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  <front>
    <journal-meta><journal-id journal-id-type="publisher">ACP</journal-id><journal-title-group>
    <journal-title>Atmospheric Chemistry and Physics</journal-title>
    <abbrev-journal-title abbrev-type="publisher">ACP</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Atmos. Chem. Phys.</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1680-7324</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/acp-20-11387-2020</article-id><title-group><article-title>Complex plant-derived organic aerosol as ice-nucleating particles – more than the sums of their parts?</article-title><alt-title>Complex plant-derived organic aerosol as ice-nucleating
particles</alt-title>
      </title-group><?xmltex \runningtitle{Complex plant-derived organic aerosol as ice-nucleating
particles}?><?xmltex \runningauthor{I.~Steinke et~al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1 aff5">
          <name><surname>Steinke</surname><given-names>Isabelle</given-names></name>
          <email>isabelle.steinke@pnnl.gov</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Hiranuma</surname><given-names>Naruki</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-7790-4807</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>Funk</surname><given-names>Roger</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Höhler</surname><given-names>Kristina</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Tüllmann</surname><given-names>Nadine</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Umo</surname><given-names>Nsikanabasi Silas</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-2571-163X</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff4">
          <name><surname>Weidler</surname><given-names>Peter G.</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Möhler</surname><given-names>Ottmar</given-names></name>
          <email>ottmar.moehler@kit.edu</email>
        <ext-link>https://orcid.org/0000-0002-7551-9814</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Leisner</surname><given-names>Thomas</given-names></name>
          
        </contrib>
        <aff id="aff1"><label>1</label><institution>Institute of Meteorology and Climate Research – Atmospheric Aerosol
Research (IMK–AAF), Karlsruhe Institute of Technology, Karlsruhe, Germany</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>Department of Life, Earth, and Environmental Sciences, West Texas A&amp;M
University, Canyon, Texas, USA</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Working Group Landscape Pedology, Leibniz Centre for Agricultural Landscape
Research (ZALF), Müncheberg, Germany</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>Institute of Functional Interfaces, Karlsruhe Institute of Technology,
Karlsruhe, Germany</institution>
        </aff>
        <aff id="aff5"><label>a</label><institution>now at: Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, USA</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Isabelle Steinke (isabelle.steinke@pnnl.gov) and Ottmar
Möhler (ottmar.moehler@kit.edu)</corresp></author-notes><pub-date><day>6</day><month>October</month><year>2020</year></pub-date>
      
      <volume>20</volume>
      <issue>19</issue>
      <fpage>11387</fpage><lpage>11397</lpage>
      <history>
        <date date-type="received"><day>25</day><month>September</month><year>2019</year></date>
           <date date-type="accepted"><day>5</day><month>June</month><year>2020</year></date>
           <date date-type="rev-recd"><day>22</day><month>May</month><year>2020</year></date>
           <date date-type="rev-request"><day>11</day><month>November</month><year>2019</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2020 </copyright-statement>
        <copyright-year>2020</copyright-year>
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://acp.copernicus.org/articles/.html">This article is available from https://acp.copernicus.org/articles/.html</self-uri><self-uri xlink:href="https://acp.copernicus.org/articles/.pdf">The full text article is available as a PDF file from https://acp.copernicus.org/articles/.pdf</self-uri>
      <abstract><title>Abstract</title>
    <p id="d1e183">Quantifying the impact of complex organic particles on the formation
of ice crystals in clouds remains challenging, mostly due to the vast
number of different sources ranging from sea spray to agricultural
areas. In particular, there are many open questions regarding the ice
nucleation properties of organic particles released from terrestrial
sources such as decaying plant material.</p>
    <p id="d1e186">In this work, we present results from laboratory studies investigating
the immersion freezing properties of individual organic compounds
commonly found in plant tissue and complex organic aerosol particles
from vegetated environments, without specifically investigating the
contribution from biological particles, which may contribute to the
overall ice nucleation efficiency observed at high temperatures. To
characterize the ice nucleation properties of plant-related aerosol
samples for temperatures between 242 and 267 <inline-formula><mml:math id="M1" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula>, we used the
Aerosol Interaction and Dynamics in the Atmosphere (AIDA) cloud
chamber and the Ice Nucleation SpEctrometer of the Karlsruhe Institute
of Technology (INSEKT), which is a droplet freezing assay. Individual
plant components (polysaccharides, lignin, soy and rice protein) were
mostly less ice active, or similarly ice active, compared to microcrystalline
cellulose, which has been suggested by recent studies to be a proxy for
quantifying the primary cloud ice formation caused by particles
originating from vegetation. In contrast, samples from ambient sources
with a complex organic matter composition (agricultural soils and leaf
litter) were either similarly ice active or up to 2 orders of
magnitude more ice active than cellulose.  Of all individual organic
plant components, only carnauba wax (i.e., lipids) showed a similarly
high ice nucleation activity as that of the samples from vegetated
environments over a temperature range between 245 and 252 <inline-formula><mml:math id="M2" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula>.
Hence, based on our experimental results, we suggest considering
cellulose as being representative for the average ice nucleation
activity of plant-derived particles, whereas lignin and plant proteins
tend to provide a lower limit. In contrast, complex biogenic particles
may exhibit ice nucleation activities which are up to 2 orders of
magnitude higher than observed for cellulose, making ambient
plant-derived particles a potentially important contributor to the
population of ice-nucleating particles in the troposphere, even though
major uncertainties regarding their transport to cloud altitude
remain.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d1e214">Ice formation in the atmosphere has a significant influence on the
microphysical and radiative properties of clouds. At temperatures
above 235 <inline-formula><mml:math id="M3" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula>, atmospheric aerosol particles may act as
ice-nucleating particles (INPs; Pruppacher and Klett, 2010; Vali
et al., 2015). In mixed-phase clouds, immersion freezing is often the
dominant ice nucleation mode (Hande and Hoose, 2017). Immersion
freezing refers to a solid<?pagebreak page11388?> particle initiating ice formation inside
a supercooled cloud droplet.</p>
      <p id="d1e225">Over the past decades, many different particle types initiating
freezing in mixed-phase clouds have been studied extensively (Hoose
and Möhler, 2012; Murray et al., 2012; Kanji et al.,
2017). Mineral dust particles emitted from desert areas have been
identified as ubiquitous INPs which initiate ice nucleation in clouds
over a wide range of temperature and humidity conditions (Boose
et al., 2016; Ullrich et al., 2017). Cloud-level concentrations of
potentially very ice-active primary biological aerosol particles
(Hoose and Möhler, 2012) are much lower than background
concentrations of mineral dust, with differences of up to 8 orders of
magnitude in some cases (Hummel et al., 2018). Nevertheless, several
laboratory studies, remote sensing measurements and studies
characterizing ice crystal residuals have found evidence for the
potential impact of these particles and more numerous nanoscale
fragments on ice formation in mixed-phase clouds (e.g., Möhler
et al., 2007; Pratt et al., 2009; Kanitz et al., 2011; O'Sullivan
et al., 2015). Also, recent studies indicate a missing source of INPs
beyond mineral dust, with biological particles from terrestrial
environments being a likely candidate for initiating freezing in
shallow mixed-phase clouds (O'Sullivan et al., 2018). One of the
potential sources for these terrestrial INPs is agricultural areas,
which may contribute between 7 % and 75 % to the regional dust
burden (Ginoux et al., 2012) due to emissions driven by wind erosion
and land management activities such as tilling and harvesting
(Hoffmann et al. 2008; Funk et al., 2008; Iturri et al.,
2017). Vegetated areas are another source for complex organic aerosol
particles associated with leaf detritus (Coz et al., 2010).</p>
      <p id="d1e228">One of the characteristics of biological INPs is that they include
a vast variety of different particle types, ranging from primary
biological particles, such as bacteria, fungi and pollen, to complex
organic particles carrying different ice-nucleating agents and
originating from biogenic sources (Schnell and Vali, 1973; Hoose and
Möhler, 2012; Murray et al., 2012; Augustin et al., 2013;
O'Sullivan et al., 2014; Tobo et al., 2014; Conen et al., 2016;
Steinke et al., 2016). An example of complex organic particles is
agricultural soil dust particles for which the observed high ice
nucleation efficiency can be linked to microbiological activity and
the presence of organic macromolecules (O'Sullivan et al., 2014; Tobo
et al., 2014; Hill et al., 2016; Steinke et al., 2016; Suski et al.,
2018). The expression of bacterial and fungal ice-active proteins is
highly variable because environmental stress (e.g., a change in
temperature) can change the structure of the ice-nucleating proteins,
resulting in a loss of functionality (Pummer et al., 2012). In
contrast, some of the organic macromolecules found in agricultural
soils are very inert as they are able to withstand physical and
chemical treatments, e.g., with heat or exposure to enzymes (Hill
et al., 2016). With decaying plant material being one of the sources
of these macromolecules (Hill et al., 2016), the need arises to better
characterize the ice nucleation properties of plant-derived particles
and their individual organic components.</p>
      <p id="d1e231">Lignin and polysaccharides are integral components of plant cell
structures and contribute up to 50 % to plant debris (Williams and
Gray, 1974). Proteinaceous components of leaf litter (e.g., enzymes,
storage proteins or structure proteins) vary considerably but have
been found to account for up to 15 % (Williams and Gray,
1974). Lipids contribute up to 10 % to dry leaf mass (Graça et
al., 2005).  Note that only 50 % of the organic matter is
accessible through chemical degradative techniques which inadvertently
impact the structure of the extracted organic matter
(Kögel-Knabner, 2002).</p>
      <p id="d1e235">In this study, we investigate the immersion freezing properties of
commercially available plant-derived organic compounds such as lignin,
polysaccharides, plant wax and plant proteins – which are the main
components of decaying plant material – as well as ambient bulk
samples rich in plant material. We used commercially available organic
compounds as analogues for plant-derived organics. Note that many of
the extraction methods for organic matter may cause significant
changes in the physicochemical properties of the extracted organic
compounds (Kögel-Knabner, 2002). Experiments were conducted at the
Aerosol Interactions and Dynamics in the Atmosphere (AIDA) cloud
chamber and complemented by drop freezing assay studies using the Ice
Nucleation SpEctrometer of the Karlsruhe Institute of Technology
(INSEKT). From our experimental results, we derived temperature-dependent parameterizations based on the ice nucleation active surface
site (INAS) densities concept (Connolly et al., 2009; Niemand et al.,
2012). These parameterizations were then used to estimate upper limits
for ambient INP concentrations for complex organic aerosols from
vegetated environments.</p>
</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Samples and methods</title>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>Samples</title>
      <p id="d1e253">In Table 1 we describe the samples used in this study, which include
commercially available plant-derived organic compounds and bulk
samples from vegetated environments.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1" specific-use="star"><?xmltex \currentcnt{1}?><label>Table 1</label><caption><p id="d1e259">Overview of samples used for ice nucleation experiments.</p></caption><oasis:table frame="topbot"><?xmltex \begin{scaleboxenv}{.98}[.98]?><oasis:tgroup cols="3">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="left"/>
     <oasis:colspec colnum="3" colname="col3" align="justify" colwidth="11.1cm"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Sample name</oasis:entry>
         <oasis:entry colname="col2">Acronym</oasis:entry>
         <oasis:entry colname="col3">Sample preparation/manufacturer</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row rowsep="1">
         <oasis:entry namest="col1" nameend="col3">Ambient bulk samples dominated by decaying plant material </oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Leaf litter</oasis:entry>
         <oasis:entry colname="col2">LEAF</oasis:entry>
         <oasis:entry colname="col3">Dry leaf debris from either spruce or maple trees in southwestern Germany, dried at 313 <inline-formula><mml:math id="M4" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula>, milled and sieved for particles smaller than 150 <inline-formula><mml:math id="M5" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula> (collected in spring and autumn in 2014, 2015 and 2016)</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Agricultural dust</oasis:entry>
         <oasis:entry colname="col2">AGDUST_HARV</oasis:entry>
         <oasis:entry colname="col3">Dry plant material collected from filters of harvesting machines after rye and wheat harvests in northwestern Germany and sieved for particles smaller than 63 <inline-formula><mml:math id="M6" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula> (collected in summer 2016)</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Agricultural soil dust</oasis:entry>
         <oasis:entry colname="col2">AGDUST_WYO</oasis:entry>
         <oasis:entry colname="col3">Topsoil samples collected in Wyoming from sugar beet fields (collected in spring 2011)</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry namest="col1" nameend="col3">Plant-related organic compounds </oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Alginate</oasis:entry>
         <oasis:entry colname="col2">ALG</oasis:entry>
         <oasis:entry colname="col3">C. E. Roeper GmbH (article no. NA 4012)</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Lignin</oasis:entry>
         <oasis:entry colname="col2">LIG</oasis:entry>
         <oasis:entry colname="col3">Sigma-Aldrich (article nos. 370959 and 471003)</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Lipids (carnauba wax)</oasis:entry>
         <oasis:entry colname="col2">LIP</oasis:entry>
         <oasis:entry colname="col3">Sigma-Aldrich (article no. 243213)</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Pectin</oasis:entry>
         <oasis:entry colname="col2">PEC</oasis:entry>
         <oasis:entry colname="col3">Herbstreith and Fox KG (article no. AU 015 H I)</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Protein (rice and soy)</oasis:entry>
         <oasis:entry colname="col2">PROT_R, PROT_S</oasis:entry>
         <oasis:entry colname="col3">Erdschwalbe (article nos. 30676 and 30744; food grade quality)</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Starch (potato)</oasis:entry>
         <oasis:entry colname="col2">STAR_P</oasis:entry>
         <oasis:entry colname="col3">Mueller's Muehle GmbH (food grade quality)</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Starch (corn)</oasis:entry>
         <oasis:entry colname="col2">STAR_C</oasis:entry>
         <oasis:entry colname="col3">Unilever (food grade quality)</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup><?xmltex \end{scaleboxenv}?></oasis:table></table-wrap>

      <p id="d1e453">Note that the agricultural dust from harvesting machines (bulk sample)
contains roughly 90 % of biological material, e.g., partially
intact plant cells and similar particles (Fig. S1 in the Supplement). The soil dust
sample from Wyoming has been investigated in a recent study by Tobo
et al. (2014), who found that organics contribute significantly to the ice
nucleation efficiency observed for size-selected particles (<inline-formula><mml:math id="M7" display="inline"><mml:mrow><mml:mi>d</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">600</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M8" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">nm</mml:mi></mml:mrow></mml:math></inline-formula>). Representative microscopy images of all other
samples used in this study are shown in the Supplement (Fig. S2).</p>
</sec>
<sec id="Ch1.S2.SS2">
  <label>2.2</label><title>AIDA immersion freezing experiments</title>
      <p id="d1e484">Immersion freezing initiated by plant-related particles was
investigated in the AIDA cloud chamber (Karlsruhe Institute<?pagebreak page11389?> of
Technology, Germany). The AIDA cloud chamber consists of a cylindrical
aluminum vessel (volume 84 <inline-formula><mml:math id="M9" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>) which is enclosed by
a thermally insulated box. The ascent of cloud parcels is simulated by
lowering the pressure from ambient levels (about 1000 <inline-formula><mml:math id="M10" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">hPa</mml:mi></mml:mrow></mml:math></inline-formula>) to
around 800 <inline-formula><mml:math id="M11" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">mbar</mml:mi></mml:mrow></mml:math></inline-formula> resulting in a decrease in the temperature and an increase in the relative humidity.</p>
      <p id="d1e514">A fan at the bottom of the AIDA chamber ensures homogeneous mixing
(also with regard to temperature and humidity) across the whole
chamber volume, except for transition zones near the chamber
walls. The overall uncertainty of the mean gas temperature is about
<inline-formula><mml:math id="M12" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>T</mml:mi><mml:mo>=</mml:mo><mml:mo>±</mml:mo><mml:mn mathvariant="normal">0.3</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M13" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula> (Möhler et al., 2006). The absolute
water vapor partial pressure is measured with a tunable diode laser
instrument and converted into humidity values by leveraging the
saturation pressure formulation given in the review by Murphy and Koop
(2005). The relative humidity values can be measured with an accuracy
of <inline-formula><mml:math id="M14" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:msub><mml:mtext>RH</mml:mtext><mml:mtext>ice</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mo>±</mml:mo><mml:mn mathvariant="normal">5</mml:mn></mml:mrow></mml:math></inline-formula> % (Fahey et al., 2014).</p>
      <p id="d1e560">Particle background concentrations within the cloud chamber are
typically below 0.1 <inline-formula><mml:math id="M15" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">cm</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>. For the immersion freezing
experiments presented in this work, aerosol samples were injected into
the cloud chamber by using a rotating brush generator (RBG 1000; Palas
GmbH) for dry dispersion.  Additionally, impactor stages were used to
eliminate particles larger than 3–5 <inline-formula><mml:math id="M16" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>. The aerosol
size distribution at the beginning of each experimental run was
measured by combining data from an aerodynamic particle sizer (APS; model 3321, TSI Incorporated) and a scanning mobility particle sizer (SMPS; model 3076, TSI Incorporated). The combined aerosol size distributions are used to
estimate the available aerosol surface area based on volume-equivalent
sphere diameters; this then results in an estimate of the geometric
surface area.</p>
      <p id="d1e587">Upon reaching water saturation during an expansion experiment, aerosol
particles within the cloud chamber are activated to droplets and may
freeze subsequently. Ice crystal number concentrations are measured
with two optical particle counters (white light aerosol spectrometer, namely WELAS1 and WELAS2; series 2300 and 2500; Palas GmbH) with size ranges
of 0.7–46 and 5–240 <inline-formula><mml:math id="M17" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>m in optical particle diameter,
respectively (Wagner and Möhler, 2013). Ice crystals are
discriminated from droplets by choosing a size threshold which is
evaluated individually for each experiment.</p>
</sec>
<sec id="Ch1.S2.SS3">
  <label>2.3</label><title>Droplet freezing assay studies</title>
      <p id="d1e606">To investigate the freezing of suspensions created with the bulk
samples and hence to account for freezing caused by particles larger
than 5 <inline-formula><mml:math id="M18" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>, a droplet freezing technique was employed. The
Ice Nucleation SpEctrometer of the Karlsruhe Institute of Technology
(INSEKT) setup (Schiebel, 2017) is based on the droplet freezing assay
originally developed at Colorado State University (Hill et al., 2014).</p>
      <p id="d1e619">Suspensions were created from bulk samples, combining 2 <inline-formula><mml:math id="M19" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">mg</mml:mi></mml:mrow></mml:math></inline-formula> of
material with 20 <inline-formula><mml:math id="M20" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">mL</mml:mi></mml:mrow></mml:math></inline-formula> of deionized water (resistivity about
18 <inline-formula><mml:math id="M21" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">M</mml:mi><mml:mi mathvariant="normal">Ω</mml:mi></mml:mrow></mml:math></inline-formula>), which were passed through a filter with
a pore diameter of 0.1 <inline-formula><mml:math id="M22" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula> (Whatman Puradisc
25). Suspensions were shaken by hand (about 1 <inline-formula><mml:math id="M23" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">min</mml:mi></mml:mrow></mml:math></inline-formula>), and the
suspension tube was then submerged in an ultrasonic bath
(5 <inline-formula><mml:math id="M24" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">min</mml:mi></mml:mrow></mml:math></inline-formula>) to promote dispersion of the particles. In addition to
the original suspensions, we also created suspensions with a dilution
factor of 15 and 225 by adding filtered deionized water in
proportion. Original and diluted suspensions were partitioned into 192
wells (aliquot volume – 50 <inline-formula><mml:math id="M25" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">L</mml:mi></mml:mrow></mml:math></inline-formula>) of a sterile polypropylene
polymerase chain reaction (PCR) tray, with<?pagebreak page11390?> 32 wells set aside for
blank measurements, i.e., freezing of particle-free filtered deionized
water. These blank measurements are used for determining the
background, which is then subtracted from the observed freezing
curves. In this study, droplet freezing was measured at a cooling rate
of 0.33 <inline-formula><mml:math id="M26" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">min</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>. Cooling is achieved by flowing chilled ethanol
through a custom-made aluminum block which encloses the bottom part
of the PCR tray. The overall temperature uncertainty is <inline-formula><mml:math id="M27" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>T</mml:mi><mml:mo>=</mml:mo><mml:mo>±</mml:mo><mml:mn mathvariant="normal">0.3</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M28" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula> (Schiebel, 2017).</p>
</sec>
<sec id="Ch1.S2.SS4">
  <label>2.4</label><title>Ice nucleation active surface site densities</title>
      <p id="d1e734">For all experiments, the ice nucleation efficiency was quantified by
calculating the ice nucleation active surface site (INAS) density
<inline-formula><mml:math id="M29" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>.  The <inline-formula><mml:math id="M30" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> values were derived by scaling
the observed ice crystal number concentration <inline-formula><mml:math id="M31" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mtext>ice</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula> with
the available aerosol surface <inline-formula><mml:math id="M32" display="inline"><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mtext>aer</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula> (Connolly et al., 2009;
Niemand et al., 2012). Exemplary size distributions for leaf litter
and lignin are shown in Fig. S3.</p>
      <p id="d1e781">For the cloud chamber experiments, the aerosol surface
<inline-formula><mml:math id="M33" display="inline"><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mtext>aer</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M34" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msup><mml:mi mathvariant="normal">cm</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>) was calculated from the
APS and SMPS size distribution data using volume-equivalent sphere
diameters (Möhler et al., 2006). In this study, it was assumed
that all aerosol particles were activated to droplets upon reaching
water saturation. Hence, the full aerosol surface area was considered
to be available for immersion freezing. The ice crystal number
concentration <inline-formula><mml:math id="M35" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mtext>ice</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula> was derived from particle size
distributions measured with the optical particle counters, WELAS1 and
WELAS2, in conjunction with a size threshold above which particles are
counted as ice crystals. Based on the measurement uncertainties of the
observed ice crystal concentration, <inline-formula><mml:math id="M36" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mtext>ice</mml:mtext></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mtext>ice</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.2</mml:mn></mml:mrow></mml:math></inline-formula>, and the aerosol surface area
concentration, <inline-formula><mml:math id="M37" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:msub><mml:mi>A</mml:mi><mml:mtext>aer</mml:mtext></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mi>A</mml:mi><mml:mtext>aer</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.35</mml:mn></mml:mrow></mml:math></inline-formula>, the
resulting uncertainty of the INAS density is <inline-formula><mml:math id="M38" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.4</mml:mn></mml:mrow></mml:math></inline-formula> (Ullrich et al., 2017).</p>
      <p id="d1e901">For the droplet freezing studies, the INAS density values were derived
from normalizing the cumulative INP concentrations <inline-formula><mml:math id="M39" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mtext>ice</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula>
with the specific aerosol surface <inline-formula><mml:math id="M40" display="inline"><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mtext>aer</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula>
(<inline-formula><mml:math id="M41" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">g</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>) derived from Brunauer–Emmett–Teller (BET)
surface measurements. For our INAS density uncertainty analysis, we
considered only the uncertainty of the cumulative INP concentrations
which is based on statistics. Confidence intervals (at 95 %) have
been estimated according to the improved Wald interval, which
implicitly assumes a normal approximation for binomially distributed
measurement errors (Agresti and Coull, 1998). Hence, in our INAS
density analysis, we neglected the uncertainties of the BET surface
measurements which are, in most cases, considerably smaller
(i.e., <inline-formula><mml:math id="M42" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:msub><mml:mi>A</mml:mi><mml:mtext>aer</mml:mtext></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mi>A</mml:mi><mml:mtext>aer</mml:mtext></mml:msub><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.1</mml:mn></mml:mrow></mml:math></inline-formula>) than the previously
described statistical uncertainties of the cumulative INP
concentrations (Hiranuma et al., 2015a). Another source of uncertainty
– which is considerably more difficult to quantify – was the
contribution of larger particles. These larger particles may sediment
quickly within the suspension and were probably underrepresented in
the sampled aliquots. Thus, the particle surface area available for
freezing was most likely overestimated in some cases. However, to
fully understand this effect, more studies are needed. Additionally,
suspending particles in water may lead to the desorption and potential
redistribution of soluble material. This change in soluble material
could also lead to differences in the observed ice nucleation
properties when comparing cloud chamber experiments with droplet
freezing studies.</p>
</sec>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Results and discussion</title>
      <p id="d1e979">In Fig. 1 we
present results from AIDA cloud chamber experiments with commercially
available plant-related organic compounds and natural samples (see
Table 1). For comparison, we show the ice nucleation activity of
microcrystalline cellulose (Hiranuma et al., 2015b), which is
a prevalent natural polymer derived from plant fragments, leaf
litter, wood fiber, nonwood fiber and/or even microbes
(Quiroz-Castañeda and Folch-Mallol, 2013; Vlachou et al.,
2018). We also show the ice nucleation efficiency of agricultural soil
dusts investigated in a study by Steinke et al. (2016) and an
estimate for leaf litter from a study by Schnell and Vali (1973). The
ice nucleation activity of each sample is expressed as the INAS
density <inline-formula><mml:math id="M43" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><?xmltex \currentcnt{1}?><label>Figure 1</label><caption><p id="d1e995">Immersion freezing results for plant-related organic compounds
compared to ambient samples. Ice nucleation efficiency is expressed as INAS
density values based on AIDA cloud chamber experiments.</p></caption>
        <?xmltex \igopts{width=312.980315pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/11387/2020/acp-20-11387-2020-f01.png"/>

      </fig>

      <p id="d1e1004">Figure 1 shows that the observed ice nucleation efficiencies of most
individual plant-related organic compounds tend to be lower in
comparison to samples from natural environments. However, there is
a large spread in INAS density values when comparing different
plant-related organic compounds. Particularly noticeable is the low
ice nucleation efficiency observed for plant protein and for which
freezing was observed only below 248 <inline-formula><mml:math id="M44" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula>. In this study, we
tested two different types of plant proteins (PROT_R and PROT_SOY),
derived from soy or rice (not differentiated in Fig. 1). Only lignin
(LIG) shows an ice nucleation activity as low as the plant protein
samples. Alginate, pectin and starch (which mainly consist of highly
complex polysaccharides) are similarly as ice active as microcrystalline
cellulose (Hiranuma et al., 2015b) and desert dusts (Ullrich et al.,
2017 – not shown in Fig. 1). Above 250 <inline-formula><mml:math id="M45" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula>, the complex
polysaccharides investigated in this study (ALG, PEC, STAR_P and
STAR_C) tend to be more ice active than cellulose. Our data also
indicate that the temperature dependence of the polysaccharides
investigated in this study is possibly less pronounced than for
cellulose. Note that this finding is based only on a few data points
due to the low observed ice nucleation efficiency above 252 <inline-formula><mml:math id="M46" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula>.</p>
      <p id="d1e1032">Of all plant-related compounds, carnauba wax (LIP) shows the highest
ice nucleation efficiency, which is comparable to decaying leaves and two
agricultural samples, i.e., dust from a sugar beet field
(AGDUST_WYO) and material collected from harvesting machines
(AGDUST_HARV). Carnauba wax is a mixture of hydrocarbons,
aliphatic esters and fatty alcohols (Vandenburg and Wilder, 1970), with
an average chain length of 50 carbon atoms (Basson and<?pagebreak page11391?> Reynhardt,
1988). Crystalline fatty alcohols (C16–C18) have been highlighted
recently in a study by DeMott et al. (2018) with regard to their
ability to nucleate ice at 261 <inline-formula><mml:math id="M47" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula> via condensation
freezing. Based on theoretical considerations, hydrocarbons with long
chains are potentially very good at initiating ice formation (Qiu
et al., 2017), but conclusive experimental evidence is still
missing. Hence, these theoretical considerations might provide an
explanation for the high ice nucleation ability of carnauba wax.</p>
      <p id="d1e1043">For samples like the agricultural soil dusts and the leaf litter
investigated in this study, some studies (e.g., Schnell and Vali, 1973;
Steinke et al., 2016) have found similarly high ice nucleation
efficiencies.</p>
      <p id="d1e1046">In contrast, at 258 <inline-formula><mml:math id="M48" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula>, leaf litter from the Arctic consisting
of birch and grass leaves (Conen et al., 2016) has been observed to
show relatively low ice nucleation efficiencies compared to leaf
litter in our study – based on AIDA results and similar efficiencies
when comparing against our droplet freezing assay.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2" specific-use="star"><?xmltex \currentcnt{2}?><label>Figure 2</label><caption><p id="d1e1059">Immersion freezing results for selected plant-related samples and
illite by comparing INSEKT-derived INAS density values to results from AIDA
experiments (Fig. 1). Ice nucleation efficiency is expressed as INAS density
values based on INSEKT droplet freezing experiments and specific surface
areas indicated in the legend.</p></caption>
        <?xmltex \igopts{width=312.980315pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/11387/2020/acp-20-11387-2020-f02.png"/>

      </fig>

      <p id="d1e1068">Hence, the high INAS density values observed in our cloud chamber
studies can be interpreted as the upper limits for the ice nucleation
efficiency of ambient plant-related aerosol particles. Note that for
our leaf litter samples we did not differentiate between samples
collected at different points in time or for different species. Due
to the high variability, it was not possible to clearly derive
a seasonal trend from the observed ice nucleation efficiencies.</p>
      <p id="d1e1072">In Fig. 2, we show INSEKT-derived INAS density values for selected
samples investigated in the previously described AIDA cloud chamber
studies. For every sample at least two experimental runs were
conducted, using freshly prepared suspensions for each run. The
PROT_S sample was investigated to establish the lower boundary of
ice nucleation activity observed for plant components whereas the
AGDUST_HARV and the LEAF samples were used to represent ambient
samples. Note that for the droplet freezing experiments, the INAS
densities are evaluated based on the specific surface areas derived
from BET measurements rather than the geometric surface areas which
were used for analyzing the AIDA experiments.  The droplet freezing
experiments are complementary to the cloud chamber studies as they
provide insights regarding the freezing properties of the bulk
material, in particular with regard to including particles larger than
5 <inline-formula><mml:math id="M49" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula> which are largely eliminated by the impactor stages in
our AIDA experiments. Also, observing the freezing of bulk suspensions
allows for quantifying the immersion freezing efficiencies at a lower
supercooling which are more difficult to quantify in AIDA cloud
chamber studies. For leaf litter we observe that INAS density values
agree well between the INSEKT and AIDA experiments. Similarly for plant
protein (PROT_S), the agreement is reasonable. For AGDUST_HARV,
there is a difference of approximately more than 1 order of
magnitude, which is possibly caused by larger particles being
undersampled due to sedimentation within the suspensions.</p>
      <p id="d1e1085">Figure 2 shows that the hierarchy in ice nucleation activities is
similar to that observed in the AIDA cloud chamber experiments, with leaf
litter and agricultural dust being the most ice-active samples. The
steep onset of ice nucleation observed for the agricultural dust at
267 <inline-formula><mml:math id="M50" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula> suggests a contribution from biological particles (Suski
et al., 2018). In contrast, the reasons for the steep onset observed
for the leaf litter sample are a bit more unclear as most studies
investigating primary biological particles have observed freezing
onsets and high ice nucleation efficiency already at temperatures
above 260 <inline-formula><mml:math id="M51" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula> (see references in Hoose and<?pagebreak page11392?> Möhler,
2012). However, one recent study has found indications for
macromolecules associated with microbial activity being ice active at
about 258 <inline-formula><mml:math id="M52" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula> (O'Sullivan et al., 2015). Soy protein particles
initiate ice formation at higher temperatures (i.e., already below
258 <inline-formula><mml:math id="M53" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula>) than observed in AIDA cloud chamber experiments, but
the overall ice nucleation efficiency is still lower than for the
complex organic samples from natural environments.  Unfortunately, it
was not possible to reliably determine INAS density values for
carnauba wax (LIP) due to its very low dispersibility. Figure 2 also
shows the INAS density values observed for illite as a proxy for
freezing induced by mineral dust.</p>
      <p id="d1e1120">In conclusion, the results from the droplet freezing studies confirm
the trend observed in our AIDA cloud chamber experiments, with
particles from vegetated and agricultural environments being highly
ice active, whereas individual organic compounds tend to be lower in
their ice nucleation efficiencies. It should be noted that the organic
compounds investigated in this study may not fully represent the
complexity of real organic compounds in plants which often include
mixtures, e.g., ligno-polysaccharide complexes with unknown chemical
structures (Kögel-Knabner, 2002). At temperatures above
260 <inline-formula><mml:math id="M54" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula>, the gap between individual plant-related compounds and
particles from natural environments may be attributed to primary
biological particles (e.g., fungi and bacteria) according to our
droplet freezing measurements of harvesting dust. For example, ice
nucleation efficiencies observed for particles generated from leaf
litter fall within the lower range of values observed for bacteria
(Hoose and Möhler, 2012).</p>
      <p id="d1e1131">There are, however, also differences between the ice nucleation
efficiencies derived from AIDA cloud chamber experiments and droplet freezing studies, which are strongly dependent on the aerosol type. Some
of these differences might be explained by differences in the
evaluation of the INAS density values, which are either related to the
geometric surface or the specific surface area. For illite,
normalizing by BET surface area results in INAS density values which
are 1 order of magnitude lower compared to values derived by using
geometric surface estimates (Hiranuma et al., 2015a). Also, for some
samples there are possibly differences in the effective size
distribution due to agglomeration or low dispersibility in the
suspensions.  In contrast, the dry dispersion method (i.e., the
rotating brush generator) is more likely to encourage the disaggregation
of particle agglomerates. Similar differences regarding the freezing
of aqueous suspensions in comparison to dry dispersion experiments
have been observed in other studies as well (Hiranuma et al., 2015a, 2019).</p>
      <?pagebreak page11393?><p id="d1e1134">Our experimental results suggest that the main components of decaying
plant material (i.e., cellulose and lignin) are not very good
predictors of ice nucleation by ambient plant-related
particles. However, the INAS density values observed for leaf litter
and agricultural dust may help to constrain the upper limits of their
respective ambient INP concentrations. The INAS density values for
leaf litter and agricultural dust can be described by
temperature-dependent functions, with the following:

              <disp-formula id="Ch1.E1" content-type="numbered"><label>1</label><mml:math id="M55" display="block"><mml:mstyle class="stylechange" displaystyle="true"/><mml:mrow><mml:mstyle class="stylechange" displaystyle="true"/><mml:msub><mml:mi>n</mml:mi><mml:mtext>s,leaf</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mi>exp⁡</mml:mi><mml:mo>(</mml:mo><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.246</mml:mn><mml:mo>⋅</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mtext>leaf</mml:mtext></mml:msub><mml:mo>+</mml:mo><mml:mn mathvariant="normal">84.681</mml:mn><mml:mo>)</mml:mo><mml:mspace linebreak="nobreak" width="1em"/><mml:msup><mml:mi>r</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.70</mml:mn><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>

        and

              <disp-formula id="Ch1.E2" content-type="numbered"><label>2</label><mml:math id="M56" display="block"><mml:mstyle class="stylechange" displaystyle="true"/><mml:mrow><mml:mstyle class="stylechange" displaystyle="true"/><mml:msub><mml:mi>n</mml:mi><mml:mtext>s,agri</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mi>exp⁡</mml:mi><mml:mo>(</mml:mo><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.541</mml:mn><mml:mo>⋅</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mtext>agri</mml:mtext></mml:msub><mml:mo>+</mml:mo><mml:mn mathvariant="normal">157.471</mml:mn><mml:mo>)</mml:mo><mml:mspace width="1em" linebreak="nobreak"/><mml:msup><mml:mi>r</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.84</mml:mn><mml:mo>.</mml:mo></mml:mrow></mml:math></disp-formula>

        Note that these functions are only valid within certain temperature
ranges, i.e., <inline-formula><mml:math id="M57" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mtext>leaf</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mo>[</mml:mo><mml:mn mathvariant="normal">243</mml:mn><mml:mo>,</mml:mo><mml:mn mathvariant="normal">258</mml:mn><mml:mo>]</mml:mo></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M58" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mtext>agri</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mo>[</mml:mo><mml:mn mathvariant="normal">245</mml:mn><mml:mo>,</mml:mo><mml:mn mathvariant="normal">255</mml:mn><mml:mo>]</mml:mo></mml:mrow></mml:math></inline-formula>, with all temperatures given in kelvin. Equations (1) and (2)
have been derived from the cloud chamber experiments exclusively and
are represented in Fig. 2. Note that, based on our droplet freezing
experiments, both of these aerosol types may have relatively sharp ice
nucleation onsets at 257 <inline-formula><mml:math id="M59" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula> (leaf litter) and 267 <inline-formula><mml:math id="M60" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula>
(agricultural dusts).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3"><?xmltex \currentcnt{3}?><label>Figure 3</label><caption><p id="d1e1301">Comparison between atmospheric INP concentrations (Petters and
Wright, 2015) and estimates for INPs from leaf litter and
agricultural dust based on AIDA cloud chamber experiments.</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/11387/2020/acp-20-11387-2020-f03.png"/>

      </fig>

      <p id="d1e1310">Figure 3 shows a comparison between ambient INP concentrations derived
from precipitation samples from several sites in the USA and Europe
(Petters and Wright, 2015) and estimates for INP concentrations from
leaf litter (Eq. 1) and agricultural dust (Eq. 2). Note that ambient
INP measurements may scatter significantly more than found in the
study by Petters and Wright (2015), with deviations of up to 4
orders of magnitude between different studies (Kanji et al., 2017).</p>
      <p id="d1e1313">Ground-based measurements for leaf litter concentrations range between
30 <inline-formula><mml:math id="M61" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">ng</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> and 1 <inline-formula><mml:math id="M62" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">g</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> (Hildemann et al.,
1996; Sánchez-Ochoa et al., 2007). Sánchez-Ochoa et al. (2007)
use cellulose found in aerosol particles as a proxy for plant debris
concentrations, relying on observations at six European sites for a time span of 2 years, and with two of the sites being located on
mountains.  Hildemann et al. (1996) used higher alkanes (e.g., occurring
in plant waxes) to fingerprint plant debris in aerosol particles
sampled in the greater Los Angeles area. For agricultural dust,
ground-based concentration varies between <inline-formula><mml:math id="M63" display="inline"><mml:mrow><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">10</mml:mn></mml:mrow></mml:math></inline-formula> and
100 <inline-formula><mml:math id="M64" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">g</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>, with up to 800 <inline-formula><mml:math id="M65" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">g</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>
observed occasionally for very strong wind erosion events (Gillette
et al., 1978; Sharratt et al., 2007; Hoffmann and Funk,
2015). Annually averaged boundary layer concentrations for desert dust
vary between 0.1 and 30 <inline-formula><mml:math id="M66" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">g</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> (Ginoux et al., 2001),
which is comparable to the aforementioned concentrations of complex
organic particles.  Anthropogenic dust sources contribute roughly
25 % to the global dust burden, with regional variations ranging
from 7 % to 75 % (Ginoux et al., 2012). In areas with intense
agricultural land use, e.g., in eastern North America, India, eastern
China and Europe, anthropogenic dust emissions contribute generally to
more than 60 % of the total dust burden (Huang et al.,
2015). Note, however, that there is a substantial uncertainty
regarding the number and size of particles emitted from agricultural activities and their transport to cloud altitudes and the resulting
atmospheric lifetime. This uncertainty is rooted in a lack of emission
flux data above 5–10 <inline-formula><mml:math id="M67" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>, which is the height at which dust
fluxes from agricultural areas are commonly observed (e.g., in the
study by Zobeck and Van Pelt, 2006). Using Eqs. (1) and (2) and assuming
an aerosol surface area of 1 and 36 <inline-formula><mml:math id="M68" display="inline"><mml:mrow class="unit"><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msup><mml:mi mathvariant="normal">g</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> as measured
by BET analysis for leaf litter and soil dust, respectively, we can derive order of magnitude estimates for the
expected atmospheric INP contributions from leaf litter and
agricultural dust. In Fig. 3, we have scaled down agricultural dust
INPs by a factor of 100 and leaf litter INPs by a factor of 10 to at
least partially account for transport losses. Scaling factors are
derived from model results presented in Hoose et al. (2010), using
vertical profiles for desert dusts and biological particles as rough
proxies for the samples investigated in this study.</p>
      <p id="d1e1448">The estimates presented in this study should be considered as the upper
limits, with emission fluxes of organic particles acting as INPs being
poorly constrained, and more detailed modeling case studies are
needed. We find that plant-derived organic INPs from leaf litter and
agricultural areas are within the same order of magnitude as INP
concentrations derived from precipitation measurements and field
campaigns (Petters and Wright, 2015; Kanji et al., 2017). This finding
further emphasizes the potential of plant-related sources
contributing to ambient INPs.</p>
</sec>
<sec id="Ch1.S4" sec-type="conclusions">
  <label>4</label><title>Conclusions</title>
      <p id="d1e1459">Complex organic particles are emitted from terrestrial sources, with
wind erosion, soil cultivation and harvesting crops as potential main
drivers for emissions of organic matter associated with plant debris
and decomposed residues (Funk et al., 2008; Hoffmann et al., 2008; Coz
et al., 2010; Ginoux et al., 2012). These sources are becoming
increasingly important in the global view as climate change, soil
degradation and excessive land use will promote dust emissions from
agriculturally used areas. In this study, we investigated the
immersion freezing properties of plant-related organic particles and
samples from vegetated environments. We used a combination of the AIDA
cloud chamber and INSEKT droplet freezing experiments to cover
a temperature range between 242 and 267 <inline-formula><mml:math id="M69" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">K</mml:mi></mml:mrow></mml:math></inline-formula>. Our experiments
show that<?pagebreak page11394?> the samples with a complex organic composition are equally as
ice active as (or more ice active than) individual plant-related compounds. Lignin and
plant protein samples are inefficient INPs, whereas starches, alginate
and pectin show moderate to high ice nucleation
efficiencies. Surprisingly, carnauba wax – which is a mixture of
aliphatic esters and fatty acids – shows the highest ice nucleation
activity of all the organic compounds investigated in this study. INP
estimates based on our cloud chamber experiments lend themselves to
the hypothesis that aerosolized particles from leaf litter and
agricultural areas are potentially important contributors to
atmospheric INPs. However, the high ice nucleation efficiency of these
particles could not be fully explained by the ice nucleation activity
of individual organic compounds commonly found in plant tissue,
potentially indicating a contribution from primary biological
particles or organics associated with microbial activity.  Thus,
further future studies are indeed demanded and warranted.</p>
</sec>

      
      </body>
    <back><notes notes-type="dataavailability"><title>Data availability</title>

      <p id="d1e1475">All data in this manuscript will be made available as part of a KITopen data
repository.</p>
  </notes><app-group>
        <supplementary-material position="anchor"><p id="d1e1478">The supplement related to this article is available online at: <inline-supplementary-material xlink:href="https://doi.org/10.5194/acp-20-11387-2020-supplement" xlink:title="pdf">https://doi.org/10.5194/acp-20-11387-2020-supplement</inline-supplementary-material>.</p></supplementary-material>
        </app-group><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e1487">IS and NH designed and conducted the experiments, with contributions from
KH, OM and NSU. PGW conducted the BET surface measurements, and NT provided
the SEM images. IS and NH analyzed the data. IS prepared the paper with
input from all coauthors.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e1493">The authors declare that they have no conflict of interest.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e1499">Hinrich Grothe (Vienna University of Technology) is acknowledged for having
provided several organic samples (lignin, carnauba wax, pectin, alginate and
starches) investigated in this study.</p><p id="d1e1501">The IMK–AAF technicians team (Georg Scheurig, Rainer Buschbacher, Tomasz Chudy, Olga Dombrowski and Steffen Vogt) is acknowledged for their continued support in ensuring the smooth operation of the AIDA cloud chamber.</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d1e1506">This study was conducted with financial support from the
Carl Zeiss Foundation and the German Science Foundation (DFG) through the
research unit INUIT (grant nos. FOR 1525 and MO668/4-2). Isabelle Steinke was also funded in
part by the US Department of Energy (DOE; BER) through the Early Career
Program. Nsikanabasi Silas Umo was funded by the Alexander von Humboldt Foundation, Germany (grant no. 1188375). Some microscopy research and sample
precharacterizations were performed in the Environmental Molecular Sciences
Laboratory (under the user proposal 49077), which is a DOE Office of Science User
Facilities, sponsored by the Office of Biological and Environmental Research,
and located at the Pacific Northwest National Laboratory.<?xmltex \hack{\newline}?><?xmltex \hack{\newline}?> The article processing charges for this open access <?xmltex \hack{\newline}?> publication were covered by a Research<?xmltex \hack{\newline}?> Centre of the Helmholtz Association.</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e1519">This paper was edited by Hinrich Grothe and reviewed by two anonymous referees.</p>
  </notes><ref-list>
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    <!--<article-title-html>Complex plant-derived organic aerosol as ice-nucleating particles – more than the sums of their parts?</article-title-html>
<abstract-html><p>Quantifying the impact of complex organic particles on the formation
of ice crystals in clouds remains challenging, mostly due to the vast
number of different sources ranging from sea spray to agricultural
areas. In particular, there are many open questions regarding the ice
nucleation properties of organic particles released from terrestrial
sources such as decaying plant material.</p><p>In this work, we present results from laboratory studies investigating
the immersion freezing properties of individual organic compounds
commonly found in plant tissue and complex organic aerosol particles
from vegetated environments, without specifically investigating the
contribution from biological particles, which may contribute to the
overall ice nucleation efficiency observed at high temperatures. To
characterize the ice nucleation properties of plant-related aerosol
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Aerosol Interaction and Dynamics in the Atmosphere (AIDA) cloud
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of Technology (INSEKT), which is a droplet freezing assay. Individual
plant components (polysaccharides, lignin, soy and rice protein) were
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cellulose, which has been suggested by recent studies to be a proxy for
quantifying the primary cloud ice formation caused by particles
originating from vegetation. In contrast, samples from ambient sources
with a complex organic matter composition (agricultural soils and leaf
litter) were either similarly ice active or up to 2 orders of
magnitude more ice active than cellulose.  Of all individual organic
plant components, only carnauba wax (i.e., lipids) showed a similarly
high ice nucleation activity as that of the samples from vegetated
environments over a temperature range between 245 and 252&thinsp;K.
Hence, based on our experimental results, we suggest considering
cellulose as being representative for the average ice nucleation
activity of plant-derived particles, whereas lignin and plant proteins
tend to provide a lower limit. In contrast, complex biogenic particles
may exhibit ice nucleation activities which are up to 2 orders of
magnitude higher than observed for cellulose, making ambient
plant-derived particles a potentially important contributor to the
population of ice-nucleating particles in the troposphere, even though
major uncertainties regarding their transport to cloud altitude
remain.</p></abstract-html>
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