Introduction
Sampling locations, elevations, and dates (given in mm/dd/yy) for all of the harvests
are given.
Sample
Location
Latitude, longitude
Elevation (m)
Sampling date
Pre-soybean harvest
Colby, KS
39.394, -101.066
966
10/14/14
Soybean
Colby, KS
39.394, -101.066
966
10/14/14
Sorghum
Colby, KS
39.394, -101.066
966
10/15/14
Wheat 1
Colby, KS
39.394, -101.066
966
06/30/15
Wheat 2
Colby, KS
39.394, -101.066
966
07/01/15
Corn
Lingle, WY
42.126, -104.403
1309
11/09/15
Currently, the accuracy of climate change predictions is limited by large
uncertainties associated with quantifying aerosol–cloud interactions (IPCC,
2013). One step toward narrowing these uncertainties is identifying and
quantifying key sources of aerosol particles that can aid in the formation of
ice crystals in clouds, termed ice-nucleating particles (INPs). INPs are rare
in the atmosphere (DeMott et al., 2010), and their sources are not well
characterized or quantified. Some INP sources have been identified including mineral dust (DeMott et al., 2003), lofted biological
particles (Pratt et al., 2009; Creamean et al., 2013), biomass burning
aerosol (McCluskey et al., 2014; Prenni et al., 2012), sea spray (DeMott et
al., 2016; Wilson et al., 2015), decaying leaf litter (Conen et al., 2016;
Schnell and Vali, 1976), macromolecules on pollen (Pummer et al., 2012), and
certain strains of fungi (Fröhlich-Nowoisky et al., 2015; O'Sullivan et
al., 2016; Morris et al., 2013). Additionally, changes in emission rates of
INPs have been correlated with rain events (Huffman et al., 2013; Prenni et
al., 2013) and high relative humidity (Wright et al., 2014). Recently, soil
dust and its ice nucleation potential have gained attention (Conen et al.,
2011; Tobo et al., 2014; O'Sullivan et al., 2014; Hill et al., 2016). Soil
dust contains both mineral and organic components, and it has been suggested
that the organic and biological fractions of soil dust are responsible for a
majority of its ice nucleation ability (Tobo et al., 2014; Conen et al.,
2011; O'Sullivan et al., 2014; Hill et al., 2016). A variety of organic
sources of INPs in soils including bacteria, fungi, and other soil organic
matter classes have been identified (Hill et al., 2016); however, a thorough
understanding and quantification of INPs from soil organic matter is lacking.
Aside from soils, plant fragments pulverized during harvesting and
biological particles released from the surface of plants can serve as INPs.
For instance, large numbers of ice-nucleating (IN) bacteria have been measured on
leaf surfaces (Hill et al., 2014; Georgakopoulos and Sands, 1992), and
elevated levels of INPs were observed during active harvesting of a corn
field, some of which were identified as ice-nucleating bacteria (Garcia et
al., 2012). Harvesting can loft these biological particles into the air
(Lighthart, 1984), and once lofted they can travel long distances (Aylor,
1986; Nagarajan and Singh, 1990). Thus, harvesting can be a large daily and
seasonal emission source of biological particles and INPs that could have
regional impacts on precipitation.
Arable land makes up roughly 11 % of land surfaces on Earth (FAO, 2010),
and in the central United States the majority of land is used for
agriculture. Characterizing the sources of INPs in such intensively disturbed
land is, thus, essential to accurately predicting their role in cloud
development and precipitation events over agricultural regions. Data
presented here were collected during harvesting of four crops in the US High
Plains over 2 years at agricultural research centers in Kansas and Wyoming.
This work builds upon previous studies of harvest emissions by also utilizing
pre- and post-treatments of the samples and electron microscopy to
investigate the various mineral, organic, and biological components that
contribute to the ice-nucleating ability of harvest emissions.
Methods
Harvest sampling description
Measurements were made at the Kansas State University Northwest Research-Extension Center in Colby, KS, and at the University of Wyoming Sustainable
Agriculture Research and Extension Center (SAREC) in Lingle, WY. Harvests of
soybean, sorghum, and wheat were sampled in Kansas, and a corn harvest was
sampled in Wyoming. Photographs of the fields pre-, during, and post-harvest
are shown in Figs. S1 and S2 in the Supplement. The harvests usually lasted 2–4 h. Details of
the sampling locations and dates are listed in Table 1. A mobile laboratory
and gasoline-powered generators were used to sample in the fields during the
harvests. Generators were always positioned downwind of the mobile
laboratory. Additionally, the mobile laboratory was positioned downwind from
the field being harvested and was repositioned if the plume was no longer
reaching the laboratory due to a shift in wind direction or position of the
combine harvester. Figure S3 shows an example GPS track of a combine
harvester during a corn harvest and the mobile laboratory position in
relation to the combine. Aerosols were sampled through an inlet comprised of
a stainless-steel rain hat located 3.5 m above the ground with a 1/2 in.
OD stainless-steel tube attached. From this tube, y-splitters were used to
split the aerosol flow among various instruments. A schematic of the various
sampling configurations used during the study is provided in Fig. S4 in the
Supplement.
Aerosol instrumentation
Ice-nucleating particle concentrations were measured online with the Colorado State University (CSU)
Continuous Flow Diffusion Chamber (CFDC) (Rogers et al., 2001; Eidhammer et
al., 2010). Aerosols are introduced into the CFDC chamber, which has two
cylindrical walls that are coated with ice and held at different
temperatures. The temperature difference results in a supersaturation
gradient that permits calculation of the supersaturation and temperature at
the predicted position of the aerosol lamina ring within a particle-free
sheath flow between the walls. For the measurements performed here, the CFDC
was operated at 5 % supersaturation with respect to water over a
temperature range of -15 to -32 ∘C. As discussed in DeMott et
al. (2015), supersaturation uncertainty ranges from < 1.6 to
> 2.4 % over this range of temperatures. While these settings
potentially permit deposition, immersion, and condensation-freezing modes of
ice nucleation to occur, conditions in the supersaturated (or growth) region
of the CFDC emphasize aerosols growing into water droplets via condensation
and then droplets that contain INPs freezing into ice crystals. Hence, data
collected during operation in this manner are often compared to methods that
explicitly examine immersion-freezing nucleation. Downstream of the growth
region, droplet evaporation is stimulated (evaporation region) by holding the
two iced walls at the same temperature to create water subsaturated
conditions. This design feature amplifies the size difference between larger
ice crystals and smaller aerosol particles. All particles are sized and
counted by an optical particle counter (OPC, Climet CI-3100), and particles
larger than 3 µm are counted as ice crystals. A 2.4 µm
impactor (50 % aerodynamic cut-size diameter) was used upstream of the
CFDC inlet to limit the size of particles entering the CFDC, as large
particles (> 3 µm) would otherwise interfere with
counting the ice crystals that form in the instrument. The uncertainty in INP
concentration is calculated by adding in quadrature the Poisson counting-statistics-derived standard deviations of the sample and background periods,
which are measured by sampling through a particle filter upstream of the CFDC
(Schill et al., 2016). A statistical significance test is also performed on
the data. If INP concentrations are greater than the error in INPs multiplied
by 1.64 (INP > (INPError ⋅ 1.64)), which
corresponds to the Z statistic at 95 % confidence, then the data are
considered statistically significant (Schill et al., 2016).
For select sampling periods, particles that formed ice in the CFDC were
collected for chemical analysis via impaction onto electron microscopy (EM)
grids (SPI Supplies Coated Grids
Formvar®/Carbon, 200 mesh, nickel) with a
2.9 µm single-stage inertial impactor (Kreidenweis et al., 1998).
Scanning electron microscopy (SEM; Quanta FEG MK2) was used to image the
particles, and energy dispersive X-ray (EDX; Oxford Instruments X-Max EDS
detector) analysis was performed to obtain elemental composition. Analysis
was completed at the University of Wyoming Materials Characterization
Laboratory. Analysis was done by analyzing individual particles on the
filters (73 particles for the sorghum sample and 67 and 72 particles for the
corn samples). Characteristic combinations of elements were identified and
then used to group the individual particles into classes. Particles
containing at least one mineral dust marker, such as silicon, aluminum, or
iron, were labeled as dust particles. These particles typically contained
oxygen and sometimes carbon as well. Particles with oxygen, carbon, and
either sulfur or nitrogen were labeled as organic. If particles contained
phosphorous, along with organic markers (O, C, N or S), they were labeled as
biological (Pratt et al., 2009). Mixtures of these particle types were
labeled as both types. For example, if a particle contained silicon, carbon,
oxygen, and nitrogen, it was labeled as dust-organic.
The CFDC sample flow rate of 1.5 L min-1 sets a limit of detection
that restricts its useful temperature range for assessing INP number
concentrations. This detection limit is also dependent on the background
concentration measured. For the 10 min integrated sampling periods typically
used and when background counts are low, this detection limit is
∼ 0.2 L-1. To help overcome this limitation, an aerosol
concentrator (MSP 4240) was used upstream of the CFDC to concentrate aerosol
using virtual impaction (Fig. S4b in the Supplement) (Romay et al., 2002). The concentrator was
located on a cart 1 m above the ground. This method has been used in
previous studies (e.g., Tobo et al., 2013). Measurements were taken for
10 min with and 10 min without the concentrator. A concentration
factor (CF) was calculated by taking the number concentration of INPs during
concentrated periods divided by the INP concentration during
temporally adjacent non-concentrated periods. During harvesting, there were
large spikes in concentration due to the passing of a combine harvester. This
made CF calculations difficult when the concentrated and non-concentrated INP
values were not equally affected by the spikes. It was important to use a
time period within the overall harvesting experimental period that included
stable aerosol concentrations during periods on and off the concentrator.
Therefore, the CF calculated during a pre-soybean harvest period in Colby, KS
(CF = 90 ± 3), was used as the CF for all of the harvests. We may
note that this value is within 15 % of the value found by Tobo et
al. (2013) in prior studies with the same aerosol concentrator and CFDC
instrument. This is physically expected if most INPs reside in the size range
above 0.5 mm. CF uncertainty was calculated by propagating the uncertainties
in the INP values used to calculate it. Then, the INP number concentrations
during periods using the concentrator were corrected by dividing INP number
concentrations by the CF.
The Ice Spectrometer (IS) immersion freezing method uses aerosols collected
onto filters over periods of 2–4 h, achieving 800–3500 L sample volumes
that can extend the range of INP measurements to warmer temperatures and a
detection limit of ∼ 0.001 INPs L-1. Although creating
difficulties for comparing methods when higher-frequency changes in INP
concentrations are occurring, these two methods are complementary, offering
colder temperatures and higher time resolution with the CFDC and warmer
temperatures and lower INP detection limits with the IS. For IS analysis,
aerosols were collected onto 47 mm diameter, 0.2 µm pore diameter
(sometimes 0.05 µm) polycarbonate Nuclepore filters (Whatman, GE
Healthcare Life Sciences) fitted within open-faced Nalgene sterile filter
units (Thermo Fisher Scientific Inc.). During the wheat and corn harvests, a
2.5 µm cyclone (50 % aerodynamic cut-size diameter at
16.7 L min-1, URG Corporation) was also used, upstream of a 47 mm
diameter inline aluminum filter holder (Pall Corporation) fitted with a
0.2 µm diameter pore Nuclepore membrane. This limited the size of
the particles collected to the same size range as the CFDC. Filters and
dissembled filter holders were cleaned before use by immersion in 10 %
H2O2 for 30 min followed by three rinses in deionized water
(18 MΩ and 0.2 µm pore diameter filtered) and then dried
by removal of excess water and placement on foil in a clean-air laminar flow
cabinet.
For processing in the laboratory, filters were transferred to sterile, 50 mL
Falcon polypropylene tubes (Corning Life Sciences); 7–10 mL of
0.02 µm pore diameter filtered water (Anotop syringe filter,
Whatman) with 2 mM KCl was added to maintain activity of K feldspar, if present.
Prior tests on dilute suspensions of pure K feldspar found that the use of
deionized water reduced IN activity, presumably due to desorption of K+;
the use of a suspension containing ≥ 0.1 mM K+ prevented this and so
was used for dilutions. Tubes were tumbled end-over-end at 1 cycle s-1
for 20 min (Roto-Torque, Cole-Palmer) to resuspend particles. Measurements
of immersion freezing were made on this suspension and 20-, 400- and
8000-fold dilutions of it. Thirty-two 50 μL aliquots of each dilution
and a negative control (2 mM KCl) were then dispensed into two 96-well polymerase chain reaction (PCR) trays (μCycler, Life Science Products), which were then transferred to
the cold blocks in the IS. The trays were then slowly cooled by lowering the
temperature at a rate of 0.3 ∘C min-1 from 0 to
-27 ∘C, and the numbers of wells frozen were counted at 0.5 or
1 ∘C intervals. Cumulative numbers of INPs per volume of liquid as a
function of temperature were estimated using the formula -lnfu(T)/V,
where fu(T) is the proportion of droplets not frozen at a given
temperature and V is an aliquot volume (Vali, 1971). Values were then
converted to concentrations per liter air samples. Uncertainties are given as
binomial sampling confidence intervals (95 %) (formula no. 2, Agresti and
Coull, 1998). For a detailed description of the IS, see Hiranuma et
al. (2015).
Ambient aerosols were sized at aerodynamic sizes larger than
0.542 µm using an aerodynamic particle sizer (APS; TSI 3321) and
counted using a condensation particle counter (CPC; TSI 3010). The wideband
integrated bioaerosol sensor (Droplet Measurement Technologies WIBS-4A) was
used to collect information on fluorescent and biological aerosols. The
WIBS-4A, from here onward referred to as the WIBS, gives fluorescence
information in three channels: FL1 (fluorescence at 310–400 nm, excited at
280 nm), FL2 (fluorescence at 420–650 nm, excited at 280 nm), and FL3
(fluorescence at 420–650 nm, excited at 370 nm). Particles from 0.8 to
20 µm are sized by light scattering. Based on fluorescence signatures, the data were classified into four particle classes: any particles measured
with the WIBS (total particles), particles that fluoresced in at least one
channel (FP), particles that fluoresced in two channels (termed fluorescent
biological aerosol particles, FBAPs), and particles that fluoresced strongly
in channel FL1 and weakly or not at all in channels FL2 and FL3 (FP3), as
described in Wright et al. (2014). The WIBS instrument was non-functional
during the corn harvest on 9 November 2015; thus, fluorescence data supplied
for corn are from a corn harvest on a different day (4 November 2015) but at
the same field in Wyoming.
Pre- and post-treatments
This work utilized two different types of treatments to tease out the various
biological and chemical compositional influences on INPs measured in the
ambient environment. Upstream of the CFDC, a tube heated to 300 ∘C
was used to deactivate organic components before they entered the CFDC. The
heating tube setup, shown in Fig. S4c in the Supplement, consists of two
tube furnaces (Thermolyne 21100) placed next to each other in series with a
1 in. diameter quartz tube
running through the center of the tube furnaces. By measuring INP
concentrations with and without passage through the heating tube, the
fraction of organic INPs, which are deactivated by heating, can be measured
in situ. Previously, heat and peroxide have been used to degrade organic INP
components in bulk soil samples and aerosol generated from it post treatment
(Tobo et al., 2014; Hill et al., 2016). While useful, these methods do not
provide precise time resolution on single particles, which is important for
episodic events. Thus, to enable higher time resolution of single ambient
particles, an online heating technique was developed and used in this work.
For initial optimization, to ensure that the heating tube method was
comparable to the previous bulk heating results, the same soil sample (soil
from a sugar beet crop collected in Wyoming) used by Tobo et al. (2014) in
the bulk heating analysis was aerosolized and run with the heating tube
setup. The previous study measured aerosolized pre-treated soil particles
size-selected at 0.6 µm using a differential mobility analyzer
(DMA; TSI 3080) before sampling with the CFDC. In this study, particles are
size-selected at 0.5 µm. However, this difference in size did not
greatly change the results. Figure S5 in the Supplement shows the previous
results using the bulk heating method (Tobo et al., 2014) plotted with the
results using the heating tube. Data from the heating tube at 300 ∘C
agree well with results from the bulk heating experiment. This comparison
demonstrates that, even though particles only pass through the heating tube
for 98 s with a flow rate of 1.5 L min-1, the heating tube technique
is as effective at degrading organic components as the bulk heating method,
which entailed heating to 300 ∘C in an oven for 2 h.
WIBS data collected during the harvests showing the percentages and
concentrations of fluorescent particles (FP), particles that fluoresce in
channel 1 (FP3), and fluorescent biological aerosol particles that fluoresce
in two channels (FBAPs). INP concentrations measured with the IS with (bold)
and without (normal) a cyclone are presented at -15, -20, and
-25 ∘C.
FP
FP3
FBAP
FP
FP3
FBAP
INP -15 ∘C
INP -20 ∘C
INP -25 ∘C
Sample
%
%
%
(L-1)
(L-1)
(L-1)
(L-1)
(L-1)
(L-1)
Pre-soybean harvest
85.7
3.6
6.8
128.6
5.4
10.2
0.08
1.6
*100
Soybean
81.2
4.9
17.8
156.9
9.5
34.4
0.26
3.0
180
Sorghum
88.5
2.6
11.8
348.5
10.1
46.6
0.51
3.5
180
Wheat 1
90.5
11.1
0.7
1580.0
193.2
12.7
76/3.1
180/4.7
610
Wheat 2
65.7
4.5
0.3
415.1
28.7
2.2
14/0.22
33/1.2
721/91
Corn
88.8
17.8
33.5
285.3
57.2
107.7
2.7/0.16
8.1/0.33
200*/29
Pre-corn
0.05
0.70
6.9
* The pre-soybean harvest INP concentration is extrapolated
from the limit of measures at -24.5 ∘C and the corn from
-23.5 ∘C.
Post-treatments were also applied to the IS filter wash water of the wheat
harvest sample to selectively deactivate different INP components. To
denature labile organic components (e.g., proteins), an aliquot was heated to
95 ∘C for 20 min, while to decompose and remove all organic INPs, an
aliquot of the wash water was digested with hydrogen peroxide. The latter
used the same method as detailed in McCluskey et al. (2018), except we
used a more powerful UV source for hydroxyl radical generation. Briefly, this
entailed adding 30 % H2O2 (Sigma Aldrich) to the aliquot to
achieve a final concentration of 10 % and then immersing the suspension in
water heated to 95 ∘C for 20 min while being illuminated with two
26 W UVB fluorescent bulbs (Exo Terra) to generate hydroxyl radicals. To
remove residual H2O2, catalase (cat. number 100429, MP Biomedicals)
was added in 20 µL aliquots to the cooled solution, allowing
several minutes between each addition, until no further effervescence
occurred. To lyse all bacteria (including known IN species) another aliquot
was incubated with lysozyme to digest their cell walls (lysozyme also
hydrolyzes fungal chitin oligosaccharides but not the chitin polymer itself).
An aliquot of the aerosol suspension was amended to contain 4 mg mL-1
lysozyme, 10 mM Tris buffer, and 5 mM EDTA (both at pH 8) and incubated at
24 ∘C for 3 h. For a detailed description of this method, see Hill
et al. (2016).
Results
Harvest INP emissions
Measurements were made during a soybean harvest on 14 October 2014, a sorghum
harvest on 15 October 2014, and during a wheat harvest (30 June and
1 July 2015, referred to as Wheat 1 and Wheat 2, respectively) in Colby,
KS, and during a corn harvest in Lingle, WY, on 9 November 2015. Figure 1
shows CFDC and IS INP number concentrations measured during the harvests. The
corn and wheat IS data were sampled through a 2.5 µm cyclone, in
addition to open-faced Nalgene sterile filter units, to limit the size range
of particles that were collected. While the use of the cyclone will not
capture the IN activity of larger particles, these larger particles will
sediment out faster than smaller particles and likely do not make it to cloud
level. Therefore, the use of the cyclone and impactor offer a better
representation of particles that could impact clouds. IS data with and
without the cyclone are provided in Table 2 for a comparison of INP
concentrations with and without larger particles.
The CFDC INP number concentrations are averaged over 3 to 5 min periods, and
the IS INP number concentrations represent the average over the
whole harvest sampling period (typically 2–4 h). For a given CFDC operating
temperature, there was a broad range of INP number concentrations for a given
harvest due to the nature of harvest sampling: the concentrations vary
rapidly in time due to the movement of the combine harvester up and down the
field, laterally across, and closer and further away from the mobile
laboratory and due to the stopping and starting of the harvesting. Thus, the difference
in time resolution between the CFDC and IS techniques can explain some of the
greater spread in CFDC data. Even so, the IS and CFDC data generally agree
well in the overlap region between -15 and -25 ∘C. INP
concentrations ranged from 0.5 to 147 L-1 at -30 ∘C as
measured with the CFDC, and the IS data showed a maximum INP concentration of
922 L-1 at -25.5 ∘C during the wheat harvest. In general,
these concentrations are very high compared to background INP concentrations
and global averages (e.g., DeMott et al., 2010). This result was consistent
with limited previous harvest sampling (Garcia et al., 2012) but not
consistent when comparing crops harvested or even between harvests of the
same crop (i.e., wheat). Further, measurements from a corn harvest in
Nebraska described by Garcia et al. (2012) showed INP concentrations from
drop freezing analysis between 30 and 80 L-1 at -20 ∘C and
an average CFDC INP concentration of 5.9 L-1. In this study, average
number concentrations of 8.1 and 3.6 L-1 were measured with the IS and
CFDC, respectively, at this temperature. While the CFDC results agree quite
well between the two studies, there was up to an order of magnitude
difference between the immersion freezing measurements. The average distance
from the combine harvester during sampling or differences in plant and soil
properties at the time of harvesting could contribute to this. While the soil was dry
during the Nebraska harvest, it was wetter during this study due to recent
heavy rain, thus limiting the amount of soil dust kicked up during sampling.
These results illustrate the complexity of harvest emissions due in part to
varying concentrations in time, distance from the source, and soil moisture.
INP number concentrations divided by the concentration factor (CF)
measured using the CFDC (squares) and IS (circles) during four harvests are
shown as soybean (a), sorghum (b),
wheat (c), and corn (d). The smaller squares represent
particles sampled on the concentrator, while the larger squares are sampled
without the concentrator. Both significant and non-significant data are
shown. Panels (c) and (d) are data collected through a
2.5 µm cyclone.
The shapes of all of the IS harvest spectra are similar, with a “hump” at
the warm end (-5 to -22 ∘C), which is accentuated in wheat
sample 1. This warm temperature hump, which is a frequent feature in
terrestrial INP spectra and is commonly observed in precipitation samples, is
suggested to be from biological sources (Petters and Wright, 2015).
Interestingly, the crop dust emissions from the wheat field were considered
particularly strong due its infestation with rust (Marv Farmer, personal
communication, 2015), a parasitic fungal infection. Rust breaks down plant cell
walls, which can result in more and finer plant dust particles being produced
during harvesting. Furthermore, rust damage to leaf tissues would have
allowed many adventitious phylloplane bacteria and fungi to have flourished.
IN bacteria have been measured on wheat at populations of 108 g-1
of fresh green leaf in Wyoming (Hill et al., 2014) and at
3.5 × 106 g-1 of fresh dry leaf at harvest in Colorado
(Garcia et al., 2012), and rust has been shown to be IN active at warm
temperatures (Morris et al., 2013). Thus, these various biological particles
could have contributed to the INP concentrations seen in the pronounced hump
in the IS spectrum for this case. Also, total aerosol numbers, the
concentration of fluorescent particles (see below), and INP concentrations on
this day were the highest observed in all of the harvest measurements. This
suggests that the direct and indirect consequences of the fungal infection of
the wheat crop could be contributing to the large number of particles and
could be altering the characteristics of the emitted particles.
It should be noted that the CFDC and IS INP spectra have different slopes and
the concentrations can be quite different at colder temperatures. The CFDC
INP concentrations are generally higher than or similar to the IS at warmer
temperatures but lower at temperatures below -25 ∘C. The reasons
for this are not fully understood; however, there are some possible
explanations, as discussed in DeMott et al. (2017) and revisited here.
Particle conglomerates could break up while in the IS wash water, which could
provide more INPs in the bulk solution than are present as single particles
measured in the CFDC. Alternately, small ice-nucleating entities (INEs), such
as protein complexes (Hartmann et al., 2013) or macromolecules on pollen
(Augustin et al., 2013; Pummer et al., 2012), could be present on and then
released from the particle surfaces, and these INEs might be especially active
at lower temperatures. Size could also play a role, as INP sizes are
generally larger during harvests (Mason et al., 2016) and are not as
effectively sampled into the CFDC. During the soybean and sorghum harvests, no
size restriction was placed on the IS filters; thus larger particles
(> 2.5 µm) are underrepresented in the CFDC data as
compared to the IS. This is less of a concern (and indeed may be reflected in
the data) for the corn and wheat harvests because a 2.5 µm cyclone
was used to restrict particle sizes on the IS filters. Finally, there may be
a time dependence of ice nucleation that accentuates differences between the
CFDC and IS measurements at lower temperatures. Particles are in the CFDC
growth region for approximately 5 s, while they are at a particular
temperature for several minutes in the IS. However, previous results suggest
there is little temperature dependence to stochasticity (Wright and Petters,
2013), and thus it is unlikely that the time difference is the cause of the
discrepancy that occurred at the lowest temperatures.
Fluorescent particles
Fluorescent and biological particle concentrations and types measured by the
WIBS were grouped into particle classes as described in the “Methods” section.
These data are shown in Fig. 2 and Table 2. Interestingly, FBAPs were observed
before the start of the soybean harvest period, which has been termed the
“pre-soybean harvest” period. During the soybean harvest, the FBAP
percentage increased from 6.8 % during the pre-soybean harvest period to
17.8 %, indicating that additional biological particles were emitted
during the harvest. This pre-harvest period was likely strongly influenced by
harvesting in the region even though we were not directly in a fresh harvest
plume. The corn harvest produced the largest percentage and concentration of
FBAPs out of all of the harvests sampled (33.5 %,
107.7 L-1). These results suggest an abundance of biological particles
are emitted during corn harvests, which is supported by a previous study that
showed corn harvests emit bacteria (Garcia et al., 2012), and could also
indicate that more plant fragments are emitted during corn harvests than for
other plant harvests. While wheat harvests emitted the highest number of
fluorescent particles, they had the lowest percentages of FBAPs and Wheat 2
had the lowest concentration of FBAPs (2 L-1). This indicates that the
wheat emissions only fluoresced in one channel and could point to the greater
presence of plant material or soil dust, as opposed to other biological
particles. Lignin is present in wheat and absorbs at 280 nm and emits at
∼ 360 nm, which would give a signal at FL1 (Albinsson et al., 1999).
Wheat lignin also autofluoresces with excitation at 330–385 nm and
detection at 420 nm, so it will give a signal at FL3. Therefore, if wheat
lignin made up a bulk of the emitted particles, they might show up as FBAPs;
however, FBAPs made up a low percentage of particles. This suggests that the
bulk of the fluorescent wheat particles lack lignin and could be
non-lignified plant cells or dead microbes.
The FP3 particles did not make up a significant percentage of particles,
except during the first wheat harvest and the corn harvest, and average
concentrations ranged from 5 L-1 in the pre-soybean harvest period to
193 L-1 during the Wheat 1 harvest. These particles have not been
biologically or chemically identified but have been shown to correlate with
INP concentrations (Wright et al., 2014). FP3 is indicative of the presence
of tryptophan and the absence of nicotinamide adenine dinucleotide + hydrogen (NADH) and could indicate dead plant and
microbial material containing protein (e.g., dead phloem cells, dead
bacteria, and fungi). Figure 2 and Table 2 also include IS INP concentrations
at three temperatures for comparison to the fluorescent particle
concentrations. In general, the INP concentrations measured without a cyclone
at -20 ∘C are on the same order of magnitude as the FP3
concentrations, and thus FP3 concentrations show potential as an indicator of
INP concentration at -20 ∘C. The Wheat 1 case is somewhat of an
outlier with the highest INP concentration at -15 ∘C, which was on
the same order of magnitude as the FP3 concentration. Overall, the best
agreement was between the FP concentrations and the -25 ∘C INP
concentrations, which agreed very well for the three samples that had IS data at
-25 ∘C.
WIBS data showing the concentration (circle markers) and
percentage (bars) of three different classes of fluorescent particles (FP,
FP3, and FBAP) during each harvest and one pre-soybean harvest period. The
corn WIBS data were collected from the same field but on a different day than
the corn IS data. The IS INP concentrations for -15 ∘C (light
grey), -20 ∘C (dark grey), and -25 ∘C (black) are shown
in diamond markers for non-cyclone samples only.
Relative amounts of different particle types collected via impaction
and analyzed using SEM-EDX (a) and example SEM images with the
corresponding elemental composition measured with EDX shown in
white (b). The colored circle in the left corner of the images
indicates which chemical class the particles were classified as. These data
were collected during a sorghum harvest with a CFDC operating temperature of
-17 ∘C. In total, 73 particles were analyzed to make this pie chart.
Chemical composition of INPs
To chemically characterize the harvest INPs, particles were collected via
impaction onto SEM grids downstream of the CFDC. During the sorghum harvest,
mineral dust made up 41 % of INPs at -17 ∘C, as shown in
Fig. 3a. Organic material also made up a large fraction (29 %) of the
INPs along with mixtures of dust and organics (13 %) and a small
percentage of purely biological particles (2 %). Images of the particles,
shown in Fig. 3b, include interesting structures that could be indicative of
plant material or biological particles. There were plate-like structures with
potassium, thread-like filaments containing silicon, which could be plant
material (Lux et al., 2002), and flaky structures with the
elemental composition of mineral dust. These results indicate that organic
components make up a large percentage of sorghum harvest INPs at
-17 ∘C.
SEM-EDX data collected during a corn harvest. Panels (a) and
(b) show relative amounts of each particle type as sampled using
EDX, while (c) and (d) show example images of particles
analyzed with SEM. The colored circle in the top left corner of the images
indicates which chemical class the particles were classified as. Elemental
composition is given in white on each of the example images. The data were
collected at a CFDC operating temperature of -27 ∘C with the left-hand side (a, c) showing data without heat and the right-hand side
(b, d) showing data for particles that had passed through a heating
tube at 300 ∘C upstream of the CFDC. Overall, 67 and 72 particles were
analyzed to make the pie charts in (a) and (b),
respectively.
The fraction of INPs out of the total number of particles greater
than 0.5 µm as measured by the CFDC (n0.5µm) is
plotted against CFDC operating temperature for four crop harvests:
soybean (a), sorghum (b), wheat (c), and
corn (d). Data collected through the heating tube at 300 ∘C
are shown in red and non-heated data are in blue. The larger markers represent
periods sampled without the concentrator, and the smaller markers represent
periods sampled through the concentrator. Note the difference in scale on the
y axes.
The fractional change in INP number concentrations due to heating at
300 ∘C for four crop harvests for soybean (a),
sorghum (b), wheat (c), and corn (d). The
fractional change is shown for each CFDC operating temperature (x axis)
where measurements were made. Heating was done in situ using a heating tube
upstream of the CFDC.
SEM-EDX data from the corn harvest at -27 ∘C, shown in Fig. 4a,
indicate mineral dust again comprised a significant portion of INPs
(32 %), along with dust mixtures with organics (Dust-org, 19 %),
biological particles (Dust-bio, 13 %), and sulfate (Dust-S, 10 %).
Additionally, there was a significant number of biological particles
(18 %), which were identified by the presence of carbon, nitrogen, and
phosphorous (Pratt et al., 2009). Many of the measured INPs also had
structured forms similar to the sorghum harvest emissions. These particles
had oblong, granular shapes and some appeared to have tiny hairs on the
surface, suggesting they were of biological origin. The large percentage of
biological INPs agrees well with previous measurements during corn harvests,
which also showed the presence of several genera of bacteria among CFDC
residuals (Garcia et al., 2012); this included 19 IN bacteria L-1 air,
quantified directly using quantitative PCR of the Ina gene. While
SEM-EDX elemental compositions indicated the presence of biological
particles, a full characterization of the biological components cannot be
achieved with this method. Future work will utilize post-treatments of the
filtered particles to further identify the types of biological particles that
served as INPs. The WIBS data also revealed that the corn harvest produced
the largest fraction and concentrations of FBAPs out of all the sampling locations. Taken together, these
results indicate that organics and biological particles, along with mineral
dust, make up a large percentage of harvest INPs between -17 and
-27 ∘C. SEM samples were not collected during the soybean and
wheat harvests; therefore, a comparison of organic and mineral components
cannot be directly assessed from these crops.
Inferences regarding INP compositions through the use of heat and
post-treatments
In situ heating during real-time CFDC measurements was utilized to assess the
contribution of minerals and organics to INPs emitted from harvests. Heating
at 300 ∘C has a similar impact on organics as peroxide digestion and
will degrade heat-labile organics and biological particles (Tobo et al.,
2014), and thus a comparison of heated and non-heated INP concentrations
reveals the percentage of organic versus inorganic INPs. SEM-EDX results
presented in Fig. 4b show the chemical changes in INPs that occurred with
heating at 300 ∘C during the corn harvest. The percentage of mineral
dust increased, which is expected because, as organics are degraded with
heating, minerals will remain IN active and make up a larger percentage of
INPs. The percentage of biological INPs was reduced from 18 % to 7 %,
but they were not totally deactivated. The heat treatment dramatically
reduced the percentage of Dust-bio INPs from 13 % to 1 %, which
suggests the IN-active biological components were degraded with heat,
suggesting that the biological components played a larger role in the IN
activity than the minerals within this class. The latter scenario is
consistent with previous studies that show organic and protein residues on
mineral surfaces can enhance the ice nucleation ability of the minerals
(O'Sullivan et al., 2014, 2016; Conen et al., 2011).
Interestingly, the percentage of Dust-org particles was not reduced with
heating. This could indicate that the organics that were internally mixed
with minerals were not susceptible to heat at 300 ∘C or that, after
the organics were degraded, the INP activity was unchanged because the
minerals in these mixed particles were serving as the active sites for ice
nucleation. Alternatively, heating the Dust-org particles could have
evaporated off some volatile organics uncovering active sites on the dust.
This study cannot differentiate between those scenarios, but future studies
should investigate the physical changes caused by heating, including how
heating might change mixing state and surface morphology.
The quantitative changes to ice-nucleating ability with heating are shown in
Fig. 5. The fraction of INPs with respect to the concentration of total
particles larger than 0.5 µm, as measured with the CFDC OPC
(n0.5µm), is plotted on the y axis. INP fraction is shown
instead of INP concentration to allow for direct comparison between heated
and non-heated sampling periods. This is because there were large changes in
particle concentrations due to sampling in and out of the harvesting plume
and changing wind directions, which complicate a direct concentration
comparison. This figure displays only statistically significant data points,
as determined with the significance test described in the “Methods” section,
unless otherwise noted. Results indicate that heating had a large impact on
INP number concentration for soybean harvest emissions as cold as
-25 ∘C. Similarly, for the sorghum harvest, INPs were reduced by
heating to below detection levels at -18 ∘C, but a smaller impact
was noted at temperatures ≤ -22 ∘C. During the corn harvest,
heating reduced the fraction of INPs at warm temperatures (-19 ∘C)
to below the instrumental detection limits, but at colder temperatures
(-28 ∘C) there was only a slight change in the fraction. A similar
situation appears for the wheat harvest, although data were not collected for
heating trials below -22 ∘C. These results suggest a general,
albeit variable, impact in which organic (including biological) particles
from harvesting exert more influence at warmer temperatures, while at colder
temperatures mineral dust components likely dominated the ice nucleation
activity. Further characterization of the emissions is necessary to identify
the nature of the organic particles, but these results suggest harvest
emissions are distinct for different crops.
The observed decrease in INPs with heating is presented in a different way in
Fig. 6. Fractional change in INPs is shown for each temperature and crop in
cases where heating measurements were made. At temperatures between -17 and
-19 ∘C (the warmest temperature accessible for comparison via CFDC
data), all of the harvest samples had large decreases in INP activity with
heating. The fractional changes at these relatively warm temperatures were
between -0.7 and -0.98, which suggests that a large percentage of these
warm temperature INPs are of organic or biological origin. At
-32 ∘C, the INPs fractionally decreased by ∼ 0.5 for all
crops, indicating that minerals and possibly 300 ∘C heat-stable
organics are contributing up to 50 % of the INPs. However, the fact that
there was still a reduction in INPs at these cold temperatures agrees with
previous results that showed that organics contributed significantly to the
INP population of soil dust even at cold temperatures where, traditionally,
minerals are expected to dominate the activity (Tobo et al., 2014).
INP number concentrations resulting from a wheat harvest on
30 June 2015: measured by the IS (green); wash water treated with
lysozyme to selectively remove bacterial INPs (blue); after heating to
95 ∘C for 20 min (red); after peroxide digestion and
heating to 95 ∘C (black). The reduction in INP concentrations by
removal of bacteria and heat-labile and heat-stable organics can be seen by the
shaded areas. The dashed black line is representative of the likely
underlying mineral INP spectrum.
Measured CFDC (a–c), IS (d–f), and IS behind a
2.5 µm cyclone (g–i). INP number concentrations plotted
against predicted INP number concentrations using the D10 (a, d, g),
D15 (b, e, h), and T13 (c, f, i) parameterizations. The
markers are colored by the different harvests; the size of the CFDC square
markers indicates if the concentrator was used (smaller squares) or not
(larger squares). The grey dashed line represents a 1:1 line for measured
versus predicted INP.
During the wheat harvest, heat treatment resulted in a 98 % reduction in
INP number concentrations at -18 ∘C. This suggests that biogenic
particles make up almost all of the INPs at temperatures
≥ -18 ∘C. Additional focus in the wheat sampling was placed
on the evaluation of the contributions to this degradation observed in situ and
in real time with the CFDC. Post-treatments on the wheat harvest sample via
IS immersion freezing measurements, shown in Fig. 7, revealed a variety of
biological/organic INP compositions contributing to the IN activity
> -20 ∘C, along with an underlying mineral or
non-organic contribution to the IN activity, as suggested by the dashed grey
line. Lysozyme digestion indicated that bacteria likely contributed foremost
to the INP population. By digesting bacterial cell walls, lysozyme will cause
rupturing of all bacteria. For the known species of ice nucleation active
bacteria (e.g., Pseudomonas syringae, Pantoea agglomerans,
and Xanthomonas campestris), clusters of the protein anchored in the
outer membrane will, as the outer membrane disintegrates, disaggregate into
smaller clusters active at ∼ -7 to -10 ∘C or into single
proteins active at -12 to -13 ∘C (Govindarajan and Lindow,
1988). However, in the wheat harvest sample the effect was observed as
cold as -21 ∘C, suggesting that other, as yet unidentified, IN
bacteria were not only present but abundant in the wheat dust. While WIBS
data suggested that < 1 % of particles were FBAPs, the lysozyme
digestion shows that a large amount of bacteria was generated from the
harvest.
Bulk heating of the IS sample to 95 ∘C resulted in a larger
reduction of INPs that can be attributed to heat-labile INPs, such as
proteins in bacteria and fungi, on the plants, and in soil dusts raised by the
harvester. There was also a modest amount of organic material that was not
susceptible to 95 ∘C heat, but was degraded with peroxide digestion,
that was contributing to INP concentrations and is evident in the shaded
regions between the red markers and the black markers in Fig. 7. If arable
soil dust contributed largely to the INP concentrations, peroxide treatment
would show a greater reduction in INPs than was observed here. The large
reduction due to heating indicates that biogenic particles make up a large
percentage of INPs at temperatures warmer than -18 ∘C. These
biogenic particles come from a variety of sources, which highlights the
complex nature of INPs emitted from agricultural and soil perturbation
activities. No one particle type can accurately describe the nature of INPs
for agricultural areas in general, but rather a mixture of biogenic particle
types best represents these emissions. The findings in this study suggest
that harvesting and plant litter emissions stimulated by wind at the surface provide the most viable explanation of the ubiquity of heat-labile INPs in
the High Plains boundary layer even in the absence of harvesting, as found by
Garcia et al. (2012).
Discussion and atmospheric implications
Results presented herein, especially those shown in Figs. 6 and 7, emphasize
the potential need to include harvesting INP emission impacts in regional
cloud models to assess their subsequent impacts on clouds and precipitation
in both agricultural and naturally vegetated regions. Harvesting emits
mineral, organic, and biological particles into the atmosphere in large
quantities. PM10 emission factors ranging from 10 to over
1000 kg km-2 have been reported for different crops harvested in
California, and these emission factors vary based on crop, relative humidity,
and soil moisture (Flocchini et al., 2001). A full characterization of the
emitted organic matter is beyond the scope of this work and would involve
intensive chemical, biological, and plant pathological investigations. Even
from a single source such as harvesting, there are several distinct inputs
including but not limited to pulverized plant tissues, dust, bacteria, fungi
and other biological particles present on plant surfaces, various biological,
organic and mineral INPs lofted from the soil, and even residual fertilizer
on the soil surface. This complex combination of sources is difficult to
untangle, especially because it can change with geographic location, crop
type, plant and soil states, environmental conditions during harvesting, and
year-to-year differences in the many parameters. Additionally, different
pathogens can grow on the crops, as was shown with the rust-infected wheat
crop sampled in Colby, KS. All of these factors can change the ratio of
mineral to organic components in the INPs, which has implications for how
these emissions should be represented in models.
To assess the ability of existing INP parameterizations to model harvesting
INP concentrations, the measured CFDC and IS INP number concentrations were
compared to predicted INPs using parameterizations for average global INP
concentrations (D10) (DeMott et al., 2010), mineral dust (D15) (DeMott et
al., 2015), and biological particles (T13) (Tobo et al., 2013) (Fig. 8). The
D10 and D15 parameterizations predict INP concentrations at a given
temperature based on particle number concentrations above 0.5 µm
(n0.5µm). For comparison with the IS, particle number
(n0.5µm from the CFDC) was averaged over the IS sampling
times. The D15 parameterization results presented here do not include the
factor of 3 increase suggested in DeMott et al. (2015) for use in predicting
atmospheric concentrations of relevance to clouds, because comparisons here
are made to uncorrected CFDC and IS INP concentrations. The T13
parameterization uses biological particle concentrations, derived from the
WIBS FBAP concentrations in this study, instead of n0.5µm
to predict INP concentrations. FBAP concentrations were averaged over the
CFDC and IS sampling times to compare to CFDC- and IS-derived INP
concentrations, respectively. In applying WIBS FBAPs within the Tobo et
al. (2013) parameterization, we must note that FBAP concentrations used to
develop the parameterization were based on an ultraviolet aerodynamic
particle sizer (UV-APS) that senses FBAPs at sizes above 0.5 µm,
while the WIBS FBAP signal is for > 0.8 µm particles.
Hence, we expect that predicted values may be somewhat underestimated in this
case. Note that all temperatures are integrated into such an analysis, so
that biases may enter due to changes in the contributions of different
compositions at different temperatures, as has been discussed. Also, the CFDC
data presented here cover a narrower temperature range (-17 to
-32 ∘C) than that used in developing these parameterizations
(e.g., -9 to -34 ∘C for D10).
Comparisons shown in Fig. 8 indicate that different crops have different
relationships with CFDC-derived n0.5µm as described by
different parameterizations. For instance, the corn CFDC and IS with cyclone
INP data are predicted most accurately by the D15 parameterization. D15 is
used to model dust INP activity; thus the correlation suggests that dust was
serving as a source of INPs during the corn harvest. The SEM-EDX results
presented in Fig. 4a confirm this and show that dust and dust mixtures made
up 74 % of INPs measured at -27 ∘C. However, the IS data for
all sizes (no cyclone) are predicted best with the D10 parameterization. The
WIBS instrument was inoperable during the corn harvest; therefore, the T13
parameterization could not be tested against the corn data.
The first wheat harvest (Wheat 1) INP concentrations are predicted well with
the D10 parameterization for CFDC and IS with cyclone data. This “global”
INP parameterization represented a diverse range of INP sources (i.e., not
distinct to one source), which may explain why it captures the diverse range
of INPs that wheat harvests emit. Furthermore, this again supports the idea
that elevated INP activity at temperatures higher than -20 ∘C
observed in data compilations like DeMott et al. (2010) have their major
sources from plant and microbial INPs. The cyclone-IS INP concentrations are
predicted best by the D15 parameterization, although they are consistently
overpredicted. The relationship between n0.5µm and CFDC
INPs during the second wheat harvest, Wheat 2, is not captured by any of the
tested parameterizations. This could indicate changing emissions throughout
the harvest or a mixture of minerals, organics, and biological particles that
does not have a consistent relationship between n0.5µm or
FBAPs and INPs. It might also suggest a nonfluorescing population or an
especially active biological population not being represented by the T13
parameterization, which was modeled on data collected in a region rich in
fungal spores and which might not capture the behavior of other biological
types. To explore this scenario, the markers in Fig. 8 were colored by CFDC
and IS operating temperature and are displayed in Fig. S6 in the Supplement.
The Wheat 2 data points, as well as some Wheat 1 and soybean points that are
not well predicted by T13, are mostly at warm temperatures (around
-20 ∘C). Thus, this could indicate a non-spore biological particle
type was contributing to the INP population during these harvests.
The soybean harvest was modeled well by both the D10 and D15
parameterizations for both CFDC and non-cyclone IS data; however, the T13
parameterization also accurately predicted the CFDC data except for a few
points. This might suggest that the soybean emissions had contributions to
INPs from both dust and biological particles. The soybean emissions had a
large percentage of FBAPs (17.8 %) according to the WIBS and a strong
reduction in INP activity after heat treatment, which is indicative of
biological INPs. However, the heat did not totally wipe out the IN activity,
which suggests the presence of minerals in the INPs as well.
Sorghum CFDC INP concentrations are modeled well by both the D10 and T13
parameterizations, while the IS non-cyclone data were best represented by D15.
However, there is a hump in the data between -15 and -18 ∘C,
which is better predicted by T13. This again suggests there is a mix of
particle types including mineral dust and biological INPs present. Indeed,
∼ 11.8 % of particles measured on the WIBS were FBAPs, and heating
reduced the INP activity of the sorghum to below detection limit at
-17 ∘C and by 63 % at -32 ∘C, indicating a strong
organic contribution to INPs. SEM-EDX analysis of INPs active at
-17 ∘C shows 41 % of INPs were mineral dust and, while a small
fraction (2 %) of particles were of biological origin, a large percentage
of INPs had organic components (42 %). Some of these organic particles
could be from biological sources but based on their low (undetectable)
levels of phosphorus, they were labeled as organic. Phosphorus is often a limiting
nutrient in plants and is re-mobilized into living tissues or seeds when
plants are senescing. The high organic but low biological signature could
indicate that phosphorous was relocated from leaf and stem tissues to the
sorghum grain before the harvest, which has been observed in sorghum when
phosphorous is limited (Roy and Wright, 1974). Images of some of these
particles (Fig. 3b) show structured shapes indicative of biological origin.
This evidence points to the importance of organic and biological particles as
well as mineral dust serving as INPs during the sorghum harvest.
The results presented here suggest that different crops have different
relationships between aerosol number concentrations and INP concentrations.
No single INP parameterization accurately predicts INPs released during
harvest periods for all crops, but both D10 and D15 could be used in
agricultural regions to predict ambient INP concentrations during harvest
months, given measurements and/or forecasts of aerosol concentrations. FBAP
concentration data are not readily available, and thus the comparison to the
T13 parameterization is provisional at this point.
The large seasonal increases in harvest emissions could have effects on
precipitation, especially in the Plains states where deep convection is
frequently occurring. Several modeling studies have investigated the effects
of increased aerosol concentrations on convection. One study showed that
increases in aerosols modify storm structure but have minimal effects on
warm-front precipitation (Igel et al., 2013), while another suggested deep
convection in the Great Plains is modified by larger aerosol loading, by
raising cloud-top height in mixed-phase clouds and by increasing precipitation
rates in clouds with large amounts of liquid water (Li et al., 2011).
Increases in biomass burning aerosols have been linked to increases in severe
weather (Wang et al., 2009) and the likelihood of tornado formation (Saide et
al., 2015), while mineral dust has been shown to have competing effects on
squall lines with an overall weakening due to larger dust concentrations
(Seigel et al., 2013). It is important to note that these studies have
focused on the effects of aerosols serving as cloud condensation nuclei and
have not included aerosols serving as INPs. The varying effects of aerosols
on convection highlight the need to further investigate these scenarios and
include INPs in these simulations, which could change the results and lead
to a better representation of clouds and precipitation in agricultural
regions in models.
Conclusions
Measurements made during the harvesting of four crops in the Great Plains
indicate that highly complex mixtures of different organic particle types
along with mineral components make up the spectrum of activity in
harvest-derived INPs. SEM-EDX analysis confirms the presence of organic
components in the harvest INP emissions as well as biological particles,
mineral dust, and mixtures of these types. High-heat tests suggested
contributions of both labile and stable organic INPs over the full
temperature range measured, accounting for up to half of the INP activity
even at -30 ∘C but dominating at temperatures above
-20 ∘C for all crops. Soybean harvest emissions showed the largest
contribution of organic components at colder temperatures
(-32 ∘C), while corn harvests produced the largest fraction of
biological particles in the total aerosol and showed a large fraction of
biological INPs even at -27 ∘C.
Organic particles, especially those of biogenic origin, contribute
substantially to the ice-nucleating efficiency of harvest emissions. This was
demonstrated by the effect of heating, which greatly reduced INP
concentrations for all crops, with the most pronounced effects at warm
temperatures. For example, during the wheat harvest, CFDC INP concentrations
at -18 ∘C were reduced by 98 % with heat treatment.
Post-treatments on the wheat harvest sample indicated the presence of IN
active bacteria, mineral dust, and an extraordinarily high proportion of 95 ∘C heat-labile (e.g., proteinaceous) INPs. The large contribution of
heat-labile material to INPs is unique for the harvest emissions and has not
been observed as being so abundant in soil dusts (Hill et al., 2016). A small
number of 95 ∘C heat-stable organic INPs that were degraded only
with peroxide digestion were also observed. Heat-stable organics make up a
larger fraction of arable soil dust than were observed here, again suggesting
that harvest emissions include plant fragments and other biogenic particles
not commonly found in soil dust.
With the ultimate goal of incorporating these data into cloud models, INP
parameterizations were used to compare predicted and measured INP
concentrations. These comparisons suggested that INP emissions from several
crops are complex mixtures of various types of organic, mineral, and
biological particles. The inability of the T13 parameterization to predict
warm temperature INPs for several crops is due to the low number of FBAPs
observed and suggests the presence of unidentified warm temperature INPs that
are distinct from the spore-dominated scenario in Tobo et al. (2013). Due to
the variety of components that contribute to the INPs, the complexity of the
INP spectrum is not accurately modeled by existing INP parameterizations.
However, the D10 and D15 parameterizations could be used to give estimates of
INPs in agricultural regions. WIBS data can also be used to give estimates of
-20 and -25 ∘C INP concentrations using FP3 and FP
concentrations, respectively. Corn, soybean, and wheat are the top three most
planted crops in the United States. Over 2014 and 2015, corn, soybean, wheat,
and sorghum crops were planted over 960 000 km2 of land in the United
States alone (National Agricultural Statistics Service, 2018). The increasing
homogenization of crops grown in this part of the US may not have changed
the overall number of INPs released compared with the greater heterogeneity
of species and strains grown previously. This is because previous crops would
have produced a mix of both higher and lower emissions. For example,
Georgakopoulos and Sands (1992) recorded a 5000-fold range in populations of
IN P. syringae among 23 barley lines and cultivars grown in Bozeman, Montana.
However, greater patchiness of the landscape would have required a longer
period, over which harvesting emissions occurred in each region due to
differences in maturation times. In summary, harvest emissions can have
a large impact on clouds in agricultural regions and this characterization of
harvest-emitted INPs can be used to inform quantitative models using aerosol
concentration inputs and will hopefully lead to a better understanding of the
role of harvest-emitted INPs in convective clouds in these regions.