ARTICLE IN PRESS

1352-2310/$ - se

doi:10.1016/j.at

�Correspond16-52-01.

E-mail addr1Present addr

DK-1401 Cope

Atmospheric Environment 38 (2004) 6349–6359

www.elsevier.com/locate/atmosenv

Prediction of indoor concentration of 0.5–4 mm particles ofoutdoor origin in an uninhabited apartment

Thomas Schneidera,�, Keld Alstrup Jensena, Per A. Clausena, Alireza Afsharib,Lars Gunnarsenb, Peter Wahlinc, Marianne Glasiusc, Finn Palmgrenc,

Ole J. Nielsend, Christian L. Foghe,1

aNational Institute of Occupational Health, Lerso Parkalle 105, DK-2100 Copenhagen, DenmarkbDanish Building and Urban Research, P.O. Box 119, DK-2970 Horsholm, Denmark

cNational Environmental Research Institute, DK-4000, Roskilde, DenmarkdDepartment of Chemistry, University of Copenhagen, Universitetsparken 5, DK-2100 Copenhagen, Denmark

eRisoe National Laboratory, P.O. Box 49, DK-4000, Roskilde, Denmark

Received 5 February 2004; received in revised form 23 July 2004; accepted 4 August 2004

Abstract

Indoor and outdoor particle size distributions, indoor–outdoor pressure difference, indoor air-exchange rate, and

meteorological conditions were measured at an uninhabited apartment located in a busy street in Copenhagen during 1-

month long fall, winter and spring campaigns. Particle penetration was estimated from concentration rebound

measurements following HEPA filtering of the indoor air by fitting a simple deterministic model. The model included

measured air exchange rates and published surface deposition loss rates. This model was then used to predict indoor

particle concentration. The model predicted well the indoor concentration of coarse (1.2–4 mm) particles of outdoororigin for the fall and spring campaign. The model performed less well for the fine (0.5–1.2 mm) particle concentrationand the winter campaign. The association between the ratio measured/predicted indoor concentration and factors not

included in the deterministic model was analysed statistically and the result was used to determine a correction factor to

the model prediction. The correction factor was found to depend on wind velocity, outdoor relative humidity, and air

exchange rate. Including the correction factor reduced the ratio of the 95 percentile to the 5 percentile by an average of

26% for the fine particles and 12% for the coarse particles. The ratio measured/predicted concentration using the

correction factor was found to be the highest during periods where it was most likely that occupants were present in

other apartments. The results suggest that factors such as particle chemical composition, within building transport

patterns, and occupant behaviour in other apartments should be identified and quantified in future studies, and that

these factors need to be included in predictive models.

r 2004 Elsevier Ltd. All rights reserved.

Keywords: Air exchange; Humidity; Penetration; Temperature; Wind

e front matter r 2004 Elsevier Ltd. All rights reserve

mosenv.2004.08.002

ing author. Tel.: +45-39-16-52-95; fax: +45-39-

ess: ts@ami.dk (T. Schneider).

ess: Danish Environmental Protection Agency,

nhagen, Denmark.

1. Introduction

Evidence is accumulating that health effects of

ambient air-pollution are related mainly to particles of

near- to sub-micrometer size (Kunzli et al., 2000;

d.

ARTICLE IN PRESS

Bedroom

Kitchen

Living room

Hall

Window with slitBA

Street

APS

Backyard

Window

T. Schneider et al. / Atmospheric Environment 38 (2004) 6349–63596350

Schwartz et al., 2002; Pope, III et al., 2002; Ibald-Mulli

et al., 2002). The health effects have been related to

emissions from combustion of fossil fuels, particularly

traffic exhaust. However, epidemiological studies have

relied strongly on urban background concentration

measurements where the contributions from local high-

traffic areas are highly diluted. In addition, people spend

most of their time indoors. The indoor concentration of

particles from street environments may thus be a key

determinant of city dwellers’ exposure to traffic-related

particulate air-pollution. An improved understanding of

the factors determining the indoor concentration of

these particles can lead to an improved assessment of the

quantitative health risks of urban air-pollution.

The indoor fraction of particles from the street

environment is determined by the rate of air exchange,

particle loss from the outdoor air during passage through

the building shell and the air handling system, by

evaporation and chemical processes, including decom-

position, and by deposition to room surfaces. Resuspen-

sion will generate airborne particles including particles of

outdoor origin having sizes modified by agglomeration

and fragmentation. Penetration (defined as the fraction of

particles not removed from the outdoor air as it moves

indoors) of particles through a building shell depends on

particle size, crack geometry and pressure drop and has a

maximum in the size range 0.1–1mm (Mosley et al., 2001;

Liu and Nazaroff, 2003). Also removal by the air

handling system is size dependent. Deposition to room

surfaces depends on particle size and on other factors,

e.g., air turbulence intensity and surface roughness, and

can exceed loss rates of 0.5 h�1 for particle sizes outside

the range 0.1–1mm, as reviewed by Lai (2002). Resuspen-sion (Thatcher and Layton, 1995) is very low for particles

below 1mm and increases rapidly for increasing sizes

above 1mm. Particle loss owing to decomposition of

outdoor ammonium nitrate aerosols into ammonia and

nitric acid gases upon entering low-humidity indoor

environments occurs at rates ranging from 0.3 to 18h�1

(Lunden et al., 2003). Thus, it is important to include the

time and size dependence of particle penetration and loss

in modelling the fraction of outdoor particles in indoor

air and in studies of health effects of particles of outdoor

and of indoor origin.

The objective of the present work was to develop and

validate a model for predicting the indoor concentration

of 0.5–4mm particles of outdoor origin in an unin-

habited apartment. This work is part of a long-term

study of health effects of ambient air-pollution including

pollution generated by local traffic.

Bath

Exhaust ductStaircase

Fig. 1. Apartment. Measuring positions A and B of the APS

particle sizer, outdoor air intake slit, and exhaust fan. During

measurements, the kitchen and entrance doors were sealed.

2. Materials and experimental methods

Indoor and outdoor particle size distributions, in-

door–outdoor pressure difference, indoor air exchange

rates, and meteorological conditions were measured at

an uninhabited two-room 4th floor apartment (Fig. 1) in

a five-storey building in Copenhagen. The apartment

complex is located in a narrow two-way street canyon

with a traffic load of �26,000 vehicles (4–5% heavy duty

vehicles) each working day (Municipality of Copenha-

gen, 2002). The study was conducted in an uninhabited

apartment in order to limit interference from indoor

particle sources. To prevent contamination from the

measuring equipment all exhaust lines from pumps and

instruments were led to the kitchen, which was sealed off

from the rest of the apartment. The apartment door to

the front door staircase was not tight and was

consequently sealed prior to beginning the measure-

ments in order to limit infiltration of particles from the

stairway, which would contain a mixture of particles

originating from the street canyon and from other

apartments. The doors between the living room, bed-

room and the small hall were kept open and the air was

stirred in each room with a small fan. It was assumed

that this caused the airborne particles in the ‘‘study

space’’, consisting of the three rooms, to be well mixed

within a time scale of half an hour. In previous

comparable studies, particle deposition on fan blades

has been reported to be less than about 10% (Thatcher

et al., 2002) and 2% (Byrne et al., 1995) of the total

deposition, respectively, and was hence assumed

negligible.

The volume of the study space was 78.6m3. The

ceiling height was 2.7m, floor area was 29m2 and the

wall area was 95m2. Furniture and instrument racks,

tubes, etc., subtracted an estimated volume of 6.6m3 and

added an estimated horizontal area of 3.5m2 and

vertical area of 10m2. Floors were covered with wall-

to-wall carpets, and walls with wallpaper. The ceilings

were painted with acrylic paint.

Measurements were conducted during the fall, winter,

and spring campaign 2001–2002. Each campaign lasted

ARTICLE IN PRESST. Schneider et al. / Atmospheric Environment 38 (2004) 6349–6359 6351

between 4 and 5 weeks. Due to limitations in instrument

and apartment availability, measurements could not be

performed during a summer campaign. Since half-hour

averaging already was the standard reporting format for

outdoor measurements, all results in the present study

were calculated as half-hour averages.

Particles were sampled outdoors and in the living

room (pos A and B, Fig. 1) every alternating 6.5min

(and a 1min flushing time between each change) through

two 13mm ID copper tube sampling lines of identical

geometry using an aerosol particle sizer (APS) Model

3320 from TSI Inc. A computer-controlled valve was

used to switch between the indoor and outdoor sampling

line. The indoor sampling inlet was positioned 1.6m

from the wall, 1.5m above the floor. The outdoor

sampling inlet was positioned 0.25m from the wall,

facing down at an angle of 451. The sampling inlet was a

blunt-edged cylindrical nozzle with ID 1.5 cm. The APS

sampling flow rate was 5 lmin�1 resulting in an inlet

velocity of 47 cm s�1. A coarse wire mesh placed 3 cm

downstream prevented entrainment of large objects. It

was assumed that the aspiration efficiency of the

sampling nozzle for particles below 4mm was identical

for indoor and outdoor sampling conditions.

Particle mass was calculated from the APS data

assuming that the particles were spherical and had

density, r ¼ 1:0 g cm�3: The calibration of the APS 3320given by Armendariz and Leith (2002) was used for

correcting the particle counting efficiency. Prior to the

field campaigns, the APS size calibration was checked

using 5.1 mm latex spheres. Recent studies have shown

that when measuring sub-micrometer particles a small

fraction of small particles circulates back into the

sensing volume of the APS 3320 resulting in detection

of phantom particles larger than about 4mm (Armen-

dariz and Leith, 2002; Thatcher et al., 2002). This also

became evident in the present study, and for this reason

only particle sizes up to 4 mm were included for later

analysis. The appearance of phantom particles would

not affect diameter calibration using 5.1 mm particles.

The air exchange rate, l; in the study space was

maintained at a low (target 0.5 h�1) and a high (target

1.0 h�1) level, respectively, every alternating week by a

mechanical fan in the bathroom exhaust duct. The

exhaust volumetric flow rate was adjusted by varying the

slit opening in the exhaust air valve. Outdoor air was

supplied directly via a specially mounted air intake with

two standard ventilation slits (each of size 0.9� 30.5 cm

covered by a perforated plate screen) in the living room

window facing the street. One and two slits in the air

intake were open during periods with low and high l;respectively. l was determined by the constant concen-

tration method using SF6 as tracer gas, a multipoint

dosing and sampling system, and a photo-acoustic IR

detector (INNOVA Multipoint sampler and doser, type

1303, and INNOVA Gas Monitor, type 1312). Tracer

gas was injected and sampled in each of the three rooms

of the study space. Air exchange rate for the study space

was calculated from the total injection rate of tracer gas

and the effective volume of the study space. Air

exchange rate measurements showed several single

half-hour peaks that were likely due to instabilities

in the measurement procedure. These peaks were

smoothed using a five-point moving window median

method (Mathcad, 2002). A PFT multiple tracer gas

technique (Dietz et al., 1986) was used to estimate the

order of magnitude of within building air flow. The

airflows between the study space, the apartment

adjacent to the kitchen, hall and bathroom, and the

apartment below the study space were determined. One

measurement was made, lasting 12 days during the fall

campaign.

The pressure difference, Dp, across the outdoor air

intake slit was monitored taking simple precautions to

minimize dynamic pressure. The volumetric flow rate,

Qslit, through the slit was calculated from the measured

pressure difference using a laboratory calibration. The

calibration was obtained by measuring Qslit and Dp and

fitting a straight line to the points Qslit versusffiffiffiffiffiffiDp

p: This

gave the following relation:

Qslit ¼ 1:08ffiffiffiffiffiffiDp

pl s�1; one slit open;

Qslit ¼ 1:48ffiffiffiffiffiffiDp

pl s�1; two slits open; ð1Þ

where p is measured in Pa.

Air velocity (average and standard deviation) was

measured on a single occasion in the living room at 12

equally spaced positions in a plane 1m from the

outer wall, using a multidirectional air velocity probe

(B&K analyser, type 1213). The position-averaged air

velocity was 7.9 cm s�1 and standard deviation 3.5 cm

s�1. Free-stream wind velocity and direction were

measured at an urban background measuring site

(Kemp, 1993).

Penetration and deposition occurs simultaneously,

and it is difficult to decouple these effects when fitting a

model to measurement data obtained under natural

conditions. Thatcher et al. (2003) solved this problem by

manipulating the indoor environment by short periods

of resuspension activities or supply of HEPA filtered air

and measuring the decay or build-up, respectively, of

particle concentration. In the present study the HEPA

filter method was used during a short period of the fall

campaign. The filter unit was placed in the living room,

and was switched on, operated for 1 h, and switched off

by a timer. The starting time was set to coincide with

morning and with afternoon rush hours, where outdoor

concentrations were assumed to be the highest. The

procedure was repeated eight times.

Visits to the kitchen or study space for the purpose of

instrument supervision were recorded in a log-book.

ARTICLE IN PRESST. Schneider et al. / Atmospheric Environment 38 (2004) 6349–63596352

3. Model development

An instantaneously mixed single compartment model

was used to predict the concentration of outdoor

particles in the study space. The model used a time

resolution of 0.5 h and time-independent penetration, P,

and loss rate, kdep. The assumption of time indepen-

dence of kdep is justified because kdep is governed by air

turbulence, which was kept constant by the fans in the

study space. The consequences of assuming a time-

independent penetration are assessed in a later section.

The air exchange rate, l; is divided into two parts. One

part, lslit, is the air entering through the outdoor air

intake, given Dp40: The other part, l� lslit; is the airentering from outside via cracks in the outer walls and/

or from interior regions of the building through open-

ings and cracks in the interior building structure. When

Dpo0; the air passing through the slit becomes part of

the air leaving the study space. It was assumed that there

were no indoor particle sources. The indoor concentra-

tion, Cmod(Cout, P, l; lslit, kdep) was modelled using the

parameters:

Cout o

utdoor particle concentration

P p

enetration, assumed to be independent of time.

For the fraction of outdoor particles entering

through the slit, penetration is one for all particle

diameters

l m

easured air exchange rate in the study space

lslit c

ontribution to air exchange by air entering

through the slit calculated by Eq. (1)

kdep s

urface deposition loss rate, assumed to be

independent of time.

All parameters, except l and lslit are diameter

dependent but the diameter variable is omitted in the

equations to follow.

The mass balance equation for the model is:

dCðtÞ

dt¼ ½ðlðtÞ � lslitðtÞÞP þ lslitðtÞ

�CoutðtÞ � ðlðtÞ þ kdepÞCðtÞ; ð2Þ

where C is the indoor concentration. This equation

assumes that the instant values of the time-dependent

parameters are known. In the present study only half-

hour average values were available. The mass balance

equation was thus modified using the approach given by

Switzer and Ott (1992) for piecewise constant inputs and

time averaged outputs. The modelling procedure was:

(1) Assume that the instantaneous outdoor concentra-

tion is equal to the piecewise constant function, defined

by the half-hour averages, Cout(ti) and similarly for land lslit: (2) Calculate the real-time indoor concentrationduring a half-hour period using Eq. (2). To do this, one

needs to know the initial condition, i.e. the instanta-

neous indoor concentration at the beginning of the time

period. This value was estimated as 0:5ðCi�1 þ CiÞ;where Ci�1 is the half-hour average for the preceding

time period and Ci the half-hour average for the actual

time period. (3) Calculate the average indoor concentra-

tion, Cmod, over the half-hour time period. The

equations for any time period ti were:

CmodðtiÞ ¼ Sðentering through slit during time

period tiÞ þ Bðentering by other

pathways during time period tiÞ

þ Rðfraction remaining

from previous time period ti�1Þ;

S ¼lslitðtiÞ � CoutðtiÞ

Dt � ðlðtiÞ þ kdepÞ2½Dt � ðlðtiÞ þ kdepÞ

þ expð�Dt � ðlðtiÞ þ kdepÞÞ � 1;

B ¼ðlðtiÞ � lslitðtiÞÞ � P � CoutðtiÞ

Dt � ðlðtiÞ þ kdepÞ2

½Dt � ðlðtiÞ þ kdepÞ

þ expð�Dt � ðlðtiÞ þ kdepÞÞ � 1;

R ¼0:5ðCðti�1Þ þ CðtiÞÞ

Dt � ðlðtiÞ þ kdepÞ

�½1� expð�Dt � ðlðtiÞ þ kdepÞÞ: ð3Þ

4. Results

4.1. Measurements

Some particle concentration data were lost due to

instrument malfunction. This created four gaps in the

concentration and air exchange time series and (not

coinciding) three gaps in the meteorology time series. In

total, there were eight peaks of 1/2 to 1 h duration in the

indoor concentration with peak to background ratios

exceeding 10 and showing no exponential decay and five

peaks in the outdoor concentration with peak to

background ratios exceeding 10, not mirrored indoors.

These peaks were considered to be outliers and were

replaced by the average of the reading before and after

the peak. Several site visits with entry into the sealed

study space had to be made. The site visit and HEPA

filtration intervention time periods were excluded from

the general data set used for model prediction. The total

number of half-hour data obtained and used for

prediction, respectively, is given in Table 1.

The number and volume weighted distributions

indoors and outdoors averaged over all campaigns are

shown in Fig. 2. The size distribution measured

with a custom built Vienna-type Differential Mobility

Analyser (Wahlin et al., 2002) is shown for comparison.

The DMA results indicate that the calibration by

ARTICLE IN PRESS

Table 1

Arithmetic, AM, and geometric, GM mean and geometric standard deviation, GSD for outdoor and indoor fine and coarse particle

concentrations and arithmetic mean of total background nitrate concentrations

Campaign N AM/GM/GSD AM/GM/GSD Npr Median

Outdoor concentration (mgm�3) Indoor concentration (mgm�3) Ratio M/Pcorr

Fine Coarse Nitrate (*) Fine Coarse Fine Coarse

Fall 1430 5.8/3.3 14.0/11.4 0.7 1.9/1.4 2.0/2.4 1311 0.86 0.95

3.0 2.0 2.1 1.8

Winter 1003 7.7/5.1 16.8/12.7 1.4 2.5/2.1 7.3/5.2 927 0.71 1.34

2.5 2.2 1.7 2.4

Spring 1775 5.4/3.2 8.9/7.6 0.9 1.7/1.4 2.0/1.7 1361 0.74 1.19

2.9 1.8 1.9 1.8

N: Number of half-hour data. Npr: Number of predicted half-hour values. (*) GM of whole-day sample data. Number of nitrate data

per campaign ranged between 31 and 36.

The median of the ratios M/Pcorr of the measured to predicted (corrected by Eq. (7)) particle concentration is also given.

0.01

0.1

1

10

100

1000

10000

0.1 1 10Diameter, µm

dN/d

(log(

Dia

)), c

m-3

1

10

100

dV/d

(log(

Dia

)),µ

m3 c

m-3

DMA number, in

Number, out

Volume, out

DMA volume, in

Volume, in

Number, in

Fig. 2. Particle number per cm3 and particle volume in

mm3 cm�3 averaged over all campaigns for particles measured

indoors in the living room (in) and outdoors (out). Results

obtained using a DMA (Wahlin et al., 2003) are shown for

comparison. Diameter is aerodynamic diameter for APS and

mobility diameter for DMA measurements.

T. Schneider et al. / Atmospheric Environment 38 (2004) 6349–6359 6353

Armendariz and Leith (2002) under-corrects the APS

results for particles smaller than 0.7 mm. This error onlyaffects absolute concentration measurements, and not

relative measures, such as penetration, given the likely

assumption that the error is linearly related to the true

concentration.

Fig. 2 shows that both outdoor and indoor particles

measured with the APS have two volume modes,

separated at 1.2mm. Thus, two mass fractions were

calculated: a coarse (1.2 mmoDaeo4mm) and a fine

(0.5 mmoDaeo1.2 mm) mass fraction. The averaged

outdoor and indoor concentrations for the fine and

coarse particle fraction are given in Table 1 for each

campaign. Results from 24 h filter sampling of total

suspended nitrate+nitric acid from a rural site close to

Great Copenhagen were available (Ellerman et al.,

2003). These data were used as an estimate of the urban

background values (Table 1) assuming that nitrates

constituted 85% of the total nitrate+nitric acid

concentration. No information was available on the

size distribution of the suspended nitrate.

Visual inspection of the average daily outdoor

concentration time profile revealed a minor peak during

morning and afternoon rush hours for the fine and

coarse mass concentration but only during the winter

and spring campaigns.

The meteorological conditions during the campaigns

are summarized in Table 2. The pressure difference

across the outdoor air-intake slit varied with wind

direction and had a maximum when the street fac-ade

was in the windward side and a minimum when in the

leeward side. There was a 1801 difference between the

directions for minimum and maximum pressure differ-

ence, but the direction for maximum pressure difference

was shifted 301 relative to the facade normal.

When the exhaust fan was set to the high air exchange

target value, the average measured air exchange rate was

1.1 h�1 and was 0.51 h�1at the low target value. The air

exchange rates fluctuated around the mean, but were

clearly separated by the value 0.81 h�1. Thus, measured

rates below, respectively, above 0.81 h�1 will be termed

low, respectively, high air exchange rates. The PFT

tracer gas measurements showed that about one tenth of

the total make-up air in the study space was supplied

from the two selected apartments.

ARTICLE IN PRESS

Table 2

Lower quartile, median and upper quartile of conditions prevailing during the HEPA experiments and during the three campaigns

Wind velocity (m s�1) Wind direction normal Tin�Tout (1C) RHout (%) RHin (%) Pressure difference (Pa)

HEPA, quartiles 4.2/5.6/6.5 �0.79/0.73/0.42 9/11/14 69/76/82 45/48/50 0.7/1.5/2.5

Fall, quartiles 3.3/4.4/6.1 �0.71/0.44/0.5 8/13/20 76/83/89 49/51/52 2.6/4.4/5.8

Winter, quartiles 3.6/4.6/5.8 0.22/0.34/0.42 13/15/19 83/89/93 30/34/36 5.1/6.1/7.1

Spring, quartiles 2.3/3.8/4.4 0.35/0.6/0.78 10/12/15 62/75/87 28/34/38 2.1/3.4/5.0

0.1

1

1

10

0.1 10

Diameter, µm

Sur

face

dep

ositi

on lo

ss r

ate,

h-1 Thatcher et al., 2002

Medium furnished, high kdepTh

Low carpeted, low kdepTh

kdepfit

Fig. 3. Surface deposition loss rate. Experimental data by

Thatcher et al. (2002) obtained for low-level turbulence/

carpeted floor (low kdepTh ), medium-level turbulence/furnished

(high kdepTh ), and average of these two, kdep

fit .

T. Schneider et al. / Atmospheric Environment 38 (2004) 6349–63596354

4.2. Model fitting

The HEPA filtering experiments were used to estimate

penetration. For this purpose the original time resolu-

tion of 6.5min of the particle concentration measure-

ments was used. Since indoor and outdoor

measurements were alternating, each indoor measure-

ment was taken to represent a 15min time period, and

for the outdoor concentration for that same period the

average of the measurement before and after a given

indoor measurement was used. Only data for the first

hour after HEPA filtering stopped were used. Consider-

ing the involved loss rate time constants, deposition

losses could not be entirely neglected. Thus, experi-

mental surface deposition loss rates determined by

Thatcher et al. (2002) were used in Eq. (3). Their

conditions were close to the present conditions. Their

experimental room was stirred with two small opposing

fans creating three turbulence levels (urms=2.2, 4.8, and

5.1 cm s�1, respectively). Their experiments were made

for bare floors, carpeted floors, and with furnishing and

particles were measured with an APS. In the present

study, urms was 3.5 cm s�1 and it was thus assumed that

the loss rates obtained by Thatcher et al. (2002) for

urms=2.2 cm s�1 and carpeted floor, and for

urms=4.8 cm s�1 and furnished room, respectively (in the

following termed low kdepTh and high kdep

Th , see also Fig. 3)

embraced the loss rates in the study space. The loss rates

kdepTh were corrected for difference in ceiling height (2.4m

compared with 2.7m in the present study) by multi-

plying their deposition rates with 2.4/2.7. This value was

used because as a first approximation, the loss rate for

super-micron particles is dominated by stirred gravita-

tional settling giving

kdep ffiparticle settling speed

ceiling height: (4)

No further correction was made for the difference in

surface-to-volume ratio (2.5m�1 versus 3.4m�1 in the

present study).

The penetration, P, was estimated by minimizing the

sum

SAEðPÞ ¼X

i

CðtiÞ � CmodðtiÞ�� ��

CðtiÞ(5)

for each diameter interval using the surface deposition

loss rate kdepfit =0.5 (low kdep

Th +high kdepTh ). Use of

absolute and not square deviation reduces the influence

of large deviations caused by short duration peaks. The

results for each HEPA experiments were limited to

Po1, since a value P41 would indicate that indoor

sources were active. One of the eight HEPA interven-

tions experiments resulted in a fitted penetration that

fluctuated with diameter much more than the penetra-

tion obtained from any of the other experiments and this

experiment was excluded. Penetration was also esti-

mated for low and high kdepTh to determine the sensitivity

of the fit to the loss rate, see Fig. 4. It is seen that P is

insensitive to the surface deposition loss rate for the fine

particles, but is sensitive to the choice of values for

coarse particles.

The conditions prevailing during the seven HEPA

experiments and the entire campaigns are compared in

Table 2. The air exchange rate was low for five and high

for two of the seven experiments.

4.3. Prediction

The model was used to predict the indoor average

concentration during the half-hour time periods ti for all

ARTICLE IN PRESS

0

0.2

0.4

0.6

0.8

1

0.1 101Diameter, µm

Pen

etra

tion

High kdepTh

kdepfit

Low kdepTh

Fig. 4. Penetration estimated from the HEPA experiments

using kdepfit (heavy line) with standard error of the mean.

Penetration as estimated using low kdepTh and high kdep

Th is also

shown.

T. Schneider et al. / Atmospheric Environment 38 (2004) 6349–6359 6355

three campaigns, using the measured half-hour values of

Cout, l; and Dp. The procedure for prediction was: (1)

calculate how many particles that penetrated into the

study space during an earlier time period tj (joi); (2)

calculate how many of these particles remain in the

study space at the beginning of the time period ti, using

the measured air exchange rates for each time interval;

(3) sum over all j (i�12ojoi). Only the preceding 12

half-hour periods were included, since a negligible

fraction of particles that had entered from still

earlier periods would have remained in the study space

air at time period ti; (4) use this sum as the initial

condition for calculating the time-varying concentration

during the time period ti and average this time varying

concentration over the time period ti as described in the

section 3.

Measured and predicted half-hour averages of indoor

mass concentrations are shown in Fig. 5 for the fall

campaign. Fig. 6 shows scatter plots of measured versus

predicted fine and coarse particle mass concentrations

for all three campaigns. The results show that there are

deviations between predicted and measured concentra-

tions, most pronounced for the winter campaign. During

1 week of the winter campaign where the air exchange

rate was low, the measured air exchange rates exhibited

an unusual short-term variability. Results obtained

during this week are shown separately in Fig. 6c and

d. The ratio, M/P, of the measured concentration to

predicted concentration was calculated and for

each campaign and the 5, 25, 50, 75, and 95 percentiles

of the distribution of M/P were determined. The results

are shown in Fig. 7. It is seen that the model over-

predicts fine particle concentrations and under-predicts

coarse particle concentrations during the winter

campaign.

4.4. Model correction

It is unlikely that differences between predicted and

measured concentrations were caused by differences in

the intake efficiency of the outdoor and indoor sampling

nozzle, since it was found that the wind dependence of

the ratio M/P of measured to predicted concentration

calculated for fine particles, medium particles

(1.2 mmoDaeo2.3mm) and very coarse particles

(2.3 mmoDaeo 4mm) were similar. It is also unlikely

that particulate ozone reaction products have contrib-

uted, as these particles generally are below 0.5 mm(Weschler and Shields, 1999).

The particle concentration at the back fac-ade was

likely lower than Cout and this could have affected the

ratio M/P. However this concentration was unknown.

As it was not obvious how to include additional factors

in the deterministic model, a statistical analysis was

made of the association between M/P and other factors

as described below. The analysis was based on a step-

wise forward linear regression analysis of data from all

campaigns pooled. Linearity, as assessed visually from

scatter plots, could be obtained by using the log-

transformed values of the dependent variable M/P and

the independent variables wind velocity (WV), wind

direction normal to fac-ade (this factor was not log-

transformed), 100�RH, where RH is outdoor relative

humidity in %, air exchange rate (l), pressure drop (Dp)

if positive, and difference between indoor and outdoor

temperature. The stepwise regression analysis lead to

inclusion of WV, l; and 100�RH, i.e.

lnMeasured

Predicted

� �¼ a þ b � lnðWV Þ þ c � lnðlÞ

þ d � lnð100�RHÞ: ð6Þ

A Durbin–Watson test showed that the residuals were

autocorrelated. This does not bias the estimates of the

coefficients, but inferences regarding significance are

unreliable. The dependency of M/P on WV, l; andRH was used to correct the original model by multi-

plying the predicted concentrations for each time

interval ti by

Fi ¼WVi

exp ðlnðWV iÞÞ

� �b li

exp ðlnðliÞÞ

� �c

�100� RHi

exp ðlnð100� RHiÞÞ

� �d

; ð7Þ

where the bar indicates averaging over all time intervals

ti. The ratio of the measured to corrected predicted

concentrations, M/Pcorr, were calculated and for

each campaign the 5, 25, 50, 75, and 95 percentiles of

the distribution of M/Pcorr determined. The results

(Fig. 7) show that the correction did indeed improve

prediction.

ARTICLE IN PRESS

0.1

1

10

100

05-10-01 10-10-01 15-10-01 20-10-01 25-10-01 30-10-01 04-11-01Date

Con

cent

ratio

n, µ

gm

-3

0.1

1

10

100

5.10.01 10.10.01 15.10.01 20.10.01 25.10.01 30.10.01 4.11.01

Date

Con

cent

ratio

n, µ

gm

-3

(a)

(b)

Fig. 5. Measured and predicted concentrations for the fall campaign. Circles: measured indoor concentration. Line: predicted indoor

concentrations. a: fine particle mass concentration: coarse particle mass concentration.

T. Schneider et al. / Atmospheric Environment 38 (2004) 6349–63596356

No relationship could be found between M/P and the

measured total suspended nitrate+nitric acid concen-

tration measured at the rural site. Since there was no

information on variations during a day, on particle size,

and on the concentration of nitrate, especially ammo-

nium nitrate, a possible influence of the nitrate fraction

of Cout on M/P could not be studied further.

The half-hour ratio M/Pcorr was averaged over a

campaign for each given half-hour. The averages

showed a daily pattern for the fine particle fraction,

both for work days and week ends for the fall campaign

(Fig. 8). The pattern was similar for the spring and less

pronounced for the winter campaign. For coarse

particles the patterns were much less pronounced.

5. Discussion

The penetration, Pfit, was found to be high for

particles below 1mm and decreased to about 0.1 at

4mm. This is in qualitative agreement with the findings

of Liu and Nazaroff (2003) and others.

For the fall and spring campaign the original model

predicted the measured coarse particle concentration

well. The median ratio of M/P was close to one, and the

ratio R95/5 of the 95 percentile to 5 percentile was 2.3 for

the fall and 2.6 for the spring campaign, respectively

(Fig. 7). The original model performed less well for all

other cases as seen from Fig. 7. Including the correction

(Eq. (7)) reduced R95/5 on average by 26% for the fine

ARTICLE IN PRESS

2:1

1:1

1:2

0.1

1

10

100

0.1(a) (b)

(c) (d)

1 10 100

Predicted, µg m-3

Mea

sure

d, µ

g m

-3

0.1

1

10

100

0.1 1 10 100

Predicted, µg m-3

Mea

sure

d, µ

g m

-3

2:1

1:1

1:2

0.1

1

10

100

0.1 1 10 100Predicted, µg m-3

Mea

sure

d, µ

g m

-3

2:11:1

1:2

0.1

1

10

100

0.1 1 10 100Predicted, µg m-3

Mea

sure

d, µ

g m

-3

2:11:1

1:2

Fig. 6. Scatter plot of predicted versus measured indoor mass concentrations. a, c: Fine fraction. b, d: Coarse fraction. a, b: Fall and

spring. c, d: Winter. Grey circles show results obtained during 1 week where the air exchange rate, which was low, showed unusually

large short-term variability.

T. Schneider et al. / Atmospheric Environment 38 (2004) 6349–6359 6357

particles and by12% for the coarse particles. For R75/25

the average reduction was 25% and 6%, respectively

(Fig. 7). It is noteworthy, that a major part of the

aberrant coarse particle results from the winter cam-

paign were obtained during a 1-week period where the

measured air exchange rates showed an unusually large

short-term variability (Fig. 6c and d). It is not known

what caused this instability and no explanation as to

why this appeared to influence the ratio M/P can be

given.

The wind velocity coefficient b (Eqs. (6) and (7)) was

positive and the air exchange rate coefficient c was

negative indicating that penetration increases with

increasing wind velocity and decreases with increasing

air exchange rate. An increase in penetration with

increasing air exchange rate could have been expected

since it has been found that penetration increases with

increasing pressure difference Dp (Mosley et al., 2001;

Liu and Nazaroff, 2003). On the other hand, a separate

effect of Dp or of wind direction was not identified in the

stepwise regression analysis. The outdoor air intake slit

was of a commonly used type. When the mechanical fan

provided a low air exchange rate (average 0.51 h�1), the

slit replaced 18% of the exhausted air, and the average

pressure difference was 4.0 Pa. At the high air exchange

rate (average 1.1 h�1), the average pressure difference

was 5.2 Pa and the slit only replaced 14% of the

exhausted air. Hence, a relatively larger fraction of

ARTICLE IN PRESS

0.1

1

10M

/P

M/P

corr

M/P

M/P

corr

M/P

M/P

corr

M/P

M/P

corr

M/P

M/P

corr

M/P

M/P

corr

Fall Winter Spring Fall Winter SpringFine Coarse

5, 2

5, 5

0, 7

5, 9

5 pe

rcen

tiles

Fig 7. The 5, 25, 50, 75, and 95 percentiles of the cumulative

distribution of the ratio M/P of the measured to predicted and

M/Pcorr of the measured to predicted (corrected by Eq. (7)) fine

and coarse particle concentration for fall, winter and spring

campaigns.

0

0.5

1

1.5

2

0 4 8 12 16 20 24Time of day

Mea

sure

d/P

redi

cted

WorkDay

WeekEnd

Fig. 8. Half-hour ratio M/Pcorr of the measured to predicted

(corrected by Eq. (7)) concentration for each half-hour

averaged over the fall campaign for work days and for week

ends, respectively. The error bars are the standard deviations

(only one-side shown).

T. Schneider et al. / Atmospheric Environment 38 (2004) 6349–63596358

make-up air penetrated through cracks at the high air-

exchange rate. Since the average pressure difference

across the front fac-ade only increased from 4.0 to 5.2 Pa

(when doubling the air exchange rate from 0.51 to

1.1 h�1), a larger part of this fraction must have entered

through the back fac-ade or from adjacent apartments.

The pressure drop across the outdoor air intake slit in

the front fac-ade varied with wind direction in agreement

with the expected pressure conditions in a street canyon

and this has the following implication. When the street

fac-ade is in the windward side, the ambient pressure is

increased and the concentration Cout at the street fac-ade

will be close to urban background. When the street fac-

ade is in the leeward side, the wind vortex within the

canyon transports street emissions up along the fac-ade.

However, the ambient pressure at the street fac-ade is

reduced thereby reducing direct penetration from this

fac-ade (Vardoulakis et al., 2003; Ni Riain et al., 2003).

The significance of this effect could not be assessed since

concentration at the back fac-ade was not measured and

simultaneous measurements of urban background for

particle sizes in the range 0.5–4 mm were not available. In

summary, the present findings suggest that both wind

velocity and air exchange rate affect the within-building

transport, and thus the amount of make-up air

originating from the interior of the building. It can also

be concluded that in future studies pressure differences

across all study space boundaries should be monitored.

The outdoor relative humidity coefficient d (Eqs. (6)

and (7)) was positive and larger for fine rather than for

coarse particles indicating that penetration increases

with increasing value of 100�RH, i.e. decreases with

increasing outdoor relative humidity. This could suggest

that there were losses due to evaporation. From Table 1

it is seen that outdoor nitrate concentrations can be

significant but it was not possible to estimate the

contribution of ammonium nitrate decomposition to

this loss. The results of Lunden et al. (2003) show that

the loss can be significant.

Fig. 8 shows a daily variation in M/Pcorr and the

highest values coincide with the time where occupants

are most likely to be present in other apartments. This

strongly indicates that occupant activities in adjacent

apartments influenced the concentration in the study

space. Opening doors and windows could facilitate

transport of outdoor particles into and within the

building and household activities are known to generate

airborne particles. The PFT measurements showed that

about one tenth of the make-up air originated from the

apartment adjacent to and the apartment below the

study space. Since the PFT measurement was made only

once and lasted 12 days, a daily variation of this air

transport could not be estimated and linked to wind

velocity and air exchange rate in the study space.

6. Conclusions

The simple dynamic model predicted well the indoor

concentration of coarse particles of outdoor origin for

the fall and spring campaign. The model performed less

well for the fine particle concentration and the winter

campaign.

The simple model could be improved by including a

correction factor obtained from a statistical model of the

association between the ratio measured to predicted

ARTICLE IN PRESST. Schneider et al. / Atmospheric Environment 38 (2004) 6349–6359 6359

indoor concentration and variation in wind velocity, air

exchange rate and outdoor relative humidity.

Indirect evidence indicated that there is transport of

particles to the study space from other apartments, and

that this transport depends on wind velocity, air

exchange rate and behaviour of occupants in other

apartments.

To improve prediction of the indoor fraction of

outdoor particles the role of particle chemical composi-

tion, penetration through all external walls, within

building transport, and occupant behaviour in other

apartments should be identified and quantified and

corresponding deterministic models be developed for

inclusion in exposure prediction models.

Acknowledgements

This work was part of Project C1 of the Centre for

Transport Research on Environmental and Health

Impacts and Policy, supported by the Danish Strategic

Environmental Research Program. Suggestions by Bill

Nazaroff during the initial phase of the project are

greatly appreciated.

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