Introduction
Particles of biological origin, or bioaerosols, make up a substantial
fraction of atmospheric aerosols and have the potential to influence
environmental processes and to negatively impact human health (Després et
al., 2012; Douwes et al., 2003; Fröhlich-Nowoisky et al., 2016; Shiraiwa
et al., 2017). In order to understand the impact bioaerosols, such as pollen,
fungal spores, and bacteria, play in various systems, it is important to be
able to identify and characterize these biological particles in the
atmosphere. One common method for the detection of bioaerosols is ultraviolet
laser/light-induced fluorescence (UV-LIF) because it can provide particle
detection in near real time and at a high particle size resolution (Fennelly
et al., 2017; Huffman and Santarpia, 2017; Sodeau and O'Connor, 2016). Many
commercial UV-LIF instruments have become available for bioaerosol detection,
but all of these techniques are challenged with the need to differentiate
between small differences in fluorescence properties in order to detect and
quantify biological aerosols. Recently commercialized instruments show an
improved ability to discriminate between particle types, for example by
utilizing multiple excitation sources or other particle data (e.g., size and
shape). UV-LIF techniques are, however, inherently limited by the broad
nature of fluorescence spectra, and so instruments face a ubiquitous problem
of poor selectivity between particle types. By applying improved data
thresholding and particle classification techniques, particle
characterization can be further improved, but important limitations still
remain (Hernandez et al., 2016; Huffman et al., 2012; Perring et al., 2015;
Savage et al., 2017; Toprak and Schnaiter, 2013; Wright et al., 2014). One
strategy to improve the quality of differentiation between particles types
has been to collect full, resolved emission spectra, each at multiple
excitation wavelengths. This can lead to a high instrumental purchase cost,
and such instruments have not been widely applied or commercialized (Huffman
et al., 2016; Kiselev et al., 2013; Pan et al., 2009b; Ruske et al., 2017;
Swanson and Huffman, 2018). Most commercial UV-LIF instruments for bioaerosol
detection utilize one to two excitation wavelengths and integrate
fluorescence signals into a small number of emission bands. To extend the
improvements in particle classification for these commercial UV-LIF
instruments, a number of multivariate analysis techniques have been applied
to ambient particle analysis. The most common of these techniques include
principal component analysis, factor analysis, and cluster analysis
strategies. Classification algorithms, including several clustering
techniques in particular, have shown successful results in providing unbiased
insights into the classification of bioaerosols (Crawford et al., 2015;
Pinnick et al., 2013; Robinson et al., 2013; Swanson and Huffman, 2018).
Cluster analysis is a broad class of data mining methods in which data
objects placed in the same group (or cluster) are more similar to one
another than to those objects placed in other groups. Classification
algorithms can be divided into two central models: (1) supervised and (2) unsupervised learning. Both models have associated advantages and
disadvantages. Supervised learning methods allow the “training” of data
and grouping to better reflect the data observations (Eick et al., 2004;
Ruske et al., 2017, 2018). This type of method enhances
(trains) the classification algorithm in that the output groups are
predetermined rather than discovered, as is the case for unsupervised
methods. Supervision requires the user to have appropriate starting
conditions to put into the model, which are often difficult or impossible to
determine. Supervised training methods are also much more time-efficient
compared to unsupervised methods, which is important when analyzing ambient
data sets where particle counts (individual objects) can be greater than
106 (Ruske et al., 2017). In contrast, unsupervised training
methods present less bias and can adapt to unique situations because the
resultant clusters are based on models that have not been previously
trained. To access some of the advantages of supervised methods, however, it
is important to first apply unsupervised models to wide collections of
laboratory data of known particle types in order to gain insight into how
these models interpret data inputs and to learn how algorithms can best be
trained (Ruske et al., 2017).
Hierarchical agglomerative clustering (HAC) is an unsupervised learning
method that has been most commonly applied for bioaerosol-related studies
(e.g., Crawford et al., 2015, 2016; Gosselin et al.,
2016; Pan et al., 2009a, 2007; Pinnick et al., 2004, 2013; Robinson et al., 2013; Ruske et al., 2017, 2018).
Other unsupervised clustering techniques, such as the k-means clustering
method, have shown poor results when applied to ambient data sets because
the number of clusters used to represent the data are required a priori, and
this information is usually unknown prior to analysis (Ruske et al.,
2017). There are several different HAC methods or linkages including the following: single, complete, average, weighted, Ward's, centroid, and median
(Crawford et al., 2015; Müllner, 2013). Ruske et
al. (2017) compared a variety of HAC linkages and determined that Ward's
linkage had a higher percentage of correctly classifying particles in
comparison to other HAC methods.
Recently, Savage et al. (2017) published a
comprehensive laboratory study applying the wideband integrated bioaerosol sensor (WIBS-4A) to a large and diverse set of biological and nonbiological
aerosol types. Following on to that work, the study presented here utilizes
those data as inputs to evaluate and challenge the HAC strategy of particle
differentiation using Ward's linkage of unsupervised clustering.
Previous HAC studies have focused primarily on (a) the analysis of simple
particle standards (i.e., fluorescent microbeads) and (b) the clustering of
particles from ambient data sets. There have been relatively few published
attempts to differentiate between biological particles and interfering
particles by clustering methods using controlled laboratory UV-LIF data or
to separate different kinds of biological particles from one another.
Presented here are results of the HAC method applied to data from a
comprehensive WIBS-4A laboratory study showing that clustering can dramatically
improve the removal of nonbiological particle types from data sets if operated
under appropriate conditions.
Experimental and computational methods
The WIBS-4A (Droplet Measurement Techniques, Longmont, CO, USA) is a commonly
used UV-LIF based instrument for the detection and characterization of
biological particles. The instrument collects particles in the size range
0.8–20 µm and interrogates them in real time as particles flow
along the path between optical sources. The WIBS collects information
about fluorescence intensity in three channels (FL1, FL2, and FL3), particle
size, and particle asymmetry for each interrogated particle. The bands of
excitation and fluorescence emission are FL1 (λex=280,
λem=310–400 nm), FL2 (λex=280,
λem=420–650 nm), and FL3 (λex=370,
λem=420–650 nm). The excitation and emission
wavelengths chosen for each of the three fluorescence channels were designed to
maximize the information gained about key biological fluorophores present in
a broad range of bioparticles (Kaye et al., 2005; Pöhlker et al., 2012).
Early generations of UV-LIF bioaerosol spectrometers were often interpreted
to be able to detect proteins via channels similar to FL1 and products of
active cellular metabolism (i.e., riboflavin and NAD(P)H) via channels
similar to FL3, but these approximations are gross simplifications that
confound a more detailed investigation of particle types. For more information
on the design, operation, and calibration of this instrument, see, e.g., the
papers listed here and references therein: Foot et al. (2008), Healy
et al. (2012a, b), Hernandez et al. (2016), Kaye et al. (2005), Perring et al. (2015), Robinson et al. (2017), Savage et
al. (2017), and Stanley et al. (2011).
All aerosol materials utilized have been listed previously in Table 2 of
Savage et al. (2017), where an overview of
size and fluorescence properties of particles utilized for this study are
also reported. No additional laboratory experiments were performed here
beyond the results presented previously.
The fluorescence threshold applied to the differentiation of fluorescent
from nonfluorescent particles is a key step in UV-LIF data analysis.
Traditionally, a fluorescence threshold has been determined as the average
baseline fluorescence intensity measured in each of the three channels
during the forced trigger (FT) mode when no particles are present plus
3 times the standard deviation (σ) of that measurement (i.e., FT +3σ) (Gabey et al., 2010).
Savage et al. (2017) also reported that additional particle
discrimination is possible by using FT +9σ as the threshold. Both
threshold definitions will be discussed here. After choosing a threshold of
minimum fluorescence, the fluorescence characteristics of a particle can be
classified into seven different particle types introduced by Perring et al. (2015) and summarized in Fig. 1
of Savage et al. (2017).
Schematic diagram showing the data preparation
process resulting in the generated clustering products. Parameters within
the pink box are the focus of this paper.
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Clustering strategy
Hierarchical clustering methods work by grouping objects from the bottom up,
meaning that each object (particle) starts as its own “cluster,” and
clusters are merged together based on similarities until a greatly reduced
number of clusters are presented as a final solution. Ward's method for
clustering is among the most popular approaches for HAC and is the only
method based on a classical sum-of-squares criterion, minimizing the
within-group sum of squares (or variance) (Müllner, 2013). The
WIBS-4A used here for data collection provides five parameters of information
for each individual particle detected (three fluorescence channels, size, and
asymmetry factor (AF)), resulting in five dimensions of data.
The clustering analysis was performed using the open-source software R
package “fastercluster” (Müllner, 2013) using a Dell Latitude E7450
laptop computer with an Intel® Core™ Processor
(i7-5600U CPU @ 2.60 GHz, 16 GB RAM).
Data preparation
Saturation of fluorescence intensity occurs at 2047 analog-to-digital counts
(ADCs) for each of the three FL channels in the WIBS-4A, at which point the
photomultiplier tube (PMT) reaches its upper limit of detection. A study by
Ruske et al. (2017) investigated whether nonfluorescent (in that case,
particles below the FT +3σ fluorescence threshold) and/or
saturating data points included in the clustering analysis hindered the
efficiency of the cluster output. The authors determined that removing both
saturating and nonfluorescent particles before HAC analysis resulted in a
better clustering performance in terms of correctly classifying ambient
particles. The quality of the clustering results is likely to be impacted by
the types of particles involved and the assumptions placed on those. As shown by
Savage et al. (2017), many biological particles
present a large fraction that saturates one or more of the fluorescence
detectors. Conversely, many nonbiological particles present a large
fraction of very weakly fluorescent particles with an intensity below a given
threshold, which are thus classified as nonfluorescent. To limit
the premodification of particle populations before clustering, the only filter
applied before clustering was to remove particles smaller than the lower
particle size detection limit of the WIBS-4A (0.8 µm), similar to
Ruske et al. (2017). In contrast, both saturating and nonfluorescent
particles were analyzed and the clustering results will be evaluated. Figure 1 outlines the data preparation process, including the conceptual process of
normalization, clustering, and validation of data, which is explained in
detail below.
Data normalization
Normalization of the raw data is necessary before executing the clustering
algorithm because data parameters delivered from the instrument are
measured on different respective scales. For example, fluorescent intensity
values range from 0 to 2047 ADCs, size ranges from 0 to ∼20 µm, and AF ranges from 0 to 100 arbitrary units.
Crawford et al. (2015) performed an analysis on polystyrene latex spheres
(PSLs) using several different normalization techniques, concluding that
z-score normalization was the best technique when looking at cluster
performance using Ward's linkage for the separation of PSLs. As a result, we
utilize the z-score normalization of Ward's linkage HAC for the presented
study. By this type of normalization, the mean value of all data points is
subtracted from each individual data point, and then each data point is
divided by the standard deviation of all points. Standardization using the
z-score method compares results to a normal (Gaussian) population, and we
have chosen to standardize our variables to a mean of 0 and a variance of 1
so that the output variables would be on comparable scales.
HAC scenarios
Hierarchical agglomerative clustering performs optimally if all variables
(1) are independent of one another and (2) can be described well by a normal
(Gaussian) distribution (Norusis, 2011). To achieve meaningful results
from the clustering analysis, data values must, therefore, be input into the
clustering algorithm with an understanding of how specific preparatory
conditions can significantly impact results. To investigate optimal input conditions, a total of six clustering scenarios was explored, with conditions
summarized in Table 1. The impact of two separate variables was explored
within these scenarios by varying (i) whether fluorescence intensity was pre-normalized by particle size and (ii) whether the data values were input
after logarithmic transformation to produce a normal distribution.
Six scenarios explored, with varying combinations of
pre-analysis treatment. “Fluorescence normalization” refers to whether
fluorescence intensity values were input to HAC as reported by the
instrument (No) or after normalizing to particle size (Yes). “Variables
logged” refers to whether data values were input as reported (No) or
manipulated to produce a normal distribution by using log(value) (Yes).
Parameters
A
B
C
D
E
F
Fluorescence
No
No
Yes
Yes
Yes
No
normalization
Variables
No
Yes
No
Yes
Yes,
Yes,
logged
only
only
AF/size
AF/size
variables
variables
Ambient particle number vs. size distributions can often be well approximated
by lognormal distributions, although specific groups of particles, including
some bacteria, spores, and pollen, may not always exhibit a lognormal
distribution. Further, fluorescence intensity has been shown to scale with
particle size (e.g., Hill et al., 2001; Sivaprakasam et al., 2011).
Several previous studies attempted to utilize HAC for ambient
lognormally distributed particle size data (Crawford et al., 2014, 2015; Robinson et al., 2013) but applied the assumption
that particle fluorescence is normally distributed in a group of particles.
If this assumption is not correct, however, weakly fluorescing
particles are likely to be grouped into a single cluster based on the high
abundance of these particles (Robinson et al., 2013).
Scenarios C, D, and E (Table 1) utilize data input to the clustering
algorithm after fluorescence intensity was normalized to particle size (by
dividing fluorescence intensity value by light scattering signal when a
particle interacts with the diode laser beam) in order to explore the assumption that laboratory data should be treated like previously
explored ambient data sets and not logged. Scenarios B and D take into
account the logging of all parameters, producing normal distributions of all
variables (AF, particle size, three channels of fluorescence). By this process,
data values were input into the algorithm as a log(value) without separately
binning the points. For comparison, scenarios E and F explore log-spaced
distributions of size and AF, while retaining the assumption that the
fluorescence output is normally distributed. Scenario A data are neither
logged nor normalized. For comparison, scenario F represents the input
conditions that have been used frequently (e.g., Crawford et al., 2015;
Ruske et al., 2017).
Cluster validation
An important feature of HAC is that it provides clusters in an unsupervised
manner, and the user must determine the number of clusters that makes
physical sense. One useful tool to systematically determine the optimal
number of final clusters is the Calinski–Harabasz (CH) index, which uses the
interclass–intraclass distance ratio (Liu et al., 2010). For each
clustering output the CH index was calculated for cluster solutions with 1 through 10 clusters, and the solution with the highest CH value was
generally determined to be the optimal number of clusters. Figure 2 shows an
example CH value versus cluster number plot for a mixture of Aspergillus niger fungal spores mixed
with diesel soot particles. The curve suggests the optimal result to be a
two-cluster solution for this trial, as was generally the case for
investigations where two particle types were mixed before clustering. In
order to reduce the length and complexity of discussion, the analysis of results
in Sects. 4.1–4.3 was limited to using cluster products only from the
two-cluster solution. In some cases, a three-cluster solution may have produced
higher-quality results, but these cases were not investigated.
Results and discussion
The analysis of clustering quality was performed systematically and with
increasing complexity. Section 4.1 utilizes three pairs of particles types
to explore the effect of particle ratio and normalization strategies on
cluster performance. Using conclusions from this section, Sect. 4.2 then
expands the exploration to 20 additional pairs of particle types. Section 4.3 explores the effect of three different fluorescence thresholding
strategies on cluster output. Finally, Sect. 4.4 investigates the ability
of HAC analysis to separate particle types from mixed populations of
particle types.
Example of a Calinski–Harabasz index plot for the cluster
experiment with input from Aspergillus niger and diesel soot (50:50 ratio). The optimal number of
clusters is determined by the highest CH value. For Sects. 4.1–4.3 only
two-cluster solutions were analyzed.
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Investigating pre-normalization scenarios and particle input ratio
To explore the ability to separate two distinct populations of particles
from one another, three different clustering trials are presented in this
section as one-to-one matchups: (1) Aspergillus niger (fungal spores, F2) vs.
standard diesel
soot (S4), (2) Pseudomonas stutzeri (bacteria, B3) vs. standard diesel soot (S4), and
(3) Aspergillus niger (fungal spores, F2) vs. California sand (mineral dust, D12). These four
particle materials were chosen to represent key classes of coarse particles
observed in ambient air. For each trial, a subset of particles from each
material type was selected randomly for HAC analysis. The clustering process
includes (i) the evaluation of cluster performance based on particle assignment
and cluster composition and (ii) the visual representations of cluster outputs
using the particle type classification introduced by Perring et al. (2015). For each of these three trials, the clustering process
was run separately using each of the six scenarios A–F described in Table 1.
Additionally, while exploring the optimal data preprocessing scenario, the
influence that different concentration ratios of particle types could play
in the clustering output was also explored. The cluster process for each
trial was performed using four different ratios of particles in each
particle set including situations with an equal ratio and where the
concentration of each particle type was significantly mismatched. In total,
this section represents 57 individual clustering experiments (3 trials ×6 scenarios ×3 particle ratios +3 additional ratio trials) exploring three
independent input variables. The results will be utilized to explore many
more individual particle type matchups in the following sections.
The first two trials include diesel soot particles because light-absorbing
carbon aerosol is commonly observed in aerosol samples with anthropogenic
influence (Bond et al., 2013) and because it can have fluorescence
characteristics difficult to distinguish from small biological particles
(e.g., Huffman et al., 2010; Pan et al., 2012; Savage et al., 2017; Yu et
al., 2016). For example, when excited by photons with a wavelength of 280 nm, diesel soot can be misinterpreted as single bacterial cells using the
WIBS, and so we explored here whether the two particle types could be
clustered separately (Pöhlker et al., 2012). The three trials
include two examples of biological particles, both exhibiting fluorescent
properties but with different excitation–emission characteristics and with a different average particle size.
The output of the algorithm reports the particle type from which each
particle was input in order to evaluate the accuracy of the clustering. The
resulting output of each particle with an assigned cluster number is then
compared to the originating particle type to determine classification
accuracy. Figure 3 summarizes the relative accuracy of individual clustering
experiments by representing the percent of particles misclassified with
respect to known input identities (blue bar corresponding to correct
classification, red bar and overlaid value corresponding to incorrect
classification). The clustering process was generally effective for
separating particles correctly when two particle types were considered, but
results vary widely across the six scenarios. Several previous studies that
used HAC to separate particles within an ambient data set assumed that
particle fluorescence is already normally distributed (Crawford et al.,
2014, 2015; Robinson et al., 2013). As a result, these
previous studies did not normalize fluorescence data and thus used data
preparation scenario F in their clustering analysis. For comparison,
scenarios B and D were explored to test whether the clustering efficiency
would be improved or hindered by fluorescence normalization. Scenarios A and
F produced inconsistent results, with some experiments (i.e., a 50:50 ratio of
fungal spores : diesel) producing a misclassification < 1.1 %, whereas
other experiments (i.e., a 20:80 ratio of bacterial : diesel) produced a misclassification of up to 80 %. In contrast, scenarios B and D produced
consistently more accurate results. Scenario B, in particular, consistently
exhibited the most accurate classification of particles for almost every
individual experiment. No experiment involving scenario B produced a greater
than 9 % misclassification of particles, regardless of the particle input
ratio, and most experiments produced results with 0.1 %–3 % error. These
observations taken together suggest that particle fluorescence properties
may not be well described by normal distributions and that normalizing
fluorescence data prior to analysis may be more effective.
Cluster misclassification shown for three
computational combinations of fungal spores (F2), bacteria (B3), diesel soot
(S4), and mineral dust (D12). Each combination explored with respect to the ratio of input particle number using scenario B and a two-cluster solution
for each experiment. Scenario letters A–F refers to the scenarios summarized in
Table 1. Red shaded regions (and values) indicate the percent of particles
misclassified. Blue shaded regions represent the percentage of particles
correctly classified.
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Particle type stacked-category size distributions for
input and output clustering results, using FT +3σ threshold
definition. Each experiment (row) shows matchups of two particle types
computationally mixed using 50:50 ratios, scenario B, and two-cluster
solutions. Panels (a)–(f) show the properties of input particles; (g)–(l) show the properties of cluster outputs.
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The results of these experiments also highlight how important the ratio of
input particles can be. While scenario B was relatively consistent, varying
only between 0.1 % and 3.8 % error for different ratios of the fungal spore
versus diesel matchup, other experiments depended strongly on particle
ratio. It is clear that the input ratio of particle types cannot be
controlled during an ambient study, and so these results suggest that it is
important to keep the possibility of varying concentration ratios in mind
when interpreting time- or air-mass-associated changes in cluster
composition or when relaying the relative confidence in clustering results.
For the remainder of the discussion, experiments will be limited to a 50:50
ratio following scenario B. In each case the input particles are a random
subset taken from the pool of particles in the experimental data. As a
result, individual samples selected from the same experiments (i.e., Fig. 4a, e) can show slightly different average properties. In some cases (i.e.,
diesel soot; Fig. 4d) the number of particles originally analyzed was small, and so to keep the input particle ratio at 50:50, the corresponding particle
type was also limited to small numbers.
To extend the investigation of the particle input ratio, the three matchups
presented in Fig. 3 were investigated using scenario B with 1 %
bioparticles and 99 % non-bioparticles in each case. In these
experiments the bacteria : diesel soot and fungal spores : dust particles
separated relatively well (6.6 % and 13.5 % misclassification,
respectively). The fungal spores : diesel soot separation was poor, however,
because the diesel soot particles were nearly evenly split into both
clusters, and the fungal spore particles were too low in concentration to
influence the cluster properties. More investigation is needed to explore
how extreme disparities in particle ratio could negatively influence cluster
quality in real-world settings.
Misclassification of two-cluster solutions for 23 matchups of two
individual particle types (equal ratio of particle number, B
scenario, no fluorescence threshold applied) computationally combined
before clustering analysis. Misclassification calculated as the sum
percentage of particles misclassified in each cluster divided by the total
number of particles. Three biological particle types (F2, B3, P9) compared
separately to (a) nonbiological particle materials and
(b) biological particle materials. Particle number input was a
subset of the total population of particles experimentally analyzed. Bold
values show a misclassification > 15 %.
(a)
Nonbiological particle materials
Diesel
California
Arizona
Suwannee
Methyl-
Glyoxal
White
Wood
soot
sand
Test Dust
River
glyoxal +
+ amm.
t-shirt
smoke
(Soot 4)
(Dust 2)
(Dust 12)
Humic
glycine
sulfate
(Misc. 2)
(Soot 6)
Acid
aerosol
aerosol
(HULIS 2)
(Brown
(Brown
carbon 1)
carbon 3)
S4
D2
D12
H2
BC1
BC3
WT
WS
Aspergillus niger
(1)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(Fungi 2)
0.1 %
2.6 %
6.1 %
4.8 %
2.5 %
23.0 %
40.5 %
7.2 %
P. stutzeri
(2)
(10)
(11)
(12)
(13)
(14)
(15)
(Bacteria 3)
1.2 %
1.9 %
1.2 %
1.3 %
6.1 %
41.7 %
4.7 %
Phleum pratense
(16)
(17)
(Pollen 9)
22.7 %
23.2 %
(b)
Biological particle materials
S. cerevisiae
Phleum pratense
P. stutzeri
Taxus baccata
B. atrophaeus
(Fungi 4)
(Pollen 9)
(Bacteria 3)
(Pollen 5)
(Bacteria 1)
F4
P9
B3
P5
B1
Aspergillus niger
(18)
(19)
(20)
(Fungi 2)
27.9 %
36.4 %
10.3 %
P. stutzeri
(21)
(22)
(Bacteria 3)
18.3 %
65.4 %
Phelum pratense
(23)
(Pollen 9)
46.8 %
An important tool readily applied to the analysis of ambient data is the
categorization of particles into eight fluorescent particle types (Perring et
al., 2015). Thus, to further investigate the quality of cluster accuracy,
Fig. 4 shows inputs and cluster outputs from three clustering experiments
stacked as a function of fluorescence particle type and particle size.
Figure 4a, b, g, and h show the input data for Aspergillus niger
and diesel soot (Fig. 4a–b) paired with the outputs of the two-cluster
solution (Fig. 4g–h). It can be seen that both particle materials have
predominantly particle type A characteristics, meaning that they are
fluorescent only in channel FL1. The fungal material also presents roughly
one-third of AB (green) and a small minority of nonfluorescent (gray)
characteristics. The size distribution of the fungal spores peaks at ∼3 µm, whereas diesel soot peaks at ∼1 µm in size.
While not shown in this plot style, the spores exhibit moderately higher FL1
channel fluorescence, with a median of 543 ADCs, whereas diesel soot exhibits
a median of 751 ADCs in this channel (see Savage et al., 2017; Table 2). Both
particle types show almost no fluorescent characteristics in either FL2 or
FL3. In summary, the particle distributions are relatively similar in
fluorescence particle type and their differences are largely related to
particle size, so separation of these particles through Trial 1 was
hypothesized to represent a relatively challenging initial exercise. The
clustering outputs presented in Fig. 4g–h, however, visually highlight the
conclusion represented by Fig. 3, which is that the particles in this trial
separated very well. Cluster 1 was comprised predominantly of fungal
particles and presented fluorescence and size traits qualitatively similar to
the input fungal particles, whereas cluster 2 was comprised predominantly of
diesel soot particles.
Results from the 50:50 ratio of the scenario B experiments for the other
two trials are also shown in Fig. 4c, d, i, and j and Fig. 4e, f, k, and l.
In each case, the qualitative properties of the input particles are extremely
well represented by the corresponding output cluster, corroborating the
conclusion from Fig. 3 that the scenario B cases accurately separated the
particle groups investigated through these experiments. It is also important
to note here that the method of aerosolization for each particle type plays
an important role in the observed size distribution, and so results involving
laboratory particles should be interpreted with this in mind. Observed
fluorescence properties, in contrast, are expected to be conserved at a given
particle size and are intrinsically
related to particle composition.
Investigating cluster quality without fluorescence threshold
After concluding that scenario B exhibited the most consistently accurate
clustering results using two-cluster solutions from mixtures comprised of two
particle type inputs, the analysis was expanded to include a broader range of
particle types. Using 50:50 ratios of two types of input particles,
prepared using scenario B (leaving fluorescence data un-normalized and logarithmically transforming data vaules), 20 new individual experiments were
performed. The results of all 23 experiments (3 from Sect. 4.1 and 20
introduced in Sect. 4.2) are summarized in Table 2 as the percentage of
particle misclassification. These trials were chosen to represent a broad
range of individual matchups that might be expected in ambient air. Of the
original 69 types of particles analyzed by Savage et al. (2017), 14 were used
in experiments here: 8 types of nonbiological particles and 6 types of
biological particles (2 each of fungal spores, bacteria, and pollen species).
Supplement Fig. S4 from Savage et al. (2017) shows size distributions stacked
by fluorescence particle type for each of the particle species discussed.
Table 2a organizes clustering results into three rows, showing the
misclassification of F2 (Aspergillus niger fungal
spores), B3 (Pseudomonas stutzeri bacteria), and P9 (Phelum pratense pollen) particles with
respect to a variety of other particle types represented by table column. Of
the 15 cluster experiments between fungal spores or bacteria and nonbiological material, only 3 showed a
misclassification greater than 7.5 % (bold text) and 7 were less than
3 %. The three outliers were experiment 7 (F2 vs. BC3;
glyoxal + ammonium sulfate brown carbon aerosol), 8 (F2 vs. WT; white
t-shirt particles), and 14 (B3 vs. WT). Looking first at experiment 7, F2
particles show A-type fluorescence characteristics and are dominated by a
mode between 1.5 and 4 µm. BC3 particles are primarily
nonfluorescent < 1.5 µm but are primarily A-type between
1.5 and 3 µm, suggesting similar size and fluorescence properties.
The white t-shirt particles separated poorly (∼41 %
misclassification) from both the fungal spore and bacterial particles. All
three particle types (WT, F2, and B3) exhibit medium fluorescent intensity in
the FL1 channel. The poor ability to separate WT from both F2 and B3 was
surprising, however, given that WT exhibited significantly higher mean
fluorescence in each of the FL2 and FL3 channels. As first mentioned by
Savage et al. (2017), great care should be taken when interpreting
fluorescent particle results from indoor environments where increased
concentrations of bleached fibers from clothing, bedding, paper, and cleaning
products may be present.
While the results show that the fungal spores and bacterial particles investigated could generally be well
separated from most potentially interfering nonbiological species, the
results were much less successful for differentiation from pollen. P9 pollen
particles separated poorly in all experiments (versus D12, H2, or P5), with a
rate of misclassification ranging from 22 to 47 %. It is important to
keep in mind, however, that the WIBS was operated using a standard gain
setting that limits analysis of particle size to below approximately
20 µm. As a result, the WIBS is insensitive to whole pollen grains,
and so most of the particles observed during pollen experiments are small
pollen fragments. Any intact pollen grains that navigate the flow system to
be detected are likely to be binned together in the channel representing the
largest particles. Clustering results including pollen should be interpreted
accordingly. Pollen grains can fragment in ambient air as a function of
increased relative humidity (Miguel et al., 2006; Suphioglu et al., 1992;
Taylor et al., 2004), but the relative ratio of whole / fragmented
particles is hard to predict under ambient conditions. Smaller fragments can
also exhibit different fluorescent properties to whole grains (Pöhlker et
al., 2013). O'Connor et al. (2014) operated a WIBS-4 (Univ. Hertfordshire) at
a lower gain in order to improve the pollen detection efficiency, but these
results are not explored directly here.
The WIBS instrument is frequently used to differentiate between airborne
biological particles and material of nonbiological origin. A secondary goal
of differentiating more finely between types of biological aerosols is also
frequently pursued. To investigate this goal, six additional experiments
were conducted by pairing two different types of nonbiological particles
(Table 2b). In contrast to the results shown in Table 2a, the clustering
algorithm showed a generally poor ability to separate between two biological
particle types. Only one of the six experiments resulted in an error < 15 % (F2 vs. B3, 10.3 % error), whereas error for the other five
experiments ranged from 18 % to 65 %. The worst accuracy was
demonstrated by experiment 22 (B1 vs. B3) and experiment 23 (P5 vs. P9). Both
of these experiments attempted to separate between different species of a
single particle type (i.e., between two bacteria or two pollen). Overall, these results suggest that the clustering strategy
may be quite useful at aiding the differentiation of biological material
from nonbiological material but that separating more finely to quantify
differences between types of individual biological particles is
significantly more challenging and not likely to be possible in most
situations.
Further exploration of two-cluster solutions for the 10
matchups of two individual particle types shown in Table 2 with
a misclassification > 15 %. Each matchup is shown using three
separate fluorescence threshold strategies in advance of particle input into
the cluster algorithm: (I) all particles included (no fluorescence threshold),
(II) particles with fluorescence intensity < FT +3σ
removed, and (III) particles with fluorescence intensity < FT +9σ removed. (a) Particle misclassification. (b) Total particle
number used for clustering experiment.
(a)
Percent misclassified
Bio + Nonbio
Input
(7)
(8)
(14)
(16)
(17)
F2 + BC3
F2 + WT
B3 + WT
P9 + D12
P9 + H2
(I) All particles
23.0 %
40.5 %
41.7 %
22.7 %
23.2 %
(II) Fluor. > FT + 3σ
10.3 %
36.2 %
24.3 %
19.3 %
3.4 %
(III) Fluor. > FT + 9σ
41.4 %
32.6 %
31.8 %
45.3 %
14.0 %
Bio + Bio
Input
(18)
(19)
(21)
(22)
(23)
F2 + F4
F2 + P9
B3 + P9
B1 + B3
P9 + P5
(I) All particles
27.9 %
36.4 %
18.8 %
65.4 %
46.8 %
(II) Fluor. > FT + 3σ
13.3 %
31.0 %
20.0 %
77.5 %
24.9 %
(III) Fluor. > FT + 9σ
29.0 %
28.6 %
29.0 %
66.7 %
33.9 %
(b)
Number of particles
Bio + Nonbio
Input
(7)
(8)
(14)
(16)
(17)
F2 + BC3
F2 + WT
B3 + WT
P9 + D12
P9 + H2
(I) All particles
1959
565
565
10 359
8902
(II) Fluor. > FT + 3σ
1000
393
393
171
207
(III) Fluor. > FT + 9σ
471
319
319
38
37
Bio + Bio
Input
(18)
(19)
(21)
(22)
(23)
F2 + F4
F2 + P9
B3 + P9
B1 + B3
P9 + P5
(I) All particles
10 000
8900
10 000
10 000
10 000
(II) Fluor. > FT + 3σ
9600
8500
9800
10 000
10 000
(III) Fluor. > FT + 9σ
9200
8100
9700
10 000
7895
Investigating the impact of fluorescence thresholding strategy on
cluster quality
In previously published studies, removing particles from clustering analysis
that exhibited a particle fluorescence intensity below the threshold (i.e.,
nonfluorescent) or at the saturating point improved the efficiency of
clustering (Crawford et al., 2015; Ruske et al., 2017). In Sects. 4.1–4.2, particles with either of these characteristics were left in the
analysis to prevent the underestimation of the particles clustered. In this
section, however, we investigated whether removing nonfluorescent particles
could improve cluster accuracy for the experiments that performed poorly in
Sect. 4.2. Of the 23 trials represented in Table 2, 10 experiments
exhibited a 15 % or greater misclassification and were subjected to further
analysis in order to investigate whether using a more discriminating
fluorescence thresholding strategy could improve cluster results. In all 10 cases, fluorescence saturating particles were retained, and three separate
thresholding conditions were compared by (i) keeping all nonfluorescent
and saturating particles, (ii) removing nonfluorescent particles by
applying a fluorescence threshold of FT baseline +3σ, and (iii) removing nonfluorescent particles by applying a fluorescence threshold
of FT baseline +9σ. Savage et al. (2017) showed evidence that applying a FT +9σ improved WIBS results by removing a higher fraction of
nonbiological material from analysis than the more commonly
used FT +3σ, without negatively impacting observations of
biological particles. Table 3 shows the percentage of particles
misclassified in each of the three scenarios investigated here (Table 3a) as
well as the number of particles subjected to the clustering algorithm (Table 3b).
Each scenario, with exception of the B3 vs. B9 experiment 21, shows a
decrease in particle misclassification from scenario I (no fluorescence
threshold applied) to scenario II (FT +3σ). In contrast, 8 of
the 10 scenarios increase in particle misclassification when raising the
fluorescence threshold from 3σ (II) to 9σ (III). The
exceptions to this trend are experiments 8 (F2 vs. WT) and 19 (F2 vs. P9),
which show a nominal improvement in error (2 %–4 % reduction) with an increased
threshold. We hypothesize that the 9σ results degrade, in most
cases, because the threshold becomes high enough that most weakly
fluorescing particles have been removed from analysis. This reduces the
ability of the cluster to group into low- and high-fluorescence categories,
and so remaining particles are separated less efficiently. Secondly,
removing particles at higher fluorescence thresholds leads to increasingly
poor counting statistics, as represented in Table 3b by the number of
particles included in each experiment. Overall, these results suggest that
inputting particles into the clustering analysis with at least a nominal
fluorescence threshold (i.e., FT +3σ) can improve the clustering
results in many cases; however, increasing the threshold further may
decrease cluster quality.
Particle fraction for each type and total particle
number used as inputs for simulated mixtures. PBAP: primary biological aerosol particle.
F2
B3
P9
S4
D12
H2
BC1
WS
WT
Mixture
Mixture
Asp. niger
P. stutzeri
Phleum
Diesel
AZ Test
Suwannee
Brown
Wood
White
Total
number
name
(fungi)
(bacteria)
pratense
soot
Dust
River
carbon 1
smoke
t-shirt
particle
(pollen)
Humic
number
Acid
1
Four-comp. A
25 %
25 %
25 %
25 %
680
2
Four-comp. B
25 %
25 %
25 %
25 %
680
3
High PBAP
25 %
25 %
20 %
20 %
10 %
850
4
Low PBAP
12.5 %
12.5 %
15 %
15 %
15 %
15 %
15 %
1134
5
Pollen
30 %
10 %
20 %
20 %
10 %
10 %
850
6
Indoor air
20 %
20 %
20 %
20 %
20 %
850
Investigating the capability to separate particles in
simulations of complex mixtures
To this point, our investigation has focused on a variety of individual
matchups between two distinct particle types. To better simulate real-world
scenarios, we computationally simulated six mixtures of particles by pooling
existing WIBS data from selected particle types at prescribed ratios. Each
simulated mixture was assembled to roughly represent a different
hypothetical mixture of particles that might be expected. Also, the
particles in each simulated mixture are assumed to be so diluted that any
agglomeration is negligible. Table 4 provides an overview of the percentage
of each particle type included as well as the total number of particles in
the mixture. Mixtures 1 and 2 were simulated arbitrarily to test if a
minority (25 %) of one type of fungal spores (F2) could be separated from
a majority (75 %) of a mixture of three different nonbiological
materials. Mixtures 3 and 4 synthesized arbitrary mixtures of two types of
bioaerosol (F2 and B3) with three or five types of nonbiological particles,
respectively. Mixture 5 was simulated to examine the separation of pollen
(P9) from a set of five nonbiological particles. Mixture 6 was simulated to
be similar to an indoor environment that might have a mixture of biological
particles (F2 and B3) with nonbiological materials, including bleached
fibers (WT). These mixtures are not intended to closely mimic any set of
individual ambient conditions but are rather used as very rough simulations for discussion and to prompt discussion related to future experiments
within the community. In a real-world sampling environment, one would also
expect a high concentration of nonfluorescent particles (e.g., most
organic aerosols, sea salt, dusts), but these were generally not sampled as
a part of the Savage et al. (2017) study, which
focused on fluorescent particles. As a result, relatively nonfluorescent
particles like D12 and H2 were included here as “fillers” in most mixtures
as surrogates for other types of nonfluorescent particles. Clustering
analysis was performed using the ratios listed in Table 4, the B scenario of
pre-normalization conditions, and the filtering of nonfluorescent particles below
the FT +3σ threshold. In all cases, the number of clusters
retrieved after HAC was pre-defined to be the same as the number of particle
types input.
Overview
of computationally simulated mixtures. Six
mixtures shown as groups of rows, with input particle fractions defined in
Table 4. Panel (a) shows the particle number retrieved by each
individual cluster (horizontal rows) categorized by each input particle
type (vertical columns). Panel (b) shows the particle number
categorized and grouped by particle classes (i.e., nonbiological and
biological). Panel (c) shows the misclassification of groups of
particles. Colors: light green – fungal spores; blue – bacteria; pink – pollen; dark green – grouped biologically; brown – all nonbiological.
![]()
Cluster results from all six mixtures are summarized in Fig. 5. Figure 5a shows the number of particles from each type assigned to each
cluster, and panels (b) and (c) show results grouped by general particle
classification (brown for nonbiological and dark green for biological).
Overall, the ability of the HAC analysis to separate the biological
particles from the nonbiological particles was high. In some cases, the
quality of separation of one or two biological species from a mixture of
nonbiological materials was even higher than the two-material matchups shown
in Sects. 4.1–4.3. The two four-component mixtures showed a 22.4 % and
14.8 % misclassification of fungal spores. In both cases, a small fraction
of each of the nonbiological materials was mixed into the spore cluster,
whereas almost none (1.5 % and 0.6 %) of the spores were incorrectly
mixed into the sum of the nonbiological clusters.
Mixtures 3 and 4 showed a similar misclassification for fungal spores
(11.9 % and 13.8 %, respectively), whereas the bacterial particles
clustered with amazing quality. For Mixture 3, no particles other than
bacterial particles were grouped into Cluster 1, and only 16 of 213
bacterial particles were assigned to other clusters. For Mixture 4, 135 of
137 particles in Cluster 6 were bacterial in origin and 135 of 142 bacterial
particles were assigned to the cluster. The combination of fungal and
bacterial particles in mixtures 3 and 4 resulted in a total of 5.0 % and
5.3 % misclassification of all biological particles.
In contrast to the poor separation of pollen from other particle types
discussed in Sect. 4.2, Mixture 5 showed a higher quality of separation
between pollen (9.4 % misclassified) and the sum of five other
nonbiological particle types. Lastly, the mixture designed to roughly mimic
an indoor environment included white t-shirt particles. In this mixture the
WT particles confounded the spore separation, but the bacterial separation
was nearly flawless.
Another surprising observation from the analysis of these simulated mixtures
was that the diesel soot particles (mixtures 1, 2, 4, and 5) separated into
their own cluster in almost all cases with very high quality (1.8 %,
2.9 %, 0.6 %, and 9.4 %, respectively, of diesel soot particles
misclassified into a different cluster). The quality of the separation of
bacterial particles and diesel soot (Mixture 4) was especially good,
given the qualitative similarity of the two particle populations. For
example, size distributions of each particle type show primarily A-type
particles with similar mean fluorescent intensity values in FL1, FL2, and
FL3 (Savage et al., 2017).
Conclusions
The application of results from a recent set of systematic laboratory
experiments (Savage et al., 2017) by the commonly used hierarchical
agglomerative clustering analysis helps to reveal areas where the tool can be
used well and other areas where it struggles. First (Sect. 4.1) it was
observed that differing ratios of particle input into the clustering
algorithm can produce dramatically different results. It will be important
for anyone applying HAC to ambient particle sets, where particle ratios are
not independently verified, to interpret results somewhat loosely. In
Sect. 4.1 the clustering quality of scenario B, where fluorescence intensity
was not normalized to particle size and where all variables were input in
logarithmic space as log(value), was determined to consistently demonstrate
the highest-quality results. Further, the ability of the HAC analysis to
separate between two groups of individual particle types using no
fluorescence threshold (Sect. 4.2) and comparing three separate threshold
strategies (Sect. 4.3) was shown to be relatively high in many cases but
confounded in others. Lastly, Sect. 4.4 explored the ability of HAC analysis
to separate biological components from more complex mixtures of four to seven
types of input particles.
A standard fluorescence threshold of FT +3σ has been commonly
applied during WIBS analysis to separate between fluorescent and
nonfluorescent particles. Savage et al. (2017) concluded that the application
of a more aggressive threshold strategy (FT +9σ) could help
discriminate between biological and nonbiological particles more successfully
in many circumstances; however, certain types of interfering, nonbiological
particle species can still confound WIBS analysis, irrespective of the threshold. Here we have investigated an
orthogonal strategy to separate particle types by subjecting particles to HAC
computer analysis. By comparing the results of the HAC analysis with raw
separation based on fluorescence thresholding alone, the HAC analysis can
clearly increase the quality of differentiation. Interestingly, while Savage
et al. (2017) reported that the FT +9σ strategy helped improved
differentiation, using the same threshold in conjunction with HAC analysis
actually degraded results. We therefore conclude that if HAC analysis is to
be performed, the standard FT +3σ threshold is likely to produce
the highest-quality results; however, if HAC is not to be applied, the FT +9σ threshold is probably a better choice to enable the investigation
of biological particles while computationally filtering nonbiological
particles.
The overall message here is that HAC can be applied successfully to
differentiate particle types sampled by WIBS instruments and that it is most
successful at separating biological species (i.e., fungal spores and
bacteria) from nonbiological particles. In all cases the HAC method allows
the separation of particles at least at the order-of-magnitude level and
often with a misclassification of < 5 %. As mentioned by Savage
et al. (2017), however, it should always be kept in mind that different
instruments may produce slightly different signals due to physical
differences between instruments (i.e., fluorescence calibration, tuning, and
detector gain sensitivity) and between calibration strategies (Könemann
et al., 2018; Robinson et al., 2017). Results here are also generally
extendable to other UV-LIF instruments, whether they offer single or many
channels of emission spectral resolution, in that the methods of particle
pre-preparation and the impact of the particle number ratio are likely to
relay similar effects to the clustering strategy. Subtle differences in
particles observed in a real-world environment will also complicate HAC analysis or the extension of results
presented here. The UV-LIF community is encouraged to continue laboratory
investigations, including a detailed interrogation of clustering analytical
techniques, to further understand limitations to better differentiating
between particles.