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Journal of Occupational and Environmental Hygiene,2:577–585
ISSN: 1545-9624 print / 1545-9632 online
Copyright
c
2005 JOEH, LLC
DOI: 10.1080/15459620500330583
Respiratory Protection Provided by N95 Filtering Facepiece
Respirators Against Airborne Dust and Microorganisms in
Agricultural Farms
Shu-An Lee, Atin Adhikari, Sergey A. Grinshpun, Roy McKay,
Rakesh Shukla, Haoyue Li Zeigler, and Tiina Reponen
University of Cincinnati, Department of Environmental Health, Cincinnati, Ohio
Anew system was used to determine the workplace pro-
tection factors (WPF) for dust and bioaerosols in agricultural
environments. The field study was performed with a subject
wearing an N95 filtering facepiece respirator while performing
animal feeding, grain harvesting and unloading, and routine
investigation of facilities. As expected, the geometric means
(GM) of the WPFs increased with increasing particle size
ranging from 21 for 0.7–1 µm particles to 270 for 5–10 µm
particles (p < 0.001). The WPF for total culturable fungi
(GM = 35) was significantly greater than for total culturable
bacteria (GM = 9) (p = 0.01). Among the different microor-
ganism groups, the WPFs of Cladosporium, culturable fungi,
and total fungi were significantly correlated with the WPFs of
particles of the same sizes. As compared with the WPFs for dust
particles, the WPFs for bioaerosols were found more frequently
below 10, which is a recommended assigned protection factor
(APF) for N95 filtering facepiece respirators. More than 50%
of the WPFs for microorganisms (mean aerodynamic diameter
<5 µm) were less than the proposed APF of 10. Even lower
WPFs were calculated after correcting for dead space and lung
deposition. Thus, the APF of 10 for N95 filtering facepiece
respirators seems inadequate against microorganisms (mean
aerodynamic size <5 µm). These results provide useful pilot
data to establish guidelines for respiratory protection against
airborne dust and microorganisms on agricultural farms.
The method is a promising tool for further epidemiological
and intervention studies in agricultural and other similar
occupational and nonoccupational environments.
Keywords agricultural farms, airborne dust, airborne microorgan-
isms, respirators, workplace protection factor, WPF
Address correspondence to: Tiina Reponen, University of
Cincinnati, Department of Environmental Health, P.O. Box 670056,
Cincinnati, OH 45267-0056; e-mail: Tiina.Reponen@uc.edu.
F
armers are at high risk of exposure to airborne dust
and microorganisms. These exposures can cause
respiratory diseases.
(1−4)
According to the U.S.
Bureau of Labor Statistics,
(5)
around 13 million
people gain some earnings from farming in the United States.
Of these, 6 million people are family members living and
working on the farms.
The application of engineering controls for preventing
farmers and their family members from exposure to airborne
particles, including microorganisms, is limited because of
the diverse nature of the dust and bioaerosol sources in
agricultural settings. Personal protection by respirators is
often the only feasible option for farmers to minimize their
exposure to airborne dust and microorganisms. However, the
Respiratory Protection Standard (29 CFR Part 1910.134) is not
applicable to many agricultural environments,
(6)
and there is
limited guidance for respiratory protection against biological
particles.
Respirators used by agricultural workers should be certified
by NIOSH in accordance with 42 CFR Part 84.
(7)
Under these
certification guidelines, N95 filtering facepiece respirators
have the filtration efficiency of at least 95% for the most
penetrative particle size of 0.3 µm. These respirators have
been recommended by the Centers for Disease Control and
Prevention (CDC) for health care workers to protect them
from infectious aerosols, which can cause diseases such as
SARS (severe acute respiratory syndrome) and tuberculosis.
Qian et al.
(8)
found that the filtration efficiency of some
N95 filtering facepiece respirators is 99.5% or higher for
the NaCl and PSL particles in the size range of 0.75 to
1 µmaswell as for Bacillus subtilis (the mean aerodynamic
diameter = 0.8 µm) and Bacillus megatherium (the mean
aerodynamic diameter = 1.2 µm). The aerodynamic sizes of
most bacteria and fungal spores are between 0.7 and 10 µm
(9,10)
and thus the filtration efficiency by N95 filtering facepiece
respirators should be even higher than 99.5%. However, these
contaminants enter the respirator cavity not only through the
filter material but also through the faceseal leaks, which is
the primary pathway for contaminants to penetrate inside
negative pressure respirators (especially those that have poor
respirator fit). N95 filtering facepiece respirators are relatively
comfortable for workers because they are lightweight and
do not obstruct vision or hinder communication as much as
Journal of Occupational and Environmental Hygiene November 2005 577
elastomeric respirators. Therefore, in the present study, the
N95 filtering facepiece respirator was investigated for its field
performance in protecting farmers against airborne dust and
microorganisms.
The workplace protection factor (WPF) is commonly used
to assess respirator performance in the workplace. The WPF,
which is defined as a ratio of the particle concentration outside
the respirator to that inside the respirator, is a measure of the
protection provided in the workplace under the conditions
of that workplace, by a properly selected, fit tested, and
functioning respirator that is correctly worn and used.
(7,11)
In our previous study,
(12)
we developed a new personal
sampling system for determining the protection provided by
respirators against airborne dust and microorganisms. This
personal system was tested for its capability of measuring and
reflecting the nearly instant changes in the aerosol concen-
trations inside and outside the respirator through laboratory
and field evaluation.
(13)
Both laboratory and field studies
showed this system to be a promising tool in determining
the protection provided by respirators against particles and
microorganisms. The objective of the current study was to use
the newly developed personal sampling system in agricultural
environments to determine the protection provided to farmers
by N95 filtering facepiece respirators against airborne dust
and microorganisms of different particle size ranges. Our
concurrent article
(14)
will characterize exposures, whereas this
article focuses on respiratory protection.
MATERIALS AND METHODS
Field Study Design
Field samples were collected using a personal sampling
system previously described in detail by Lee et al.
(12,13)
In
short, the sampling system consisted of two sampling lines
(in-facepiece and ambient sampling lines) that were used to
collect particle samples inside and outside the respirator. N95
filtering facepiece respirators (model 8210, 8110S; 3M, St.
Paul, Minn.) were used in the field experiments. Airborne
dust and microorganisms were sampled through the sampling
probes at a flow rate of 10 L/min and drawn through Tygon
tubing to a metal sampling chamber at the end of each
sampling line. A portion of each aerosol flow (2.8 L/min) was
sampled from the chamber into an optical particle counter
(OPC, model HHPC-6; ARTI Inc., Grants Pass, Ore.) for
dust measurement. The rest of the aerosol flow (7.2 L/min)
passed through a filter sampler that collected the airborne
microorganisms.
The selected flow rate of 10 L/min is five times the conven-
tional in-facepiece sampling flow rate used for fit testing. As
described in Lee et al.,
(12)
a higher flow rate was selected to
decrease the respirator purge time and the potential sampling
bias for nonhomogenous distributions of the particle concen-
tration inside the respirator. In addition, the high flow rate
decreases the detection limit of particle measurements when
measuring for a specific sampling period, which is especially
important for evaluating the respirator performance against
low concentrations of airborne microorganisms. This high
flow rate, however, may lead to the overestimation of particle
penetration into the mask particularly at low respiration flow
rate.
Our field measurements were conducted in six farms—three
types of animal confinements (swine, poultry, and dairy), and
three grain farms. Detail information on farming activities and
farm characteristics, as well as on the methods for enumeration
of airborne dust and microorganisms, are presented in Lee
et al.
(14)
All subjects recruited in the study had to pass the medical
clearance evaluation and fit test before participating in field
testing. The medical clearance evaluation was conducted using
the questionnaire, specified in OSHA standard 1910.134,
Appendix C.
(6)
The medical clearance was authorized by
a licensed physician. Before starting the field test, subjects
signed an Institutional Review Board consent form, where the
possible risks of the field test were addressed. All study subjects
were required not to have beard or stubble on their face and
not to smoke 1 hour before the test.
The respirator fit test was performed once for each subject
prior to his or her involvement in the field testing. Before
fit testing, each subject was trained and instructed to wear
the respirator properly. The instructions followed the man-
ufacturer’s guidance on the use of the respirator. Fit testing
was conducted with a TSI Portacount Plus in connection with
N95 companion (TSI, Inc., St. Paul, Minn.) in compliance
with the 6-exercise protocol.
(15)
With the quantitative fit
test, a fit factor of 100 or above constituted a pass. The
subject then donned the respirator equipped with the personal
sampling system. In each farming environment, one to four
subjects were involved in the experiment that lasted for 30
to 60 min. The testing time covered the time it took the
subject to complete the specific work task under investigation.
Subjects were recruited primarily from agricultural farms,
while students and staff of the University of Cincinnati also
participated.
Correction on WPF Data Based on the Respirator
Dead Space and Lung Retention
Several studies have shown that respirator dead space and
lung retention decrease the concentration inside the respirator
during inhalation, resulting in the overstating of the WPF.
(16,17)
Hinds and Bellin
(16)
developed a model to predict the average
true concentration inside the respirator after accounting for
the effects of lung retention and respirator dead space. In their
study, the ratio of an average full breathing cycle concentration
to an average inhalation concentration (C
full
/C
in
)was related
to the ratio of the respirator dead volume to the tidal volume
(V
ds
/V
t
). The association was described in detail for five values
of fractional particle depositions in the respiratory tract (F
dep
)
in the absence of faceseal leakages. Based on the information
obtained for V
ds
/V
t
and F
dep
in our study, the ratio of C
full
/C
in
can be interpolated from a figure presented in Hinds and
Bellin’s paper.
(16)
Thus, the corrected WPF (WPF
corr
) can be
578 Journal of Occupational and Environmental Hygiene November 2005
calculated as following:
(16)
WPF
corr
= WPF ×
C
full
C
in
(1)
where the WPF value is measured during the full breathing
cycle.
To use this model, information is needed on the respirator
dead space volume, tidal volume, and fractional deposition
of particles in the respiratory tract. For an N95 filtering
facepiece respirator, the respirator dead volume was measured
by immersing a human face into an N95 respirator filled with
water and measuring the remaining water volume inside the
respirator. The average of three repeats was of 123 mL. Tidal
volume of 1250 mL was selected to represent an adult male
performing light work.
(18)
The respiratory deposition of particles was calculated using
an existing computer-based deposition model.
(18)
These cal-
culations were performed separately for each microorganism
group/species and for dust particles in the five OPC particle
size classes. All of the physiological data, which were required
for the respiratory deposition model, were specified for an
average height of American adult male (176 cm) under light
workload.
(18)
Data Analysis
The data analysis was performed by analysis of variance
(ANOVA), t-test, and correlation model by Statistical Analysis
System (SAS) version 8.0 (SAS Institute Inc., Cary, N.C.).
P-values of <0.05 were considered significant. Intra- and
intersubject variability in WPFs was investigated by repeated
measure analysis using PROC MIXED procedure in SAS.
Two different models were considered for both dust and
microorganisms. The first model was one-way random effect
model used to examine the between-subject and within-subject
variability without including the covariate of particle size and
microbial type in the model. The second model was a two-
factor mixed-effect model, in which one additional covariate
(particle size for nonbiological particles, and microbial type
for biological particles) was included. Particle size had two
levels: 0.7–2 µm and 2–10 µm, and microbial type had two
levels: culturable fungi and culturable bacteria.
The between-subject and within-subject variability were es-
timated by the restricted maximum likelihood (REML) method
due to the unbalanced nature of data. In addition, the difference
in mean WPFs among five particle sizes as well as among
the predominant fungal spores were examined by ANOVA
followed by pair-wise comparison using Tukey’s studentized
range (HSD) test. The t-test was used to examine the difference
in the protection factors for biological and nonbiological
particles, specifically comparisons between culturable fungi
and culturable bacteria and comparisons between two particle
size ranges: 0.7–2 µm (bacteria) versus 2–10 µm (fungi). The
correlation coefficient was obtained to examine the association
between the WPF for airborne microorganisms and the WPF
for dust of similar particle sizes. All WPF data used for
statistical analyses were log-transformed to achieve normal
distribution. When the concentration inside the respirator was
undetectable, one-half of the detection limit was used to
calculate the WPF.
RESULTS AND DISCUSSION
F
igure 1 presents the percentile and mean values of the
WPFs, determined as a ratio of the concentration outside
the respirator to that inside the respirator. The WPFs provided
by N95 filtering facepiece respirators against airborne dust
were found to be associated with the particle size. The
geometric means (GM) of the WPFs were 24 for the particles in
the range of 0.7 to 2 µm and 75 for the particles of 2 to 10 µm.
With specific size fractions, GM = 21 for 0.7–1 µm particles,
28 for 1–2 µm particles, 51 for 2–3 µm particles, 115 for 3–5
µm particles, and 270 for 5–10 µm particles. The difference
in WPFs for particles in the five size classes was statistically
significant (p < 0.001). The WPF for the particle size fraction
of 2–10 µm, representing the size of most airborne fungi, was
significantly higher than that for 0.7–2 µm, representing the
size of most bacteria (p = 0.01). Correspondingly, the WPF for
total culturable fungi (GM = 35) was significantly greater than
for total culturable bacteria (GM = 9; p = 0.01).
For the most common fungal genera or groups, the geomet-
ric means of the WPFs were 5 for Aspergillus/Penicillium,6
for Ascospores, 15 for Basidiospores, 68 for Cladosporium,
15 for smut spores, 15 for Epicoccum, and 22 for Alternaria.
Their corresponding calculated mean aerodynamic sizes were
3.7, 5.6, 6.8, 8.1, 9.7, 14.5, and 18.9 µm.
(14)
Thus, similarly to
the situation with nonbiological particles, the WPF increased
with an increase in the microorganism size with the exception
of Cladosporium.
When the individual fungal genera and groups were inves-
tigated, Cladosporium had a wide range of WPF values com-
pared with other fungi. The large variability in the spore size
of Cladosporium can explain this phenomenon. The physical
size and aerodynamic size of Cladosporium cladosporioides
are 3.6 ± 0.7 µm and 1.8 ± 0.7 µm, respectively, as reported
by Reponen et al.
(19)
The size of Cladosporium spp. measured
in our study ranged from 7.8–16.6 µminlength and 4.3–
9.2 µminwidth, while Ellis
(20)
reported wider ranges of
the spore size (3–40 µm [in length] × 2–13 µm [in width])
that reflect different species of Cladosporium.Inaddition, the
agglomeration of the Cladosporium was observed under the
microscope for the air samples. The abovementioned factors
were attributed to a decrease in the penetration of Cladospo-
rium through the faceseal leaks and a greater variation in the
WPFs.
Fungal spores as large as Cladosporium are not able to
remain suspended in the air for a long time. Most of them
will settle down on the ground before they reach the sampling
probe. The concentration of spores in the air may not rise
to considerable levels as long as there is no continuous
aerosolization source in the environment. As reported in our
concurrent study,
(14)
most of the concentrations of individual
fungal genera and groups outside the respirator were close to
Journal of Occupational and Environmental Hygiene November 2005 579
the detection limit in animal confinements. This caused the
concentration of these spores inside the respirator to remain
below detection limit in several cases. In these cases, the WPFs
were calculated by using one-half of the detection limit for the
concentration inside the respirator. This might introduce a bias
in estimating the respiratory protection against fungal spores
as large as Cladosporium.
In addition, if the leak size is close to particle size, the
shape of the fungal spores, as well as the shape and size of
faceseal leaks, might affect the penetration of spores through
the faceseal leaks. For example, nonspherical fungal spores,
such as Cladosporium, penetrate differently when the large
end of a spore encounters a small faceseal leak, compared with
when it encounters a large faceseal leak. Likewise, penetration
is different when the large end of a spore encounters a slit
shape faceseal leak compared with a circular faceseal leak.
FIGURE 1. Percentile and mean values of workplace protection factor (without correction for respirator dead space and lung retention)
against dust and microorganisms. The boxplot shows the following: • geometric mean; vertical lines from left represent 5%, 25%, 50%, 75%,
95% percentiles; n represents the number of observations; n
∗
represents number of those observations, in which the in-facepiece concentrations
were below LOD. Legend: FF = fit factor; APF = assigned protection factor.
Thus, large variation in the WPF was expected for nonspherical
fungal spores.
Figure 1 also shows the proposed assigned protection factor
(APF) and the pass/fail criterion for N95 filtering facepiece
respirators, which are 10
(21)
and 100,
(6)
respectively, for N95
filtering facepiece respirators. Among 19 observations, 68%
of the WPFs for particles in the lowest size range of 0.7–1 µm
were above 10, whereas for large particles, this percentage
was greater. However, more than 50% of the WPFs for
microorganisms, such as culturable bacteria (62%), cultur-
able actinomycetes (64%), Aspergillus/Penicillium (65%), and
Ascospores (64%), were below 10. The mean aerodynamic
diameter of these microorganisms was estimated to be below
5 µm. Most of the WPF values for larger fungal spores
were higher as discussed above: 59% of smut spores, 78%
of Alternaria, and 67% of Epicoccum in WPF values were
580 Journal of Occupational and Environmental Hygiene November 2005
above 10. Although the WPF for dust and microorganisms
showed similar increasing trend with increasing particle size,
the WPF for particles were found to be higher than that for
microorganisms of the same size range. This might be due
to differences in the particle losses occurring in the faceseal
leaks due to different shape and density of biological and
nonbiological particles.
Another reason could be related to the measurement bias of
the OPC in size-selective count of dust particles. The optical
particle counter operates by projecting light on particles and
detecting light scattering from particles; thus, factors such
as shape and color of particles interfering light scattering
can affect the instrumental measurement on particles. When
the air samples were analyzed under the microscope, dust
particles with irregular shape and different colors were ob-
served. Unlike the dust particles, fungal spores have close-
to-regular shapes. The difference in reflective index and
shape of the dust particles is expected to cause significant
variability of the measured particle sizes and number con-
centrations in a specific environment when using the OPC.
Furthermore, the irregular shape of dust particles increases
particle losses through the faceseal leaks due to the interception
mechanism.
The density of particles may also play a role. The aerody-
namic sizes of dust particles and fungal spores were calculated
based on the assumption that ρ = 1 g/cm
3
.However, the density
of dust particles, such as sand and clay, can be higher than
1 g/cm
3
whereas for many fungal spores the density is smaller
than 1 g/cm
3
.
(19)
This is likely to result in the underestimation
of the aerodynamic size of dust particles and the overestimation
TABLE I. WPF Correlations
Dust Particle Size Fractions Within the 0.7–10 µm Range
Five Fractions Two Fractions
Microorganism 0.7–1 µm 1–2 µm 2–3 µm 3–5 µm 5–10 µm 0.7–2 µm 2–10 µm
Total
0.7–10 µm
Aspergillius/Penicillium
A
(n = 17) 0.34 0.43 0.46 0.37 0.35
B
0.37 0.41 0.37
Ascospores
A
(n = 12) 0.05 0.23 0.19 0.03 0.14
B
0.13 0.17 0.21
Basidiospores
A
(n = 7) 0.36 0.38 0.15 −0.19 −0.08
B
0.39 0.15 0.44
Cladosporium
A
(n = 15) 0.58 0.58 0.57 0.52 0.61
B
0.59 0.57 0.61
(p = 0.02) (p = 0.02) (p = 0.03) (p = 0.05) (p = 0.02) (p = 0.02) (p = 0.03) (p = 0.02)
Smut spores
A
(n = 14) 0.05 0.06 −0.05 −0.22 −0.22
B
0.06 −0.07 −0.09
Alternaria
A
(n = 8) 0.13 0.22 0.28 0.28 0.29 0.17 0.32 0.28
Epicoccum
A
(n = 12) −0.24 −0.28 −0.30 −0.33 −0.34 −0.25 −0.29 −0.22
Total fungal
spores
A
(n = 18)
0.40 0.47
(p = 0.05)
0.50
(p = 0.04)
0.44 0.51
B
(p = 0.04)
0.44 0.50
(p = 0.03)
0.51
(p = 0.03)
Culturable fungal
spores (n = 18)
0.49
(p = 0.04)
0.54
(p = 0.02)
0.57
(p = 0.01)
0.56
(p = 0.02)
0.74
B
(p = 0.001)
0.52
(p = 0.03)
0.61
(p = 0.01)
0.62
(p = 0.01)
Culturable bacteria (n = 18) 0.17 0.23 0.20 0.09 0.11
B
0.20 0.19 0.25
Notes: Values denote Pearson correlation coefficients (R) for log-transformed WPFs. Significant correlations in bold;anentry in the parentheses indicates the p
values; n = number of observations.
A
Based on the microscopic analysis.
B
For some microorganism, one observation was lost.
of the aerodynamic size of fungal spores. Following this logic,
the aerodynamic sizes of dust particles may be underestimated
whereas those for fungal spores may be overestimated. When
the physical sizes of dust particles and fungal spores are about
the same and their Stokes numbers are close to 1, even small
variation in the density of particles can have a pronounced
effect on the dust particle losses in the faceseal leaks due to the
impaction mechanism. The concentration of the dust particles
inside the respirator was lower than that of the fungal spores,
resulting in the higher protection factor. These findings deserve
further research. Since there are no OSHA required guide-
lines for respiratory protection against biaerosols, and OSHA
has proposed to change the APF for filtering facepieces,
(21)
the results obtained in this study provide important pre-
liminary information to consider for respiratory protection
against airborne dust and microorganisms in agricultural
farms.
Nearly all the WPF studies used to justify the APF = 10
for half-mask respirators have involved particulate contami-
nants, and many of these studies have been done with large
particles. However, large particles, which comprise most of
the total mass, were found to be less penetrative than small
ones. Thus, by determining the total mass concentration
inside and outside respirator, one may lead to underestimate
the WPF values for small particles, which would result in
the overestimation of the APF values. Our data show that the
particle size should be taken into account when assigning APF
values.
Since the WPF for both airborne dust and microorgan-
isms was found to be associated with the particle size, we
Journal of Occupational and Environmental Hygiene November 2005 581
investigated whether the WPF of airborne microorganisms
and the WPF of dust of the same size range correlate with
each other. Table I shows the correlation between the WPF of
dust and the WPF of fungal spores for predominant groups
and genera. The WPF for Cladosporium, total fungi, and
culturable fungi showed a significant association with the WPF
for total dust (0.7–10 µm) as well as with the WPF of dust
in the corresponding size range: 5–10 µm for Cladosporium
(r = 0.61, p = 0.02), 2–10 µm for total fungal spores (r = 0.50,
p = 0.03) and culturable fungal spores (r = 0.61, p = 0.01).
Although the WPF of Aspergillus/Penicillium did not signif-
icantly correlate with the WPF of dust, the best correlation
between the WPF for Aspergillus/Penicillium and dust was
observed with the particles in the size range of 2–3 and 3–5
µm, which coincides with the size of Aspergillus/Penicillium
spores. The WPFs obtained for other microorganisms were
found to have much lower correlation with those obtained for
the particles.
As mentioned above, the variation in the shape and re-
flection index of nonbiological particles may play a role in
the measurement of their size and may explain the poor
correlation between the WPF of airborne microorganisms and
the WPF of dust. In addition, the mean aerodynamic size of
Alternaria (14.5 µm) and Epicoccum (18.9 µm) were greater
than 10 µm, which exceeded the upper limit of the particle
sizes that the OPC can measure at about 10% efficiency. Thus,
for further study involving these two large fungi or similar
ones, the OPC should be customized to measure particle sizes
up to 20 µm. Moreover, the sampling losses through the
sampling line should be carefully addressed for these large
particles.
Figure 2 presents the regression plots for the associations
that were found to be significant: WPF of Cladosporium,
total fungi, and culturable fungi vs. the WPF of dust particles
for the corresponding size range. The equations provided
in Figure 2 can be used to estimate the WPF of these
microorganisms when only dust measurement is performed.
It means the WPFs of microbes can be obtained by import-
ing the WPFs of particles of the same aerodynamic size
in the equation. Considerable time and expense could be
saved for the microbiological analysis. However, as we found
significant correlations for only a few microbial types, our
data indicate that the WPFs for most of the microorganism
genus/groups cannot be estimated utilizing WPFs measured for
particles.
Several subjects recruited among students and staff at the
University of Cincinnati repeated the experiment in different
farming environments. This allowed us to investigate the
difference in WPFs between and within subjects. Table II
shows the variability of the WPFs between subjects and within
subjects for airborne dust and microorganisms. The Appendix
shows the raw data used in these calculations. The first model
(without covariates) shows that the between-subject variability
was 1.08 for dust and 0.13 for microorganisms, while the
within-subject variability was 1.43 and 3.9, respectively. This
demonstrates that the within-subject variability in WPFs was
FIGURE 2. Correlation between the WPF for microorganisms
and the WPF for particles of corresponding particle size: (A)
Cladosporium vs. 5–10 µm particles, (B) total fungi vs. 2–10 µm
particles, and (C) culturable fungi vs. 2–10 µm particles. y = log
(the WPF for microorganisms); x = log (the WPF for particles).
greater than the between-subject variability for half-mask
respirators when there were no other covariates included in
the model. The findings support the results presented by Nicas
and Neuhaus.
(22)
However, when the covariate, such as particle size or
microbial type, was included in the model, the within-subject
variability decreased as seen in Table II. This may result
582 Journal of Occupational and Environmental Hygiene November 2005
TABLE II. Variability of WPFs Between Subjects (σ
2
B
) and Within Subjects (σ
2
w
)for Dust and Microorganisms
σ
2
B
σ
2
w
σ
2
B
σ
2
B
+σ
2
w
GSD
A
B
GSD
B
W
Reduction for Within-Subject
Variation Compared with Model
with No Covariates (%)
Particles
No covariates 1.08 1.43 0.43 2.83 3.31
Size only 1.18 1.07 0.52 2.96 2.81 25
Microorganisms
No covariates 0.13 3.9 0.03 1.43 7.21
Type only 0.18 3.49 0.05 1.53 6.48 11
A
GSD
B
= between-subject geometric standard deviation.
B
GSD
W
= within-subject geometric standard deviation.
in the within-subject variability being equal to or smaller
than the between-subject variability at least for nonbiological
particles. Table II also shows that the fraction of the between-
subject variability versus the total variability for dust increased
from 43% to 52% when the particle size was accounted for
in the model. Note that the between-subject variability in
microorganisms was much smaller than the within-subject
variability. Also, the latter for biological particles was two to
three times larger than that for nonbiological ones. It is likely
that different farming activities involved different particle size
distributions, different microbial composition, and different
faceseal leakage.
TABLE III. Differences in WPF Data Before and After Accounting for Lung Deposition and Respirator Dead
Volume
Mean Aerodynamic
Size (µm)
A
F
B
dep
(%) C
full
/C
C
in
WPF WPF
D
corr
Bias E (%)
Dust
0.7–1 µm0.9390.82 92 76 22
1–2 µm1.5700.68 146 99 47
2–3 µm2.5920.59 343 202 69
3–5 µm4.0960.57 932 531 75
5–10 µm7.5890.60 2563 1538 67
Fungal spores
Aspergillius/Penicillium 3.7950.57 3 2 75
Ascospores 5.6930.58 5 3 72
Basidiospores 6.8920.59 240 142 69
Cladosporium 8.1870.61 406 248 64
Smut spores 9.7840.62 60 37 61
Alternaria 14.5720.67 256 172 49
Epicoccum 18.9630.71 90 64 41
A
Mean aerodynamic size is the same as that presented in authors’ concurrent paper.
(14)
B
F
dep
: fractional deposition of particles in the respiratory tract.
(18)
C
C
full
/C
in
: ratio of an average concentration measured during the full breathing cycle to that measured during inhalation.
(16)
D
WPF
corr
: WPF corrected after accounting for lung deposition and respirator dead volume (WPF
corr
= WPF × C
full
/C
in
).
E
Bias: [WPF-WPF
corr
]/[WPF
corr
] × 100%, calculated with nonrounded numbers.
When comparing the within-subject and between-subject
variability in WPFs for airborne dust and microorganisms,
other covariates such as particle size, microbial types, and
farming activities should be carefully addressed, and the effect
of these factors on WPF measurements in agricultural envi-
ronments should be further investigated. From our small-scale
study results, it appears that the WPF distributions between
biological and nonbiological particles are very different from
each other. Therefore, more detailed research will help to better
characterize WPFs.
Previous studies showed that respirator dead space and
lung retention decrease the concentration inside the respirator
Journal of Occupational and Environmental Hygiene November 2005 583
during inhalation, resulting in overestimation of the WPF.
(16,17)
So far, only a few WPF studies have investigated the effects
of respirator dead space and lung retention because the
information on the distribution of the particle size inside the
respirator was not readily available. In this study, the OPC
provided the size distribution of particles inside the respirator
for five different size fractions in the particle size range of 0.7
to 10 µm. In addition, the size information for fungal spores
was obtained from the data presented by Lee et al.
(14)
Table III presents the differences in the WPF data before
and after accounting for the lung deposition and respirator
dead volume. As seen from the table, the total deposition
in human respiratory tract (F
dep
) ranged from 39 to 96%
for particles in the size range of 0.7 to 10 µm, and from
63% to 95% for fungal spores, which cover the particle
size range from 3.7 to 18.9 µminmean aerodynamic size.
The measured WPF values were corrected by accounting
for respirator dead space and lung retention using Hind and
Bellin’s approach.
(16)
The bias was calculated by dividing
the difference between the protection factors before (WPF)
and after correction (WPF
corr
)bythe protection factor after
correction. For particles in the size range of 0.7 to 10 µm,
the WPFs before correction were overestimated by 22% to
75%. For fungal spores in the mean aerodynamic size of
3.7 to 18.9 µm, the protection factors before correction
resulted in the overestimation ranging from 41% to 75%.
This information provided the possible bias caused by res-
pirator dead space and lung retention when we evaluated
the respiratory protection against airborne dust and microor-
ganisms in agricultural farms. As compared with the WPF
values presented in Figure 1, the percentage of WPF values
less than 10 would be increased after correction for dead
respirator space and lung retention. For example, the percent-
age of WPF values less than 10 for Aspergillus/Penicillium
was increased from 65% to 71% resulting from the
correction.
CONCLUSIONS
T
he protection provided by N95 filtering facepiece respira-
tors against dust and airborne microorganisms varied with
particle size, shape, and density. The WPFs for microorganisms
were smaller than those for nonbiological (dust) particles of
the same size range measured by OPCs. This may be due
to pronouncedly irregular shape and higher density of dust
particles as compared to biological particles. More than 50% of
the measured WPFs for microorganisms (mean aerodynamic
size <5 µm) were less than the proposed APF of 10. Even
lower WPFs were calculated after correcting for respirator dead
space and lung deposition. As a consequence, the APF of 10
for N95 filtering facepiece respirators against microorganisms
(mean aerodynamic size <5 µm) seems to be inadequate for
more than 50% of wearings. Our data shows that particle
size and the nature of particles (nonbiological/biological)
should be taken into account when computing APF values
for particulate respirators. In order to establish respiratory
protection guidelines against airborne microorganisms in
agricultural farms, more field data must be obtained. The
present results provide preliminary data toward developing
such guidelines, and the method developed can be used for
further epidemiological and intervention studies in agricul-
tural and other environments with considerable bioaerosol
contamination.
ACKNOWLEDGMENT
T
he authors are grateful to farm owners for providing access
and help in field measurements.
The authors also thank students and staff members who
volunteered to be human subjects in the field testing when
needed.
Special thanks go to Mike Brugger and LingYing Zhao
(Ohio State University) for helping us find field sites.
This research was supported by the National Institute for
Occupational Safety and Health (NIOSH R01 OH04085) and
through the Pilot Project Research Training Program of the
University of Cincinnati Education and Research Center.
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APPENDIX I. Relicated WPF Values of Airborne
Dust and Microorganisms
0.7–2 µm 2–10 µm
Culturable
Fungi
Culturable
Bacteria
Subject 1 17.6 117.81.21.5
45.3 337.754.03.1
Subject 2 27.0 194.958.09.0
34.3 269.2 400.02.2
35.3 145.7 2575.01.6
55.1 414.1 520.6 116.7
28.729.716.013.8
6.02.2
Subject 3 74.3 258.8 192.0 709.5
2.94.774.412.5
4.57.52.43.3
8.619.814.55.6
11.127.75.83.6
31.315.12.014.0
2.02.5
Subject 4 40.3 232.370.025.3
8.812.518.04.3
Subject 5 160.7 712.578.12.5
39.4 145.8 293.57.5
115.3 612.6 262.5 169.2
Subject 6 4.37.921.462.4
Journal of Occupational and Environmental Hygiene November 2005 585
