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Indoor Particulate Matter of Outdoor Origin: Importance of Size-Dependent Removal Mechanisms


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Adverse human health effects have been observed to correlate with levels of outdoor particulate matter (PM), even though most human exposure to PM of outdoor origin occurs indoors. In this study, we apply a model and empirical data to explore the indoor PM levels of outdoor origin for two major building types: offices and residences. Typical ventilation rates for each building type are obtained from the literature. Published data are combined with theoretical analyses to develop representative particle penetration coefficients, deposition loss rates, and ventilation-system filter efficiencies for a broad particle size range (i.e., 0.001-10 microm). We apply archetypal outdoor number, surface area, and mass PM size distributions for both urban and rural airsheds. We also use data on mass-weighted size distributions for specific chemical constituents of PM: sulfate and elemental carbon. Predictions of the size-resolved indoor proportion of outdoor particles (IPOP) for various conditions and ambient particle distributions are then computed. The IPOP depends strongly on the ambient particle size distribution, building type and operational parameters, and PM metric. We conclude that an accurate determination of exposure to particles of ambient origin requires explicit consideration of how removal processes in buildings vary with particle size.
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Indoor Particulate Matter of Outdoor
Origin: Importance of
Size-Dependent Removal
Indoor Environment Department, Environmental Energy
Technologies Division, E.O. Lawrence Berkeley National
Laboratory, 1 Cyclotron Road, Berkeley, California 94720, and
School of Public Health and Department of Civil and
Environmental Engineering, University of California,
Berkeley, California, 94720
Adverse human health effects have been observed to
correlate with levels of outdoor particulate matter (PM),
even though most human exposure to PM of outdoor origin
occurs indoors. In this study, we apply a model and
empirical data to explore the indoor PM levels of outdoor
originfortwomajorbuildingtypes: officesandresidences.
Typical ventilation rates for each building type are obtained
from the literature. Published data are combined with
theoretical analyses to develop representative particle
system filter efficiencies for a broad particle size range
(i.e., 0.001-10 µm). We apply archetypal outdoor number,
surface area, and mass PM size distributions for both
urban and rural airsheds. We also use data on mass-
ofPM: sulfateandelementalcarbon.Predictionsofthesize-
resolved indoor proportion of outdoor particles (IPOP)
for various conditions and ambient particle distributions
particle size distribution, building type and operational
parameters, and PM metric. We conclude that an accurate
determination of exposure to particles of ambient origin
requires explicit consideration of how removal processes
in buildings vary with particle size.
Recent epidemiological studies have shown strong correla-
tionsbetween elevatedoutdoorparticulate matter(PM) levels
anda rangeofadverse healtheffects, including earlymortality
(1,2), exacerbation of respiratory tractdisease,reducedlung
function (3), and cardiovascular disease (4, 5). The mech-
anisms by which PM exposure affects human health are
unclear and are the subject of much current research (6).
Understanding the impact of outdoor PM on human
healthrequires recognitionthatpeople spend alargefraction
(90%) of their time indoors (7, 8). Indeed, recent studies
indicate that most of the population exposure to PM occurs
in buildings (9, 10). These studies show that indoor particle
concentrationscan beattributedtoboth outdoorandindoor
sources. Particle removal by a ventilation-system filter,
deposition to the building shell during air infiltration, and
deposition onto indoor surfaces can significantly affect the
indoor concentration of particles originating outdoors (11).
These loss processes vary with building conditions and
operation and are strongly particle-size dependent. An
understanding of how the outdoor PM properties are
modified indoors is needed to accurately estimate human
exposure based on ambient measurements.
Until recently, studies designed to understand relation-
ships between indoor and outdoor PM levels focused on
integralmeasures of PMwithoutsize discrimination (12, 13).
Furthermore, the integrated PM measurements have often
mixed PM originating outdoors with contributions from
indoorsources. More recentstudies(14-19) haveattempted
to differentiate indoor-outdoor concentration ratios based
onparticlesize and to separate indoor and outdoorsources.
In this paper, we expand on such studies by specifically
treating building operational characteristics (i.e., filtration,
penetration, deposition, and ventilation) and their size-
dependent effects on indoor PM levels. We apply this
approach to determine the size-resolved indoor particle
PM2.5 andPM10 mass concentrations, and selected chemical
species. We also perform analyses for characteristic rural
and urban PM size distributions.
IndoorProportion of Outdoor Particles. Figure1illustrates
the modeled processes that affect the indoor proportion of
outdoorparticles(IPOP). The model complexity was chosen
tomatchthe data available for parametrization and is based
on work by Alzona et al. (20). This approach to modeling
indoorPM levelshasalsobeen appliedinstudiesby Nazaroff
and Cass (21) and Leaderer et al. (22), among others.
Assuming isothermal conditions, no resuspension or co-
agulation of particles, and no phase change processes, the
size-specific mass balance for particles of outdoor origin is
where Cois the outdoor PM concentration (µgm
-3); Cis the
indoor concentration of PM of outdoor origin (µgm
-3); tis
time (s); jis an index referencing each of the three major
surface orientations in the building (upward facing, down-
ward facing, and vertical); vd,jis the deposition velocity for
orientation j(m s-1); Sjis the surface area with orientation
j(m2); ηmand ηrare the makeup and recirculation filter
Vis the room volume (m3); and Qm,Qr,Qn, and Qiare the
makeup, recirculation, natural ventilation, and infiltration
airflowrates (m3s-1), respectively. Natural ventilation refers
* Correspondingauthor phone: (510)486-5036;fax: (510)486-7070;
E. O. Lawrence Berkeley National Laboratory.
School of Public Health, University of California, Berkeley.
§Present address: School of Mechanical and Production Engi-
neering, Nanyang Technological University, 50 Nanyang Ave.,
Singapore 639798.
|Departmentof Civil andEnvironmental Engineering, University
of California, Berkeley.
dt)QmCo(1 -ηm)+QnCo+pQiCo-QrηrC-
Environ. Sci. Technol.
200 9ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 36, NO. 2, 2002 10.1021/es010723y CCC: $22.00 2002 American Chemical Society
Published on Web 12/13/2001
toairflow throughopenwindows anddoors,while infiltration
referstoairflow through the remainder of the buildingshell.
Applying a time average to eq 1 while neglecting the change
of PM mass within the building and assuming that Coand
Care not correlated in time with Qm,Qr,Qi,Qn,orvd,jyields
where C/Cois the size-specific, time-averaged IPOP and β
is the deposition loss rate coefficient (s-1), defined as
(vd,jSj)/V. For simplicity, in the calculations that follow we
assume that the makeup and recirculation filter efficiencies
are equivalent (i.e., ηm)ηr). Equation 2 also describes the
size-specific IPOP for a particular chemical constituent of
PM. Although the form of eq 2 is the same as that obtained
byassuming that steady-state conditions apply, the terms in
eq 2 represent time averages and not instantaneous values.
Assuming uncorrelated ventilation rates and particle con-
centrations is an important restriction on eq 2. However,
thisrestriction is less severe thanthoseimposedby a steady-
state assumption, which requires that all input parameters
be constant in time. Because we are most interested in
illustrating the importance of the PM size distribution, we
believe that the model assumptions do not unduly limit the
value of the work presented here.
The analysis in this paper does not consider indoor
sources. It is well-known that indoor sources exist and can
contribute significantly to indoor PM levels. In particular,
environmental tobacco smoke, cooking, and cleaning activi-
ties are important indoor particle sources (23). We focus on
theindoor proportionof outdoorparticlesand excludeindoor
sources since the epidemiological literature has focused on
health effects associated with ambient particle levels. The
methods and concepts developed here can be extended to
thestudy ofindoorPM sources, butsuchan effortwillrequire
a more thorough characterization of these sources.
Resuspension would increase the time-averaged IPOP
above that calculated by eq 2; this effect would be most
pronouncedfor coarsemodeparticles (24).Modelshave been
developed to estimate resuspension rates (25), but more
research is required to thoroughly characterize this effect.
We do not include the effects of resuspension in the current
Building Simulation Scenario. We considered five rep-
resentative building scenarios: (i) an office building with a
40% ASHRAE filter (Ofc40); (ii) an office building with an
85% ASHRAE filter (Ofc85); (iii) a closed residence with
continuouscentralairandastandard furnace filter (ResCA);
(iv)a residence with a typicalinfiltrationventilationrate and
nocentral air (ResTV);and(v) a residencewitha high natural
ventilation rate as may occur with open windows (ResHV).
For the office building scenarios, we applied the results of
Persily (26), who reported makeup ventilation rates for 14
U.S. office buildings (Table 1). Murray and Burmaster (27)
reported on a set of 2844 measurements of U.S. residential
ventilation rates. For the ResTV scenario, we used their
geometric mean (GM) and standard deviation (GSD) of the
full data set; for the ResHV scenario, we applied the 95th
percentile ventilation rate. The remaining ventilation pa-
rameters in Table 1 were chosen based on the authors’
scientific judgment. In cases where we did not have
information on ventilation rate variability, we assumed the
distribution to be lognormal with a default GSD of 1.5.
Filtration Efficiency. Hanley et al. (28) measured the
removalefficiencyofseveral filtersforparticles withdiameters
between 0.01 and 2.4 µm. In the current study, we use their
datafor loadedhomefurnacefilters andtwocommonlyused
commercial filters: the 40% and 85% ASHRAE filters (29).
We applied fibrous-bed filtration theory to augment these
results for particles smaller than 0.01 µm and larger than 2.4
µm(30). We generated a best fit to the Hanley et al. (28) data
set by minimizing the log-squared difference between
theoretical and measured removal efficiencies. Bed solidity
andfiber diameter were used as fitting parameters.We were
unable to fit the filter efficiency data over the entire range
of particle diameters with a single combination of solidity
and fiber diameter. Therefore, different fits were generated
to apply for particles with diameters less than 0.01 µm and
greater than 2.4 µm for each filter (Figure 2). Linear
interpolationbetween measured data was used for particles
with diameters between 0.01 and 2.4 µm.
Deposition. Our deposition model is based on results
fromexperimental studies(31-36)that measured theindoor
depositionlossrateover a range of particle sizes, ventilation
conditions, and indoor surface area to volume ratios. We
characterize a representative indoor deposition loss rate
coefficient across a broad particle size range by extending
these experimental results using a theoretical analysis for
ultrafine particles. Experimental results are summarized in
Figure 3, along with a least-squares cubic polynomial fit to
thelogarithmically transformeddata.We applied thesmooth
indoorsurface particle depositiontheoryof Lai andNazaroff
(37) to estimate deposition for particles with diameters
smaller than 0.06 µm. For this analysis, we used a surface
area to volume ratio of 3 m-1, which is a typical value in
furnishedrooms (38). Thetheoreticalloss rate coefficient for
ultrafine particles onto smooth surfaces is a function of the
shear velocity. To match the curve fit to the empirical data
at a particle diameter of 0.06 µm required a shear velocity
of 3.0 cm s-1, a value within the range of expected shear
velocities in indoor spaces.
PMPenetration. Wedefinethe particlepenetrationfactor
(p)as the fraction of particles of a specific diameter that pass
through the building shell along with infiltrating air. Sub-
stantial uncertainty in the penetration factor exists in the
literature. Two studies indicate that pis close to 1.0 for a
widerange of particlediameters(13, 24), whileseveralrecent
studies (14, 18, 19, 39) have presented data indicating that
the penetration factor may be significantly less than 1 and
varies with particle size.
Inthe current study, we use a baseline value for pdefined
by the idealized crack theory of Liu and Nazaroff (40). The
FIGURE 1. Schematic of the processes that affect the indoor
proportion of outdoor particles for a generic single-zone building.
See text for symbol definitions.
V(1 -ηm)+Qn
theory assumes idealized rectangular cracks with regular
geometry, smooth inner surfaces, and steady airflow. Depo-
sition occurs by gravitational settling, Brownian diffusion,
and inertial impaction. In the model, we assume a mean
crack height of 0.8 mm, a crack flow length of 9 cm, a total
crack length throughout the building shell of 1000 m, and a
4Pa pressuredrop acrossthebuilding shell.Thiscombination
of parameters results in a penetration factor that closely
matches the experimental results presented in Long et al.
(14) for one house (their Figure 7b). In real buildings, crack
size, length, and geometry can vary substantially. The large
uncertaintyin the particle penetration factor implies a need
to better characterize this parameter. Our model structure
easily permits analysis of the effect of pon the predicted
IPOP as more information on this parameter becomes
Outdoor PM Concentrations. Jaenicke (41) described
archetypalatmospheric aerosolsizedistributions asthesums
of three lognormal distributions. By combining PM distri-
butionsfrom a number of studies, he developedparameters
to describe several distributions; in this study, we apply his
approximationsfor PMdistributionsin rural andurbanareas
(Table 2). With a particle density, F(g cm-3),of1gcm
-3, the
integratedPM2.5 and PM10 mass concentrationsforthe urban
distribution are 43 and 60 µgm
-3, respectively, and for the
rural distribution are 12 and 59 µgm
-3, respectively.
Because of differences in sources, particle density may
not be constant across the particle size distribution. Coarse
particlesconsistofsoil dustandother mechanicallygenerated
material,while fine modeparticlescontain primary particles
from combustion sources and secondary aerosol material
(42). Thus, smaller particles will have densities closer to 1 g
cm-3,whilelarger particles may have densities closer to that
of soil grains (i.e., 2.5 g cm-3). However, there is insufficient
information in the literature to accurately characterize
ambientparticle density as a function ofsize. For the results
presented here, we take the particle density to be1gcm
To estimate the impact of particle density variability on the
IPOP,wemakecomparisons to a binary density distribution
with fine mode particles having a density of1gcm
coarse mode particles having a density of 2.5 g cm-3.
Becausehuman healtheffectsof ambientPMmay depend
on particle composition as well as size distribution, we
developed IPOP relationships for particles of different
composition. We present results here for two constituents:
sulfate and elemental carbon.
Whitby (43) compiled data from 5 studies of 15 urban
sites to characterize the size composition of sulfate in
atmospheric aerosol. He summarizes the size composition
data as a single log-normal distribution, with a geometric
meanparticle diameter(Dpm )0.48(0.10 µm)andgeometric
standard deviation (σg)2.0 (0.29).
Offenbergand Baker(44) measuredelementalcarbon (EC)
and organic carbon (OC) during the summer and winter in
Chicago. Several studies have linked EC levels with human
health effects (e.g., ref 45). In the current work, we include
an estimate of the IPOP for the winter EC size distribution
as ambient EC levels tend to be highest in the winter. The
measured particle size distributions we applied are sum-
marizedas follows: for Dp)0.15, 0.45, 1.4, 4.1, and12.2 µm,
respectively, the values of the normalized elemental carbon
mass size distribution (C/[Ctotallog Dp]) were 0.0, 0.49,
0.73, 0.49, and 0.19.
Integral PM Measures. We compute integrated number
-3), surface area (A,µm2m-3), and mass (M,µgm
concentrations for three particle size ranges [Dpe10 µm
(PM10),Dpe2.5 µm (PM2.5), and 2.5µmeDpe10µm(coarse
mode)]bynumericallyintegrating theappropriatelyweighted
moment of the size distribution over the respective particle
size range. We also compute the effective integrated PM2.5
and PM10 recirculation and makeup filter efficiencies and
TABLE 1. Geometric Mean (GSD) Ventilation Parameters for Each Building Scenarioa
building type and operation
parameter Ofc40 and Ofc85 ResCA ResTV ResHV
(h-1) 0.73 (1.8)
(h-1) 3 (1.5) 4 (1.5) 0 0
(h-1) 0 0 0 2.2
(h-1) 0.25 (1.5) 0.75 (1.5) 0.53 (2.27)
filter 40 or 85% ASHRAE (1.5
) standard furnace filter (1.5
iarethe makeup,recirculation, naturalventilation, andinfiltration airflowrates, respectively.Ofc40 andOfc85, officebuildings.
ResCA, residence closed with continuous central air. ResTV, residence closed with typical ventilation. ResHV, residence with high air exchange
Estimatedfrom Figure 19 of ref
GMand GSD for full data set (
95%percentile value (
Filterefficiencies are limited to be lower
than 1.0 in the Monte Carlo simulations.
na, not applicable.
FIGURE 2. Filter efficiency vs particle size, as predicted from the
dataof Hanley etal.(
)(squares) and extrapolatedusingthe theory
of Hinds (
FIGURE3. Deposition lossratecoefficient (β)vs particlesize.Shown
are the data sets referenced in the text, a least-squares third-order
polynomialfit tothe log-transformed data,and predictionsforparticle
diametersless than 0.06µmfrom the smooth indoorsurface particle
deposition theory of Lai and Nazaroff (
particle deposition velocity for each scenario by integrating
the mass-weighted filtration efficiency over the appropriate
particle size ranges.
The integrated parameters differ among simulation
scenarios because of changes in the outdoor PM size
distribution. These integrated measures can be useful for
estimating the IPOP for integral measures of PM in the
absenceof asize-dependentcharacterization ofambientPM,
penetration, deposition loss rate, or filter performance.
Addressing Uncertainty. Because of limitations in the
data and theories to support IPOP modeling, there is a need
to characterize uncertainty and variability in the model
approach and input parameters. The framework for the
analysis of uncertainty in human exposure and health risk
assessment developed by Morgan and Henrion (46) and
Cullen and Frey (47) distinguishes among parameter un-
certainty, model uncertainty, and decision rule uncertainty
and calls for a separate treatment of these different types of
uncertainty. In the current study, we explicitly address
parameter uncertainty.
of parameter uncertainty on our IPOP predictions. Log-
normal distributions are used to represent building ventila-
tion rates, and when possible, literature values are used to
obtainthe characteristics of the distribution. In cases where
literature data were inconclusive or unavailable, we used
scientificjudgment to obtain distributioncharacteristics.To
express uncertainty in outdoor particle concentrations,
particlepenetration, filterefficiency,and deposition lossrate
parameters, we used lognormal distributions with a GSD of
1.5 and a GM based on available data. Parameter values
sampled from the distributions were limited to a range
definedby afactorof 2.5fromthe GM, andthefilter efficiency
andpenetration rate were limited to valuesbelow 1. For this
study, the Monte Carlo results were computed from an
ensemble of 1000 model simulations.
Results and Discussion
Size-Resolved Particulate Matter. Figure 4 illustrates the
outdoor and predicted indoor number, surface area, and
volume concentrations for the archetypal urban and rural
distributionsand thetwo officebuildingscenarios. Forclarity,
we present only the mean of the Monte Carlo simulations.
The overall impact of filtration and deposition differ among
thenumber, surfacearea,and massPMdistributions because
each metric has different size dependence. For example, a
large fraction of the number concentration distribution
occurs below particle diameters of about 0.05 µm, where
both deposition and filtration are efficient removal mech-
anisms (Figure 4a,d). The urban indoor surface area con-
centration (Figure 4b) for the building equipped with a 40%
ASHRAE filter is relatively unaffected by the building since
theremoval mechanisms areinefficientinthe accumulation
TABLE 2. Mean Diameters and Standard Deviations of the Three Lognormal Modes for Characteristic Aerosol Distributions in
Rural and Urban Areas (41)
mode I mode II mode III integral measures
(µm) log σi
(µm) log σi
(µm) log σi
(-)PM2.5 mass
-3)PM10 mass
urban 9.93 ×1040.013 0.245 1.1 ×1030.014 0.666 3.64 ×1040.05 0.337 43 60
rural 6650 0.015 0.225 147 0.054 0.557 1990 0.084 0.266 12 59
Assuming a particle density of1gcm
FIGURE4. Archetypal urban and rural aerosol distributions and predicted indoor particle number, surface area, andvolumeconcentrations
for the office building with 40% (Ofc40) and 85% (Ofc85) ASHRAE filters. Panels a-c show number, surface area, and volume distributions,
respectively, for urban aerosol; panels d-f show the corresponding distributions for rural aerosol.
mode where a large portion of the PM surface area exists.
Note that the 85% ASHRAE filter substantially reduces the
indoor PM surface area concentration for both rural and
urban conditions since this filter is more efficient than the
40% filter in the accumulation mode and because the office
buildingrecirculation flowrateis relatively highascompared
to the makeup ventilation rate. For the urban distribution,
the 85% ASHRAE filter reduces the indoor volume concen-
tration substantially as compared to the 40% filter. Much of
the PM volume in the rural scenario is in the coarse mode
(Figure 4f), resulting in a large removal of PM10 volume in
both office building scenarios.
Figure5 summarizestheoffice building IPOPpredictions.
Values range from 0.05 for the coarse mode mass in all four
cases to 0.75 for particle surface area with urban PM and a
40% ASHRAE filter. For the archetypal urban atmosphere,
the 85% ASHRAE filter reduces all the IPOP values (except
thecoarse mode mass) byafactorof about 3-4 as compared
to the 40% ASHRAE filter (Figure 5a,c). The lesser impact in
thecoarse modemassis a resultofthe highefficiency(>99%)
ofboth filters for particles with diameters greaterthanabout
3µm (Figure 2).
Figure 6 shows outdoor and predicted mean indoor
number, surface area, and volume concentrations for the
three residential scenarios. Patterns similar to those in the
office building scenarios (Figure 4) are found here. For the
ResHV building scenario in both urban and rural atmo-
spheres, the surface area and volume concentrations below
1µm are essentially unchanged across the building shell.
Comparedto the officebuildingscenarios, the residential
scenarios show a larger fraction of the outdoor PM con-
centration indoors (Figure 7). For the residential scenarios,
IPOP values for most metrics are again lower in the rural
distributionthaninthe urban distribution. The largest IPOP
valuesoccur for theResHVscenariowhere, for the urbanPM
distribution,PM10 number andmassIPOP areabove0.8 while
the surface area and PM2.5 mass IPOP are above 0.9. The
ResCA scenario has the smallest IPOP for both archetypal
ambient distributions and for all indoor metrics. Applying
the binary particle density (i.e., higher density in the coarse
mode) resulted in coarse mode and PM10 mass IPOP values
between 5 and 20% lower than with the uniform particle
density of1gcm
Integrated Deposition Loss Coefficient and Filtration
Efficiency. Integrated PM2.5 and PM10 deposition loss rate
coefficients and filter efficiencies are presented in Table 3.
These parameters differ markedly between PM2.5 and PM10
integrated values. For example, the Ofc40 scenario in the
archetypal urban PM distribution has makeup filter efficien-
ciesof 8%and31% for thePM2.5 andPM10 particle sizeranges,
respectively. Furthermore, for a particular ambient PM
distribution and building scenario, the recirculation and
makeupfilters canhavedifferent integratedefficiencies,even
though they are identical filters. The recirculation and
makeup filters operate on indoor and ambient airstreams,
respectively, and these airstreams have different PM size
distributions. The largest difference occurs for PM10 for the
urban particle size distribution and the Ofc40 building
scenario where the makeup filter was about four times as
efficient as the recirculation filter. Differences between the
integrated filter efficiencies were smaller for the Ofc85
scenario since the efficiency of the 85% ASHRAE filter is less
variablewith particlesizethan isthe40% filter.Theintegrated
PM2.5 recirculation and makeup filter efficiencies are com-
parable for each scenario.
Theintegrated PM10 deposition lossratecoefficient varies
by more than an order of magnitude among the simulated
scenarios.Simulations with theurbanPM distribution result
in smaller PM10 deposition loss rate coefficients than with
the rural distribution. This observation is consistent with
the larger fraction of coarse mode mass in the rural PM size
distributions.The integrated deposition loss rate coefficient
for PM2.5 is smaller than for PM10 and varies by a factor of
about 3 across scenarios. For comparison, Ozkaynak et al.
(13) reported mean integrated PM2.5 and PM10 deposition
lossrates of 0.39 and 0.65 h-1, respectively, from the PTEAM
study of residences in southern California. Their fitted PM10
deposition loss rate is consistent with that found under the
urban high ventilation residential scenario presented here.
However,theirPM2.5 integrated deposition loss rate is about
threetimes largerthanour predictions. Thediscrepancymay
be due to differences in the outdoor particle size distri-
butionor theinclusionof indoor sourcesinthe PTEAM study
These differences suggest that care must be taken when
choosing representative values for exposure studies. We
emphasize that the recirculation filter efficiency and the
deposition loss rate coefficient values for integral PM
measures can vary with any parameter that changes the
indoorparticle size distribution, such as the ventilation rate.
Despite these limitations, the information in Table 3 can
provide guidance for direct application of a mass balance
modelof integralPM measuresinthe absenceofsize-resolved
Simulation Results for Individual Compounds in PM.
Table 4 summarizes the IPOP values for PM2.5 and PM10
fractions of sulfate and elemental carbon. For offices,
FIGURE 5. Predicted indoor proportion of outdoor particles (IPOP)
for the office building with 40% (Ofc40) and 85% (Ofc85) ASHRAE
filtersand the archetypal urban and rural aerosol distributions. The
IPOP values are shown for PM10 number, PM10 surface area, PM2.5
mass, coarse mode mass, and PM10 mass.
differences in filter efficiency have a large impact on the
IPOP for both constituents. For example, with the 40%
ASHRAEfilter,the sulfate PM10 mass IPOP is about 0.72.The
more efficient filter (85% ASHRAE) lowers the PM10 mass
IPOP to 0.18.
Inresidences, thehighventilation scenariohasPM2.5 mass
IPOP values above 0.9 for both constituents. Filtration and
the reduced infiltration rate in the residence with a con-
tinuously operating central air handler (ResCA) lowered the
PM10 and PM2.5 mass IPOP values for both species to below
0.5.Comparison with EmpiricalEvidence.We identifiedtwo
recent studies that provide an opportunity for comparison
of field observations with our simulation results. Ott et al.
(48)computedthePM10 IPOP forthree large-scalefieldstudies
of residences by removing the impact of indoor sources on
measured indoor PM levels with their random component
superposition statistical model. They inferred mean PM10
IPOP values for these three studies as 0.54, 0.55, and 0.61,
respectively. They did not report IPOP values for PM2.5.
Because our model is parameterized with deposition loss
rates, ventilation rates, and filtration efficiencies based on
many studies with varying conditions and we are applying
archetypal ambient PM size distributions, we do not expect
our IPOP predictions to correspond directly to the results of
Ott et al. Nevertheless, our results for the PM10 mass IPOP
of about 0.4, 0.6, and 0.8 for the urban size distribution and
three residential scenarios bracket the values they report.
Furthermore, the typical ventilation residential scenario
(ResTV) closely matches the results from the field studies.
Ozkaynak et al. (13) described results from the PTEAM
study of 178 participants in southern California. In addition
to PM10 measurements, elemental analyses of sulfur were
alsoperformed. Themassmedian diameterofsulfur particles
was less than 1 µm; these particles represent a good proxy
for PM2.5 sulfate. Their data suggest an IPOP of 0.7, which
matches the typical ventilation residential scenario (ResTV)
predicted IPOP of 0.8 (0.1 well.
FIGURE6. Archetypal urban and rural aerosol distributions and predicted indoor particle number, surface area, andvolumeconcentrations
forthe three residentialscenarios(ResCA, closed withcentralair; ResTV, typicalinfiltrationventilation; and ResHV,highnatural ventilation).
Panels a-c show number, surface area, and volume distributions, respectively, for urban aerosol; panels d-f show the corresponding
distributions for rural aerosol.
TABLE 3. Mean Integrated PM2.5 and PM10 Deposition Loss Rate Coefficient and Recirculation and Makeup Filter Efficiencies (SD)
for the Archetypal Urban and Rural PM Distributions and the Five Building Scenarios
PM2.5 PM10
distribution building
scenario deposition loss
rate coeff (h-1)recirculation
filter efficiency (%) makeup filter
efficiency (%) deposition loss
rate coeff (h-1)recirculation
filter efficiency (%) makeup filter
efficiency (%)
archetypal Ofc40 0.10 (0.01) 6 (0.3) 8 (0.3) 0.17 (0.02) 8 (0.8) 31 (1.0)
urban Ofc85 0.09 (0.01) 56 (1.8) 64 (1.5) 0.32 (0.05) 59 (2.0) 72 (1.2)
ResCA 0.09 (0.01) 19 (0.9) -
0.32 (0.04) 24 (1.5) -
ResTV 0.11 (0.01) --0.31 (0.10) --
ResHV 0.13 (0.01) --0.53 (0.07) --
archetypal Ofc40 0.24 (0.02) 13 (0.9) 19 (1.1) 0.96 (0.2) 32 (4.2) 77 (1.8)
rural Ofc85 0.23 (0.03) 67 (2) 79 (1.9) 1.9 (0.2) 81 (2.4) 91 (1.7)
ResCA 0.25 (0.02) 32 (2) -1.9 (0.1) 62 (2.6) -
ResTV 0.27 (0.03) --1.5 (0.3) --
ResHV 0.33 (0.02) --2.1 (0.2) --
-, not applicable.
In summary, a full understanding of the adverse
health effects of airborne particles requires knowledge of
how health-related ambient PM metrics are transformed
withinbuildings. Usingavariant of awell-establishedmodel,
in combination with empirical information and theoretical
analyseson key parameters,wehave shown thatIPOPvalues
can vary from 0.05 to more than 0.9. Ambient size distribu-
tions of the PM metric and building design and operation
play key roles influencing IPOP values and thus have a
significant effect on estimates of human exposure. The
approach presented here, especially if combined with more
complete parameter data, could serve to strengthen epi-
demiologicalstudies byimprovingexposure assessment.The
methods and information may also contribute to the
developmentand evaluation of risk reductionstrategiesthat
arebased onmodifyingexposure bychanging building design
and operation.
This work was supported by the U.S. Environmental Protec-
tion Agency (EPA) National Exposure Research Laboratory
through Interagency Agreement DW-988-38190-01-0 and
carried out at the Lawrence Berkeley National Laboratory
(LBNL) through the U.S. Department of Energy under
Contract Grant DE-AC03-76SF00098. The authors thank Bill
Fisk, David Faulkner, Rich Sextro, and two anonymous
reviewers for their constructive comments.
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Deposition on indoor surfaces is an important removal mechanism for tobacco smoke particles. We report measurements of deposition rates of environmental tobacco smoke particles in a room-size chamber. The deposition rates were determined from the changes in measured concentrations by correcting for the effects of coagulation and ventilation. The airflow turbulent intensity parameter was determined independently by measuring the air velocities in the chamber. Particles with diameters < 0.25 μm coagulate to form larger particles of sizes between 0.25 and 0.5 μm. The effect of coagulation on the particles > 0.5 μm was found to be negligible. Comparison between our measurements and calculations using the theory of Crump and Seinfeld (1981) showed smaller measured deposition rates for particles from 0.1 to 0.3 μm in diameter and greater measured deposition rates for particles larger than 0.6 μm at three mixing intensities. Comparison of Nazaroff and Cass' model (1989a) for natural convection flow showed good agreement with the measurements for particles > 0.1 μm in diameter; however, measured deposition rates exceeded model predictions by a factor of approximately 4 for particles in size range of 0.05–0.1 μm in diameter. These results were used to predict deposition of sidestream smoke particles on interior surfaces. Calculations predict that in 10 hours after smoking one cigarette, 22% of total sidestream particles by mass will deposit on interior surfaces at 0.03 air change per hour (ACH), 6% will deposit at 0.5 ACH, and 3% will deposit at 1 ACH.
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Several recent studies have indicated significant health risks associated with exposure to fine particles as measured outdoors. However, much of the exposure is believed to have occurred indoors. Consequently, there is considerable interest in the relationship between indoor and outdoor fine particles. This paper describes some results from a study in which the processes of particle removal from infiltrating air by building envelopes are simulated in a chamber. The chamber consists of two compartments, each having a volume of 19 m. Particles with aerodynamic diameters in the range of 0.05 to 5
The air in a room was cleaned, and then airborne particulate matter was collected for various subsequent time intervals simultaneously in the room and outdoors nearby by pumping air through filters; the filters were analyzed by X-ray excitation for elements known to be primarily of outdoor origin (Fe, Zn, Pb, Br, Ca). Within several hours an equilibrium is reached in which the indoor/outdoor ratio is typically 0.3. A model is developed incorporating filtration in passing through walls, and deposition on, and resuspension from surfaces in the room. Experiments were carried out with windows “cracked” open and wide open, and with windows and/or other room surfaces covered with plastic sheet to determine the importance of various terms. Several rooms of various types and two automobiles were studied and it is concluded that a person remaining indoors with doors and windows closed probably inhales no more than as much dust of outdoor origin as he would if he were outdoors.
Objects in Southern California museums may become perceptibly soiled within periods as short as a year due to the deposition of airborne particles onto their surfaces. Methods for reducing the soiling rate include reducing the building ventilation rate, increasing the effectivenes of particle filtration, reducing the particle deposition velocity onto surfaces of concern, placing objects within display cases or glass frames, managing a site to achieve low outdoor aerosol concentrations, and eliminating indoor particle sources. A mathematical model of indoor aerosol dynamics and experimental data collected at an historical museum in Southern California are combined to illustrate the potential effectiveness of these control techniques. According to model results, the soiling rate can be reduced by at least two orders of magnitude through practical application of these control measures. Combining improved filtration with either a reduced ventilation rate for the entire building or low-air-exchange display cases is a very effective approach to reducing the soiling hazard in museums.