Ice-Nucleating Particles in a Coastal Tropical Site

Abstract

Atmospheric aerosol particles that can nucleate ice are referred to as ice-nucleating particles (INPs). Recent studies have confirmed that aerosol particles emitted by the oceans can act as INPs. This very relevant information can be included in climate and weather models to predict the formation of ice in clouds, given that most of them do not consider oceans as a source of INPs. Very few studies that sample INPs have been carried out in tropical latitudes, and there is a need to evaluate their availability to understand the potential role that marine aerosol may play in the hydrological cycle of tropical regions.

This study presents results from the first measurements obtained during a field campaign conducted in the tropical village of Sisal, located on the coast of the Gulf of Mexico of the Yucatan Peninsula in Mexico in January–February 2017, and one of the few data sets currently available at such latitudes (i.e., 21∘ N). Aerosol particles sampled in Sisal are shown to be very efficient INPs in the immersion freezing mode, with onset freezing temperatures in some cases as high as −3 ∘C, similarly to the onset temperature from Pseudomonas syringae. The results show that the INP concentration in Sisal was higher than at other locations sampled with the same type of INP counter. Air masses arriving in Sisal after the passage of cold fronts have surprisingly higher INP concentrations than the campaign average, despite their lower total aerosol concentration.

The high concentrations of INPs at warmer ice nucleation temperatures (T>−15 ∘C) and the supermicron size of the INPs suggest that biological particles may have been a significant contributor to the INP population in Sisal during this study. However, our observations also suggest that at temperatures ranging between −20 and −30 ∘C mineral dust particles are the likely source of the measured INPs.

Full text

Atmos. Chem. Phys., 19, 6147–6165, 2019
https://doi.org/10.5194/acp-19-6147-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Ice-nucleating particles in a coastal tropical site
Luis A. Ladino1, Graciela B. Raga1, Harry Alvarez-Ospina2, Manuel A. Andino-Enríquez3, Irma Rosas1,
Leticia Martínez1, Eva Salinas1, Javier Miranda4, Zyanya Ramírez-Díaz1, Bernardo Figueroa5, Cedric Chou6,
Allan K. Bertram6, Erika T. Quintana7, Luis A. Maldonado8, Agustín García-Reynoso1, Meng Si6, and
Victoria E. Irish6
1Centro de Ciencias de la Atmosfera, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
2Facultad de Ciencias, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
3School of Chemical Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Ecuador
4Instituto de Fisica, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
5Laboratorio de Ingenieria y Procesos Costeros, Instituto de Ingenieria, Universidad Nacional Autonoma
de Mexico, Sisal, Yucatan, Mexico
6Chemistry Department, University of British Columbia, Vancouver, Canada
7Escuela Nacional de Ciencias Biologicas, Instituto Politecnico Nacional, Mexico City, Mexico
8Facultad de Quimica, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
Correspondence: Luis A. Ladino (luis.ladino@atmosfera.unam.mx)
Received: 19 November 2018 – Discussion started: 6 December 2018
Revised: 22 April 2019 – Accepted: 24 April 2019 – Published: 9 May 2019
Abstract. Atmospheric aerosol particles that can nucleate
ice are referred to as ice-nucleating particles (INPs). Recent
studies have confirmed that aerosol particles emitted by the
oceans can act as INPs. This very relevant information can
be included in climate and weather models to predict the for-
mation of ice in clouds, given that most of them do not con-
sider oceans as a source of INPs. Very few studies that sam-
ple INPs have been carried out in tropical latitudes, and there
is a need to evaluate their availability to understand the po-
tential role that marine aerosol may play in the hydrological
cycle of tropical regions.
This study presents results from the first measurements ob-
tained during a field campaign conducted in the tropical vil-
lage of Sisal, located on the coast of the Gulf of Mexico of the
Yucatan Peninsula in Mexico in January–February 2017, and
one of the few data sets currently available at such latitudes
(i.e., 21◦N). Aerosol particles sampled in Sisal are shown
to be very efficient INPs in the immersion freezing mode,
with onset freezing temperatures in some cases as high as
−3◦C, similarly to the onset temperature from Pseudomonas
syringae. The results show that the INP concentration in Sisal
was higher than at other locations sampled with the same
type of INP counter. Air masses arriving in Sisal after the
passage of cold fronts have surprisingly higher INP concen-
trations than the campaign average, despite their lower total
aerosol concentration.
The high concentrations of INPs at warmer ice nucleation
temperatures (T > −15 ◦C) and the supermicron size of the
INPs suggest that biological particles may have been a sig-
nificant contributor to the INP population in Sisal during this
study. However, our observations also suggest that at temper-
atures ranging between −20 and −30 ◦C mineral dust parti-
cles are the likely source of the measured INPs.
1 Introduction
Clouds are essential to the hydrological cycle of the planet
and also play a significant role in the radiative balance
of the climate system (Ramanathan et al., 1989; Lohmann
and Feichter, 2005; Andreae and Rosenfeld, 2008; Stevens
and Feingold, 2009). Cloud formation depends on the pres-
ence of cloud condensation nuclei (CCN) and most pre-
cipitation from mixed-phase clouds involves also the pres-
ence of ice-nucleating particles (INPs). Aerosol–cloud in-
teractions are one of the main sources of uncertainty in cli-
mate projections as assessed by the Intergovernmental Panel
on Climate Change (Stocker et al., 2013), prompting a large
Published by Copernicus Publications on behalf of the European Geosciences Union.

6148 L. A. Ladino et al.: Ice-nucleating particles in the Yucatan Peninsula
amount of research effort from the scientific community in
recent years. Nevertheless, the formation and evolution of
ice crystals in mixed-phase and cirrus clouds still remain
highly uncertain (Seinfeld et al., 2016; Kanji et al., 2017;
Field et al., 2017). Several pathways have been proposed
to be potentially responsible for ice formation: condensa-
tion freezing, contact freezing, immersion freezing, and de-
position nucleation (Vali et al., 2015). Murray et al. (2012)
and Ladino et al. (2013) have suggested that contact freez-
ing and immersion freezing are the most efficient mech-
anisms leading to ice nucleation in clouds; however, the
atmospheric relevance of contact freezing is still unclear
given the contradictory results (Hobbs and Atkinson, 1976;
Ansmann et al., 2005; Cui et al., 2006; Phillips et al., 2007;
Seifert et al., 2011; Kanji et al., 2017).
Most of the precipitation from deep convection in the
tropics, e.g., in the intertropical convergence zone, forms
via the ice phase (Mülmenstädt et al., 2015). Given the
ice-nucleating potential of a variety of aerosol particles
such as mineral dust, biological particles, crystalline salts,
carbonaceous particles, and secondary organic aerosol,
the main source of INPs at tropical latitudes is highly
uncertain (Kanji et al., 2017; Yakobi-Hancock et al., 2014;
DeMott et al., 2010). Although it is yet not fully understood
what exactly makes an aerosol particle an efficient INP (e.g.,
its composition, active sites, crystal structure, size, or hy-
groscopicity), there is evidence that their composition is
one of the key factors (Kanji et al., 2017). On a global
scale, the large tropospheric concentrations and the good
ice-nucleating abilities of mineral dust have been widely
reported as an important INP source (Hoose and Möhler,
2012; Nenes et al., 2014; Atkinson et al., 2013; Kanji et al.,
2017). Bioaerosol has also been identified as very efficient
INP (Kanji et al., 2017; Hoose and Möhler, 2012; Fröhlich-
Nowoisky et al., 2016; Hill et al., 2017), with onset freezing
temperatures reported as high as −2◦C (Yankofsky et al.,
1981; Després et al., 2012; Fröhlich-Nowoisky et al., 2015;
Wex et al., 2015; Stopelli et al., 2017). Global climate mod-
els parameterize cloud droplet and ice crystal formation from
observational studies and results from such modeling sug-
gest that on a global scale bioaerosol is not a major source
of INPs, and therefore, have a lower impact on ice cloud
formation in comparison to mineral dust particles (Hoose
et al., 2010; Sesartic et al., 2012). However, this may not
be the case on a regional scale (Burrows et al., 2013; Ma-
son et al., 2015a). Marine organic matter, likely of biological
origin, has been suggested to be an important oceanic source
of INPs in the southern oceans, North Atlantic, and North
Pacific (Burrows et al., 2013; Yun and Penner, 2013; Wilson
et al., 2015; Vergara-Temprado et al., 2017). However, the
maritime source suggestion was made with little or no data
from tropical latitudes.
Important efforts were made during the 1950–1970s to un-
derstand the role of the oceans in ice cloud formation (Bigg,
1973; Schnell and Vali, 1975; Schnell, 1975, 1977, 1982;
Rosinski et al., 1987, 1988). There is recent new and robust
evidence that biological material from the marine environ-
ment could act as efficient INPs (Knopf et al., 2011; Wilson
et al., 2015; Mason et al., 2015b; DeMott et al., 2016; Ladino
et al., 2016; McCluskey et al., 2017; Irish et al., 2017; Welti
et al., 2018). Most of the past available INP data were ob-
tained from middle- and high-latitude studies, with tropical
latitudes heavily underrepresented (Schnell, 1982; Rosinski
et al., 1987, 1988; Boose et al., 2016; Welti et al., 2018; Price
et al., 2018). Marine and coastal INP concentration ([INP])
typically ranges from 10−4to 10−1L−1for temperatures be-
tween −10 and −25 ◦C (Kanji et al., 2017) but have shown
to be higher at tropical coastal sites (Rosinski et al., 1988;
Boose et al., 2016; Welti et al., 2018; Price et al., 2018).
This large [INP] range may strongly depend on the micro-
biota concentration, the marine biological activity, and the
organic matter enrichment in the sea surface microlayer as
shown in Wilson et al. (2015).
At marine and coastal sites, a large variety of bacteria have
been identified with Proteobacteria,Firmicutes, and Bac-
teroidetes as the main reported phyla (Després et al., 2012).
Also, airborne fungi are common in both continental and
marine environments, with Cladosporium,Alternaria,Peni-
cillium,Aspergillus, and Epicoccum being the main iden-
tified genera (Després et al., 2012). Besides bacteria and
fungal spores, viruses, algae, and pollen have been iden-
tified in the bioaerosol of marine environments (Després
et al., 2012; Fröhlich-Nowoisky et al., 2015; Michaud et al.,
2018). Therefore, the concentration, ice-nucleating abilities,
and variability of tropical bioaerosol need to be better char-
acterized to quantify their role in cloud formation and precip-
itation development at regional levels and within the tropical
zonal band.
The Yucatan Peninsula, surrounded by the Gulf of Mex-
ico to the west and by the Caribbean Sea to the east, with
a large variety of tropical vegetation, is a great source of
both terrestrial and marine microorganisms (Guzmán, 1982;
Videla et al., 2000; Morales et al., 2006). Tropical cyclones
(TCs) and cold fronts are some of the meteorological phe-
nomena that seasonally affect the Yucatan Peninsula every
year (Whigham et al., 1991; Landsea, 2007; Knutson et al.,
2010). DeLeon-Rodriguez et al. (2013) show that TCs can
significantly enhance the concentration of biological parti-
cles throughout the troposphere and can also efficiently trans-
port biological particles far away from their sources. More-
over, Mayol et al. (2017) has shown that ocean and terrestrial
microorganisms can be efficiently transported long distances
from their sources over the tropical and subtropical oceans.
This study presents results of the INP concentration as a
function of temperature and particle size, and the concen-
tration and composition of biological particles at a tropical
coastal site (Sisal, Yucatan) to infer the potential relevance
of biological particles in mixed-phase cloud formation and
precipitation development.
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L. A. Ladino et al.: Ice-nucleating particles in the Yucatan Peninsula 6149
Figure 1. Map showing the sampling location. The red star shows the location of the Engineering Institute building where the sampling took
place, while the yellow star shows the center of Sisal (Google Maps).
Table 1. List of the measured variables and the corresponding instrumentation.
Measured variable Instrument
INP concentration MOUDI-DFT (Mason et al., 2015a)
Aerosol concentration Condensation particle counter (CPC, TSI 3010)
Coarse aerosol size distribution LasAir Optical particle counter (MSP)
Chemical composition X-Ray fluorescence (XRF) and High-performance liquid chromatography (HPLC)
Bacterial and fungal concentration Biostage impactor (SKC)
Meteorology Weather station (Davis)
2 Methods
2.1 Sampling site
Ambient aerosol particles were collected between 21 January
and 2 February 2017 in the coastal village of Sisal, located in
the northwest corner of the Yucatan Peninsula (21◦0905500 N
90◦0105000 W), as shown in Fig. 1. Sisal had 1837 inhabitants
in 2015 (SEDESOL, 2015), with fishing and tourism recog-
nized as the main economical activities. The closest industry
is located approximately 25 km away from the village and
the nearest city is Merida, 75 km away.
The instruments used in this study were located on the roof
of the Engineering Institute building of the Universidad Na-
cional Autonoma de Mexico (UNAM, Sisal Campus), which
is 50 m from the shoreline and about 1.7 km from the center
of Sisal (Fig. 1). The roof is 25 m above ground level and
directly faces the ocean.
January and February are part of the cold dry season in
Mexico, with isolated events of rain associated with cold
fronts reaching the deep tropics. The arithmetic mean ±
standard deviation for air temperature and relative humid-
ity (RH) during the sampling period were 22.3±3.6◦C and
68.9±6.2 %.
2.2 Instrumentation
A suite of instrumentation was deployed in Sisal to char-
acterize the aerosol chemical composition, concentration,
size distribution, biological content, INP concentration, and
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6150 L. A. Ladino et al.: Ice-nucleating particles in the Yucatan Peninsula
meteorological variables (Table 1). Most instruments were
run simultaneously and next to each other (less than 10 m
apart), and only wet aerosol particles were sampled (mean
RH =69 %). Additionally, none of the instruments used an
impactor or cyclone ahead of their inlets. The inlets were lo-
cated around 1.5–2.0 m above the roof surface. The meteo-
rological data were obtained with a meteorological station
(Davis, VANTAGE PRO2) placed in a different building ap-
proximately 20 m away from the other instruments.
2.2.1 Aerosol concentration and size distribution
The aerosol particle concentration and size distribution were
monitored with a condensation particle counter (CPC 3010,
TSI) and with an optical particle counter (LasAir II 310A,
PMS), respectively. In the CPC, the size of the aerosol parti-
cles is increased in a heated saturator and cooled condenser
system prior to their detection. The particles grown are di-
rected towards a laser beam and the dispersed light is col-
lected by a photodetector that converts it to particle concen-
tration. Similarly to the CPC, aerosol particles in the LasAir
are counted by being passed through a laser beam (without
any prior treatment). Based on the pulses (or voltage) and
their amplitude the dispersed light by the particles is then
converted to particle concentration and size. The total parti-
cle concentration reported by the CPC was collected every
second at a flow rate of 1 Lmin−1, whereas the aerosol con-
centration as a function of their optical diameter (cut sizes at
0.3, 0.5, 1.0, 5.0, 10.0, and 25 µm) was recorded every 11 s
with the LasAir at a flow rate of 28.3 L min−1.
2.2.2 Ice-nucleating particles
Aerosol particles were collected on hydrophobic glass cover
slips (HR3-215; Hampton Research) with the help of a
Micro-Orifice Uniform Deposit Impactor (MOUDI 110R,
MSP) to determine INP concentrations in ambient air. Iden-
tical substrate holders to those described in Mason et al.
(2015a) were used to keep the glass cover slips at a loca-
tion on the impaction plate where particle concentrations
varied by a relatively small amount. The MOUDI has eight
stages for particle separation and collection as a function
of their aerodynamic diameter (cut sizes are 10.0, 5.6, 3.2,
1.8, 1.0, 0.56, 0.32, and 0.18 µm). The particle size range for
each MOUDI stage is given in Table S1 in the Supplement.
The flow through the MOUDI is 30 L min−1and the typi-
cal sampling time was 6 h. It has been recognized that when
sampling with a MOUDI under dry conditions (i.e., RH be-
low approximately 60 %), aerosol particles can bounce from
the impaction plates moving to lower stages (Winkler, 1974;
Chen et al., 2011; Bateman et al., 2014). Although this is a
known artifact when using this technique, this may not have
been an issue in the current study given that the ambient RH
was typically above 67 %. The glass substrates containing the
ambient aerosol particles were stored in petri dishes at 4 ◦C
prior to their analysis.
The INP concentrations were measured with a cold cell
coupled to an optical microscope with an EC Plan-Neofluar
5 X objective (Axiolab, Zeiss) following the MOUDI-DFT
method described by Mason et al. (2015a). The cold-cell mi-
croscope system used here is the same one used in previous
studies (Mason et al., 2015a, b, 2016; DeMott et al., 2016;
Si et al., 2018). The following steps encompass the analy-
sis: (i) the samples collected on glass cover slips were placed
in the cold cell at room temperature, (ii) the cold cell was
isolated and kept at 0 ◦C while humid air (RH >100 %) was
injected into the cell to induce liquid droplet formation by
water vapor condensation, and (iii) dry air (N2) was then in-
jected into the cold cell to prevent the newly formed droplets
from touching. This is a key step that minimizes the prob-
ability of liquid droplets freezing by contact, and (iv) once
the droplet sizes and thermodynamic conditions were stable,
the cold cell was closed. The activation scans were conducted
between 0 and −40 ◦C at a cooling rate of −10 ◦C per minute
for particles collected on stages 2 to 7. Stage 1 (>10.0 µm)
was not taken into account given that the aerosol concentra-
tion on the glass substrates was typically very low, whereas
in stage 8 (0.18–0.32 µm) the number concentration of par-
ticles deposited on the glass substrates was so high that it
inhibited the proper formation of water drops. The tempera-
ture at which each droplet froze was determined by analyzing
the video from the CCD camera (XC-ST50, Sony) connected
to the microscope and the data reported by the resistance
temperature detector (RTD) located at the center of the cold
cell with a ±0.2◦C uncertainty (Mason et al., 2015b). Ho-
mogeneous freezing experiments were performed on labora-
tory blanks exposed during the preparation of the MOUDI,
while heterogeneous freezing experiments were run on am-
bient particles deposited on the glass cover slips (Fig. S1 in
the Supplement). The [INP] was calculated using the follow-
ing expression:
[INPs(T )]=−ln Nu(T )
No·Adeposit
ADFTV·No·fne
·fnu,0.25−0.10 mm ·fnu,1 mm,(1)
where Nu(T) is the number of unfrozen droplets at tempera-
ture T,Nothe total number of droplets, Adeposit the total area
of the aerosol deposit on the hydrophobic glass cover slip,
ADFT the area of the hydrophobic glass cover slip analyzed
in the DFT experiments, Vthe total volume of air sampled,
fne a correction factor to account for uncertainty associated
with the number of nucleation events in each experiment,
fnu,0.25–0.10 mm and fnu,1 mm a non-uniformity factor which
corrects for aerosol deposit inhomogeneity on scales of 0.25–
0.10 and 1 mm (Mason et al., 2015a). The upper and lower
detection limits of the MOUDI-DFT are 30 and 0.01 L−1. We
refer the readers to Mason et al. (2015a, b) for more details
on the MOUDI-DFT operational principle.
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L. A. Ladino et al.: Ice-nucleating particles in the Yucatan Peninsula 6151
2.2.3 Chemical composition
A second eight-stage MOUDI (100NR, MSP) was operated
simultaneously to collect aerosol particles for chemical com-
position analysis with particle sizes ranging from 0.18 to
10.0 µm. Particles were collected on 47 mm Teflon filters
(Pall Science) for 48 h at a flow rate of 30 L min−1. Filters
were weighed prior to and after the sampling and stored in
petri dishes at 4 ◦C until they were analyzed. Two different
analyses were performed on each filter: elemental composi-
tion followed by ion-cation concentration analysis.
Elemental composition of the aerosol samples was deter-
mined by X-ray fluorescence (XRF), using the X-ray spec-
trometer at Laboratorio de Aerosoles, Instituto de Fisica,
UNAM (Espinosa et al., 2012). The samples were mounted
on plastic frames with no previous treatment. The anal-
ysis was carried out using an Oxford Instrument (Scotts
Valley, CA, USA) X-ray tube with an Rh anode and an
Amptek (Bedford, MA, USA) Silicon Drift Detector (resolu-
tion 140 eV at 5.9 keV). The tube operated at 50kV and a cur-
rent of 500 µA, irradiating during 900s per spectrum. The ef-
ficiency of the detection system was measured using a set of
thin film standards (MicroMatter Co., Vancouver, Canada).
The spectra obtained for the samples were deconvolved with
the WinQXAS computer code (IAEA, 1997), and the exper-
imental uncertainties in elemental concentrations were com-
puted according to the method described by Espinosa et al.
(2010).
After the XRF analysis, the Teflon filters were analyzed
for NO−
3, SO2−
4, Cl−, K+, Na+, Ca2+, Mg2+, and NH+
4us-
ing a Dionex model ICS-1500 equipped with an electrical
conductivity detector, following Chow and Watson (1999).
NO−
3, Cl−, and SO2−
4were separated using a Thermo Scien-
tific Dionex IonPac AS23-4 µm Analytical Column (4 mm ×
250 mm) with Thermo Scientific Dionex CES 300 Capil-
lary Electrolytic Suppressor module. The injection volume
was 1000 µL, the mobile phase was 4.5 mM Na2CO3–
0.8 mM NaHCO at 1 mL min−1flow rate. For NH+
4, Na+,
Ca2+, Mg2+, and K+, volumes of 1000 µL were injected in
a Thermo Scientific Dionex IonPac CS12A Cation-Exchange
Column (4 mm ×250 mm) with the Thermo Scientific Dionex
CES 300 Capillary Electrolytic Suppressor. The mobile
phase was a solution CH4SO320 mM and 1 mL min−1flow
rate.
2.2.4 Biological particles
Air samples were collected using two Quick Take 30 Sample
Pump BioStage viable cascade impactor (SKC Inc. USA),
which is a one-stage portable battery-powered instrument op-
erated at a constant airflow rate (28.3 L min−1) for a sampling
time of 5 min. Petri dishes containing Trypticase soy agar
(TSA; BD Bioxon) media, supplemented with 100 mg L−1
cycloheximide (Sigma-Aldrich) to prevent fungal growth,
were used for capture cultivable total bacteria, and malt ex-
tract agar (MEA; BD Bioxon) for cultivable airborne propag-
ule fungi. The two impactors, one with the TSA and the
other one with MEA growing media, were run in parallel.
After exposure, the plates were incubated at 37 ◦C during
24–48 h for cultivable total bacteria and at 25 ◦C during 48–
72 h for propagule fungi. After incubation, colonies growing
on each plate were counted and concentrations were calcu-
lated by taking the sampling rates into account. They were
reported as colony-forming units per cubic meter (cfu m−3)
of air. The petri dishes with the grown colonies were stored
at 4 ◦C prior to their analysis. Fungi were identified to genus
level by macroscopic characteristics of the colonies and mi-
croscopic examination of the spore structure. Representative
bacterial colonies were selected and purified using several
transfer steps of single colonies on TSA and checked by
Gram staining and microscopy. Fresh biomass of the bacte-
rial isolates were suspended in 30 % glycerol LB broth (Al-
pha Biosciences, Inc.) and stored at −72 ◦C for further anal-
ysis.
Bacteria isolated from the pure cultures were identified
by 16S rRNA sequencing. DNA was extracted using the QI-
Aamp DNA Mini kit (QIAGEN), according to the manufac-
turer’s protocol. Partial 16S rRNA gene sequences were am-
plified by polymerase chain reaction (PCR) using universal
bacterial primers 27F (5-AGA GTT TGA TCM TGG CTC
AG-3) and 1492R (5-TAC GGY TAC CTT GTT ACG ACT
T-3) (Lane, 1991). PCRs were performed in a total volume of
50 µL including 2 µL of bacterial DNA, 35.4 µL of ddH2O,
5 µL of 10 X buffer, 1.5 µL of MgCl2(1.5 mM), 1 µL of
dNTPs (10 mM), 0.1 µL of Taq DNA polymerase (5 U µL−1),
and 2.5 µL of each primer (10µM). Cycle conditions were as
follows: initial denaturation at 94 ◦C for 1 min followed by
35 cycles at 94 ◦C for 1 min, 56◦C for 30 s, 72 ◦C for 1.5 min;
and a final extension at 72 ◦C for 5 min. The PCR products
were examined for size and yield using 1.0 % (w/v ) agarose
gels in the TAE buffer. After successful amplification, the ob-
tained products were sequenced using a PRISM 3730 auto-
mated sequencer (Applied Biosystem Inc.). DNA sequences
were edited and assembled using the SeqMan and EditSeq
software (Chromas Lite, Technely Slom Pty Ltd. USA). Se-
quence similarity analysis was performed using the BLAST
software (https://www.ncbi.nlm.nih.gov/BLAST, last access:
8 May 2018).
Although specific growing media for actinobacteria were
not used in this study, some actinobacteria colonies were able
to grow on the TSA petri dishes; therefore, in some cases
they were isolated and identified as follows. Genomic DNA
was extracted using standard protocols reported previously
for actinobacteria (Maldonado et al., 2009). The DNA prepa-
rations were then used as a template for 16S rRNA gene am-
plification using the universal set of bacterial primers 27f
and 1525r (Lane, 1991). The following components for the
PCR mix were employed: 0.5 µL DNA template (for a final
concentration of 100 ng µL−1, 5 µL 10X DNA polymerase
buffer, 1.5 µL MgCl2(50 mM stock solution), 1.25 µL dNTP
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6152 L. A. Ladino et al.: Ice-nucleating particles in the Yucatan Peninsula
(10 mM stock mixture), 0.5 µL of each primer (20µM stock
solution), and 2.0 units of Taq polymerase made up to 50 µL
with deionized sterile distilled water.
The PCR amplification was achieved using a Techne 512
gradient machine using the protocol described in Maldonado
et al. (2008). The expected product (size approx. 1500 bp)
was checked by horizontal electrophoresis (70 V, 40 min)
and then purified using the QIAquick PCR purification kit
(QIAGEN, Germany) following the manufacturer’s instruc-
tions. Purified 16S rRNA gene PCR products were sent
for sequencing to Macrogen (Korea) for the BigDye Ter-
minator Cycle Sequencing Kit (Applied Biosystems). As-
sembly of each 16S rRNA gene sequence was performed
using Chromas (http://www.technelysium.com.au, last ac-
cess: 21 March 2018) and checked manually with the SeaV-
iew software (Galtier et al., 1996). Each assembled se-
quence was compared against two databases, namely, (a)
the GenBank database (https://www.ncbi.nlm.nih.gov, last
access: 21 March 2018) by using the BLAST option and
(b) the EZCloud (https://www.ezbiocloud.net, last access:
21 March 2018) under its EZTaxon option. Both databases
generated a list of the closest phylogenetic neighbors to each
sequence and the EZTaxon specifically provided the list of
the closest described (type) species. At least 650 bp was em-
ployed for the analyses.
3 Results and discussion
3.1 Aerosol concentration and meteorology
Two cold fronts affected Sisal during the sampling period
between 21 January and 2 February 2017, providing differ-
ent air mass characteristics. The periods affected by each of
the fronts are indicated in Fig. 2 by vertical grey bars and la-
beled cold front A and cold front B, associated with increased
wind speed and shifts in wind direction. Figure 2b–d shows
the time series of the aerosol particle concentration between
21 January and 2 February 2017. There is a large diurnal vari-
ability for the aerosol particle concentration measured by the
CPC (particles >30 nm, Fig. 2b) and the LasAir (particles
>300 nm, Fig. 2c). Assuming log-normal distributions, the
geometric mean concentration and multiplicative standard
deviation (cf. Limpert et al., 2001) for the entire sampling
period were 758.51x/1.76 and 1.00x/1.37 cm−3. From the
CPC data shown in Fig. 2b, there seems to be a daily cycle
with most of the highest concentration taking place between
7 and 12 h (local time), most notably on days without the
influence of cold fronts. The data reported by the CPC and
the LasAir indicate that most of the aerosol particles were
smaller than 300 nm. A similar result was found by Rosin-
ski et al. (1988) in the Gulf of Mexico (GoM), who found
that the aerosol concentration for particles ranging between
0.5 and 1.0 µm was 3 to 4 orders of magnitude smaller than
particles ranging between 0.003 and 0.1 µm. A decrease in
aerosol particle concentration was observed at the arrival and
during the passage of two cold fronts during the sampling
period, associated with an increase in horizontal wind speed
of at least a factor of 3 (Fig. 2a). During the passage of cold
front A, precipitation events were not observed which was
not the case for cold front B. This could partially explain the
lower aerosol concentration during the passage of the cold
front B in comparison to cold front A. Also note that, dur-
ing the influence of the cold front A, the wind direction was
almost constant from approximately 270◦, while during cold
front B the wind direction varied between 270 and 360◦, with
a more northerly component and a larger influence from the
GoM compared to winds associated with cold front A.
Back trajectories from the measurement site were esti-
mated using the HYSPLIT model (Stein et al., 2015). They
were run on each day of the campaign for 72 h. In the ab-
sence of cold fronts A and B, air masses arriving in Sisal
had a predominantly continental influence, associated with
southerly winds (Fig. S2). However, when the cold fronts A
and B reached the Yucatan Peninsula, northerly and north-
westerly winds prevailed and contributed a more maritime
influence. The arrival of the cold fronts was also confirmed
by the surface weather maps for 22 and 29 January (Fig. S3)
provided by the National Oceanic and Atmospheric Admin-
istration (NOAA).
Air masses behind both cold fronts, flowing over the GoM,
were characterized by lower aerosol particle concentrations
than air masses coming from the south to the site. This re-
sult agrees well with a large body of evidence indicating
that marine air masses have lower aerosol particle concentra-
tion than continentally influenced air masses (Patterson et al.,
1980; Fitzgerald, 1991). As for the total aerosol concentra-
tion (Fig. 2), the number size distributions of the aerosol
particles larger than 300 nm were also impacted by the cold
fronts. For example, the concentration of particles smaller
than 5.0 µm was lower during the passage of the cold front B
(Fig. S4). As shown in Fig. 3 (and Fig. S5), the XRF anal-
ysis indicates that, although there are small differences in
the bulk chemical composition of the aerosol particles, the
overall composition is generally comparable in the presence
or absence of cold fronts. Note, however, that this is not a
completely fair comparison given that sampling time for the
chemical analysis was 48 h, while sampling time for deter-
mining the influence of the cold-front air masses on INP pop-
ulations was on the order of 36 h. Therefore, the periods de-
noted as cold fronts contain aerosol particles that may not
technically correspond to cold-front air masses.
3.2 Ice-nucleating particle concentration
A total of 41 samples (eight stages each) were collected dur-
ing the Sisal field campaign to calculate the [INP] as a func-
tion of temperature and particle size. Some of these sam-
ples showed a high ice-nucleating activity with onset freez-
ing temperatures found to occur at temperatures as high as
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L. A. Ladino et al.: Ice-nucleating particles in the Yucatan Peninsula 6153
Figure 2. Time evolution of wind and aerosol particle concentration time series for the entire campaign (21 January–2 February 2017).
(a) Time series of the wind speed (yellow) and wind direction (green), (b) particle concentration measured by the CPC, (c) particle concen-
tration measured by the LasAir full-size range (0.3 to 25 µm), and (d) particle concentration measured by the LasAir for particles >500 nm
(0.5 to 25 µm). Grey areas denote the periods affected by cold fronts A and B. Each tick mark on the xaxis corresponds to midnight local
time.
Figure 3. Time series of the ambient aerosol mass concentration and
bulk chemical composition as measured by the XRF. Each sample
was collected for 48 h starting at 12:00 h local time. A and B indi-
cate that those samples were partially influenced by the passage of
the cold front A and the cold front B, respectively.
−3◦C (Fig. S6). Figure 4 summarizes the [INP] as a func-
tion of temperature and particle size for 29 analyzed sam-
ples. Due to technical issues it was not possible to analyze
the samples collected after 30 January. Figure 4 also shows
recent literature data obtained at coastal and marine regions
from DeMott et al. (2016), Welti et al. (2018), and Irish et al.
(2019).
At −15 ◦C the [INP] measured in Sisal are in relatively
good agreement with those found at Cabo Verde (Welti et al.,
2018) but are 1 to 2 orders of magnitude higher than the val-
ues reported by Irish et al. (2019) from the Arctic boundary
layer and by DeMott et al. (2016) from sea spray laboratory-
generated particles and ambient marine boundary layer par-
ticles. As temperature decreases from −20 to −30 ◦C there
is a better agreement between the Sisal [INP] and data from
DeMott et al. (2016). It is important to note that the large
variability of the [INP] from Welti et al. (2018) is related to
the large amount of data summarized on each dotted line (i.e.,
from 2009 to 2013).
The high [INP] found at −15 ◦C can be explained in part
by the very efficient INPs shown in Fig. S6 with sizes ranging
from 1.0 to 1.8 µm. However, it is important to note that parti-
cles with diameters between 1.8 and 10 µm also contribute to
the total [INP] at warm temperatures. Aerosol particles act-
ing as INPs at −15 ◦C are usually biological, given that other
aerosol particles such as metals, crystalline salts, combus-
tion particles (e.g., soot), and organics are not efficient INPs
under these conditions. Moreover, for typical atmospheric
concentrations of mineral dust, ice nucleation at these tem-
peratures seems to be of secondary importance (Hoose and
Möhler, 2012; Murray et al., 2012; Kanji et al., 2017). The
potential sources of the measured INPs in Sisal are discussed
below.
Figure 5 is based on Mason et al. (2016) and shows the
average [INP] for three different temperatures (−15, −20,
and −25 ◦C) at different locations around the globe using the
same sampling and analysis methods. The Sisal data corre-
spond to particle diameters ranging between 0.32 and 10 µm;
full information in all size stages was obtained in 16 out
of the 29 samples analyzed. At −15 ◦C the average [INP]
in Sisal was lower than Colby (USA), an agricultural site,
and Labrador Sea; however, the obtained values are compa-
rable to those found at UBC (Canada), Saclay (France), and
Ucluelet (Canada). At −20 and −25 ◦C the average [INP] in
Sisal was comparable or higher than at the other locations. As
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6154 L. A. Ladino et al.: Ice-nucleating particles in the Yucatan Peninsula
Figure 4. Summary of average INP concentrations as a function of temperature and particle size (solid symbols). Total [INP] are represented
by the grey triangles, whereas the brown asterisks, light blue dotted lines, and purple stars are data from DeMott et al. (2016), Welti et al.
(2018), and Irish et al. (2019), respectively. The upper and lower detection limits of the MOUDI-DFT are 30 and 0.01 L−1.
Figure 5. Mean INP number concentrations at droplet freezing temperatures of −15 (light gray), −20 (dark gray), and −25 ◦C (black). The
blue and red stars represent the mean INP concentration during the cold fronts A and B and cold front B, respectively. Uncertainties are given
as the standard uncertainty of the mean (adapted from Mason et al., 2016).
shown by the stars on top of the Sisal bars, the [INP] during
the passage of the cold fronts was found to be higher than
the average [INP], although the obtained values are within
the uncertainty bars. For example, at −15 ◦C the [INP] in-
creases from 0.33 to 0.59 L−1in the cold air mass after the
passage of cold front B. Recalling that the air masses behind
cold front B contained a lower aerosol particle concentra-
tion, this suggests that the marine particles in that air mass
are more efficient INPs than in the air masses with more con-
tinental influence. Given that the bulk chemical composition
as shown in Fig. S5 (and Fig. 3) is comparable before, dur-
ing, and after the passage of the cold front B, it is possible
that the observed differences in the ice-nucleating abilities
are linked to the biological content in the cold air masses.
This is further discussed below.
The majority of the field studies performed to measure
the [INP] have been conducted at midlatitudes; nevertheless,
here we compare our observations with the results presented
by Rosinski et al. (1988), who measured the [INP] in the con-
densation freezing mode for particles in the GoM during a
cruise between 20 July and 30 August 1986, during midsum-
mer. The study reports very efficient INPs with onset freezing
temperatures as high as −4◦C for particles with diameters
between 0.1 and 0.4 µm. On 6 August 1986 (the closest sam-
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L. A. Ladino et al.: Ice-nucleating particles in the Yucatan Peninsula 6155
Figure 6. Mean INP concentration as a function of aerosol particle size at (a) −15, (b) −20, (c) −25, and (d) −30 ◦C. Uncertainties are
given as the standard uncertainty of the mean.
pling site to Sisal in the GoM) the study shows that the [INP]
at −15 ◦C was on the order of 10−2L−1for particles with
sizes between 0.1 and 0.4 µm. In contrast, our results indicate
that the [INP] at −15 ◦C varied between 10−1and 100L−1
for particles ranging between 0.32 and 10 µm. This discrep-
ancy could be attributed to the differences in the size of the
particles sampled and could also be influenced by seasonal
variability. If supermicron particles are excluded, the [INP] at
−15 ◦C from the present study is 1 order of magnitude lower
(Fig. 4). As shown by DeMott et al. (2010) particles larger
than 500 nm are the more likely potential INPs and as stated
by Mason et al. (2016) and as shown in Fig. 4, super-micron
particles are a large contributor to the INP population. Ad-
ditionally, the chemical composition of the aerosol particles
collected by Rosinski et al. (1988) indicate that the air masses
in the GoM in July–August were significantly influenced by
mineral dust particles. African dust episodes reached Florida
between May and October (Lenes et al., 2012), and there
have been no reported episodes during the sampling period
of this study (January–February).
3.2.1 [INP] vs. particle size
Figure 6 shows the mean [INP] concentration as a function of
particle size between 0.32 and 10 µm at four different temper-
atures (−15, −20, −25, and −30 ◦C). Note that the INP size
distributions are different for each of the temperatures con-
sidered, in contrast with the results from Mason et al. (2015b)
on the Pacific coast of Canada. At −15 ◦C the peak [INP]
corresponds to particles ranging between 1.0 and 1.8 µm; this
range has been reported as the typical size for airborne bac-
teria (Burrows et al., 2009). Similar size distributions were
obtained at −20 and −25 ◦C with peak concentration for
particles ranging in size between 3.2 and 5.6 µm. Finally, at
−30 ◦C the peak was observed at smaller sizes (i.e., between
1.8 and 3.2 µm). The discrepancies between the present re-
sults and those from Mason et al. (2015b) at −15 and −30 ◦C
could be explained by differences in air mass history. Al-
though both studies were conducted at coastal locations, the
back-trajectories from the present study indicate that during
“normal” days (i.e., 70 % of the time) the sampled air masses
had a significant continental contribution (Fig. S2). In con-
trast, air masses were mostly maritime in the Mason et al.
(2015b) study. Also, it is important to note that, although
the cold air masses that reached Sisal behind the cold fronts
had crossed the GoM, the aerosol particles found in them are
likely a mixture of particles originated in the Central Great
Plains and the GoM (Figs. S2b–c and S5).
Figure 6 also shows that most of the INPs are in the su-
permicron size range, where submicron particles represent
less than 10 % of the total [INP] independent of tempera-
ture, in agreement with Mason et al. (2015b, 2016). To con-
firm the size dependence and the importance of supermicron
particles to the [INP] in Sisal, the fraction of particles act-
ing as INPs was calculated by combining the DFT and La-
sAir data (Fig. 7). The [INP] was normalized for four size
bins (i.e., 0.3–0.5, 0.5–1.0, 1.0–5.0, and 5.0–10.0 µm). As
expected (from Figs. 4 and 6), the fraction of particles act-
ing as INPs increases with increasing particle size and with
decreasing temperature. This trend is in agreement with the
results shown by Si et al. (2018), with the present results be-
ing higher. Figure 7 also shows that the fraction of aerosol
particles acting as INP is higher when influenced by the cold
fronts (black symbols), especially for particles ranging be-
tween 1.0 and 5.0 µm.
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6156 L. A. Ladino et al.: Ice-nucleating particles in the Yucatan Peninsula
Figure 7. The fraction of aerosol particles acting as an INP ([INP]/NTot) as a function of particle size at −15, −20, and −25 ◦C. NTot refers
to the number of aerosol particles in a given size range measured by the LasAir. The solid colored symbols represent the entire campaign,
while the black symbols represent the samples collected under the influence of the cold fronts.
Figure 8. (a) Mean mass concentration of 14 detected elements for the collected aerosol particles using XRF, (b) mean mass concentration
of eight detected ions for the collected aerosol particles using HPLC, (c) and (d) mean mass size distribution of the main five detected
elements/ions with the XRF and HPLC. These results are the average for the whole sampling period.
3.3 Identification of the potential INP sources
The chemical analysis of the sampled aerosol particles (for
the whole sampling period) indicates that a large fraction of
the particle mass (for sizes between 0.18 and 10.0 µm) are
likely of marine origin (Figs. 3 and 8a–b). Both techniques,
i.e., XRF and HPLC, found that the main elements and ions
are sodium and chlorine. The low concentrations of Ti, Cu,
K, and Zn show the very low probability of anthropogenic
influence at the sampling site. However, although sulfate and
ammonium can be emitted by natural sources, their presence,
in addition to nitrates, indicate that the influence of anthro-
pogenic activities to the aerosol population is not completely
negligible. Finally, the low concentration of Al, Fe, Ca, and
Si suggest that mineral dust is not a major contributor of
aerosol particle mass during the sampling period. However,
although the long-range transport of mineral dust particles
from Africa to the Yucatan Peninsula and the GoM is very
rare between January and February, mineral dust particles
are frequently found in the Caribbean including the GoM
(Rosinski et al., 1988; Prospero and Lamb, 2003; Doherty
et al., 2008; Kishcha et al., 2014).
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L. A. Ladino et al.: Ice-nucleating particles in the Yucatan Peninsula 6157
Table 2. Correlation coefficients (r2) of the average chemical composition and the average [INP] per sample at −15, −20, −25, and −30 ◦C.
Bold text highlights the r2with p < 0.05 (Table S1) for each temperature. The correlations were obtained for five sample points at each
temperature.
Temperature Na Mg Al Si P S Cl K Ca Ti Mn Fe Cu Zn
−15 ◦C 0.02 0.70 0.08 0.15 0.01 0.02 0.07 0.02 0.08 0.21 0.61 0.31 0.03 0.25
−20 ◦C 0.18 0.02 0.44 0.86 0.24 0.33 0.27 0.77 0.64 0.19 0.47 0.74 0.35 0.26
−25 ◦C 0.54 0.00 0.33 0.45 0.27 0.45 0.02 0.40 0.89 0.06 0.40 0.49 0.17 0.07
−30 ◦C 0.53 0.00 0.65 0.74 0.46 0.56 0.00 0.65 0.79 0.02 0.34 0.81 0.06 0.03
Figure 9. (a) Time series of the [INP] at −15 (blue), −20 (brown), and −25 ◦C (yellow), (b) time series of the [INP] at −15 ◦C (blue)
together with bacteria concentration (red), and (c) time series of the [INP] at −15 ◦C (blue) together with fungal concentration (black). Each
x-axis tick corresponds to 06:00 local time. The horizontal uncertainty bars indicate the time span of the MOUDI-DFT measurements, i.e.,
6 h. Grey areas denote the periods affected by cold fronts A and B.
Figure 8c–d shows the mean mass size distribution for the
whole sampling period of the main five elements/ions deter-
mined by the XRF and HPLC techniques. For the XRF anal-
yses Na, Cl, and Ca have a single peak at 3.2 µm, whereas
the S and Mg reported two peaks at 0.32 and 3.2 µm. Simi-
larly to the XRF results, the HPLC analyses for Na+and Cl−
also showed a single peak at 3.2 µm. SO2−
4, NO−
3showed two
peaks at 0.32 and 3.2 µm, whereas for NH+
4the peaks were
located at 0.32 and 5.6 µm. The obtained size distributions
are in agreement with those of sea-salt-type particles as re-
ported elsewhere (O’Dowd et al., 2004; Prather et al., 2013).
Although Al, Si, Ca, and Fe were found at low concen-
trations (Fig. 8a), Tables 2 and S2 suggest that mineral dust
particles are an important source of INPs in Sisal at temper-
atures ranging from −20 to −30 ◦C. This is in close agree-
ment with the results obtained by Si et al. (2019) in the Cana-
dian High Arctic. From the correlation of the [INP] and the
aerosol chemical composition at −15 ◦C, Mg was the only el-
ement showing a correlation that is statistically significant at
the 95 % confidence interval (p < 0.05). Although Mg can be
found in mineral dust particles in low percentages, it can also
be found in marine environments linked to sea spray aerosol
(e.g., Savoie and Prospero, 1980; Andreae, 1982; Casillas-
Ituarte et al., 2010). Given that mineral dust particles are un-
likely the source of the measured INPs above −15 ◦C (as sug-
gested by Table 2), and as secondary organic aerosol and soot
are not typically efficient INPs at temperatures above −15 ◦C
(Kanji et al., 2017), in addition to the supermicron size of ca.
90 % of the INPs (Fig. 6), bioaerosol is a potential source of
the INPs measured at warm temperatures. Note that bioparti-
cles have been shown to efficiently nucleate ice at those high
temperatures (Hoose and Möhler, 2012; Murray et al., 2012;
Ladino et al., 2013). Efficient INPs such as those measured
in Sisal could be very important for cloud glaciation. Addi-
tionally, they can trigger ice multiplication or secondary ice
formation at such high temperatures via the Hallett–Mossop
mechanism (Hallett and Mossop, 1974; Field et al., 2017)
and impact precipitation formation.
To confirm the presence of bioparticles around Sisal and to
determine their potential role in the ice-nucleating abilities
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6158 L. A. Ladino et al.: Ice-nucleating particles in the Yucatan Peninsula
Table 3. Bacterial isolation for (top) 21–22 January, (middle) at cold front A and (bottom) cold front B.
Phylum Genus/species Source
Actinobacteria aKocuria palustris Soil, rhizoplane
aMicrococcus spp. Water, soil, dust, and skin
aRhodococcus corynebacterioides Soil, water and eukaryotic cells
Firmicutes aStaphylococcus kloosii Human and animal skin
aStaphylococcus lugdunensis Human and animal skin
aStaphylococcus nepalensis Mucocutaneous zones of humans and animals
aStaphylococcus arlettae Animal skin, mucosal zones, polluted water
aStaphylococcus epidermidis Human skin, mucosal microbiota
aBacillus aryabhattai Upper atmosphere, rhizosphere
aBacillus gibsonii Alkaline soil
aBacillus aeris Soil
aStaphylococcus lentus Soil
Alphaproteobacteria aSphingomonas mucosissima Water and soil
Actinobacteria aMicrococcus spp. Water, soil, dust, and skin
Firmicutes aBacillus oceanisediminis Marine sediments
Gammaproteobacteria aProteus mirabilis Water and soil
aPseudomonas stutzeri Soil
Actinobacteria a,b Micrococcus spp. Water, soil, dust, and skin
aMicrococcus lentus Soil, dust, water and air
bMicrococcus yunnanensis Roots of Polyspora axillaris
bStreptomyces spp. Cosmopolitan
Firmicutes aBacillus spp. Cosmopolitan
aBacillus niacini Soil
aBacillus subtilis Soil, gut commensal in ruminants and humans
aPlanomicrobium koreense Fermented seafood
aStaphylococcus spp. Human and animal skin, mucous zones, soils
bSolibacillus isronensis Air
aStaphylococcus equorum Human and animal skin
Gammaproteobacteria aPseudomonas reactants Soil
aVibrio alginolyticus Marine
aVibrio natriegens Marine
aVibrio neocaledonicus Marine
aVibrio parahaemolyticus Marine
aZobellella sp. Marine and estuarine environments
aIsolated on TSA media. bIsolated on GYM media.
of the collected aerosol particles, bacteria and fungi iden-
tification was performed. As stated by Islebe et al. (2015)
both bacteria and fungi need to be properly documented
in the peninsula and the GoM to fully understand their re-
gional importance. Samples for viable bacteria and fungi
were collected every day at 06:00, 08:00, 10:00, and 12:00
local time. However, a single daily profile was performed be-
tween 22 and 23 January. Bacteria and fungi colony-forming
units (cfu) m−3were usually above zero, with the highest
concentrations found early in the morning (Fig. S7). The
bacteria and fungi concentrations showed a relatively good
correlations (r=0.55, p < 0.0005 not shown) with average
values for the whole of the sampling periods of 295 ±312
and 438 ±346 cfu m−3, respectively. The bacteria concentra-
tions are comparable to the values found by Hurtado et al.
(2014) in Tijuana, on the Pacific coast of Mexico (i.e, 230–
280 cfu m−3). Bacteria and fungi concentrations were found
to be lower when the wind was coming from the north in
comparison with southern-continental air masses (Fig. S8),
a behavior similar to the aerosol concentration shown in
Sect. 3.1.
Figure 9 shows the time series of the [INP] together with
the bacteria and fungal concentrations. Panels B and C show
a poor correlation between the bacteria and fungal concentra-
tions with the [INP] with correlation coefficients at −15 ◦C
of 0.12 (p=0.06) and 0.36 (p=0.03), respectively. This
poor correlation can be in part due to the different sampling
times of the MOUDI and the biosamplers. An additional fac-
tor is that the reported bacteria and fungi concentrations are
only a small fraction of the total population given that the
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L. A. Ladino et al.: Ice-nucleating particles in the Yucatan Peninsula 6159
Table 4. Fungal identification on MEA media for the whole sampling period.
Phylum Genus Source
Dothideomycetes Alternaria
Cladosporium
Drechslera Dead plants, soil, foods, air, indoor
Euascomycetes Curvularia environments, decaying organic matter,
Eurotiomycetes Aspergillus indoor bioaerosols, on animal systems and
Penicillium in freshwater and marine habitats.
Leotiomycetes Monilia
Sordariomycetes Fusarium
Zygomycetes Rhizopus
used method is selective to viable microorganisms only. Note
that the fraction of detected microorganisms by culture meth-
ods is typically ca. 1 % (but can be lower) of the total pop-
ulation (Lighthart, 2000; Burrows et al., 2009). From Fig. 9
it is notable that, although the bacteria and fungi concentra-
tions were very low on 29 January (i.e., under the influence
of the cold front B), the [INP] at −15 ◦C was comparable to
the average value for the entire campaign. It is therefore in-
triguing if the marine microorganisms brought to Sisal by the
cold front B could be efficient INPs.
Table 3 summarizes the identified bacteria before the ar-
rival of cold front A and after the passage of cold fronts
A and B. Additionally, Table 4 shows the fungi identifica-
tion for the whole campaign. To our knowledge this is the
first time that airborne viable bacteria, and fungi are identi-
fied at this coastal location. Although biological microorgan-
ism characterization has been previously conducted in Mex-
ico, those studies focused mainly on health effects (Santos-
Burgoa et al., 1994; Guzman, 1998; Maldonado et al., 2009;
Frías-De León et al., 2016; Ríos et al., 2016). Note that
76 % of the detected bacteria were Gram positive with Mi-
crococcus,Staphylococcus, and Bacillus as the main identi-
fied genera (Fig. S9). As shown in Table 3, before the arrival
of cold front A (21–22 January), a large variety of bacteria
species were found with different typical sources, mostly ter-
restrial. This is in contrast with the identified species found
after the passage of cold fronts A and B. Especially after
cold front B, different Vibrio species were identified, most of
which are typically of marine origin. Recently, Hurtado et al.
(2014) found that the most common genera of the bacteria in
Tijuana were Staphylococcus,Streptococcus,Pseudomonas,
and Bacillus in close agreement with the present results.
Regarding fungi, different genera were also identified as
shown in Table 4 with Cladosporium and Penicillium as the
most frequent ones (51 % and 11 %, respectively) as shown
in Fig. S9. This is in good agreement with the data reported
by Després et al. (2012).
Several studies have shown the good correlation between
the concentration of fluorescent biological particles and the
[INP]; however, from those studies it is highly uncertain
if the good ice-nucleating abilities can be attributed to a
single microorganism specie (Mason et al., 2015b; Twohy
et al., 2016). Offline methods such as the one used here have
been able to identify specific microorganisms such as Pseu-
domonas syringae,Micrococcus,Staphylococcus,Cladospo-
rium,Penicillium, and Aspergillus from rainwater and cloud
water, with some showing good ice-nucleating abilities (Am-
ato et al., 2007, 2017; Delort et al., 2010; Failor et al., 2017;
Stopelli et al., 2017; Akila et al., 2018).
4 Conclusions
Aerosol particles collected around Sisal (on the northwest
coast of the Yucatan Peninsula) from 21 January to 2 Febru-
ary 2017 were found to be efficient INPs with onset freezing
temperatures as high as −3◦C, similarly to the onset freezing
temperature of the well-known efficient INP Pseudomonas
syringae (Wex et al., 2015) and Arctic sea surface micro-
layer organic-enriched waters (Wilson et al., 2015). The re-
sults show that the INP concentrations in Sisal are com-
parable (geometric mean and multiplicative standard devia-
tion of 0.44x/1.77, 1.73x/2.56, and 6.20x/2.65 L−1at −15,
−20, and −25 ◦C, respectively) and in specific cases even
higher than at other locations studied using the same type of
INP counter. Higher INP concentrations were observed, es-
pecially under the influence of cold fronts. This is an intrigu-
ing result given that the air masses behind the cold front con-
tained lower aerosol particle concentrations. This deserves
further analysis given that the Yucatan Peninsula and the
Caribbean region are impacted regularly by this meteorolog-
ical phenomenon during the winter and early spring months.
The chemical analyses performed on the sampled aerosol
particles did not indicate the presence of mineral dust par-
ticles at high concentrations (the combined mass concen-
trations of Al, Si, and Fe correspond to 5.1 % of the total
particle mass measured by the XRF). However, Al, Si, Ca,
and Fe showed high correlation coefficients (r2above 0.64
with p < 0.05) with the [INP] at temperatures between −20
and −30 ◦C. At −15 ◦C the [INP] in Sisal was 1 to 2 orders
of magnitude higher than the concentrations reported from
other coastal and marine regions around the globe. The size
www.atmos-chem-phys.net/19/6147/2019/ Atmos. Chem. Phys., 19, 6147–6165, 2019

6160 L. A. Ladino et al.: Ice-nucleating particles in the Yucatan Peninsula
of this very efficient INPs was found to be above 1.0 µm with
a large contribution to the [INP] of particles ranging from 1.0
to 1.8 µm. A summary of the observations presented in this
study shows (i) the presence of large [INP] above −15◦C,
(ii) 90 % of the INPs are supermicron in size, (iii) poor corre-
lation between mineral dust tracers and the [INP], and (iv) the
presence of marine biological particles behind cold fronts,
coinciding with the highest [INP]. These results lead us to
hypothesize that the likely source of the INPs measured in
Sisal at high temperatures is biological particles. Therefore,
our results suggest that continental and maritime biological
particles could play an important role in ice cloud forma-
tion and precipitation development in the Yucatan Peninsula.
Although several bacteria and fungi were identified, it is un-
known if any of them were responsible for the observed ice-
nucleating abilities of the aerosol around Sisal.
The present results are important for the development of
new parameterizations to be incorporated in climate models,
given that the currently available parameterizations contain
little or no data from tropical latitudes. However, further sim-
ilar studies are needed given that the [INP] may vary season-
ally. In particular, the arrival of mineral dust particles to the
GoM and the Caribbean region from Africa in July–August
is expected to impact the [INP], and therefore, ice cloud for-
mation, as shown by Rosinski et al. (1988) and DeMott et al.
(2003).
The quantitative understanding of the importance of bi-
ological particles in ice particle formation is a challenging
task for the cloud physics community. As shown here, even
when combining biology with chemistry, physics, and mete-
orology, the results obtained are not as quantitative as would
be desired. Therefore, further studies are needed in order to
improve our current limited understanding of the role that
tropical microorganisms could play in ice cloud formation.
Data availability. Data are available upon request to the corre-
sponding author.
Supplement. The supplement related to this article is available
online at: https://doi.org/10.5194/acp-19-6147-2019-supplement.
Author contributions. LAL and GBR designed the experiments.
LAL, HAO, MAE, and BF carried out the INP and aerosol mea-
surements. IR, LM, ES, EQ, LAM, and AGR analyzed the biologi-
cal particles. HAO and JM performed the chemical analyses. LAL,
ZRD, CC, AKB, MS, and VI performed the INP analyses. LAL
wrote the paper, with contributions from all co-authors.
Competing interests. The authors declare that they have no conflict
of interest.
Acknowledgements. The authors thank Elizabeth Garcia,
Gabriel Garcia, Irma Gavilan, Rafaela Gutierrez, Joshua Munoz,
Luis Landeros, Fernanda Cordoba, Wilfrido Gutierrez, Manuel Gar-
cia, Miguel Robles, Alfredo Rodriguez, Juan Carlos Pineda,
Luis Gonzalez, Alejandra Prieto, Telma Castro, Ma. Isabel Saave-
dra, and Aline Cruz for their invaluable help. We also thank
David S. Valdes from CINVESTAV Merida for sharing the
meteorological data. Finally, we thank the National Oceanic and
Atmospheric Administration (NOAA) for facilitating the use of
the surface maps and the HYSPLIT. This study was financially
supported by the Direccion General de Asuntos del Personal
Academico (DGAPA) through grants PAPIIT IA108417 and
IN102818 and by the Consejo Nacional de Ciencia y Tecnologia
(Conacyt) through grant I000/781/2106.
Review statement. This paper was edited by Paul Zieger and re-
viewed by five anonymous referees.
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