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Atmospheric Environment 42 (2008) 3087–3098

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The impact of urban street canyons on population exposureto traffic-related primary pollutants

Ying Zhou�, Jonathan I. Levy

Department of Environmental Health, Harvard School of Public Health, Landmark Center, 4th Floor West,

P.O. Box 15677, Boston, MA 02215, USA

Received 30 August 2007; received in revised form 13 December 2007; accepted 13 December 2007

Abstract

The relationship between emissions and population exposures to traffic-related air pollutants is a necessary component

of any assessment of mobile source control strategies. In this analysis, part of the New York Metropolitan Exposure to

Traffic Study (NYMETS), we simulated atmospheric dispersion and population exposure in densely populated street

canyons in mid-town Manhattan. We estimated population exposure using the concept of an intake fraction (iF), defined

as the fraction of material released from a source that is eventually inhaled or ingested by a population. We applied the

Operational Street Pollution Model (OSPM) for inert pollutants (e.g., CO, PM2.5), reactive pollutants (e.g., NO and NO2),

and ultrafine particles. Concentrations were linked with different subpopulations, including residents, workers, and

pedestrians, incorporating time-activity patterns and differential breathing rates. For the base case scenario, the total iF

for a 100-m-long street canyon including the contribution of different subpopulations is on the order of 10�3. Daytime

office workers and pedestrians contribute most to the overall iF, together contributing over 80% for all pollutants.

Univariate sensitivity analyses show that iFs are sensitive to the street configuration and slightly sensitive to traffic volume,

speed, and percent of trucks. Our iF estimates are similar in magnitude to those found for indoor environmental tobacco

smoke and are substantially higher than previous mobile source estimates, mainly due to the higher population density in

street canyons. Our findings emphasize the importance of controlling emissions in urban street canyons, and the need to

study high-resolution near-source exposures for primary pollutants in urban settings to inform cost–benefit analyses.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: Air pollution; Exposure assessment; Intake fraction; Particulate matter; Street canyon; Traffic

1. Introduction

Cities around the world are trying different waysto solve the problems of traffic congestion and theassociated air pollution. For example, London,Stockholm, Singapore, and several cities in Norway

e front matter r 2007 Elsevier Ltd. All rights reserved

mosenv.2007.12.037

ing author. Tel.: +1617 384 8528;

8859.

ess: yzhou@hsph.harvard.edu (Y. Zhou).

have been implementing congestion pricing systems,which charge a fee for vehicles to enter a certainzone in the city (Jenkins, 2007; McKinnon, 2007).These programs are of interest because motorvehicle emissions are associated with multipleadverse health impacts (Brunekreef and Holgate,2002; Gauderman et al., 2004; Pope et al., 2002),are significant contributors to air pollution inurban areas (further worsened by congestion),and because the health risk implications per unit

.

ARTICLE IN PRESSY. Zhou, J.I. Levy / Atmospheric Environment 42 (2008) 3087–30983088

emissions may be magnified in densely populatedurban areas.

In urban areas, previous studies have shown thatstreet canyons may trap pollutants within theirboundaries (Britter and Hanna, 2003) and canincrease pollutant concentration significantly withinthe immediate areas around the roadway. Further-more, from a health risk perspective, the benefits ofemissions controls will include not just residentialpopulations, but also other groups routinely spend-ing time in or passing through street canyons, e.g.,office workers, pedestrians, bikers, and drivers. Thetypical health risk assessment approach for mobilesources involves combining ambient concentrationestimates with residential populations, assuming24 h day�1 are spent at home, which may be areasonable assumption for spatially homogeneouspollutants evaluated in coarse-resolution models butbreaks down within a single urban street canyon.

To characterize the relationship between emis-sions and population exposures in an urban streetcanyon, we use the concept of an intake fraction(iF), defined as the fraction of material or itsprecursor released from a source that is eventuallyinhaled or ingested by a population (Bennett et al.,2002). Studies have shown a wide range of iFresults. For primary pollutants, the iF for pointsources with tall stacks (e.g., power plants) aregenerally on the order of 10�6 in developedcountries (Evans et al., 2002; Levy et al., 2002;Smith, 1993) and 10�5 in developing countries withhigher population density (Smith, 1993; Zhou et al.,2003, 2006). For mobile sources, the iF is generallyon the order of 10�5–10�6 (Evans et al., 2002; Grecoet al., 2007; Marshall et al., 2003, 2005). On theother hand, for indoor environmental tobaccosmoke (ETS), the iF has been found to be on theorder of 10�3 (Klepeis and Nazaroff, 2006; Smith,1993). Few studies have incorporated personalexposure or dose concepts into iF estimates, andnone have explicitly considered the iF associatedwith emissions in an urban street canyon, a criticalcomponent in understanding the benefits of urbantraffic mitigation measures.

Thus, this study aims to characterize the influenceof urban street canyons on population exposure perunit emissions from motor vehicles. We consider therelative influence of street configuration (i.e., streetcanyons versus other line sources in open terrain)and consideration of personal exposure rather thanresidential concentrations only. Furthermore, wecompare the iF of traffic air pollution in street

canyons against previous estimates from indoor andoutdoor environments, to facilitate interpretation ofour findings. These analyses will allow us to decidewhether there is a need to explicitly consider streetcanyons in estimating the benefit associated withpolicies aiming at reducing traffic air pollution inlarge metropolitan areas.

2. Methods

We conducted our calculations in two parts—thebase case and the sensitivity analysis. In the basecase, the input data (e.g., street configuration,population count) are based on mid-town Manhat-tan in New York City, an area with tall buildingsand street canyons as well as high traffic volumesand population density. In addition, there arediverse population groups in this area, such aspedestrians, residents, office workers, and drivers,which makes it possible to study the total popula-tion exposure and the relative importance ofexposure from the different subpopulations. Thissite selection also allows us to use data from a fieldcampaign conducted in August 2006 as part of theNew York Metropolitan Exposure to Traffic Study(NYMETS), a project with the overall aim ofunderstanding the costs and benefits of differentstrategies to reduce traffic congestion in New YorkCity. This field campaign included video recordingsof roadways, allowing for estimates of traffic,pedestrian, and biker counts, as described in moredetail in the following sections.

2.1. Intake fraction definition

In a street canyon setting with multiple subpo-pulations, we defined iF slightly differently fromprevious publications, calculating it as

iF ¼

Pki¼1

R T2

T1ðPiCiðtÞBRiðtÞÞdt

� �

nEL,

in which subscript i indicates subpopulation (e.g.,pedestrians, office workers), and t indicates timespent in the street canyon. Pi is the size ofsubpopulation i. Ci is the incremental change inconcentration (gm�3) associated with the trafficemission rate described below, based on air disper-sion models. BRi is the breathing rate for sub-population i. In the denominator, n is the number ofvehicles going through the street canyon per day, E

is the average daily emission factor (g km�1 car�1),

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1

2

1

3

2

1 1

1

21

4

2

1

2

1

4

2

1

221

3

2

1

014300

013700

013100

013900

011201

014500

013300

010400011202

010200012500

LegendCensus tract boundaries

Census block selection

0 200100Meters

2

Fig. 1. Census tract and census block group distributions in mid-town Manhattan area used in the case study.

Y. Zhou, J.I. Levy / Atmospheric Environment 42 (2008) 3087–3098 3089

and L is the street canyon length in km. Thus, thenumerator calculates the potential dose of pollu-tants inhaled (in units of g day�1) while thedenominator calculates the emissions in the sameunits of mass per time, so that iF is a unitlessnumber. Operationally, multiplying iF by emissionswould provide an estimate of dose proportional tohealth risk in many settings.

2.2. Street canyon model

To model pollutant concentrations in the streetcanyon, we use the Operational Street PollutionModel (OSPM). It is a parameterized semi-empiri-cal street canyon model, developed by the NationalEnvironmental Research Institute (Berkowicz et al.,2003). Concentrations of exhaust gases are calcu-lated using a combination of a plume model forthe direct contribution and a box model for therecirculating part of the pollutants in the street. Theturbulence in the street is assumed to be composedof two parts: a part dependent on wind speed(ambient turbulence) and a part due to traffic-induced turbulence, which dominates when the windspeed is low. Nitrogen dioxide (NO2) concentrationsare calculated taking into consideration the chem-istry of nitrogen monoxide (NO), NO2, and ozone

(O3) and the residence time of pollutants in thestreet. Model validations have shown reasonableagreement with field measurement data (Kukkonenet al., 2003; Vardoulakis et al., 2007).

2.3. Street configuration

In the base case scenario, we use average buildingheights and street widths from mid-town Manhat-tan. The building heights in the mid-town Manhat-tan area under study range from 5 to 350m with anaverage of 88m and a median of 60m (NYC, 2003).In the base case scenario, we use the median heightof 60m to represent the height of buildings on bothsides of the streets. For the width of the street, GISdata (Fig. 1) shows the width of avenues in the areaof around 30–35m and that of the streets 20–25m.In the base case, we assume a width of 30m.Therefore, the aspect ratio (the ratio of width toheight) for the street canyon being modeled is 1 to 2.In the sensitivity analysis, a wider range of buildingheights and street widths will be tested.

2.4. Pollutants modeled

For particles, we focus on primary particlesincluding PM2.5 as well as primary particulate

ARTICLE IN PRESSY. Zhou, J.I. Levy / Atmospheric Environment 42 (2008) 3087–30983090

matter with an aerodynamic diameter of up to10 mm (PM10). Secondary particles are not modeledbecause the residence time in the street canyon is notlong enough for them to form. In general, we modeltwo categories of pollutants—conservative pollu-tants (e.g., CO, PM2.5, PM10) and reactive pollu-tants (e.g., NO and NO2). In addition, we alsomodeled the concentration of ultrafine particles.Although ultrafine particles could be removed bydilution, coagulation, deposition, and condensation,previous studies found dilution to be the mainprocess in the near field while other effects aremarginal (Ketzel and Berkowicz, 2004; Vignatiet al., 1999). Therefore, we treated ultrafine particlesas an inert pollutant given our focus on exposures instreet canyons.

To calculate the iF of NOx, the total concentra-tion of NO and NO2 is used in the numerator andthe total emissions of NO and NO2 is used in thedenominator. Although we modeled the concentra-tion of NO and NO2 separately, we did not quantifytheir individual iFs because of definitional andpractical reasons (i.e., the non-linear and reversiblechemistry among NO, NO2, and O3).

2.5. Population groups considered

Residential populations in mid-town Manhattanare estimated using 2000 census data from the USCensus Bureau (http://www.census.gov). The areaunder study falls in several census tracts (Fig. 1).For the base case, we use population data fromcensus tract 013700, which had a total populationof 6797. Since there are 10 blocks within this tract,the average residential population within eachblock is about 680 people. We presume for thisanalysis that only those residents immediatelyproximate to the street canyon would be affected.Since the perimeter of the block is approximately630m (250m long and 65m wide), assumingall the residents are distributed along the buildingenvelopes, there is a residential population densityof approximately one person per linear meter.Therefore, for a 100-m-long street canyon (our basecase assumption, between the lengths of the streetsand avenues), the residential population on bothsides of the street would be approximately 200,which we use in the base case. The influenceof population density on iF is discussed in thesensitivity analysis.

To estimate the office worker population, we use aday and night population database developed at Los

Alamos National Laboratory (LANL) (McPhersonand Michael, 2004a, b). Using a LANL 250m rasterdatabase, we estimated a nighttime population of5958 for census tract 013700, and a daytime popula-tion of 36,168. The daytime population includes bothdaytime workers and the residential population whostays at home during the day. The nighttimepopulation estimated using the LANL dataset isabout 10% lower than the 2000 US Census Bureauresidential population value, indicating the reason-ableness of the value.

To separate out office workers versus residentsamong the daytime population (which is not directlyindicated in the LANL data), we used two sourcesof data—the Consolidated Human Activity Data-base (CHAD) (USEPA, 2003) and the AmericanCommunity Survey (ACS) (ACS, 2005). Using datafrom the National Human Activity Pattern Survey(versions A and B) from CHAD and assumingdaytime to be from 8 a.m. to 6 p.m., we calculatedthat on average people spent 46% of daytime hoursat home. This can be compared with ACS data forNew York State, which showed that 58% of thepopulation age 16 and older does not go to workoutside the home during the day. Clearly, theseindividuals do not spend all daytime hours at home,but this value is in general agreement with the valuefrom CHAD.

Thus, we presume that there are 200 residents perstreet canyon, all of whom are at home at night and46% of whom are at home during the day. For theoffice worker estimate, we calculate the daytimepopulation as six times the nighttime population, orapproximately 1200 people, and presume that 1108are workers (1200 minus 46% of 200). These areclearly approximations and are meant to representplausible base case values that are more formallyevaluated in our sensitivity analyses.

We also estimated the number of pedestrians,bikers, and motor vehicle drivers/passengers in thestreet canyon by manually counting numerous15min videos recorded within street canyons inmid-town Manhattan during daytime in August2006 as part of NYMETS. The pedestrian fluxestimated from the video recordings ranged from1200 to 3500 pedestrians per hour across differentstreet canyons, with the flux for bikers ranging from40 to 120 per hour. Since the length of the streetcanyon is 100m and we assume that pedestriansmove at an average speed of 0.6m s�1 (given acrowded city block with traffic lights), then eachpedestrian spends approximately 3min in the street

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Table 1

Summary of inputs used in the base case intake fraction calculations

All vehicles Cars Trucks

Average daily emission factors (in g km�1, except for particle number, which is in number km�1) used in the OSPM model in the base case

scenario

NOx 2.17 0.389 18.2

CO 7.47 6.97 11.9

PM2.5 0.135 0.020 1.17

PM10 0.183 0.054 1.34

Particle number 6.17E+14 3.88E+13 5.82E+15

Street configuration and traffic count

Street canyon length (m) 100 Traffic count (average) (h�1) 417

Street canyon width (m) 30 Traffic count (day) (h�1) 672

Street canyon height (m) 60 Traffic count (night) (h�1) 234

Pedestriansa Bikersa Residents Office workers Drivers/passengersa

Daytime Night

Parameters for different subpopulations

Count 150 2 92 200 1108 2

Time (h) 12 12 10 14 10 24

Breathing rate (m3 day�1) 24 38 13 13 12 12

aCounts represent steady-state values estimated from videotape estimates of population flux.

Y. Zhou, J.I. Levy / Atmospheric Environment 42 (2008) 3087–3098 3091

canyon. For bikers, we assume their speed is threetimes that of the pedestrians and it takes themnearly 1min to pass through the street canyon. Thisimplies a steady state count of between 56 and 160pedestrians and between one and two bikers—wepresume 150 pedestrians and two bikers for our basecase, representing a more crowded urban canyon,and assume that these values are applicable for12 h day�1 (Table 1).

For drivers/passengers, the flux of cars rangedfrom 300 to 2000 per hour with an average of 900cars per hour. The flux of trucks ranged from 36 to230 per hour with an average of 100. Since therecorded traffic only covered weekdays duringworking hours, we followed one of the default 24-h traffic variation patterns in OSPM (Fig. 2),assuming that our values represented hours withmaximum traffic flow. We therefore set the averagedaily traffic to be 10,000 with 90% cars and 10%trucks, so that the hour with the highest traffic flowhas about 900 vehicles. Given a presumed averagespeed of 30 kmh�1, we arrive at a steady state countof 1.4 vehicles, and use an estimate of twoindividuals to account for passengers.

Our iF focus implies that we are evaluating theeffects of emissions within a single street canyon on

populations within that canyon. For the sake ofsimplicity, we have omitted the effects of emissionswithin the street canyon on other populations. Inaddition, there are clearly numerous street canyons,and individuals will be exposed in all of thosesettings in various capacities. For example, thepedestrians in one street canyon may be commutingand become the daytime worker in another streetcanyon. Our emphasis is on the population ex-posure benefits of emissions controls within adefined street canyon, not on individual personalexposure estimation or quantification of vehicle-specific exposure benefits.

2.6. Potential dose assessment

To move from concentrations to potential dose,we need multiple additional components. First, weneed to account for the vertical gradient of bothconcentrations and populations. Previous researchhas shown that TSP and PM10 concentrationsdecrease with height exponentially in street canyons(Chan and Kwok, 2000), and populations areclearly distributed across these tall buildings. Weassume that building residents are distributeduniformly on each floor. Assuming each floor is

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Average Diurnal Traffic

Hour of Day2

Traf

fic C

ount

(veh

icle

s/ho

ur)

0

200

400

600

800

1000All vehiclesCarsTrucks

4 6 8 10 12 14 16 18 20 22 24

Fig. 2. Distribution of average diurnal traffic pattern used in OSPM model.

Y. Zhou, J.I. Levy / Atmospheric Environment 42 (2008) 3087–30983092

3m in height, there are 20 floors in a 60-m building.We therefore assume that 5% of the office workerand residential populations can be found on each ofthe 20 floors.

We use concentration at the ground level torepresent outdoor exposures to pedestrians, bikers,and drivers/passengers. We combine the concentra-tion at different vertical heights with an averageinfiltration factor for different pollutants to repre-sent the exposures of office workers and buildingresidents. We assume an infiltration factor of 1 forgas phase pollutants. For particulate matter, theinfiltration factor ranges from 0.1 to 0.7 acrosssettings and particle size fractions (Bennett andKoutrakis, 2006; Meng et al., 2005); we assume aninfiltration factor of 0.7 for PM2.5, 0.4 for PM10,and 0.5 for ultrafine particle number. While therewill clearly be a dependence on the presence/absenceof central air conditioning and age of buildings,among other factors, such data were not availablefor this analysis.

In addition, as mentioned above, we estimatedbreathing rates for each subpopulation based ontheir activity patterns. We use breathing ratesof between 12 and 38m3 day�1 (Table 1) followingthe US EPA Exposure Factors Handbook recom-mendations in Table 5-23 (USEPA, 1997) foradults engaged in activities with various levels ofexertion.

2.7. Meteorology data

OSPM requires inputs of wind speed, winddirection, and temperature and global radiation(Berkowicz et al., 2003). Wind speed and winddirection are assumed to represent the conditionsabove roof level in the city. Temperature and globalradiation are used to calculate the chemicaltransformations among NO, NO2, and O3.

Earlier studies (Hanna et al., 2003, 2007) foundthat observed wind speeds at the top of tall buildingsare within about 10–20% of the observed wind speedat a nearby airport and the wind directions are alsosimilar (within about 101). For this study, hourlywind speed, wind direction and temperature data for2006 were taken from the nearby LaGuardia Airport,as obtained from the National Climatic Data Center(http://cdo.ncdc.noaa.gov/ulcdsw/ULCD). Hourlysolar radiation data are not reported in the airportmeteorological stations, and were instead obtainedfrom the US Climate Reference Network (http://www.ncdc.noaa.gov/crn/hourly) from the neareststation (Millbrook, NY, about 90 miles north ofthe city).

2.8. Traffic volume

Assuming an average speed of 30 kmh�1 in thestreet canyon and given the traffic patterns in Fig. 2

ARTICLE IN PRESSY. Zhou, J.I. Levy / Atmospheric Environment 42 (2008) 3087–3098 3093

and Table 1 summarizes the daily average emissionfactors for different pollutants as calculated byOSPM. iF is in many settings independent ofemission rate, as the incremental concentration inthe numerator changes proportionally in responseto the change in emission rates in the denominator.However, the change in traffic volume, speed, andpercent of trucks may also influence the dispersionof pollutants in the street canyon, which may affectthe proportional relationship between concentrationand emission rates. We test this in the sensitivityanalysis.

2.9. Intake fraction calculations

Each OSPM run includes two receptors on eachside of the street. For the street canyon model, thereare multiple runs at different vertical heights—oneat the ground level (1.5m receptor height), severalones at different building heights as well as one atthe top of the building. We interpolate betweenthese values to determine the vertical gradientacross the building. Along with the base casescenario described above, we re-ran the modelconducting univariate sensitivity analyses on build-ing height, street width, traffic volume, vehiclespeed, and percent of trucks in the total traffic.We additionally considered the relative subpopula-tion numbers and the likelihood that various

Concentration v

Recept0

Con

cent

ratio

n (µ

g/m

3 )

0

50

100

150

200

250

300

10 20

Fig. 3. Vertical profiles for CO concentration (mgm�3) during daytim

contribution from the traffic in the street canyon. The background con

subpopulations would contribute substantially tothe total iF in street canyons with differentpopulation patterns.

3. Results

For our base case run, with a street canyon ofheight 60m and width 30m, Fig. 3 shows thevertical profiles for CO modeled during daytime andat night. Except for NO2, which is formed in thestreet canyon from chemical reactions, other pollu-tants modeled are similar to CO in that about 30%of the concentration at the ground level remains atthe top of the street canyon. For NO2, 50% of theground-level concentration remains at the top of thestreet canyon in the base case scenario. The sharpestdecline happens within 15m of the ground level. Forconservative pollutants, the concentration on thefourth floor roughly represents the average concen-tration people in the 20-floor building are exposedto assuming uniform vertical population density,which is about one-third the ground-level pollutantconcentration. For NO2, the concentration on thethird floor roughly represents the average concen-tration people in the 20-floor building are exposedto, which is nearly two-thirds of the ground-levelNO2 concentration.

The total iFs including the contribution ofdifferent subpopulations modeled are on the order

s. Receptor Height

or Height (m)

CO (day)CO (night)

30 40 50 60 70

e and nighttime. Note: concentration in the graph refers to the

centration from other sources has already been removed.

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Table 2

Intake fraction results by population group and pollutant

Pollutant

CO NOx PM2.5 PM10 Particle number

Pedestrians 1.0E�03 1.0E�03 1.0E�03 1.0E�03 1.04E�03

Bikers 2.1E�05 2.2E�05 2.2E�05 2.2E�05 2.2E�05

Drivers 1.4E�05 1.4E�05 1.4E�05 1.4E�05 1.4E�05

Residents 3.5E�04 3.7E�04 2.6E�04 1.5E�04 1.9E�04

Daytime worker 1.1E�03 1.2E�03 8.2E�04 4.6E�04 5.9E�04

Total iF 2.6E�03 2.7E�03 2.2E�03 1.7E�03 1.9E�03

Y. Zhou, J.I. Levy / Atmospheric Environment 42 (2008) 3087–30983094

of 3� 10�3 for CO and NOx, and 2� 10�3 forPM2.5, PM10, and ultrafine particles (Table 2). ForCO and NOx, for example, this means that for everykg emitted from the traffic in the street canyon, 3 gare eventually inhaled by populations living, work-ing or passing through the street canyon. Thedifferences in iF between gaseous pollutants andparticles are driven in large part by assumptionsabout infiltration factors and the contribution ofoffice workers and residential populations.

Pedestrian exposures account for 40–60% of theoverall iF, versus 27–42% for daytime office work-ers and 11–17% for residential populations. Thebiker and drivers/passengers iFs for differentpollutants are on the order of 10�5.

3.1. Univariate sensitivity analysis

Among the different pollutants, there is not asignificant difference in response within our sensi-tivity analysis, so we focus only on CO. Fig. 4summarizes the percent change of CO iF from thebase case by population group given variousunivariate changes to model assumptions.

Lower building height results in a lower iF, as it iseasier for pollutants to get out of the street canyon,so that the concentration within the canyon is lower.Of note, this conclusion is dependent in part on ourfocus only on exposures within the street canyonitself. An increased building height has a relativelysmaller effect. As to the street width, when itchanges from 30m in the base case to 15m in thesensitivity analysis, iFs increase by 50–84% amongthe different population groups modeled. Similarly,when the width of the street increases to 60mfrom the base case, the iFs decrease by 40–60%,indicating the importance of the width-to-heightratio in characterizing iF for a street canyon.

The three traffic-related variables tested (trafficvolume, traffic speed, and percent of truck traffic) intheory should influence traffic emissions and pollu-tant concentrations in the street canyon proportion-ally. While the iF did not change significantly whenthe value of each of these variables was doubled orhalved, there was a non-zero change (Fig. 4). Whenthe traffic volume doubled, the iF decreased byabout 10% (other than for drivers/passengers, givenan increased size of the exposed population),explained by increased turbulence and hence slightlyincreased dispersion. Similar slight sensitivities arefound for travel speed and percent of trucks amongthe total traffic.

3.2. Robustness of relative contributions of

subpopulations

We have estimated iFs for multiple subpopula-tions given our base case assumptions and underalternative parametric assumptions within OSPM,but a key question is whether our conclusions aboutthe subpopulations that contribute most to the iFare robust across different street canyon settings.The difference in iFs across different subpopula-tions is the result of the combination of populationcount, breathing rate, infiltration rates, and timespent in the street canyon, as well as pollutantconcentration. Some of these relative values mayvary substantially across street canyons, whileothers are unlikely to differ to a great extent.

For example, we can determine the likelihoodthat bikers would contribute more to the totaliF than pedestrians. Since both populations areassumed to have the same ground-level exposureconcentration, the difference in iF is the result of thedifference in breathing rates, population flux, andtime spent in street canyons (see Table 1). Assuming

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Percent Change in CO iF from Base Case by Population Group

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

Per

cent

Cha

nge

in C

OiF

from

Bas

e C

ase height=15m

height=90m

w idth=15m

w idth=60m

traff ic=20,000/day

speed=15km/h

truck=20%

height=15m -24% -24% -24% -12% -17% -19%

height=90m 4% 4% 4% 4% 2% 3%

width=15m 50% 50% 50% 68% 84% 68%

width=60m -39% -39% -38% -49% -58% -49%

traffic=20,000 /day -12% -12% 75% -14% -8% -9%

speed=15km/h 12% 12% 13% 18% 9% 11%

truck=20% -5% -5% -6% -7% -3% -5%

Pedestrians Bikers Drivers Residents (night)

Daytime workers

Total iF

Fig. 4. Percent change from base case in intake fraction for carbon monoxide, across population groups, and parametric assumptions.

Y. Zhou, J.I. Levy / Atmospheric Environment 42 (2008) 3087–3098 3095

that the ratio of breathing rates (roughly 60%higher for bikers) and speeds (three times greater forbikers) is relatively consistent across urban streetcanyon settings, bikers will contribute more to theiF than pedestrians only when the flux of bikers isapproximately 85% greater than the flux ofpedestrians. Even assuming the lowest pedestrianvalue recorded in the NYMETS study, this wouldrequire over 2000 bikers per hour. This seemsunlikely in most cases, except for some developingcountries where bicycling is the major means ofcommuting. We can therefore conclude that inurban areas in the US with significant pedestriantraffic, bikers are unlikely to contribute substan-tially to the total iF.

We can similarly compare pedestrians withresidential populations. In addition to the termsdiscussed above, there is also a difference in theconcentrations the population groups are exposedto, given the vertical distribution of the buildingresidents, the penetration efficiency for particulatematter, and the times of day when the populationsare present. The average concentration that buildingresidents experience in our base case is estimated tobe about one-third of the ground-level concentra-tion. Given this fact, the base case infiltration factorfor PM2.5, diurnal concentration patterns, anddifferences in breathing rates and time spent in the

street canyon, the number of residents would needto be approximately five times the steady-statenumber of pedestrians in the street canyon for thecontributions to be identical. The residential popu-lation density (30,000 persons km�2) in the zonein Fig. 1 is about one-third that of census tractswith higher population density in Manhattan ofabout 91,000 persons km�2, and the pedestriancounts were a factor of 2–3 lower on some roadwaysthan our base case value, so residential populationscould dominate in some settings, especiallyhighly dense urban areas with minimal commercialdevelopment.

Finally, we can consider the likelihood that officeworkers or residential populations will dominate theiF in various street canyons. One of the key factorsin this comparison, other than relative populationvalues, would be any differences in infiltrationfactors given the structures of the buildings. In astreet canyon where the office workers are in high-rises with HVAC systems and windows that do notopen, but the residential populations in olderbuildings with window air conditioning and openwindows, the relative importance of the residentialpopulation will increase. As the iF for these twosubpopulations are similar to one another, andgiven substantial variability in relative populationsand building configurations across street canyons, it

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is likely that each will dominate the iF in somesettings and that both will need to be considered.

4. Discussion and conclusion

As mentioned earlier, previous studies foundmobile source iFs to be on the order of 10�5–10�6

(Evans et al., 2002; Greco et al., 2007; Marshallet al., 2003, 2005) while that of indoor ETS wasfound to be on the order of 10�3 (Klepeis andNazaroff, 2006; Smith, 1993). Our results aresubstantially higher than previous mobile sourceestimates and are more similar in magnitude to theiF for indoor ETS, in spite of the relatively smallspatial scale of our analysis. To determine whetherour findings are interpretable, we made somequantitative comparisons with key parameters inprevious studies.

We calculated that the population densities forprevious mobile source iF studies were on theorder of 101–102 personskm�2. For the indoor ETSstudy (Klepeis and Nazaroff, 2006), we calculatedthe population density to be on the order of104 personskm�2 using a house volume of 287m3

and assuming 3m as the height of the occupied spaceof the house. To calculate population density for thestreet canyon in our base case, we assume that all theexposed population is either inside the street canyonwhich is 100m long and 30m wide or distributedalong the building envelope. The area under study istherefore 3000m2 with a nighttime residential popula-tion of 200 and daytime combined office worker andresidential population of 1200. Therefore, the corre-sponding population density is 7� 104 personskm�2

at night and 4� 105 personskm�2 during the day,which is about three orders of magnitude higher thanpopulation densities in previous mobile source iFstudies, but of a similar magnitude as the indoor ETSiF study. This indicates that our core findings aredriven in large part by population density, as well asthe fact that a street canyon may have dispersionpatterns more like an indoor environment than a linesource with no obstructions. Of note, calculated on avolume rather than areal basis, the street canyon stillhas a similar population density to the indoorenvironment.

Our iF estimates would also be expected to exceedvalues previously published given the role of thestreet canyon, which may trap pollutants and delaypollutant transport to the freely moving air abovethe canyons (Britter and Hanna, 2003). To get asense of the relative significance of the street canyon

itself, beyond the issue of population density, wecalculated the incremental concentration fromtraffic of an open line source using the Gaussianequation C ¼ 2Q=

ffiffiffiffiffiffi2pp

Usz

� �, described in more

detail elsewhere (Zhou and Levy, 2007). We usesimilar emissions and meteorological parameters asin the OSPM model, though some simplificationsare made (e.g., using average traffic count and windspeeds rather than time-varying values). The result-ing CO concentration near the roadway is aboutone-third that of the average annual incrementalconcentration calculated by OSPM. While there areclearly some uncertainties and the relative impacton iF will depend on the population distributionin each case, these results demonstrate that thesignificance of the street canyon is related to thecombination of the high population density andthe increased concentration given emissions withinthe canyon.

Another question is related to the importance ofconsidering multiple subpopulations in detail from apotential dose perspective, as opposed to thestandard assumption of 24-h residential exposuresto ground-level concentrations. If we had run ouranalysis omitting other subpopulations as well asvertical gradients, time-activity patterns, variablebreathing rates, and infiltration rates, the iF wouldhave been about 40–62% lower than our base caseresults. While this is a relatively small difference,this is in part coincidental, because of fairly largeerrors in both directions that offset one another—for example, although the daytime worker popula-tion is six times that of the local residents, theiraverage exposure is one-third of the ground-levelconcentration for gaseous pollutants and they spendless than half of the day in the street canyon. Thus,while the simpler approach would have yielded asimilar answer (although biased low by a factorof 2), this will not be the case in all situations,depending on the relative population numbers,height of the street canyon, building ventilationsystems, and time-activity patterns, among otherfactors.

Although our results appear interpretable, thereare some clear limitations. For example, the iFresults above do not take into consideration theinfluence of the street canyon on populationsoutside the canyon. Therefore, the results reportedhere are within-canyon iFs alone, which will under-estimate the overall iFs to an undetermined degree.One previous study (Spadaro and Rabl, 2001),which analyzed 33 equally spaced canyons with

ARTICLE IN PRESSY. Zhou, J.I. Levy / Atmospheric Environment 42 (2008) 3087–3098 3097

height to width ratio of 1:1, found that the averageconcentration in the canyon with the source is anorder of magnitude higher than in the next canyondownwind, and 18 and 25 times higher than thefollowing two canyons downwind, respectively.Thus, if we assume the same population density inevery canyon, then this implies that including thethree most proximate downwind canyons will onlyincrease the iF by approximately 20%. Note thatthe height to width ratio in the base case scenario inour analysis is two to one, which may make it evenharder for pollutants to escape from the canyonunder consideration than in this earlier study.

In addition, among the different populationgroups modeled, bikers and pedestrians are closerto the traffic than people in the buildings (even onthe ground floor), which is not reflected in the iFresults. In general, the population numbers werebased on some simplifying assumptions and maynot represent general conditions, so the absolutemagnitude of the iFs should be considered some-what uncertain. That being said, office worker andresidential populations can be fairly accuratelyestimated from existing databases, and pedestrianpopulations can be readily approximated on majorstreets from existing video surveillance data. Ourfindings are also clearly dependent on the accuracyof OSPM itself, which has been validated in paststudies but not under the specific circumstancesfound in New York City. However, it appearsunlikely that OSPM would be biased to a sufficientdegree to change our qualitative conclusions aboutthe higher iF in these urban street canyons, givendifferences of multiple orders of magnitude relativeto previously published values. Finally, many motorvehicle emissions control strategies are focused atthe vehicle level. Given the fact that an individualvehicle will likely spend only a small amount of timein street canyons, the contribution of street canyonsto vehicle-specific benefits may not be large in manycases. However, congestion pricing programs tar-geted at urban settings could have disproportionateimpacts within street canyons, and may thereforehave benefits dominated by exposures in streetcanyons.

In conclusion, we found that for street canyons inmid-town Manhattan, the total iF including thecontribution of different subpopulations is on theorder of 10�3 for the different pollutants understudy. These values were largely driven by daytimeoffice workers, pedestrians, and local residents, withsmaller contributions from bikers and drivers/

passengers who spend less time in the given streetcanyon. These iFs are orders of magnitude higherthan those in regional-scale mobile source disper-sion models, emphasizing that control of emissionsin urban street canyons may have substantialpopulation exposure benefits relative to identicalemission reductions from non-street canyon mobilesources or other stationary sources. Our findingsalso emphasize the importance of studying high-resolution near-source exposures for primary pollu-tants in urban settings. When conducting cost–be-nefit analyses for different policy interventionsaimed at reducing traffic exposures in metropolitanareas, it is important to use health benefit estimatesthat take account of street canyon settings, to avoidunderestimating the benefits of these interventions.

Acknowledgments

This study was funded by the Gilbert and IldikoButler Foundation. We thank Michael Brown atLos Alamos National Laboratory for providingGIS data to estimate daytime population in NewYork City. We are grateful for the support fromEnvironmental Defense through the NYMETSproject. We appreciate Ruwim Berkowicz andMatthias Ketzel at National Environmental Re-search Institute in Denmark for addressing ourOSPM related questions. Finally, we thank thefollowing colleagues at Harvard School of PublicHealth—Steven Melly for help with GIS data;Leonard Zwack for collecting and analyzing thefield data from NYMETS; and Yang Liu andSteven Hanna for the valuable input and comments.The contents of this manuscript reflect the views ofthe authors alone and do not necessarily reflect theviews of the reviewers or the funder.

References

ACS, 2005. American Community Survey. /http://www.census.

gov/acs/www/S.

Bennett, D.H., Koutrakis, P., 2006. Determining the infiltration

of outdoor particles in the indoor environment using a

dynamic model. Journal of Aerosol Science 37, 766–785.

Bennett, D.H., McKone, T.E., Evans, J.S., Nazaroff, W.W.,

Margni, M.D., Jolliet, O., Smith, K.R., 2002. Defining

intake fraction. Environmental Science and Technology 36,

206A–211A.

Berkowicz, R., Olesen, H.R., Jensen, S.S., 2003. User’s guide to

WinOSPM, Operational Street Pollution Model. NERI

Technical Report.

ARTICLE IN PRESSY. Zhou, J.I. Levy / Atmospheric Environment 42 (2008) 3087–30983098

Britter, R.E., Hanna, S.R., 2003. Flow and dispersion in urban

areas. Annual Review of Fluid Mechanics 35, 469–496.

Brunekreef, B., Holgate, S.T., 2002. Air pollution and health.

Lancet 360, 1233–1242.

Chan, L.Y., Kwok, W.S., 2000. Vertical dispersion of suspended

particulates in urban area of Hong Kong. Atmospheric

Environment 34, 4403–4412.

Evans, J.S., Wolff, S.K., Phonboon, K., Levy, J.I., Smith, K.R.,

2002. Exposure efficiency: an idea whose time has come?

Chemosphere 49, 1075–1091.

Gauderman, W.J., Avol, E., Gilliland, F., Vora, H., Thomas, D.,

Berhane, K., McConnell, R., Kuenzli, N., Lurmann, F.,

Rappaport, E., Margolis, H., Bates, D., Peters, J., 2004. The

effect of air pollution on lung development from 10 to 18

years of age. New England Journal of Medicine 351,

1057–1067.

Greco, S.L., Wilson, A.M., Spengler, J.D., Levy, J.I., 2007.

Spatial patterns of mobile source particulate matter emis-

sions-to-exposure relationships across the United States.

Atmospheric Environment 41, 1011–1025.

Hanna, S.R., Britter, R., Franzese, P., 2003. A baseline urban

dispersion model evaluated with Salt Lake City and

Los Angeles tracer data. Atmospheric Environment 37,

5069–5082.

Hanna, S., White, J., Zhou, Y., 2007. Observed winds,

turbulence, and dispersion in built-up downtown areas of

Oklahoma City and Manhattan. Boundary-Layer Meteorol-

ogy 125, 441–468.

Jenkins, H.W.J., 11 July 2007. Wall Street Journal. Business

World: Bloomberg vs. The Car.

Ketzel, M., Berkowicz, R., 2004. Modelling the fate of ultrafine

particles from exhaust pipe to rural background: an analysis

of time scales for dilution, coagulation and deposition.

Atmospheric Environment 38, 2639–2652.

Klepeis, N.E., Nazaroff, W.W., 2006. Modeling residential

exposure to secondhand tobacco smoke. Atmospheric

Environment 40, 4393–4407.

Kukkonen, J., Partanen, L., Karppinen, A., Walden, J., Kar-

tastenpaa, R., Aarnio, P., Koskentalo, T., Berkowicz, R.,

2003. Evaluation of the OSPM model combined with an

urban background model against the data measured in 1997

in Runeberg Street, Helsinki. Atmospheric Environment 37,

1101–1112.

Levy, J.I., Spengler, J.D., Hlinka, D., Sullivan, D., Moon, D.,

2002. Using CALPUFF to evaluate the impacts of power

plant emissions in Illinois: model sensitivity and implications.

Atmospheric Environment 36, 1063–1075.

Marshall, J.D., Riley, W.J., McKone, T.E., Nazaroff, W.W.,

2003. Intake fraction of primary pollutants: motor vehicle

emissions in the South Coast Air Basin. Atmospheric

Environment 37, 3455–3468.

Marshall, J.D., McKone, T.E., Deakin, E., Nazaroff, W.W.,

2005. Inhalation of motor vehicle emissions: effects of urban

population and land area. Atmospheric Environment 39,

283–295.

McKinnon, J.D., 5 February 2007. Wall Street Journal. Politics

and economics: bush plays traffic cop in budget request;

president suggests ‘congestion’ tolls to ease rush hour.

McPherson, T.N., Michael, J.B., 2004a. Determination of the

spatial and temporal distribution of population for air toxics

exposure assessments. In: Proceedings of the 5th AMS Urban

Environment Conference, Vancouver, BC.

McPherson, T.N., Michael, J.B., 2004b. Estimating daytime and

nightime population distributions in US Cities for emergency

response activities. In: Proceedings of the 84th AMS Annual

Meeting, Seattle, WA.

Meng, Q.Y., Turpin, B.J., Polidori, A., Lee, J.H., Weisel, C.,

Morandi, M., Colome, S., Stock, T., Winer, A., Zhang, J.F.,

2005. PM2.5 of ambient origin: estimates and exposure errors

relevant to PM epidemiology. Environmental Science and

Technology 39, 5105–5112.

New York, 2003. NYC tax lot base map 03C ed. Bytes of the big

apple. City of New York, Department of City Planning.

Pope, C.A., Burnett, R.T., Thun, M.J., Calle, E.E., Krewski, D.,

Ito, K., Thurston, G.D., 2002. Lung cancer, cardiopulmonary

mortality, and long-term exposure to fine particulate air

pollution. JAMA—Journal of the American Medical Asso-

ciation 287, 1132–1141.

Smith, K.R., 1993. Fuel combustion, air-pollution exposure, and

health—the situation in developing-countries. Annual Review

of Energy and the Environment 18, 529–566.

Spadaro, J.V., Rabl, A., 2001. Damage costs due to automotive

air pollution and the influence of street canyons. Atmospheric

Environment 35, 4763–4775.

USEPA, 1997. Exposure Factors Handbook. US EPA.

USEPA, 2003. National Human Activity Pattern Survey.

/http://www.epa.gov/chadnet1/S.

Vardoulakis, S., Marios, V., James, M., Helen, A., 2007.

Operational air pollution modelling in the UK—street canyon

applications and challenges. Atmospheric Environment 41,

4622–4637.

Vignati, E., Berkowicz, R., Palmgren, F., Lyck, E., Hummelshoj,

F., 1999. Transformation of size distributions of emitted

particles in streets. Science of the Total Environment 235,

37–49.

Zhou, Y., Levy, J.I., 2007. Factors influencing the spatial extent

of mobile source air pollution impacts: a meta-analysis. BMC

Public Health 7 (art. no. 89).

Zhou, Y., Levy, J.I., Hammitt, J.K., Evans, J.S., 2003.

Estimating population exposure to power plant emissions

using CALPUFF: a case study in Beijing, China. Atmo-

spheric Environment 37, 815–826.

Zhou, Y., Levy, J.I., Evans, J.S., Hammitt, J.K., 2006. The

influence of geographic location on population exposure to

emissions from power plants throughout China. Environment

International 32, 365–373.