Aerosol and Air Quality Research, 16: 1152–1164, 2016
Copyright © Taiwan Association for Aerosol Research
ISSN: 1680-8584 print / 2071-1409 online
doi: 10.4209/aaqr.2015.10.0579 Continuous Measurement of Ambient Aerosol Liquid Water Content in Beijing
Oscar A. Fajardo1, Jingkun Jiang1,2*, Jiming Hao1,2
1
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua
University, Beijing, 100084, China
2
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
ABSTRACT
The amount of liquid water present on ambient aerosol particles influences its radiative properties, the partitioning of
gas-phase organic and inorganic compounds to the condensed phase, and the assessment of particle health effects upon
inhalation. An improved home-made Dry-Ambient Aerosol Size Spectrometer (DAASS) was implemented and deployed
at a sampling site in Beijing to measure water content and volumetric growth factor of fine ambient aerosol during clean
and polluted days. Aerosol chemical composition was characterized by an aerosol chemical speciation monitor (ACSM).
Meteorological conditions were also assessed to trace origin of the air masses arriving at the site. A thermodynamic model,
ISORROPIA II, was used to predict aerosol water content based on inorganic compositions measured by ACSM. It was
then compared with the aerosol water content measured by DAASS. During the two-week sampling period, an aerosol
efflorescence behavior was observed around 50% relative humidity (RH). Aerosol at the sample location was
predominantly alkaline, with an average acidity ratio of 1.3 during the polluted days but very variable during the clean
days. This fact coupled with generally low ambient RH resulted in low amounts of liquid water detected during the
campaign. When RH reached its maximum of 87% and aerosol appeared to be more acidic, the maximum amount of
aerosol water content measured was 1.3 µg m–3 (44.9% wt of DAASS measured ambient aerosol). Measured aerosol water
content and model predictions seem to show a better agreement during the more polluted days, but during the clean days
aerosol organic fraction appears to have an important contribution in the water content. We argue that the organicassociated water is comparable with that associated with inorganic compositions during the clean periods at low ambient
RH conditions.
Keywords: Urban aerosol; Aerosol water content; Beijing pollution; Aerosol acidity.
INTRODUCTION
Despite the known importance of water uptake in ambient
aerosol systems (Charlson et al., 2001; Johnson et al., 2004;
Lohmann and Feichter, 2005; McFiggans et al., 2006) there
is a relative lack of aerosol water measurements. Water is
one of the most important constituents of ambient aerosol
at certain relative humidity (RH) conditions (Kreidenweis
et al., 2008) and its amount is governed by gas-liquid
equilibrium (Kreidenweis and Asa-Awuku, 2014). There is an
intrinsic complexity of the atmospheric aerosols with regard
to hygroscopicity, given the large differences in particle sizes,
varying from nanometer to micrometer, and the varying
chemical components in different size modes. Water content
is found to be a key factor in determining total ambient
*
Corresponding author.
E-mail address: [email protected];
[email protected]
particulate matter (PM) mass concentration, causing an
impact on light scattering and extinction coefficient (Day
and Malm, 2001; Garland et al., 2007; Jung et al., 2009),
and has profound implications for atmospheric chemistry
as it can enhance chemical reaction rates on the aerosol
(Ervens et al., 2011; Lee et al., 2011; Wang et al., 2012).
There are diverse methods to measure aerosol water
content, e.g., gravimetric analysis to determine the amount
of water associated with the mass of aerosol on a filter
(McInnes et al., 1996; Lee and Hsu, 2000; Spindler et al.,
2012), optical methods using Nephelometers or extinction
cells (Carrico et al., 2000; Nessler et al., 2005; Kim et al.,
2006), microscopy such as scanning electron microscope
(SEM) and environmental transmission electron microscope
(ETEM) (Ohta et al., 1998; Ebert et al., 2002; Wise et al.,
2005), electrodynamic balance to study properties of single
droplets of multicomponent systems (Tang and Munkelwitz,
1993; Tong et al., 2011; Davies et al., 2013; Zuend et al.,
2013). Among all, the most frequently used is the hygroscopic
tandem differential mobility analyzer (H-TDMA) (Liu et
al., 1978; Rader and McMurry, 1986), which measures
Fajardo et al., Aerosol and Air Quality Research, 16: 1152–1164, 2016 hygroscopic growth by size-selecting individual particles
with a Differential Mobility Analyzer (DMA), exposing
them to elevated or reduced RH and then measuring aerosol
diameter change using a second DMA (Swietlicki et al.,
2008; Kitamori et al., 2009; Ye et al., 2011; Wang et al.,
2014). However, the HTDMA cannot easily measure liquid
water content of atmospheric particles in their ambient state
because particles are often dried prior to sampling, this fact
cannot ensure that no other species but just water are removed
(Gysel et al., 2006; Gysel et al., 2007; Swietlicki et al.,
2008), or particles may effloresce or their morphology may
change (Engelhart et al., 2011). An additional inconvenience
is the troublesome and time consuming when scanning a
range of particle sizes and RH values with respect to the
fact that ambient aerosol composition changes continuously.
These methods are not suitable for near real-time continuous
measurements of aerosol water content at ambient conditions.
A Dry Ambient Aerosol Size Spectrometer (DAASS) was
proposed to meet this challenge. It assesses the amount of
aerosol water at atmospheric conditions by merging aerosol
number size distribution with the volumetric growth factor
(VGF). It was first introduced by Stanier et al. (2004) during
the Pittsburgh Air Quality Study. A reduced version of the
instrument was used to measure aerosol water content and
VGF of fine PM during the Finokalia Aerosol Measurement
Experiment (FAME-2008) in Greece (Engelhart et al., 2011).
Rapid economic and industrial development of China in
recent years is evidenced by its growing urbanization and
energy consumption. But such development has brought
the Chinese cities severe urban air pollution in recent decades
(He et al., 2002). Beijing, for example, frequently has severe
PM pollution events that normally are accompanied with
increasing ambient RH (Jia et al., 2008; Jiang et al., 2015). It
possibly could have high aerosol water content. In Guangzhou
and Shanghai, optical methods (using nephelometers,
transmissometer and photometer) were employed for the
determination of hygroscopic growth factor and evaluation
of scattering and light extinction of ambient aerosol (Liu et
al., 2008; Jung et al., 2009; Cheng et al., 2014). While in
the North China Plain a series of interrelated studies (Achtert
et al., 2009; Massling et al., 2009; Meier et al., 2009; Bian
et al., 2014; Liu et al., 2014) evaluated the chemical and
physical influence on hygroscopic growth of atmospheric
sub-micrometer particles using HTDMA, Twin Differential
Mobility Particle Sizer (TDMPS), and Humidifying
Differential Mobility Particle Sizer (HDMPS) for rural and
urban areas. So far, direct measurement of aerosol water
content at ambient conditions using a DAASS system has
not been performed in China.
In this paper we present measurements of aerosol water
content using a home-made Dry Ambient Aerosol Size
Spectrometer at a urban site in Beijing, China. Prior to its
deployment, the instrument was calibrated using laboratory
generated aerosol particles. Discrepancies between the
ambient RH and the DMA sheath RH found in the original
DAASS design were addressed in this work for a better
resemblance of the ambient conditions inside the DMA at the
moment of the measurements. Aerosol chemical composition
was characterized using an Aerosol Chemical Speciation
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Monitor (ACSM) whose data was used as inputs for the
thermodynamic model ISORROPIA II to theoretically
estimate aerosol water content for comparison with DAASS
results. Meteorological conditions and back trajectories of
air masses are employed in the explanation of aerosol mass
concentration and water content observed during the
campaign.
METHODOLOGY
Development of DAASS
The home-made DAASS was constructed following the
schematics in Fig. 1. Major components of the instrument
include an Scanning Mobility Particle Sizer (SMPS, TSI
3938) which includes an electrostatic classifier (TSI 3082), an
Soft X-ray aerosol neutralizer (TSI 3088) using a differential
mobility analyzer (TSI 3081), a condensation particle counter
(TSI 3787), an aerosol dryer (Permapure MD 110-22), a
sheath flow dryer (Permapure PD 200-24), three computercontrolled solenoid valves (Humphrey 310), and a custommade control box which controls the switch of the solenoid
valves and acquires temperature and RH from the sensing
probes.
The instrument was designed to continuously measure the
number size distributions of aerosol particles in the range of
approximately 10 to 500 nm. DAASS has two conditioning
modes and two scanning modes (i.e., Ambient mode and
Dried mode). Scan times for both Ambient and Dried modes
are 3 minutes in duration. Alternated measurements of
particle number size distributions (PNSD) in Ambient (red
lines in Fig. 1) and Dried mode (blue lines in Fig. 1) are
performed continuously in order to estimate the amount of
liquid water present on the aerosol. During a Dried scanning
mode, ambient polydisperse aerosol is initially dried in the
aerosol Nafion dryer before entering the DMA, to an RH
of about 25%, DMA sheath flow is kept in a closed loop
passing through the sheath Nafion tube dryer until an RH
of about 25% is reached, normally it is slightly lower than
the aerosol RH. After finishing this Dried scan, the system
enters into the Ambient conditioning mode where the SMPS
sheath flow changes to open loop and bypassing the sheath
dryer Nafion tube, ambient particle-free air is allowed to
enter into the instrument to increase the relative humidity
to that of the exterior ambient conditions. This conditioning
stage lasts for 10 minutes, after which the system enters into
the Ambient scanning mode. During the Ambient scanning
mode, un-dried polydisperse aerosol is sent directly into the
DMA. The sheath flow remains open allowing ambient
humid air enter the line ensuring a very close value of sheath
RH to that of the ambient RH within the DMA. After
finishing the Ambient scan, the instrument changes back to
the Dried mode configuration, remaining in a pre-dry
conditioning stage for 7 minutes before starting the following
scan. These 2 cycles are repeated continuously. After one
dry and one ambient measurement have been preformed,
PNSD’s stored in the system are automatically processed
by the Data Processing Algorithm. The amount of aerosol
liquid water is calculated and displayed automatically.
Utility dry air at –40°C dewpoint is permanently supplied
Fajardo et al., Aerosol and Air Quality Research, 16: 1152–1164, 2016 1154
Pump
Dryer
Filter
Critical Orifice
Nafion Tube
Impactor
Aerosol Probe
T/RH
Neutralizer
Dryer
Pump
Electrostatic
Classifier
Filter
Critical Orifice
Nafion Tube
Sheath Probe
DMA
CPC
T/RH
Ambient Probe
T/RH
Control
box
Fig. 1. Schematics of the home-made DAASS system. Red lines are flow lines in Ambient mode and blue lines are flow
lines in Dried mode.
to both nafion dryers when in the dried mode. The –40°C
dewpoint required for the utility air by the Nafion dryer was
achieved by passing through a combination of molecular
Sieve 3A (J&K Scientific, 4–8 mesh pellets) and Silica gel
dryers. During the Ambient mode, the utility dry air flow
is stopped in order to conserve the desiccant integrity as
long as possible and to reduce maintenance for the instrument.
The sheath to aerosol flow ratio for the DMA is 4:1 L min–1.
The instrument is controlled via USB from a computer
running a graphical user interphase (GUI) built in LABView
graphical language which displays basic information of the
instrument condition (temperature and RH at several locations,
current stage of the measurement) while the data processing
algorithm written in Python (Version 2.7) runs on the back.
Description of the procedure to calculate the amount of
water content present on aerosol particles can be found
elsewhere (Stanier et al., 2004; Engelhart et al., 2011). The
calculation is based on three fundamental assumptions:
volume additivity is considered valid for all the aerosol
components including water; a single and size-independent
VGF factor applies to different sized particles; aerosol
particles are spherical. Briefly, total measured volumes of
ambient and dried size distributions are calculated based
on PNSD’s. Using an assumed initial volumetric growth
factor, an iterative procedure is employed to determine the
upper limit dry size for the dry volume integral so that the
same number of particles is considered in both distributions.
Aerosol water is then calculated as the difference of ambient
to dry volume multiplied by the water density.
Prior to the deployment of DAASS for field measurement,
a number of calibrations were performed in the laboratory.
Sizing accuracy for the instrument was checked using
laboratory-generated monodisperse Polyestyrene Latex
spheres (PSL, Duke Scientific, USA) at diameters of 50 and
100 nm. Diffusional particle deposition within the transport
lines were estimated using empirical particle loss corrections
from Kulkarni et al. (2011) and (Wang et al., 2002) from
the processed size distributions given by the TSI SMPS
program (Fig. S1). Diffusional losses for the nafion tube
were estimated using the equivalent pipe length method
(Wiedensohler et al., 2012). Finally, the instrument was used
to measure diameters of hydrated and dried sodium chloride
particles generated in the laboratory by atomizing the solution
of 0.02 wt% NaCl in ultra-pure water. The generated NaCl
dry aerosol size distribution has a geometric mean diameter of
122 nm and a geometric standard deviation of 1.66. Changes
Fajardo et al., Aerosol and Air Quality Research, 16: 1152–1164, 2016 in aerosol RH were controlled by reducing or increasing the
length of a silica diffusion dryer placed immediately after
the atomizer. Theoretical diameter growth curves for NaCl
particles were calculated according to the Kohler equation
using salt specific surface tension, solution density, and water
activity values (Gysel, 2003). As shown in Fig. 2, measured
results have reasonable agreement with theoretical growth
data.
Uncertainties in the Measurement of Aerosol Water
Content
Sources of error in the DAASS water measurements
were reported in Stanier et al. ( 2004). Since the determnation
of aerosol water relies on particle counts in both Dried and
Ambient modes, it is important to minimize bias between the
two modes. Difussive particle losses, particularly in the Dry
mode, could directly impact particle counts. When developing
the instrument, aerosol flow lines were designed as short and
straight as possible, with no bend exeeding 90°. In the case
of losses inside the aerosol nafion dryer, correction of particle
count were applied based on the equivalent pipe length
method (Wiedensohler et al., 2012), ensuring that count
difference for laboratory-generated dry aerosol is lower
than 5% between two modes (Fig. S2). The ambient and dried
particle distributions are not measured simultaneously.
Changes in the aerosol distribution in such period could
cause error in the calculation of volume growth. Stanier et al.
(2004) presents a correction factor for the changes in the dry
aerosol volume (ß) during a lapse of time. The assumptions
made when estimating water content may also introduce error.
Urban aerosols tend to be externally mixed, with components
of different hygroscopicities and possibly various growth
factors. The growth factor calculated for DAASS is a volumeweighted average growth factor for all the species in the
aerosol. Ambient particles may deviate from the assumption
of sphericity, propagating error to the calculation of aerosol
volume. It was reported that atmospheric particles may
undergo morphological re-conformations when they are
1155
under repeated changes of RH (Mikhailov et al., 2009).
Finally, studies on particle hygroscopicity and water uptake
have commonly used the assumption of additivity and
found acceptably low errors (Kreidenweis et al., 2008).
Field Measurement
Atmospheric measurements were carried out during the
last two weeks of March 2015 (March 20th to April 2nd).
Continuous aerosol liquid water content measurements were
performed every ~20 minutes by the custom-made DAASS.
The instrument was located at the rooftop of the
Environmental Science Building (40°0’17’’N, 116°19’34’’E,
~10 m above the ground) on the campus of Tsinghua
University, a semi-residential area. This site has been use to
monitor PM2.5 concentrations and air quality in Beijing since
1999 (He et al., 2001; Yang et al., 2011; Cao et al., 2014).
To ensure a close resemblance of the ambient conditions
within the system (Stanier et al., 2004; Engelhart et al., 2011),
the instrument was placed outdoors at all times. Protection
from direct sunlight and precipitation was provided to
ensure appropriate functioning.
In the original reduced DAASS system (Engelhart et al.,
2011), their setup included an auxiliary pump to drive a
larger amount of humid air through the system during the
Ambient conditioning. This helps to speed up the replacement
of dry air inside the system. However, this auxiliary pump
was not used for the scanning stage. When the ambient RH
exceeded 70%, the sheath RH in their instrument was not
able to reach such high values at the moment of the scan,
forcing them to discard all measurements above this value
(Engelhart et al., 2011, Bian et al., 2014). In order to avoid
this problem caused by a sudden drop of the sheath RH
going into the DMA once the Ambient conditioning mode
was finished and the loop is closed back again (Stanier et
al., 2004; Engelhart et al., 2011), our setup didn't use any
additional pump or blower but maintained the sheath flow
loop open even during the scanning stage. This setup
shows good performance, normally reaching the ambient
Fig. 2. Theoretical (line) and measured (dots) growth factors for sodium chloride aerosol. Vamb and Vdry are the aerosol
volumes measured by the instrument in the Ambient and Dried modes.
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Fajardo et al., Aerosol and Air Quality Research, 16: 1152–1164, 2016 RH inside the system (Fig. 3(a) and 3(b)). In very few
occasions, this setup had a slight difference between the
exterior ambient RH and an averaged difference of 5% was
observed (Fig. 3(c)). RH changes drastically in Beijing, e.g.,
in some few hours there could be differences of up to 50%.
Nevertheless, all the calculations for aerosol water content
were done using the RH value of the Sheath Flow. The
maximum ambient RH measured during the sampling period
was 87% with an equivalent sheath flow RH of 76%. Their
difference represents the maximum difference (i.e., 11%)
between both ambient RH and sheath RH measurements
during the campaign.
During the campaign, chemical composition of submicrometer non-refractory (NR-PM1) aerosol was measured
Fig. 3. Relative humidity conditions during the sampling period. (a) Sheath RH follows closely with the ambient RH
during the sampling period; (b) Sheath RH vs. ambient RH for the sampling period (the average difference is ~5%). (c) An
example of RH in the system at different instrument modes: light green region is pre-Ambient conditioning, light red
region is pre-dry conditioning, and light yellow regions are scanning periods.
Fajardo et al., Aerosol and Air Quality Research, 16: 1152–1164, 2016 with an ACSM (Aerodyne Research, Inc. (Ng et al., 2011)).
It reported the mass concentration of sulfate, organics,
nitrate, chloride, and ammonium in NR-PM1 every 15 min.
Data was analyzed by standard ACSM software written in
Igor Pro (Wavemetrics, Inc., USA). Using the measured
compositions, aerosol acidity was estimated according to
the following expression:
Acidity Ratio 
molNH 4
 2  molSO2  molNO  molCl  
(1)
Acidity, which is greater for lower cation/anion ratios,
dictates the relative abundance of different inorganic aerosol
components (Dick et al., 2000). Molar ratios of the unity
or greater are assumed to be fully neutralized aerosol. Even
though the concentration of chloride was small during the
sampling period, it was not negligible and was included in
the acidity calculation. Chemical analysis of PM1 in Beijing
has reported low mass fractions of K+ and Na+ compared
to ammonium ion. We hypothesize there is not significant
variation in the acidity ratio without their inclusions.
Furthermore, the thermodynamic equilibrium model
(ISORROPIA II (Nenes et al., 1998; Fountoukis and Nenes,
2007)) was used to estimate aerosol water content based on
aerosol inorganic compositions for comparison with DAASS
results. ISORROPIA II can solve two kind of problems, one
is in “Reverse” mode, in which aerosol phase concentrations,
relative humidity and temperature of the system are known,
and the other is “Forward” mode where both aerosol and gas
phase concentrations with temperature and relative humidity
are known.
Meteorological conditions (temperature, pressure, dew
point, wind speed and direction) for the sampling location
were obtained from www.wunderground.com. Back
trajectories of air masses were downloaded from the
HYSPLIT website (http://www.ready.noaa.gov/HYSPLIT_
traj.php). Hourly PM2.5 mass concentrations were downloaded
form the website of the Beijing Environmental Protection
Bureau, that has 35 air quality monitoring stations located
around the city. The closest monitoring station to our
sampling site is WanLiu station, located ~ 4 km from our
sampling site with no major pollution sources in between.
RESULTS AND DISCUSSION
Pollution Characteristics during the Sampling Period
Using ambient PM2.5 concentrations from the Wanliu
monitoring station and a threshold value of 50 µg m–3, which
is twice the daily mean PM2.5 guideline value suggested by
the World Health Organization (WHO) (WHO Regional
Office for Europe, 2006), it was possible to distinguish 3
different periods with pollution characteristics during the
sampling time. A first period of clean conditions (P1) is from
21:00 on March 20th to 01:00 on March 24th. A second period
(P2) of moderate polluted conditions is from 01:00 on March
24th to 15:00 on March 28th. A third period (P3) of heavily
polluted conditions is from 15:00 on March 28th 15:00 and
to 06:00 on April 1st (Fig. 4).
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Meteorological conditions changed in several occasions
during the measurement period. These changes were
accompanied with increases in aerosol mass load, particle
number size distribution and major chemical compositions
of aerosol particles (Figs. 4 and 5). Back trajectories of the
air masses arriving at the sampling site for those three selected
periods (P1, P2 and P3) were obtained using HYSPLIT
model.
First period (P1) is characterized by northerly winds.
Relative humidity was generally low for most of the period.
WanLiu monitoring station reported a maximum hourly PM2.5
concentration of 62 µg m–3. Aerosol inorganic composition
was very low while organic compositions was predominant
and had marked peaks. A few new particle formation and
growth events happened during this period. P2 presents air
masses coming from southern regions, a largely populated
and heavy industrialized area. There is a notorious increase
in the daily relative humidity with maxima during the early
hours everyday (~4 AM) and minima in the afternoons. PM2.5
data from WanLiu reported a maximum hourly concentration
of 162 µg m–3. Comparing to the first period, the averaged
particle number distributions for the whole period shifted
toward larger particles, peaking at around 100 nm. P3 period
presents similar conditions to those in P2 with southerly
winds and peaks in particle number size distribution are
around 100 nm. However, very low wind speed during the
whole day of March 31st allowed the built-up of pollution.
PM2.5 data from WanLiu reported a maximum hourly
concentration of 219 µg m–3 during this day.
Positive matrix factorization (PMF, the PMF Evaluation
Tool PET, v2.05 (Ulbrich et al., 2009) was used for analysis
and evaluation on organic aerosol spectra measured by
ACSM to resolve distinct OA components. Two resolved OA
factors (a hydrocarbon-like OA (HOA) and an oxygenated
OA (OOA)) are reported in this study. The deconvolved
OA allowed to distinguish main composition of the organic
peaks presented during the three selected periods. P1 has
four visible peaks. The first two sharp peaks are mainly
composed of HOA (75–85%), while the third and fourth
peak, in which would be the transition period between P1
and P2 are composed of an almost equal amount of HOA and
OOA. P2 presents three major OA peaks. All are composed
mainly of a more oxygenated fraction, what is consistent with
the air mass back trajectory bringing more aged aerosol from
the southern region. P3 shows a continuously increasing
mass concentration of OA. During the first day, HOA and
OOA show equal concentrations but thereafter OOA takes
a more predominant fraction of up to 70% of the organic
aerosol composition.
Aerosol Efflorescence Behavior and Measured Liquid
Water
During the measurement period the particles showed a
small retention (below 10% in volume) of liquid water at
RH between 20–50%. At 50% RH, there is an inflection
point in the VGF trend of the particles tending to increase up
to ~1.8. Observing this, we considered it as an efflorescence
point for the ambient aerosol. At 60% RH, particles were
able to retain somewhere below 40% of water. At about
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Fajardo et al., Aerosol and Air Quality Research, 16: 1152–1164, 2016 Fig. 4. Ambient aerosol mass and number concentrations during the sampling period. (a) Contour plot of the particle
number size distributions (dN/dlog10Dp) measured by DAASS. (b) PM2.5 mass concentration at the WanLiu monitoring
station vs. ambient RH at Tsinghua sampling site.
70% RH, particles retained water in excess of 50% of their
dry volume. Fig. 6 displays a statistical analysis of the data
to quantify the correlation between VGF and ambient
relative humidity. Mean and median VGF values falling into
2.5% relative humidity bins were calculated. The resulting
curve was fitted with a polynomial regression of second
order using the least square method, which results in a high
coefficient of determination (R2 = 0.76). An efflorescence
behavior was reported previously for urban aerosol with a
seasonal variations, i.e., in winter with a value of ~ 60%
RH and in Spring with a value of ~55% when aerosol was
more alkaline (Khlystov et al., 2005). Using an individual
particle hygroscopic (IPH) system, Li et al. (2014) reported
efflorescence of acidic ambient particles in Mt Lu (North
China) at 49–53%. On the other hand, Engelhart et al. (2011)
found no evidence of efflorescence for an acidic and aged
aerosol on an island.
There was a marked influence of the aerosol acidity on
the amount of liquid water present on the aerosol. An
important difference in the acidity of the particles between
clean days (P1) and polluted days (P2–P3) is visible in
Fig. 7(b). The figure shows that during P1 aerosol acidity
ratio is extremely variable while during P2 and P3 this ratio
is more stable, with an alkaline aerosol presenting an average
ratio of 1.3 during most of the time. This low aerosol acidity
was reflected on the low amount of aerosol water detected
by DAASS. An example could be seen for March 27th.
During this day the aerosol was slightly more acidic than
that of March 26th but the atmospheric conditions were
similar. However, the amount of measured water is markedly
larger (Fig. 7(b)). This is to say, any perturbation on the
almost constant alkalinity of the aerosol accompanied by a
relative humidity over ~50% results in an increase on
aerosol water content detected by the instrument.
The acidity or alkalinity character of the aerosol is strictly
dependent on the relative abundance of chemical species in
the atmosphere. In this respect governmental policies have
had an impact on the local atmospheric aerosol composition.
Strict emission limits on coal burning power plants and
factories and vehicle emissions, and control of factories on
emissions of SO2 and NOx are measures put in place by the
government that have resulted in reduction of the
corresponding particulate components. However, very little
has been done on the reduction of ammonia emissions, which
continue in the rising (Meng et al., 2011; Wang et al., 2011),
and reflects on the alkalinity that aerosol in Beijing shows.
A comparison of ACSM aerosol mass and Acidity ratio
shows how the aerosol is predominately alkaline during the
sampling period, particularly for low aerosol masses (i.e.,
clean period) (Fig. 7(a)). Ianniello et al. (2010) had previously
reported higher ammonia concentrations were found during
northwesterly winds in Beijing.
The maximum concentration of measured aerosol water
was 1.3 µg m–3. It occurred during the most polluted day of
Fajardo et al., Aerosol and Air Quality Research, 16: 1152–1164, 2016 1159
Fig. 5. Ambient aerosol chemical composition given by the ACSM. (a) Total aerosol chemical compositions vs. time. (b)
Organic aerosol factors determined by positive matrix factorization analysis. OOA: oxygenated organic aerosol, HOA:
hydrocarbon-like organic aerosol. Wind direction as reported from wunderground.com is included as well.
Fig. 6. Correlation of Volume growth Factor with Ambient Relative Humidity. Blue hexagons are mean VGF values for the
whole sampling period. Yellow stars are median VGF values. Red line is a fitted curve to the mean values. Shaded area
represents VGF within one standard deviation from the mean. Coefficient of determination (R2) is given on the top-right side.
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Fajardo et al., Aerosol and Air Quality Research, 16: 1152–1164, 2016 Fig. 7. Progression of acidity ratio for the sampling period as calculated from Eq. (1). (a) Acidity ratio vs. ACSM inorganic
mass. (b) Acidity ratio (orange circles), DAASS measured water content (blue diamonds) and ambient relative humidity
(pink line) versus date.
sampling on the 31st of March (Fig. 7(b)). At this point,
ambient RH reached almost 80% and the aerosol appeared
to be more acidic which influenced the uptake of water.
This high RH was not found in other period, i.e., for P1
and P2 ambient RH was never higher than 60% which
limited the uptake of water in aerosol. Fig S7 in the
supplementary information evidences how from period P1
to period P3 aerosol moves towards more acidic conditions
(left side of the figure) and simultaneous increases in water
content occur.
Using an aerosol density of 1.5 g cm–3 in Beijing during
winter season (Hu et al., 2012), aerosol mass concentration
from DAASS measurements could be estimated. On the
31st of March when the maximum concentration of measured
water occurred, the mass percentage of water to DAASS
measured ambient aerosol was 44.9%.
DAASS Measured Water and ISORROPIA Estimations
Using aerosol inorganic composition, temperature and
DAASS sheath RH, estimation of aerosol water content
were performed using the ISORROPIA II model. The
calculations were carried out with the metastable mode to
ensure liquid phase (Fig. 8(a)). Because concentrations in
the gas phase were not available, we used the reverse mode.
Variability in aerosol water during P2 and P3 was driven
primarily by the inorganic fraction of the aerosol. A
comparison of the time series for ISORROPIA modeled
water and DAASS measured water shows how they progress
similarly (Fig. 8(a)). With further analysis we found that
liquid water is better correlated with nitrate. Nitrate is more
hygroscopic than sulfate (Kreidenweis et al., 2008) and was
the main inorganic component during the sampling period
with an average molar ratio NO3–/SO42– = 4.5. Nitrate is
expected to increase its importance not just in China but
worldwide because of the generalized trend in policies aimed
at reducing SO2 emissions and the increasing emissions of
NOx itself (Hodas et al., 2014).
For period P1 when the larger fraction of aerosol is
mainly of OA there seems to be no similarity with the
estimations from the model. To infer the uptake of the
inorganic fraction independently, ISORROPIA model does
not take into account the interaction of the inorganic aerosol
Fajardo et al., Aerosol and Air Quality Research, 16: 1152–1164, 2016 1161
Fig. 8. DAASS measured aerosol water (blue dots) vs. time. (a) Aerosol water as calculated from ISORROPIA II (brown
line) is better tracked by DAASS water during the polluted periods. (b) DAASS water and HOA fraction vs. time. Days
with low aerosol inorganic fraction present coincident peaks of measured water and HOA.
with the organic fraction on the uptake of water. Although
the impact of organics on water uptake is largely unknown
(Pajunoja et al., 2015), the important fraction that OA has
on the total aerosol mass reported from the ACSM (specially
during the low polluted conditions of P1) may influence
the total amount of water on the aerosol. Fig. 8(b) indicates
that during P1 the DAASS measured water content has
almost identical trend with that of HOA concentration.
Therefore, we evaluated the correlation between the organic
and inorganic aerosol fraction of the aerosol for P1 with
liquid water. Using a simple linear regression model we found
that the measured aerosol water was similarly correlated
with nitrate (R2 = 0.31) and organics (R2 = 0.28) and less
with sulfate (R2 = 0.15). This analysis suggests that organicassociated water content is comparable with that of nitrate
for the cleaner period P1 at low RH conditions.
CONCLUSIONS
Continuous measurements of ambient aerosol water
content were conducted in Beijing using a home-made
DAASS system. This system was calibrated using laboratory-
generated aerosol prior to its deployment. The campaign
was from March 20th to April 2nd in 2015. Three periods
were distinguishable based on aerosol mass concentration
and chemical composition, allowing the study of aerosol
water content in clean and polluted days. Air mass back
trajectories explain the pollution characteristics and ambient
conditions of three identified periods. Sampled ambient
aerosol was often alkaline, emphasizing the relevance that
ammonia emissions have currently in the local aerosol
chemistry and therefore on the uptake of water by the aerosol.
Although the calculated VGF showed a large variability,
particularly in the range of 60-80% RH, a visible variation
in the trend of the mean and median VGF was considered
as an efflorescence behavior for the urban sampled aerosol
at ~50% ambient RH. A similar behavior was also reported in
other urban measurements. In general, low amounts of
liquid water were observed during the campaign. Between
measured DAASS liquid water and ISORROPIA II modeled
water, better agreement occurs during the polluted periods
(Fig. S9). When the aerosol showed to be more acidic the
measured aerosol water content increased (Fig. S7). For
the cleaner days the amount of water showed to be similarly
1162
Fajardo et al., Aerosol and Air Quality Research, 16: 1152–1164, 2016 correlated with nitrate and organics, and less with sulfate.
Yet still requiring some adjustments for a more rapid
measurement, the present instrument offers an attractive
alternative for an unattended and continuous method to
evaluate local aerosol water content at ambient conditions.
ACKNOWLEDGMENTS
The authors would like to thank to Zhou Wei, Prof. He
Kebin, Prof. Qiang Zhang for providing the ACSM
instrument and helpful discussions about aerosol chemistry,
also to Profs. Daren Chen and Brent Williams for their
insightful comments during the preparation of this manuscript.
Fajardo O. would like to thank Prof. A. Nenes for his
advice on the execution of ISORROPIA model in a python
environment. Supports from Qiang Zhang to develop DAASS
is appreciated. This study was supported by National Key
Basic Research and Development Program of China
(2013CB228505), Key Technologies R&D Program of China
(2014BAC22B01), National Natural Science Foundation of
China (21190054, 41227805, and 21422703).
SUPPLEMENTARY MATERIALS
Supplementary data associated with this article can be
found in the online version at http://www.aaqr.org.
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Received for review, October 5, 2015
Revised, January 4, 2016
Accepted, January 29, 2016