Introduction
Some of the least understood atmospheric processes are aerosol–cloud
interactions and, specifically, those with aerosols that serve as ice
nucleating particles (INPs) (Boucher et al., 2013). Formation
and microphysical modulation of cloud droplets and ice crystals is highly
dependent upon the types and number of aerosols that serve as cloud
condensation nuclei (CCN) and INPs. In the absence of CCN and INPs, clouds
would in theory require > 400 % humidity and < -36 ∘C to form droplets and ice crystals, respectively; conditions
atypical of mixed-phase clouds (Pruppacher and Klett, 1997).
Aerosol-induced microphysical modifications influence cloud lifetime and
albedo (Morrison et al., 2005) as well as the production of
more or less precipitation, particularly in mixed-phase cloud systems. INPs
nucleate ice through pathways dependent upon temperature, saturation with
respect to ice, and the INP type (Hoose and Möhler,
2012). The modes of heterogeneous ice nucleation include (1) condensation
freezing, whereby ice is formed concurrently with the initial formation of
liquid on CCN at supercooled temperatures; (2) immersion freezing, whereby an
INP is immersed in an aqueous solution or water droplet via activation of
CCN during liquid cloud formation; (3) contact freezing, whereby an INP
approaches the air–water interface of a droplet (e.g., via a collision) and
initiates freezing; (4) deposition nucleation, whereby ice is formed from
supersaturated vapor with respect to ice (RHi > 100 %) on
an INP directly; and (5) pore condensation and freezing, whereby water vapor
is condensed into voids and cavities followed by glaciation (Coluzza et
al., 2017; Cziczo et al., 2017; Hoose and Möhler, 2012; Kanji et al.,
2017; Marcolli, 2014; Vali et al., 2015).
Immersion freezing is the most relevant to primary ice formation in
mixed-phase clouds and requires that INPs initially serve as, or in
conjunction with, CCN, whereas deposition nucleation is prevalent in
mixed-phase and dominant in cirrus cloud ice formation (Kanji
et al., 2017). Aerosols such as mineral dust, soil dust, sea salt, volcanic
ash, black carbon from wildfires, and primary biological aerosol particles
(PBAPs) have been shown to serve as INPs (Conen et al., 2011; Cziczo et
al., 2017; DeMott et al., 1999; Hoose and Möhler, 2012; McCluskey et
al., 2014; Murray et al., 2012; Petters et al., 2009). Among these, dust and
PBAPs are the most efficient INPs found in the atmosphere (Cziczo et al.,
2017; Murray et al., 2012). Dust is the most atmospherically abundant INP,
forming ice as warm as -10 ∘C, but primarily at temperatures
< -15 ∘C (Hoose and Möhler, 2012; Murray et al.,
2012). In contrast, PBAPs are relatively rare in the atmosphere, but
can form ice as warm as -1 ∘C (Despres et al., 2012; Schnell
and Vali, 1976; Vali et al., 1976; Vali and Schnell, 1975). However,
constraining aerosol–cloud impacts in models ranging from the
cloud-resolving to climate scales, specifically when parameterizing INPs,
remains a significant challenge due to limited observations (Coluzza et
al., 2017; Cziczo et al., 2017; DeMott et al., 2010).
A number of previous ground-based field measurements dating back to the
1950s have provided noteworthy advancements in understanding the sources and
efficiencies of INPs (e.g., Bigg, 2011; Durant et al., 2008; Garcia et
al., 2012; Huffman et al., 2013; Jayaweera and Flanagan, 1982; Mason et al.,
2015; McCluskey et al., 2014; Mossop, 1963; Murray et al., 2012; Petters et
al., 2009; Prenni et al., 2009b, 2013). Further, previous
work has evaluated INP concentrations and at times composition in detritus,
soil, water from lakes and oceans, surface microlayers, and precipitation
samples to assess INP sources (e.g., Conen et al., 2016; Creamean et al.,
2014; DeMott et al., 2016; Hill et al., 2016; Irish et al., 2017; Moffett,
2016; O'Sullivan et al., 2014; Petters and Wright, 2015; Pietsch et al.,
2017; Pouzet et al., 2017; Schnell, 1977; Schnell and Vali, 1972, 1973,
1975; Stopelli et al., 2015; Tobo et al., 2014). Analysis of INPs in
precipitation samples takes a step in the direction of vertical profiling of
INPs, making the assumption that the INPs in precipitation are what
initiated ice formation in the clouds above; however, there are caveats
associated with artifacts from scavenging during raindrop or snowflake
descent, aerosolization methods, and redistribution of residue particles in
collected liquid precipitation samples (Creamean et al., 2014; Hanlon et
al., 2017; Petters and Wright, 2015).
Although observations on the ground afford detailed information regarding
the characterization of INP sources, they may not be representative of INPs
in the atmospheric column, where they have the direct ability to impact
cloud ice formation processes and may originate from a range of local to
long-range transported sources. As a result, several INP quantification and
characterization studies have been conducted in clouds at mountaintop
atmospheric research facilities, such as Storm Peak Laboratory in the United
States (Baustian et al., 2012; Cziczo et al., 2004; Richardson et al.,
2007), Puy-de-Dôme in France (Joly et al., 2014, 2013),
and Jungfraujoch in Switzerland (Chou et al., 2011; Conen et al., 2015;
Stopelli et al., 2017, 2016). Such studies provide routine
or long-term measurements of INPs in clouds, yet one disadvantage is that
profiling is not possible. Vertical profiling of INPs can serve as a
connection between the ground and various altitudes below, in, and above
cloud. Targeted aircraft campaigns have helped explain the role of INPs in
cloud ice formation at all levels from below cloud, cloud base, in-cloud,
and cloud top (e.g., Avramov et al., 2011; Creamean et al., 2013; Curry
et al., 2000; DeMott et al., 2010, 2003; Pratt et al., 2009;
Prenni et al., 2009a; Rogers et al., 2001, 1998; Schnell,
1982). Although such campaigns yield results crucial for understanding the
vertical distribution of INPs in cloudy environments, they are intensive
with regard to personnel, cost, and time.
Overall, a key gap in ice nucleation research is routine vertical profiling
of INP abundance, efficiency, and chemical and physical characterization
(Coluzza et al., 2017). Tropospheric measurements via balloon-based
systems have been a desirable means of measuring aerosol properties on an
inexpensive and, thus, more frequent basis. However, such measurements can be
limited in terms of time, measurements made, or location. For example,
long-term records of tropospheric aerosol particle size distributions have
been reported in Wyoming, United States (i.e., 20 years) (Hofmann,
1993). The same launched balloon system was deployed in Antarctica,
demonstrating the utility of this platform in multiple environments
(Hofmann et al., 1989). Particle size distributions have
also been measured via launched balloons in several locations in China using
optical particle counters (Iwasaka et al., 2003; Kim et al., 2003; Tobo
et al., 2007). One major caveat with these studies is that it is not clear
if the balloon systems were retrievable, given that their maximum flight ceilings
were located well into the stratosphere. In addition, the launched balloon
platforms provide information on one to two aerosol profiles (i.e., ascent and
sometimes descent) and are limited by payload weight. Particle spectrometers
have also been deployed and retrieved on tethered balloon systems (de
Boer et al., 2018; Greenberg et al., 2009; Maletto et al., 2003; Renard et
al., 2016; Siebert et al., 2004; Wehner et al., 2007), affording information
on aerosol layer locations and evolution by means of multiple profiles. A
few studies have deployed miniature aerosol filter samplers on launched or
tethered balloon systems, yielding information on aerosol chemistry (Hara
et al., 2011; Rankin and Wolff, 2002); however, such samplers contained one
filter per flight, thus providing information on aerosol properties at only
one altitude (i.e., not a profile). A noteworthy study by Ardon-Dryer et al. (2011) consisted of measurements of immersion mode INP
concentrations from a tethered balloon flight in Antarctica, although only
at temperatures below -18 ∘C from three filters collected below
200 m above ground level (a.g.l.). In general, tethered balloons can handle
much larger payloads than launched systems, but are limited to lower
altitudes (i.e., up to approximately 2 km a.g.l. anywhere), have wind
condition limitations, and involve more complicated logistics (e.g., use of
a winch and personnel required to operate a winch), and thus may not be ideal for
sampling INPs in all conditions. Schrod et al. (2017) present INP
measurements from several flights using unmanned aircraft systems (UASs)
over the Eastern Mediterranean, but only in the deposition nucleation mode.
To our knowledge, the results from Ardon-Dryer et al. (2011) and
Schrod et al. (2017) are the only reported vertical INP measurements
using smaller, unmanned systems. The fact that only two published studies
exist, in addition to the limitations of such studies (and our limitations
as discussed in more detail herein), demonstrates the challenges associated
with obtaining INP measurements aloft.
Both launched and tethered balloon platforms, as well as UASs, have their
advantages and disadvantages in terms of flight ceiling, profiling,
retrievability, cost, operational logistics, and payload restrictions. A
solution to reduce the limitations of these methods is a launched balloon
system that can be controlled in terms of ascent and descent, affords
multiple profiling and payload retrieval capabilities, and collects aerosol
loadings sufficient for altitude-resolved offline ice nucleation
measurements. Here, we present a measurement system called HOVERCAT (Honing
On VERtical Cloud and Aerosol properTies) deployed on an experimental
launched balloon system that possesses such capabilities.
Methods
The first prototype of HOVERCAT was recently built and tested in Colorado,
United States, consisting of an aerosol module for measuring real-time
particle size distributions and a miniaturized filter sampler for aerosol
collection for offline ice nucleation analyses. The balloon platform, called
the Boomerang Balloon Flight Control System (BBFCS), was used to fly
HOVERCAT. The current version of HOVERCAT is experimental; thus we consider
it as Phase I of its development and described it herein. As discussed
later, we provide future directions for modification and improvement of
HOVERCAT and recommendations for non-tethered balloon systems in general for
future deployments.
Components of the complete flight system, including
(a) schematic of the TRAPS, (b) picture of the aerosol
module, (c) schematic of the BBFCS, and (d) flight train
for test flights. Note that the service module on the BBFCS was separated
approximately 1 m from the ballast module with the aerosol module (i.e.,
payload) in between. The ballast module was controlled by the onboard
computer in the control module via an extended cable that ran down the tether
string. The separated BBFCS modules were housed in foam for flights.
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HOVERCAT: the aerosol instrumentation package
The aerosol module package contains (1) an optical particle counter
(Alphasense OPC-N2) for particle size distributions (16 size bins for
0.38–17 µm in diameter) and estimated particle mass concentrations
with optical diameters of ≤ 1, 2.5, and 10 µm (PM1,
PM2.5, and PM10, respectively) and (2) the miniaturized Time-Resolved Aerosol
Particle Sampler (TRAPS) built by the National Oceanic and
Atmospheric Administration (NOAA) for collection of up to 10 samples. The time
resolution can be set at the desired rate but was set for 30 min in the
current study. The OPC-N2 operates at 175 mA in operation mode and weighs
105 g. Flow rates are adjusted based on ambient pressure to maintain a
1.2 L min-1 flow using a patented “pump-less” design. Data are
stored on a microprocessor within the OPC during collection. A default
density of 1.65 g mL-1 and refractive index of 1.5 were used to
estimate particle mass concentrations. The TRAPS design is based on the
filter components of the NOAA Continuous Light Absorption Photometer (CLAP),
without the optical components and measurements (Ogren et al., 2017)
(Fig. 1a). It is connected to a small 12 V DC vacuum pump (Brailsford &
Co., Inc., TD-4X2N), which nominally enables a flow rate of approximately
1.2 ± 0.1 L min-1 through the TRAPS when a 47 mm diameter
filter with 0.2 µm pore size is in place. A Honeywell AWM43600V
mass flow meter measures sample flow rate. Ten miniature solenoid valves
select the active sample spot and are controlled by an onboard
microprocessor preselected for the desired time resolution, which was 30 min
per sample spot for the HOVERCAT test flights. The TRAPS flow rate at 30 min
provides approximately 40 total liters of air through each spot, which is
ideal for measuring more realistic INP concentrations (Mossop and Thorndike,
1966). Sample loaded spots average to a coverage area of 19.9 mm2
(equates to a spot diameter of approximately 4.46 mm). The TRAPS has the
highest collection efficiency for particles in the 1 nm–10 µm
aerodynamic diameter range – with particle losses of less than 10 % for
5 nm–7 µm particles and less than 1 % for
30 nm–2.5 µm particles at 1.0 L min-1 – but can collect
particles with larger diameters (Ogren et al., 2017).
The TRAPS, micropump, and OPC are all operated by battery: the TRAPS and
micropump run off a battery pack containing three 18 650 rechargeable Li-ion
batteries (Panasonic NCR18650B, 12 V output, 3400 mAh) and the OPC runs off
one rechargeable battery (Anker PowerCore 5000, 5 V output, 5000 mAh). The
OPC can operate for several days on its portable battery, while the TRAPS
and pump can operate for up to 5 h on its battery pack. Both the TRAPS
and OPC are connected to inlets composed of an 8 in. segment of
0.25 in. ID black conductive tubing connected to a
stainless-steel funnel (5 cm in diameter) with the opening covered with
stainless steel mesh. All components are seated in a foam enclosure with
removable lid and inlets extending out of the bottom (Fig. 1b).
BBFCS: the balloon platform
The BBFCS is a real-time remote device that allows the user to control the
ascent and descent of standard latex weather balloons (Fig. 1c). The primary
features are a lift-gas vent valve in the control module that permits
negative buoyancy adjustments and a sand ballaster (i.e., ballast module) that
permits positive buoyancy adjustments. Buoyancy adjustments as small as 5 g
of lift are possible. For example, if a faster or slower ascent is desired,
ballast can be dropped or venting can be done, respectively. If descent is
desired, a longer and faster venting is applied. Due to the ability to slow
down the fall speed by a combination of the appropriate amount of venting and
dropping ballast, if needed, landing the system is relatively gentle and did
not result in instrumental damage during the test flights. It is possible the
balloon itself can be reused (i.e., we used the same balloon for two
flights).
Two-way communication is achieved through a 70 cm line-of-sight LoRa radio
link. The system features a 1 / 4 W
transceiver that uses a low baud rate and a slow 4 s time-division multiple
access (TDMA) cycle to achieve ranges in excess of 300 km. The system also
features redundant termination methods, anti-collision strobes, positioning,
and flight sensors. A recovery parachute is included for emergency
termination and faster fall speeds than slow balloon deflation. The BBFCS was
manually controlled for this project. We utilized a software interface on a
ground-based computer to analyze the real-time flight conditions and send the
necessary buoyancy control commands to achieve the desired flight profile. We
drove in the approximate trajectory of the balloon in order to stay within
the 300 km communications range; thus we were able to physically retrieve it
when it ultimately landed. Early morning launches were conducted to maximize
the calm low-troposphere atmospheric conditions as flight control is much
easier in such conditions. Because this project entailed low-altitude flights
that did not exceed 9.6 km above mean sea level (a.m.s.l.) or approximately
8.1 km a.g.l., 300 g latex balloons were used. These relatively small
balloons, for a 3.9 kg payload, ensured that the envelope was always under
tension and would expel lift-gas whenever the vent valve was opened, while
ensuring that the burst altitude was above the expected operational altitude.
Burst altitude was calculated to be 13–14 km a.m.s.l.
(11.5–12.5 km a.g.l.) depending on how much lift gas had been vented. The
BBFCS is designed to allow Federal Aviation Administration (FAA) part 101
exempt flights, even when carrying a reasonably sized payload (i.e., total
payload weight of less than 5.5 kg and no one module greater than 2.7 kg).
Test flight details
The overall launch mass was 4250 g with 450 g of free lift to achieve an
initial 3 m s-1 ascent rate. System masses were 350 g for the balloon
and connection spindle, 900 g for the control module and parachute, 2300 g
for HOVERCAT, and 700 g of ballast. Initial flight planning called for a
five-step flight profile with 500 m altitude steps. This allocated 100 g of
ballast per step, 1.5 m s-1 anticipated ascent rate between steps, and
a 200 g reserve for the flight to help maintain the desired altitude.
However, this plan was ultimately not executed due to flight complications
discussed herein. The flight train for this project consisted, from top to
bottom: latex balloon, valve and flight computer modules, 500 mm of line,
aerosol module, 500 mm of line, and ballast module (Fig. 1d). The recovery
parachute was attached to the bottom of the flight computer module and hung
off to the side. The parachute's apex was attached to the termination clamp
and was released by this clamp during termination or by aerodynamic drag if
the balloon had prematurely burst. The OPC was started during balloon
inflation and the TRAPS and micropump were started via Bluetooth just prior
to takeoff. Two miniature cameras (Mobius Basic ActionCam with wide angle
lens) were mounted to and facing the BBFCS valve module and HOVERCAT for time
lapse photos during takeoff, flight, and landing.
Three test flights were conducted in central Colorado during 24–26 May
2017. Two of the three flights had instrument operational issues (i.e., 24
and 26 May), so only data from the 25 May flight are presented herein.
Briefly, communications were lost during the 24 May flight and, as a result,
controlling the valve and ballast modules was not possible. The system
reached 8.1 km a.g.l. and ambient pressure was too low for the TRAPS pump to
operate. The 26 May flight reached > 2 km a.m.s.l. (> 500 m a.g.l.), in which the TRAPS pump also did not operate correctly. For
both the 24 and 26 May flights, the total volume of air pulled through the
filters was 1–12 L above 2.5 km a.m.s.l. (1.1 km ), equating to loadings
too low for offline analyses (i.e., calculated INP concentrations were below
detection limits). Based on the successful 25 May flight and unsuccessful
flights on 24 and 26 May, we have concluded that in its current
configuration, HOVERCAT can operate below 2.5 km a.m.s.l., otherwise at the
low pressures, the current micropump cannot generate sufficient flow. New,
higher volume pumps are being tested.
The three-dimensional flight path for 25 May is shown in Fig. 2. The
horizontal distance between launch and landing was 16.8 km directly.
Conditions were partly cloudy with surface air temperatures ranging from 16
to 21 ∘C, relative humidity from 35 to 47 %, and wind speeds
from 2 to 3 m s-1 from the north and south (hourly meteorological data
during flight times obtained from the Colorado Department of Public Health
and Environment (CDPHE) at the Boulder Reservoir ground site;
40.07∘ N, 105.22∘ W;
https://www.colorado.gov/pacific/cdphe/data, last access: May 2017).
HOVERCAT did not fly through the clouds present that day, but remained below
cloud base, based on visual identification of the system while tracking in
real time (i.e., the system was always in the line of sight).
Offline ice nucleation analyses
Drop freezing assay for immersion mode ice nucleation
For the 25 May flight, aerosol samples were collected on 47 mm filters
(Pallflex® EmFab™). Pre-treatment of the filters
by means of a 6 N nitric acid bath (Certified ACS Plus, Fisher Scientific),
three rinses with ultrapure water (UPW;
Barnstead™ Smart2Pure™ 6 UV/UF), and baking at 150 ∘C for 30 min was conducted to remove possible filter INP artifacts. Out of the
filters tested, EmFab™ possessed the lowest contribution from
artifacts compared to cellulose nitrate and polytetrafluoroethylene and
survived the pre-treatment process.
Four-dimensional flight path of HOVERCAT during the 25 May 2017 test
flight, colored by time in mountain daylight time (MDT). Black lines between
data points indicate missing GPS data, which occurred between 07:01–07:07
and 07:23–07:51 MDT. Meters a.g.l. were calculated by subtracting 1490 from
m a.m.s.l. to roughly show the altitude above ground.
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Immersion mode freezing was tested using a drop freezing assay (DFA) cold
plate apparatus. This cold plate technique was based on previous but slightly
modified apparatuses (Hill et al., 2016; Stopelli et al., 2014; Tobo, 2016;
Wright and Petters, 2013). For brevity, we call this system the NOAA drop
freezing cold plate (DFCP). Following collection and prior to analysis,
sample filters were stored frozen for approximately 6 months. After
removing from the freezer, each sample spot was carefully cut and separated
from the 25 May filter; six spots (i.e., samples) were successfully collected
before the battery died. Each spot was placed is a 29 mL sterile
Whirlpak® bag with 2 mL of UPW to resuspend
particles deposited on the filter. The bags were sealed and shaken at 500 rpm
for 2 h (Bowers et al., 2009). Copper discs (76 mm in diameter, 3.2 mm
thick) were prepared by cleaning with isopropanol (99.5 % ACS Grade,
LabChem. Inc.), then coated with a thin layer of petrolatum (100 %,
Vaseline®) (Bowers et al., 2009; Tobo, 2016).
Three of the spots on the filter had visible aerosol deposits that were
successfully transferred to the UPW (i.e., based on visual identification).
Following sample preparation, a sterile, single-use syringe was used to draw
0.25 mL of the suspension and 100 drops were pipetted onto the
petrolatum-coated copper disc, creating an array of ∼ 2.5 µL aliquots. Drops were visually inspected for size; however, it
is possible not all drops were the same exact volume, which could lead to a
small level of indeterminable uncertainty. However, previous studies have
elucidated that drops need to be orders of magnitude different in volume to
significantly perturb the freezing temperature from drop size alone
(Bigg, 1953; Hader et al., 2014; Langham and Mason, 1958). The copper
disc was then placed on a thermoelectric cold plate (Aldrich®) and covered with a transparent plastic dome. Small holes in the side of
the dome and copper disc permitted placement of up to four temperature
probes using an Omega™ thermometer/data logger (RDXL4SD). The
Omega™ meter has a 0.1 ∘C resolution and accuracy of
±(0.4 % + 0.5 ∘C) for the K sensor types used.
During the test, the cold plate was cooled at 1–10 ∘C min-1 from room temperature until all drops on the plate were frozen
or until the DFCP detection limit of approximately -32 to -33 ∘C. Control experiments with UPW at various cooling rates within
this range show no discernible dependency of drop freezing on cooling rate,
akin to previous works (Vali and Stansbury, 1966; Wright and Petters,
2013). Frozen drops were detected visually, but recorded through software
written in-house, providing the freezing temperature and cooling rate of
each drop frozen. For the control experiments with UPW, some experiments
resulted in unfrozen drops at the DFCP lower temperature limit; thus, the
fraction frozen was calculated from the number of drops detected, including
the unfrozen remaining, which is the reason why not all fractions frozen equal 1. However, all drops froze for tests with blanks for the sample handling
and the samples themselves. Each sample was tested three times with 100 new
drops for each test. From each test, the fraction frozen and percentage of
detected frozen drops were calculated. The results from the triplicate tests
are then binned every 0.5 ∘C to produce one spectrum per sample.
Although the methodology behind DFA is well established, control experiments
were conducted with UPW for full system characterization of the DFCP. First,
temperature differences were measured within the range of cooling rates
using UPW on petrolatum-coated copper discs between the center of the disc
(thermocouple inserted in a small diameter hole in the side of the disk) and
a drop on top of the plate with a thermocouple inserted into the drop
(Fig. 3). As expected based on previous work (Vali and Stansbury, 1966;
Wright and Petters, 2013), there was no dependence of the temperature
difference on cooling rate, but on average the drop temperature was
0.33 ± 0.15 ∘C warmer than the center of the plate. Thus, a
+0.33 ∘C correction factor was added to any temperature herein
and an uncertainty of 0.15 ∘C was added to the probe accuracy
uncertainty.
Second, various hydrophobic coatings with UPW were tested for the best
combination of materials to use with the least influence from artifacts
(Fig. 4). Materials tested were chosen based on those used in previous
work and included (1) direct petrolatum (Tobo, 2016), (2) 15 % w/v
petrolatum in xylenes (Certified ACS Reagent Grade, Ricca Chemical)
(Bowers et al., 2009), (3) silicone fluid (710 fluid, Dow
Corning®) (Polen et al., 2016), and (4) squalene
(≥ 98 %, Sigma-Aldrich®) (Hader et al., 2014;
Wright and Petters, 2013; Wright et al., 2013). The silicone fluid was
difficult to use for cold plate experimentation because droplets would
coalesce during the experiment and freezing detection by eye was difficult
due to the glare of the substance. Squalene was less viscous than the
silicone fluid, inducing more drop coalescence but freezing detection was
easier than the silicone fluid. Both materials remained in the fluid state
and
thus are not ideal for direct cold plate use, but have been proven suitable
for cold stages that use covered sample dishes or trays and smaller drop
sizes (Hader et al., 2014; Polen et al., 2016; Wright and Petters, 2013;
Wright et al., 2013). The petrolatum and xylenes solution creates a thin
layer of petrolatum after drying to evaporate the xylenes and alleviate the
coalescence problem; however, as evidenced by the freezing spectra in Fig. 4, this is not the best option in terms of limiting artifacts. To summarize, a
hydrophobic coating is needed on the copper plate and the option with the
least influence from contaminants is direct petrolatum smeared onto the
plate using UPW.
Histogram of temperature differences between measurements from a
probe at the center of the copper plate and drop on top of the plate coated
with petrolatum colored by cooling rate. The 1 s data are from three
different tests. The average difference used for the temperature correction
was 0.33 ± 0.15 ∘C.
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Lastly, the effect of drop size was tested using UPW and petrolatum-coated
copper plates (Fig. 5). Normally, 2.5 µL drops are created by hand
using a sterile syringe. Because such drops are created without the use of a
pipette, possible small variations in drop volume may occur. The same volume
drops were created with a pipette and sterile tips and tested against
syringe drops. Additionally, tests with 1.5 and 5.0 µL
drops were conducted to evaluate the effects of larger changes in volume.
One major caveat with the pipette technique is that it takes substantially
more time to create the arrays of 100 drops (approximately 5 times slower
than the syringe method). Overall, the best method in terms of onset
freezing temperatures and fraction frozen was the 2.5 µL drops
created via syringe. This test was comparable in terms of fraction frozen to
the 1.5 µL drops colder than -21 ∘C. One possible
explanation for the higher onset temperature and higher concentrations of
impurities in the 2.5 µL pipetted drops as compared to the
2.5 µL syringed drops is contamination from the pipette tips. The
5.0 µL test demonstrated that drops of this size are too large such
that they induce freezing at warmer temperatures and are subject to large
variability – in theory, the larger the drop volume, the larger the
abundance of impurities within a single drop that may facilitate ice
formation (Bigg, 1953). Overall, our drop size tests demonstrate the
efficiency and reliability of 2.5 µL drops created via syringe.
Freezing spectra for the control experiments conducted to
characterize the DFCP system. Results included here are tests evaluating the
most proficient hydrophobic coating with blank UPW drops. Error bars for the
y and x axes correspond to standard deviation per 0.5 ∘C bin and
temperature probe–plate versus drop variability standard deviation,
respectively. Spectra that do not reach a frozen fraction of 1 indicates not
all drops froze at the lower limit of the DFCP. The inset shows an example of
the appearance of frozen versus unfrozen 2.5 µL drops on the copper
disc.
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Freezing spectra for the control experiments conducted to
characterize the drop size chosen for DFCP analysis. Results included here
are tests evaluating pipetted versus syringe-aliquoted drops and at different
volumes. Error bars for the y and x axes correspond to standard deviation
per 0.5 ∘C bin and temperature probe–plate versus drop variability
standard deviation, respectively. Spectra that do not reach a frozen fraction
of 1 indicates not all drops froze at the lower limit of the DFCP.
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Freezing spectra for the three tests of each of the samples
collected from HOVERCAT during the 25 May 2017 test flight. Each data point
is colored by cooling rate and has error bars associated with Omega™
temperature probe uncertainty. The percentage of recorded frozen drops is
provided for each sample.
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Out of the 100 drops for each test, 95 ± 5 % on average (ranging
between 84 and 100 %) were detected as frozen and recorded from all tests
(Fig. 6). Some of the tests within the same sample were reproducible
within error, demonstrating the reliability of the method (e.g., samples 1
and 3). However, variability from test to test within the same sample could
occur due to (1) detection of rarer INPs at specific temperatures during 1–2 of the tests or (2) uncertainties arising from instrumental artifacts,
such as contamination between tests. These results demonstrate the
importance of running triplicate (or more) tests for DFA techniques – to
capture some of the rarer INPs that may exist in the samples or account for
test-to-test variabilities. Such rarer INPs may be missed or over accounted
for if only one test is conducted. The cooling rate was variable during each
test but maintained within the 1–10 ∘C min-1 range and
the fraction frozen did not show a noticeable dependence on the cooling
rate, as discussed above.
From the fraction of drops frozen and the known total volume of air per
sample, we calculated the estimated INP concentration (L-1 of air) with
the universally applied equation by Vali (1971):
INP(L-1)=ln(1-f)Vdrop×VsuspensionVair,
where f is the proportion of droplets frozen, Vdrop is the volume of each
drop, Vsuspension is the volume of the suspension (i.e., 2.5 mL for the
sample tests), and Vair is the volume of air per sample. We averaged the
total volume of air from the six field samples collected and applied that to
the equation to calculate INP concentrations for the blanks in order to
conduct a direct comparison and evaluate the INP concentrations in the
samples relative to the blanks.
Raman microscopy for deposition mode ice nucleation
Depositional ice nucleation was measured using a Nicolet Almega XR
Dispersive Raman Spectrometer outfitted with a Linkham THMS600 environmental
cell and a Buck Research CR-1A chilled-mirror hygrometer. The Raman
spectrometer was coupled with an Olympus BX51 research-grade optical
microscope with 10×, 20×, and 50× magnification abilities. The environmental
cell and CR-1A hygrometer allow for temperature control and dew–frost point
measurements to back calculate saturation ice ratios, Sice. The
environmental cell was connected to two UHP-grade N2 tanks: one is
humidified and the other is a “dry” tank that is not humidified. These two
were then mixed and fed through the environmental cell; lastly, the CR-1A
measures the dew and frost point. In these experiments, the water vapor was
kept constant while the temperature was decreased, which resulted in an
increase in Sice. This experimental setup, calibration, and
calculation are explained in more in detail in Baustian et al. (2010), Schill and Tolbert (2013), and Primm
et al. (2017).
An aliquot of the solutions from the previous immersion mode experiments
was used for deposition mode ice nucleation experiments (i.e., untested
sample solution). The solution derived from each spot on the collected
filter sample was nebulized onto a fused silica disc, which was then placed
into the environmental cell at ∼ 0 % RH to allow for
evaporation of water from the particles. The temperature was then decreased
at a rate of 0.1 K min-1, while water vapor was held constant.
Temperature and dew point were recorded during the entire experiment.
Sice was determined from the temperature and dew point where ice was
first visually identified. The different Sice values at different
temperatures were determined by performing the same procedure, but changing
the starting water vapor pressure. This difference in water vapor pressure
changes the Sice value at different temperatures. Temperatures which
were analyzed for depositional ice nucleation were chosen to cover a wide
range of those previously reported and relevant for several cloud regimes
(Hoose and Möhler, 2012). Nebulization onto the disc
resulted in 5000–10 000 particles, with a range of 1 to 50 µm in diameter, deposited on the surface depending on the spot from
the filter paper. Of the particles that nucleated ice, three to five particles were
analyzed for composition using Raman spectrometry for each sample. Because
the purpose of the analysis was to prove that particles could be analyzed
for depositional ice nucleation using samples collected by HOVERCAT, only
the first few particles that formed ice at each temperature regime were
recorded. A more statistical approach (i.e., analyzing more particles) to
characterize the depositional INP population during the flight is outside
the scope of this paper.
Time series of TRAPS total volume per sample (L; of air), OPC number
concentrations (cm-3), altitude (m a.g.l.), and estimated particulate
mass (PM) concentrations from the OPC (µg m-3). The width of
the TRAPS total volumes corresponds to the collection time per sample (i.e.,
30 min).
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Results and discussion
Operation of HOVERCAT instruments during test flight
Although the ability to control the exact altitude of the system was
difficult due to vertical winds – which was determined by abrupt ascent or
descent and horizontal transport while tracking in real time – we were able
to control gas venting and dropping ballast to slow down ascent and descent
and sample at altitudes from the ground level up to 2543 m a.m.s.l.
(approximately 1053 m a.g.l.) for 3 h (Fig. 7). The ability to control the
BBFCS to execute the step-wise flight plan was difficult given the winds and
the several-second delay in time when venting or dropping ballast to decrease
or increase in altitude, respectively. Minor fluctuations in BBFCS control to
maintain altitude was not possible during 25 May conditions, but may be on a
calmer day aloft. Because of such issues, the first two profiles (i.e.,
ascent followed by descent to ground) during the first hour of flight (up to
2316 and 2543 m a.m.s.l.) were abrupt and parking at desired altitudes was
not achieved. We were able to maintain altitude at
1771 ± 80 m a.m.s.l. (281 m a.g.l.) during the third profile
(08:00–09:00 MDT), with a short drop in altitude around 08:50 MDT.
Starting at 09:07 MDT, we were able to maintain altitude just above the
ground at 1536 ± 20 m a.m.s.l. (46 m a.g.l.) until 09:15 MDT, with
a final profile up to 2098 m a.m.s.l. (608 m a.g.l.) at 09:25 MDT.
Ultimately, the balloon deflated and ended the flight at 09:36 MDT.
While controlling the exact altitude of the BBFCS was difficult, the aerosol
measurements were fruitful. The OPC measured particle concentrations up to
250 cm-3 while on the ground (average of 6 cm-3), with the lowest
concentrations occurring at the highest altitudes (< 1 to 2 cm-3; average of 1 cm-3). However, episodic spikes in number
occurred when stable on the ground, indicating localized sources of high
concentrations of particles. PM concentrations followed a similar inverse
relationship with altitude (Fig. 7). The total flow though the filter in
TRAPS was fairly consistent throughout the flight, starting at 40 L for
Sample 1 and decreasing to 32 L for Sample 6. The slight decrease possibly
resulted from (1) inconsistent power supply by the battery pack to the
micropump or (2) strain on the micropump with altitude, although the latter
is less likely given the variability in altitude throughout the flight.
Cumulative INP spectra from the samples collected during the 25 May
2017 HOVERCAT test flight. Triplicate tests are binned every 0.5 ∘C.
The blanks indicate a triplicate test from UPW mixed alone in a beaker for
2 h (Blank 1), UPW mixed in a WhirlPak® bag
for 2 h (Blank 2), and an EmFab® filter
mixed in UPW in a WhirlPak® bag for 2 h
(Blank 3). The latter is closest to how the samples were prepared. Error bars
for the y and x axes correspond to standard deviation per 0.5 ∘C
bin and temperature probe–plate versus drop variability standard deviation,
respectively.
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(a) Average, minimum, and maximum altitudes HOVERCAT flew
during each sample collection time period. Error bars represent one standard
deviation. (b) The average number concentrations of total particles
from 380 nm to 17 µm in diameter measured by the OPC (left axis) and
fraction of INPs out of total OPC number at -10, -15, and -20 ∘C,
and the maximum INP concentration measured at the temperature in which the
last drop froze (right axis).
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Immersion freezing ice nucleation
From the six filter sample spots that were collected, aerosol loading was
sufficient to conduct INP measurements using the DFCP system. Cumulative INP
spectra show relatively low concentrations (i.e.,
10-2–10-1 L-1) of warm temperature INPs
(> -10 ∘C, likely of biological origin; Murray et al.,
2012) for all samples, while reaching up to 101 L-1 at
temperatures below -20 ∘C (Fig. 8). Such concentrations are within
range of those previously reported in Colorado: Prenni et al. (2013) reported
1–102 L-1 at -25 ∘C. The highest INP concentrations
were observed from Sample 3, which corresponded to the time where HOVERCAT
was closest to the ground (i.e., 69 % of sample time was
< 50 m a.g.l.), on average (Fig. 9a). Sample 6 had the highest
concentrations of INPs active between -8 and -12.5 ∘C, which
also corresponds to when HOVERCAT hovered just above ground level (19 %
of the time; Fig. 7). It is important to note that all samples aside from
Sample 4 hovered near the ground: samples 1, 2, and 5 were close to the
ground 40, 9, and 2 % of the time, respectively. Thus, altitude-dependent
results could be skewed by collection nearest to the local source of aerosol.
It is important to note that the samples that spend little to no time on the
ground corresponded to the lowest INP concentrations (i.e., samples 4 and 5).
However, based on OPC number concentrations, there was not always a clear
decrease of aerosol concentrations with altitude (e.g., Sample 5).
Additionally, concentrations were calculated and based on total volume of
air, indicating that the altitude in which the sample was collected at for
the most amount of time is representative of the overall sample INP
population. Combined, the immersion INP, OPC, and BBFCS results indicate
that (1) total particle number concentrations and INP concentrations were
highest when HOVERCAT sampled near the ground and (2) INPs of likely
biological origin remained close to the surface, which is predominantly
agricultural soils in this region (Hill et al., 2016). The relative abundance
of INPs to total particles is also consistent with previously reported values
(DeMott et al., 2010): INPs represented 1 in every 102 to 105
number of particles detected by the OPC, although the OPC does not measure
below 380 nm so the fractions might in reality be even lower (Fig. 9b).
However, INPs are thought to be relatively large (i.e., > 200 nm
in diameter) based on previous work (DeMott et al., 2010; Fridlind et al.,
2012; Kanji et al., 2017; Mertes et al., 2007; Niedermeier et al., 2015), so
the OPC may be relevant for supporting INP measurements. Although these
results may not be surprising (e.g., total particle, INP concentrations
within range of previous work and generally highest near the ground, and
biological INPs sourced from an agricultural region) and yield results
consistent with previous work (DeMott et al., 2010; Hill et al., 2016; Murray
et al., 2012; Prenni et al., 2013), they demonstrate the utility and
reliability of the collection and analytical methods of HOVERCAT and the DFCP
systems.
Depositional ice nucleation experiments on samples 1–6 plotted by
Sice versus temperature. The values plotted here are of the
onset conditions of depositional ice nucleation. For our experiments, this
refers to the first particle to nucleate ice out of the 104 particles
deposited on the disc in total, thus a percent activated fraction of
10-4. Although temperatures measured were not exactly -25, -40, and
-55 ∘C, these values are used for brevity for all samples within
each grouping shown above. Nucleation occurring at -25 ∘C could
also be due to immersion freezing.
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Deposition ice nucleation
Depositional ice nucleation analysis of the six filter samples was conducted
using the extra volume of resuspension left from the immersion freezing
analysis (i.e., the portion of the 2 mL that was not used on the DFCP). Of
the particles that nucleated ice, three to five particles were analyzed for
composition using Raman spectrometry for each sample. We assume that a
majority of the particles are of similar concentration because the whole
sample was dissolved in water, allowed to mix to a homogeneous solution, and
nebulized onto the sample disc. Indeed, the particle composition was similar
for each particle in any sample, while there was variation from sample to
sample. Although the Raman spectral and ice nucleation analyses are helpful
to observe the overall particle composition as temperature and relative
humidity are changed, the experiment does not determine the size or mixing
state of the particles as they were in the atmosphere. Further, the spectral
resolution of 1 µm in our system does not allow smaller scales to be
distinguished within the individual particles probed.
Raman spectra for a representative particle per sample.
Characteristic vibrational frequencies for functional groups of organics
(C–H; 2800–3000 cm-1), carbonates (CO3;
1070–1090 cm-1), sulfates (SO4; 972–1008 cm-1), and
nitrates (NO3; 1032–1069 cm-1) are noted for reference.
Included are images of the particles that initiated depositional freezing for
the Raman spectra shown. The length of the black line in each image
represents a scale of 20 µm.
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Overall, ice activation onset conditions between the six samples were similar
at all temperatures tested (Fig. 10). However, at -40 ∘C,
samples 3 and 4 showed first ice nucleation activity at a saturation ice
ratio of 1.12, which was lower than the other samples and may be
characterized as more efficient deposition INPs at that temperature as
compared to the remaining samples These samples contained slightly more
efficient INPs at -25 ∘C, but similar efficiencies to the
remaining samples at -55 ∘C. Raman spectrometry demonstrates that
most of the samples were compositionally disparate from each other (Fig. 11).
The first three samples show a very intense fluorescence signal (i.e., the
curve-like characteristic of the baseline), which is consistent with either
biological or organic materials (Baustian et al., 2012). Additionally,
Sample 2 contained a peak for carbonate, which is indicative of a mineral
dust signature (Baustian et al., 2012). The sample collection time periods
for these samples occurred directly over a dense agricultural region in the
Colorado plains, supporting the observation of highly fluorescent particles
(Figs. 2 and 7). Interestingly, Sample 3 contained efficient immersion mode
INPs as well that were likely of biological origin due to the relatively
higher INP concentrations at temperatures greater than -10 ∘C
(Fig. 8). Samples 4, 5, and 6 show a C–H stretch peak, as well as occasionally
sulfate (SO42-) and nitrate (NO3-) peaks, which is
consistent with the composition of typical anthropogenic aerosols in the
atmosphere (Zhang et al., 2007). Sample 5 had the most intense anthropogenic
peaks while yielding the least efficient immersion mode and deposition mode
(i.e., at the two highest temperatures measured) INPs. It is possible any
INPs present in this sample were affected by sulfate or nitrate coatings,
which have been shown to inhibit the ice nucleating abilities of aerosols
(e.g., Cziczo et al., 2009; Möhler et al., 2008; Reitz et al., 2011;
Sullivan et al., 2010). Collection of samples 5 and 6 coincided with
HOVERCAT flying close to the ground near I-25, where vehicular traffic and
industry lining the multilane interstate likely contributed to the larger
signal from anthropogenic functional groups and less efficient INPs. However,
the Raman spectrum for Sample 6 also has a weak fluorescent signature,
indicating a possible biological contribution. HOVERCAT flew from over I-25
to the west over more agricultural lands. Sample 6 also contained high
concentrations of INPs at -10 ∘C, indicating the sample also
contained biological INPs. Combined, these results from Sample 6 suggest a
mixture of biological and anthropogenic sources.
Recommendations for future airborne INP measurements on small
platforms
As indicated earlier, Phase I of the BBFCS and HOVERCAT combination exists
in its current prototype state. The priorities of Phase I were to develop a
system that is cost effective, user-friendly, versatile, and in compliance
with FAA regulations without the need for special approvals or restricted
airspace. Under these priorities, our objectives were to address if we could
develop such a system that was (1) recoverable and (2) controllable.
Recoverability was a requirement as we needed to obtain the filter samples
for the offline INP analysis, while controllability was an added benefit to
have altitude-resolved INP measurements. We successfully achieved the first
objective by recovering the system after it landed and controlling the BBFCS
such that the landing was not damaging to the instrumentation. The second
objective, however, is still in need of improvement as discussed here. The
benefits of the system as a whole are that it is cost effective and easy to
operate relative to traditional airborne measurements of INPs and it did not
require special FAA approvals, providing flexibility to fly anywhere at any
time. HOVERCAT alone has the benefit of having time-resolved filter sampling
capabilities that, if able to control altitude, would yield
vertically resolved INP measurements. However, as discussed throughout, both
the BBFCS and HOVERCAT have their limitations. Here, we discuss these
limitations and provide recommendations not only for a Phase II system for
HOVERCAT, but also recommendations generally applicable towards INP
measurements on small airborne platforms.
First, HOVERCAT could only operate in its current design up to 2.5 m a.m.s.l. (1.1 m a.g.l.). Although this is an improvement over previously
reported tethered measurements of INPs (e.g., Ardon-Dryer et al., 2011, reached 196 m a.g.l.), achieving higher altitudes is desired
to capture the profile of INPs in and above clouds using a launched platform
that affords the flexibility to essentially fly anywhere. To improve
operation for higher altitudes, modifications should be made to incorporate
a stronger micropump that would yield higher flows and operation at lower
pressures. The main issue is that to fly at free will (i.e., under FAA
compliance), payload weight must be maintained under 2.7 kg for any single
module (i.e., HOVERCAT). Thus, stronger pumps, which are by nature heavier,
may not be realistic for HOVERCAT on a launched balloon system. Implementing
a stronger pump would require either (1) a FAA Certificate of Waiver or
Authorization (COA), (2) flights in restricted airspace, or (3) flights on a
tethered balloon system, all of which do not align with the priorities to
maintain simplicity and versatility. However, we generally recommend future
parallel measurements be made with a better pump. One option could be to
reduce weight of the other components (e.g., replace the metal protective
enclosure of the TRAPS with lightweight foam). This may not afford enough
margin to incorporate the weight of a better pump, but it is a possible
alternative that needs to be tested.
Second, the hovering capability needs improvement, by either further testing
with the BBFCS or modification to a traditional launched balloon system. We
were able to control the altitude ±80 m, but executing the step-wise
flight plan proved to be more difficult than anticipated. The venting and
ballasting functioned properly, but improvement could be focused on
accounting for natural conditions (i.e., updrafts and downdrafts) that
affect the altitude and truly enable the BBFCS to hover at desired
altitudes. As another option, HOVERCAT could be deployed on a traditional
launched balloon with a slow rise rate and less helium or a reverse
parachute (i.e., less buoyancy and more drag) to afford a steady vertical
profile, although this eliminates the hovering capability of the system
unless the free lift is adjusted such that the system may hover near
inversions. For instance, an ascent rate of 0.5 m s-1 would provide a
900 m vertical resolution (at 30 min per sample). If such a system were
successful, bidirectional communication to control TRAPS
sampling intervals would not be required and it would eliminate the need for
additional hardware, receivers, batteries, and other data processing
components in HOVERCAT and for the ground station. In general, we recommend
implementing advanced controllability features into traditional launched
balloons to not necessarily hover, but afford a consistent and slow rise for
sample collection, and components to terminate the flight at the desired
altitude such that the package is still recoverable.
Third, the Phase I pilot study involved sampling in clear air to conceptually
prove HOVERCAT could perform as desired. Ideally, operation of such a system
would be in clouds and harsher conditions such as the Arctic. To function in
harsher environments, testing the modules in humidified, pressure-controlled,
and temperature-controlled conditions is required at temperatures down to
-40 ∘C. Ardon-Dryer et al. (2011) measured INPs successfully using
a filter sampler in the Antarctic, but did not collect samples in cloud.
Schrod et al. (2017) deployed their sampler on a small unmanned aircraft
system up to 2.5 km a.g.l., but did not fly in cloud or ambient
temperatures below approximately 15 ∘C. Combined, even though our
system and these previous systems are subject to limitations, they are a
significant advancement towards a more flexible and versatile manner in which
INPs above ground level can be measured. In general, additional research is
needed to continue to improve such systems with regard to cost, performance,
and enhanced spatial and temporal coverage to improve understanding of INP
impacts on clouds.