Atmos. Chem. Phys., 17, 1759–1773, 2017
www.atmos-chem-phys.net/17/1759/2017/
doi:10.5194/acp-17-1759-2017
© Author(s) 2017. CC Attribution 3.0 License.

Anthropogenic influences on the physical state of submicron
particulate matter over a tropical forest
Adam P. Bateman1 , Zhaoheng Gong1 , Tristan H. Harder2,a , Suzane S. de Sá1 , Bingbing Wang3,4 , Paulo Castillo5 ,
Swarup China3 , Yingjun Liu1 , Rachel E. O’Brien2,b , Brett B. Palm6 , Hung-Wei Shiu3,c , Glauber G. Cirino7 ,
Ryan Thalman5,d , Kouji Adachi8 , M. Lizabeth Alexander3 , Paulo Artaxo9 , Allan K. Bertram10 , Peter R. Buseck11 ,
Mary K. Gilles2 , Jose L. Jimenez6 , Alexander Laskin3 , Antonio O. Manzi7 , Arthur Sedlacek5 , Rodrigo A. F. Souza12 ,
Jian Wang5 , Rahul Zaveri3 , and Scot T. Martin1,13
1 School

of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
3 William R. Wiley Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory,
Richland, WA, USA
4 State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences,
Xiamen University, Xiamen, China
5 Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
6 Department of Chemistry and Cooperative Institute for Research in Environmental Sciences (CIRES),
University of Colorado, Boulder, CO, USA
7 National Institute of Amazonian Research, Manaus, Amazonas, Brazil
8 Atmospheric Environment and Applied Meteorology Research Department, Meteorological Research Institute,
Tsukuba, Ibaraki, Japan
9 Departamento de Física Aplicada, University of São Paulo, São Paulo, Brazil
10 Department of Chemistry, University of British Columbia, Vancouver, BC, Canada
11 School of Earth and Space Exploration & School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
12 Amazonas State University, Manaus, Amazonas, Brazil
13 Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA
a now at: Physikalisches Institut, Universität Würzburg, Am Hubland, 97074 Würzburg, Germany
b now at: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology,
Cambridge, MA, USA
c now at: Scientific Research Division, National Synchrotron Radiation Research Center, Hsinchu, Taiwan 30076
d now at: Departments of Chemistry and Natural Resources, Snow College, Richfield, UT, USA
2 Chemical

Correspondence to: Scot T. Martin (scot_martin@harvard.edu)
Received: 17 July 2016 – Published in Atmos. Chem. Phys. Discuss.: 17 August 2016
Revised: 3 January 2017 – Accepted: 5 January 2017 – Published: 6 February 2017

Abstract. The occurrence of nonliquid and liquid physical states of submicron atmospheric particulate matter (PM)
downwind of an urban region in central Amazonia was investigated. Measurements were conducted during two intensive
operating periods (IOP1 and IOP2) that took place during
the wet and dry seasons of the GoAmazon2014/5 campaign.
Air masses representing variable influences of background
conditions, urban pollution, and regional- and continental-

scale biomass burning passed over the research site. As the
air masses varied, particle rebound fraction, an indicator of
physical state, was measured in real time at ground level using an impactor apparatus. Micrographs collected by transmission electron microscopy confirmed that liquid particles
adhered, while nonliquid particles rebounded. Relative humidity (RH) was scanned to collect rebound curves. When
the apparatus RH matched ambient RH, 95 % of the parti-

Published by Copernicus Publications on behalf of the European Geosciences Union.

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A. P. Bateman et al.: Anthropogenic influences on the physical state of submicron particulate matter

cles adhered as a campaign average. Secondary organic material, produced for the most part by the oxidation of volatile
organic compounds emitted from the forest, produces liquid
PM over this tropical forest. During periods of anthropogenic
influence, by comparison, the rebound fraction dropped to
as low as 60 % at 95 % RH. Analyses of the mass spectra of the atmospheric PM by positive-matrix factorization
(PMF) and of concentrations of carbon monoxide, total particle number, and oxides of nitrogen were used to identify
time periods affected by anthropogenic influences, including both urban pollution and biomass burning. The occurrence of nonliquid PM at high RH correlated with these indicators of anthropogenic influence. A linear model having
as output the rebound fraction and as input the PMF factor loadings explained up to 70 % of the variance in the observed rebound fractions. Anthropogenic influences can contribute to the presence of nonliquid PM in the atmospheric
particle population through the combined effects of molecular species that increase viscosity when internally mixed
with background PM and increased concentrations of nonliquid anthropogenic particles in external mixtures of anthropogenic and biogenic PM.

1

Introduction

Particulate matter (PM) directly affects the Earth’s climate
by scattering and absorbing solar radiation and indirectly
through effects on clouds (Ramanathan et al., 2001). The
magnitude of these effects depends in part on the physical
and chemical properties of the particulate matter (Andreae
and Rosenfeld, 2008). The physical state of PM, as liquid
or nonliquid, can influence the growth rates of small particles and ultimately the production of cloud condensation nuclei (CCN) (Riipinen et al., 2011; Perraud et al., 2012). Liquid particles pose negligible in-particle diffusion barriers for
condensing species and therefore can grow rapidly. By comparison, nonliquid particles, referring to both semisolid and
solid particles, can have different behavior. For some conditions, semisolid particles can grow slowly because of inparticle limits on rates of molecular diffusion, and solid particles can grow even more slowly when limited to surface adsorption (Riipinen et al., 2012; Shiraiwa and Seinfeld, 2012;
Li et al., 2015). Liquid compared to nonliquid PM can also
affect reactivity (Kuwata and Martin, 2012; Li et al., 2015).
As a consequence of the differing growth mechanisms, the
growth of small particles can be relatively disfavored in a
population of liquid particles of heterogeneous sizes compared to a similar population of nonliquid particles (Zaveri
et al., 2014). An implication can be that CCN concentrations
are ultimately greater for a population of nonliquid particles
that grows to CCN sizes, as compared to a population of liquid particles.

Atmos. Chem. Phys., 17, 1759–1773, 2017

Secondary organic material (SOM), produced by the oxidation of biogenic volatile organic compounds (BVOCs), is a
major source of atmospheric PM, especially over forested regions where SOM often dominates the mass concentration of
submicron PM (Hallquist et al., 2009; Jimenez et al., 2009).
The physical state of SOM has been studied in both laboratory (Vaden et al., 2011; Kuwata and Martin, 2012; Perraud et al., 2012; Saukko et al., 2012; Renbaum-Wolff et al.,
2013; Kidd et al., 2014; Bateman et al., 2015; Li et al., 2015;
Liu et al., 2015; Pajunoja et al., 2015; Song et al., 2015) and
ambient environments (Virtanen et al., 2010; O’Brien et al.,
2014; Bateman et al., 2016; Pajunoja et al., 2016). For background conditions of the Amazonian tropical forest, a region dominated by isoprene-derived SOM and high RH, PM
was mostly liquid (Bateman et al., 2016). For a boreal forest
in northern Europe, a region dominated by pinene-derived
SOM and low RH, PM was largely nonliquid (Virtanen et
al., 2010). The combined set of laboratory and ambient studies shows that the physical state of PM with high SOM content depends on the surrounding relative humidity (RH). This
effect arises in part because organic particles are hygroscopic
to various extents depending on composition. Water absorption, which is favored at elevated RH, has a plasticizing effect
on physical state (Koop et al., 2011).
The physical state of PM affected by urban pollution over
forests remains largely unexplored. A single day of ambient
observations in central Amazonia suggested that ambient PM
affected by urban pollution tended toward a nonliquid state
(Bateman et al., 2016), and laboratory studies support the
idea of an important modulating role of pollution in the physical state of SOM. A nonliquid state was favored for SOM
produced from single-ring aromatic species (Liu et al., 2015;
Song et al., 2015) as well as SOM mixed with polycyclic aromatic hydrocarbons (PAHs) (Zelenyuk et al., 2012; Abramson et al., 2013; Berkemeier et al., 2014). Organic molecules
associated with urban pollution and industrial activities tend
to be less hygroscopic than biogenic SOM (Hersey et al.,
2013). When internally mixed within biogenic SOM, the anthropogenic molecules have a tendency to reduce water uptake and thereby increase the viscosity of the mixed particles.
A similar line of reasoning leads to an identical hypothesis
for the effects of biomass burning emissions. Compared to
prevailing background conditions of SOM dominance over
many forests, PM produced by biomass burning leads to a
net effect of decreased water uptake when the PM mixes into
the background particle population (Dusek et al., 2011).
The data sets presented herein provide observational evidence on the effects of anthropogenic influences on the physical state of ambient particulate matter. All other factors being equal, the lack of particle rebound is an indicator of liquid PM (Bateman et al., 2015). Conversely, the occurrence
of particle rebound is an indicator of nonliquid PM. Rebounding and adhering particles were separately collected
for conventional and chemical imaging. The data sets were
collected during the two intensive operating periods (IOP1
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A. P. Bateman et al.: Anthropogenic influences on the physical state of submicron particulate matter
and IOP2) of the GoAmazon2014/5 experiment, corresponding to the wet and dry seasons (Martin et al., 2016). The research site (−3.2133◦ , −60.5987◦ ), called “T3”, was located
70 km downwind of the city of Manaus, with a population
of 2 million, in central Amazonia. Air masses representing
background conditions, urban pollution, and regional- and
continental-scale biomass burning passed over the research
site. Herein, anthropogenic influence refers to all but background conditions. Under background conditions, the submicron PM in this region is dominated by biogenic SOM (Chen
et al., 2009, 2015).
2

Experimental design

An impactor apparatus was used for the study of particle rebound (Bateman et al., 2014, 2015, 2016; Li et al., 2016).
The apparatus was housed inside a temperature-controlled
research trailer at the T3 site. Particulate matter was sampled
at 5 m above ground through copper tubing with an outer diameter of 13 mm (0.5 in.). In sequence, a drying unit reduced
the sampled flow to 25 % RH or lower, a differential mobility analyzer (DMA; TSI 3085) selected a subpopulation of
dried particles having a narrow distribution of electric mobility, and a humidification unit (Nafion tubes; Perma Pure,
MD 110) elevated the RH of the mobility-filtered flow to the
targeted RH of a measurement. The drying unit consisted of
a Nafion drier in series with a silica gel diffusion drier, and
the silica gel was replaced every 2 days. After passing the
dryer, DMA, and humidifier, the resulting flow was split and
passed through three impactors operated in parallel. Labels i,
ii, and iii refer to each of the three impactors. Each impactor
was operated at a flow rate of 1.0 L min−1 , corresponding to
a set point aerodynamic diameter da∗ of 84.9 ± 5.4 nm (Bateman et al., 2014). Particle number concentrations, denoted
by Ni , Nii , or Niii , exiting the impactors were measured
by three independent condensation particle counters (CPCs;
TSI 3010). Measurements were conducted from 14 February to 16 March 2014 during IOP1 and from 4 September to
15 October 2014 during IOP2.
The three impactors differed from one another by having
uncoated, coated, or no impaction plate. The impactor with
the uncoated plate passed both nonimpacted and rebounded
particles. The impactor with the coated plate (Dow Corning high-vacuum grease) passed only nonimpacting particles.
The impactor with no plate passed all particles. Its purpose
was to serve as a compensation arm for possible miscellaneous particle losses, such as wall loss. Based on the particle number concentrations measured downstream of the impactors, the rebound fraction was calculated as follows (Bateman et al., 2014):
f=

Ni − Nii
.
Niii − Nii

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(1)

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The terms Ni , Nii , and Niii represent the particle number
concentration measured downstream of the impactors with
uncoated, coated, and no impaction plate, respectively. The
standard deviation of the rebound fraction was based on error propagation for uncertainty in N of N −1/2 (Agarwal and
Sem, 1980).
A rebound curve representing f (RH) constituted an individual data set. For most measurements, the DMA setting for
electric mobility was held constant (typically 190 nm), the
RH in the humidification unit was changed stepwise every
few minutes, and f was continuously recorded. Additional
protocols of DMA settings and RH profiles were used in a
few cases. All protocols, including differences between IOP1
and IOP2, are described in Sect. S1 of the Supplement.
In conjunction with the rebound measurements, particles were collected for imaging by transmission electron
microscopy (TEM) and scanning transmission X-ray microscopy (STXM). Images in some cases can directly suggest
the liquid or nonliquid state of individual particles (O’Brien
et al., 2014; Wang et al., 2016). Samples for imaging were
collected as follows. A fourth impactor was added in parallel with the other three impactors. Imaging substrates were
affixed to the impaction plate, and multiple substrates were
mounted to the plate for concurrent collection for the various microscopy techniques. Substrates included grids coated
with formvar (EMJapan Co., Tokyo, Japan) or lacey carbon
(Ted Pella Inc., USA) for TEM and silicon nitride membrane
windows (Silson, UK) for STXM. The flow rate and set point
aerodynamic diameter of the fourth impactor were the same
as for the other three impactors. The collected particles represented those that adhered to the substrate at impact, and an
assumption in the analysis is that rebound from the substrate
was similar to that from the uncoated plate. The flow through
this fourth impactor was pulled by an in-line TEM autosampler (Arios Inc., Tokyo, Japan; Adachi et al., 2014). In this
way, the setup separately collected particles that adhered to
the grids on the impaction plate and particles that rebounded
from the impaction plate and passed to the autosampler. The
autosampler collected particles having aerodynamic diameters from 60 to 350 nm. Particles adhered to the TEM substrates in the autosampler, yet rebounded from the TEM substrates in the impactor because of the significantly different
particle impact velocities between the two impactors (i.e., cut
point and impactor design). Samples were collected for TEM
analysis between 30 September and 15 October 2014. Samples were collected for STXM analysis between 01:00 and
10:00 UTC on 1 October 2014.
Microanalysis of individual particles of the collected PM
was performed using two instruments: (1) a transmission
electron microscope (TEM; JEOL, JEM-1400) equipped
with an energy-dispersive X-ray spectrometer (EDS; Oxford Instruments) and (2) a scanning transmission X-ray
microscope interfaced for near-edge X-ray absorption fine
structure spectroscopy (STXM/NEXAFS; Advanced Light
Source, Berkeley). For the TEM, imaging was conducted
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A. P. Bateman et al.: Anthropogenic influences on the physical state of submicron particulate matter

through bright-field microscopy, and particle composition
was investigated by EDS. For the STXM at a fixed photon
energy, an image was obtained by detecting the transmitted
light at each pixel while raster scanning the sample. Spatially resolved NEXAFS spectra were obtained from a set of
images recorded at different photon energies. The NEXAFS
spectra provided chemical bonding information and quantitative elemental ratios (Moffet et al., 2010a, b; O’Brien et
al., 2014; Piens et al., 2016). Section S2 of the Supplement
presents further technical information concerning the TEM
and STXM/NEXAFS analyses.
Additional colocated measurements used in the data analysis herein included a high-resolution time-of-flight aerosol
mass spectrometer (AMS; Aerodyne Inc.), a single-particle
soot photometer (SP2; Droplet Measurement Technologies),
a size-resolved cloud condensation nuclei counter (CCNC;
Droplet Measurement Technologies, CCNC-100), a condensation particle counter (TSI, CPC 3772) for measuring particle number concentrations, an integrated cavity output spectroscope (ICOS; Los Gatos) for measuring carbon monoxide (CO) concentration, and a trace-level enhanced detector (TLE; Thermo Scientific, Model 42i, with further customization) for the measuring concentrations of nitrogen oxides (NOy ). The latter three instruments were deployed at the
T3 site as part of the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research
Facility, including the ARM Mobile Facility One (AMF-1)
and the Mobile Aerosol Observing System (MAOS) (Mather
and Voyles, 2013; Martin et al., 2016). This facility also collected the meteorological data used herein, including temperature, relative humidity, wind direction, and wind speed.
The AMS measured the chemical composition in real time
of non-refractory submicron particles (DeCarlo et al., 2006).
The high-resolution data of the organic component in “Vmode” were used in conjunction with positive-matrix factorization (PMF) analysis (Ulbrich et al., 2009) to obtain statistical factors and the associated time series of factor loadings for each season (de Sá et al., 2016). As a surrogate
for concentrations of black carbon, the SP2 measured the
time-resolved scattering and incandescence produced by irradiated refractory, light-absorbing components of individual
particles with volume-equivalent diameters between 70 and
600 nm (Schwarz et al., 2006; Moteki and Kondo, 2007). The
size-resolved CCN activity was obtained by classifying dry
particles using a DMA (TSI 3081) and then exposing them
successively to a range of supersaturations inside the CCNC
(Thalman et al., 2017). The hygroscopicity and CCN activity were analyzed via κ-Köhler theory, which relates particle critical supersaturation to the initial dry diameter and hygroscopicity (Petters and Kreidenweis, 2007; Petters et al.,
2009).

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3
3.1

Results and discussion
Rebound observations

Particle rebound or particle adhesion at impact depends on
the balance of energies. Particle rebound occurs when the
kinetic energy before impact is greater than the sum of dissipation and surface adhesion energies after impact (Tsai et
al., 1990; Bateman et al., 2014). The dissipation energy of
liquid particles is much greater than that of solid particles
because of additional mechanisms of dissipation available to
the former. As a point of reference, the transition from rebound to adhesion occurs across a viscosity transition of 102
to 1 Pa s for sucrose particles for the operating conditions of
the impactor (Bateman et al., 2015). The viscosity range corresponding to the rebound transition can depend on particle
composition, but this aspect is not investigated herein.
The rebound curves of particles with 190 nm in mobility diameter are shown in Fig. 1a for IOP1 and IOP2. For
RH < 50 %, the rebound fraction was between 0.8 and 1.0.
For RH > 50 %, the rebound fraction decreased monotonically to a low value, typically 0. The shape of the rebound
curve in Fig. 1a is similar to that for particles of secondary
organic material produced in the Harvard Environmental
Chamber (Bateman et al., 2015). The rebound fraction of
SOM particles produced from isoprene or α-pinene became
0 for RH > 90 %.
The subset of the data for which the apparatus RH matched
the ambient RH is shown in Fig. 1b (see Sect. S3 of the Supplement). In light of the distribution of ambient RH values
(see Fig. 1c), the data set in Fig. 1b implies that submicron
PM in this tropical environment was liquid most of the time.
Bateman et al. (2016) reported such a result for observations
under background conditions at this site for data sets collected in 2013 a few months before GoAmazon2014/5 began.
The results reported for GoAmazon2014/5 represent a longer
time series (i.e., 550 compared to 30 rebound curves) and reinforce the generality of the earlier results found by Bateman
et al. (2016) on the prevalence of liquid PM for this forested
region under background conditions.
In the larger set of observations in the present study, what
also emerges is that the rebound fraction remained at a median value of 0.05, even at 95 % RH, implicating the presence of externally mixed PM in the atmosphere. Approximately 5 % of the particles were nonliquid, even to 95 % RH,
in both the wet and dry seasons (i.e., IOP1 and IOP2) (Fig. 1a
and b). At times, rebound fractions of up to 0.4 occurred for
RH > 90 %. Elevated rebound fractions were observed more
frequently in IOP2 than IOP1. During IOP2, which was extensively influenced by biomass burning, nonliquid particles
at the median constituted a fraction of 0.3 at 70 % RH and a
fraction of 0.1 at 90 %. These RH values prevailed approximately 10 % of the time. Events of elevated rebound fraction
were associated with the pollution plume from Manaus during IOP1 in the wet season and with both this urban plume
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A. P. Bateman et al.: Anthropogenic influences on the physical state of submicron particulate matter

Figure 1. Rebound fraction as a function of apparatus relative humidity during IOP1 (blue) and IOP2 (red) corresponding to the wet
and dry seasons, respectively, in central Amazonia. Panel (a) shows
all measurements. Panel (b) shows the subset of data for which the
apparatus RH matched the ambient relative humidity (see Supplement Sect. S3). Panel (c) represents the probability density function
of relative humidity at the T3 site during the wet and dry seasons of
2014. Points represent rebound measurements for particles having
mobility diameters of 190 nm. Statistics for measurements groups
by relative humidity are represented in box–whisker format. A horizontal line within the box indicates the median of the points, the
horizontal lines at the box boundaries indicate quartiles, and the
horizontal lines most distant from the box indicate 10 and 90 %
quantiles. For comparison, the black line shows the rebound curve
for particles of secondary organic material produced by photooxidation of an isoprene/α–pinene mixture in the Harvard Environmental
Chamber (Bateman et al., 2015).

and increased regional biomass burning during IOP2 in the
dry season (see Sect. 3.2).
Probability density functions (PDFs) of the rebound fraction at 75 % RH based on the data sets of Fig. 1a are shown in
Fig. 2 for (a) IOP1 and (b) IOP2 for three types of air masses
during day- and nighttime-periods. The daytime period is
12:00 to 16:00 LT (local time) (16:00 to 20:00 UTC) and the
nighttime period is 23:00 to 04:00 LT (03:00 to 08:00 UTC).
Air masses were identified as influenced by local to regional
biomass burning, as influenced by Manaus pollution, or as
background. The classification scheme was based on conwww.atmos-chem-phys.net/17/1759/2017/

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Figure 2. The probability density function of rebound fraction during (a) IOP1 and (b) IOP2 for categorization by type of air mass
and time of day: background conditions (green), Manaus pollution
(red), and biomass burning (orange). Solid and dashed lines represent daytime (12:00 to 16:00 LT; 16:00 to 20:00 UTC) and nighttime
measurements (23:00 to 04:00 LT; 03:00 to 08:00 UTC). Results are
shown for an apparatus RH of 75 % and for particles having a mobility diameter of 190 nm. No data sets fit the classification of Manaus
pollution during the nighttime of IOP2 (i.e., absence of a red dashed
line in panel b).

centration regimes of particle number, carbon monoxide, and
odd nitrogen (NOy ), as presented in Sect. S4 of the Supplement. Background conditions included contributions from
both natural processes and the long-distance transport and
extensive oxidation of biomass burning emissions (Chen et
al., 2009; Martin et al., 2010). Natural processes in this region were dominated by the production of secondary organic
material from oxidation of plant emissions (Martin et al.,
2010; Pöschl et al., 2010). Figure 2a shows that the mode
value of the PDF became higher under the influence of Manaus pollution compared to background conditions, meaning
that rebound became more probable, indicating an increasing
presence of nonliquid PM at 75 % RH. In Fig. 2b, the mode
value became even higher under the influence of biomass
burning. For each type of air mass, the nighttime mode values were higher than the daytime equivalents. The day–night
differences were lowest under background conditions.
The increase in rebound at night might be explained by a
combination of interacting factors. A shallow stable nocturnal boundary layer can trap and thereby concentrate anthroAtmos. Chem. Phys., 17, 1759–1773, 2017

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A. P. Bateman et al.: Anthropogenic influences on the physical state of submicron particulate matter

pogenic nonliquid particles from local emissions at night, including smoldering fires during IOP2. During the day, the
boundary layer expands and dilution is more effective. In addition, the production of liquid secondary organic material
by biogenic processes is comparatively more rapid.
For further analysis, the rebound curves recorded under
background conditions were averaged separately for IOP1
and IOP2 (see Fig. 1a and Sect. S5 of the Supplement). These
two background-average curves served as references against
which deviations in rebound fraction were calculated for all
air masses. Rebound deviation represents the excess nonliquid PM over background conditions after detrending the data
for dependence on relative humidity. The rebound deviations
for IOP1 and IOP2 are plotted in Fig. 3. Rebound deviations
as high as +0.5 in rebound fraction were observed. Statistics
for Fig. 3 are summarized in Table S1, including the subset of
measurements matched with the ambient RH. For the full set
of data, including the full range of RH, rebound deviations
greater than 0.1 represented 17 and 35 % of the observations
during IOP1 and IOP2, respectively. These deviations, corresponding to increased rebound and thus indicating an increasing presence of nonliquid PM, corresponded to the anthropogenic influences, as developed further in Sect. 3.2.
3.2

3.2.1

Relationships between rebound and other
observations
Case studies

Transitions between background and polluted conditions
across 24 h periods are presented in Fig. 4 as representative
examples for each IOP. The bottom panel shows the deviation in rebound fraction relative to the background-average
curve (see Sect. S5 of the Supplement). Color coding distinguishes relative humidity. From bottom to top, other panels
in the figure show temperature, wind direction, wind speed,
relative fractions of two groups of AMS PMF factor loadings, black carbon (BC) concentration, sulfate concentration,
and total submicron PM mass concentration. Processes contributing to the loading of PMF group A are largely associated with the background atmosphere of Amazonia, including the possibility of long-range transport and extensive oxidation of biomass burning emissions. Processes contributing
to the loading of PMF group B are largely associated with
urban pollution and local and regional biomass burning (see
Sect. S6 of the Supplement).
Shifts in the deviation of the rebound fraction from the
background-average curve are apparent in the time series in
the bottom panels of Fig. 4. At these times, the fractional
loading of PMF group A decreased and that of PMF group B
increased, indicating a shift away from background conditions. Background conditions were characterized by high
loadings of PMF group A and small rebound deviations. In
the left panel (IOP1) is an example of one type of anthropogenic event: black carbon concentration and the fractional
Atmos. Chem. Phys., 17, 1759–1773, 2017

Figure 3. Deviation in rebound fraction relative to the average curve
for background conditions during (a) IOP1 and (b) IOP2. Red, purple, and green correspond to the RH regions of rebound, transition,
and adhesion, respectively, for the background-average curve. Results are shown for particles having a mobility diameter of 190 nm.

loading of PMF group B abruptly increased together. The rebound deviation simultaneously increased, indicating an increasing presence of nonliquid PM, especially above 70 %
RH. In the right panel (IOP2) is an example of a second type
of anthropogenic event: the background air mass was gently replaced by an air mass characterized by small increases
in rebound deviation and fractional loading of PMF group B,
yet it was lacking an associated increase in black carbon concentration. In an example of a third type of anthropogenic
event, this air mass was later replaced by an air mass characterized by a strong increase in the fractional loading of PMF
group B, total PM mass concentration, and rebound deviation. Around 15:00 UTC, a convective event associated with
rainfall decreased temperature, changed wind direction, and
increased wind velocity; background conditions returned.
TEM images collected during the time periods of elevated
rebound corroborate the foregoing interpretation of liquid
and nonliquid PM as adhering compared to rebounding particles. Images of particles that adhered to the TEM substrates
in the impactor at 95 % RH are shown in Fig. 5a. The images
were taken at a tilt angle of 60◦ so that the aspect ratio of
the particles could be viewed. The images show that most
adhering particles spread out across the substrate, indicating flattening upon impact, as expected for liquid particles.
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Figure 4. Time series of representative species during two pollution events for IOP1 and IOP2 (left and right sides of figures, respectively).
Total mass concentration plotted in the figure represents the sum of the AMS and black carbon mass concentrations measured for submicron
PM. The PMF mass fractions are expressed in relative terms to one another and necessarily sum to unity. Time on the abscissa is expressed
in UTC.

Some of these adhering particles had a mixed composition,
appearing as solid cores surrounded by halos of flattened liquid shells. The horizontal dimensions of the flattened particles approached 1 to 2 µm for vertical dimensions of tens of
nanometers. Pöschl et al. (2010) previously recorded similar images for ambient particles in the wet season of 2008
in central Amazonia during the Amazonian Aerosol Characterization Experiment (AMAZE-08) (Martin et al., 2010).
For comparison to the images of the adhering particles, images of particles that rebounded at 95 % RH from the impaction plate are shown in Fig. 5b. These particles were collected downstream of the impactor. The images show that
these particles had high vertical dimensions and spherical or
dome-like morphologies and thus suggest that the particles
experienced little deformation upon impact, as expected for
solid particles.

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Figure 5. Representative TEM images of (a) adhering and (b) rebounding PM at 95 % RH collected between 14:15 and 18:15 UTC
on 30 September 2014 during IOP2. The rebound fraction during the time period of collection approached 0.3. The arrows in
panel (b) highlight the locations of particles having high vertical
dimensions and spherical or dome-like morphologies, as expected
for solid particles.

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Figure 6. Deviation in rebound fraction for categorization by type of air mass and time of day. The box–whisker representation of the 10, 25,
50, 75, and 90 % quantiles of statistics for each RH bin is explained in the caption to Fig. 1. Air mass categorization is as for Fig. 2. Results
are shown for particles having a mobility diameter of 190 nm.

3.2.2

Descriptive statistics

The results of Figs. 3 and 4 are further analyzed in Fig. 6.
Rebound deviation statistics are shown by box–whisker representations for different windows of relative humidity. Figure S1 of the Supplement presents an additional level of
detail. The data sets were segregated for presentation by
IOP1-IOP2, daytime-nighttime, and air mass type. For background conditions, the rebound deviations relative to their average were mostly 0, indicating that there was low variability
among different background air masses. The exception was
for the night periods of IOP1. By comparison, under anthropogenic influences, the rebound deviation was positive for
both IOPs. Positive deviations were most significant between
65 and 95 % RH. In all cases, the nighttime deviations were
greater than the daytime counterparts. For IOP2, the prevalence of biomass burning confounded separate classifications
of urban pollution and biomass burning, and a classification
of biomass burning took precedent. Rebound deviations were
strongest during these time periods. Statistics of the analysis
are further summarized in Table S2 (see Sect. S7 of the Supplement). The average O : C ratios for the various air-mass
classifications can be found in Table S3 of the Supplement.
Atmos. Chem. Phys., 17, 1759–1773, 2017

3.2.3

Statistical correlations

Scatter plots of rebound deviation with environmental variables of ambient temperature, wind speed, and wind direction show no correlation for daytime or nighttime data sets
(see Fig. S2 of the Supplement). Scatter plots of rebound
deviation with some possible anthropogenic influences are
presented in Fig. S3. Soot, typically characterized by a solid
core region of black carbon, is expected in abundance both
in urban pollution and biomass burning emissions. There
was, however, no correlation between rebound deviation and
black carbon concentrations. There was also no correlation
between rebound deviation and total particle mass concentration. Rebound deviation and sulfate concentration weakly anticorrelated, which might be expected given the hygroscopicity of sulfate. Sulfate concentrations, however, were a poor
indicator of anthropogenic influence in this region because
the variability in background concentrations was comparable
in magnitude to any urban influence (de Sá et al., 2016). As
a caveat, an assumption in correlation tests is that variance
arises from a single variable, and the possibility of two or
more contributing or interacting factors is not directly considered.
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A. P. Bateman et al.: Anthropogenic influences on the physical state of submicron particulate matter

1767

Given that water uptake is an important process for softening organic material, scatter plots of the rebound deviation at
75 % RH with the hygroscopicity parameter κ are shown in
Fig. 7a and b for the data sets from IOP1 and IOP2, respectively. Lower values of κ represent decreased equilibrium in
water uptake for a fixed RH (Petters and Kreidenweis, 2007).
The plots show that the rebound deviation increased as the κ
value decreased, meaning that particles of lower hygroscopicity were less prone to being in liquid form.
The apparent shift to higher κ values from IOP1 to IOP2
might be explained by instrumental methods. During IOP1,
κ values were measured using the impactor apparatus at subsaturated RH (i.e., < 100 %) (see Sect. S8 of the Supplement).
During IOP2, κ values were measured using a size-resolved
CCN instrument at supersaturated RH (i.e., > 100 %) (Thalman et al., 2017). Sub- and supersatured κ values can be systematically different (Petters and Kreidenweis, 2007; Ruehl
et al., 2016).
3.2.4

Chemical characteristics of rebounded particles

An analysis of the relationship between rebound deviation
and chemical characteristics is presented in Fig. 8 based on
the fractional loading of PMF group B. The data sets are segregated for presentation by IOP1-IOP2, daytime-nighttime,
and four bands of fractional loading. Within each panel, box–
whisker statistics of rebound deviation are shown for different windows of relative humidity, ranging from 50 to 95 %.
The figure shows that the rebound deviation increased at all
RH values as the fractional loading of group B increased.
The background-average curve used as the reference for rebound deviation corresponded to a fractional loading of 0.00
to 0.15 for group B and 0.85 to 1.00 for group A. An increasing fractional loading of group B represented greater anthropogenic influence. The inference is that anthropogenic influences, represented by a combination of urban pollution and
biomass burning, affected chemical composition in ways that
increased the presence of nonliquid PM above 50 % RH.
Scatter plots between rebound deviation at 75 % RH and
the fractional loading of group B are shown in Fig. 9a and
b for the data sets of the two IOPs. The data points are colored according to the value of the hygroscopicity parameter κ. The plots show that rebound deviation increased for
low hygroscopicity and high fractional loading of group B,
in agreement with the presentation in Figs. 7a, b, and 8.
Figures 9a and b further show that the highest rebound deviations occurred during time periods affected by biomass
burning and urban pollution, as characterized by the lowest
values of κ and the highest fractional loadings of group B.
Smaller κ values for lower fractional loadings of group B
can be explained by the differences in O : C ratios between
group A and B (Massoli et al., 2010): the O : C ratios were
0.95 : 0.95 (IOP1/IOP2) and 0.42 : 0.54 for groups A and B,
respectively.

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Figure 7. Scatter plot of rebound deviation with the hygroscopicity parameter κ for (a) IOP1 and (b) IOP2. For clarity of presentation, data points are shown for apparatus RH values between 73
and 78 %, although the trend applies more broadly. As a guide to
the eye, in each panel data are divided into four groups, and medians and quartiles of the groups are plotted as black circles and
whiskers, respectively. Different techniques were used to measure
κG and κCCN , as described in the main text.

A model to predict rebound deviation based on chemical
characteristics was constructed. The fractional loadings of
PMF group A and B were used as model inputs, and model
coefficients represented the effects of RH across nine bands
(see Sect. S9 of the Supplement). The observed and predicted
rebound deviations are plotted in Fig. 10. The corresponding
coefficients R 2 of determination were 0.65 and 0.72 for IOP1
and IOP2, respectively. Predicted values were biased high for
low rebound deviation and biased low for high rebound deviation. The magnitudes of the model coefficients represented
the relative importance of the two PMF groups in predicting
rebound deviation (Table S5). In this regard group B dominated during both IOPs. This result is consistent with the role
of anthropogenic influences in shifting the PM population to
fewer liquid particles and more nonliquid particles.
Analysis by STXM/NEXAFS supports the foregoing narrative of anthropogenic influence as a modulator between liquid and nonliquid PM. Rebounded particles were collected
on 1 October 2014 during a time period classified as influAtmos. Chem. Phys., 17, 1759–1773, 2017

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A. P. Bateman et al.: Anthropogenic influences on the physical state of submicron particulate matter

Figure 8. Deviation in rebound fraction for categorization by chemical characteristics. The box–whisker representation of the 10, 25, 50, 75,
and 90 % quantiles of rebound fraction is explained in the caption to Fig. 1. The chemical characteristics are categorized by the fractional
loading of PMF group B as < 0.15 (green), 0.15 to 0.3 (blue), 0.3 to 0.6 (orange), and > 0.6 (red).

enced by biomass burning emissions. The carbon K-edge
spectrum is shown in Fig. 11a, and the STXM image is
shown in Fig. 11b. A notable feature of the NEXAFS spectrum of the rebounded particles is the strong double bond.
Pöhlker et al. (2012) previously collected NEXAFS spectra
for samples collected at a background site in central Amazonia, and the strong feature of a double bond was absent. The
spectra instead resembled those of different types of reference biogenic secondary organic material.
For comparison to the spectrum collected of the rebounded
particles, carbon K-edge spectra are shown for carbonaceous particles collected in other field and laboratory studies. Based on these results as well as those of the rebound measurements, a hypothesis of soot or black carbon
to explain the rebounding particles was ruled out on three
grounds: the double-bond feature was homogeneously distributed throughout the particles (Fig. 11b) compared to inclusions that are typical for soot (Moffet et al., 2013; Knopf
et al., 2014; O’Brien et al., 2014), rebound deviation and
black carbon concentrations did not correlate (Fig. S3), and
the spectroscopic signatures of rebounded particles and soot
did not match (Fig. 11a). A hypothesis of VOC-derived sec-

Atmos. Chem. Phys., 17, 1759–1773, 2017

ondary organic material, including possible changes because
of shifts from HO2 - to NO-dominant chemistry (P. Liu et al.,
2016; Y. Liu et al., 2016), was also eliminated by comparison of the NEXAFS spectrum of the rebounded particles to
the reference spectra for laboratory samples. An important
caveat to these interpretations is that some of the hypotheses
ruled out for this particular day might still have a role to play
on other days.
There was exceptional uniformity in the particle population characterized by STXM/NEXAFS, which could suggest
that the rebounding PM represented distant sources or, alternatively, a strong single nearby source. The gray region
around the red line in Fig. 11a illustrates the low variability across the population of analyzed particles (Fig. 11b).
Moreover, the variability in the O : C ratio determined by the
NEXAFS analysis was just ±0.01 for O : C = 0.34 (±0.03)
[±0.01], where the value in parentheses was the uncertainty
of the measurement and the value in the brackets was the
variability across the image in Fig. 11b. During the same time
period, the O : C ratio of the ambient PM was 0.77 ± 0.04 by
AMS measurements. Hence, the rebounding particles were
significantly less oxidized, as consistent with increased dou-

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A. P. Bateman et al.: Anthropogenic influences on the physical state of submicron particulate matter

1769

Figure 9. Scatter plot of rebound deviation with the fractional loading of PMF group B for (a) IOP1 and (b) IOP2. The data points are
colored according to the corresponding hygroscopicity parameter
κ. For clarity of presentation, data points are shown for apparatus
RH values between 73 and 78 %, although the trend applies more
broadly. As a guide to the eye, in each panel data are divided into
four groups, and medians and quartiles of the groups are plotted as
black circles and whiskers, respectively.

ble bond (C=C) content and increased fractional loading of
PMF group B.
Several speculations can be made for the origins of the
particles leading to the data set from 1 October. The chemical constituents giving rise to the double bonds might derive from biological degradation products from incomplete
combustion, such as in biomass burning (Tivanski et al.,
2007; Keiluweit et al., 2010). Unexplained by this speculation, however, is the absence of a potassium signature in the
NEXAFS spectra, which is typical of most biomass burning.
Future collection of NEXAFS spectra would be advisable for
the several different types of biomass burning in an Amazonian context, such as from nearby fields, regionally around
Manaus, from other regions of South America 2 or 3 days
away, and from Africa up to a week away. An alternative
speculation for this data set is that solid organic particles produced by the impact of raindrops on wet soil surfaces could
be making a contribution to the rebounded PM analyzed here
(Joung and Buie, 2015). Wang et al. (2016) recently reported
the detection of airborne soil organic particles generated by
this mechanism over agricultural fields in the central plains
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Figure 10. Scatter plot of observed compared to predicted rebound
deviation for (a) IOP1 and (b) IOP2. Predictions are based on linear
combinations of the loadings of the two PMF groups. The linear
coefficients used in the prediction were optimized as a function of
RH (Table S5). The solid line represents a one-to-one correlation,
and the dashed line represents the best linear fit. Coefficients R 2 of
determination were 0.65 and 0.72 for the IOP1 and IOP2 data sets,
respectively. Points are color-coded by relative humidity.

of the USA, and the corridor from Manaus to T3 has many
agricultural fields. The rebounded particles collected at T3
and the agricultural particles reported in Wang et al. (2016)
both had a homogeneous distribution of double bonds and
similar elemental ratios and absorption features. Even so,
preliminary analysis across the extended data set at T3 between rebound and nearby precipitation did not show a clear
correlation. Another speculation relates to the importance of
aromatic compounds as hardening agents. Several gas-phase
aromatic compounds, laden with double bonds, were measured during IOP1 and IOP2, including toluene, benzene,
Atmos. Chem. Phys., 17, 1759–1773, 2017

1770

A. P. Bateman et al.: Anthropogenic influences on the physical state of submicron particulate matter
trimethylbenzene, and xylenes, by proton-transfer mass spectrometry (Y. Liu et al., 2016). Rebound deviation correlated
positively with the concentrations of these compounds during both IOPs, and the correlation was stronger during the
night. Laboratory studies show that the uptake of polycyclic
aromatic hydrocarbons during the formation of biogenic PM
can increase the viscosity of the PM (Vaden et al., 2011; Zelenyuk et al., 2012; Abramson et al., 2013; Liu et al., 2015).
4

Conclusions

Under background conditions, particles composed primarily
of highly oxidized biogenic PM were hygroscopic, and they
were liquid for the RH values prevailing over Amazonia at
surface level. Anthropogenic influences of urban pollution
and biomass burning decreased hygroscopicity, and nonliquid PM became more favored. The shift in physical state correlated with increasing concentrations of C=C functionalities and increasing fractional loadings of AMS PMF group B,
both of which were indicative of anthropogenic influences.
These results demonstrate the importance of anthropogenic
influences in altering the physical properties of ambient particulate matter over tropical forests.
5

Data availability

The underlying time series of data is provided as text files in
the Supplement.

The Supplement related to this article is available online
at doi:10.5194/acp-17-1759-2017-supplement.

Figure 11. STXM/NEXAFS analysis of particles collected after rebound from the impaction plate. Samples were collected between
01:00 and 10:00 UTC on 1 October 2014. (a) Carbon K-edge spectrum of rebounded particles (red). Lines at 285.4 and 288.5 eV
highlight absorption by double bonds (C=C) and carboxylic acids
(-COOH), respectively. The line in the region of 286.5 to 286.7 eV
can have contributions from ketones, carbonyl-substituted aromatics, and phenolic species. For comparison, spectra are shown for
carbonaceous particles collected in other field and laboratory studies: soil organic particles from the central USA (Wang et al.,
2016), soot from the ambient environment, atmospheric particles
collected at a background site in central Amazonia (Pöhlker et al.,
2012), and three laboratory samples of secondary organic material. Data sources: isoprene-derived SOM produced under HO2 dominant conditions (O’Brien et al., 2014); isoprene-derived SOM
produced under NO-dominant conditions (O’Brien et al., 2014),
and toluene-derived SOM produced under NO-dominant conditions
(this study). (b) STXM image from which the NEXAFS spectrum
of panel (a) was obtained. Coloring is red for absorption at 285.4 eV
(i.e., double bonds). Coloring is green for other types of functionalities. The pixelation visible in the image corresponds to the STXM
spatial resolution during data collection.

Atmos. Chem. Phys., 17, 1759–1773, 2017

Competing interests. The authors declare that they have no conflict
of interest.

Acknowledgements. Institutional support was provided by the
Central Office of the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), the National Institute of Amazonian
Research (INPA), and Amazonas State University (UEA). The
Office of Biological and Environmental Research of the Office
of Science of the United States Department of Energy is acknowledged for funding, specifically the Atmospheric Radiation
Measurement (ARM) Climate Research Facility, the Atmospheric
System Research (ASR) Program, the Division of Chemical
Sciences, Geosciences, and Biosciences (Advanced Light Source
at Lawrence Berkeley National Laboratory, Beamlines 5.3.2
and 11.0.2), the Environmental Molecular Sciences Laboratory
(EMSL), and Pacific Northwest National Laboratory (PNNL).
Further funding was provided by the Amazonas State Research
Foundation (FAPEAM), the São Paulo State Research Foundation

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A. P. Bateman et al.: Anthropogenic influences on the physical state of submicron particulate matter
(FAPESP), the Brazil Scientific Mobility Program (CsF/CAPES),
the US National Science Foundation, and the Japanese Ministry of
the Environment. The work was conducted under scientific licenses
001030/2012-4 of the Brazilian National Council for Scientific and
Technological Development (CNPq).
Edited by: T. Karl
Reviewed by: two anonymous referees

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