was observed during the dust episodes in the wet season, including those
periods when regional fires were active (IQR: 0.08–0.24). Other periods,
like the dry season, with higher BC mass concentrations exhibited a narrower
and lower BC-to-OA mass ratio range (IQR: 0.03–0.08). A scatter plot of the
absorption wavelength dependence, åabs, as a function
of the BC-to-OA mass ratio during the northern African LRT events in the wet
season can be found in Fig. 11. We have found a trend where
åabs increases with decreasing BC-to-OA mass ratio
following an exponential function. These results are comparable to those
presented by Pokhrel et al. (2016) and Saleh et al. (2014), with slightly
lower åabs values in our study, however. This pattern
could be related to a dominant presence of primary organic aerosol (POA) that
has characteristically lower absorption wavelength dependence compared to SOA
(Saleh et al., 2013). However, more experimental studies are required to
investigate the optical properties of aerosol produced by burning different
tropical forest fuels.
Summary and conclusions
This study presents the optical properties of aerosol particles at the remote
ATTO site for a measurement period of 5 years (2012–2017). The atmospheric
seasonality at ATTO strongly affects aerosol light scattering and absorption,
with significant increases from wet to dry season conditions due to intense
biomass burning in South America and Africa. The wet season background
aerosol was dominated by biogenic particles with occasional interruptions by
long-range transported dust and BB aerosols from Africa to ATTO, leading to
decreases in scattering Ångström exponent,
åsca, and single scattering albedo, ω0
(637 nm). The average ω0 during the wet season was 0.93±0.04,
which is higher than the dry season average of 0.87±0.03. The absorption
wavelength dependence, åabs, was relatively low, with
an average of 0.93±0.16, and varied only slightly between seasons. The
highest åabs were measured during BB events, but no
effect on åabs was observed due to the presence of
dust, most likely due to a size effect, given that after May 2014 absorption
coefficients were measured only for sub-micron aerosol particles. The BC mass
absorption coefficient (MAC) at 637 nm calculated from MAAP and SP2
measurements agrees with other studies; however, it is higher than
“typical” values that are commonly used in the literature to convert
σap into BC mass concentrations. The calculated wet season MAC
average was 11.4±1.2 m2 g-1, and increased slightly during the
dry season to an average of 12.3±1.3 m2 g-1 at 637 nm. These
values are consistent with a strong “lensing effect” by organic coatings
attached to BC aerosol particles. High OA amounts in the Amazonian atmosphere
resulted in low BC-to-OA mass ratios, in the range of 0.04 to 0.10 (IQR). A
significant correlation between BC-to-OA mass ratio and
åabs was observed during the wet season under the
influence of regional and remote BB emissions. The ΔBC /ΔCO enhancement ratios (ERBC) were mostly lower than
8 ng m-3 ppb-1, mainly due to the aging and deposition of BB
aerosol particles during transport to ATTO. A higher and wider range of
ERBC values was observed during the dry season due to the influence
of different biomass combustion phases that varied from smoldering to flaming
fires.
Theoretical wavelength-dependent BC åabs were
calculated and used to estimate the BrC contribution to near-UV (370 nm)
light absorption. This approach resulted in medians of 27 % and 22 %
BrC contributions in the wet and dry seasons, respectively. Higher BrC
contributions were measured during the El Niño period at the end of 2015,
when BrC absorption at 370 nm increased to a median of 37 %. We observed
that winds coming from ESE directions in the dry season were more likely to
bring aerosols with a high absorption wavelength dependence, implying a
higher BrC content.
In the case of prolonged drought periods in the Amazon Basin, significant
increases in BrC absorption contribution could be expected due to increased
fire occurrence. Long-term monitoring of light absorbing aerosol particles is
required to reduce uncertainty in global climate models. The data presented
here provide a contribution in this direction and can help to understand how
different climatic phenomena, like El Niño, can affect the Amazon
atmospheric aerosol cycling. Further investigations on the BC mixing state
and morphology will be required to improve modeled calculations and BrC
retrievals.
The data of the key results presented here have been
deposited in supplementary data files for use in follow-up studies. They are
available in NASA Ames format under 10.17617/3.1r (Saturno et al.,
2018b, available data for ATTO BC and BrC study). For data requests beyond
the available data, please refer to the corresponding
authors.
List of frequently used symbols and
acronyms.
DescriptionAcronymSymbolUnitsBlack carbonBCBrown carbonBrCEquivalent black carbonBCeRefractory black carbonrBCOrganic carbonOCOrganic aerosolOALight-absorbing carbonaceous matterLACΔBC /ΔCO enhancement ratioERBCAttenuation coefficientATNσATNm-1Absorption coefficientσapm-1Scattering coefficientσspm-1Absorption Ångström exponentAAEåabsScattering Ångström exponentSAEåscaWavelength dependence of åabsWDAMass attenuation cross sectionαatnm2 g-1(BC) Mass absorption cross sectionMACαabsm2 g-1Backscattering coefficientσbspm-1Single scattering albedoSSAω0Aerosol optical depthAODCondensation nuclei number concentration (>10 nm)NCNcm-3Accumulation mode particle number concentration (100–430 nm)Nacccm-3Precipitation at ATTO region of interest (ROI), Fig. 1aPATTOmmEquivalent potential temperatureθeKAmazon Tall Tower ObservatoryATTOBackward trajectoryBTLong-range transportLRTEl Niño–Southern OscillationENSOOceanic Niño IndexONIBiomass burningBBFossil fuelFFCoordinated universal timeUTCLocal timeLTInter-quartile rangeIQRDomain of interest, Fig. 2aDOI
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-12817-2018-supplement.
JS, BAH, CP, FD, QW, DMZ, JB, SC, YC, XC, JVL, MLP, LVR, DW, SW, PA,
UP and MOA designed the research. JS, BAH, CP, FD, QW, DMZ, JB, SC, YC, XC,
JD, IHA, TK, JVL, HP, MLP, LVR, PS, DW, SW and MOA performed the measurements
and/or contributed to the data analysis. All authors contributed to the
discussion and interpretation of the results and to writing the paper.
The authors declare that they have no conflict of
interest.
This article is part of the special issues “Amazon Tall Tower
Observatory (ATTO) Special Issue” and “Observations and Modeling of the
Green Ocean Amazon (GoAmazon2014/5)”. It is not associated with a
conference.
Acknowledgements
This work has been supported by the Max Planck Society (MPG) and the Paul
Crutzen Graduate School (PCGS). 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 the PhD scholarship from the
Fundación Gran Mariscal de Ayacucho (Fundayacucho). 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, Alberto Quesada, Niro Higuchi, Susan Trumbore,
Matthias Sörgel, Thomas Disper, Andrew Crozier, Thomas Klimach,
Björn Nillius, 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,
Feliciano de Souza Coehlo, Thiago de Lima Xavier,
Josué Ferreira de Souza, Roberta Pereira de Souza, Bruno Takeshi, and
Wallace Rabelo Costa.The article processing
charges for this open-access publication were covered by the
Max Planck Society. Edited by: Markku
Kulmala Reviewed by: two anonymous referees
ReferencesACTRIS: ACTRIS Intercomparison Workshop for Integrating Nephelometer and
Absorption Photometers, Leipzig, Germany, available at:
http://www.wmo-gaw-wcc-aerosol-physics.org/files/ACTRIS-intercomparison-workshop-integrating-nephelometer-and-absorption-photometer-02-03-2013.pdf
(last access 1 June 2017), 2014.Ahlm, L., Nilsson, E. D., Krejci, R., Mårtensson, E. M., Vogt, M., and
Artaxo, P.: Aerosol number fluxes over the Amazon rain forest during the wet
season, Atmos. Chem. Phys., 9, 9381–9400,
10.5194/acp-9-9381-2009, 2009.Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S.,
Karl, T., Crounse, J. D., and Wennberg, P. O.: Emission factors for open and
domestic biomass burning for use in atmospheric models, Atmos. Chem. Phys.,
11, 4039–4072, 10.5194/acp-11-4039-2011, 2011.Anderson, T. L., Covert, D. S., Marshall, S. F., Laucks, M. L., Charlson, R.
J., Waggoner, A. P., Ogren, J. A., Caldow, R., Holm, R. L., Quant, F. R.,
Sem, G. J., Wiedensohler, A., Ahlquist, N. A., and Bates, T. S.: Performance
Characteristics of a High-Sensitivity, Three-Wavelength, Total
Scatter/Backscatter Nephelometer, J. Atmos. Ocean. Tech., 13, 967–986,
10.1175/1520-0426(1996)013<0967:PCOAHS>2.0.CO;2, 1996.
Andreae, M. O.: Biomass burning: Its history, use and distribution and its
impact on environmental quality and global climate, Glob. Biomass Burn.
Atmos. Clim. Biosph. Implic., 15–42, 1991.Andreae, M. O.: Raising dust in the greenhouse, Nature, 380, 389–390,
10.1038/380389a0, 1996.Andreae, M. O.: The dark side of aerosols, Nature, 409, 671–672,
10.1038/35055640, 2001.Andreae, M. O.: Aerosols before pollution, Science, 315, 50–51,
10.1126/science.1136529, 2007.Andreae, M. O. and Gelencsér, A.: Black carbon or brown carbon? The
nature of light-absorbing carbonaceous aerosols, Atmos. Chem. Phys., 6,
3131–3148, 10.5194/acp-6-3131-2006, 2006.Andreae, M. O. and Merlet, P.: Emission of trace gases and aerosols from
biomass burning, Global Biogeochem. Cy., 15, 955–966,
10.1029/2000GB001382, 2001.Andreae, M. O., Browell, E. V., Garstang, M., Gregory, G. L., Harriss, R. C.,
Hill, G. F., Jacob, D. J., Pereira, M. C., Sachse, G. W., Setzer, A. W.,
Dias, P. L. S., Talbot, R. W., Torres, A. L., and Wofsy, S. C.:
Biomass-burning emissions and associated haze layers over Amazonia, J.
Geophys. Res., 93, 1509–1527, 10.1029/JD093iD02p01509, 1988.Andreae, M. O., Anderson, B. E., Blake, D. R., Bradshaw, J. D., Collins, J.
E., Gregory, G. L., Sachse, G. W., and Shipham, M. C.: Influence of plumes
from biomass burning on atmospheric chemistry over the equatorial and
tropical South Atlantic during CITE 3, J. Geophys. Res., 99, 12793–12808,
10.1029/94JD00263, 1994.Andreae, M. O., Artaxo, P., Beck, V., Bela, M., Freitas, S., Gerbig, C.,
Longo, K., Munger, J. W., Wiedemann, K. T., and Wofsy, S. C.: Carbon monoxide
and related trace gases and aerosols over the Amazon Basin during the wet and
dry seasons, Atmos. Chem. Phys., 12, 6041–6065,
10.5194/acp-12-6041-2012, 2012.Andreae, M. O., Acevedo, O. C., Araùjo, A., et al.: The Amazon Tall Tower
Observatory (ATTO): overview of pilot measurements on ecosystem ecology,
meteorology, trace gases, and aerosols, Atmos. Chem. Phys., 15, 10723–10776,
10.5194/acp-15-10723-2015, 2015.Ångström, A.: On the Atmospheric Transmission of Sun Radiation and on
Dust in the Air, Geogr. Ann., 11, 156–166, 10.2307/519399, 1929.Aragão, L. E. O., Malhi, Y., Roman-Cuesta, R. M., Saatchi, S., Anderson,
L. O., and Shimabukuro, Y. E.: Spatial patterns and fire response of recent
Amazonian droughts, Geophys. Res. Lett., 34, 1–5, 10.1029/2006GL028946,
2007.Aragão, L. E. O., Malhi, Y., Barbier, N., Lima, A., Shimabukuro, Y.,
Anderson, L., and Saatchi, S.: Interactions between rainfall, deforestation
and fires during recent years in the Brazilian Amazonia, Philos. Trans. R.
Soc. B Biol. Sci., 363, 1779–1785, 10.1098/rstb.2007.0026, 2008.Artaxo, P., Martins, J. V., Yamasoe, M. A., Procópio, A. S., Pauliquevis,
T. M., Andreae, M. O., Guyon, P., Gatti, L. V., and Cordova Leal, A. M.:
Physical and chemical properties of aerosols in the wet and dry seasons in
Rondônia, Amazonia, J. Geophys. Res., 107, 8081,
10.1029/2001JD000666, 2002.Artaxo, P., Rizzo, L. V., Brito, J. F., Barbosa, H. M. J., Arana, A., Sena,
E. T., Cirino, G. G., Bastos, W., Martin, S. T., and Andreae, M. O.:
Atmospheric aerosols in Amazonia and land use change: from natural biogenic
to biomass burning conditions, Faraday Discuss., 165, 203–235,
10.1039/c3fd00052d, 2013.Barbosa, P. M., Stroppiana, D., Grégoire, J.-M., and Cardoso Pereira, J.
M.: An assessment of vegetation fire in Africa (1981–1991): Burned areas,
burned biomass, and atmospheric emissions, Global Biogeochem. Cy., 13,
933–950, 10.1029/1999GB900042, 1999.Bilbao, B. A., Leal, A. V., and Méndez, C. L.: Indigenous Use of Fire and
Forest Loss in Canaima National Park, Venezuela, Assessment of and Tools for
Alternative Strategies of Fire Management in Pemón Indigenous Lands, Hum.
Ecol., 38, 663–673, 10.1007/s10745-010-9344-0, 2010.
Bohren, C. F. and Huffman, D. R.: Absorption and scattering of light by small
particles, Wiley, Hoboken, NJ, 1983.Bond, T. C. and Bergstrom, R. W.: Light Absorption by Carbonaceous
Particles?: An Investigative Review, Aerosol Sci. Technol., 40, 27–67,
10.1080/02786820500421521, 2006.Bond, T. C., Streets, D. G., Yarber, K. F., Nelson, S. M., Woo, J.-H., and
Klimont, Z.: A technology-based global inventory of black and organic carbon
emissions from combustion, J. Geophys. Res., 109, D14203,
10.1029/2003JD003697, 2004.Bond, T. C., 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, 10.1002/jgrd.50171,
2013.Brito, J., Rizzo, L. V., Morgan, W. T., Coe, H., Johnson, B., Haywood, J.,
Longo, K., Freitas, S., Andreae, M. O., and Artaxo, P.: Ground-based aerosol
characterization during the South American Biomass Burning Analysis (SAMBBA)
field experiment, Atmos. Chem. Phys., 14, 12069–12083,
10.5194/acp-14-12069-2014, 2014.Cachier, H., Bremond, M.-P., and Buat-Ménard, P.: Determination of
atmospheric soot carbon with a simple thermal method, Tellus B, 41, 379–390,
10.1111/j.1600-0889.1989.tb00316.x, 1989.Caponi, L., Formenti, P., Massabó, D., Di Biagio, C., Cazaunau, M.,
Pangui, E., Chevaillier, S., Landrot, G., Andreae, M. O., Kandler, K.,
Piketh, S., Saeed, T., Seibert, D., Williams, E., Balkanski, Y., Prati, P.,
and Doussin, J.-F.: Spectral- and size-resolved mass absorption efficiency of
mineral dust aerosols in the shortwave spectrum: a simulation chamber study,
Atmos. Chem. Phys., 17, 7175–7191, 10.5194/acp-17-7175-2017,
2017.
Carbone, S., Brito, J. F., Xu, L., Ng, N. L., Rizzo, L. V., Stern, R.,
Cirino, G. G., Holanda, B. A., Senna, E., Wolff, S., Saturno, J., Chi, X.,
Souza, R. A. F., Arana, A., de Sá, M., Pöhlker, M. L., Andreae, M.
O., Pöhlker, C., Barbosa, H. M. J., and Artaxo, P.: Long-term chemical
composition and source apportionment of submicron aerosol particles in the
central Amazon basin (ATTO), Atmos. Chem. Phys. Discuss., in preparation,
2018.Carslaw, K. S., Lee, L. A., Reddington, C. L., Pringle, K. J., Rap, A.,
Forster, P. M., Mann, G. W., Spracklen, D. V., Woodhouse, M. T., Regayre, L.
A., and Pierce, J. R.: Large contribution of natural aerosols to uncertainty
in indirect forcing, Nature, 503, 67–71, 10.1038/nature12674, 2013.Chand, D., Guyon, P., Artaxo, P., Schmid, O., Frank, G. P., Rizzo, L. V.,
Mayol-Bracero, O. L., Gatti, L. V., and Andreae, M. O.: Optical and physical
properties of aerosols in the boundary layer and free troposphere over the
Amazon Basin during the biomass burning season, Atmos. Chem. Phys., 6,
2911–2925, 10.5194/acp-6-2911-2006, 2006.Chi, X., Winderlich, J., Mayer, J.-C., Panov, A. V., Heimann, M., Birmili,
W., Heintzenberg, J., Cheng, Y., and Andreae, M. O.: Long-term measurements
of aerosol and carbon monoxide at the ZOTTO tall tower to characterize
polluted and pristine air in the Siberian taiga, Atmos. Chem. Phys., 13,
12271–12298, 10.5194/acp-13-12271-2013, 2013.Chow, J. C., Yu, J. Z., Watson, J. G., Hang Ho, S. S., Bohannan, T. L., Hays,
M. D., and Fung, K. K.: The application of thermal methods for determining
chemical composition of carbonaceous aerosols: A review, J. Environ. Sci.,
42, 1521–1541, 10.1080/10934520701513365, 2007.Clarke, A. D. and Charlson, R. J.: Radiative Properties of the Background
Aerosol: Absorption Component of Extinction, Science, 229, 263–265,
10.1126/science.229.4710.263, 1985.Cochrane, M. A.: Fire science for rainforests, Nature, 421, 913–919,
10.1038/nature01437, 2003.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,
10.5194/amt-3-457-2010, 2010.
Crutzen, P. J. and Andreae, M. O.: Biomass burning in the tropics: Impact on
atmospheric chemistry and biogeochemical cycles, Science, 250, 1669–1678,
10.1126/science.250.4988.1669, 1990.Das, S., Harshvardhan, H., Bian, H., Chin, M., Curci, G., Protonotariou, A.
P., Mielonen, T., Zhang, K., Wang, H., and Liu, X.: Biomass burning aerosol
transport and vertical distribution over the South African-Atlantic region,
J. Geophys. Res.-Atmos., 6391–6415, 10.1002/2016JD026421, 2017.Davidson, E. A., de Araújo, A. C., Artaxo, P., Balch, J. K., Brown, I. F.
C., Bustamante, M. M., Coe, M. T., DeFries, R. S., Keller, M., Longo, M.,
Munger, J. W., Schroeder, W., Soares-Filho, B. S., Souza, C. M., and Wofsy,
S. C.: The Amazon basin in transition, Nature, 481, 321–328,
10.1038/nature10717, 2012.Denjean, C., Cassola, F., Mazzino, A., Triquet, S., Chevaillier, S., Grand,
N., Bourrianne, T., Momboisse, G., Sellegri, K., Schwarzenbock, A., Freney,
E., Mallet, M., and Formenti, P.: Size distribution and optical properties of
mineral dust aerosols transported in the western Mediterranean, Atmos. Chem.
Phys., 16, 1081–1104, 10.5194/acp-16-1081-2016, 2016.
Draxler, R. R. and Hess, G. D.: An overview of the HYSPLIT 4 modelling system
for trajectories, dispersion and deposition, Aust. Met. Mag., 47, 295–308,
1998.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, 10.5194/amt-8-1965-2015, 2015.
Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M.
D., Tanré, D., and Slutsker, I.: Variability of Absorption and Optical
Properties of Key Aerosol Types Observed in Worldwide Locations, J. Atmos.
Sci., 59, 590–608, 2002.Falster, D. S., Warton, D. I., and Wright, I. J.: SMATR: Standardised major
axis tests and routines, ver 2.0, available at:
http://www.bio.mq.edu.au/ecology/SMATR/ (last access: 1 June 2017),
2006.Favez, O., El Haddad, I., Piot, C., Boréave, A., Abidi, E., Marchand, N.,
Jaffrezo, J.-L., Besombes, J.-L., Personnaz, M.-B., Sciare, J., Wortham, H.,
George, C., and D'Anna, B.: Inter-comparison of source apportionment models
for the estimation of wood burning aerosols during wintertime in an Alpine
city (Grenoble, France), Atmos. Chem. Phys., 10, 5295–5314,
10.5194/acp-10-5295-2010, 2010.Fioletov, V. E., McLinden, C. A., Krotkov, N., Li, C., Joiner, J., Theys, N.,
Carn, S., and Moran, M. D.: A global catalogue of large SO2 sources and
emissions derived from the Ozone Monitoring Instrument, Atmos. Chem. Phys.,
16, 11497–11519, 10.5194/acp-16-11497-2016, 2016.Fisch, G., Tota, J., Machado, L. A. T., Silva Dias, M. A. F., da F. Lyra, R.
F., Nobre, C. A., Dolman, A. J., and Gash, J. H. C.: The convective boundary
layer over pasture and forest in Amazonia, Theor. Appl. Climatol., 78,
47–59, 10.1007/s00704-004-0043-x, 2004.Formenti, P., Andreae, M. O., Lange, L., Roberts, G., Cafmeyer, J., Rajta,
I., Maenhaut, W., Holben, B. N., Artaxo, P., and Lelieveld, J.: Saharan dust
in Brazil and Suriname during the Large-Scale Biosphere-Atmosphere Experiment
in Amazonia (LBA) – Cooperative LBA Regional Experiment (CLAIRE) in March
1998, J. Geophys. Res.-Atmos., 106, 14919–14934, 10.1029/2000JD900827,
2001.Forrister, H., Liu, J., Scheuer, E., Dibb, J., Ziemba, L., Thornhill, K. L.,
Anderson, B., Diskin, G., Perring, A. E., Schwarz, J. P., Campuzano-Jost, P.,
Day, D. A., Palm, B. B., Jimenez, J. L., Nenes, A., and Weber, R. J.:
Evolution of brown carbon in wildfire plumes, Geophys. Res. Lett., 42,
4623–4630, 10.1002/2015GL063897, 2015.Fuller, K. A., Malm, W. C., and Kreidenweis, S. M.: Effects of mixing on
extinction by carbonaceous particles, J. Geophys. Res.-Atmos., 104,
15941–15954, 10.1029/1998JD100069, 1999.Fuzzi, S., Decesari, S., Facchini, M. C., Cavalli, F., Emblico, L., Mircea,
M., Andreae, M. O., Trebs, I., Hoffer, A., Guyon, P., Artaxo, P., Rizzo, L.
V., Lara, L. L., Pauliquevis, T., Maenhaut, W., Raes, N., Chi, X.,
Mayol-Bracero, O. L., Soto-García, L. L., Claeys, M., Kourtchev, I.,
Rissler, J., Swietlicki, E., Tagliavini, E., Schkolnik, G., Falkovich, A. H.,
Rudich, Y., Fisch, G., and Gatti, L. V.: Overview of the inorganic and
organic composition of size-segregated aerosol in Rondônia, Brazil, from
the biomass-burning period to the onset of the wet season, J. Geophys.
Res.-Atmos., 112, D01201, 10.1029/2005JD006741, 2007.Garg, S., Chandra, B. P., Sinha, V., Sarda-Esteve, R., Gros, V., and Sinha,
B.: Limitation of the Use of the Absorption Angstrom Exponent for Source
Apportionment of Equivalent Black Carbon: a Case Study from the North West
Indo-Gangetic Plain, Environ. Sci. Technol., 50, 814–824,
10.1021/acs.est.5b03868, 2016.GES-DISC: Goddard Earth Sciences Data and Information Services Center,
available at: https://giovanni.gsfc.nasa.gov/giovanni/, last access: 1
June 2017.Gläser, G., Wernli, H., Kerkweg, A., and Teubler, F.: The transatlantic
dust transport from North Africa to the Americas-Its characteristics and
source regions, J. Geophys. Res.-Atmos., 120, 11231–11252,
10.1002/2015JD023792, 2015.Guyon, P., Graham, B., Beck, J., Boucher, O., Gerasopoulos, E.,
Mayol-Bracero, O. L., Roberts, G. C., Artaxo, P., and Andreae, M. O.:
Physical properties and concentration of aerosol particles over the Amazon
tropical forest during background and biomass burning conditions, Atmos.
Chem. Phys., 3, 951–967, 10.5194/acp-3-951-2003, 2003a.Guyon, P., Boucher, O., Graham, B., Beck, J., Mayol-Bracero, O. L., Roberts,
G. C., Maenhaut, W., Artaxo, P., and Andreae, M. O.: Refractive index of
aerosol particles over the Amazon tropical forest during LBA-EUSTACH 1999, J.
Aerosol Sci., 34, 883–907, 10.1016/S0021-8502(03)00052-1, 2003b.Guyon, P., Graham, B., Roberts, G. C., Mayol-Bracero, O. L., Maenhaut, W.,
Artaxo, P., and Andreae, M. O.: Sources of optically active aerosol particles
over the Amazon forest, Atmos. Environ., 38, 1039–1051,
10.1016/j.atmosenv.2003.10.051, 2004.Guyon, P., Frank, G. P., Welling, M., Chand, D., Artaxo, P., Rizzo, L.,
Nishioka, G., Kolle, O., Fritsch, H., Silva Dias, M. A. F., Gatti, L. V.,
Cordova, A. M., and Andreae, M. O.: Airborne measurements of trace gas and
aerosol particle emissions from biomass burning in Amazonia, Atmos. Chem.
Phys., 5, 2989–3002, 10.5194/acp-5-2989-2005, 2005.Gysel, M., Laborde, M., Olfert, J. S., Subramanian, R., and Gröhn, A. J.:
Effective density of Aquadag and fullerene soot black carbon reference
materials used for SP2 calibration, Atmos. Meas. Tech., 4, 2851–2858,
10.5194/amt-4-2851-2011, 2011.Hamilton, D. S., Lee, L. A., Pringle, K. J., Reddington, C. L., Spracklen, D.
V., and Carslaw, K. S.: Occurrence of pristine aerosol environments on a
polluted planet, P. Natl. Acad. Sci. USA, 111, 18466–18471,
10.1073/pnas.1415440111, 2014.Hansen, A. D. A., Rosen, H., and Novakov, T.: The aethalometer – An
instrument for the real-time measurement of optical absorption by aerosol
particles, Sci. Total Environ., 36, 191–196,
10.1016/0048-9697(84)90265-1, 1984.Hoffer, A., Gelencsér, A., Guyon, P., Kiss, G., Schmid, O., Frank, G. P.,
Artaxo, P., and Andreae, M. O.: Optical properties of humic-like substances
(HULIS) in biomass-burning aerosols, Atmos. Chem. Phys., 6, 3563–3570,
10.5194/acp-6-3563-2006, 2006.
IPCC: Climate Change 2013: The Physical Science Basis, Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental Panel
on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K.,
Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and
Midgley, P. M., Cambridge University Press, Cambridge, UK, New York, NY, USA,
1535 pp., 2013.Janhäll, S., Andreae, M. O., and Pöschl, U.: Biomass burning aerosol
emissions from vegetation fires: particle number and mass emission factors
and size distributions, Atmos. Chem. Phys., 10, 1427–1439,
10.5194/acp-10-1427-2010, 2010.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, D21208, 10.1029/2004JD004999, 2004.Kondo, Y., Matsui, H., Moteki, N., Sahu, L., Takegawa, N., Kajino, M., Zhao,
Y., Cubison, M. J., Jimenez, J. L., Vay, S., Diskin, G. S., Anderson, B.,
Wisthaler, A., Mikoviny, T., Fuelberg, H. E., Blake, D. R., Huey, G.,
Weinheimer, A. J., Knapp, D. J., and Brune, W. H.: Emissions of black carbon,
organic, and inorganic aerosols from biomass burning in North America and
Asia in 2008, J. Geophys. Res., 116, D08204, 10.1029/2010JD015152, 2011.Laborde, M., Crippa, M., Tritscher, T., Jurányi, Z., Decarlo, P. F.,
Temime-Roussel, B., Marchand, N., Eckhardt, S., Stohl, A., Baltensperger, U.,
Prévôt, A. S. H., Weingartner, E., and Gysel, M.: Black carbon
physical properties and mixing state in the European megacity Paris, Atmos.
Chem. Phys., 13, 5831–5856, 10.5194/acp-13-5831-2013, 2013.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, 10.5194/acp-13-10535-2013, 2013.Lack, D. A., Cappa, C. D., Covert, D. S., Baynard, T., Massoli, P., Sierau,
B., Bates, T. S., Quinn, P. K., Lovejoy, E. R., and Ravishankara, A. R.: Bias
in Filter-Based Aerosol Light Absorption Measurements Due to Organic Aerosol
Loading: Evidence from Ambient Measurements, Aerosol Sci. Technol., 42,
1033–1041, 10.1080/02786820802389277, 2008.Lack, D. A., Bahreini, R., Langridge, J. M., Gilman, J. B., and Middlebrook,
A. M.: Brown carbon absorption linked to organic mass tracers in biomass
burning particles, Atmos. Chem. Phys., 13, 2415–2422,
10.5194/acp-13-2415-2013, 2013.Laskin, A., Laskin, J., and Nizkorodov, S. A.: Chemistry of Atmospheric Brown
Carbon, Chem. Rev., 115, 4335–4382, 10.1021/cr5006167, 2015.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., 113, D16203, 10.1029/2007JD009699, 2008.Lewis, S. L., Brando, P. M., Phillips, O. L., van der Heijden, G. M. F., and
Nepstad, D.: The 2010 Amazon Drought, Science, 331, 554–554,
10.1126/science.1200807, 2011.Liu, D., Flynn, M., Gysel, M., Targino, A., Crawford, I., Bower, K.,
Choularton, T., Jurányi, Z., Steinbacher, M., Hüglin, C., Curtius,
J., Kampus, M., Petzold, A., Weingartner, E., Baltensperger, U., and Coe, H.:
Single particle characterization of black carbon aerosols at a tropospheric
alpine site in Switzerland, Atmos. Chem. Phys., 10, 7389–7407,
10.5194/acp-10-7389-2010, 2010.Liu, D., Taylor, J. W., Young, D. E., Flynn, M. J., Coe, H., and Allan, J.
D.: The effect of complex black carbon microphysics on the determination of
the optical properties of brown carbon, Geophys. Res. Lett., 42, 613–619,
10.1002/2014GL062443, 2015.Liu, D., Whitehead, J., Alfarra, M. R., Reyes-Villegas, E., Spracklen, D. V.,
Reddington, C. L., Kong, S., 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,
10.1038/ngeo2901, 2017.Martin, S. T., Andreae, M. O., Althausen, D., Artaxo, P., Baars, H.,
Borrmann, S., Chen, Q., Farmer, D. K., Guenther, A., Gunthe, S. S., Jimenez,
J. L., Karl, T., Longo, K., Manzi, A., Müller, T., Pauliquevis, T.,
Petters, M. D., Prenni, A. J., Pöschl, U., Rizzo, L. V., Schneider, J.,
Smith, J. N., Swietlicki, E., Tota, J., Wang, J., Wiedensohler, A., and Zorn,
S. R.: An overview of the Amazonian Aerosol Characterization Experiment 2008
(AMAZE-08), Atmos. Chem. Phys., 10, 11415–11438,
10.5194/acp-10-11415-2010, 2010a.Martin, S. T., Andreae, M. O., Artaxo, P., Baumgardner, D., Chen, Q.,
Goldstein, A. H., Guenther, A., Heald, C. L., Mayol-Bracero, O. L., McMurry,
P. H., Pauliquevis, T., Pöschl, U., Prather, K. A., Roberts, G. C.,
Saleska, S. R., Silva Dias, M. A., Spracklen, D. V., Swietlicki, E., and
Trebs, I.: Sources and properties of Amazonian aerosol particles, Rev.
Geophys., 48, RG2002, 10.1029/2008RG000280, 2010b.Martin, S. T., Artaxo, P., Machado, L. A. T., Manzi, A. O., Souza, R. A. F.,
Schumacher, C., Wang, J., Andreae, M. O., Barbosa, H. M. J., Fan, J., Fisch,
G., Goldstein, A. H., Guenther, A., Jimenez, J. L., Pöschl, U., Silva
Dias, M. A., Smith, J. N., and Wendisch, M.: Introduction: Observations and
Modeling of the Green Ocean Amazon (GoAmazon2014/5), Atmos. Chem. Phys., 16,
4785–4797, 10.5194/acp-16-4785-2016, 2016.Martin, S. T., Artaxo, P., Machado, L., Manzi, A. O., Souza, R. A. F.,
Schumacher, C., Wang, J., Biscaro, T., Brito, J., Calheiros, A., Jardine, K.,
Medeiros, A., Portela, B., de Sá, S. S., Adachi, K., Aiken, A. C.,
Albrecht, R., Alexander, L., Andreae, M. O., Barbosa, H. M. J., Buseck, P.,
Chand, D., Comstock, J. M., Day, D. A., Dubey, M., Fan, J., Fast, J., Fisch,
G., Fortner, E., Giangrande, S., Gilles, M., Goldstein, A. H., Guenther, A.,
Hubbe, J., Jensen, M., Jimenez, J. L., Keutsch, F. N., Kim, S., Kuang, C.,
Laskin, A., McKinney, K., Mei, F., Miller, M., Nascimento, R., Pauliquevis,
T., Pekour, M., Peres, J., Petäjä, T., Pöhlker, C., Pöschl,
U., Rizzo, L., Schmid, B., Shilling, J. E., Dias, M. A. S., Smith, J. N.,
Tomlinson, J. M., Tóta, J., and Wendisch, M.: The Green Ocean Amazon
Experiment (GoAmazon2014/5) Observes Pollution Affecting Gases, Aerosols,
Clouds, and Rainfall over the Rain Forest, B. Am. Meteorol. Soc., 98,
981–997, 10.1175/BAMS-D-15-00221.1, 2017.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,
10.1016/j.atmosenv.2015.02.058, 2015.Mikhailov, E. F., Mironova, S., Mironov, G., Vlasenko, S., Panov, A., Chi,
X., Walter, D., Carbone, S., Artaxo, P., Heimann, M., Lavric, J., Pöschl,
U., and Andreae, M. O.: Long-term measurements (2010–2014) of carbonaceous
aerosol and carbon monoxide at the Zotino Tall Tower Observatory (ZOTTO) in
central Siberia, Atmos. Chem. Phys., 17, 14365–14392,
10.5194/acp-17-14365-2017, 2017.Mishchenko, M. I., Dlugach, J. M., Yanovitskij, E. G., and Zakharova, N. T.:
Bidirectional reflectance of flat, optically thick particulate layers: an
efficient radiative transfer solution and applications to snow and soil
surfaces, J. Quant. Spectrosc. Ra., 63, 409–432,
10.1016/S0022-4073(99)00028-X, 1999.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, 10.5194/acp-11-1217-2011, 2011.Moran-Zuloaga, D., Ditas, F., Walter, D., Saturno, J., Brito, J., Carbone,
S., Chi, X., Hrabe de Angelis, I., Baars, H., Godoi, R. H. M., Heese, B.,
Holanda, B. A., Lavric, J. V., Martin, S. T., Ming, J., Pöhlker, M. L.,
Ruckteschler, N., Su, H., Wang, Y., Wang, Q., Wang, Z., Weber, B., Wolff, S.,
Artaxo, P., Pöschl, U., Andreae, M. O., and Pöhlker, C.: Long-term
study on coarse mode aerosols in the Amazon rain forest with the frequent
intrusion of Saharan dust plumes, Atmos. Chem. Phys., 18, 10055–10088,
10.5194/acp-18-10055-2018, 2018.Moteki, N. and Kondo, Y.: Method to measure time-dependent scattering cross
sections of particles evaporating in a laser beam, J. Aerosol Sci., 39,
348–364, 10.1016/j.jaerosci.2007.12.002, 2008.Müller, T., Henzing, J. S., de Leeuw, G., Wiedensohler, A., Alastuey, A.,
Angelov, H., Bizjak, M., Collaud Coen, M., Engström, J. E., Gruening, C.,
Hillamo, R., Hoffer, A., Imre, K., Ivanow, P., Jennings, G., Sun, J. Y.,
Kalivitis, N., Karlsson, H., Komppula, M., Laj, P., Li, S.-M., Lunder, C.,
Marinoni, A., Martins dos Santos, S., Moerman, M., Nowak, A., Ogren, J. A.,
Petzold, A., Pichon, J. M., Rodriquez, S., Sharma, S., Sheridan, P. J.,
Teinilä, K., Tuch, T., Viana, M., Virkkula, A., Weingartner, E., Wilhelm,
R., and Wang, Y. Q.: Characterization and intercomparison of aerosol
absorption photometers: result of two intercomparison workshops, Atmos. Meas.
Tech., 4, 245–268, 10.5194/amt-4-245-2011, 2011a.Müller, T., Laborde, M., Kassell, G., and Wiedensohler, A.: Design and
performance of a three-wavelength LED-based total scatter and backscatter
integrating nephelometer, Atmos. Meas. Tech., 4, 1291–1303,
10.5194/amt-4-1291-2011, 2011b.Nepstad, D. C., Stickler, C. M., Filho, B. S., and Merry, F.: Interactions
among Amazon land use, forests and climate: prospects for a near-term forest
tipping point, Philos. T. Roy. Soc. B, 363, 1737–1746,
10.1098/rstb.2007.0036, 2008.Ng, N. L., Herndon, S. C., Trimborn, A., Canagaratna, M. R., Croteau, P. L.,
Onasch, T. B., Sueper, D., Worsnop, D. R., Zhang, Q., Sun, Y. L., and Jayne,
J. T.: An Aerosol Chemical Speciation Monitor (ACSM) for Routine Monitoring
of the Composition and Mass Concentrations of Ambient Aerosol, Aerosol Sci.
Technol., 45, 780–794, 10.1080/02786826.2011.560211, 2011.Petzold, A. and Schönlinner, M.: Multi-angle absorption photometry – a
new method for the measurement of aerosol light absorption and atmospheric
black carbon, J. Aerosol Sci., 35, 421–441,
10.1016/j.jaerosci.2003.09.005, 2004.Petzold, A., Ogren, J. A., Fiebig, M., Laj, P., Li, S.-M., Baltensperger, U.,
Holzer-Popp, T., Kinne, S., Pappalardo, G., Sugimoto, N., Wehrli, C.,
Wiedensohler, A., and Zhang, X.-Y.: Recommendations for reporting “black
carbon” measurements, Atmos. Chem. Phys., 13, 8365–8379,
10.5194/acp-13-8365-2013, 2013.Pöhlker, C., Saturno, J., Krüger, M. L., Förster, J.-D., Weigand,
M., Wiedemann, K. T., Bechtel, M., Artaxo, P., and Andreae, M. O.:
Efflorescence upon humidification? X-ray microspectroscopic in-situ
observation of changes in aerosol microstructure and phase state upon
hydration, Geophys. Res. Lett., 41, 3681–3689, 10.1002/2014GL059409,
2014.Pöhlker, C., Walter, D., Paulsen, H., Könemann, T.,
Rodríguez-Caballero, E., Moran-Zuloaga, D., Brito, J., Carbone, S.,
Degrendele, C., Després, V. R., Ditas, F., Holanda, B. A., Kaiser, J. W.,
Lammel, G., Lavric, J. V., Ming, J., Pickersgill, D., Pöhlker, M. L.,
Praß, M., Ruckteschler, N., Saturno, J., Sörgel, M., Wang, Q., Weber,
B., Wolff, S., Artaxo, P., Pöschl, U., and Andreae, M. O.: Land cover and
its transformation in the backward trajectory footprint region of the Amazon
Tall Tower Observatory, Atmos. Chem. Phys. Discuss.,
10.5194/acp-2018-323, in review, 2018.Pöhlker, M. L., Ditas, F., Saturno, J., Klimach, T., Hrabe de Angelis,
I., Araùjo, A. C., Brito, J., Carbone, S., Cheng, Y., Chi, X., Ditz, R.,
Gunthe, S. S., Holanda, B. A., Kandler, K., Kesselmeier, J., Könemann,
T., Krüger, O. O., Lavric, J. V., Martin, S. T., Mikhailov, E.,
Moran-Zuloaga, D., Rizzo, L. V., Rose, D., Su, H., Thalman, R., Walter, D.,
Wang, J., Wolff, S., Barbosa, H. M. J., Artaxo, P., Andreae, M. O.,
Pöschl, U., and Pöhlker, C.: Long-term observations of cloud
condensation nuclei over the Amazon rain forest – Part 2: Variability and
characteristics of biomass burning, long-range transport, and pristine rain
forest aerosols, Atmos. Chem. Phys., 18, 10289–10331,
10.5194/acp-18-10289-2018, 2018.Pokhrel, R. P., Wagner, N. L., Langridge, J. M., Lack, D. A., Jayarathne, T.,
Stone, E. A., Stockwell, C. E., Yokelson, R. J., and Murphy, S. M.:
Parameterization of single-scattering albedo (SSA) and absorption
Ångström exponent (AAE) with EC / OC for aerosol emissions from
biomass burning, Atmos. Chem. Phys., 16, 9549–9561,
10.5194/acp-16-9549-2016, 2016.Pöschl, U., Martin, S. T., Sinha, B., Chen, Q., Gunthe, S. S., Huffman,
J. A., Borrmann, S., Farmer, D. K., Garland, R. M., Helas, G., Jimenez, J.
L., King, S. M., Manzi, A., Mikhailov, E., Pauliquevis, T., Petters, M. D.,
Prenni, A. J., Roldin, P., Rose, D., Schneider, J., Su, H., Zorn, S. R.,
Artaxo, P., and Andreae, M. O.: Rainforest Aerosols as Biogenic Nuclei of
Clouds and Precipitation in the Amazon, Science, 329, 1513–1516,
10.1126/science.1191056, 2010.Prospero, J. M., Glaccum, R. A., and Nees, R. T.: Atmospheric transport of
soil dust from Africa to South America, Nature, 289, 570–572,
10.1038/289570a0, 1981.Raatikainen, T., Brus, D., Hyvärinen, A.-P., Svensson, J., Asmi, E., and
Lihavainen, H.: Black carbon concentrations and mixing state in the Finnish
Arctic, Atmos. Chem. Phys., 15, 10057–10070,
10.5194/acp-15-10057-2015, 2015.R Development Core Team: R: A language and environment for statistical
computing, available at: http://www.r-project.org (last access: 1 June
2017), 2009.Reid, J. S., Hobbs, P. V, Ferek, R. J., Blake, D. R., Martins, J. V., Dunlap,
M. R., and Liousse, C.: Physical, chemical, and optical properties of
regional hazes dominated by smoke in Brazil, J. Geophys. Res.-Atmos., 103,
32059–32080, 10.1029/98JD00458, 1998.Reid, J. S., Eck, T. F., Christopher, S. A., Koppmann, R., Dubovik, O.,
Eleuterio, D. P., Holben, B. N., Reid, E. A., and Zhang, J.: A review of
biomass burning emissions part III: intensive optical properties of biomass
burning particles, Atmos. Chem. Phys., 5, 827–849,
10.5194/acp-5-827-2005, 2005.Ridley, D. A., Heald, C. L., and Prospero, J. M.: What controls the recent
changes in African mineral dust aerosol across the Atlantic?, Atmos. Chem.
Phys., 14, 5735–5747, 10.5194/acp-14-5735-2014, 2014.Rincón, A. G., Guzmán, M. I., Hoffmann, M. R., and Colussi, A. J.:
Thermochromism of Model Organic Aerosol Matter, J. Phys. Chem. Lett., 1,
368–373, 10.1021/jz900186e, 2010.Rizzo, L. V., Correia, A. L., Artaxo, P., Procópio, A. S., and Andreae,
M. O.: Spectral dependence of aerosol light absorption over the Amazon Basin,
Atmos. Chem. Phys., 11, 8899–8912, 10.5194/acp-11-8899-2011,
2011.Rizzo, L. V., Artaxo, P., Müller, T., Wiedensohler, A., Paixão, M.,
Cirino, G. G., Arana, A., Swietlicki, E., Roldin, P., Fors, E. O., Wiedemann,
K. T., Leal, L. S. M., and Kulmala, M.: Long term measurements of aerosol
optical properties at a primary forest site in Amazonia, Atmos. Chem. Phys.,
13, 2391–2413, 10.5194/acp-13-2391-2013, 2013.Roberts, G. C., Nenes, A., Seinfeld, J. H., and Andreae, M. O.: Impact of
biomass burning on cloud properties in the Amazon Basin, J. Geophys. Res.,
108, 4062, 10.1029/2001JD000985, 2003.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, 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,
10.1038/ngeo2220, 2014.Salvador, P., Almeida, S. M., Cardoso, J., Almeida-Silva, M., Nunes, T.,
Cerqueira, M., Alves, C., Reis, M. A., Chaves, P. C., Artíñano, B.,
and Pio, C.: Composition and origin of PM10 in Cape Verde:
Characterization of long-range transport episodes, Atmos. Environ., 127,
326–339, 10.1016/j.atmosenv.2015.12.057, 2016.Sandradewi, J., Prévôt, A. S. H., Szidat, S., Perron, N., Alfarra, M.
R., Lanz, V. A., Weingartner, E., and Baltensperger, U.: Using aerosol light
absorption measurements for the quantitative determination of wood burning
and traffic emission contributions to particulate matter, Environ. Sci.
Technol., 42, 3316–3323, 10.1021/es702253m, 2008.Saturno, J., Pöhlker, C., Massabò, D., Brito, J., Carbone, S., Cheng,
Y., Chi, X., Ditas, F., Hrabe de Angelis, I., Morán-Zuloaga, D.,
Pöhlker, M. L., Rizzo, L. V., Walter, D., Wang, Q., Artaxo, P., Prati,
P., and Andreae, M. O.: Comparison of different Aethalometer correction
schemes and a reference multi-wavelength absorption technique for ambient
aerosol data, Atmos. Meas. Tech., 10, 2837–2850,
10.5194/amt-10-2837-2017, 2017.Saturno, J., Ditas, F., Penning de Vries, M., Holanda, B. A., Pöhlker, M.
L., Carbone, S., Walter, D., Bobrowski, N., Brito, J., Chi, X., Gutmann, A.,
Hrabe de Angelis, I., Machado, L. A. T., Moran-Zuloaga, D., Rüdiger, J.,
Schneider, J., Schulz, C., Wang, Q., Wendisch, M., Artaxo, P., Wagner, T.,
Pöschl, U., Andreae, M. O., and Pöhlker, C.: African volcanic
emissions influencing atmospheric aerosols over the Amazon rain forest,
Atmos. Chem. Phys., 18, 10391–10405, 10.5194/acp-18-10391-2018, 2018a.Saturno, J., Holanda, B., Pöhlker, C., Ditas, F., Wang, Q.,
Moran-Zuloaga, D., Brito, J., Carbone, S., Cheng, Y., Chi, X., Ditas, J.,
Hoffmann, T., Hrabe de Angelis, I., Könemann, T., Lavric, J., Ma, N.,
Ming, J., Paulsen, H., Pöhlker, M., Rizzo, L., Schlag, P., Su, H.,
Walter, D., Wolff, S., Zhang, Y., Artaxo, P., Pöschl, U., and Andreae, M.
O.: Available data for ATTO BC and BrC study, Max Planck Society,
10.17617/3.1r, 2018b.Schkolnik, G., Chand, D., Hoffer, A., Andreae, M. O., Erlick, C., Swietlicki,
E., and Rudich, Y.: Constraining the density and complex refractive index of
elemental and organic carbon in biomass burning aerosol using optical and
chemical measurements, Atmos. Environ., 41, 1107–1118,
10.1016/j.atmosenv.2006.09.035, 2007.Schuster, G. L., Dubovik, O., Arola, A., Eck, T. F., and Holben, B. N.:
Remote sensing of soot carbon – Part 2: Understanding the absorption
Ångström exponent, Atmos. Chem. Phys., 16, 1587–1602,
10.5194/acp-16-1587-2016, 2016.Schwarz, J. P., Gao, R. S., Fahey, D. W., Thomson, D. S., Watts, L. A.,
Wilson, J. C., Reeves, J. M., Darbeheshti, M., Baumgardner, D. G., Kok, G.
L., Chung, S. H., Schulz, M., Hendricks, J., Lauer, A., Kärcher, B.,
Slowik, J. G., Rosenlof, K. H., Thompson, T. L., Langford, A. O.,
Loewenstein, M., and Aikin, K. C.: Single-particle measurements of
midlatitude black carbon and light-scattering aerosols from the boundary
layer to the lower stratosphere, J. Geophys. Res., 111, D16207,
10.1029/2006JD007076, 2006.Seinfeld, J. H., Bretherton, C., Carslaw, K. S., Coe, H., DeMott, P. J.,
Dunlea, E. J., Feingold, G., Ghan, S., Guenther, A. B., Kahn, R., Kraucunas,
I., Kreidenweis, S. M., Molina, M. J., Nenes, A., Penner, J. E., Prather, K.
A., Ramanathan, V., Ramaswamy, V., Rasch, P. J., Ravishankara, A. R.,
Rosenfeld, D., Stephens, G., and Wood, R.: Improving our fundamental
understanding of the role of aerosol–cloud interactions in the climate
system, P. Natl. Acad. Sci. USA, 113, 5781–5790,
10.1073/pnas.1514043113, 2016.Snelling, D. R., Smallwood, G. J., Liu, F., Gülder, Ö. L., and
Bachalo, W. D.: A calibration-independent laser-induced incandescence
technique for soot measurement by detecting absolute light intensity, Appl.
Opt., 44, 6773, 10.1364/AO.44.006773, 2005.Stephens, M., Turner, N., and Sandberg, J.: Particle Identification by
Laser-Induced Incandescence in a Solid-State Laser Cavity, Appl. Opt., 42,
3726, 10.1364/AO.42.003726, 2003.
Stull, R. B.: An Introduction to Boundary Layer Meteorology, Springer, the
Netherlands, 1988.Subramanian, R., Kok, G. L., Baumgardner, D., Clarke, A., Shinozuka, Y.,
Campos, T. L., Heizer, C. G., Stephens, B. B., de Foy, B., Voss, P. B., and
Zaveri, R. A.: Black carbon over Mexico: the effect of atmospheric transport
on mixing state, mass absorption cross-section, and BC / CO ratios,
Atmos. Chem. Phys., 10, 219–237, 10.5194/acp-10-219-2010,
2010.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. Technol. Lett., 4, 540–545, 10.1021/acs.estlett.7b00393, 2017.Talbot, R. W., Andreae, M. O., Berresheim, H., Artaxo, P., Garstang, M.,
Harriss, R. C., Beecher, K. M., and Li, S. M.: Aerosol chemistry during the
wet season in central Amazonia: The influence of long-range transport, J.
Geophys. Res., 95, 16955, 10.1029/JD095iD10p16955, 1990.Tasoglou, A., Saliba, G., Subramanian, R., and Pandis, S. N.: Absorption of
chemically aged biomass burning carbonaceous aerosol, J. Aerosol Sci., 113,
141–152, 10.1016/j.jaerosci.2017.07.011, 2017.Tuch, T. M., Haudek, A., Müller, T., Nowak, A., Wex, H., and
Wiedensohler, A.: Design and performance of an automatic regenerating
adsorption aerosol dryer for continuous operation at monitoring sites, Atmos.
Meas. Tech., 2, 417–422, 10.5194/amt-2-417-2009, 2009.Virkkula, A., Backman, J., Aalto, P. P., Hulkkonen, M., Riuttanen, L.,
Nieminen, T., dal Maso, M., Sogacheva, L., de Leeuw, G., and Kulmala, M.:
Seasonal cycle, size dependencies, and source analyses of aerosol optical
properties at the SMEAR II measurement station in Hyytiälä, Finland,
Atmos. Chem. Phys., 11, 4445–4468, 10.5194/acp-11-4445-2011,
2011.Wang, Q., Huang, R.-J., Cao, J., Han, Y., Wang, G., Li, G., Wang, Y., Dai,
W., Zhang, R., and Zhou, Y.: Mixing State of Black Carbon Aerosol in a
Heavily Polluted Urban Area of China: Implications for Light Absorption
Enhancement, Aerosol Sci. Technol., 48, 689–697,
10.1080/02786826.2014.917758, 2014.Wang, Q., Saturno, J., Chi, X., Walter, D., Lavric, J. V., Moran-Zuloaga, D.,
Ditas, F., Pöhlker, C., Brito, J., Carbone, S., Artaxo, P., and Andreae,
M. O.: Modeling investigation of light-absorbing aerosols in the Amazon Basin
during the wet season, Atmos. Chem. Phys., 16, 14775–14794,
10.5194/acp-16-14775-2016, 2016a.Wang, X., Heald, C. L., Sedlacek, A. J., de Sá, S. S., Martin, S. T.,
Alexander, M. L., Watson, T. B., Aiken, A. C., Springston, S. R., and Artaxo,
P.: Deriving brown carbon from multiwavelength absorption measurements:
method and application to AERONET and Aethalometer observations, Atmos. Chem.
Phys., 16, 12733–12752, 10.5194/acp-16-12733-2016, 2016b.Warton, D. I., Wright, I. J., Falster, D. S., and Westoby, M.: Bivariate
line-fitting methods for allometry, Biol. Rev. Camb. Philos. Soc., 81,
259–291, 10.1017/S1464793106007007, 2006.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, 10.1016/S0021-8502(03)00359-8, 2003.Winderlich, J., Chen, H., Gerbig, C., Seifert, T., Kolle, O., Lavric, J. V.,
Kaiser, C., Höfer, A., and Heimann, M.: Continuous low-maintenance
CO2/CH4/H2O measurements at the Zotino Tall Tower Observatory
(ZOTTO) in Central Siberia, Atmos. Meas. Tech., 3, 1113–1128,
10.5194/amt-3-1113-2010, 2010.
Womack, C., Manfred, K., Wagner, N., He, Q., Rudich, Y., Brown, S., and
Washenfelder, R.: Characterizing the optical properties of brown carbon
aerosol from biomass burning using broadband cavity enhanced spectroscopy, in
Atmospheric Chemistry Gordon Research Conference, 2017.Wong, J. P. S., Nenes, A., and Weber, R. J.: Changes in Light Absorptivity of
Molecular Weight Separated Brown Carbon Due to Photolytic Aging, Environ.
Sci. Technol., 51, 8414–8421, 10.1021/acs.est.7b01739, 2017.Zanatta, M., Gysel, M., Bukowiecki, N., Müller, T., Weingartner, E.,
Areskoug, H., Fiebig, M., Yttri, K. E., Mihalopoulos, N., Kouvarakis, G.,
Beddows, D., Harrison, R. M., Cavalli, F., Putaud, J. P., Spindler, G.,
Wiedensohler, A., Alastuey, A., Pandolfi, M., Sellegri, K., Swietlicki, E.,
Jaffrezo, J. L., Baltensperger, U., and Laj, P.: A European aerosol
phenomenology-5: Climatology of black carbon optical properties at 9 regional
background sites across Europe, Atmos. Environ., 145, 346–364,
10.1016/j.atmosenv.2016.09.035, 2016.