Introduction
Atmospheric aerosols have a major impact on the radiative balance of the
Earth's climate system by changing the microphysical structure, lifetime, and
coverage of clouds. For the same liquid water content, high aerosol
concentration leads to more, smaller cloud droplets and therefore higher
cloud albedo (Twomey, 1977). The smaller droplet size also delays or inhibits
warm precipitation, leading to increases in both cloud lifetime and coverage
(Albrecht, 1989) and ultimately invigoration of convective clouds (Rosenfeld
et al., 2008). Currently, the effects of aerosol on clouds remain one of the
largest uncertainties in simulated climate change during the industrial era,
and a large portion of this uncertainty is due to the natural aerosol
properties and processes represented in models (Carslaw et al., 2013; Ghan
et al., 2013). The Amazon represents more than half of the planet's
rainforest and is a rapidly changing region where deforestation, human
activity, and natural resource needs are all at play in changing the
ecosystem (Andreae et al., 2015; Batistella et al., 2009; Davidson et al.,
2012). The Amazon basin also represents at times one of the cleanest
continental regions on the planet where it is still possible to find extended
periods with little or no impact of anthropogenic activity, although the
long-distance transport of pollution is occasionally observed (Andreae
et al., 2015; Hamilton et al., 2014; Martin et al., 2010b; Wang et al.,
2016a, b; Williams et al., 2002). This makes the Amazon basin an ideal
location to characterize aerosol under near-natural conditions and assess the
impact due to urban emissions and biomass burning (Kuhn et al., 2010). The
biogenic activity of this region makes it a major source of organic carbon
released into the atmosphere via isoprene and monoterpenes (Guenther et al.,
2006, 2012; Kesselmeier et al., 2002; Kuhn et al., 2007) which are mediated
by biotic stress through heat, sunlight, and changes in CO2 (Heald
et al., 2009).
To understand the impact of aerosol on clouds and climate requires
knowledge of the concentration of cloud condensation nuclei (CCN), which are
particles that are able to form cloud droplets under relevant
atmospheric conditions. The minimum supersaturation required to
activate a particle into a cloud droplet can be predicted using
the κ-Köhler theory based on particle size and the single
hygroscopicity parameter κ, which combines a number of
thermodynamic properties required for the description of water
activity of the growing droplets (Petters and Kreidenweis, 2007). The
value of κ is determined by the physicochemical properties of
the solutes, including their molar volume, activity coefficient, and
the effect on surface tension. For multicomponent particles, κ
is the volume average of participating species. Hygroscopicity also
describes particle growth under subsaturated conditions and can be
derived from the particle growth factor (GF). However, particles
sometime exhibit larger κ values for droplet activation
(derived from CCN measurements under supersaturated conditions) than
for particle growth (derived from particle GF under subsaturated
conditions; e.g., Duplissy et al., 2008; Good et al., 2010; Mikhailov
et al., 2013; Pajunoja et al., 2015; Wex et al., 2009). In this
paper, “hygroscopicity” represents κ associated with droplet
activation derived from CCN measurements unless noted otherwise.
The hygroscopicities of typical inorganic species in ambient particles are
relatively well known (Petters and Kreidenweis, 2007). However, atmospheric
aerosols consist of a large number of organic compounds, which often dominate
the total fine aerosol mass, especially in forested areas (e.g., de Sá
et al., 2017a; Jimenez et al., 2009; Zhang et al., 2007). The hygroscopicity
of aerosol organics (κorg) have been examined in both
laboratory (e.g., Asa-Awuku et al., 2009; Duplissy et al., 2011; King et al.,
2009; Lambe et al., 2011; Massoli et al., 2010; Prenni et al., 2007; Raymond
and Pandis, 2003) and field studies (e.g., Cerully et al., 2015; Chang
et al., 2010; Dusek et al., 2010; Gunthe et al., 2009; Jimenez et al., 2009;
Lathem et al., 2013; Mei et al., 2013a, b; Moore et al., 2011, 2012;
Pöhlker et al., 2016; Rose et al., 2010; Shantz et al., 2008; Wang
et al., 2008). Overall, these studies show that aerosol organics exhibit
a wide range of κ values from 0 to ∼0.3, and κorg often increases substantially during aerosol aging in the
atmosphere (e.g., Duplissy et al., 2011; Jimenez et al., 2009; Lambe et al.,
2011; Massoli et al., 2010; Mei et al., 2013a, b).
A number of recent studies examined the sensitivity of predicted CCN
concentration and cloud droplet number concentration to aerosol
properties (e.g., Ervens et al., 2010; Kammermann et al., 2010;
McFiggans et al., 2006; Mei et al., 2013b; Reutter et al., 2009;
Rissman et al., 2004; Roberts et al., 2002; Wang, 2007; Wang et al.,
2008). These studies show that the predicted CCN concentration is
often sensitive to κorg, especially for aerosol under
background conditions where organics tend to dominate submicron
aerosol mass (Liu and Wang, 2010; Mei et al., 2013b). Using
a constant κorg may lead to large biases in predicted
CCN concentrations and aerosol indirect forcing (Liu and Wang, 2010).
Therefore, it is imperative to understand organic hygroscopicity under
background conditions, such as in the Amazon forest, as well as the
variation of organic hygroscopicity due to anthropogenic emissions.
There have been several studies of aerosol hygroscopicity in the
Amazon basin over the past 20 years (Gunthe et al., 2009; Mikhailov
et al., 2013; Pöhlker et al., 2016; Rissler et al., 2006; Roberts
et al., 2001; Vestin et al., 2007; Whitehead et al., 2016; Zhou
et al., 2002). Gunthe et al. (2009) performed size-resolved CCN
measurements during the wet season in February and March 2008 as part
of the AMAZE-08 campaign (Martin et al., 2010a). That study reported
no diel cycle in the CCN concentration during periods with little or
no influence of pollution. Pöhlker et al. (2016) measured
size-resolved CCN spectra at a remote background site (Amazon Tall
Tower Observatory, ATTO) over a 1-year period from March 2014 to
February 2015 and observed no diel cycle and only weak seasonal trends
in derived particle hygroscopicity (κCCN), while CCN concentrations had
a pronounced seasonal cycle as the background aerosol concentration
was strongly influenced by regional biomass burning during the dry
season. During the SMOCC-2002 campaign (LBA–SMOCC; Large-Scale Biosphere–Atmosphere Experiment in Amazonia – SMOke, Aerosols, Clouds,
Rainfall, and Climate), particle hygroscopicity was derived from the Humidified Tandem Differential Mobility
Analyzer measurements in the state of Rondônia in the southwest of the
Amazon region during the dry season from September to November of 2002
(Rissler et al., 2006; Vestin et al., 2007). The study concluded that
the diel variation in the aerosol hygroscopicity could be linked to
the structure and dynamics of the boundary layer. Local sources
dominated nighttime aerosol properties with downward mixing from the
residual layer aloft as the day progressed. All of these studies found
that particle hygroscopicity increased with particle size (from the
Aitken to accumulation modes), consistent with higher sulfate content
at larger sizes (Gunthe et al., 2009). The same boundary layer
evolution has been found to influence particle number and CCN
evolution in a number of other related studies (Fisch et al., 2004;
Martin et al., 2010a; Rissler et al., 2006; Vestin et al., 2007;
Whitehead et al., 2010; Zhou et al., 2002).
In this study we present measurements of size-resolved CCN spectra at
five particle diameters ranging from 75 to 171 nm downwind of
Manaus, Brazil, in central Amazonia for a period of 1 year from
12 March 2014 to 3 March 2015. Particle hygroscopicity, mixing state,
and organic hygroscopicity are derived from the size-resolved-CCN
activated fraction and concurrent aerosol composition
measurements. The diel variations of these properties are examined for
different seasons (i.e., wet season, dry season, and transition
seasons) and for different types of representative air masses,
including background conditions, as well as influences of urban
pollution plumes and/or local biomass burning. During the wet season,
the background air mass represents near-natural conditions, with
occasional impact from anthropogenic emissions, while, in the dry
season, the background is dominated by regional biomass burning
aerosol particles. The relationship between organic hygroscopicity and
particle oxidation level (i.e., O : C atomic ratio) is examined for
both dry and wet seasons. Hygroscopicities associated with organic
factors from aerosol mass spectrometry (AMS) positive matrix
factorization (PMF) analysis are derived, and their relationship with
the O : C ratio is compared with those from previous laboratory
studies.
Experimental setup
Measurement sites
Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5),
sponsored by the US Department of Energy and several Brazilian and German
agencies, took place at multiple surface sites surrounding Manaus, Brazil,
from January 2014 through December 2015 (Martin et al., 2016). This work
focuses on the measurements carried out at a downwind site (T3;
3∘12′47.82′′ S,
60∘35′55.32′′ W; 60 km west of
Manaus) from March 2014 to March 2015. Depending on the wind direction, the
T3 site experienced conditions ranging from nearly natural to heavily
polluted. More detailed characterizations of aerosol- and gas-phase chemical
composition were carried out at the T3 site during Intensive Operating
Periods (IOPs) from 1 February 2014 to 31 March 2014 and from 15 August 2014
to 15 October 2014. In addition, data from two sites normally upwind of
Manaus are also used in this study. These background sites include the T0a
site (ATTO; 2∘8′47.88′′ S,
59∘0′18.00′′ W; Andreae et al., 2015) and
the T0 t site (2.6091∘ S, 60.2093∘ W; Martin
et al., 2010a).
Activated fraction of size-selected particles
The CCN activation fraction of size-selected particles was measured
using a differential mobility analyzer (DMA) coupled to a condensation
particle counter (CPC; TSI Inc., 3010) and a cloud condensation nuclei
counter (CCNC; Droplet Measurement Technologies, Boulder, CO; Frank
et al., 2006; Mei et al., 2013a; Moore et al., 2010; Petters et al.,
2007). Aerosol particles were sampled with a total flow rate (Qa)
of 1.53 Lmin-1 from a height of 5 ma.g.l. and
were dried to relative humidity (RH) below 20 % by a Nafion dryer immediately upon
entering the instrument container. The dried aerosol particles then
reached steady state charge distribution inside a Kr-85 aerosol
charger (TSI, model 3077A) prior to being introduced into the DMA
operated with a sheath flow rate (Qsh) of
15.3 Lmin-1 to maintain a 10:1 sheath to aerosol flow
ratio (Qsh/Qa). The aerosol particles were
size-selected by the DMA, and the size-selected particles were
simultaneously characterized by a CPC (QCPC=0.53 Lmin-1) and a CCNC (see Fig. S1 in the Supplement
for further details). This system had been operated in previous field
campaigns by scanning the particle size while CCNC supersaturation was
held constant (Mei et al., 2013a, b). During GoAmazon2014/5 the
particle size classified by the DMA was stepped through seven particle
diameters (51, 75, 94, 112, 142, 171, and 222 nm), while the
CCNC supersaturation was also changed at each diameter by stepping the
flow rates (QCCN ranging from 0.2 to
1.0 Lmin-1) and temperature gradient (ΔT=4.5,
5.5, 6.5, 8.0, and 10.0 ∘C). At a given supersaturation, data
were acquired for a minimum of 30 s and until 1500 particles
were counted by the CPC or up to a maximum time of 120 s.
Depending on the aerosol number size distribution, the measurement
cycled through the seven particle sizes in 1–2 h (see Figs. S1
and S2 for further details of the measurement setup and sampling
protocol). The sampling sequence was designed so that the change of
CCNC supersaturation was mostly accomplished by stepping flow rates,
as the CCNC reaches steady state faster following flow changes than
temperature changes. Change in the temperature gradient was kept at
a minimum frequency but was necessary given the wide range of
supersaturation explored. Given the low particle number concentration
(e.g., ∼200 cm-3 under background conditions during
the wet season), these approaches were important to achieve adequate
counting statistics with good time resolution to capture changes of
air mass within 10–20 min (Liu et al., 2016). The supersaturation of
the CCNC was calibrated using ammonium sulfate aerosol, as described
previously in the literature (e.g., Mei et al., 2013a), at each
operational set point (QCCN and ΔT), ranging from
0.075–1.1 %. Fluctuation of the temperature inside the
instrument container, ranging from 20 to 30 ∘C over the
course of a day, led to substantial variation in the absolute
temperature inside the CCNC growth chamber. Calibrations were
therefore repeated under a range of container and associated-growth-chamber
temperatures. The dependence of the supersaturation on the
temperature at the top of the CCNC column (instrument temperature T1)
was derived for each QCCN and ΔT pair and used to
retrieve the supersaturation over the range of the instrument
operating conditions (see Fig. S3 and further description in the
Supplement).
Aerosol chemical composition
Non-refractory submicron aerosol composition (organics, sulfate, nitrate,
ammonium, and chloride) was measured by a high-resolution time-of-flight
aerosol mass spectrometer (HR-ToF-AMS; Aerodyne Research Inc.; DeCarlo
et al., 2006) during the two IOPs and by an Aerosol Chemical Speciation
Monitor (ACSM; Aerodyne Research, Inc.; Ng et al., 2011) from July 2014 to
March 2015. The AMS sampled from an inlet equipped with a PM2.5
cyclone located at 5 ma.g.l. The ambient sample was first dried
outside the container by a polytube Nafion dryer (Perma Pure, model PD-100T).
Once inside the container the sample was further dried by a monotube Nafion
dryer (Perma Pure, model MD-110) to achieve RH <40 % and was split
between the AMS and a Scanning Mobility Particle Sizer (SMPS, TSI, model
3081). Ambient measurements were obtained every 4 of 8 min. Further details
of the AMS setup and operation are described in de Sá et al. (2017a).
The ACSM was a part of the Atmospheric Radiation Measurement (ARM) Mobile
Facility-1 (AMF1) mobile aerosol observing system (MAOS). Aerosol
was sampled through an inlet located 10 m above the
ground. The aerosol sample was first dried through five large (40×1.75 cm I.D.) Nafion dryers before being distributed
among various instruments including the ACSM. The ACSM sampling
alternated between with and without an in-line filter using a three-way
valve, such that aerosol-free background could be subtracted from the
ambient measurement. A total of 28 ambient and background scans of the
quadrupole mass spectrometer (unit mass resolution) were averaged to
give one measurement every 30 min. The mass concentrations of
organic species, sulfate, nitrate, ammonium, and chloride were derived
from measurements using approaches described in Ng et al. (2011).
Refractory black carbon (rBC) was measured using both a Single
Particle Soot Photometer (SP2; Droplet Measurement Technologies,
Boulder, CO) and an aethalometer (Magee Scientific) co-located with
the AMS and ACSM. The SP2 measures rBC using laser-induced
incandescence, whereas the aethalometer measures equivalent black
carbon (BCe; Andreae and Gelencsér, 2006) using light
absorption from particles collected onto a filter. While these are
fundamentally different aerosol properties, both species (rBC and
BCe) were treated as equivalent in this study, and
BCe concentration was adjusted to match that of rBC using
the approach detailed in Sect. 2.2 of the Supplement.
Additional relevant measurements
Additional measurements of aerosol microphysics, trace gas concentrations,
and atmospheric conditions used in this study are briefly described here.
These measurements were part of the deployment of the ARM AMF1 facility
during GoAmazon2014/5 (Martin et al., 2016). Relevant aerosol measurements
include dry-particle number size distributions from 10 to 480 nm by
an SMPS and the number concentration of particles with diameters greater than
10 nm by a CPC (TSI Inc., model 3772). Mixing ratios of CO and
O3 were characterized by an Off-Axis Integrated Cavity Output
Spectroscopy CO, N2O, and H2O analyzer (model number
908-0014, Los Gatos) and a UV Photometric O3 analyzer (model 49i,
Thermo Scientific Inc.), respectively. Oxides of nitrogen (NO, NOx,
NO2, NOy) were measured using a chemiluminescence
technique (details given in Sect. S2.1 of the Supplement). Meteorological
data included relative humidity, ambient temperature, wind speed and
direction, and rain accumulation. The vertical profiles of atmospheric
backscatter (clouds and aerosol) and boundary layer heights were estimated
from ceilometer (model CL31, Vaisala) measurements.
Methods
Derivation of particle hygroscopicity and mixing state
The particle hygroscopicity parameter, κ (Petters and
Kreidenweis, 2007), was derived from the activation spectrum (i.e.,
activated fraction as a function of supersaturation S) at the
individual particle sizes using approaches detailed in the literature
(Bougiatioti et al., 2011; Cerully et al., 2011; Lance et al., 2013;
Mei et al., 2013a; Rose et al., 2008). The activation spectrum of
size-selected particles was first corrected for the influence of
multiply charged particles, which is estimated using the size
distribution measured by the SMPS in MAOS and the activation spectrum
measured at the sizes of the doubly and triply charged particles (see
Sect. S3.1). The corrected activation spectrum of size-selected
particles was then fit with a cumulative lognormal (Mei et al., 2013a;
Rose et al., 2008) functional form (see Fig. S6 for examples):
RaS=E21+erflnS-lnS∗2σs2,
where Ra is the activated fraction as a function of
supersaturation S, E is maximum activated fraction, and (1-E)
represents the number fraction of particles consisting of only
nonhygroscopic species (e.g., uncoated rBC) that cannot serve as CCN
under typical atmospheric supersaturations. S∗ is the
supersaturation at which Ra reaches 50 % of E and
represents the median critical supersaturation of size-selected
particles that serve as CCN. The value of σs is related to
the slope of the increasing Ra with S near S∗ and
reflects the heterogeneity of critical supersaturation, which to
a large degree arises from the heterogeneity of the hygroscopicity
among size-selected particles (Cerully et al., 2011; Mei et al.,
2013a). The probability density function of hygroscopicity for
size-selected particles is derived from the Ra(S). The average
hygroscopicity κ‾CCN and dispersion of the
hygroscopicity σκ/κ‾CCN for the size-selected CCN were then derived
from the probability density function of hygroscopicity using the
approach detailed in Sect. S3.4 in Supplement. The dispersion of the
hygroscopicity reflects the composition heterogeneity (i.e., mixing
state) among size-selected particles (Mei et al., 2013a). For
simplicity, we use κCCN to represent the average
hygroscopicity of size-selected CCN in the following sections. As
hygroscopicities reported in this study were derived from particle dry
diameter and critical supersaturation, they represent “apparent
hygroscopicity”, which includes the potential impact due to the
limited solubility of organics and the reduction of surface tension by
surface active species (Sullivan et al., 2009).
Derivation of organic aerosol hygroscopicity
The average particle hygroscopicity was then combined with the chemical
composition data to derive the hygroscopicity of the organic component of the
size-selected particles, κorg. Collectively, the AMS, ACSM,
SP2, and aethalometer provided mass concentrations of organic species,
sulfate, nitrate, ammonium, and rBC. The concentration of chloride was
negligible (≫1 % of aerosol mass) and was not included in the
analysis. Given the low concentrations during GoAmazon2014/5, size-resolved
mass concentrations at the time resolution of the CCN measurement were not
directly derived from AMS particle-time-of-flight (PToF) mode data. For IOP1,
measurements were classified into three groups based on bulk organic mass
fraction and the characteristic mass size distribution of each species was
averaged from measurements in each group. For the dry season, the
measurements were classified into three groups each for day and night periods
based on the bulk aerosol organic mass fraction, and the mass size
distribution of each species was averaged from measurements in each of the
six groups. The size-resolved mass concentrations of sulfate, nitrate, and
organics at the time resolution of the CCN measurement were then derived by
scaling the total mass concentration using the average mass size
distributions for the corresponding group (based on the bulk organic mass
fraction) of either wet or dry season (de Sá et al., 2017b). The shape of
the NH4+ mass size distribution was assumed to be the same as
that of sulfate, as ammonium cations were primarily associated with sulfate.
rBC was assumed to have the same size distribution shape as the total aerosol
mass (i.e., mass fraction of rBC was independent of particle size); though
this assumption may not always be appropriate, the effect is expected to be
very small as the monthly average volume fraction of rBC was always less than
4 % (Fig. 1). A detailed description of the derivation of the
size-resolved mass concentrations is given in Sect. S4.1.
Seasonal variations of aerosol properties observed at the T3
site from March 2014 to March 2015, including (a)
κCCN; (b) κorg; and
the size-resolved volume fraction of (c) organics; (d)
sulfate, including (NH4)2SO4 and NH4HSO4; (f) refractory black
carbon; and
(e) nitrate. Data
points are the monthly mean; error bars represent the 25th and
75th percentiles.
In most cases, NH4+ was insufficient to completely neutralize
SO42-. The concentrations of both the organonitrate and
inorganic nitrate during the two IOPs were retrieved from AMS data based on
the ratio of ions NO+ and NO2+ (de Sá et al.,
2017b; Fry et al., 2009). When the inorganic nitrate mass concentration was
negligible (i.e., less than 30 ngm-3), as in most of the cases,
the contributions of ammonium sulfate and ammonium bisulfate were calculated
based on the mass concentrations of SO42- and NH4+
(Nenes et al., 1998). In rare cases when the mass concentration of inorganic
nitrate was greater than 30 ngm-3, sulfate was assumed to be
ammonium sulfate. During non-IOP periods, only the total nitrate mass
concentration is available, all nitrate was assumed to be an organonitrate
(Cerully et al., 2015; Lathem et al., 2013; Nenes et al., 1998; Zhang et al.,
2005), and sulfate was fully neutralized by ammonium (see Sect. S4.2 in the
Supplement for a sensitivity study of these assumptions for non-IOP periods).
The –ONO2 portion of the organonitrate was added back to the organic
mass. We note that the amount of –ONO2 added back was typically
small given the low mass fraction of nitrate in the aerosol. Cloud
condensation nuclei were assumed to be internal mixtures of
(NH4)2SO4, NH4HSO4, NH4NO3, organics, and
rBC, and the volume fractions of the species were derived from the mass
concentrations and densities. Densities of organics were estimated from the
ratios of O : C and H : C measured by the AMS (Kuwata et al., 2012) and
were on average 1450±100 and 1470±80 kgm-3 for IOP1
and IOP2, respectively. Densities of 1770, 1790, 1730, and
1800 kgm-3 were used for (NH4)2SO4,
NH4HSO4, NH4NO3, and rBC (Bond and Bergstrom, 2006; Park
et al., 2004), respectively. In very rare cases, E was less than 100 %,
suggesting some of the size-selected particles were nonhygroscopic. The
nonhygroscopic particles were assumed to consist entirely of rBC (Mei et al.,
2013a). The volume concentration of the nonhygroscopic particles was derived
as the product of (1-E) and the total volume concentration (i.e., the sum
of volume concentrations of organics, (NH4)2SO4,
NH4HSO4, NH4NO3, and rBCs) at the size classified by the
DMA. The volume concentration of rBC internally mixed within the CCN-active
particles was then calculated as the difference between the total rBC volume
concentration and the volume concentration of the nonhygroscopic particles.
(Mei et al., 2013a, b). Assuming a κ value of zero for rBC, we can
derive the hygroscopicity of the organic component of the CCN
κorg as
κorg=1xorgκCCN-xNH42SO4κNH42SO4-xNH4HSO4κNH4HSO4-xNH4NO3κNH4NO3,
where xi is the volume fraction of the respective species. The
κ values are 0.61, 0.7, and 0.67 for (NH4)2SO4,
NH4HSO4, and NH4NO3, respectively (Petters and
Kreidenweis, 2007). The uncertainty in κorg using
these calculations has been derived using the approach detailed in
earlier studies (Mei et al., 2013a, b) and is on the order of
0.01–0.02 (which was generally between 10 and 20 %) for this dataset.
Derivation of κ for AMS PMF factors
PMF was applied to the AMS mass spectra (Lanz et al., 2008; Ulbrich et al.,
2009), and six organic factors were identified for each of the two IOPs (de
Sá et al., 2017b). For IOP2 the PMF analysis included data from 24 August
to 15 October 2014, excluding a major regional biomass burning event from 16
to 23 August, which was treated separately in the PMF analysis. For IOP1 (wet
season), the six factors were isoprene-epoxydiol-derived secondary organic
aerosol (IEPOX-SOA), more-oxidized oxygenated organic aerosol (MO-OOA, i.e.,
highly oxidized organics), less-oxidized oxygenated organic aerosol (LO-OOA),
biomass burning organic aerosol (BBOA) with characteristic peaks at m/z=60 and 73 and correlated with the concentrations of levoglucosan and
vanillin, a factor with high contribution from m/z=91 (Fac91) and
correlated with anthropogenic emissions of aromatics, and hydrocarbon-like
organic aerosol (HOA). The six factors for IOP2 included IEPOX-SOA, MO-OOA,
LO-OOA, an aged biomass burning organic aerosol factor (aged BBOA), a fresh
biomass burning organic aerosol factor (fresh BBOA), and HOA. Further details
of PMF analysis and the characteristics of the factors can be found in de
Sá et al. (2017b). The O : C ratio and calculated density for each
factor are presented in Table 1. In this study, the O : C ratio was derived
using the Improved-Ambient method (Canagaratna et al., 2015).
Density, O : C ratio, and hygroscopicity associated with organic
factors derived from positive matrix factorization (PMF) analysis. For IOP1
and IOP2, hygroscopicities of PMF organic factors were derived from time
series of particle hygroscopicity under all conditions and background
conditions, respectively.
IOP1 (wet season)
IOP2 (dry season)
PMF factor
ρ (gcm-3)
O : C
κ
PMF factor
ρ (gcm-3)
O : C
κ (Bkgd)*
IEPOX-SOA
1.47
0.798
0.18±0.02
IEPOX-SOA
1.42
0.711
0.08±0.03
MO-OOA
1.80
1.19
0.20±0.02
MO-OOA
1.81
1.24
0.21±0.03
LO-OOA
1.48
0.786
0.12±0.02
LO-OOA
1.52
0.883
0.20±0.03
BBOA
1.42
0.712
0.04±0.03
Aged BBOA
1.37
0.666
0.08±0.03
Fac91
1.14
0.328
0.10±0.03
Fresh BBOA
1.23
0.536
0.00±0.07
HOA
0.95
0.163
0
HOA
1.02
0.223
0
* κ (Bkgd) refers to κ values of PMF factors derived from
the time series of particle hygroscopicity under background conditions (see
Sects. 4.2.2 and 4.3).
For each IOP, hygroscopicities associated with the six factors were
attributed based on multilinear regression of κorg
with respect to the volume fractions of the factors
(Levenberg–Marquardt algorithm, IGOR Pro, Wavemetrics):
κorg=∑inκixi,
where κi and xi are the hygroscopicity and volume
fraction of the individual organic PMF factors. The volume fraction
was derived from mass concentrations and the densities of the
factors. κorg represents the average organic
hygroscopicity at particle diameters (Dp) of 142 and
171 nm. As the PMF analysis is based on the mass spectra of
the bulk submicrometer aerosol (i.e., MS mode measurements), an
implicit assumption of Eq. () is that the bulk volume
fractions of the factors represented those over the sizes at which
κorg was derived (i.e., Dp=142 and
171 nm). The validity of this assumption is discussed in the
results section. The robustness of the factor hygroscopicity derived
through linear regression depends on the variation of the factor
volume fraction during the measurement period. The HOA hygroscopicity
was assumed as zero based on the results from previous studies (Cappa
et al., 2009, 2011; Jimenez et al., 2009), and the hygroscopicity of
the other five factors were derived by multilinear regression as
described above.
Classification of seasons and air masses
The 1-year sampling period was divided into different seasons by
grouping months according to the similarity of the aerosol properties
and trace gas concentrations measured at the two background sites, T0a
and T0t, as well as monthly accumulated rainfall. In this study, the
seasons were defined as follows: the first wet season – March, April,
and May of 2014; the first transition season – June and July 2014; the
dry season – August and September of 2014; the second transition
season – October, November, and the first half of December 2014; and the
second wet season – the second half of December 2014 and January,
February, and the first few days of March 2015.
For each season, the air masses arriving at the T3 site were
classified into three different types: background, urban pollution,
and local biomass burning based on trace gas and aerosol measurements
at all sites. During the wet season, the background air mass
represented near-natural conditions, with occasional impact from
anthropogenic emissions, while, in the dry season, the background was
dominated by regional biomass burning aerosol particles. Polluted air
masses represent those with strong influence from urban emissions,
which were mostly from Manaus. The local-biomass-burning type
describes those air masses strongly influenced by local (i.e., fresh)
biomass burning activities, which dominated over the impact from urban
pollution, if any. For each season, background conditions were
identified when CO and condensation nuclei (CN) concentrations were below the thresholds
derived from measurements at the background T0a and T0t sites, and the
NOy mixing ratio was below 1.5 ppb. Non-background
conditions were identified by condensation nuclei and CO
concentrations above the respective threshold levels. As biomass
burning aerosol typically has a higher fraction of accumulation-mode
particles, and the emissions from Manaus were more dominated by Aitken-mode particles, the fraction of particles with diameter less than
70 nm was used to differentiate air masses strongly influenced
by local biomass burning from those with more impact from urban
pollution (see Table S2 in Supplement for details). Contamination by
the emissions from an on-site diesel generator, grass cutting
activities, tractors, and other vehicles were evidenced by rBC
concentrations above 1.0 µgm-3 or CN concentration
above 10 000 cm-3. Over the 1-year measurement period,
background, urban pollution, and local biomass burning represented
12.4, 38.5, and 28.4 % of the CCN measurements, respectively
(Table S3). We note that the air masses arriving at the T3 site often
included contributions from different sources. The classification of
the air masses using the above three types clearly represents
a simplification but is very helpful for understanding the properties
of aerosols influenced by the various major sources.
Results
Seasonal trend and size dependence of hygroscopicity and
chemical composition
The monthly average κCCN at the T3 site varied from
0.1 to 0.2 at five particle diameters ranging from 75 to
171 nm (Fig. 1a) and was substantially lower than the value
of 0.3±0.1 suggested for continental sites (Andreae and
Rosenfeld, 2008). This was due to the large organic volume fraction,
up to 95 %, observed at the T3 site. In this study, measurements at
51 and 222 nm were not included, because the range of
supersaturation sampled inside the CCN counter only adequately
captured the activation spectrum for 51/222 nm particles with
relatively high/low κCCN values, leading to
a positive/negative bias of the average κCCN. The
value of κCCN exhibited similar seasonal variations
at all five sizes. During the transition from wet to dry season,
κCCN decreased by 20–30 % with the absolute
minimum of 0.116 occurring at 75 nm in September and October
(Fig. 1a).
The seasonal trend of κCCN was mainly driven by the
variation of κorg, which shows the lowest value in
September during the dry season (Fig. 1b). Despite a strong increase
in aerosol mass concentration from wet to dry season due to biomass
burning emissions, the organic volume fraction exhibited little seasonal
variation and was ∼90 % or higher at the four sizes from 94
to 171 nm (Fig. 1c). A minor increase in organic volume
fraction in October might have also contributed to the lower
κCCN value observed. The species volume fractions at
75 nm are not shown due to the very low signal-to-noise ratio
of the AMS PToF data in the small particle diameter range. No clear
seasonal trend was observed for sulfate volume fraction, which ranged
from 3 to 9 % at the four sizes. The lack of clear seasonal trends
of sulfate and organic fractions is consistent with observations at
the T0a site (Andreae et al., 2015). Nitrate and rBC represented
a small fraction of aerosol volume and were less than 1 and
∼4 %, respectively.
The average κCCN increased with increasing
particle size for all three air mass types and during all the seasons
(Fig. 2), consistent with decreasing organic volume fraction with increasing
particle size (Fig. 1c). The κCCN at 75, 94, 112, 142, and
171 nm averaged for the 1-year measurement period were 0.130±0.028, 0.144±0.039, 0.148±0.043, 0.164±0.046, and 0.167±0.042, respectively. The value of κCCN and its size
dependence under background conditions were largely consistent among
different seasons and were in good agreement with those observed under
near-natural conditions during the AMAZE-08 campaign at T0t in the wet season
(Gunthe et al., 2009) and during the 1-year period from March 2014 to
February 2015 at the background T0a site (Pöhlker et al., 2016). For the
air masses with strong influence from local biomass burning, the value of
κCCN and its size dependence are consistent with the
κ value derived from particle growth factor measurements in July 2001,
during a “recent biomass burning period” of the CLAIRE-2001 study (Rissler
et al., 2004), which took place at Balbina, about 125 km northeast of
Manaus. In contrast, κ values derived from particle growth factor
measurements from 11 September to 8 October 2002, during the dry period of
the LBA–SMOCC (Rissler et al., 2006), are substantially lower than
κCCN values observed at the T3 site for local biomass
burning air masses at all sizes. As LBA–SMOCC took place in the state of
Rondônia in southwestern Amazonia with extensive biomass burning
activities during the dry season, the difference in κ could be due to
the differences in fire condition and the type of vegetation burned. Previous
studies show particles sometime exhibit larger κ values for droplet
activation (derived from CCN measurements under supersaturated conditions)
than for particle growth (derived from particle growth factor under
subsaturated conditions); this could also contribute to the differences in
κ values. Compared to κCCN, κorg was
largely independent of particle size for all three air mass types, indicating
that the size dependence of κCCN was mainly due to the size
dependence of the organic volume fraction and particle composition
(Fig. 1c–f). During the dry season, aerosols classified as urban pollution
and local biomass burning exhibited lower κorg values
compared to background aerosols, contributing to the lower values of overall
κCCN.
The variation of κCCN and
κorg with particle diameter during different
seasons for each of three air mass types. Data points are the mean
values; error bars are the 25th and 75th percentiles. The top-left
panel also includes κCCN observed under near-natural
conditions during the AMAZE-08 campaign at T0t in the wet season
(Gunthe et al., 2009) and during the 1-year period from March 2014
to February 2015 at the background T0a site (Pöhlker et al.,
2016). The top-right panel includes κ derived from particle
growth factor measurements in July 2001, during a “recent biomass
burning period” of the CLAIRE-2001 study (Rissler et al., 2004), and
from 11 September to 8 October 2002, during the dry period of the
LBA–SMOCC (Rissler et al., 2006).
Diel trends of particle and organic hygroscopicities
The diel variations of aerosol properties are presented in Figs. 3–7
for different air masses during the two IOPs. Aerosol properties
derived from the size-resolved CCN measurements, including
κCCN, σκCCN/κ‾CCN, and κorg, and the volume fractions
of different species were averaged at the three largest sizes (Dp=112, 142, and 171 nm). The fraction of organic mass at m/z=44 (f44) and O : C were derived from the AMS bulk
measurements. Also shown are diel variations of planetary boundary
layer (PBL) height, CN, and aerosol volume concentrations based on 5 min
average data.
Diel variations of aerosol properties and meteorological
parameters under background conditions during the wet season,
including (a) κCCN, (b)
σκCCN/κ‾CCN,
(c) fraction of the organic mass at m/z=44 (f44)
and the elemental ratio O : C, (d) κorg
derived using size-resolved particle composition, (e) the
total number of condensation nuclei (NCN), (f)
the total aerosol volume derived from size distribution measured by
the SMPS in MAOS, (g) planetary boundary layer height as
estimated using the ceilometer data, (h) the volume
fractions of aerosol species, and (i) the number of samples
in each hour bin corresponding to the data by the same colors and
symbols in their respective panel. The values of
κCCN, σκCCN/κ‾CCN, κorg, and the volume fraction of
aerosol species were averaged over three particle diameters of 112,
142, and 171 nm. The values of f44 and O : C were
derived from the AMS bulk measurements. Data include the last 2 weeks
of March 2014 when valid data from both size-resolved CCN
system and AMS were available. Data points are hourly averaged mean
values; error bars represent the 25th and 75th percentiles of the
data. Yellow shading represents the local daytime
(10:00–22:00 UTC).
Diel variations of aerosol properties and meteorological
parameters for urban pollution air masses during the wet season
(analogous to Fig. 3).
Diel variations of aerosol properties and meteorological
parameters under background conditions during the dry season
(analogous to Fig. 3).
Diel variations of aerosol properties and meteorological
parameters for urban pollution air masses during the dry season
(analogous to Fig. 3).
Diel variations of aerosol properties and meteorological
parameters for local biomass burning air masses during the dry
season (analogous to Fig. 3).
Wet season aerosol
Background conditions
Figures 3 and 4 show the diel variations of aerosol properties during
the wet season of 2014 for background and urban pollution air masses,
respectively. Only 0.7 % of the data were classified as local
biomass burning (see Table S3), which is insufficient to evaluate the
diel trends. During the wet season, the background air mass
represents near-natural conditions, with minimum impact from
anthropogenic emissions, although long-distance transport of African
biomass burning may contribute to the aerosols observed (Chen et al.,
2009; Wang et al., 2016b). Background aerosol constantly exhibited
relatively high hygroscopicity of ∼0.19 throughout the day. The
lack of a diel trend in κCCN is also in agreement
with the results from the T0a site (ATTO), which is upwind of Manaus
and served as a background site (Fig. 8). The particle composition
averaged for the three particles diameters was dominated by organics,
representing ∼90 % of the aerosol volume. The lack of a diel
trend in κCCN and κorg suggests
little variation in particle composition throughout the day. The
nearly constant κorg of ∼0.16 is also
consistent with the lack of a diel trend in f44 and O : C. The
values of f44 and O : C are ∼0.2 and ∼0.8,
respectively, indicating that the aerosol under background condition
during the wet season was dominated by the aged regional aerosol
particles consisting of highly oxygenated organic compounds.
Comparison κCCN values derived from
measurements at the T0a (ATTO) site (Pöhlker et al., 2016) and
at the T3 site under background conditions (this study) during the
(a) wet season (April and May 2014) and (b) dry
season. The size-resolved CCN data at T0a was collected by stepping
the particle size at given CCNC supersaturations (Rose et al.,
2008). Data displayed for T0a are averaged over critical particle
diameters ranging from 44 to 175 nm, while the T3 data are
averaged from measurements at 112, 142, and 171 nm.
Aerosol number and volume concentrations exhibited a minimum at ∼310 cm-3 and ∼0.3 µm3cm-3,
respectively, just before sunrise. The number and volume
concentrations started increasing after sunrise and peaking at
400 cm-3 and 0.8 µm3cm-3 in the
afternoon. These diel variations are partially attributed to the wet
scavenging of accumulation-mode particles, which dominate the
submicron particle concentrations under background conditions, and the
mixing of the particles from the residual layer aloft down to the
surface as the boundary layer develops in the morning. During the
night, the radiative cooling at the surface leads to a shallow
nocturnal boundary layer with low and variable winds. RH near surface
was near 100 %, and fog or mist was identified by the weather
station (Present Weather Detector, Visalia) 62 % of the time
during the 1-year measurement period. The gradual decreases of
particle number and volume concentration during these fog events were
due to the wet deposition of the accumulation-mode particles activated
into droplets. Similar decreases of particle number concentration were
previously reported during night fog events in the tropical rainforest
in Borneo (Whitehead et al., 2010). After sunrise, the boundary layer
deepened on average from less than 200 up to 800 m as a result
of solar heating (Fig. 3g). Consequently, particles in the residual
layer aloft (Fisch et al., 2004; Rissler et al., 2006), which were
not impacted by the fog, were mixed down to the surface, leading in
part to the observed increases in both number and volume
concentrations. Such mixing of particles from the residual layer in
the morning had been observed previously in the Amazon basin during
the dry season (Rissler et al., 2006). The formation of secondary
organic aerosol (SOA) as a result of photochemical oxidation (in both
gas and particle phases) likely contributed to the increase in volume
concentration (Chen et al., 2015, 2009; Martin et al., 2010a;
Pöschl et al., 2010).
Air masses impacted by urban pollution
Air masses arriving at the T3 site frequently had passed over urban and
industrial areas upwind. When the air mass was influenced by the urban
pollution, κCCN and its dispersion exhibited clear diel
variations (Fig. 4). The value of κCCN was lower during the
night at 0.15, and it increased from the early morning hours, peaking at
a value of 0.19 around noon (LT, UTC -4 h). The
dispersion was anticorrelated with κCCN, exhibiting higher
values (i.e., increased heterogeneity in particle chemical composition)
during night and a minimum value around noon. To a large degree, the diel
trend of κCCN was due to the variation of
κorg. The value of κorg was lower during
night at 0.10 and increased to 0.16 at noon. The increase in
κorg is consistent with the variation of O : C, which
increased during the early morning and reached the highest value of 0.8
around noon time. The pollution strongly affected the particle number and
volume concentrations, both exhibiting similar diel trends. Under polluted
conditions, particle number concentration ranged on average from 1500 to
2300 cm-3, which is an increase by a factor of ∼5 from that
under background conditions. In comparison, the increase in the volume
concentration was only about a factor of 2 (i.e., from a range of 0.3–0.75
to 1.0 to 1.3 µm3cm-3), as the urban pollution is
dominated by Aitken-mode particles that make a relatively small contribution
to aerosol mass and volume concentration.
The diel variations of κCCN, its dispersion,
κorg, and O : C are explained as follows. During night,
particles in freshly emitted pollution, which are dominated by primary
organic aerosol (POA) and have low hygroscopicity, are mixed with more aged
particles within a shallow nocturnal boundary layer (Bateman et al., 2017).
In the absence of photochemical oxidation and aging, this external mixture
leads to higher dispersion of particle hygroscopicity as well as overall
lower O : C and κorg. As the pollution aerosols are mainly
from isolated point sources, they are confined in the shallow nocturnal
boundary layer during night, and the residual layer above the T3 site is
expected to consist of aged background aerosols. Therefore, unlike under
background conditions, the mixing of aerosol aloft in the residual layer down
to the surface cannot by itself explain the increase in particle number and
volume concentration during the day, both of which were substantially above
the background values. These increases under polluted conditions might be due
to the stronger urban influence at T3 during the day. The strong increase in
CN concentration at 16:00 UTC (12:00 LT) could be caused by the arrival at
the T3 site of the Manaus plume emitted during early morning traffic hours.
To a large degree, the increases in O : C and κorg are due
to the formation and aging of SOA in the pollution, while the development of
the daytime boundary layer, which leads to dilution of pollution and mixing
with aged particles from the residual layer, can also contribute to the
increases. The condensation of secondary species and photochemical aging also
leads to more homogenous composition among particles (Mei et al., 2013a), and
therefore lower dispersion of κCCN, as was observed. The
O : C reached a maximum average value of 0.8, similar to that under the
background conditions. This suggests that the formation and photochemical
aging of SOA quickly led to highly oxygenated organic compounds (i.e., within
several hours; de Sá et al., 2017b).
Dry season aerosol
Background conditions
Figures 5–7 show the diel variations of the aerosol properties observed
during the dry season for background, urban pollution, and local biomass
burning air masses, respectively. During the dry season, the background
aerosol (Fig. 5) is strongly influenced by regional biomass burning, and air
masses arriving at the T3 site often pass through urban and industrial areas
along the Amazon River and in northeast Brazil (Andreae et al., 2015),
indicating that the background aerosol is also impacted by more aged urban
and industrial emissions (Martin et al., 2017). Despite different aerosol
sources and processes, the particle hygroscopicity, dispersion, and
κorg exhibited similar values as those of background aerosol
during the wet seasons and a lack of obvious diel variations. This is also
consistent with the absence of a significant diel trend of
κCCN observed at T0a (ATTO) during the dry season (Fig. 8).
The O : C value increased slightly from 0.8 during night to 0.9 in the
afternoon, possibly due to further oxidation and aging of background aerosols
during the daytime. The high value of O : C is consistent with the
relatively high value of κorg (0.15) and is close to that
observed under background condition during the wet season, indicating highly
oxygenated organic aerosol. The number and volume concentrations were lower
just before dawn and increased during the early morning, again a result of
wet scavenging of particles by fog followed by the mixing of aerosol aloft in
the residual layer down to the surface as the boundary layer develops in the
morning.
Air masses impacted by urban pollution and local
biomass burning
For urban pollution (Fig. 6) and local biomass burning (Fig. 7) air
masses during the dry season, κCCN (urban pollution:
0.12–0.20; local biomass burning: 0.10–0.17), its dispersion (urban
pollution: 0.4–1.0; local biomass burning: 0.4–0.9),
κorg (urban pollution: 0.10–0.15; local biomass
burning: 0.08–0.14), and O : C (polluted: 0.7–0.85; biomass
burning: 0.7–0.86) showed similar values and diel variations as those
under polluted conditions during the wet season. This is consistent
with the picture that freshly emitted particles (in either the case of
urban pollution or local biomass burning) lead to overall lower
O : C, κorg, and higher κCCN
dispersion during night, followed by increases in O : C,
κorg, and a decrease in the dispersion during
daytime, which are mainly driven by the formation and photochemical aging
of SOA, with contributions from the mixing of background aerosol aloft
in the residual layer down to the surface and dilution of fresh
emission as the boundary layer develops. Compared to urban pollution,
local biomass burning air masses exhibited lower κCCN and κorg values during night and stronger
diel variations. In the afternoon, κorg and O : C
reached high values of 0.14 and 0.86, respectively, as observed for
background aerosols.
For urban pollution air masses, aerosol number and volume
concentrations showed similar trends for both seasons. The increases
in number and volume concentration from early morning to noon were
similar, about 1000 cm-3 and
0.5 µm3cm-3, respectively, for both wet and dry
seasons. The percentage increases were less pronounced in the dry
season due to the higher background values. In contrast to urban
pollution, local biomass burning showed higher aerosol number and
volume concentrations at night and decreased during the
morning. Local biomass burning activities typically peaked during
evening hours, consistent with frequent classification of the
nighttime aerosol as local biomass burning (Fig. 7i; Vestin et al.,
2007). Despite the wet removal of particles by fog, the strong
emission from local biomass burning, largely confined within the
shallow nocturnal boundary layer, led to higher surface aerosol
concentrations than those in the residue layer aloft, which likely
represented the regional background. As the boundary layer deepened in
the morning, the mixing with aerosol from the residual layer led to
decreases in both aerosol number and volume concentration observed at
the surface (Fig. 7e and f).
Hygroscopicity of PMF factors and the variation of organic
hygroscopicity with oxidation level
The hygroscopicities associated with the AMS PMF factors were
estimated through multivariable linear regression using different
subsets of the data as well as the entire dataset for each of the two
IOPs (IOP1 in Fig. 9 and IOP2 in Fig. 10). The different subsets
included measurements during day, night, certain sampling periods, and
ranges in particle hygroscopicity dispersion. Comparison of the
hygroscopicities derived from the different subsets of data allowed us
to examine the robustness of this approach. Uncertainty in the
derived κ for individual factors was determined by the number
of points available to fit in the time series, with greater data
coverage and therefore lower uncertainty during the dry season. For
the wet season (IOP1), the hygroscopicities associated with PMF factors
derived using different subsets of the data are largely in agreement
with those derived from the entire dataset. There are notable
differences between the hygroscopicities of MO-OOA and Fac91 factors
derived using data under background conditions only and those derived
using the entire dataset. Such difference could be partially due to
the limited data under the background conditions during IOP1
(Fig. 3). For the dry season (IOP2), the hygroscopicities of PMF
factors derived using measurements under background conditions or data
with hygroscopicity dispersion less than 0.4 are quite different from
those derived using other data subsets and the entire dry season
dataset. The agreement among the PMF factor hygroscopicities derived
using different sub-datasets during the wet season and the
disagreements for the dry season are attributed to the applicability
of the underlying assumption that the bulk volume fractions of PMF
factors (i.e., derived from MS mode data) represented those at the
sizes of CCN measurements. For the wet season, the average f44
was largely independent of particle size from 130 to 400 nm
(Fig. S10), which is the size range that dominated the bulk aerosol mass
concentration measured by AMS. This is consistent with the assumption
that the bulk volume fractions of the PMF factors represent those at
the two CCN sizes (142 and 171 nm). For the dry season, the
f44 averaged over local biomass burning air masses and the entire
IOP2 exhibited an increase with particle diameter from 100 to
300 nm (Fig. S10). For periods with hygroscopicity dispersion
less than 0.4 or under background conditions, the average size
distribution of f44 was noisier due to fewer data
points. Nevertheless, the size distribution shows f44 was largely
independent of the particle size under these conditions, consistent
with the assumption described above. In the following discussion, we
focus on the PMF factor hygroscopicities derived using all data during
the wet season and under background conditions in the dry season.
Hygroscopicity of AMS PMF factors for IOP1 (i.e., wet season)
retrieved by multilinear regressions using all data (red square),
data from UTC 12:00–24:00 (cyan circle), data from UTC 00:00–12:00
(blue triangle), data under background conditions (red triangle),
data under polluted conditions (red diamond), and data with
hygroscopicity dispersion σκCCN/κ‾CCN less than 0.6 (black diamond).
Hygroscopicity of PMF factors for IOP2 (i.e., dry season)
retrieved by multilinear regressions using all data (red square),
data from UTC 12:00–24:00 (cyan circle), data from UTC 00:00–12:00
(blue triangle), data with strong influence from local biomass
burning (red right triangle), data under background conditions (red
upside-down triangle), data from 21 August to 14 September 2014
only (red left-pointing triangle), data from 15 September to 15 October
only (brown diamond), and data with a dispersion (σκCCN/κ‾CCN)<0.4 (black diamond).
The MO-OOA factors for the two IOPs exhibit very similar O : C and
κ values. The O : C values were 1.19 and 1.24, and
κ values were 0.20 and 0.21, for IOP1 and 2, respectively
(Table 1). The O : C and κ values are consistent with those
of some typical SOA compounds, such as malonic acid, which has an
O : C value of 1.33 and a κ value of 0.23 (Kumar et al.,
2003), and succinic acid, which has an O : C value of 1 and
a κ value of 0.23 (Hori et al., 2003). For the LO-OOA and
IEPOX-SOA factors, the hygroscopicities vary between the two IOPs. The
κ values of IEPOX-SOA were 0.18 and 0.08 during IOP1 and IOP2,
respectively, and the κ values of the LO-OOA factor varied from 0.12 to
0.20 between IOP1 and IOP2. The difference in κ may be
partially due to the change of O : C values of the factors derived
for the two IOPs. The difference in SOA precursors and therefore
composition in LO-OOA (Ng et al., 2010) may also contribute to the
difference in its κ values between the two IOPs. The variation
of IEPOX-SOA κ between the two IOPs could be a result of the
different RH conditions, which may strongly influence the composition
of IEPOX-SOA (Riva et al., 2016).
During the wet season, a factor with high contribution from m/z=91 was
identified. The Fac91 factor correlates with several tracers for
anthropogenic emissions, including NOx, benzene, toluene,
trimethylbenzene (TMB), and xylenes, but not high NOx isoprene
products (e.g., methylglyceric acid; de Sá et al., 2017b). This factor
likely represents SOA formed from aromatics emitted from urban areas,
possibly combined with a mixture of freshly oxidized biogenic compounds
within the urban-influenced air (de Sá et al., 2017b). The Fac91 factor
has a hygroscopicity value of 0.10 and a much lower O : C ratio of 0.328
compared to those of MO-OOA and LO-OOA.
The less-oxidized organic factors identified by the PMF analysis were HOA for
both IOPs, BBOA for IOP1, and fresh and aged BBOA for IOP2. These factors
represent primary OA, except that the aged BBOA of IOP2 likely included
contributions from oxidized POA or SOA. The hygroscopicity of the HOA factors
was fixed as zero in the multivariate regressions. All BBOA factors have
a distinctive m/z=60 peak and correlate with biomass burning traces
including levoglucosan and vanillin (de Sá et al., 2017b). The retrieved
hygroscopicity values for the BBOA factors are substantially lower than those
of SOA factors, especially for the fresh-BBOA factor during IOP2. The
extremely low hygroscopicity suggests that the fresh BBOA, likely produced by
local fires, behaves very similar to HOA in terms of CCN activation despite
a substantially higher O : C.
Figures 11 and 12 show that for SOA factors, including IEPOX-SOA,
LO-OOA, MO-OOA for both IOPs, and Fac91 for IOP1, the κ value
increases with increasing O : C, and the variation of κ with
O : C agrees with the linear relationship derived from laboratory
studies of SOA CCN activities (Lambe et al., 2011). The low
hygroscopicities of the HOA and the BBOA factors, which are below the
linear relationship for SOAs, are also consistent with laboratory
results of POA and oxidized POA (Lambe et al., 2011). Cerully
et al. (2015) derived κ of LO-OOA, MO-OOA, and IEPOX-SOA
factors from data collected in the southeastern US during the Southern
Oxidant and Aerosol Study (SOAS). A different name, isoprene-OA, was
used for the IEPOX-SOA factor in Cerully et al. (2015), as, while this
factor is mainly attributed to SOA formed from IEPOX uptake, it might
not be entirely due to IEPOX (Schwantes et al., 2015; Xu et al.,
2015a, b). The O : C values calculated using the Improved-Ambient
method (Canagaratna et al., 2015) are 0.59, 0.61, and 0.92 for the
IEPOX-SOA, LO-OOA, and MO-OOA factors reported in Cerully
et al. (2015), respectively (personal communication, L. Xu and
N.L. Ng). For the LO-OOA and MO-OOA factors reported in Cerully
et al. (2015), the κ and O : C values are largely consistent
with the linear relationship between κ and O : C derived
from Lambe et al. (2011). Cerully et al. (2015) also reported
a IEPOX-SOA (i.e., called isoprene-OA in their study) factor κ
of 0.2, similar to the 0.18 derived for IOP1. The O : C value of the
IEPOX-SOA factor during the SOAS study was 0.59, which is somewhat lower than
those derived from both IOPs of GoAmazon2014/5. While the IEPOX-SOA
factors identified using different datasets share many similar
features, they are not identical and can consist of different groups
of compounds. Such differences may be due to varying degrees of
oxidation in different environments between the two field campaigns.
The variation of PMF factor hygroscopicity, 1 h diel average
of organic hygroscopicity, and O : C ratio at 142 and
171 nm for urban pollution air masses. Also shown are the
relationships between κorg and O : C reported by
earlier field and laboratory studies.
For comparison with earlier field studies, the values of
κorg and O : C were averaged according to the hours of the
day over particle diameters of 142 and 171 nm for data under polluted
conditions during IOP1 and all data during IOP2 (Figs. 11 and 12). For the
1 h diel averages, the slope of κorg vs. O : C, derived
through a bivariate least squares fit (i.e., orthogonal distance regression),
is steeper than that derived from laboratory studies of SOA hygroscopicity,
especially during IOP2. This steep slope during IOP2 is consistent with the
results from earlier field studies (Mei et al., 2013b), although there is
a clear offset between the two relationships. The O : C ratios from Mei
et al. (2013b) and Lambe et al. (2011) were scaled by a factor of 1.27 to
account for changes in the method of calculating the O : C ratio
(Improved-Ambient, Canagaratna et al., 2015), while all O : C values from
this work were calculated using the Improved-Ambient method. This offset
between the field studies may be partially due to the different precursors of
the SOA for the campaigns, with a higher anthropogenic volatile organic
compound fraction expected for CalNex and CARES, which took place near Los
Angeles and Sacramento, respectively. In addition, biomass burning
represented a much smaller fraction of the organics during CalNex and CARES
(Mei et al., 2013a, b). The factors associated with secondary processes
(e.g., MO-OOA, LO-OOA, and IEPOX-SOA), which have higher O : C values,
exhibited higher volume fractions during the day, whereas the factors
associated with primary emissions (e.g., HOA and BBOA), which have lower
O : C, had higher volume fractions during the night (de Sá et al.,
2017b). As a result, the diel trend of overall O : C was to a large degree
driven by the variations in volume fractions of the POA and SOA factors with
very different O : C values. This is in contrast to laboratory studies, in
which the increase in O : C was mainly driven by oxidation. As POA exhibits
hygroscopicity values well below the linear fit between SOA hygroscopicity
and O : C, mixtures with different POA and SOA fractions lead to a steeper
slope for the increase in κorg with O : C, as shown by
the results from this and previous field studies (Mei et al., 2013b).
Conclusions
Size-resolved CCN spectra at five particle diameters ranging from 75
to 171 nm were characterized downwind of Manaus, Brazil, in
central Amazonia for a period of 1 year from 12 March 2014 to
3 March 2015 during GoAmazon2014/5. For each season, the air masses
arriving at the site were classified into different types, including
background, urban pollution, and local biomass burning. During the wet
season, the background air mass represented near-natural conditions,
at times with impact from anthropogenic emissions, while, in the dry
season, the background was dominated by regional and long-distance
biomass burning aerosol particles. Polluted air masses represented
those with strong influence from urban emissions, which were mostly
from Manaus. The local-biomass-burning type describes those strongly
influenced by local (i.e., fresh) biomass mass burning activities that
dominate the impact from urban pollution, if any.
Particle hygroscopicity, mixing state, and
organic hygroscopicity were derived from
size-resolved CCN activation fraction and concurrent aerosol
composition measurements. The monthly mean κCCN
exhibits the lowest values during the dry season, largely due to lower
κorg when aerosol was often strongly influenced by
local biomass burning. The κCCN increased with
particle size during all seasons, consistent with decreasing organic
volume fraction with increasing particle size. Under background
conditions, the value of κCCN and its size dependence
were largely consistent among different seasons, despite the very
different aerosol sources. During the dry season, aerosols classified
as urban pollution and local biomass burning exhibited lower
κorg values compared to background aerosols,
contributing to the lower values of overall κCCN.
The variation of PMF factor hygroscopicity, 1 h diel average
of organic hygroscopicity, and O : C ratio at 142 and
171 nm for local biomass burning air masses. Also shown are
the relationships between κorg and O : C
reported by earlier field and laboratory studies.
Under background conditions during both wet and dry seasons, the largely
constant diel trends of κCCN and κorg
suggest little variation in particle composition throughout the day. The
constant κorg of ∼0.15 is consistent with the lack of
a diel trend in f44 and O : C. The high values of f44 and O : C
indicate that the aerosols under background conditions are dominated by the
aged regional aerosol particles consisting of highly oxygenated organic
compounds. When the air mass is influenced by urban pollution or local
biomass burning, κCCN, κorg,f44, and
O : C exhibit clear diel variations. The value of κCCN
(0.1–0.2) is lower during the night and increases from the early morning
hours, peaking around noon (LT, UTC -4 h). This diel trend of
κCCN is largely driven by the variation in
κorg (0.08–0.15), consistent with the variation of O : C.
The dispersion of κCCN is anticorrelated with
κCCN, exhibiting higher values during night and a minimum
value around noon, indicating an increased heterogeneity in particle chemical
composition during nighttime. These diel variations for air masses strongly
influenced by urban pollution and local biomass burning indicate that, during
the night, freshly emitted particles, dominated by POA and with low
hygroscopicity, are mixed with more aged particles within a shallow nocturnal
boundary layer. In the absence of photochemical oxidation and aging, this
external mixture leads to higher dispersion of particle hygroscopicity as
well as overall lower O : C and κorg. The increases in
O : C and κorg during daytime are driven by the formation
and aging of SOA and dilution of POA emissions into a deeper boundary layer,
while the development of the boundary layer, which leads to mixing with aged
particles from the residual layer, likely also contributes to the increases.
The hygroscopicities associated with individual PMF organic factors were
derived through multivariable linear regression. For the SOA factors,
κ increases within increasing O : C, and the variation of κ
with O : C agrees well with the linear relationship derived from laboratory
studies of SOA hygroscopicity (Lambe et al., 2011). The low hygroscopicity of
HOA and the BBOA factors, which are below the linear relationship, are also
consistent with laboratory results of POA and oxidized POA (Lambe et al.,
2011). In contrast, the slope of κorg (i.e., overall organic
hygroscopicity) vs. O : C is much steeper when compared to that derived
from laboratory studies of SOA hygroscopicity, especially for IOP2. Such
difference is due to the increase in O : C being driven primarily by
oxidation in laboratory SOA studies, while the variation in O : C of
ambient organics is to a large degree due to the variations in volume
fractions of POA and SOA factors, which have very different O : C values.
As POA factors show hygroscopicity values well below the linear fit between
SOA hygroscopicity and O : C, mixtures with different POA and SOA fractions
lead to a steeper slope for the increase in κorg with
O : C, as shown by the results from this and earlier field studies (Mei
et al., 2013b).