ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-7193-2017Improved identification of primary biological aerosol particles using single-particle mass spectrometryZawadowiczMaria A.FroydKarl D.MurphyDaniel M.CziczoDaniel J.djcziczo@mit.eduDepartment of Earth, Atmospheric and Planetary Sciences,
Massachusetts Institute of Technology, Cambridge,
Massachusetts, UKNOAA Chemical Sciences Division, Boulder,
Colorado, USACooperative Institute for Research in Environmental
Sciences, University of Colorado, Boulder, Colorado, USADepartment of Civil and Environmental Engineering,
Massachusetts Institute of Technology, Cambridge, MA, USADaniel J. Cziczo (djcziczo@mit.edu)16June201717117193721212December201615December201618April20176May2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/7193/2017/acp-17-7193-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/7193/2017/acp-17-7193-2017.pdf
Measurements of primary biological aerosol particles
(PBAP), especially at altitudes relevant to cloud formation, are scarce.
Single-particle mass spectrometry (SPMS) has been used to probe aerosol
chemical composition from ground and aircraft for over 20 years. Here we
develop a method for identifying bioaerosols (PBAP and particles containing
fragments of PBAP as part of an internal mixture) using SPMS. We show that
identification of bioaerosol using SPMS is complicated because
phosphorus-bearing mineral dust and phosphorus-rich combustion by-products
such as fly ash produce mass spectra with peaks similar to those typically
used as markers for bioaerosol. We have developed a methodology to
differentiate and identify bioaerosol using machine learning statistical
techniques applied to mass spectra of known particle types. This improved
method provides far fewer false positives compared to approaches reported in
the literature. The new method was then applied to two sets of ambient data
collected at Storm Peak Laboratory and a forested site in Central Valley,
California to show that 0.04–2 % of particles in the 200–3000 nm
aerodynamic diameter range were identified as bioaerosol. In addition,
36–56 % of particles identified as biological also contained spectral
features consistent with mineral dust, suggesting internal dust–biological
mixtures.
Introduction
Biological atmospheric aerosol (or bioaerosol) has recently garnered interest
because certain species of bacteria and plant material might impact climate
via the nucleation of ice in clouds (Hiranuma et al., 2015; Möhler et
al., 2008). However, many field-based measurements of ice nuclei and ice
residuals do not indicate that bioaerosol is a major class of ice-active
particles (Cziczo et al., 2013; DeMott et al., 2003; Ebert et al., 2011).
While modeling efforts suggest that biological material is not significant in
ice cloud formation on a global scale, uncertainties continue to exist
because field measurements of ice-nucleating particles are currently sparse
(Hoose et al., 2010; Sesartic et al., 2012).
In this paper, “bioaerosol” is defined as primary biological aerosol
particles (PBAP) (i.e., airborne whole and fragmentary bacteria, pollen and
spores) and particles that contain fragments of PBAP as a part of an internal
mixture. Measurement techniques specific to bioaerosol include the collection of
aerosol on filters followed by analysis with microscopy techniques, either
electron microscopy (EM) or optical microscopy coupled with fluorescent
staining of the samples (Amato et al., 2005; Bauer et al., 2002, 2008; Bowers
et al., 2009, 2011; Griffin et al., 2001; Matthias-Maser and Jaenicke, 1994;
Pósfai et al., 2003; Sattler et al., 2001; Wiedinmyer et al., 2009; Xia
et al., 2013). Aerosol samples collected in the atmosphere have been cultured
for the identification of the microbial strains present (Amato et al., 2005,
2007; Fahlgren et al., 2010; Fang et al., 2007; Griffin et al., 2001, 2006;
Prospero et al., 2005).
In situ techniques specific to biological samples are typically based on
the fluorescence of biological material following UV excitation. Examples include
the wide-band integrated bioaerosol sensor (WIBS) which is available
commercially (Kaye et al., 2000, 2005). WIBS has been successfully deployed
in several locations (Gabey et al., 2010; O'Connor et al., 2014; Toprak and
Schnaiter, 2013). Using fluorescence to detect biological aerosol can have
interferences, however. For example, polycyclic aromatic compounds or humic
acids can have similar fluorescent properties (Gabey et al., 2010; Pan et
al., 1999). Cigarette smoke has similar fluorescent properties to bacteria
(Hill et al., 1999). In an attempt to address interferences, WIBS collects
fluorescence information using several channels with different wavelengths
while also measuring the size and shape of the particles. Table 1 summarizes
some recent measurements of bioaerosol. More information can be found in
recent reviews focused on bioaerosols in the atmosphere, such as Després
et al. (2012).
Measurements of biological aerosol in the atmosphere (NR – not
reported; FBAPs – fluorescent particles, attributed to bioaerosol). TEM: transmission electron microscopy.
SiteElevation (m)TechniqueConcentration ofbioaerosol detected(particles m-3)Percentage oftotal particles(size range)Type of bioaerosolReferenceGround sites Jungfraujoch3450Fluorescentmicroscopy3.4 × 104(free troposphere) 7.5 × 104(over surface)NRBacteriaXia et al. (2013)Storm Peak Lab3220Fluorescentmicroscopy9.6 × 105–6.6 × 106NRBacteria (51 %) Fungi (45 %) Plant material (4 %)Wiedinmyer et al. (2009)Storm Peak Lab3220Flow cytometry3.9 × 105 (spring) 4.0 × 104 (summer) 1.5 × 105 (fall) 2.7 × 104 (winter)22 %(0.5–20 µm)BacteriaBowers et al. (2012)Mt. Rax (Alps)1644Fluorescentmicroscopy1.1 × 104 (bacteria) 3.5 × 102 (fungi)NRBacteria and fungiBauer et al. (2002)Various locationsin Colorado1485–2973Fluorescentmicroscopy1.0 × 105–2.6 × 106NRBacteriaBowers et al. (2011)Vienna150–550Fluorescentmicroscopy3.6 × 103–2.9 × 104NRFungiBauer et al. (2008)US Virgin IslandsNRFluorescentmicroscopy3.6 × 104–5.7 × 105NRBacteria and possible virusesGriffin et al. (2001)Various sites in the UK50–130Fluorescentmicroscopy5.3 × 103–1.7 × 104 (spring) 8.3 × 103–1.5 × 104 (summer) 6.0 × 103–1.4 × 104 (fall) 2.9 × 103–1.0 × 104 (winter)NRBacteriaHarrison et al. (2005)Danum Valley,Malaysian Borneo150–1000WIBS2.0 × 105(above forest canopy) 1.5 × 106(below forest canopy)NRFBAPGabey et al. (2010)Karlsruhe, Germany112WIBS2.9 × 104 (spring) 4.6 × 104 (summer) 2.9 × 104 (fall) 1.9 × 104 (winter)4–11 %(0.5–16 µm)FBAPToprak and Schnaiter (2013)
Continued.
SiteElevation (m)TechniqueConcentration ofbioaerosol detected(particles m-3)Percentage oftotal particles(size range)Type of bioaerosolReferenceAircraft campaigns Cape Grim30–5400TEMNR1 % (> 0.2 µm)BacteriaPósfai et al. (2003)Flights around the Gulf of Mexico, Californiaand Florida3000–10 000Fluorescentmicroscopy3.6 × 104–3.0 × 1053.6–276 % (0.25–1 µm)*Mostly bacteriaDeLeon-Rodriguez et al. (2013)Flights oversoutheastern US(SEAC4RS)Vertical profiles up to 12 000WIBS3.4 × 105 (average, < 0.5 km) 7.0 × 104 (average, 3 km) 1.8 × 104 (average, 6 km)5–10 % (0.6–5 µm)FBAPZiemba et al. (2016)Flights over Colorado,Wyoming, Nebraskaand South DakotaVertical profiles up to 10 000WIBS1.0 × 104–1.0 × 105 (< 2.5 km) 0–3.0 × 103 (> 2.5 km)NRFBAPTwohy et al. (2016)
* Comment
in response to DeLeon-Rodriguez et al. (2013) by Smith and Griffin (2013).
Measurements of bioaerosol in the free and upper troposphere, where they
could be relevant to cloud formation, remain scarce. Four of the recent
studies reported in Table 1 used an aircraft to access altitudes higher than
4000 m (DeLeon-Rodriguez et al., 2013; Pósfai et al., 2003; Twohy et
al., 2016; Ziemba et al., 2016). Two of these used the WIBS to report
vertical profiles of fluorescent particles (Twohy et al., 2016; Ziemba et
al., 2016). In the remaining two cases, aerosols were collected on filters
and analyzed off-line. There can exist significant uncertainty in these
measurements. A recent aircraft-based study by DeLeon-Rodriguez et al. (2013)
reports analysis of high-altitude (8–15 km) samples taken before, after and
during two major tropical hurricanes. The abundances of microbes, mostly
bacteria, were reported to be between 3.6 × 104 and
3.0 × 105 particles m-3 in the 0.25–1 µm
size range. The methods and conclusions of this study were re-evaluated by
Smith and Griffin (2013), who argued that in some instances the reported
concentrations of bioaerosol were not possible because they exceeded the total
aerosol by several factors. The samples were also taken over periods of
hours, possibly including sampling in clouds when the high-speed impaction of
droplets and ice can dislodge particles from the inlet (Cziczo and Froyd,
2014; Froyd et al., 2010; Murphy et al., 2004).
Although difficult, measurements of bioaerosol in the upper troposphere are
necessary in order to constrain their influence on atmospheric properties and
cloud formation processes. All of the techniques discussed above, except for
WIBS, are off-line and require expertise in sample processing and
decontamination. WIBS is a possible in situ detection technique for
bioaerosols, but it is relatively new and, as a result, has a short
deployment history. There has been considerable interest in using aerosol
mass spectrometry techniques to measure bioaerosol. Single-particle mass
spectrometry (SPMS) has been successfully used since the mid-1990s to
characterize the chemical composition of atmospheric aerosol particles in situ
and in real time (Murphy, 2007). The ability of SPMS to simultaneously
characterize volatile and refractory aerosol components makes it an
attractive tool for investigating the mechanisms of cloud formation (Cziczo
et al., 2013; Friedman et al., 2013). The general principle behind SPMS, and
in particular the instrument discussed in this paper, the Particle Analysis
by Laser Mass Spectrometry (PALMS), is the use of a pulsed UV laser for the
ablation and ionization of single aerosol particles. Ions are then
accelerated into a time-of-flight mass spectrometer. Laser
ablation or ionization used with SPMS produces ion fragments and clusters and is
susceptible to matrix effects such that quantitative results are possible
only with careful calibration and consistent composition (Cziczo et al.,
2001).
Biological aerosols have been studied with SPMS, in particular the aerosol
time-of-flight mass spectrometer (ATOFMS; Cahill et al., 2015; Creamean et
al., 2013; Fergenson et al., 2004; Pratt et al., 2009b). A property of SPMS
bioaerosol spectra that has been exploited for their detection is the
presence of phosphate (PO-, PO2-, PO3-) and organic
nitrogen ions (CN-, CNO-) (Cahill et al., 2015; Fergenson et al.,
2004). Those ions have also previously been shown to be present in
nonbiological particles with the same instrument, however, such as vehicular
exhaust (Sodeman et al., 2005) and soil dust (Silva et al., 2000). Particles
that contain phosphates, organic nitrates and silicates have historically
been classified as mixtures of bioaerosol and dust (Creamean et al., 2013).
This work examines the prevalence of these ions in the context of spectra
collected with PALMS.
Phosphorus was chosen as the focus of this paper because of its abundance in
spectra of bioaerosol but also because it does not undergo gas-phase
partitioning in the atmosphere (Mahowald et al., 2008). Therefore, the
presence of phosphorus on a particle can often constrain its source, and only
the classes of particles that are most likely to contain phosphorus are
examined here. Emission estimates qualitatively agree that mineral dust,
combustion products and biological particles constitute the principal
phosphate emission sources. The global phosphorus budget has been modeled by
Mahowald et al. (2008), indicating that 82 % of the total burden is
emitted in the form of mineral dust. Bioaerosol accounts for 12 % and
anthropogenic combustion sources, including fossil fuels, biofuels and
biomass burning, account for 5 % (Mahowald et al., 2008). Recently, Wang
et al. (2014) provided a higher estimate of phosphorus emissions from
anthropogenic combustion sources: 31 %. In this estimate, mineral dust
was responsible for 27 %, bioaerosol for 17 % and natural combustion
sources for 20 % of total phosphorus emissions (Wang et al., 2014).
In this work, calcium-phosphate-rich minerals (apatite and monazite) and fly
ash are chosen to represent dust and industrial combustion particle classes,
respectively. In atmospheric particles, the composition can be mixed,
containing some phosphate from inorganic sources, such as calcium phosphate,
and some phosphate from microbes. For instance, soils can contain minerals,
live microbes and biogenic matter at all stages of decomposition. Therefore,
classifying soil-derived particles with a binary biological–nonbiological
classifier has uncertainties. These uncertainties are quantified here for
soils using soil samples collected in various locations.
In this work, the presence of phosphorus in a mass spectrum is evaluated as
a proxy for bioaerosol. All biological cells contain phosphorus because it is
a component of nucleic acids and cell membranes. Distinguishing the specific
mass spectral phosphate signature of biological cells from other
nonbiological phosphorus is the topic of the analysis in this paper. The
goal of this paper is to develop a method that can differentiate PALMS
bioaerosol spectra from spectra of dust and combustion by-products.
Experimental setup
The objective of this work is to describe and validate a new SPMS-based data
analysis technique that allows for the selective measurement of bioaerosol.
A dataset of bioaerosol, phosphate-rich mineral and coal fly ash single-particle spectra – the three largest sources of phosphorus in atmospheric
aerosols – was used to derive a classification algorithm for biological and
nonbiological phosphate-containing material. This classifier was then
applied to an ambient dataset collected at the Storm Peak Laboratory during
the Fifth Ice Nucleation workshop, phase 3 (FIN03).
PALMS
The NOAA PALMS instrument has been discussed in detail elsewhere (Cziczo et
al., 2006; Thomson et al., 2000). Currently, there are two copies of the
PALMS instrument, both of which were used in this work. The laboratory PALMS
is a prototype for the flight PALMS, which is more compact and can be
deployed unattended at field sites and on aircraft (Thomson et al., 2000).
Briefly, PALMS uses an aerodynamic lens to sample aerosols and impart them
with a size-dependent velocity (Zhang et al., 2002, 2004). Aerodynamic
particle diameter is measured by timing the particles between two
continuous-wave laser beams (532 nm Nd : YAG in laboratory PALMS and
405 nm diode in flight PALMS). The particles are ablated and ionized in one
step by a 193 nm excimer laser. A unipolar reflectron time-of-flight mass
spectrometer is then used to acquire mass spectra. PALMS acquires spectra in
either positive or negative polarity, but not simultaneously. For field
datasets presented in this paper, sampling polarity was switched every 5 min for FIN03 and every 30 min for the Carbonaceous
Aerosol and Radiative Effects Study (CARES).
Due to the high laser fluence used for desorption and ionization
(∼ 109 W cm-2), PALMS spectra show both atomic ions and ion
clusters, which complicate spectral interpretation. SPMS is considered a
semiquantitative technique because the ion signal depends on the abundance
and ionization potential of the substance rather than solely on its abundance
(Murphy, 2007). Additionally, the ion signals can depend on the overall
chemical composition of the particle, known as matrix effects (Murphy, 2007).
The lower particle size threshold for PALMS is ∼ 200 nm diameter and
is set by the amount of detectable scattered light. The upper size threshold
is set by transmission in the aerodynamic lens at ∼ 3 µm
diameter (Cziczo et al., 2006). In PALMS, Particles toward the larger end of
this size range are transmitted into the laser beam more efficiently than
smaller particles. The 193 nm excimer laser can ionize all
atmospherically relevant particles within this size range with a little
detection bias (Murphy, 2007). The ionization region is identical in the
laboratory and flight PALMS instruments. Raw PALMS spectra are processed
using a custom IDL software. Mass peak intensities used in this paper refer
to integrated peak areas normalized by the total ion current.
Aerosol standards
Table 2 shows numbers of negative spectra for all analyses in this paper. A
portion of the data from each of the bioaerosol and nonbiological phosphate
samples was used as “training data” to build the classification algorithm.
The remaining test data were classified using the trained algorithm.
Summary of particle statistics for samples used to both train and
test the classifier.
A collection of phosphorus-containing samples of biological and inorganic
origin were used to train the classification algorithm used in this work.
Some of the samples were analyzed with the laboratory PALMS at the Aerosol
Interaction and Dynamics in the Atmosphere (AIDA) facility at Karlsruhe
Institute of Technology (KIT) during the Fifth International Ice Nucleation
Workshop, phase 1 (FIN01), with the remainder sampled at MIT.
Biological aerosol sampled at AIDA included two aerosolized cultures of
Pseudomonas syringae bacteria, Snomax (Snomax International, Denver,
CO) (irradiated, desiccated and ground Pseudomonas syringae) and
hazelnut pollen wash water. The Snomax and P. syringae cultures were
suspended in water and aerosolized with a Collison-type atomizer. The growth
medium for P. syringae cultures was Pseudomonas Agar Base (CM0559,
Oxoid Microbiology Products, Hampshire, UK).
Biological aerosol sampled at MIT included giant ragweed (Ambrosia trifida) pollen, oak (Quercus rubra) pollen, European white birch
(Betula pendula) pollen, Fusarium solani spores and yeast.
Samples of dried pollens and F. solani spores were purchased from
Greer (Lenoir, NC). Information supplied by the manufacturer indicates that
F. solani fungus was grown on enriched trypticase growth medium and
killed with acetone prior to harvesting the spores. Ragweed and oak pollen
originated from wild plants, while the birch pollen originated from a
cultivated plant. Pollen was collected, mechanically sieved and dried. The
yeast used in this experiment was commercial active dry yeast (Star Market
brand). The yeast powder was sampled by PALMS from a vial subjected to slight
manual agitation. Pollen grains were too large (18.9–37.9 µm
according to manufacturer's specification) to sample with PALMS. They were
suspended in ultrapure water (18.2 MΩ cm, Millipore, Bedford, MA), and the suspensions were sonicated in an ultrasonic bath for ∼ 30 min
to break up the grains. Large material was allowed to settle to the bottom, and a few drops of the clear solution from the top of the suspensions were
further dissolved in ultrapure water, and the resulting solutions were
aerosolized with a disposable medical nebulizer (Briggs Healthcare, Waukegan,
IL). A diffusion dryer was used to remove condensed-phase water prior to
sampling with PALMS. F. solani spores were sampled in two different
ways: (1) dry and unprocessed, in the same way as the yeast, and
(2) fragmented in an ultrasonic bath and wet-generated, in the same way as
pollen samples. Examination of PALMS spectra revealed no changes in chemistry
resulting from different processing methods.
Samples of fly ash from four coal-fired US power plants were used as a proxy
for combustion aerosol: J. Robert Welsh Power Plant (Mount Pleasant, TX),
Joppa Power Station (Joppa, IL), Clifty Creek Power Plant (Madison, IN) and
Miami Fort Generating Station (Miami Fort, OH). The samples were obtained
from a commercial fly ash supplier (Fly Ash Direct, Cincinnati, OH). Fly ash
was dry-generated with the shaker.
Apatite and monazite-Ce mineral samples were generated from ∼ 7.5 cm pieces of rock. The rocks were ground and the samples aerosolized with the
shaker. Both apatite and monazite were sampled and processed at MIT. The
apatite rock was contributed by Adam Sarafian (Woods Hole Oceanographic
Institution, Woods Hole, MA).
Two samples of German soil were used as an example of agricultural soil that
was known to be fertilized with inorganic phosphate. These were also sampled
at the AIDA facility during FIN01. Note that while all other soil samples
are used as test aerosols for a completed classifier, those two in
particular are used in the training set to account for the presence of
inorganic fertilizer.
Samples of apatite and J. Robert Welsh Power Plant fly ash were also
subjected to processing with nitric acid to approximate atmospheric aging.
Powdered sample was aerosolized from the shaker to fill a 9 L glass mixing
volume. A hot plate below the volume was used to heat the air inside to
31 ∘C, measured in the center of the volume with a thermocouple.
PALMS sampled at a flow rate of 0.44 slpm (STP (standard temperature and pressure): 0 ∘C, 1 atm) from
the 9 L volume. This constituted unprocessed aerosol. Then, 80 % HNO3 was placed with a Pasteur pipette at the heated bottom of the mixing volume.
Two experiments were conducted: for experiments using 0.1 mL of nitric acid,
the entire volume of HNO3 evaporated, producing an estimated partial
pressure of about 0.005 atm in a static situation. In 1 mL experiments some
liquid HNO3 remained at the bottom of the volume with an estimated
partial pressure of HNO3 of 0.04 atm. The aerosol and gas-phase
HNO3 were allowed to interact for 2 min, at which point PALMS began
sampling from the volume.
Test dataset
Samples of natural soil dust were collected from various locations listed in
Table 3. Five samples were investigated at the AIDA facility during FIN01
(Bächli soil, Argentina soil, Ethiopian soil, Moroccan soil and Chinese
soil) with the remaining analysis at MIT (Storm Peak and Saudi Arabian soil).
Soil dust samples used in this work. The last column shows the
results of analysis with the SVM classifier developed here as a percentage
of negative spectra acquired.
SampleSite descriptionApprox.PercentagecollectionbiologicalcoordinatesparticlesBächliOutflow sediment of a glacier in a feldspar-rich granitic environment. No vegetation.46.6∘ N, 8.3∘ E6.0MoroccoRock desert with vegetation. Close proximity to a road.33.2∘ N, 2.0∘ W20.4EthiopiaCollected in Lake Shala National Park from a region between two lakes. Area vegetated by shrubs and acacia trees.7.5∘ N, 38.7∘ E32.1Storm Peak LabCollected near Storm Peak Lab. Grass and shrubs present.40.5∘ N, 106.7∘ W31.3ArgentinaLa Pampa province. Top soil collected from arable land with sandy loam (Steinke et al., 2016).37∘ S, 64∘ W21.3China/Inner MongoliaXilingele steppe. Top soil collected from a pasture with loam (Steinke et al., 2016).44∘ N, 117∘ E2.0Saudi ArabiaVarious samples from several locations. Arid, sandy soils.24.6–26.3∘ N, 46.1–49.6∘ E14.5
Internally mixed biological–mineral particles were also analyzed at MIT.
Illite NX (Clay Mineral Society) without bioaerosol was sampled dry, using a
shaker (Garimella et al., 2014), and wet-generated, using a medical nebulizer
containing ultrapure water. A second disposable medical nebulizer was then
used to aerosolize a suspension of illite NX and F. solani spore
fragments. This wet-generated aerosol was also dried with a diffusion dryer
prior to PALMS sampling.
Statistical analysis
A support vector machine (SVM), a supervised machine learning algorithm
(Cortes and Vapnik, 1995), was used as the statistical analysis method for
analysis of these data. In this case a nonlinear binary classifier was
constructed, using nonlinear kernel functions (Ben-Hur et al., 2001; Cortes
and Vapnik, 1995). A Gaussian radial basis function kernel was empirically
determined to provide the best performance in this case. For this work, the
SVM algorithm was implemented in MATLAB 2016a (MathWorks, Natick, MA) using
the Statistics and Machine Learning toolbox.
Field data
The method was employed on two ambient datasets: one acquired at the Desert
Research Institute's (DRI's) Storm Peak Laboratory located in Steamboat
Springs, CO, and the other acquired at the Cool, CA, site during the CARES study. Storm Peak Laboratory is located
on Mt. Werner at 3220 m elevation at 106.74∘ W, 40.45∘ N.
This high-altitude site is often in free-tropospheric air, mainly during
overnight hours, with minimal local sources (Borys and Wetzel, 1997). Ambient
air was sampled using the Storm Peak facility inlet with the flight PALMS
instrument in September 2015. Measurements were made during the Fifth
International Ice Nucleation Workshop, phase 3 (FIN03). The measurements were
carried out between 14 and 27 September 2015.
The CARES study was carried out in the summer of 2010 and included the deployment of
instruments at two different ground sites: one urban (Sacramento, CA) and
another in the Sierra Nevada foothills area rich in biogenic emissions (Cool,
CA, site) (Zaveri et al., 2012). Thermally driven winds tend to transport the
urban plume into the Sierra Nevada foothills and sometimes back again into
the Sacramento area (Zaveri et al., 2012). The laboratory PALMS instrument
was deployed at the Cool, CA, site at 450 m elevation at 121.02∘ W,
38.87∘ N in a trailer throughout the campaign. It sampled ambient
air between 4 and 24 June 2010.
Results
Figure 1 shows the spectra of biological species: P. syringae
bacteria, Snomax and hazelnut pollen wash water particles. These particles
contain both organic and inorganic compounds. Because they are easy to
ionize, the inorganic ions sodium and potassium stand out in the positive
spectra despite their minor fraction by mass. Sulfates, phosphates and
nitrates are present, and visible in their associations with potassium.
Negative spectra are dominated by CN-, CNO-, phosphate
(PO2- and PO3-) and sulfate (HSO4-). Higher mass
associations of potassium, sulfates, phosphates and nitrates occur
(K3H2SO3-, K2H3NO4-,
K3H2PO2- and K3H3SO3-). Chlorine is
present on some particles. Chlorine is a known contaminant from the agar
growth medium since spectra of aerosolized agar devoid of bacteria contain
large amounts of chlorine (not shown here).
Figure 2 shows spectra of apatite. In positive polarity, apatite spectra are
dominated by calcium, its oxides and associations with phosphate
(CaPO+, CaPO2+, CaPO3+, Ca2PO3+ and
Ca2PO4+) and fluorine (CaF+, Ca2OF+ and
Ca3OF+). Negative spectra are dominated by phosphates (PO-,
PO2- and PO3-), and fluorine is often present. Lab-generated
apatite spectra analyzed in this study contain little organic matter. This may be a
result of the post-processing of the apatite sample, in particular of the use of
ethanol as a grinding lubricant. In contrast, ethanol was not used in
grinding the monazite sample here, and its spectra exhibit peaks associated
with organic matter (C2H-).
Representative PALMS spectra of bioaerosol. (a, b) Snomax. (c, d)P. syringae. (e, f) Hazelnut wash water. Right and left columns are positive and
negative polarity, respectively. Red dotted lines are features indicated in
the literature as markers for biological material.
Representative PALMS spectra of phosphorus-rich minerals and
ambient aerosol. (a, b) Unprocessed apatite. (c, d) Apatite processed
with HNO3 (see text for details). (e, f) Monazite-Ce. (g, h)
Ambient particles sampled at Storm Peak matching monazite chemistry. Right
and left columns are positive and negative polarity, respectively. Red
dotted lines are features indicated in the literature as markers for
biological material.
Figure 3 shows spectra of coal fly ash from the J. Robert Welsh Power Plant.
The positive spectra contain sodium, aluminum, calcium, iron, strontium,
barium and lead. As in apatite, calcium–oxygen, calcium–phosphate
and calcium–fluorine fragments are present. Fly ash particles also
contain sulfate (H3SO3+). The negative spectra contain
phosphates (PO2-, PO3-), sulfates (HSO4-) and
silicate fragments, such as (SiO2)2-, (SiO2)2O-,
(SiO2)2Si- and (SiO2)3-.
Representative PALMS spectra of coal fly ash from the J. Robert Welsh power plant. (a, b) Unprocessed fly ash. (c, d) Fly ash processed
with HNO3 (see text for details). Right and left columns are positive
and negative polarity, respectively. Red dotted lines are features indicated
in the literature as markers for biological material.
The results of HNO3 processing experiments are also shown in Figs. 2 and
3. The processing with nitric acid had an effect on both apatite and fly ash: the
calcium–fluorine positive markers (CaF+, Ca2OF+ and
Ca3OF+) and the negative fluorine marker (F-) are either
reduced in intensity or completely absent after processing. Additionally,
CN- and CNO- appear and/or intensify after processing.
A classifier was designed to use the ratios of phosphate (PO2-,
PO3-) and organic nitrogen (CN-, CNO-) spectral peaks.
Those spectral peaks were used for several reasons: (1) they are clearly
visible in all biological spectra that were acquired as a part of this study
(Fig. 1); (2) they were used to distinguish bioaerosol from other species in
previous studies (Creamean et al., 2013; Pratt et al., 2009b); and (3) sources
of phosphorus on aerosol particles are well-defined and documented in the
literature (Mahowald et al., 2008). The only requirement for this analysis
was that each spectrum used in the training set contains both phosphate and
organic nitrogen (otherwise the ratios used here become undefined). This was
ensured by selecting spectra, where PO2- > 0.001 and
CNO- > 0.001. Nearly all biological spectra in the training
set satisfied this criterion (Table 2). Figure 4a shows normalized histograms
of the PO3-/ PO2- ratio for the laboratory aerosol. The
aerosols that contain only inorganic phosphorus, such as apatite, monazite
and fly ash cluster at PO3-/ PO2- less than 4 and often
less than 2. The bioaerosols cluster at PO3-/ PO2-
greater than 2 and often greater than 4. Ragweed pollen is an exception, with
a wide cluster in PO3-/ PO2- from 1 to 5. The processing of
apatite with nitric acid tends to shift the PO3-/ PO2-
ratio to larger values, decreasing the disparity from the bioaerosols. Soil
dusts are shown in Fig. 4, even though they are not used as training aerosol;
their histogram shows a broad distribution with a tail extending into the PO3-/ PO2- > 2 region, indicating a mixed
inorganic–biological composition. In comparison, fertilized soil dusts show a
similar distribution to apatite (PO3-/ PO2- < 4) due to the presence of inorganic
fertilizer, which is calcium phosphate.
(a) Normalized histograms of the PO3-/ PO2-
ratio for the laboratory aerosol. (b) Normalized histograms of the
CN-/ CNO- ratio for the same laboratory aerosol as in (a).
Delineation between the clusters at a PO3-/ PO2- ratio of
3 results in a 70–80 % classification accuracy depending on the types of
particles considered. Note that soil dusts were not used as part of the
training dataset and that not all training aerosols are shown here for
clarity.
The SVM algorithm was used here to optimize boundaries between clusters. To
do this, the algorithm needs a training dataset, where the classes are known
a priori. In this paper, the training dataset is defined in Table 2. Once
an optimized boundary is drawn, some of the training data can still fall on
the incorrect side of the boundary when the clusters are not perfectly
separable. Accuracy here is defined as the percentage of correctly classified
particles in the training set once the optimized boundary is found. A simple
1-D classifier can be made based only on the ratio of phosphate peaks
PO3-/ PO2- greater or less than 3. The accuracy of this
simple filter is 70–80 % for the materials considered here, with ragweed
pollen and fly ash as the greatest sources of confusion between the
bioaerosol and nonbiological classes. A higher accuracy for differentiation
of the bioaerosol and nonbiological classes can be achieved if the ratio of
organic nitrogen peaks is also taken into account. Figure 4B shows normalized
histograms of CN-/ CNO- ratios for the test aerosol. In
contrast to PO3-/ PO2- ratios, CN-/ CNO-
ratios do not, by themselves, exhibit a clear difference between the classes.
A superior separation is achieved when data are plotted in a
CN-/ CNO- vs. PO3-/ PO2- space, as shown
in Fig. 5. In this case, two clusters appear. The soil dust class was left
out of the training set because it is not known a priori if and how much
biological material it contains (classification of soil dusts with the SVM
algorithm is discussed later). The boundary between the classes in
CN-/ CNO- vs. PO3-/ PO2- space is
nonlinear, as shown in Fig. 5. The accuracy in this 2-D classification is
97 %. As before, ragweed pollen is the cause of most errors; if it is
removed from the training dataset, the accuracy increases to 99 %. Processed
mineral dust had a smaller impact on the accuracy: removing it from the
training dataset increased the accuracy to 97.5 %.
Inorganic and biological particle clusters in CN-/ CNO-
vs. PO3-/ PO2- space. The SVM algorithm
“draws” a boundary between the clusters with the red dashed line with an overall 97 %
classification accuracy. Solid red lines indicate the uncertainty boundary
(see text for further details).
For every observation, a distance from the SVM boundary can be calculated
(otherwise known as score). Those distances can then be converted to the probability of correct identification. An optimized function to convert
scores to probabilities was found by 10-fold cross-validation (Platt, 1999).
Because in this experiment the classes are not perfectly separable, the
conversion function is a sigmoid. Posterior probabilities near 0 and 1
indicate high-confidence identification. An uncertainty boundary was defined
between 0.2 and 0.8. This boundary is shown in Fig. 5. Points that lie within
this boundary are marked as low-confidence assignments. Those correspond to
shaded areas in Figs. 6 and 7.
(a) The percentage of ambient aerosol particles from the FIN03 dataset
categorized as biological and inorganic (phosphate-bearing mineral dust or
fly ash) phosphate using the criteria developed in this work. Hatched
regions indicate uncertain assignments as per the boundaries in Fig. 5. Note
that at this location and time of year inorganic phosphate dominates
biological particles. (b) HYSPLIT back trajectories plotted for 10 measurement days at
Storm Peak Laboratory. Locations of REE, phosphate and carobonatite
deposits, sourced from US Geological Survey, are co-plotted
(Berger et al., 2009; Chernoff and
Orris, 2002; Orris and Grauch, 2002). Dates are given as MM/DD.
(a) The percentage of ambient aerosol particles from the CARES dataset
categorized as biological and inorganic (phosphate-bearing mineral dust or
fly ash) phosphate using the criteria developed in this work. Hatched
regions indicate uncertain assignments per the boundaries in Fig. 5. (b)
HYSPLIT back trajectories plotted for 10 measurement days at the Cool, CA, site. Locations of REE, phosphate and carobonatite deposits, sourced from
US Geological Survey, are co-plotted
(Berger et al., 2009; Chernoff and
Orris, 2002; Orris and Grauch, 2002) along with locations of major urban
centers. Dates are given as MM/DD.
Once trained with the training set, the SVM algorithm was used to analyze
the FIN03 and CARES field datasets collected at Cool, CA, and Storm Peak. As
a first step, “phosphorus-containing” particles were identified in both
datasets. The criterion for phosphorus-containing particles used for this work is the
presence of both PO2- and PO3- ions at fractional peak
area (area of peak of interest/total spectral signal area) greater than
0.01. This threshold was set by examination of the ambient mass spectra to
determine when the phosphate peaks are distinct. Ambient particles commonly
have numerous small peaks at masses below ∼ 200 due to a
diversity of organic components. The height of this background is
∼ 0.01, and data below this level are considered uncertain.
Phosphorus-containing ambient spectra were then classified by the SVM
algorithm as bioaerosol or inorganic phosphorus if the CNO- ion was
also present at fractional peak area greater than 0.001. If CNO-
fractional area was less than 0.001, the spectrum was also classified as
inorganic phosphorus.
During the FIN03 campaign, phosphorus-containing particles represented from
0.2 to 0.5 % by number of the total detected particles in negative ion
mode depending on the sampling day and a 0.4 % average for the entire
dataset. As shown in Fig. 6a when the binary classifier described in this
work was applied to the phosphorus-containing particles, bioaerosol
represented a 29 % subset by number (i.e., 0.1 % of total analyzed
particles). During the CARES campaign, phosphorus-containing particles were
1.1 to 4.2 % by number of the total particles detected in negative ion
mode, with a 2.4 % average for the dataset (Fig. 7a). Bioaerosol particles
represented a 63 % subset by number (i.e., 1.2 % of total analyzed
particles). This range (0.1–1.2 %) is within, and towards the lower end,
of previous estimates with biological-specific techniques (Table 1). This
lower-end estimate may, in part, be due to PALMS sampling particles in the
200–500 nm diameter range as well as larger sizes. Previous estimates tend
to show increased bioaerosol in the super-micrometer range, and data are often
unavailable for the numerous particles smaller than 500 nm diameter.
The origin of the nonbiological phosphate particles is likely
phosphate-bearing mineral dust or fly ash. The CARES site experienced
influences of aged marine, urban and local biogenic sources. Within the urban
plumes, a likely source of inorganic phosphate is industrial combustion
aerosol. At Storm Peak a likely source is the mining of phosphate rock and nearby
monazite deposits. Figure 6b shows HYSPLIT back trajectories for the 10 days
of the FIN03 campaign; the air masses sampled cross deposits of either
phosphate rock (apatite) or rare-earth elements (monazite or carbonatite). As
examples, on 27 September the back trajectory intersects the vicinity of an active
rare-earth element (REE) mine in Mountain Pass, CA, and on 18 September and 20 September the
air mass intersected active phosphate mines in Idaho. Although negative
spectra of apatite and monazite cannot be definitively differentiated from
fly ash or soil dust spectra, positive spectra acquired during FIN03
additionally suggest that monazite-type material was present. Figure 2g and
h show nonbiological phosphate-rich ambient spectra from FIN03. Figure 2e
and f (monazite) contain similar features and matching rare-earth elements.
In total, 56 and 36 % of phosphate-containing particles analyzed in FIN03
and CARES, respectively, categorized as biological also contained silicate
features. Considered in more detail in the next section, a subset of these
may represent internal mixtures of biological and mineral components.
Discussion
The method of identification of bioaerosol described here is based on ratios
of phosphate and organic nitrogen peaks. This work is specific to PALMS but
can be considered a starting point from which identification and
differentiation can be made with similar instruments. Previous work with
PALMS shows this ratio approach can be used to identify differences in
chemistry, for example among mineral dusts (Gallavardin et al., 2008). In
this case the classes are bioaerosol and nonbiological phosphorus; Fig. 4a
shows that phosphorus ionizes differently in these classes. In apatite and
monazite, phosphorus occurs as calcium phosphate. In biological particles,
phosphorus occurs mostly in phospholipid bilayers and nucleic acids. In these
experiments, the PO3-/ PO2- ratio of those two forms is
different (Fig. 4a). The agricultural soils considered here cluster with the
minerals and fly ash, and we assume the phosphorus is due to the use of
inorganic fertilizer, which is derived from calcium phosphate (Koppelaar and
Weikard, 2013). Fly ash aerosol clusters similarly to apatite and monazite
but with a wider distribution; this is likely because the chemical from of
phosphorus in fly ash is different than in the minerals. Phosphorus present
in coal is volatilized and then condenses into different forms during the
combustion process (Wang et al., 2014).
During the FIN03 campaign at Storm Peak, 0.2–0.5 % of particles by
number detected in negative polarity contained measureable phosphorus
(Fig. 6a). On most days, the majority of phosphorus-rich particles were
inorganic. Particles with positive spectra showing the characteristics of
monazite coupled to back trajectories over source areas suggest the origin
of the inorganic phosphate particles. Although apatite or monazite particles
make up a small portion of ambient particles at Storm Peak, they are
potentially interesting not only due to their possible confusion with
biological phosphate but also as a tracer for industrial mining and
processing activities. Currently, such activities are taking place in Idaho
and until very recently at Mountain Pass, CA (US Geological Survey, 2016a,
b). Smaller exploration activities are also taking place at the Bear Lodge,
WY, and the REE-rich areas in Colorado, Idaho and Montana are of interest (US
Geological Survey, 2016a).
During the CARES campaign more particles contained phosphorus (1–4.2 %)
and a higher percentage of phosphate-rich particles were identified as
biological (63 % vs. 29 % in FIN03). Because the site contains strong
local biogenic and urban influences, the sources of biological particles are
probably local. As shown in Fig. 7b, aged marine particles were also present
on many days; however, only 4 % of particles identified as biological
also contained markers associated with sea salts.
Comparison with existing literature
Previous studies have attempted to identify bioaerosol with SPMS based on the
presence of phosphate and organic nitrate components. Creamean et al. (2013)
and Pratt et al. (2009b) suggested a “Boolean criterion” where the
existence of CN-, CNO- and PO3- in a particle resulted in
its classification as biological. If silicate components were additionally
present, the particle was classified as an internal mixture of mineral dust
and biological components (Creamean et al., 2013, 2014). Such “Boolean”
criteria for particle identification, can be helpful in distinguishing
aerosol types when the signatures are unique to one particle type.
Percentage of particles that include PO3-, CN- and
CNO- markers in five classes of atmospherically relevant aerosol
spectra acquired with PALMS in this work. Note that the green bars indicate
the percentage of particles of each type identified as biological using
literature criteria. In the case of bioaerosol the identification is
correct. In all other aerosol classes the green bar denotes a typical level
of misidentification.
The selectivity of this simple three-component filter (presence or absence of
CN-, CNO- and PO3-) for biological particles was
investigated for PALMS using the test aerosol database with results shown in
Fig. 8. Note that previous literature does not provide information on the
thresholds used to determine the presence or absence of ions in an analysis of
ATOFMS spectra. Furthermore, because of hardware differences, detection
limits of PALMS and ATOFMS are known to be different (Murphy, 2007). This
analysis focuses on PALMS and the threshold for “presence” was chosen as
0.001, which was observed to be the detection limit for CN-, CNO-
and PO3- in the laboratory aerosol database used here. The simple
filter successfully picks biological material. However, it also has a high
rate of false positives. For the material that contains inorganic phosphorus
(i.e., samples known to be devoid of biological material), the three-component
filter selects 56 % of fly ash, 56 % of agricultural dust and
32 % of apatite and monazite. Soil dust is identified as biological
78 % of the time.
Abundance of bioaerosol, mineral dust and fly ash in the
atmosphere constructed using emissions estimates in Table 3. (a) Highest
estimate for bioaerosol coupled to lowest estimates for dust and fly ash. (b)
Lowest estimate of bioaerosol in the atmosphere coupled to highest estimates
for dust and fly ash. (c, d) Effect of misidentification of phosphate- and
organic-nitrogen-containing aerosol as biological using the emissions in (a)
and (b), respectively. The hatched regions correspond to the misidentified
fractions of mineral dust and fly ash. In these estimates the correct
emissions (solid green region) in (a) and (b) (17 and 2 %, respectively) are
overestimated (hatched green region of misidentified aerosol plus solid
green region) in (c) and (d) (as 81 and 77 %, respectively).
The effect of the misidentification of inorganic phosphate as biological can be
considered in the context of the atmospheric abundance of the three major
phosphate bearing aerosols: mineral dust, fly ash and bioaerosol (estimates
given in Table 4). Because the emissions estimates vary, the highest fraction
of bioaerosol is the case of the highest estimate of bioaerosol coupled to
the lowest estimate of fly ash and mineral dust (Table 4 and Fig. 9a).
Conversely, the lowest fraction of bioaerosol is the case of the lowest
estimate of bioaerosol coupled to the highest estimate of fly ash and mineral
dust (Table 4 and Fig. 9b).
The misidentification rates noted above are then propagated onto the high and
low estimates. As an example, the fraction of aerosol phosphate due to fly
ash (1 % in the high and 5 % in the low bioaerosol estimate) is
multiplied by 0.56 to indicate the fraction of fly
ash that would be misidentified as biological phosphate with the simple
three-component filter. This misidentification effect is repeated for the
mineral dust emission rate and misidentification fraction. For simplicity, we
considered the mineral dust fraction to be desert soils, termed Aridisols and
Entisols, which are predominantly present in dust-productive regions, such as
the Sahara or the dust bowl (Yang et al., 2013). According to Yang and
Post (2011), the organic phosphate content of those soils is 5–15 %, but
this is a second-order effect when compared to misclassification. In the high-bioaerosol scenario, 17 % of the phosphate aerosol is biological
(Fig. 9a), but when misidentification is considered, 81 % of particles is identified
as such (Fig. 9c). In the low-bioaerosol scenario, 2 % of the phosphate
aerosol is biological (Fig. 9b), but when misidentification is considered, 77 % of the particles is identified as such (Fig. 9d). This illustrates
that simplistic identification can lead to large misclassification errors of
aerosol sources.
Misidentification can also lead to misattribution. Pratt et al. (2009b)
analyzed ice residuals sampled in an orographic cloud and suggested a
biological source using the simple three-component filter applied to spectra
containing calcium, sodium, organic carbon, organic nitrogen and phosphate.
The processed apatite spectrum in Fig. 2, devoid of biological material,
contains all of these markers. Similar to the Storm Peak dataset, the Pratt
et al. (2009b) wave cloud occurred in west–central Wyoming, which is near the
Idaho phosphate rock deposits (Fig. 6), and four US states with active mining
of phosphate rock for use as inorganic fertilizer in agriculture (US
Geological Survey, 2016b).
As noted above, the Pratt et al. (2009b) and Creamean et al. (2013, 2014)
studies were performed with a different SPMS, the ATOFMS (Gard et al., 1997;
Pratt et al., 2009a). Because the ATOFMS uses a desorption–ionization laser
of a different wavelength (266 nm), the SVM algorithm used here may not
directly translate to that instrument (Murphy, 2007). Instead, the
calculation above assumes only that the misidentification rates between the
simple three-component filter and the SVM algorithm apply.
Soil dust and internal dust–biological mixtures
Soil dust is an important but complicated category of phosphate-containing
atmospheric particles. Modeling studies, such as Mahowald et al. (2008),
treat all phosphorus in soil dust aerosol as inorganic. However, the
phosphorus in soil investigated here took both organic and inorganic forms.
Walker and Syers (1976) proposed a conceptual model of transformations of
phosphorus depending on the age of the soil. At the beginning of its
development, all soil phosphorus is bound in its primary mineral form,
matching that of the parent material, which is primarily apatite (Walker and
Syers, 1976; Yang and Post, 2011). As the soil ages, the primary phosphorus
is released. Some of it enters the organic reservoir and is utilized by
vegetation, some is adsorbed onto the surface of secondary soil minerals
(non-occluded phosphorus) and then gradually encapsulated by secondary
minerals (Fe and Al oxides) into an occluded form. The total phosphorus
content of the soil decreases as the soil ages, due to leaching. The organic
fraction can encompass microorganisms, their metabolic by-products and other
biological matter at various stages of decomposition. Soil microorganisms are
the key players in converting organic phosphorus back into the mineral form
(Brookes et al., 1984). Yang and Post (2011) estimated organic and inorganic
phosphorus content of various soils based on available data. Spodosols (moist
forest soils) have the highest fraction of organic phosphorus
(∼ 45 %), and Aridisols (sandy desert soils) have the lowest
(∼ 5 %) (Yang and Post, 2011). Yang et al. (2013) compiled a global
map of soil phosphorus distribution and its forms and found that 20 %, on
average, of total phosphorus is organic. Wang et al. (2010) arrive at
34 % of soil phosphorus as organic globally.
The biological PALMS filter was applied to several soil dust samples
(Table 3). As would be expected, soils collected in areas with less
vegetation exhibit smaller biological contributions. We note that organic
phosphorus content is not necessarily a direct indicator of microbes since it
also encompasses decomposed biogenic and organic matter. At this time, we are
not able to delineate between primary biological, biogenic or simply complex
organic (such as humic acids) material.
Literature estimates of emission rates of primary biological
particles, dust and fly ash.
ParticleEmissions (Tg yr-1) low estimatehigh estimateDust1490 (Zender, 2003)7800 (Jacobson and Streets, 2009)Primary biological186 (Mahowald et al., 2008)298 (Jacobson and Streets, 2009)Fly ash14.9 (Garimella, 2017)390 (Garimella, 2017)
Exemplary PALMS negative polarity spectra of (a): dry-dispersed
illite NX; (b): wet-dispersed illite NX from a distilled, deionized water
slurry; (c): similarly wet-dispersed illite NX but from a water slurry that
also contained F. solani spores. Note that phosphate features are absent in (a) and (b)
but present in (c) due to the addition of biological material to the mineral dust.
In the FIN03 field dataset, 56 % of particles identified as biological
also contained silicate markers normally associated with mineral dust. In
the CARES dataset the percentage of such particles was 36 %. This
represents an upper limit of particles that are an internal mixture of dust
and biological material. As stated in the last paragraph, this biological
material probably does not consist of whole cells sitting on mineral
particles; such internally mixed mineral dust particle with whole or
fragments of biological material are not supported by EM (Peter Buseck,
personal communication). It currently remains unclear if such internally
mixed particles would be counted as biological with an optical microscope
after fluorescent staining.
Internal mixtures of biological and mineral components were generated in the
laboratory in order to investigate this; an exemplary spectrum of such a particle is shown in Fig. 10. The spectrum contains alumino-silicate markers
consistent with mineral dust together with phosphate markers that, in this
case, come from the biological material. In spectra of pure illite, no
phosphate markers are present. Using the classifier developed in this paper
on the laboratory-generated internally mixed particles correctly identifies
the phosphate signatures to be biological.
Uncertainty in bioaerosol identification in PALMS spectra
Phosphorus peak ratios in biological particles cluster differently than in
inorganic phosphorus particles with ragweed pollen being an exception (Fig. 4a). No
satisfactory explanation for this observation has been found although
contamination with phosphate fertilizer cannot be ruled out. The accuracy of
the biological filter using PO3-/ PO2- and
CN-/ CNO- ratios is 97 %, with ragweed alone being the source of
most of the error. This unexplained behavior is a cause for concern, as the
list of biological samples used as a training set is extensive but not
exhaustive and other exceptions could exist.
The basic classifier presented in this paper is binary: all phosphate- and
organic-nitrogen-containing particles are classified either as biological or
inorganic. However, spectra whose PO3-/ PO2- and
CN-/ CNO- ratios are very close to the SVM boundary have more
uncertain assignments than those whose PO3-/ PO2- and
CN-/ CNO- ratios fall far away from the boundary. In order to
provide an additional measure of classification uncertainty, a probability
bound was defined as shown in Fig. 5. According to this definition, 96 %
of particles in the training dataset were classified with high confidence
(Fig. 5). In the FIN03 and CARES field datasets, 79 % of
phosphate-containing particles were classified with high confidence. The
low-confidence assignments are shown in Figs. 6a and 7a with shaded areas.
The low-confidence assignments in field datasets can be related to chemical
processing of particles (either at the source like in soils or during
transport) or to internal mixing of biological and inorganic phosphate.
Because soil dusts are a special category, where lines between biological and
inorganic phosphorus sources can be blurry because of ongoing chemical
transformations, they have higher classification uncertainties than other
types of phosphate-containing aerosols. In the field data, dust–biological
mixtures (defined as particles classified as biological with silicate
features) are overrepresented in the low-confidence assignments.
Dust–biological mixtures constitute 26 % (CARES)–46 % (FIN03) of
high-confidence assignments and 64 % (CARES)–68 % (FIN03) of
low-confidence assignments. Moreover, only 75 % of phosphate-containing
soil dust particles were classified with high confidence. However, in simple
two-component internal mixtures of dust and biological fragments (Fig. 10), phosphate features can be identified as biological with high confidence
(98 %).
Because the field studies were performed during different time periods, it
was difficult to control for a constant excimer laser fluence. However, laser
fluence was similar for all laboratory samples acquired (3–5 mJ pulse
energy). This is a possible source of uncertainty, as fragmentation patterns
can differ depending on pulse energy.
Conclusions
This paper examines criteria that can be used with SPMS instruments to
identify bioaerosol. We propose a new technique of bioaerosol detection and
validate it using a database of phosphorus-bearing spectra. A simple binary
classification scheme was optimized using an SVM algorithm, with 97 %
accuracy. Ambient data collected during FIN03 and CARES campaigns are then
analyzed with this binary classifier. Particles with phosphorus were up to
0.5 % for FIN03 and 4.2 % for CARES by number of all ambient
particles in the 200–3000 nm size range. On average, 29 % (FIN03) and
63 % (CARES) of these particles were identified as biological.
Our work expands on previous SPMS sampling that used a more simple Boolean
three-marker criterion (CN-, CNO- and PO3-) to classify
particles as primary biological or not (Creamean et al., 2013, 2014). We show
that the presence of these markers is necessary but not sufficient. We show a
false positive rate of the Boolean filter between 64 and 75 % for a
realistic atmospheric mixture of soil dust, fly ash and primary biological
particles.
The trained SVM algorithm was also used to measure the biological content of
soil dusts. Different soil dust samples can have different contents of
biological material with a range from 2 to 32 % observed here. Consistent
with the literature, samples taken from areas with vegetation exhibit a
higher biological content.
Data used to generate the figures are included in a Harvard Dataverse dataset with the same name as this paper (Zawadowicz, 2017, 10.7910/DVN/C6V7FL).
The authors declare that they have no conflict of interest.
Acknowledgements
The authors gratefully acknowledge funding from NASA grant no. NNX13AO15G,
NSF grant no. AGS-1461347, NSF grant no. AGS-1339264 and DOE grant no.
DE-SC0014487. M. A. Z. acknowledges the support of a NASA Earth and Space
Science Fellowship. The authors would like to thank Ottmar Moehler and the
KIT AIDA facility staff for hosting the FIN01 workshop and Gannet Hallar, Ian
McCubbin and DRI Storm Peak Laboratory for hosting the FIN03 workshop. The
authors thank the entire CARES, FIN01 and FIN03 teams for support and Peter
Buseck for useful discussions. Edited by:
A. Huffman Reviewed by: four anonymous referees
ReferencesAmato, P., Ménager, M., Sancelme, M., Laj, P., Mailhot, G., and Delort,
A.-M.: Microbial population in cloud water at the Puy de Dôme:
Implications for the chemistry of clouds, Atmos. Environ., 39, 4143–4153,
10.1016/j.atmosenv.2005.04.002, 2005.Amato, P., Parazols, M., Sancelme, M., Laj, P., Mailhot, G., and Delort,
A.-M.: Microorganisms isolated from the water phase of tropospheric clouds at
the Puy de Dôme: major groups and growth abilities at low temperatures,
FEMS Microbiol. Ecol., 59, 242–254, 10.1111/j.1574-6941.2006.00199.x,
2007.Bauer, H., Kasper-Giebl, A., Löflund, M., Giebl, H., Hitzenberger, R.,
Zibuschka, F., and Puxbaum, H.: The contribution of bacteria and fungal
spores to the organic carbon content of cloud water, precipitation and
aerosols, Atmos. Res., 64, 109–119, 10.1016/S0169-8095(02)00084-4, 2002.Bauer, H., Schueller, E., Weinke, G., Berger, A., Hitzenberger, R., Marr, I.
L., and Puxbaum, H.: Significant contributions of fungal spores to the
organic carbon and to the aerosol mass balance of the urban atmospheric
aerosol, Atmos. Environ., 42, 5542–5549,
10.1016/j.atmosenv.2008.03.019, 2008.
Ben-Hur, A., Horn, D., Siegelmann, H. T., and Vapnik, V.: Support Vector
Clustering, J. Mach. Learn. Res., 2, 125–137, 2001.
Berger, V. I., Singer, D. A., and Orris, G. J.: Carbonatites of the world,
explored deposits of Nb and REE; database and grade and tonnage models: US
Geological Survey Open-File Report 2009–1139, 17 pp., 2009.Borys, R. D. and Wetzel, M. A.: Storm Peak Laboratory: A Research, Teaching,
and Service Facility for the Atmospheric Sciences, Bull. Am. Meteorol. Soc.,
78, 2115–2123, 10.1175/1520-0477(1997)078<2115:SPLART>2.0.CO;2, 1997.Bowers, R. M., Lauber, C. L., Wiedinmyer, C., Hamady, M., Hallar, A. G.,
Fall, R., Knight, R., and Fierer, N.: Characterization of Airborne Microbial
Communities at a High-Elevation Site and Their Potential To Act as
Atmospheric Ice Nuclei, Appl. Environ. Microbiol., 75, 5121–5130,
10.1128/AEM.00447-09, 2009.Bowers, R. M., McLetchie, S., Knight, R., and Fierer, N.: Spatial variability
in airborne bacterial communities across land-use types and their
relationship to the bacterial communities of potential source environments,
ISME J., 5, 601–612, 10.1038/ismej.2010.167, 2011.Bowers, R. M., McCubbin, I. B., Hallar, A. G., and Fierer, N.: Seasonal
variability in airborne bacterial communities at a high-elevation site,
Atmos. Environ., 50, 41–49, 10.1016/j.atmosenv.2012.01.005, 2012.Brookes, P. C., Powlson, D. S., and Jenkinson, D. S.: Phosphorus in the soil
microbial biomass, Soil Biol. Biochem., 16, 169–175,
10.1016/0038-0717(84)90108-1, 1984.Cahill, J. F., Darlington, T. K., Fitzgerald, C., Schoepp, N. G., Beld, J.,
Burkart, M. D., and Prather, K. A.: Online Analysis of Single Cyanobacteria
and Algae Cells under Nitrogen-Limited Conditions Using Aerosol
Time-of-Flight Mass Spectrometry, Anal. Chem., 87, 8039–8046,
10.1021/acs.analchem.5b02326, 2015.
Chernoff, C. B. and Orris, G. J.: Data set of world phosphate mines,
deposits, and occurrences – Part A, Geologic Data; Part B, Location and
Mineral Economic Data: US Geological Survey Open-File Report 02-156,
2002.Cortes, C. and Vapnik, V.: Support-vector networks, Mach. Learn., 20,
273–297, 10.1007/BF00994018, 1995.Creamean, J. M., Suski, K. J., Rosenfeld, D., Cazorla, A., DeMott, P. J.,
Sullivan, R. C., White, A. B., Ralph, F. M., Minnis, P., Comstock, J. M.,
Tomlinson, J. M., and Prather, K. A.: Dust and Biological Aerosols from the
Sahara and Asia Influence Precipitation in the Western US, Science, 339,
1572–1578, 10.1126/science.1227279, 2013.Creamean, J. M., Lee, C., Hill, T. C., Ault, A. P., DeMott, P. J., White, A.
B., Ralph, F. M., and Prather, K. A.: Chemical properties of insoluble
precipitation residue particles, J. Aerosol Sci., 76, 13–27,
10.1016/j.jaerosci.2014.05.005, 2014.Cziczo, D. J. and Froyd, K. D.: Sampling the composition of cirrus ice
residuals, Atmos. Res., 142, 15–31, 10.1016/j.atmosres.2013.06.012,
2014.Cziczo, D. J., Thomson, D. S., and Murphy, D. M.: Ablation, Flux, and
Atmospheric Implications of Meteors Inferred from Stratospheric Aerosol,
Science, 291, 1772–1775,
10.1126/science.1057737, 2001.Cziczo, D. J., Thomson, D. S., Thompson, T. L., DeMott, P. J., and Murphy, D.
M.: Particle analysis by laser mass spectrometry (PALMS) studies of ice
nuclei and other low number density particles, Int. J. Mass Spectrom.,
258, 21–29, 10.1016/j.ijms.2006.05.013, 2006.
Cziczo, D. J., Froyd, K. D., Hoose, C., Jensen, E. J., Diao, M., Zondlo, M.
A., Smith, J. B., Twohy, C. H., and Murphy, D. M.: Clarifying the Dominant
Sources and Mechanisms of Cirrus Cloud Formation, Science, 340, 1320–1324,
2013.DeLeon-Rodriguez, N., Lathem, T. L., Rodriguez-R, L. M., Barazesh, J. M.,
Anderson, B. E., Beyersdorf, A. J., Ziemba, L. D., Bergin, M., Nenes, A., and
Konstantinidis, K. T.: Microbiome of the upper troposphere: Species
composition and prevalence, effects of tropical storms, and atmospheric
implications, P. Natl. Acad. Sci. USA, 110, 2575–2580,
10.1073/pnas.1212089110, 2013.DeMott, P. J., Cziczo, D. J., Prenni, A. J., Murphy, D. M., Kreidenweis, S.
M., Thomson, D. S., Borys, R., and Rogers, D. C.: Measurements of the
concentration and composition of nuclei for cirrus formation, P. Natl. Acad.
Sci. USA, 100, 14655–14660, 10.1073/pnas.2532677100, 2003.Després, V. R., Alex Huffman, J., Burrows, S. M., Hoose, C., Safatov, A.
S., Buryak, G., Fröhlich-Nowoisky, J., Elbert, W., Andreae, M. O.,
Pöschl, U., and Jaenicke, R.: Primary biological aerosol particles in the
atmosphere: a review, Tellus B, 64, 1–58,
10.3402/tellusb.v64i0.15598, 2012.Ebert, M., Worringen, A., Benker, N., Mertes, S., Weingartner, E., and
Weinbruch, S.: Chemical composition and mixing-state of ice residuals sampled
within mixed phase clouds, Atmos. Chem. Phys., 11, 2805–2816,
10.5194/acp-11-2805-2011, 2011.Fahlgren, C., Hagstrom, A., Nilsson, D., and Zweifel, U. L.: Annual
Variations in the Diversity, Viability, and Origin of Airborne Bacteria,
Appl. Environ. Microbiol., 76, 3015–3025, 10.1128/AEM.02092-09,
2010.Fang, Z., Ouyang, Z., Zheng, H., Wang, X., and Hu, L.: Culturable Airborne
Bacteria in Outdoor Environments in Beijing, China, Microb. Ecol., 54,
487–496, 10.1007/s00248-007-9216-3, 2007.Fergenson, D. P., Pitesky, M. E., Tobias, H. J., Steele, P. T., Czerwieniec,
G. A., Russell, S. C., Lebrilla, C. B., Horn, J. M., Coffee, K. R.,
Srivastava, A., Pillai, S. P., Shih, M.-T. P., Hall, H. L., Ramponi, A. J.,
Chang, J. T., Langlois, R. G., Estacio, P. L., Hadley, R. T., Frank, M., and
Gard, E. E.: Reagentless Detection and Classification of Individual
Bioaerosol Particles in Seconds, Anal. Chem., 76, 373–378,
10.1021/ac034467e, 2004.Friedman, B., Zelenyuk, A., Beranek, J., Kulkarni, G., Pekour, M., Gannet
Hallar, A., McCubbin, I. B., Thornton, J. A., and Cziczo, D. J.: Aerosol
measurements at a high-elevation site: composition, size, and cloud
condensation nuclei activity, Atmos. Chem. Phys., 13, 11839–11851,
10.5194/acp-13-11839-2013, 2013.Froyd, K. D., Murphy, D. M., Lawson, P., Baumgardner, D., and Herman, R. L.:
Aerosols that form subvisible cirrus at the tropical tropopause, Atmos. Chem.
Phys., 10, 209–218, 10.5194/acp-10-209-2010, 2010.Gabey, A. M., Gallagher, M. W., Whitehead, J., Dorsey, J. R., Kaye, P. H.,
and Stanley, W. R.: Measurements and comparison of primary biological aerosol
above and below a tropical forest canopy using a dual channel fluorescence
spectrometer, Atmos. Chem. Phys., 10, 4453–4466,
10.5194/acp-10-4453-2010, 2010.Gallavardin, S., Lohmann, U., and Cziczo, D.: Analysis and differentiation of
mineral dust by single particle laser mass spectrometry, Int. J. Mass
Spectrom., 274, 56–63,
10.1016/j.ijms.2008.04.031, 2008.Gard, E., Mayer, J. E., Morrical, B. D., Dienes, T., Fergenson, D. P., and
Prather, K. A.: Real-Time Analysis of Individual Atmospheric Aerosol
Particles: Design and Performance of a Portable ATOFMS, Anal. Chem., 69,
4083–4091, 10.1021/ac970540n, 1997.
Garimella, S.: A vertically-integrated approach to climate science: from measurements and machine learning to models and policy, PhD dissertation MIT, 2017.Garimella, S., Huang, Y.-W., Seewald, J. S., and Cziczo, D. J.: Cloud
condensation nucleus activity comparison of dry- and wet-generated mineral
dust aerosol: the significance of soluble material, Atmos. Chem. Phys.,
14, 6003–6019, 10.5194/acp-14-6003-2014, 2014.Griffin, D. W., Garrison, V. H., Herman, J. R., and Shinn, E. A.: African
desert dust in the Caribbean atmosphere: Microbiology and public health,
Aerobiologia, 17, 203–213, 10.1023/A:1011868218901, 2001.Griffin, D. W., Westphal, D. L., and Gray, M. A.: Airborne microorganisms in
the African desert dust corridor over the mid-Atlantic ridge, Ocean Drilling
Program, Leg 209, Aerobiologia, 22, 211–226, 10.1007/s10453-006-9033-z,
2006.Hill, S. C., Pinnick, R. G., Niles, S., Pan, Y.-L., Holler, S., Chang, R.
K., Bottiger, J., Chen, B. T., Orr, C.-S., and Feather, G.: Real-time
measurement of fluorescence spectra from single airborne biological
particles, F. Anal. Chem. Technol., 3, 221–239,
10.1002/(SICI)1520-6521(1999)3:4/5<221::AID-FACT2>3.0.CO;2-7, 1999.Hiranuma, N., Möhler, O., Yamashita, K., Tajiri, T., Saito, A., Kiselev,
A., Hoffmann, N., Hoose, C., Jantsch, E., Koop, T., and Murakami, M.: Ice
nucleation by cellulose and its potential contribution to ice formation in
clouds, Nat. Geosci., 8, 273–277, 10.1038/ngeo2374, 2015.Hoose, C., Kristjánsson, J. E., and Burrows, S. M.: How important is
biological ice nucleation in clouds on a global scale?, Environ. Res. Lett.,
5, 024009, 10.1088/1748-9326/5/2/024009, 2010.Jacobson, M. Z. and Streets, D. G.: Influence of future anthropogenic
emissions on climate, natural emissions, and air quality, J. Geophys. Res.,
114, D08118, 10.1029/2008JD011476, 2009.Kaye, P. H., Barton, J. E., Hirst, E., and Clark, J. M.: Simultaneous light
scattering and intrinsic fluorescence measurement for the classification of
airborne particles, Appl. Opt., 39, 3738, 10.1364/AO.39.003738, 2000.Kaye, P. H., Stanley, W. R., Hirst, E., Foot, E. V., Baxter, K. L., and
Barrington, S. J.: Single particle multichannel bio-aerosol fluorescence
sensor, Opt. Express, 13, 3583, 10.1364/OPEX.13.003583, 2005.Koppelaar, R. H. E. M. and Weikard, H. P.: Assessing phosphate rock depletion
and phosphorus recycling options, Global Environ. Chang., 23, 1454–1466,
10.1016/j.gloenvcha.2013.09.002, 2013.Mahowald, N., Jickells, T. D., Baker, A. R., Artaxo, P., Benitez-Nelson, C.
R., Bergametti, G., Bond, T. C., Chen, Y., Cohen, D. D., Herut, B., Kubilay,
N., Losno, R., Luo, C., Maenhaut, W., McGee, K. A., Okin, G. S., Siefert, R.
L., and Tsukuda, S.: Global distribution of atmospheric phosphorus sources,
concentrations and deposition rates, and anthropogenic impacts, Global
Biogeochem. Cy., 22, GB4026, 10.1029/2008GB003240, 2008.Matthias-Maser, S. and Jaenicke, R.: Examination of atmospheric bioaerosol
particles with radii >0.2 µm, J. Aerosol Sci., 25,
1605–1613, 10.1016/0021-8502(94)90228-3, 1994.Möhler, O., Georgakopoulos, D. G., Morris, C. E., Benz, S., Ebert, V.,
Hunsmann, S., Saathoff, H., Schnaiter, M., and Wagner, R.: Heterogeneous ice
nucleation activity of bacteria: new laboratory experiments at simulated
cloud conditions, Biogeosciences, 5, 1425–1435, 10.5194/bg-5-1425-2008,
2008.Murphy, D. M.: The design of single particle laser mass spectrometers, Mass
Spectrom. Rev., 26, 150–165, 10.1002/mas.20113, 2007.Murphy, D. M., Cziczo, D. J., Hudson, P. K., Thomson, D. S., Wilson, J. C.,
Kojima, T., and Buseck, P. R.: Particle Generation and Resuspension in
Aircraft Inlets when Flying in Clouds, Aerosol Sci. Technol., 38, 401–409,
10.1080/02786820490443094, 2004.O'Connor, D. J., Healy, D. A., Hellebust, S., Buters, J. T. M., and Sodeau,
J. R.: Using the WIBS-4 (Waveband Integrated Bioaerosol Sensor) Technique for
the On-Line Detection of Pollen Grains, Aerosol Sci. Technol., 48, 341–349,
10.1080/02786826.2013.872768, 2014.
Orris, G. J. and Grauch, R. I.: Rare earth element mines, deposits, and
occurrences: US Geological Survey, Open-File Report 02-189, 2002.Pan, Y., Holler, S., Chang, R. K., Hill, S. C., Pinnick, R. G., Niles, S.,
and Bottiger, J. R.: Single-shot fluorescence spectra of individual
micrometer-sized bioaerosols illuminated by a 351- or a 266-nm ultraviolet
laser, Opt. Lett., 24, 116–118, 10.1364/OL.24.000116, 1999.
Platt, J. C.: Probabilistic outputs for support vector machines and
comparisons to regularized likelihood methods, in: Advances in Large Margin
Classifiers, MIT Press, 61–74, 1999.Pósfai, M., Li, J., Anderson, J. R., and Buseck, P. R.: Aerosol bacteria
over the Southern Ocean during ACE-1, Atmos. Res., 66, 231–240,
10.1016/S0169-8095(03)00039-5, 2003.Pratt, K. A., Mayer, J. E., Holecek, J. C., Moffet, R. C., Sanchez, R. O.,
Rebotier, T. P., Furutani, H., Gonin, M., Fuhrer, K., Su, Y., Guazzotti, S.,
and Prather, K. A.: Development and Characterization of an Aircraft Aerosol
Time-of-Flight Mass Spectrometer, Anal. Chem., 81, 1792–1800,
10.1021/ac801942r, 2009a.
Pratt, K. A., DeMott, P. J., French, J. R., Wang, Z., Westphal, D. L.,
Heymsfield, A. J., Twohy, C. H., Prenni, A. J., and Prather, K. A.: In situ
detection of biological particles in cloud ice-crystals, Nat. Geosci., 2,
398–401, 2009b.Prospero, J. M., Blades, E., Mathison, G., and Naidu, R.: Interhemispheric
transport of viable fungi and bacteria from Africa to the Caribbean with soil
dust, Aerobiologia, 21, 1–19, 10.1007/s10453-004-5872-7, 2005.Sattler, B., Puxbaum, H., and Psenner, R.: Bacterial growth in supercooled
cloud droplets, Geophys. Res. Lett., 28, 239–242,
10.1029/2000GL011684, 2001.Sesartic, A., Lohmann, U., and Storelvmo, T.: Bacteria in the ECHAM5-HAM
global climate model, Atmos. Chem. Phys., 12, 8645–8661,
10.5194/acp-12-8645-2012, 2012.Silva, P. J., Carlin, R. A., and Prather, K. A.: Single particle analysis of
suspended soil dust from Southern California, Atmos. Environ., 34,
1811–1820, 10.1016/S1352-2310(99)00338-6, 2000.Smith, D. J. and Griffin, D. W.: Inadequate methods and questionable
conclusions in atmospheric life study, P. Natl. Acad. Sci. USA, 110,
E2084–E2084, 10.1073/pnas.1302612110, 2013.Sodeman, D. A., Toner, S. M., and Prather, K. A.: Determination of Single
Particle Mass Spectral Signatures from Light-Duty Vehicle Emissions, Environ.
Sci. Technol., 39, 4569–4580, 10.1021/es0489947, 2005.Steinke, I., Funk, R., Busse, J., Iturri, A., Kirchen, S., Leue, M.,
Möhler, O., Schwartz, T., Schnaiter, M., Sierau, B., Toprak, E., Ullrich,
R., Ulrich, A., Hoose, C., and Leisner, T.: Ice nucleation activity of
agricultural soil dust aerosols from Mongolia, Argentina, and Germany, J.
Geophys. Res.-Atmos., 121, 13559–13579,
10.1002/2016JD025160, 2016.
Thomson, D. S., Schein, M. E., and Murphy, D. M.: Particle analysis by laser
mass spectrometry { WB}-57 instrument overview, Aerosol Sci. Technol., 33,
153–169, 2000.Toprak, E. and Schnaiter, M.: Fluorescent biological aerosol particles
measured with the Waveband Integrated Bioaerosol Sensor WIBS-4: laboratory
tests combined with a one year field study, Atmos. Chem. Phys., 13, 225–243,
10.5194/acp-13-225-2013, 2013.Twohy, C. H., McMeeking, G. R., DeMott, P. J., McCluskey, C. S., Hill, T. C.
J., Burrows, S. M., Kulkarni, G. R., Tanarhte, M., Kafle, D. N., and Toohey,
D. W.: Abundance of fluorescent biological aerosol particles at temperatures
conducive to the formation of mixed-phase and cirrus clouds, Atmos. Chem.
Phys., 16, 8205–8225, 10.5194/acp-16-8205-2016, 2016.
US Geological Survey: 2013 Minerals Yearbook, Rare Earths, 2016a.US Geological Survey: Mineral commodity summaries 2016, 128–129, available
from: 10.3133/70140094, last access: 6 January 2016b.Walker, T. W. and Syers, J. K.: The fate of phosphorus during pedogenesis,
Geoderma, 15, 1–19, 10.1016/0016-7061(76)90066-5, 1976.Wang, R., Balkanski, Y., Boucher, O., Ciais, P., Peñuelas, J., and Tao,
S.: Significant contribution of combustion-related emissions to the
atmospheric phosphorus budget, Nat. Geosci., 8, 48–54,
10.1038/ngeo2324, 2014.Wang, Y. P., Law, R. M., and Pak, B.: A global model of carbon, nitrogen and
phosphorus cycles for the terrestrial biosphere, Biogeosciences, 7,
2261–2282, 10.5194/bg-7-2261-2010, 2010.Wiedinmyer, C., Bowers, R. M., Fierer, N., Horanyi, E., Hannigan, M., Hallar,
A. G., McCubbin, I., and Baustian, K.: The contribution of biological
particles to observed particulate organic carbon at a remote high altitude
site, Atmos. Environ., 43, 4278–4282, 10.1016/j.atmosenv.2009.06.012,
2009.Xia, Y., Conen, F., and Alewell, C.: Total bacterial number concentration in
free tropospheric air above the Alps, Aerobiologia, 29,
153–159, 10.1007/s10453-012-9259-x, 2013.Yang, X. and Post, W. M.: Phosphorus transformations as a function of
pedogenesis: A synthesis of soil phosphorus data using Hedley fractionation
method, Biogeosciences, 8, 2907–2916, 10.5194/bg-8-2907-2011, 2011.Yang, X., Post, W. M., Thornton, P. E., and Jain, A.: The distribution of
soil phosphorus for global biogeochemical modeling, Biogeosciences, 10,
2525–2537, 10.5194/bg-10-2525-2013, 2013.Zaveri, R. A., Shaw, W. J., Cziczo, D. J., Schmid, B., Ferrare, R. A.,
Alexander, M. L., Alexandrov, M., Alvarez, R. J., Arnott, W. P., Atkinson, D.
B., Baidar, S., Banta, R. M., Barnard, J. C., Beranek, J., Berg, L. K.,
Brechtel, F., Brewer, W. A., Cahill, J. F., Cairns, B., Cappa, C. D., Chand,
D., China, S., Comstock, J. M., Dubey, M. K., Easter, R. C., Erickson, M. H.,
Fast, J. D., Floerchinger, C., Flowers, B. A., Fortner, E., Gaffney, J. S.,
Gilles, M. K., Gorkowski, K., Gustafson, W. I., Gyawali, M., Hair, J.,
Hardesty, R. M., Harworth, J. W., Herndon, S., Hiranuma, N., Hostetler, C.,
Hubbe, J. M., Jayne, J. T., Jeong, H., Jobson, B. T., Kassianov, E. I.,
Kleinman, L. I., Kluzek, C., Knighton, B., Kolesar, K. R., Kuang, C.,
Kubátová, A., Langford, A. O., Laskin, A., Laulainen, N., Marchbanks,
R. D., Mazzoleni, C., Mei, F., Moffet, R. C., Nelson, D., Obland, M. D.,
Oetjen, H., Onasch, T. B., Ortega, I., Ottaviani, M., Pekour, M., Prather, K.
A., Radney, J. G., Rogers, R. R., Sandberg, S. P., Sedlacek, A., Senff, C.
J., Senum, G., Setyan, A., Shilling, J. E., Shrivastava, M., Song, C.,
Springston, S. R., Subramanian, R., Suski, K., Tomlinson, J., Volkamer, R.,
Wallace, H. W., Wang, J., Weickmann, A. M., Worsnop, D. R., Yu, X.-Y.,
Zelenyuk, A., and Zhang, Q.: Overview of the 2010 Carbonaceous Aerosols and
Radiative Effects Study (CARES), Atmos. Chem. Phys., 12, 7647–7687,
10.5194/acp-12-7647-2012, 2012.Zender, C. S.: Mineral Dust Entrainment and Deposition (DEAD) model:
Description and 1990s dust climatology, J. Geophys. Res., 108, 4416,
10.1029/2002JD002775, 2003.Zhang, X., Smith, K. A., Worsnop, D. R., Jimenez, J., Jayne, J. T., and Kolb,
C. E.: A Numerical Characterization of Particle Beam Collimation by an
Aerodynamic Lens-Nozzle System: Part I. An Individual Lens or Nozzle,
Aerosol Sci. Technol., 36, 617–631, 10.1080/02786820252883856, 2002.Zhang, X., Smith, K. A., Worsnop, D. R., Jimenez, J. L., Jayne, J. T., Kolb,
C. E., Morris, J., and Davidovits, P.: Numerical Characterization of Particle
Beam Collimation: Part II Integrated Aerodynamic-Lens–Nozzle System,
Aerosol Sci. Technol., 38, 619–638, 10.1080/02786820490479833, 2004.Ziemba, L. D., Beyersdorf, A. J., Chen, G., Corr, C. A., Crumeyrolle, S. N.,
Diskin, G., Hudgins, C., Martin, R., Mikoviny, T., Moore, R., Shook, M.,
Thornhill, K. L., Winstead, E. L., Wisthaler, A., and Anderson, B. E.:
Airborne observations of bioaerosol over the Southeast United States using a
Wideband Integrated Bioaerosol Sensor, J. Geophys.
Res.-Atmos., 121, 8506–8524,
10.1002/2015JD024669, 2016.Zawadowicz, M. A.: Improved identification of primary biological aerosol particles using single particle mass spectrometry, 10.7910/DVN/C6V7FL, Harvard
Dataverse, 2017.