Introduction
Primary biological aerosols particles (PBAPs) are of keen interest within the
scientific community, partially because methods for their quantification and
characterization are advancing rapidly (Huffman and Santarpia, 2017; Sodeau
and O'Connor, 2016). The term PBAPs, or equivalently bioaerosols, generally
comprises several classes of airborne biological particles including viruses,
bacteria, fungal spores, pollen, and their fragments (Després et al.,
2012; Fröhlich-Nowoisky et al., 2016). Fungal spores are of particular
atmospheric interest because they can cause a variety of deleterious health
effects in humans, animals, and agriculture, and it has been shown that they
can represent a significant fraction of total organic aerosol emissions
(Deguillaume et al., 2008; Gilardoni et al., 2011; Madelin, 1994), especially
in tropical regions (Elbert et al., 2007; Huffman et al., 2012; Pöschl et
al., 2010; Zhang et al., 2010). Current estimates of the atmospheric
concentration of fungal spores range from 100 to more than 104 m-3 (Frankland and Gregory, 1973; Gregory and Sreeramulu, 1958;
Heald and Spracklen, 2009; Hummel et al., 2015; Sesartic and Dallafior,
2011). Fungal spores may also impact the hydrological cycle as giant cloud
condensation nuclei or as ice nuclei (Haga et al., 2013; Morris et al., 2013;
Sesartic et al., 2013). Additionally, several classes of bioaerosols and
their constituent components, such as (1→3)-β-D-glucan and
endotoxins, have been implicated in respiratory distress and allergies
(Burger, 1990; Douwes et al., 2003; Laumbach and Kipen, 2005; Linneberg,
2011; Pöschl and Shiraiwa, 2015). For example, asthma and allergies have
shown notable increases during thunderstorms due to elevated bioaerosol
concentrations (Taylor and Jonsson, 2004) especially when attributed to
fungal spores (Allitt, 2000; Dales et al., 2003).
Molecular tracers have long been utilized as a means of aerosol source
tracking (Schauer et al., 1996; Simoneit and Mazurek, 1989; Simoneit et al.,
2004). In recent years, analysis of molecular tracers has been utilized for
the quantification of PBAPs in atmospheric samples and has been compared, for
example, with results from microscopy (Bauer et al., 2008a) and culture
samples (Chow et al., 2015b; Womiloju et al., 2003). Three organic molecules
have been predominately utilized as unique tracers of fungal spores:
ergosterol, mannitol, and arabitol. The majority of atmospherically relevant
fungal spores are released by active wet-discharge processes common in
Ascomycota and Basidiomycota, meaning that the fungal organism actively
ejects spores at a time most advantageous for the spore dispersal and
germination processes, often when relative humidity (RH) is high (Ingold,
1971). While there are several mechanisms of active spore emission (e.g.,
Buller's drop (Buller, 1909) and osmotic pressure canons (Ingold, 1971)),
they each involve the secretion of fluid containing hygroscopic compounds,
such as arabitol, mannitol, potassium and chloride ions, as well as other solutes (Elbert et al., 2007),
released near the site of spore growth. When the spores are ejected, some of
the fluid adheres to the spores and becomes aerosolized. Several of these
secreted compounds are thought to enter the atmosphere linked uniquely with
spore emission processes, and so these tracers have been used to estimate
atmospheric concentrations of fungal spores. Arabitol and mannitol are both
sugar alcohols (polyols) that serve as energy stores for the spore
(Feofilova, 2001). Arabitol is unique to fungal spores and lichen, while
mannitol is present in fungal spores, lichen, algae, and higher plants (Lewis
and Smith, 1967). Ergosterol is found within the cell membranes of fungal
spores (Weete, 1973) and has been used as an ambient fungal spore tracer (Di
Filippo et al., 2013; Miller and Young, 1997). Comparing the seasonal trends
of arabitol and mannitol with ergosterol, Burshtein et al. (2011) showed
positive correlations between arabitol or mannitol and ergosterol only in the
spring and autumn, suggesting that the source of these polyols is unlikely to
be solely fungal in origin or that the amount of each compound emitted varies
considerably between species type and season. While ergosterol has been
directly linked to fungal spores in the air, ergosterol is prone to
photochemical degradation and is difficult to analyze and quantify directly.
Quantification of ergosterol typically requires chemical derivatization by
silylation before analysis via gas chromatography (Axelsson et al., 1995;
Burshtein et al., 2011; Lau et al., 2006). In contrast, analysis of sugar
alcohols by ion chromatography involves fewer steps and has been successfully
applied to monitor seasonal variations of atmospheric aerosol concentration
at a number of sites (Bauer et al., 2008a; Caseiro et al., 2007; Yang et al.,
2012; Yttri et al., 2011a; Zhang et al., 2010, 2015) including pg m-3
(picograms per cubic meter) levels in the Antarctic (Barbaro et al., 2015).
By measuring spore count and tracer concentration in parallel at one urban
and two suburban sites in Vienna, Austria, Bauer et al. (2008a) estimated the
amount of each tracer per fungal spore emitted. Potassium ions have also been
linked to emission of biogenic aerosol (Pöhlker et al., 2012b) and are
co-emitted with fungal spores; however, application of potassium as a fungal
tracer is uncommon because it is predominantly associated with biomass
burning (Andreae and Crutzen, 1997). Additionally, (1→3)-β-D-glucan (fungal spores and pollen) and endotoxins (gram-negative bacteria)
have also been widely used to measure other bioaerosols (Andreae and Crutzen,
1997; Cheng et al., 2012; Rathnayake et al., 2016b; Stone and Clarke, 1992).
The direct detection of PBAPs has historically been limited to analysis
techniques that require culturing or microscopy of the samples. These
systems are time-consuming, costly, and often substantially under-count
biological particles by an order of magnitude or more (Gonçalves et al.,
2010; Pyrri and Kapsanaki-Gotsi, 2007). The sampling methods associated with
these measurements also offer relatively low time resolution and low
particle size resolution. Recently, techniques utilizing ultraviolet
laser/light-induced fluorescence (UV-LIF) for the real-time detection of
PBAPs have been developed and are being utilized by the atmospheric community
for bioaerosol detection. Thus far, the most widely applied LIF instruments
for ambient PBAP detection have been the ultraviolet aerodynamic particle sizer
(UV-APS; TSI Inc. Model 3314, St. Paul, MN, USA) and the wideband integrated bioaerosol sensor (WIBS; University of Hertfordshire, Hertfordshire, UK, now
licensed to Droplet Measurement Technologies, Boulder, CO, USA). Both of
these commercially available instruments can provide information in
real-time about particle size and fluorescence properties of supermicron
atmospheric aerosols. Characterization and co-deployment of these
instruments over the past 10 years has expanded the knowledge base
regarding how to analyze and utilize the information provided from these
instruments (Crawford et al., 2015; Healy et al., 2014; Hernandez et al.,
2016; Huffman et al., 2013; Perring et al., 2015; Pöhlker et al., 2012a, 2013; Ruske et al., 2016), though the interpretation of
UV-LIF results from individual particles is complicated by interfering
material that is not biological in nature (Gabey et al., 2010; Huffman et
al., 2012; Lee et al., 2010; Saari et al., 2013; Toprak and Schnaiter,
2013).
Here we present analysis of atmospheric concentrations of arabitol and
mannitol in relation to results from real-time, ambient particle
measurements reported by UV-APS and WIBS. We interrogate these relationships
as they pertain to rain conditions (rainfall and RH) that have previously
been shown to increase the concentrations of fluorescent aerosols and ice
nuclei (Crawford et al., 2014; Huffman et al., 2013; Prenni et al., 2013;
Schumacher et al., 2013; Yue et al., 2016). Active wet discharge of
ascospores and basidiospores has frequently been reported to correspond with
increased RH (Elbert et al., 2007), and fungal spore concentration has also
been shown to increase after rain events (e.g., Jones and Harrison, 2004).
Here we estimate airborne fungal concentrations in a semi-arid forest
environment utilizing a combination of real-time fluorescence methods,
molecular fungal tracer methods, and direct-to-agar sampling and culturing
as parallel surrogates for spore analysis. This study of ambient aerosol
represents the first quantitative comparison of real-time aerosol UV-LIF
instruments with molecular tracers or culturing.
Methods
Sampling site
Atmospheric sampling was conducted as a part of the BEACHON-RoMBAS
(Bio–hydro–atmosphere interactions of Energy, Aerosols, Carbon, H2O,
Organics, and Nitrogen – Rocky Mountain Biogenic Aerosol Study) field
campaign conducted at the Manitou Experimental Forest Observatory (MEFO)
located 48 km northwest of Colorado Springs, Colorado (39∘06′0′′ N, 105∘5′03′′ W; 2370 m elevation) (Ortega et al., 2014). The site is
located in the central Rocky Mountains and is representative of the semi-arid
montane pine-forested regions of North America. During BEACHON-RoMBAS, a
large, international team of researchers conducted an intensive set of
measurements from 20 July to 23 August 2011. A summary of results from the
campaign are published in the BEACHON campaign special issue of Atmospheric Chemistry and Physics (https://acp.copernicus.org/articles/special_issue247.html). All the data reported here were gathered from instruments
and sensors located within a < 100 m radius (Fig. 1).
Online fluorescent instruments
UV-APS and WIBS-3 (model 3; University of Hertfordshire) instruments were
operated continuously as a part of the study, and particle data were
integrated to 5 min averages before further analysis. The UV-APS was
operated under procedures defined in previous studies (Huffman et al., 2013;
Schumacher et al., 2013). A total suspended particle (TSP) inlet head
∼ 5.5 m above the ground, mounted above the roof of a
climate-controlled, metal trailer, was used to sample aerosol directed
towards the UV-APS. Bends and horizontal stretches in the 0.75 inch tubing
were minimized to reduce losses of large particles (Huffman et al.,
2013). The UV-APS detects particles between 0.5 and 20 µm and
records aerodynamic particle diameter and integrated total fluorescence
(420–575 nm) after pulsed excitation by a 355 nm laser (Hairston et al.,
1997). Both UV-APS and WIBS instruments report information about particle
number concentration, but it is instructive here to show results in particle
mass for comparison between all techniques. Total particle number size
distributions (irrespective of fluorescence properties) obtained from the
UV-APS and WIBS were converted to mass distributions assuming spherical
particles of unit particle mass density, unless otherwise stated, as a first
approximation. Total particle concentration values (in µg m-3)
were obtained for each 5 min period by integrating over the size range
0.5–15 µm, and these mass concentration values were averaged over
the length of the filter sampling periods. Uncertainty in mass concentration
values reported here is influenced by assuming a single value for particle
mass density and because of slight dissimilarities between size bins of the
UV-APS and WIBS instruments at particle sizes above 10 µm that
dominate particle mass.
A WIBS-3 was used to continuously sample air at a site ∼ 50 m
from the UV-APS trailer and 1.3 m above the ground. Briefly, the diameter of
individual particles sampled by the WIBS is estimated by the intensity of
the elastic side scatter from a continuous wave 635 nm diode laser and
analyzed by a Mie scattering model (Foot et al., 2008; Kaye et al., 2005).
Particles that pass through the diode laser activate two optically filtered
Xenon flash lamps. The first lamp excites the particle at 280 nm and the
second at 370 nm. Emission from the 280 nm excitation is filtered separately
for two photomultiplier tubes (PMTs), one which detects in a band at 320–400 nm and the other in a
band at 410–650 nm. These excitation and emission wavelengths result in a
total of three channels of detection: λex 280 nm, λem 320–400 nm (FL1 or channel A); λex 280 nm,
λem 410–650 nm (FL2 or channel B); and λex
370 nm, λem 410–650 nm (FL3 or channel C) (Crawford et al.,
2014). Individual particles are considered fluorescent here if they exceed
fluorescent thresholds for any channel, as defined as the average of a
“forced trigger” baseline plus 3 standard deviations (SD) of the
baseline measurement (Gabey et al., 2010).
Aerial overview of BEACHON-RoMBAS field site at the Manitou
Experimental Forest Observatory located northwest of Colorado Springs, CO.
Locations of all instruments and sensors discussed here are marked and were
located within a 50 m radius. Figure adapted from Fig. 1a of Huffman et
al. (2013).
WIBS particle-type analysis is utilized to define types of particles that
have specific spectral patterns. As defined by Perring et al. (2015), the
three
different fluorescent channels (FL1, FL2, and FL3) can be combined to
produce seven unique fluorescent categories. Observed fluorescence in channel
FL1 alone, but without any detectable fluorescence in channel FL2 or FL3,
categorizes a particle as type A. Similarly, observed fluorescence in
channels FL2 or FL3, but in no other channels, places a particle in the B or
C categories, respectively. Combinations of fluorescence in these channels,
such as a particle that exhibits fluorescence in both FL1 and FL2,
categorizes a particle as type AB and so on for a possible seven particle
types as summarized in Fig. S1 in the Supplement.
As a separate tool for particle categorization, the University of Manchester
has recently developed and applied a hierarchical agglomerative cluster
analysis tool for WIBS data, which they have previously applied to the
BEACHON-RoMBAS campaign (Crawford et al., 2014, 2015;
Robinson et al., 2013). Here we utilize clusters derived from WIBS-3 data as
described by Crawford et al. (2015). Cluster data presented here were
analyzed with the open-source Python package FastCluster (Müllner, 2013).
Briefly, hierarchical agglomerative cluster analysis was applied to the
entire data set and each fluorescent particle was uniquely clustered into
one of four groups. Cluster 1, assigned by Crawford et al. (2015) as fungal
spores, displayed a 1.5–2 µm mode and a daily peak in the early
morning that paralleled relative humidity (Schumacher et al., 2013).
Clusters 2, 3, and 4 have strong, positive correlations with rainfall and
exhibit size modes that peak at < 1.2 µm and were initially
described by Crawford et al. (2014) as bacterial particles. Here we have summed
clusters 2–4 to a single group referred to as ClBact, for simplicity
when comparing with molecular tracers. It should be noted that assignment of
name and origin (e.g., fungal spores or bacteria) to clusters is approximate
and does not imply naming accuracy or particle homogeneity. Each cluster
likely contains an unknown fraction of contaminating particles, but the
clusters are beneficial to group particles more selectively than using
fluorescent intensity alone. For more details see Robinson et al. (2013) and
Crawford et al. (2015).
The WIBS-3 utilized here has since been superseded by the WIBS-4 (Univ.
Hertfordshire, UK) and WIBS-4A (Droplet Measurement Technologies, Boulder,
CO, USA). One important difference between the models is that the optical
chamber design and filters of the WIBS-4 models were updated to enhance the
overall sensitivity of the instrument (Crawford et al., 2014). Additionally,
slight differences in detector gain between models and individual units can
impact the relative sensitivity of the fluorescence channels. This may
result in differences in fluorescent channel intensity between instrument
models, as will be discussed later.
High-volume sampler
Total suspended particle samples were collected for molecular tracer and
molecular genetic analyses using a high-volume sampler (Digitel DHA-80)
drawing 1000 L min-1 through 15 cm glass fiber filters (Macherey-Nagel
GmbH, Type MN 85/90, 406015, Düren, Germany) over a variety of sampling
times ranging from 4 to 48 h (Supplement Table S1). The sampler was located
< 50 m from each of the UV-LIF instruments described here,
approximately between the WIBS-3 and UV-APS. Prior to sampling, all filters
were baked at 500 ∘C for 12 h to remove DNA and organic contaminants.
Samples were stored in pre-baked aluminum bags after sampling at -20 ∘C
for 1–30 days and then at -80 ∘C after overnight, international
transport cooled on dry ice. Due to the low vapor pressure of the molecular
tracers analyzed, loss due to volatilization is considered unlikely (Zhang et
al., 2010). A total of 36 samples were collected during the study, in addition to
handling field blanks and operational field blanks. Handling blanks were
acquired by placing a filter into the sampler and immediately removing it,
without turning on the airflow control. Operational blanks were placed into
the sampler and exposed to 10 s of airflow.
Slit sampler
A direct-to-agar slit sampler (Microbiological Air Sampler STA-203, New
Brunswick Scientific Co, Inc., Edison, NJ) was used to collect culturable
airborne fungal spores. The sampler was placed ∼ 2 m above the
ground on a wooden support surface with 5 cm × 5 cm holes to allow airflow
both up and down through the support structure. Sampled air was drawn over
the 15 cm diameter sampling plate filled with growth media at a flow rate of
28 L min-1 for sampling periods of 20 to 40 min. Growth media (malt
extract medium) was mixed with antibacterial agents (40 units streptomycin,
Sigma Aldrich; 20 units ampicillin, Fisher Scientific) to suppress bacterial
colony growth. Plates were prepared several weeks in advance and stored in a
refrigerator at ca. 4 ∘C until used for sampling. Before each sampling
period, all surfaces of the samplers were sterilized by wiping with
isopropyl alcohol. Handling and operational blanks were collected to verify
that no fungal colonies were being introduced by handling procedures. A total of 14 air
samples were collected over 20 days and immediately moved to an incubator
(Amerex Instruments, Incumax IC150R) set at 25 ∘C for 3 days prior to
counting fungal colonies formed. Each colony, present as a growing dot on
the agar surface, was assumed to have originated as 1 colony-forming unit
(CFU; i.e., fungal spore) deposited onto the agar by impaction during
sampling. The atmospheric concentration of CFU per air volume was calculated
using the sampler airflow. Further discussion of methods and initial
results from the slit sampler were published by Huffman et al. (2013).
Offline filter analyses
Carbohydrate analysis
Approximately one-eighth of each frozen filter was cut for carbohydrate
analysis using a sterile technique, meaning that scissors were cleaned and
sterilized, and cutting was performed
in a positive-pressure laminar flow hood. In order to precisely determine the
fractional area of the filter to be analyzed, filters were imaged from a
fixed distance above using a camera and compared to a whole, intact filter.
Using ImageJ software (Rasband, 1997), the area of each filter slice showing
particulate matter (PM) deposit was referenced to a whole filter, and thereby
the amount of each filter utilized could be determined. The total PM mass was
not measured and so this technique allowed for an estimation of the fraction
of each sample used for the analysis, which corresponds to the fraction of PM
mass deposited. The uncertainty on the filter area fraction is estimated at
2 %, determined as the percent of variation in the area of the filter
edge (no PM deposit) as compared to the total filter area.
Water-soluble carbohydrates were extracted from glass fiber filter samples and
analyzed following the procedure described by Rathnayake et al. (2016a). A
total of 36 samples were analyzed along with field and lab blanks. All lab
and field blanks fell below method detection limits. Extraction was
performed by placing the filter slice into a centrifuge tube that had been
pre-rinsed with Nanopure™ water (resistance > 18.2 MΩ cm-1; Barnstead EasyPure II, 7401). A volume of 8.0 mL of
Nanopure™ water was added to the filter in the centrifuge tube to
extract water-soluble carbohydrates. Samples were then exposed to rotary
shaking for 10 min at 125 rpm, sonication for 30 min at 60 Hz (Branson 5510,
Danbury, CT, USA), and rotary shaking for another 10 min. After shaking, the
extracted solutions were filtered through a 0.45 µm polypropylene
syringe filter (GE Healthcare, UK) to remove insoluble particles, including
disintegrated filter pieces. One 1.5 mL aliquot of each extracted solution
was analyzed for carbohydrates within 24 h of extraction. A duplicate
1.5 mL aliquot was stored in a freezer and analyzed if necessary, due to
lack of instrument response or invalid calibration check, within 7 days of
extraction. Analysis of carbohydrates was done using a high-performance
anion-exchange chromatography system with pulsed amperometric detection
(HPAEC-PAD; Dionex ICS 5000, Thermo Fisher, Sunnyvale, CA, USA). Details of
the instrument specifications and quality standards for carbohydrate
determination are available in Rathnayake et al. (2016a). Calibration curves
for mannitol, levoglucosan, glucose (Sigma-Aldrich), arabitol, and erythritol
(Alfa Aesar) were generated with 7 points each, ranging in aqueous
concentration from 0.005 to 5 ppm. The method detection limits for mannitol,
levoglucosan, glucose, arabitol, and erythritol were determined to be 2.3, 2.8,
1.6, 1.0, and 0.6 ppb, respectively, by measuring the instrument response to filter extracts (Rathnayake et al.,
2016a).
One filter each was spiked with 10 ppb of the five compounds, followed by one extraction
per filter from which seven aliquots were each analyzed by the instrument.
The variability (3 SD) of the measured response was taken as the method detection
limit. All calibration curves were checked
daily using a standard solution to ensure all concentration values were
within 10 % of the known value. Failure to maintain a valid curve resulted
in recalibration of the instrument.
DNA analysis
Methods and initial results from DNA analysis from these high-volume filters
were published by Huffman et al. (2013). Briefly, fungal diversity was
determined by previously optimized methods for DNA extraction,
amplification, and sequence analysis of the internal transcribed spacer
regions of ribosomal genes from the high-volume filter samples
(Fröhlich-Nowoisky et al., 2009, 2012).
Upon sequence determination, fungal sequences were compared with known
sequences using the Basic Local Alignment Search Tool (BLAST) at the
National Center for Biotechnology (NCBI) and identified to the lowest
taxonomic rank common to the top BLAST hits after chimeric sequences had
been removed. When sequences displayed > 97 % similarity, they
were grouped into operational taxonomic units (OTUs).
Endotoxin and glucan analysis
Sample preparation for quantification of endotoxin and (1→3)-β-D-glucan included extraction of five punches (0.5 cm2 each) of the
glass filters with 5.0 mL of
pyrogen-free water (Associates of Cape Cod Inc., East Falmouth, MA, USA),
utilizing an orbital shaker (300 rpm) at room temperature for 60 min,
followed by centrifuging for 15 min (1000 rpm). A 0.5 mL aliquot of
supernatant was submitted to a kinetic chromogenic limulus amebocyte lysate
(Chromo-LAL) endotoxin assay (Associates of Cape Cod Inc., East Falmouth, MA,
USA), using a ELx808IU (BioTek Instrument Inc., Winooski, VT, USA) incubating
absorbance microplate reader. For (1→3)-β-D-glucan measurement,
0.5 mL of 3 N NaOH was added to the remaining 4.5 mL of extract and the
mixture was agitated for 60 min. Subsequently, the solution was neutralized
to pH 6–8 by the addition of 0.75 mL of 2 N HCl. After centrifuging for
15 min (1→3)-β-D-glucan concentration was determined in the
supernatant using the Glucatell® LAL kinetic
assay (Associates of Cape Cod, Inc., East Falmouth, MA, USA). The minimum
detection limits (MDLs) and reproducibility were 0.046 endotoxin units (EU)
m-3 ± 6.4 % for endotoxin and
0.029 ng m-3 ± 4.2 % for (1→3)-β-D-glucan,
respectively. Laboratory and field blank samples were analyzed as well, with
lab blank values being below detection limits, while field blank values were
used to subtract background levels from sample data. More details about the
bioassays can be found elsewhere (Chow et al., 2015a).
Meteorology and wetness sensors
Meteorological data were recorded by a variety of sensors located at the
site. Precipitation was recorded by a laser optical disdrometer (PARticle
SIze and VELocity sensor – “PARSIVEL”; OTT Hydromet GmbH, Kempton, Germany)
and separately by a tipping-bucket rain gauge. The disdrometer provides
precipitation occurrence, rate, and physical state (rain or hail) by
measuring the magnitude and duration of disruption to a continuous 780 nm
laser that was located in a tree clearing (Fig. 1), while the tipping-bucket
rain gauge measures a set amount of precipitation before tipping and
triggering an electrical pulse. A leaf wetness sensor (LWS; Decagon Devices,
Inc., Pullman, WA, USA) provided a measurement of condensed moisture by
measuring the voltage drop across a leaf surface to determine a proportional
amount of water on or near the sensor. Additional details of these
measurements can be found in Huffman et al. (2013) and Ortega et al. (2014).
Time series of key species concentrations
and meteorological data over entire campaign. (a) Fluorescent particle number size distribution measured with UV-APS instrument. Color scale
indicates fluorescent particle number concentration (L-1). (b)
Meteorological data: relative humidity (RH), disdrometer rainfall (millimeters per
15 min), leaf wetness (mV). (c) Wetness category indicated as colored
bars:
green, Rainy; brown, Dry; pink, Other. Bar width corresponds to filter
sampling periods. Lightened colored bars extend vertically to highlight
categorization. (d) Colored traces show fungal spore concentrations
estimated from molecular tracers (circles) and WIBS Cl1 data (squares). (e)
Stacked bars show relative fraction of fluorescent particle type
corresponding to each WIBS category.
Results and discussion
Categorization and characteristic differences of Dry and Rainy
periods
Increases in PBAP concentration have been frequently associated with
rainfall (e.g., Bigg et al., 2015; Faulwetter, 1917; Hirst and Stedman, 1963;
Jones and Harrison, 2004; Madden, 1997). Fungal polyols have also been
reported to increase after rain and have been used as indicators of
increased fungal spore release (Liang et al., 2013; Lin and Li, 2000; Zhu et
al., 2015). Recently, it was shown that the concentration of fluorescent
aerosol particles (FAPs) measured during BEACHON-RoMBAS increased
dramatically during and after periods of rain (Crawford et al., 2014;
Huffman et al., 2013; Schumacher et al., 2013) and that these particles were
associated with high concentrations of ice nucleating particles that could
influence the formation and evolution of mixed-phase clouds (Huffman et al.,
2013; Prenni et al., 2013; Tobo et al., 2013). It was observed that a mode
of smaller fluorescent particles (2–3 µm) appeared during rain
episodes, and several hours after rain ceased a second mode of slightly
larger fluorescent particles (4–6 µm) emerged, persisting for up to
12 h (Huffman et al., 2013). The first mode was hypothesized to result from
mechanical ejection of particles due to rain splash on soil and vegetated
surfaces, and the second mode was suggested as actively emitted fungal
spores (Huffman et al., 2013). While the UV-APS and WIBS each provide data
at a high enough time resolution to see subtle changes in aerosol
concentration, the temporal resolution of the chemical tracer analysis was
limited to 4–48 h periods defined by the collection time of the high-volume
sampler. To compare the measurement results across the sampling platforms,
UV-LIF measurements were averaged to the lower time resolution of the filter
sampler periods, and the periods were grouped into three broad categories:
Rainy, Dry, and Other, as will be defined below.
Time periods were wetness-categorized in two steps: first at 15 min
resolution and then averaged for each individual filter sample. During the
first stage of categorization each 15 min period was categorized into one of
four groups: rain, post-rain, dry, or other. To categorize each filter
period, an algorithm was established utilizing UV-APS fluorescent particle
fraction and accumulated rainfall. The ratio of the integrated number of
fluorescent particles to total particles was used as a proxy for the
increased emission of biological particles. Figure 2a presents a time series
of the size-resolved fluorescent particle concentration, showing increases
during rain periods in dark red. A relatively consistent diurnal cycle of
increased FAP concentration in the 2–4 µm range is apparent almost
every afternoon, which corresponds to near-daily afternoon rainfall during
approximately the first half of the measurement period. Disdrometer and
tipping-bucket rainfall measurements were each normalized to unity and
summed to produce a more robust, unitless measure of rainfall rate because
it was observed that often only one of the two systems would record a given
light rain event. If a point was described by total rainfall accumulation
greater than 0.201 it was flagged as rain. A point was flagged as post-rain
if it immediately followed a rain period and also exhibited a fluorescent
particle fraction greater than 0.08. The purpose of this category was to
reflect the observation that sustained elevated concentrations of FAPs
persisted for many hours even after the rain rate, RH, and leaf wetness
returned to pre-rain values. The only measurement that adequately reflected
this scenario was of the fluorescent particles measured by UV-APS and WIBS
instruments. The post-rain flag was continued until the fluorescent particle
fraction fell below 0.08 or if it started to rain again (with calculated
rain values greater than 0.201). Points were flagged as dry periods if they
exhibited rainfall accumulation and fluorescent particle fraction below the
thresholds stated above. Several periods were not easily categorized by this
system and were considered in a fourth category as other. This occurred when
fluorescent particle fraction above the threshold value was observed with no
discernable rainfall.
Once wetness categories were assigned by the algorithm at 15 min resolution,
each high-volume filter sample was categorized by a similar nomenclature,
but using only three categories. These were defined as Dry, Rainy
(combination of rain and post-rain categories), or Other based on the
relative time fraction in each of the four original 15 min categories. For
each sample, if a given category represented more than 50 % of the 15 min
periods, the sample was assigned to that category. Despite the effort to
categorize samples systematically, several sample periods (5 of 35) appeared
mis-categorized by looking at FAP concentration, rainfall, RH, and leaf
wetness in more detail. In some circumstances, this was because light
rainfall produced observable increases in FAPs, but without exceeding the
rainfall threshold. Or in other circumstances a period of rainfall occurred
at the very end or just before the beginning of a sample, and so the
many-hour period was heavily influenced by aerosol triggered by a period of
rain just outside of the sample time window. As a result, several samples
were manually re-categorized as described here. Samples 20 and 21 (Table S1)
were 4 h samples that displayed high relative humidity and rainfall;
thus, samples were originally characterized as Rainy. This period was
described by an extremely heavy rain downpour (7.5 mm in 15 min); however,
that seemingly placed the samples in a different regime of rain–aerosol
dynamics than the other Rainy samples and so these two samples were moved to
the Other category. Sample 23, originally Rainy, presented a FAP fraction
marginally above the 0.08 threshold, but visually displayed a trend
dissimilar to other post-rain periods and so was re-categorized as Dry.
Sample 28 showed no obvious rainfall, but the measurement team observed
persistent fog in three consecutive mornings (samples 25, 27, 28), and the
concentration of fluorescent particles (2–6 µm) suggested a source of
particles not influenced by rain, and so this Rainy sample was
re-categorized as Other. Sample 38 displayed a fluorescent number ratio just
below the threshold value, and was first categorized as Dry; however, the
measurement team observed post-rain periods at the beginning and end of the
sample, and the sample was re-categorized as Other. For all samples other
than these five, the categorization was determined using the majority
(> 0.50) of the 15 min periods. In no cases other than the five
that were re-categorized was the highest category fraction less than 0.50 of
the sample time. Note that we have chosen to capitalize Rainy, Dry, and
Other to highlight that we have rigorously defined the period using the
characterization scheme described above and to separate the nomenclature
from the general, colloquial usage of the terms. Wetness category assignment
for each high-volume filter sample period is shown in Fig. 2 as a
background color (brown for Dry samples, green for Rainy samples,
and pink for Other samples) and Table S1.
Characteristic differences between wetness periods (Dry, Rainy, Other). (a) Relative fraction of
fluorescent particle number corresponding to each WIBS category. Bars show
relative standard deviation of category fraction in each wetness group (Dry,
19 samples; Rainy, 11 samples; Other, 6 samples). (b, c) Distribution of
fungal OTU (operational taxonomic unit) values. (b) Fungal community
composition at phylum and class level with Agaricomycetes (dominant class with
consistently ∼ 60 % of diversity) removed. Relative
proportion of OTUs assigned to different fungal classes and phyla for each
sample category shown. (c) Venn diagram showing the number of unique
(wetness category-specific) and shared OTUs (represented by numbers in
overlapping areas) among the sample categories (Dry, 11 samples; Rainy, 7
samples; Other, 3 samples). OTUs classified as cluster of sequences with
≥ 97 % similarity. Taxonomic assignments were performed using BLAST
against NCBI database. In total, 3902 sequences, representing 406 fungal
OTUs from 3 phyla and 12 classes were detected. Despite differences in
community structure across the sample categories, phylogenetic
representation appears largely similar.
Number concentration of fluorescent
particles as a function of instrument channel, averaged over entire
measurement period. (a) Box-and-whisker plot of fluorescent particle number
concentration for WIBS FL1, FL2, FL3, and UVAPS. Circle markers shows mean
values, internal gray horizontal line shows median, top and bottom of box show
inner quartile, and whiskers show 5th and 95th percentiles. (b)
WIBS FL1 vs. UV-APS, (c) WIBS FL2 vs. UV-APS, and (d) WIBS FL3
vs.
UV-APS. Crosses represent 5 min average points. Linear fits assigned for
data in each wetness category.
To validate the qualitative differences between wetness categories described
in the last section, we present observations about each of these groupings.
First, we organized the WIBS data according to the particle categories
introduced by Perring et al. (2015). By this method, every fluorescent
particle detected by the WIBS can be defined uniquely into one of seven
categories (i.e., A, AB, ABC). By plotting the relative fraction of
fluorescent particles described by each particle type, temporal differences
between measurement periods can be observed, as shown in Fig. 2e. To a first
approximation, this analysis style allows for coarse discrimination of
particle types. For example, a given population of particles would ideally
exhibit a consistent fraction of particles present in the different particle
categories as a function of time. By this reasoning, sample periods
categorized as Dry (most of the latter half of the study; brown bars in
Fig. 2) would be expected to have a self-consistent particle-type trend,
whereas sample periods categorized as Rainy (most of the first half of the
study; green bars in Fig. 2) would have a self-consistent particle-type
trend, but different from the Dry samples. This is broadly true. During Rainy
periods, as seen in Fig. 3a, there is
a relatively high fraction (> 65 %) of ABC type particles
(light blue) and a relatively low fraction (< 15 %) in BC
(purple) and C (yellow) type particles, suggesting heavy influence from the
FL1 channel. In contrast, during Dry periods the fraction of ABC particles
(light blue) is reduced (< 25 %), whereas BC (purple) and C
(yellow) type particles increase in relative fraction (> 30 and
> 40 %, respectively), which suggested a diminished influence
of FL1 channel.
It is important to note a few caveats here. First, the ability of the WIBS
to discriminate distinctly between PBAP types is relatively poor and it is still
unclear exactly how different particle types would appear by this analysis
method. Particles of different kinds and from different sources are likely
convolved into a single WIBS particle type, which could either soften or
enhance the relationships with rain discussed here. Second, the assignment
of particle types is heavily size dependent and sensitive to subtle
instrument parameters, and so it is unclear how different instruments would
present similar particle types. For example, Hernandez et al. (2016) used
two WIBS instruments and found differences in relative fraction of particle
categories for samples aerosolized in the lab. They reported fungal spores
to be predominately A, AB, and ABC type particles, whereas Rainy sample
periods, suggested to have a heavy fungal spore influence by Huffman et al. (2013), show predominantly C, BC, and ABC type particle fractions. These
discrepancies may be due to the comparison of ambient particles to
laboratory-grown cultures. The highly controlled environment of a laboratory
may not always accurately represent the humidity conditions in which fungal
spore release occurs in this forest setting (Saari et al., 2015). This could
impact the fluorescence properties of fungal spore particles that have
different amounts of adsorbed or associated water (Hill et al., 2009, 2013,
2015). More likely, however, is that the WIBS-3 used here exhibits
differences in fluorescence sensitivity from the WIBS-4A used by Hernandez
et al. (2016). Even a slight increase in sensitivity in the FL3 channel with
respect to the FL1 or FL2 channels could explain the shift here towards
particles with C-type fluorescence. One piece of evidence for this is the
quantitative comparison of particle measurements presented by the UV-APS and
WIBS-3 instruments co-deployed here (Fig. 4). The number concentration of
particles exhibiting fluorescence above the FL2 baseline of the WIBS-3 is
approximately consistent with the number of fluorescent particles measured
by the UV-APS, and significantly below the concentration of FL3 particles.
The UV-APS number concentration shows the highest correlation with the
WIBS-3 FL2 channel: during Rainy periods, R2=0.70; Dry,
R2=0.82; and Other, R2=0.92. These observations are in stark
contrast to the trends reported by Healy et al. (2014) that the UV-APS
fluorescent particle concentration correlated most strongly with the WIBS-4
FL3 and that the number concentration of FL3 was the lowest out of all three
channels. Given that the FL3 channel of the WIBS and the UV-APS cover
similar excitation and emission wavelengths, it is expected that these two
channels should correlate well. Based on these data, we suggest that the
WIBS-3 utilized here may present a very different particle-type breakdown
than if a WIBS-4 had been used. So, while caution is recommended when
comparing the relative breakdown of WIBS particle categories shown here
(Fig. 3) with other studies, the data are internally self-consistent, and
comparing qualitative differences between, e.g., Rainy and Dry periods, is
expected to be robust. The main point to be highlighted here is that there
is indeed a qualitative difference in particles present in the three wetness
categories, as averaged and shown in Fig. 3a, which generally supports the
effort to segregate these samples.
Mass concentrations of molecular tracers and
fluorescent particles (calculated assuming unit density particle mass and spherical
particles): arabitol – top row and mannitol – bottom row. Average mass
concentration of arabitol (a) and mannitol (b) in each wetness category.
Central marker shows mean value of individual filter concentration values,
bars represent standard deviation (SD) range of filter values, and individual
points show outliers beyond mean ± SD. Correlation of arabitol (c) and
mannitol (d) with fluorescent particle mass from UV-APS. Correlation of
arabitol (e) and mannitol (f) with fluorescent particle mass from WIBS
cluster 1. R2 values shown for each fit in (c–f). Linear fit parameters
are shown in Table S2.
Further evidence that there is a qualitative difference in the three wetness
categories is shown using molecular genetic analysis (Fig. 3b, c). The
analysis of fungal DNA sequences from 21 of the high-volume samples found
406 OTUs belonging to different fungal
classes and phyla. When organized by wetness type it was observed that 106
of these occurred only on Rainy samples, 148 of these occurred on Dry
samples, and 37 on Other samples, with some fraction occurring in overlaps
of each (Fig. 3c). This shows that the number of OTUs observed uniquely in
either the Rainy or Dry periods is greater than the number of OTUs present
in both wetness types, suggesting that the fungal communities in each
grouping are relatively distinct. Further, Fig. 3b shows a breakdown of
fungal taxonomic groupings for each wetness group. This analysis shows that
there is a qualitative difference in taxonomic breakdown between periods of
Rainy and Dry. Specifically, during Dry periods there is an increased
fraction of Pucciniomycetes (green bar, Fig. 3c), Chytridiomycota (yellow), Sordariomycetes (orange), and Eurotiomycetes (pink) when
compared to the Rainy periods.
Campaign-average concentrations of molecular tracers (top) and their
respective mass contributions (bottom). Values are mean ± standard
deviation; n shows number of samples used for averaging. Total particulate
matter mass calculated from UV-APS number concentration (m-3), converted
to mass over aerodynamic particle diameter range 0.5–15 µm using
1.5 g cm-3 density.
Mass concentration
Arabitol (ng m-3)
Mannitol (ng m-3)
Erythritol (ng m-3)
Levoglucosan (ng m-3)
Glucose (ng m-3)
Endotoxins(EU m-3)
(1→3)-β-D-glucan (pg m-3)
Dry
10.6 ± 2.5 n = 18
11.9 ± 3.2 n = 18
0.840 ± 0.610 n = 16
14.2 ± 10.7 n = 15
38.7 ± 21.3 n = 18
0.192 ± 0.0970 n = 18
8.85 ± 7.68 n = 18
Rainy
35.2 ± 10.5 n = 11
44.9 ± 13.8 n = 11
1.12 ± 0.38 n = 3
12.4 ± 19.1 n = 8
73.2 ± 50.5 n = 11
1.43 ± 1.22 n = 10
10.6 ± 8.2 n = 11
Other
20.2 ± 8.9 n = 6
22.7 ± 8.3 n = 6
0.664 ± 0.515 n = 6
9.21 ± 1.66 n = 5
56.5 ± 39.2 n = 6
0.311 ± 0.159 n = 6
6.08 ± 6.08 n = 6
Mass contribution (%)
Dry
0.18 % ± 0.05 n = 18
0.20 % ± 0.073 n = 18
0.014 % ± 0.011 n = 16
0.21 % ± 0.17 n = 15
0.67 % ± 0.49 n = 18
0.16 % ± 0.16 n = 18
Rainy
0.83 % ± 0.32 n = 11
1.07 % ± 0.44 n = 11
0.032 % ± 0.009 n = 3
0.27 % ± 0.41 n = 8
1.60 % ± 1.09 n = 11
0.25 % ± 0.21 n = 11
Other
0.25 % ± 0.28 n = 6
0.37 % ± 0.29 n = 6
0.013 % ± 0.015 n = 6
0.15 % ± 0.11 n = 5
0.83 % ± 0.64 n = 6
0.12 % ± 0.19 n = 6
Square of correlation coefficients (R2) comparing total mass
concentration of molecular tracers to each other. EU: endotoxin units. Boxes
colored by coefficient value (bold > 0.7; 0.7
> italic > 0.4).
Arabitol
Mannitol
(1→3)-β-D-glucan
Rainy
Dry
Rainy
Dry
Rainy
Dry
Mannitol
Rainy
0.839
Dry
0.312
(1→3)-β-D-glucan
Rainy
0.000
0.003
Dry
0.000
0.327
Endotoxins
Rainy
0.116
0.126
0.427
Dry
0.012
0.113
0.103
Atmospheric mass concentration of arabitol, mannitol, and fungal
spores
To estimate fungal spore emission to the atmosphere, the concentration of
arabitol and mannitol (Fig. 5a, b, Table 1) in each aerosol sample was
averaged for all samples in each of the three wetness categories. The average
concentration of arabitol collected on Rainy TSP samples
(35.2 ± 10.5 ng m-3) increased by a factor of 3.3 with respect
to Dry samples, and the average mannitol concentration on Rainy samples was
higher by a factor of 3.7 (44.9 ± 13.8 ng m-3). Figure 5a, b
show the concentration variability for each wetness category, observed as the
standard deviation from the distribution of individual samples. For each
polyol, there is no overlap in the ranges shown, including the outliers of
the Rainy and Dry category, suggesting a definitive and conceptually distinct
separation between dry periods and those influenced by rain. The
concentrations observed during Other periods is between those of the Dry and
Rainy averages, as expected, given the difficulty in confidently assigning
these uniquely to one of these categories. The observations here are roughly
consistent with previous reports of polyol concentration, despite differences
in local fungal communities and concentrations. For example, Rathnayake et
al. (2016a) observed 30.2 ng m-3 arabitol and 41.3 ng m-3
mannitol in PM10 samples collected in rural Iowa, USA. In addition,
Zhang et al. (2015) reported arabitol and mannitol concentrations in PM10 samples of 44.0 and 71.0 ng m-3, respectively, from a study in the
mountains on Hainan Island off the coast of southern China. More recently,
Yue et al. (2016) studied a rain event in Beijing and observed increased
polyol concentrations at the onset of the rain. The observed mannitol
concentration (45 ng m-3) was approximately consistent with
observations reported here and with previous reports, while the arabitol
concentration values observed were approximately an order of magnitude lower
(0.3 ng m-3).
Square of correlation coefficients (R2) comparing fluorescent
particle measurements from UV-LIF instruments to measurements from molecular
tracers and direct-to-agar sampler. Columns marking tracer mass indicate
correlations between time-averaged UV-LIF and tracer mass concentrations
(left side). Columns marking fungal spore count indicate correlations between
fungal spore number concentrations estimated from time-averaged UV-LIF and
tracer or culture measurements (right side). FL1, FL2, FL3 represent
individual channels from the WIBS. FL represents particles exhibiting
fluorescence in any channel. Cl1, Cl2, Cl3, Cl4 are clusters that estimate
particle concentrations as a mixture of various channels (Crawford et al.,
2015). ClBact is a sum of the “bacteria” clusters Cl2-4. Boxes
colored by coefficient value (bold > 0.7; 0.7
> italic > 0.4).
Correlation based on tracer mass
Correlation based on spore counts
Arabitol
Mannitol
(1→3)-β-D-glucan
Endotoxins
Arabitol
Mannitol
CFU
Rainy
Dry
Rainy
Dry
Rainy
Dry
Rainy
Dry
Rainy
Dry
Rainy
Dry
Rainy
Dry
UV-LIF mass or number concentration
UVAPS
0.732
0.127
0.877
0.160
0.006
0.012
0.153
0.067
0.483
0.278
0.504
0.571
0.469
0.491
WIBS
FL
0.554
0.250
0.810
0.255
0.128
0.010
0.068
0.066
0.159
0.200
0.088
0.314
0.330
0.737
FL1
0.602
0.445
0.819
0.412
0.042
0.001
0.090
0.012
0.667
0.339
0.863
0.621
0.470
0.546
FL2
0.617
0.248
0.843
0.342
0.092
0.001
0.039
0.094
0.485
0.302
0.442
0.340
0.560
0.543
FL3
0.561
0.222
0.818
0.251
0.124
0.008
0.071
0.065
0.178
0.181
0.104
0.306
0.367
0.736
Cl1
0.824
0.764
0.799
0.109
0.000
0.134
0.229
0.011
0.679
0.543
0.775
0.423
0.128
0.690
Cl2
0.005
0.002
0.004
0.006
0.002
0.047
0.006
0.017
0.052
0.056
0.001
0.075
0.081
0.930
Cl3
0.267
0.164
0.261
0.198
0.003
0.011
0.016
0.066
0.052
0.116
0.087
0.439
0.262
0.383
Cl4
0.048
0.046
0.172
0.118
0.115
0.011
0.179
0.145
0.062
0.089
0.001
0.065
0.120
0.000
ClBact
0.041
0.081
The square of the correlation coefficient (R2) here between
concentration values of arabitol and mannitol during Rainy samples is very
high (0.839; Table 2) suggesting that arabitol and mannitol originated
primarily from the same source, likely active-discharge fungal spores. The
correlation is similar to the 0.87 R2 reported by Bauer et al. (2008a)
and the 0.93 R2 reported by Graham et al. (2003). In contrast, the same
correlation seen between mannitol and arabitol concentrations for Dry samples
is relatively low (0.312). This is consistent with reports that arabitol can
be used more specifically as a spore tracer, but that mannitol has additional
atmospheric sources besides fungal spores. The same correlation was also
performed between arabitol or mannitol and other molecular tracers
(endotoxins and (1→3)-β-D-glucan), but all R2 value were
less than 0.43, suggesting that the endotoxins and glucans analyzed were not
emitted uniquely from the same sources as arabitol and mannitol.
Results from the two UV-LIF instruments were averaged over high-volume sample
periods, and a correlation analysis was performed between tracer mass and
fluorescent particle mass showing positive correlations in all cases. The FAP
mass from the UV-APS shows high correlation with the fungal polyols during
Rainy periods, with R2 of 0.732 and 0.877 for arabitol and mannitol,
respectively (Table 3; Fig. 5c, d). The same tracers correlate poorly with
the UV-APS during Dry conditions. This is expected, because Ascomycota and
Basidiomycota spores emitted by wet-discharge methods are the only fungal
spores reported to be associated with arabitol and mannitol (Elbert et al.,
2007; Feofilova, 2001; Lewis and Smith, 1967). This high correlation suggests
that the UV-APS does a good job of detecting these wet-discharge spores, and
corroborates previous statements that particles detected in ambient air by
the UV-APS are often predominately fungal spores (Healy et al., 2014; Huffman
et al., 2012, 2013). In contrast, the low slope value and the poor
correlation during Dry periods suggest that the UV-APS is also sensitive to
other kinds of particles, as designed. The small positive x offset
(FAP mass; Table S2, Fig. 5c, d) during Rainy periods is likely due to
particles that are too weakly fluorescent to be detected and counted by the
UV-APS, which is consistent with observations made in Brazil (Huffman et al.,
2012).
Particle mass from WIBS Cl1, assigned to fungal spores (Crawford et al.,
2015), also correlate strongly with the same two molecular tracers. Both
Rainy periods (R2 0.824) and Dry periods (R2 0.764) correlate well
with arabitol (Fig. 5e), while mannitol (Fig. 5f) only shows a strong
correlation during the Rainy periods (R2 0.799). Mannitol is a common
polyol in higher plants while arabitol is only found in fungal spores and
lichen (Lewis and Smith, 1967). So the strong correlation of each polyol with
UV-LIF mass during Rainy periods when actively discharged spores are expected
to dominate and the similarly strong correlations associated with arabitol
suggest that the Cl1 cluster does a reasonably good job of selecting fungal
spore particles. The poor correlation between mannitol and Cl1 during dry
periods illustrates that the background mannitol concentration is likely not
due to fungal spores alone, but has contributions from other higher plants
that contain mannitol. Particle concentrations detected by individual WIBS
channels and in the other clusters were also compared with polyol
concentrations, but each correlation is relatively poor compared to that with
respect to Cl1. As seen in Table 3 and Figs. S2–S3, correlations in FL1, 2,
and 3 with arabitol are poor (< 0.4) in the Dry category and good
(0.4 < R2 < 0.7) in the Rainy category. For mannitol,
all the UV-LIF instruments show high correlation (> 0.7) in all
cases. This is likely due to mannitol being a non-specific tracer and
suggests that the majority of UV-LIF particles observed during all periods
was dominated by PBAPs.
Estimated fungal spore number concentration, calculated using mass
of arabitol and mannitol per spore reported by Bauer et al. (2008a).
Estimates from arabitol (top row) and mannitol (bottom row). Average fungal
spore concentration, calculated using arabitol mass (a), mannitol
mass (b), and colony-forming units (c) in each wetness
category. Central marker shows mean value of individual filter concentration
values, bars represent standard deviation (SD) range of filter values, and
individual points show outliers beyond mean ± SD. Correlation of fungal
spore number calculated from arabitol (d), mannitol (e),
and colony-forming units (f) concentrations with estimated
fluorescent particle mass from UV-APS. Correlation of fungal spore number
calculated from arabitol (g), mannitol (h), and
colony-forming unit (i) concentrations with fluorescent particle
concentrations from WIBS cluster 1. R2 value shown for each fit (right
two columns). Linear fit parameters are shown in Table S3.
Estimated number concentration of fungal spore aerosol
Bauer et al. (2008a) reported measurements of fungal spore number
concentration in Vienna, Austria, using epifluorescence microscopy, and also
measured fungal tracer mass collected onto filters in order to estimate the
mass of arabitol (1.2 to 2.4 pg spore-1) and mannitol (0.8 to 1.8 pg spore-1) associated with each emitted spore. Bauer et al. (2008a) and
Yttri et al. (2011b) reported ratios of mannitol to arabitol of
ca. 1.5 (± standard deviation of 26 %) and 1.4 ± 0.3, respectively. Our measurements show slightly lower ratios of mannitol
to arabitol, but that the ratio is dependent on wetness category: Rainy,
1.29 ± 0.17; Dry, 1.12 ± 0.23; and Other, 1.24 ± 0.54. The
mannitol to arabitol ratio would be expected to vary as a function of fungal
population present in the aerosol, whether between different wetness periods
at a given location or between different physical localities.
Using the approximate mid-point of the Bauer et al. (2008a) reported ranges,
1.7 pg mannitol per spore and 1.2 pg arabitol per spore, atmospheric number
concentrations of spores collected onto the high-volume filters were
calculated from the polyol mass concentrations measured here. Based on these
values, and assuming all polyol mass originated with spore release, the mass
concentration averages (Fig. 5) were converted to fungal spore number
concentrations (Fig. 6). The trends of spore concentration averages are the
same as with the polyol mass, because the numbers were each multiplied by
the same scalar value. After doing so, the analysis reveals an estimated
spore concentration during Dry periods of 0.89×104 (± 0.21) spores m-3 using the arabitol concentration and 0.70×104
(± 0.19) spores m-3 using the mannitol concentration (Table 4).
The estimated concentration of spores increased approximately 3-fold
during Rainy periods to 2.9×104 (± 0.8) spores m-3
(arabitol estimate) and 2.6×104 (± 0.8) spores m-3
(mannitol estimate) (Fig. 6a, b). These estimates match reasonably well with
estimates reported by Spracklen and Heald (2014), who modeled the
concentration of airborne fungal spores across the globe as an average of
2.5×104 spores m-3, with ca. 0.5×104
spores m-3 over Colorado.
The UV-LIF instruments discussed here are number-counting techniques and in
this instance have been applied as spore counters. As a first approximation,
each particle detected by the UV-APS was assumed to be a fungal spore with
the same properties used in the assumptions by Bauer et al. (2008a). Figure 6d, e, g, h show correlations of fungal spore number concentration estimated
from polyol mass on the y axes and from UV-LIF measurements on the x axes.
The first, and most important observation is that the estimated fungal spore
concentration from each technique is on the same order of magnitude,
104 m-3. Looking at individual correlations reveals a finer layer
of detail. These results show that the number concentration of fungal spores
estimated by the UV-APS is greater than the number of fungal spores
estimated by the tracers, as evidenced by slope values of ca. 0.2
and 0.35 for Rainy and Dry conditions, respectively (Table S3, Fig. 6d,
e). Again, this suggests that the UV-APS detects fungal spores as well as
other types of fluorescent particles. The R2 values (∼ 0.5) during Rainy periods indicate that the additional source of particles
detected by the UV-APS is likely to have a similar source, such as PBAPs
mechanically ejected from soil and vegetative surfaces with rain splash
(Huffman et al., 2013). The magnitude of the over-estimation is higher
during Dry periods, which would be expected because Rainy periods exhibited
much higher particle number fractions associated with polyol-containing
spores.
The Cl1 cluster from WIBS data shows correlations with estimated fungal
spores from arabitol and mannitol that have slopes much closer to 1.0 than
correlations with UV-APS number (Fig. 6g, h, Table S3). For example, the
slope of the Cl1 correlations with each polyol during Rainy periods is ca.
0.87. This suggests only a 13 % difference between the spore
concentration estimates from the two techniques during Rainy periods. The
average number concentration of Cl1 during Rainy periods is 1.6×104 (± 0.8) spores m-3. In both cases the slopes with
respect to Cl1 are greater than 1.0 during Dry periods, suggesting that the
cluster method may be missing some fraction of weakly fluorescent particles.
Huffman et al. (2012) similarly suggests that particles that are weakly
fluorescent may be below the detection limit of the
instrument and Healy et al. (2014)
suggested that both UV-APS and WIBS-4 instruments significantly under-count
the ubiquitous Cladosporium spores that are most common during dry
weather and often peak in the afternoon when RH is low (De Groot, 1968;
Oliveira et al., 2009). Fundamentally, however, the results from the UV-APS,
and even more so the numbers reported by the clustering analysis by Crawford
et al. (2015), reveal broadly similar trends with the numbers estimated from
polyol-to-spore values reported by Bauer et al. (2008a).
Campaign-average fungal spore concentration and mass contribution
estimated from arabitol and mannitol mass measurements. Values are
mean ± standard deviation; n shows number of samples used for
averaging. Fungal spore mass assumption of 33 pg spore-1 (Bauer et
al., 2008b). Total particulate matter mass calculated from UV-APS number
concentration (m-3), converted to mass over aerodynamic particle
diameter range 0.5–15 µm using 1.5 g cm-3 density.
Fungal spore number concentration (m-3)
Dry
8900 ± 2100 n = 18
6900 ± 1900 n = 18
Rainy
29 300 ± 8700 n = 11
26 400 ± 8100 n = 11
Other
16 900 ± 7400 n = 6
13 400 ± 4900 n = 6
Fungal spore mass contribution (%)
Dry
4.8 % ± 1.43 n = 18
3.7 % ± 1.1 n = 18
Rainy
22.9 % ± 8.8 n = 11
20.7 % ± 8.5 n = 11
Other
9.8 % ± 7.7 n = 6
7.3 % ± 5.6 n = 6
The fungal culture samples show similar division during Rainy and Dry periods
as arabitol and mannitol concentrations (Fig. 6c), with an increase of ca.
1.6 × during
Rainy periods. The trend of a positive slope with respect to the UV-LIF
measurements is also similar between the tracer and culturing methods. In
general, however, the R2 value correlating CFU to fungal spore number
calculated from the UV-LIF number is lower than between tracers and UV-LIF
numbers (Table 3, Fig. S4). This is not unexpected for several reasons.
First, the short sampling time of the culture samples (20 min) leads to
poor-counting statistics and high number concentration variability, whereas
each data point from the high-volume air samples represents a period of
4–48 h. Second, culture samplers, by their nature, only account for
culturable fungal spores. It has been estimated that as low as 17 % of
aerosolized fungal species are culturable, and so it is expected that the CFU
concentration observed is significantly less than the total airborne
concentration of spores (Bridge and Spooner, 2001; Després et al., 2012).
Nonetheless, the culturing analysis here supports the tracer and UV-LIF
analyses and the most important trends are consistent between all analysis
methods. The concentration of fungal spores is higher during the Rainy
periods, and there is a positive correlation between both tracer and CFU
concentration and UV-LIF number.
Estimated fraction of total aerosol mass contributed by fungal
spores. Fungal spore mass concentration (µg m-3) calculated
separately from mannitol and arabitol concentration and using average mass
per spore reported by Bauer et al. (2008b). Total particulate matter mass
calculated from UV-APS number concentration (m-3) and converted to mass
over aerodynamic particle diameter range 0.5–15 µm using density
of 1.5 g cm-3. Central marker shows mean value of individual filter
concentration values, bars represent standard deviation (SD) range of filter
values, and individual points show outliers beyond mean ± SD.
Endotoxin mass concentration as an approximate indicator of
gram-negative bacteria concentration. (a) Averaged concentration in
each wetness category. Central marker shows mean value of individual filter
concentration values, bars represent standard deviation (SD) range of filter
values, and individual points show outliers beyond mean ± SD.
(b) Correlation of endotoxin mass concentration with estimated
fluorescent particle mass from UV-APS. (c) Correlation of endotoxin
mass concentration with estimated fluorescent particle mass summed from
clusters 2, 3, and 4 from Crawford et al. (2015).
In a pristine environment, such as the Amazon, supermicron particle mass has
been found to consist of up to 85 % biological material (Pöschl et
al., 2010). Total particulate matter mass was calculated here from the UV-APS
number concentrations (m-3) and converted to mass for particles of
aerodynamic diameter 0.5–10 µm. In only
this case a density of 1.5 g cm-3 was utilized to calculate a first
approximation of total particle mass to which all other mass measurements
were compared. An average TSP mass density of 1.5 g cm-3 was utilized,
because organic aerosol is typically estimated with density
< 1.0 g cm-3, biological particles are often assumed to have
ca. 1.0 g cm-3 density, and mineral dust particles have densities of
up to ca. 3.5 g cm-3 (Dexter, 2004; Tegen and Fung, 1994). Fungal
spore mass was estimated here using the fungal spore concentrations
calculated from arabitol and mannitol mass (Fig. 6) and then using an
estimated 33 pg reported by Bauer et al. (2008b) as an average mass per
spore. Dividing the resultant fungal spore mass by total particulate mass
provides a relative mass fraction for each high-volume sample period. These
calculations suggest that fungal spores represent ca. 23 % ± 9
(using arabitol) or 21 % ± 8 (using mannitol) of total particulate
mass during Rainy periods (Table 4, Fig. 7). This represents a nearly 6-fold
increase in percentage compared to Dry periods (4.8 % ± 1.4 and
3.7 % ± 1.1, respectively). A similar increase during Rainy periods
was also seen in the mass fraction of fungal cluster Cl1, which represented
17 % ± 10 of the particle mass during Rainy and 2 % ± 1
during Dry periods (Table S4).
Variations in endotoxin and glucan concentrations
Endotoxins measured in the atmosphere are uniquely associated with
gram-negative bacteria (Andreae and Crutzen, 1997). Here, we show
correlations between total endotoxin mass and WIBS ClBact, which
was assigned by Crawford et al. (2015) to be bacteria due to the small
particle size (< 1 µm) and high correlation with rain.
This assignment of particle type to this set of clusters is quite uncertain,
however, and should be treated loosely. The correlation between endotoxin
mass and UV-APS and the WIBS clusters was very poor, in most cases
R2 < 0.1 (Table 3, Fig. 8), suggesting no apparent
relationship. Analysis of bacteria by both UV-LIF techniques is hampered by
the fact that bacteria can be < 1 µm in size and because
both instruments detect particles with decreased efficiency at sizes below
0.8 µm. So weak correlations may not have been apparent due to
reduced overlap in particle size. Despite the lack of apparent correlation
between the techniques, the relatively variable endotoxin concentrations were
elevated during Rainy periods, consistent with Jones and Harrison (2004), who
showed that bacteria concentrations were elevated after rainy periods.
Glucans, such as (1→3)-β-D-glucan, are components of the cell
walls of pollen, fungal spores, plant detritus, and bacteria (Chow et al.,
2015b; Lee et al., 2006; Stone and Clarke, 1992). In contrast to the observed
difference in endotoxin concentration during the different wetness periods,
(1→3)-β-D-glucan showed no correlations with UV-LIF
concentrations (Table 3) and no differentiation during the different wetness
periods.