Introduction
Formation of the ice phase in clouds can be modulated by aerosols emitted from anthropogenic and natural sources (Morris et al., 2014;
Murray et al., 2012; Rosenfeld et al., 2008) via heterogeneous ice nucleation (Pruppacher et al., 1998). This results in a significant
impact on the extent, lifetime, formation of precipitation and radiative properties of clouds (DeMott et al., 2010). Currently,
four mechanisms are proposed for heterogeneous ice nucleation in mixed-phase clouds: deposition ice nucleation, condensation
freezing, immersion freezing and contact freezing (Vali et al., 2015; Hoose and Möhler, 2012). It is under discussion
whether condensation freezing is different from immersion freezing on a fundamental level (Wex et al., 2014) and whether at least some of the
observed deposition ice nucleation can be attributed to pore condensation and freezing (Marcolli, 2014). For mixed-phase clouds,
immersion freezing has been widely reported to be the most important ice nucleation mechanism (Ansmann et al., 2008; Murray et al.,
2012; Westbrook and Illingworth, 2013). During the past decades, great efforts have been dedicated to understanding heterogeneous ice
nucleation. However, it has become obvious that many fundamental questions in this field are still unsolved (Kanji et al., 2017).
Numerous studies have attempted to quantify the ice nucleation ability of selected aerosol particles of a specific composition in
immersion mode, such as dust (DeMott et al., 2015; Kaufmann et al., 2016; DeMott et al., 2003), marine (Wilson et al., 2015; DeMott
et al., 2016; Alpert et al., 2011) and biological particles (Pummer et al., 2012; Hartmann et al., 2013; Fröhlich-Nowoisky et al.,
2015). Szyrmer and Zawadzki (1997), Hoose and Möhler (2012), Murray et al. (2012) and Kanji et al. (2017) are all reviews which
give a more extensive overview over materials that can induce ice nucleation. In general, biogenic particles have been assumed to
provide atmospheric ice-nucleating particles (INP) which are ice-active in the immersion mode at comparably high temperatures (above
-15 ∘C, Murray et al., 2012; Petters and Wright, 2015). Ice activity at lower temperatures is attributed to mineral dust
particles (Murray et al., 2012), while the role of soot particles in atmospheric ice nucleation is still debated (Kanji et al., 2017).
Biogenic particles in general have long been known to be able to induce ice nucleation at comparably high temperatures above
-10 ∘C (e.g., Schnell and Vali, 1972). It has been widely accepted that biological particles can act as efficient INP, with
some bacteria and fungi reported to possess the ability to arouse freezing at temperatures as high as -2 to -5 ∘C
(Lundheim, 2002). Fungal spores (O'Sullivan et al., 2016; Pummer et al., 2015) and lichen (Moffett et al., 2015) are known to nucleate
ice in the temperature range above -10 ∘C, while pollen (Augustin et al., 2013; Pummer et al., 2012) may compete with
mineral dust particles in terms of their ability to nucleate ice, albeit not in terms of their atmospheric abundance.
Recognized as the dominant INP in mixed-phase clouds (Kamphus et al., 2010), particles from various mineral dusts were found to
catalyze ice formation effectively in chamber experiments (Murray et al., 2012; Kanji et al., 2017). Among mineral dust particles,
those containing K feldspar might be particularly ice-active (Atkinson et al., 2013).
In general, burning of liquid fuels produces soot particles (i.e., particles
that are mostly organic), while burning of solid material, e.g., biomass or
coal, will also produce ash
particles which contain the inorganic components that made up the fuel. Umo
et al. (2015) and Grawe et al. (2016) examined the ice activity of ash
particles from wood and coal burning in the immersion mode and both found
that these particles are ice-active. In Grawe et al. (2016), ash particles
with atmospherically relevant sizes of 300 nm were examined and the
most active particles came from a sample of fly ash from a coal burning power
plant, inducing immersion freezing below -22 ∘C. Both Umo
et al. (2015) and Grawe et al. (2016) suggest that ash particles might play
a role in the atmosphere; however, they point to a lack of knowledge of their
atmospheric abundance. Also, different ash samples showed different ice
activities, and large differences in the results between the methods used for
the examination were also described; i.e., it is still inconclusive whether
ash particles might play an important role in atmospheric INP.
Although there has been a considerable number of studies aimed at
understanding the ability of particles that contain black carbon
(BC) acting as INP, the results are still controversial. Some studies show that BC-containing
particles did not act as good INP (Schill et al., 2016; Chou et al., 2013).
Chou et al. (2013) observed that soot particles from diesel engines and wood
burning form ice at -40 ∘C, and unrealistically high relative
humidity (RH) was needed for freezing initiation above this temperature.
Schill et al. (2016) showed that neither fresh nor aged emissions from diesel
engines contributed appreciably to atmospheric INP concentrations. However,
some studies considered BC-containing particles as possible INP (Cozic
et al., 2008; Levin et al., 2016; Cozic et al., 2007). Observation of
abundant BC in ice particle residuals in field experiments suggested that
some BC-containing particles may preferentially act as INP (Cozic et al.,
2008). In the experiments conducted by Levin et al. (2016), emissions of
different types of biomass fuel produced measurable concentrations of INP
(0.1–10 cm-3) associated with higher BC concentration accounting
for about 0–70 %. Determination of ice-nucleating properties of
physically and chemically aged soot particles suggests that the heterogeneous
ice nucleation activity of freshly emitted diesel soot particles is sensitive
to some of the aging processes (Kulkarni et al., 2016).
In the atmosphere of urban areas with dense populations, various sources and complex aging transformations (such as coagulation,
condensation of vapor and chemical reactions) of particles can be found. Particularly, urban aerosol may be rich in BC-containing particles
resulting from anthropogenic activities, such as fossil fuel combustion and biomass burning (Bond et al., 2013), which were speculated
to play a role in the formation of ice in clouds (Kanji et al., 2017). However, the ice-nucleating properties of particles produced in
urban regions have rarely been the focus of previous studies. Exceptions are Knopf et al. (2010) and Corbin et al. (2012), examining
the ice nucleation activity of particles in the anthropogenically influenced atmospheric aerosol in Mexico City and Toronto,
respectively. In both studies the relative humidity at which measurements were made were below water vapor saturation (with respect to
liquid water). Using filter samples, Knopf et al. (2010) state that organic particles included in their samples might potentially
induce ice nucleation at conditions relevant to cirrus formation. Corbin et al. (2012) used a CFDC (Continuous-Flow Diffusion Chamber)
operating at -33 ∘C together with a particle mass spectrometer. Statistical limitations impeded a statistical sound
analysis, but their data suggest that dust particles, particles from biomass burning and particles containing elemental carbon might
be sources of INP in their experimental conditions. They explicitly encourage further studies of these particle types concerning their
role as possible INP.
In the present study, we measured the ice-nucleating activity of urban aerosols in parallel with BC and PM2.5 mass
concentration and particle number concentrations in the atmosphere of the megacity Beijing, which is frequently experiencing heavy
pollution. During heavy haze episodes, PM2.5 mass concentration can be several hundred micrograms per cubic meter and
composed of a complex mixture of different chemical components (organic matter, inorganic ions and black carbon) (Zheng et al.,
2016). The goal of this project is to find out whether anthropogenic sources which are dominant in the urban atmosphere significantly
contribute to the local INP concentrations, focusing particularly on the ice-nucleating ability of BC in urban aerosols.
Materials and methods
Sample collection and particle number measurement
The sampling site for particle collection was on the roof of a six-floor
building (about 30 m above ground level) on the campus of Peking University
(39∘59′20′′ N,
116∘18′26′′ E), located in the northwestern
urban area of Beijing.
Particles with an aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5) were collected on quartz fiber
(Whatman, 1851-865) and PTFE filters (Whatman, 7592-104) using a 4-channel sampler with 2.5 µm impactors from 27
November 2016 to 1 December 2016 and 13 December 2016 to 22 December 2016. Daytime filters were collected from 08:00 to
20:00 and nighttime filters were collected from 20:00 to 08:00 with an air flow rate of 16.7 Lmin-1,
resulting in a total volume of air sampled on each filter of ∼12000 L. Note that all sample volumes used herein were
converted to standard volumes. The quartz filters were treated before the sampling by heating them to 550 ∘C for
6 h. After sampling, all filters were kept at ≤-18 ∘C during storage, and the INP analysis was done within
20 days, starting on 5 February 2017.
A scanning mobility particle sizer (SMPS, TSI Inc., USA) system was used to
obtain particle number distribution in the 3–700 nm (electrical
mobility diameter) size range during the sampling period, while an
aerodynamic particle sizer (APS, TSI model 3321, TSI Inc., USA) measured
particle number size distributions between 800 nm and
2.5 µm (aerodynamic diameter). The APS results were transformed
from aerodynamic diameter to Stokes diameter with a particle density of
1.5 gcm-3, which was measured by a CPMA (centrifugal particle mass analyzer) and combined with the
measured and inverted size distributions obtained from the SMPS. From these
combined size distributions, we calculated the total particle number
concentration of particles in the diameter range from 3 nm to
2.5 µm (Ntotal) and number concentrations of
particles larger than 500 nm (N>500nm). When comparing
with filter results, we use 12 h average values of Ntotal
and N>500nm, where the averages were always made from 08:00 to
20:00 for daytime data and from 20:00 to 08:00 for nighttime data.
N>500nm was derived, as in general larger particles are
expected to be more efficient INP, and also as this size range was selected
in DeMott et al. (2010, 2015) to serve as a base for parameterizations of INP
number concentrations.
Concentrations of BC were continuously measured by a multi-angle absorption
photometer (5012 MAAP, Thermo Fisher Scientific, Waltham, MA, USA) utilizing
a 637 nm LED as a light source (Müller et al., 2011). The
instrument measures the absorption of particles collected on a filter with
a time resolution of 5 min and automatically derives BC mass
concentration from the measurement while accounting for multiple scattering
occurring on the filter. It might be worth noting that a comparison of BC
concentrations obtained from the MAAP with concentrations of elemental carbon
(EC) determined by a filter-based SUNSET EC/OC analyzer during a different field campaign
showed that both instruments measured the same trends, while the mean ratio
of concentrations of BC to EC was about 1.35.
Chemical analysis
Two PTFE filters
were always sampled in parallel, and while one was used for INP analysis, the
other was selected for the total mass and water-soluble ion analysis.
PM2.5 mass concentration was obtained with an analytical balance by
the gravimetric method (Mettler Toledo AG285) (Yang et al., 2011). As for
water-soluble inorganic compounds analysis, Guo et al. (2012) described the
method for seven major ions (K+, Mg2+, Ca2+,
NH4+, NO3-, SO42- and Cl-) measured by
an ion chromatograph (DIONEX, ICS-2500/2000) based on the usage of
PTFE filters.
Post-sampling, all filters were stored in the refrigerator at
-18 ∘C before analysis.
INDA and LINA analysis
Two devices called INDA (Ice Nucleation Droplet Array) and LINA (Leipzig Ice Nucleation Array) have been set up at the Leibniz
Institute for Tropospheric Research (TROPOS) in Germany following the design described in Conen et al. (2012) and in Budke and Koop
(2015), respectively. INDA was used to investigate the immersion freezing properties of the quartz fiber filter samples, while LINA was
used to test the particles on PTFE filters.
INDA consists of a thermostat (JULABO FP40) with a 16 L cooling bath.
A total of 96 circles (1 mm in diameter each) of each quartz filter
were cut out by a punch and immersed separately in the tubes of a PCR
(polymerase chain reaction) tray which each contained 50 µL
distilled water. While Conen et al. (2012) originally used separate Eppendorf
Tubes®, the use of PCR trays for immersion freezing
studies was suggested before in Hill et al. (2016) and was adapted in the
LINA setup. The PCR trays were placed on a sample holder and exposed to
decreasing temperatures with a cooling rate of approximately
1 Kmin-1 in the cooling bath down to -30 ∘C.
Real-time images of the PCR trays were recorded every 6 s by a CCD
(charge-coupled device) camera. A flat light that was fixed at the bottom of
the cooling bath helped to yield proper contrast between frozen and unfrozen
droplets on the recorded pictures, so that frozen droplets could be
identified according to the brightness change during the freezing process.
A program recorded the current temperature of the cooling bath and related it
to the real-time images from the CCD camera. The temperature in the PCR trays
had been calibrated previously as described in Sect. of the
Appendix.
For the measurement of ice-nucleating particles at lower temperature, LINA
was built according to an optical freezing array named BINARY, which was
described in detail by Budke and Koop (2015). PTFE filters collected during
the same period as quartz fiber filters were used for LINA. Half of the PTFE filter of each day was immersed
in 10 mL distilled water and shaken for 1 h to wash particles
off. For each measurement, 90 droplets with the volume of 1 µL
were pipetted from the resulting suspension onto a thin hydrophobic glass
slide (diameter 40 mm, thickness 0.13–0.16 mm, obtained from
Marienfeld-Superior), with each droplet being contained in a separate
compartment. These compartments were round holes with diameters of
3 mm, drilled into an aluminium plate with a diameter of
40 mm and a thickness of 14 mm. Both the hydrophobic glass
slide and the aluminium plate with the compartments were surrounded by an
aluminium ring with an inner diameter of 40 mm, which acted to keep
the glass slide and aluminium plate in place. The slide, plate and ring were
all arranged before the droplets were pipetted. A second thin glass slide was
put on top of the plate so that the compartments were all separated from each
other and evaporation of the droplets was prevented. The droplets were cooled on a Peltier element
with a cooling rate of 1 Kmin-1. There was a thin oil (squalene)
film between the hydrophobic glass slide and the Peltier element for optimal
heat conductivity. The temperature on the glass slide had been determined
previous to the experiments as described in Sect. 1.2 of the Appendix, and
the temperature shift between that set on the Peltier element and that
observed on the glass slide was accounted for in the data presented herein.
The freezing process again was recorded by taking pictures with a CCD camera
every 6 s and detecting the freezing based on a change in the reflectance of
the droplets upon freezing.
As mentioned above, the temperature calibration for these two instruments is described in detail in Sects. and
of the Appendix. The background freezing signal of pure distilled water and circles cut from clean filters were tested as
well. These results are shown in Sect. of the Appendix.
The measurements resulted in frozen fractions (fice) as defined in Eq. ():
fice=NfrozenNt,
where Nfrozen is the number of frozen tubes or droplets at a certain temperature and Nt is the total number of tubes
in PCR trays (i.e., 96) or droplets on the slides (i.e., 90).
The temperature-dependent cumulative number concentration of INP (NINP) per volume of sampled air was calculated according
to Eq. (), similarly to Vali (1971) and Conen et al. (2012):
NINP(T)=-ln1-ficeTVsampled,
where Vsampled is the volume of air
converted to standard conditions (0 ∘C and 1013 hPa) from
which the particles were collected that were suspended in each of the
droplets in LINA or that were collected on each filter punch used for INDA
measurements, respectively.
The chemical ion analysis in Sect. 3.1 and the determination of the PM2.5 mass concentration was done at Peking
University. The filters used for INP measurements were brought to TROPOS where the INP measurements were then done. Filters were
continuously cooled below 0 ∘C in a portable ice box during transport.
Results and discussion
Severe PM2.5 pollution in Beijing
Figure 1 shows the time series of PM2.5 mass concentrations and chemical composition during the sampling period. The
PM2.5 mass concentration with a mean value of 97.30±77.9 µgm-3 ranged from 6.54 µgm-3
up to 273.06 µgm-3. Here, the cases with PM2.5 above 50 µgm-3 were defined as polluted
days, whereas the rest were defined as clean days. On average, the sulfate, nitrate and ammonia (SNA) accounted for around 35 % of
PM2.5 during the whole period with an obvious enhancement on polluted days (53 %), indicating that generation of
secondary particulate mass is one major contributor to the formation of particulate pollution, as has previously been described in
Guo et al. (2010) and Zheng et al. (2016). In this study, when we refer to secondarily formed particulate matter, this will always mainly
stand for SNA and secondary organic substances. Dust particles are in the coarse mode and only contribute little to the total
PM2.5 load (Lu et al., 2015; Li and Shao, 2009). In these studies, Ca2+ as a tracer for dust particles showed a low
proportion in PM2.5, suggesting that the dust particles also only contributed little to PM2.5 during our
observations as well.
The time series of PM2.5 concentrations and chemical composition. Data are shown for 15 different days; the
dates are indicated in the x axis labeling and “D” and “N” stand for daytime and nighttime, respectively.
The 2-day back-trajectories obtained by the NOAA HYSPLIT model color-coded with respect to PM2.5 mass
concentration determined by PTFE filters.
Minutely recorded data for wind direction and wind speed color-coded with respect to PM2.5 mass concentration.
During the sampling period, BC mass concentrations varied from 0.50 µgm-3 on clean days up to
17.26 µgm-3 on polluted days. On average, the mean mass concentration of BC, 7.77±5.23 µgm-3,
accounted for about 13 % of PM2.5. During nighttime, BC concentrations were higher than those during daytime due to
stronger diesel engine emissions and a lower boundary layer (Guo et al., 2012). Our previous studies showed that secondarily and
primarily formed organic particulate matter contributed to around 36 % of non-refractory PM1 detected by an aerosol mass
spectrometer during wintertime in the atmosphere of Beijing (Hu et al., 2017).
Additionally, Fig. 2 shows 2-day back-trajectories obtained by the NOAA HYSPLIT model, with one trajectory related to each sampled
filter, starting at the median sampling time of each filter. Figure 3 shows minutely recorded data for wind direction and wind speed
collected by an Auto weather station (Met One Instruments Inc.) located on the same roof top as the aerosol sampling equipment. Both
pictures are color-coded with respect to PM2.5 mass concentrations. The air masses that came from north or northwestern
directions were generally coincident with higher wind speeds. They brought clean air with lower PM2.5 mass
concentrations. They did cross desert regions; however, Beijing was reported to be affected by desert dust mainly only in spring (Wu
et al., 2009). Typically, the air masses coming from the south and southwest of Beijing moved slowly and spent much more time over
industrialized regions, resulting in high particulate matter mass concentrations. This pattern observed here is typical for Beijing,
and these connections between wind direction and pollution levels in Beijing have been analyzed in detail previously in Wehner
et al. (2008).
The time series of Ntotal, N>500nm and 12 h average N>500nm at -16 ∘C.
Particle number concentrations
Figure 4 shows the time series of the total number concentration of particles from 3 nm up to 2.5 µm
(Ntotal) and the number concentration of particles larger than 500 nm (N>500nm), where
Ntotal varied from 3×103 to 7×104 cm-3 and N>500nm varied from 10 to
4×103 cm-3. Obviously, in the atmosphere of Beijing during the sampling period, small particles less than
500 nm account for a large faction of the total particle number concentration, but during strong pollution events, a large
increase in N>500nm is also seen.
NINP as a function of temperature. Panel (a) and (b) show INDA results colored by PM2.5
mass concentration and 12 h average N>500nm, and panel (c) and (d) show 10 comparable results of INDA
and LINA colored by PM2.5 mass concentration and 12 h average N>500nm. Dotted lines represent LINA
results, while solid lines represent INDA results.
Figure 5a and b show INP number concentrations (NINP) as a function of temperature for INDA measurements. The lines are
color-coded depending on the PM2.5 mass concentration (Fig. 5a) and 12 h average N>500nm (Fig. 5b)
during the respective filter sampling, where each line (30 in total) represents an individual result of a filter. Exemplary measurement
uncertainties are given in Sect. of the Appendix. All filter samples had INP that were active at -12.5 ∘C and the highest
freezing temperature was observed to be -6 ∘C. Overall, NINP varied from 10-3 to 1 L-1. Already
at a first glance, there is no clear trend in NINP with PM2.5 mass concentration and 12 h average
N>500nm, indicating that the dominant pollutants of urban atmosphere may not significantly contribute to INP active down
to roughly -16 ∘C in an urban region.
To verify the results observed in INDA at lower temperatures, PM2.5
collected by PTFE filters in the same period was used for LINA which can test
the ice-nucleating properties of droplets down to below -20 ∘C.
Washing particles off from the PTFE filters was more complete for some
filters than for others. This was evident in varying large deviations in
NINP from INDA and LINA measurements in the overlapping
temperature range, where results determined from INDA were always similar to
or higher than those from LINA, as particle removal by washing the filters
was frequently incomplete. It is mentioned in Conen et al. (2012) that
a quantitative extraction of particles from quartz fiber filters was not
possible without also extracting large amounts of quartz fibers. We tried to
overcome this issue by using PTFE filters, as degradation of the PTFE filter
during washing does not occur due to the hydrophobic properties of the filter
material. But we observed that not all particles were released into the water
during the washing procedure (likely those collected deep within the filter),
as filters frequently still looked greyish after washing, independent of the
washing procedure (we experimented with different washing times of up to 4 h
and with the use of an ultrasonic bath).
For our INDA measurements, punches of quartz filters were measured after they
were immersed in water, representing the ice-nucleating properties of all
particles collected (Conen et al., 2012). However, as already mentioned
above, NINP derived from LINA measurements were lower than from
INDA, due to particles that did not come off during washing. Based on our
observations, we cannot recommend the use of sampling on PTFE filters
followed by particle extraction in water. But we still decided to select
those data from LINA measurements that showed the lowest deviation to the
respective INDA results in the overlapping temperature range for use in this
study. After calculating the deviation between INDA and LINA results,
represented as the factor (NINP of INDA/NINP of
LINA), 10 LINA measurements from different days were selected to be used. For
these measurements, the factor representing the deviation was in a range from
1.3 to 4.4. These data are shown in Fig. 5c and d. The LINA data are
represented by the dotted lines and the respective INDA data from the same
sampling periods are represented by solid lines. In the temperature range
from -20 to -25 ∘C, results of LINA also show no clear trend in
NINP with PM2.5 mass concentration and 12 h
average N>500nm, even though a lower temperature has been
measured, extending our statement that urban pollution might not contribute to INP
down to -25 ∘C.
NINP at -16 ∘C as a function of mass concentrations of BC (a) and PM2.5 (b)
and of 12 h average values of Ntotal (c). Furthermore, we show N>500nm (d) and
NINP at -16 ∘C derived based on DeMott et al. (2010) (e) and DeMott et al. (2015) (f) for
daytime (red round symbols) and nighttime (black square symbols) samples.
Correlation of NINP with PM2.5, BC mass or particle number concentrations
There have been many studies carried out in field and laboratory experiments focusing on the ice-nucleating properties of BC particles,
however, with inconclusive results. Some held the view that BC is not an efficient ice nucleation active species (Kamphus et al., 2010; Schill
et al., 2016), whereas some described BC particles as possible INP (Cozic et al., 2008, 2007).
Coefficient of determination (R2) and a measure for the
statistical significance of the assumption of a linear correlation (p) for
the comparison of NINP at -16 ∘C with the different
parameters shown in Fig. 5.
Parameter
R2
p
BC concentration
0.003
0.79
PM2.5 concentration
0.006
0.71
Ntotal
0.005
0.73
N>500nm at -16 ∘C
0.008
0.67
NINP at -16 ∘C, based on DeMott et al. (2010)
0.005
0.73
NINP at -16 ∘C, based on DeMott et al. (2015)
0.007
0.67
Wind speed
<0.001
0.99
Here we selected NINP derived from INDA measurements at
-16 ∘C and plotted them against BC (Fig. 6a), PM2.5 mass
concentrations (Fig. 6b) and 12 h average values of
Ntotal (Fig. 6c), N>500nm (Fig. 6d) and
NINP at -16 ∘C derived from DeMott et al. (2010)
(Fig. 6e) and DeMott et al. (2015) (Fig. 6f). To determine the latter two,
the 12 h averages of N>500nm shown in Fig. 4 were used,
following parameterizations suggested by DeMott et al. (2010, 2015). Linear
fits are included in all panels of Fig. 6, and values for R2 and p for
these fits are shown in Table 1. Our results discussed in the following,
based on NINP at -16 ∘C, are similarly valid for all
other temperatures down to -25 ∘C.
Figure 6a–f show that there was no clear trend between NINP and any of the displayed parameters, be it BC or
PM2.5 mass concentration or any of the 12 h average particle number concentrations. Also the R2 and p values
given in Table 1 clearly show that there was no correlation between NINP and any of the examined parameters. In the urban
region of Beijing during winter, the INP could be assumed to be soot or ash particles from traffic emissions, biomass burning and coal
combustion, or to be dust particles advected from the desert regions during prevailing northern and northwestern wind, or to originate
from the biosphere. While mineral dust and biological particles are generally assumed to be the most abundant INP in the atmosphere
(Murray et al., 2012; Kanji et al., 2017), the role of particles from combustion, i.e., of soot and ash particles, in INP is still
controversial (Kanji et al., 2017). Our results indicate that BC particles did not correlate with INP concentrations in the urban
atmosphere. It is possible that the BC particles emitted from coal burning, biomass burning and traffic emissions are not ice-active
in the first place, or that they underwent atmospheric aging processes (such as coagulation, condensation upon vapor and chemical
reactions), resulting in more internally mixed particles after emission (Pöschl, 2005), which might inactivate their potential to act
as INP. In the atmosphere of Beijing, the aging timescale is much shorter than in cleaner urban environments, which was shown in Peng
et al. (2016). For example, to achieve a complete morphology modification for BC particles in Beijing, the aging timescale was
estimated to be 2.3 h, compared to 9 h in Houston (Peng et al., 2016). PM2.5 chemical composition indicated
that the BC particles may be aged and coated by secondarily formed chemical components (SNA and other secondary organic materials)
during the heavy haze episodes (Peng et al., 2016), thereby resulting in weakened heterogeneous ice nucleation activity of freshly
emitted diesel soot particles (Kulkarni et al., 2016).
However, if a possible coating was soluble, it would dissolve both during immersion freezing and during our experiments and would not
impede the ice activity of BC particles, unless it reacted chemically with an ice-active site. It has been observed that a coating did
not impede the ice activity of mineral dust particles coated with nitric acid in Sullivan et al. (2010), and Wex et al. (2014)
observed similar for particles coated with succinic acid or levoglucosan.
Another study conducted in Ulaanbaatar in Mongolia, a city suffering from severe air pollution, showed a low ice activity towards
heterogeneous ice nucleation when the sulphur content of particles was highest (Hasenkopf et al., 2016). It is interesting to note that
we observe the opposite in our study; i.e., the increase of PM2.5 mass concentration and percentage of SNA in PM2.5
during haze periods also seem to have no negative impact on INP concentrations. Not only did increased BC mass concentrations not
increase the observed INP concentrations, but also, INP concentrations were not particularly low during pollution episodes. Furthermore,
we conclude that the strong formation of secondary particulate matter during haze days would not contribute to INP. In addition, there
is no clear difference of ice nucleation between day- and nighttime samples.
The time series of measured NINP and NINP parameterized according to DeMott et al. (2010, 2015) at -16 ∘C.
The size distribution measurements show that the largest fraction of all
particles occurred in the size range below 500 nm. However, during
the strongest pollution event towards the end of our measurement period (17
December during daytime (1217D) till the night from 21 December to 22
December (1221N)), N>500nm also increased noticeably to much
larger values than before. In general, particles in this size range were also
affected by the pollution, e.g., by an increase in the size of preexisting
particles via atmospheric aging processes (such as coagulation, condensation
and chemical reactions) in which particles advected from southern industrial
areas of Beijing might also contribute. This is at the basis of the
explanation as to why the parameterizations for NINP by DeMott
et al. (2010, 2015) were not able to describe the measured values, as seen in
Fig. 6e and f. Additionally, the time series of NINP at
-16 ∘C, based on DeMott et al. (2010, 2015), are shown as
blue and green squares in Fig. 7. Also shown are values for NINP
at -16 ∘C as measured by LINA (red circles), i.e., the same values
used in Fig. 6. Mostly, the parameterization by DeMott et al. (2015) yields
larger values and a larger spread compared to the parameterization by DeMott
et al. (2010), but naturally, both follow the trends in
N>500nm. A correction factor of 3, as suggested in DeMott
et al. (2015), was not applied, as this would simply increase all respective
values by this factor; i.e., it will not change the results. Indeed, during
the pollution phase, the parameterizations overestimate the observed values
by more than 2 orders of magnitude. But also during clean phases, neither
N>500nm nor the parameterizations by DeMott et al. (2010, 2015)
correlate with NINP. In summary, this shows that pollution events
not only did not add INP, but also that for the aerosol observed during our
study, a parameterization of NINP based on particles in the size
range >500 nm is not feasible. Interestingly, as will be briefly
discussed in the next section, a much older parameterization by
Fletcher (1962) captures NINP as measured in this study rather
surprisingly well, at least within 1 order of magnitude (Fig. 8). In summary,
during polluted days, the increase of BC concentration, secondary components
(SNA) and other compounds contributing to PM2.5, as well as
particle concentrations, have no impact on INP concentrations down to
-25 ∘C in the urban region we examined in our study. This means
that anthropogenic pollution did not contribute to the INP concentration. But
it also indicates that anthropogenic pollution in Beijing did not deactivate
the present INP, as polluted periods did not show particularly low INP
concentrations, although aging and formation of secondary particulate matter
typically are intense during times of strong pollution.
NINP as derived from precipitation samples collected in Petters and Wright (2015) (grey area) and
a parameterization based on Fletcher (1962) (black line), together with our results (dark green and brownish lines from INDA and LINA
measurements, respectively).
The 2-day back-trajectories obtained by the NOAA HYSPLIT model color-coded with respect to INP concentration.
In addition to what we discussed above, also no correlation was observed between NINP and wind speed, as can be seen by the
respective values for R2 and p given in Table 1. Figure 9 indicates that there was also no correlation with wind direction. The
fact that we find no correlation with either wind speed or wind direction agrees with the desert regions towards the northwest not
being efficient dust sources in winter and is an indication that we may have observed average background INP concentrations in Beijing
during our measurements.
Additionally, also no correlation was found between any of the water-soluble constituents that were analyzed with ion chromatography
and INP concentrations. This is not too astounding, as INP make up only a small fraction of all particles, as can be seen when
comparing number concentrations from Figs. 4 and 7, and hence they make up only a small fraction of the mass, likely too small to be
detected. Furthermore, a number of different components might contribute to INP, e.g., biological INP that are generally ice-active at
higher temperatures (>-15 ∘C) and mineral dusts which are ice-active at lower temperatures; therefore one common tracer for
INP might not be applicable. As far as K is concerned, which might be connected to K feldspar containing mineral dust particles with
high ice activity (Atkinson et al., 2013), we only analyzed the water-soluble fraction; i.e., K related to feldspar would not have been
analyzed. Moreover, K is also emitted by biomass burning and hence influenced by anthropogenic pollution. It remains to be seen
whether a simple correlation between chemical constituents of the atmospheric aerosol and INP concentrations can be established at all.
Comparison of INP measurements in different regions of China,
including NINP (i.e., INP number concentrations) and
corresponding temperature.
Sampling site
Citation
Sampling date
Instruments
Temperature (∘C)
Average INP(L-1air)
Mode
Huangshan(mountain site)
Jiang et al.(2015)
Sep–Oct 2012
Vacuum water vapordiffusion chamber
-15 ∼-23
0.27∼7.02
Deposition
Huangshan(mountain site)
Jiang et al.(2014)
May–Sep 2011
Mixing cloud chamberThe static diffusioncloud chamber
-20
16.6
Deposition/condensation
Huangshan(mountain site)
Hang et al.(2014)
May–Sep 2011; Sep–Oct 2012
Static vacuum watervapor diffusion cloudchamber
-20
18.74
All modes
Tianshan(mountain site)
Jiang et al.(2016)
14–24 May 2014
Vacuum water vapordiffusion chamber
-20
11 (non-dust)Hundreds (dust)
Deposition
Nanjing(suburban site)
Yang et al.(2012)
May–Aug 2011
Mixing cloud chamberThe statistic diffusionchamber
-20
20.11
All modes
Qing Hai(plateau site)
Shi et al.(2006)
5–26 Oct 2003
The Bigg mixingcloud chamber
-15, -20, -25
23.3∼85.4
Deposition
Beijing(suburban site)
You andShi (1964)
18 Mar–30 Apr 1963
Mixing cloud chamber
-20
3.9∼4.8
All modes
Beijing(suburban site)
You et al.(2002)
18 Mar–30 Apr 1995
The Bigg mixingcloud chamber
-15, -20
21, 78.9(non-dust)604 (dust)
All modes
Beijing(urban site)
This study
27 Nov–22 Dec 2016
Ice Nucleation DropletArray
-10 ∼-28
0.001∼10
Immersion
Comparison with literature
First, we compare our results with results of NINP derived from
precipitation samples as collected in Petters and Wright (2015) as shown in
Fig. 8. These literature data were mostly collected in various locations in
North America and Europe, and none of these locations was one with strong
anthropogenic pollution, different from the sample location in the present
study. The NINP in our study varied from
10-3–10 L-1⋅air in the temperature range of -10 to
-25 ∘C. The data of this study (dark green and brownish lines) are
within the range of values given in Petters and Wright (2015), in the whole
temperature range for which INP concentrations were derived here.
A comparison with Corbin et al. (2012) and Knopf et al. (2010), who also both
examined INP in urban air in Toronto and Mexico City, respectively, is not
possible due to different examined ice nucleation modes and also because they
only measured at -34 ∘C (Corbin et al., 2012), i.e., outside of
the temperature range examined in this study, or only ice onset temperatures
were reported (Knopf et al., 2010). But we want to point out the fact that an
older parameterization based on Fletcher (1962), which has been used for
large-scale modeling, agrees well with our data (see Fig. 8) down to
-20 ∘C. It should, however, also be pointed out that the
variability that occurs in the data certainly cannot be captured by such
a single line. But the increase in NINP towards lower
temperatures as parameterized in Fletcher (1962) is similar to that of our
data; though it should also be said that this parameterization is known to
overestimate atmospheric observations at lower temperatures (roughly below
-25 ∘C; see e.g., Meyers et al., 1992). A similar observation was
recently described in Welti et al. (2017), in which the temperature trend of
NINP down to -20 ∘C derived from filter samples taken
on Santo Antão, Cabo Verde, also agreed well with the parameterization by
Fletcher (1962), while at lower temperatures, the parameterization exceeded
the measurements. In general, for the case of the Beijing air masses examined
in this study, both the range of NINP given in Petters and Wright
(2015) as well as the parameterization by Fletcher (1962) agree better with
our measurements than the parameterizations by DeMott et al. (2010, 2015).
All of this is again indicative of the fact that severe air pollution in Beijing did not increase or decrease INP concentrations above or
below values typically observed in other nonurban areas on the Earth, and hence, that the background INP concentrations, at least
down to -25 ∘C, might in general not be directly anthropogenically influenced.
Measurements of NINP in China have been done as early as 1963 by
You and Shi (1964), and a few further studies listed in Table 2 have been
carried out in recent years. Table 2 includes some campaigns completed in
different regions of China including mountains, plateaus and suburban
districts with low PM2.5 concentration and BC-containing particles.
In contrast to these observations, our study shows NINP detected
in an urban region during highly polluted days with complex particle sources.
In our study, immersion freezing was examined, while not all studies listed
in Table 2 examined this ice nucleation mode. But due to the scarcity of
data, we include the results from all these studies in our discussion here.
Apparently, compared with results in Table 2, NINP determined for
the urban site of this study (1 L-1 air at -20 ∘C) were
at the lower end of reported values, which were up to roughly
20 L-1 air at -20 ∘C for non-dust events. The highest
concentrations were observed for dust events with values up to
604 L-1air at -20 ∘C detected at a suburban site in
Beijing, showing that INP from mineral dust contribute to the overall
NINP already at this temperature (You et al., 2002). Despite the
difference among methods and ice-nucleating modes, this again suggests that
urban pollution aerosol particles might not be efficient immersion freezing
INP and that the ice-nucleating ability of particles in urban aerosols might
originate from the nonurban background aerosol particles that are included in
the urban aerosol, i.e., that INP observed in urban environments might have
the same sources among bioaerosols and dust particles as nonurban INP. An
additional contribution from urban biogenic or dust particles to the INP
observed in this study cannot be fully excluded, but the agreement between
our data and rural data presented in literature (see Fig. 8 and Table 2)
corroborates our assumption that atmospheric INP in general originate from
nonurban sources.