Science of the Total Environment 412 (2011) 336–343

Contents lists available at SciVerse ScienceDirect

Science of the Total Environment

j ourna l homepage: www.e lsev ie r .com/ locate /sc i totenv

Dispersion modelling of traffic induced ultrafine particles in a street canyon inAntwerp, Belgium and comparison with observations

Irina Nikolova a,b,⁎, Stijn Janssen a, Peter Vos a,b, Karl Vrancken a,b, Vinit Mishra b, Patrick Berghmans a

a Flemish Institute for Technological Research VITO, Boeretang 200, 2400 Mol, Belgiumb University of Antwerp, Bio-Engineering Department, Groenenborgerlaan 171, Antwerp, Belgium

⁎ Corresponding author at: University of Antwerp,Groenenborgerlaan 171, Antwerp, Belgium. Tel.: +32 14

E-mail addresses: irina.nikolova@vito.be, irinanikolo

0048-9697/$ – see front matter © 2011 Elsevier B.V. Alldoi:10.1016/j.scitotenv.2011.09.081

a b s t r a c t

a r t i c l e i n f o

Article history:Received 20 July 2011Received in revised form 28 September 2011Accepted 28 September 2011Available online 26 October 2011

Keywords:Ultrafine particlesModellingCFDStreet canyonSimultaneous measurementsP-TRAK

The aim of this study is to investigate the dispersion of ultrafine particles and its spatial distribution in a streetcanyon and its neighbourhood with the 3D CFD model ENVI-met®. The performance of the model at streetscale is evaluated and the importance of the boundary conditions like wind field and traffic emissions onthe UFP concentration is demonstrated. To support and validate the modelled results, a short-term measure-ment campaign was conducted in a street canyon in Antwerp, Belgium. The UFP concentration was measuredsimultaneously with P-TRACK (TSI Model 8525) at four different locations in the canyon. The modelled UFPconcentrations compare well with the measured data (correlation coefficient R from 0.44 to 0.93) within thestandard deviation of the measurements. Despite the moderate traffic flow in the street canyon, UFP concen-trations in the canyon are in general double of the background concentrations, indicating the high local con-tribution for this particle number concentration. Some of the observed concentration profiles are notresembled by the model simulations. For these specific anomalies, further analysis is performed and plausibleexplanations are put forward. The role of wind direction and traffic emissions is investigated. The perfor-mance evaluation of ENVI-met® shows that in general the model qualitatively and quantitatively describesthe dispersion of UFP in the street canyon study.

Bio-Engineering Department,33 67 59; fax: +32 14 32 11 85.va@mail.bg (I. Nikolova).

rights reserved.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

Traffic is recognised as the main contributor to the ultrafine particle(UFP) pollution in the ambient air of many cities (Kittelson et al., 2004;Gidhagen et al., 2005). Typical concentrations range between 104 and106 particles/cm3 depending on factors such as driving speed, composi-tion of the fleet and meteorological conditions (Morawska et al., 2008).It has been observed that ultrafine particles (diameter less than 0.1 μm)are associated with adverse effects on human health (Oberdorster andUtell, 2002;Donaldson et al., 2005; Politis et al., 2008; Crüts et al., 2008).

Currently there is no limit value to control the number concentra-tion of UFP. On a European level, a euro standard for number concentra-tions “is to be defined as soon as possible and at the latest upon entryinto force of Euro 6” (Regulation (EC) No 715/2007). The regulationstipulates that “In order to ensure that emissions of ultrafine particulatepollutants (PM0, 1 μm and below) are controlled (Regulation (EC)No 595/2009) the Commission should also establish specific proce-dures, tests and requirements for type approval, as well as a revisedmeasurement procedure for particulates and a particle number basedlimit value, and to adoptmeasures concerning the use of defeat devices,

access to vehicle repair and maintenance information and test cyclesused to measure emissions”. This is a positive action initiated by thecommunity and a step forward to introduce a standardised UFP emis-sions procedure. Such a step would reduce the uncertainties in theUFP emissions (Kumar et al., 2011). Another key step would be to in-clude UFP measurements in standardised monitoring stations. Themain difficulties in comparing the results of various monitoring cam-paigns arise from the different operating principles and size cut-offs ofthe instruments (Kumar et al., 2010). Including standardised measure-ments of UFP in air qualitymonitoring networks would provide compa-rable datasets, more detailed analysis and last but not least the sonecessary data for validation of dispersion models.

Holmes and Morawska (2006) presented an overview of differentdispersion models. Their conclusions state that many factors influencethe concentration of UFP, notably fluctuations in the wind flow andemissions. Often, for example in cities, the areas show a complex ge-ometry leading to complex air flows and turbulence that can have agreat impact on the concentration of the ultrafine particles. To dealwith these aspects, the CFD (Computational Fluid Dynamics) methodsbecome of interest. In recent years the use of CFD models in air qualitymodelling gained a lot of attention. These models are useful for stud-ies requiring high resolution within the urban canopy although theyare complex, resource demanding and a thorough validation of aCFD model setup is not straightforward. CFD models allow to simulatethe flow characteristics and the dispersion of pollutants at the urban

337I. Nikolova et al. / Science of the Total Environment 412 (2011) 336–343

micro-scale. Uhrner et al. (2007) and Albriet et al. (2010) presented aUFP formation and dispersion simulation in the wake of a vehiclestailpipe. Gidhagen et al. (2004) and Kumar et al. (2009) studied thedispersion in a street canyon. Wang et al. (2008) presented interest-ing results on the UFP dispersion in the vicinity of a bus station andBirmili et al. (2009) showed how UFP disperse on a German highway.All these studies have dealt with modelling and observations of UFP atthe microscale but in general the UFP dispersion studies remain ratherscarce as compared to other pollutant dispersion studies (for exampleparticulate matters PM10 or NO2), especially when it comes to the dis-persion of UFP in complex urban terrain. If a policy for ultrafine parti-cles is to be introduced in the future, performance evaluation of newand existing models against measured data in various conditions willbe needed (Kumar et al., 2011). Abatement strategies for ultrafineparticles can be analysed and evaluated based on new developmentsor improvements in the abilities of existing dispersion models. Inthis paper we present the first performance model evaluation of the3D CFD model ENVI-met® (Bruse and Fleer, 1998) with ultrafine par-ticles. The aim of this study is (1) to investigate the dispersion ofultrafine particles and its spatial distribution in a street canyon andits neighbourhood, (2) to evaluate the performance of ENVI-met® atstreet scale and (3) to show the importance of the boundary condi-tions like wind direction and traffic emissions on the UFP concentra-tions observed. The paper is organised as follows. Section 2 presentsthe methodology divided in four subsections — Section 2.1 describesthe experimental set up; Sections 2.2 and 2.3 briefly describe the esti-mation of the emissions and the initialization of the CFD model, re-spectively and Section 2.4 explains the role of coagulation and thereason why this process is not taken into account in the calculations.Results and discussion are presented in Section 3. Conclusions aregiven in Section 4.

2. Methodology

2.1. Site description and measurements

A short term UFPmeasurement campaign of 14 days was set up in astreet canyon in theWolfstraat, Antwerp, Belgium. Time slots over 6 dif-ferent days are selected based on the availability of reliable data at allmeasurement locations. The selected time slots are given in Table 1.The area is a typical busy urban commercial/residential area. It is locatedat 51°12′32.00″N and 4°25′56.00″E. A map of the location of the streetcanyon is given in Fig. 1.

TheWolfstraat ismarkedwith four red arrowswith labels B, L, R andE. B stands for Beginning of the canyon, facing towards a busy boulevard(Plantin en Moretuslei) with traffic lights. L and R stand for Left andRight sides in the middle of the street, respectively. E stands for End ofthe canyon facing towards a roundabout. The average height of thebuildings is 11 m. The street is 12 m wide and approximately 120 m

Table 1Time period of the short term UFP measurements campaign, vehicles count and the ini-tial meteorological parameters to initialize ENVI-met®.

Date Timewindow

Numbervehicles/timewindow

Winddirection

Windspeed

T

LDV HDV

[°] [m/s] [K]

17/07/2009 12:17–13:30 255 12 211 7 29224/07/2009 11:55–13:32 297 14 230 4 29126/07/2009 13:37–16:16 618 24 210 3 29528/07/2009 16:11–18:27 703 27 232 3 2943/08/2009 11:32–15:39 776 36 240 3 2905/08/2009-1 15:04–15:31 138 6 170 2 2995/08/2009-2 15:40–18:03 679 25

long. The building-height-to-street-width (aspect) ratio H/W is on av-erage 0.92 although detailed information about building height istaken into account The street canyon has two driving lanes in the mid-dle, parking lanes on both sides and pedestrian lanes next to the build-ings. There is no vegetation in the canyon.

Particle number concentration was measured using P-TRAK (TSIModel 8525) in the size range from 0.02 μm to over 1 μm placed at1.5 m above the ground. The P-TRAK is a portable condensation parti-cle counter, which operates with a continual fixed sample flow of100 cm3 per min to draw the air through a saturator tube soakedwith isopropyl alcohol, which condenses on the particles causingthem to grow. The formed droplets are large enough to be detectedoptically by passing them through a focused laser beam producingflashes of light which are counted using a photo detector. This instru-ment has a rather high cut off size in the nucleation mode and doesnot measure the UFP below 0.02 μm. Previous studies (Wehner et al.(2002), Gidhagen et al. (2004), Kumar et al. (2008)) have shownthat in street canyons a part of the traffic emitted UFP is found inthe nucleation mode (diameterb0.03 μm) with a peak below0.02 μm. With P-TRAK it is likely to underestimate the actual UFP con-centration in this nucleation mode due to the large cut off size of theinstrument.

Water based CPC (Condensational Particle Counter) was used tomeasure the background concentration in a street next to the canyon.The location is pointed with the label BKGR on Fig. 1. This street isblocked for traffic and only local residential vehicles are allowed toenter the street. The instrument measured the number concentrationfrom 0.003 to 1 μm. Overall, it is expected that the difference betweenCPC and P-TRAK readings would not exceed 20%. Matson et al. (2004)observed a difference between a CPC and P-TRAK of less than 20% inall readings with almost half of the readings differing by less than5%. Those limitations of the measurement campaign will be properlytaken into account in the model validation phase. All UFP concentra-tions mentioned further on (both measured and modelled) will bebased on the 0.02 to 1 μm size range in order to account for the differ-ent size ranges of the instruments.

At the beginning and at the end of the street canyon, the instru-ments were positioned next to the traffic lane on the same (left)side of the street. In the middle of the street, the instruments wereplaced in opposite positions on the pedestrian lanes. In addition toUFP measurement, meteorological parameters (wind direction andspeed, temperature, relative humidity, pressure) were measuredusing an integrated multi gas measurement platform (Airpointer®,Recordum Austria, www.recordum.com). The platform was placed at2 m height at points B, R and E inside the canyon. Light duty vehicles(LDV) and heavy duty vehicles (HDV) were counted in the canyonusing double inductive loop detectors. The total number of vehiclesfor the time window of the UFP measurements is given in Table 1.The regional meteorological information was obtained from the localairport in Antwerp. The airport is located approximately 2.5 kmaway from the street canyon in south-east direction. The meteorolog-ical information is reported every three hours. The parameters aresynthesised in Table 1. For periods between two observations, thevalues are linearly interpolated.

2.2. UFP emissions

The UFP emissions are estimated according to the methodologypresented in Nikolova et al. (2011). In brief, the parameterization isbased on the emission factors for the total number of particles [parti-cles/km.veh] from the Emission Inventory Guidebook, 2006, the par-ticle size distribution given in PARTICULATES (Samaras et al., 2005)and traffic activity data for the location of interest (TA). It has to bestressed that the emission factors are representative for emitted UFPat a distance of about 1 m after the tailpipe. Therefore, the size distri-bution parameterization implicitly accounts for the fast nucleation

BKGR

B

L R

E

Fig. 1. Aerial view of the Wolfstraat. The location of the measuring points is marked with arrows.

338 I. Nikolova et al. / Science of the Total Environment 412 (2011) 336–343

process of particles at the tailpipe base. The emissions were calculat-ed according to the formula:

E ¼ ∫Dpmax

Dpmin

∑i;j;k

srEFijk1000

∗TAijk ð1Þ

where E is the total emissions in [particles/m.s], Dp_min and Dp_maxare the lower and upper particle diameter limits of P-TRAK, i is vehi-cle's type (passenger cars and heavy duty vehicles); j is fuel used(diesel and petrol); k is the legislation standard (pre-Euro to Euro4); srEFijk are size resolved emission factors per vehicle's type, fuelused and legislation norm [particles/km.vehicle]; TAijk is the mea-sured number of vehicles per second by vehicles type, fuel used andstandards. The split of traffic according to different legislation normsand fuel used is based on statistical data for 2006 provided by the Bel-gian Federal ministries for mobility, transportation and finance (FOD–DIV, 2006). This split is presented in Fig. 2 for LDV (light duty vehicles)and HDV (heavy duty vehicles). All HDV are assumed to be dieselfuelled while LDV can be diesel or petrol fuelled. The share of petrolfuelled LDV is emphasised in Fig. 2 with the pieces detached from thepie chart . As can be seen, the diesel fuelled LDV vehicles dominatethe fleet with a share of 71%. Euro 3 and Euro 4 legislated vehicles arethe most abundant in the fleet for LDV while the most abundantamong the HDV are Euro II and Euro III.

The traffic flow for an average working day in the canyon was3269 vehicles per day of which around 5% HDV. The traffic rushhour was more prominent in the afternoon (17 h–18 h) than duringthe morning (see Fig. 3).

According to Eq. (1) the estimated modelled average daily emis-sions for the size range between 0.02 and 1 μm in the canyon were6.29E+09 particles/m.s with a minimum in the early morning

(6.86E+08 particles/m.s) and maximum during the afternoon trafficjam (1.27E+10 particles/m.s).

2.3. CFD model

The dispersion of ultrafine particles is modelled with the three-dimensional (3D) computational fluid dynamics (CFD) model ENVI-met®. ENVI-met® is RANS (Reynolds Averaged Navier–Stokes) equa-tions based, non-hydrostatic micro scale obstacle-resolving modelwith advanced parameterizations for the simulation of surface–plant–air interactions (Bruse and Fleer, 1998). More information can befound in section “Scientific docs” on the official web page of the model(http://www.envi-met.com). The model is extended with a modulefor the dispersion of gases (NO, NO2, O3, SO2) and particles (massbased PM10, PM2.5, elemental carbon and number based total numberof UFP). The model has a dry deposition scheme taking into accountthe aerodynamic, the quasi-laminar layer and surface resistancesbased on the scheme of resistances presented in Seindfeld and Pandis(1998). Deposition can occur on different surfaces — building wallsand roofs, vegetation, and soils. ENVI-met® has features that are notcommon for other CFD dispersion codes, i.e. a detailed micro climatemodule and a vegetation module.

The initial parameters required as an input in ENVI-met® are me-teorological data (wind speed and direction at 10 m height, relativehumidity and temperature), emissions and domain characteristics.Steady-state CFD simulations for neutral conditions were performedfor different wind directions and speeds according to the meteorolog-ical input for each selected time slot of the short term measurementcampaign.

Pollution sources in ENVI-met® can be line or area sources. Linesource emissions were estimated according to the procedure pre-sented in Section 2.2 and estimated with respect to the lower andupper limit of P-TRAK, i.e. the number of particles was integrated

Fig. 3. Traffic flow during an average working day in the canyon. PC is passenger cars,HDV is heavy duty vehicles and Total is the sum of all vehicles.

339I. Nikolova et al. / Science of the Total Environment 412 (2011) 336–343

for the interval 0.02–1 μm. They were placed at 0.3 m above theground along the entire width of the traffic lanes, excluding the pe-destrian and the parking lanes. By distributing the emissions overthe width of the traffic lane, an initial rapid mixing in the source re-gion is partly taken into account (Kumar et al. 2009). It should benoted that the effect of traffic induced turbulence is not considered.Such effect can be ignored when the regional (above roof-top) windspeed is exceeding 1.5 m/s. (Di Sabatino et al., 2003; Mazzeo andVenegas, 2005). During the selected days the background wind variedbetween 2 and 7 m/s (refer to Table 1).

The modelled area has a grid resolution of 1×1 m in x and y direc-tions and z varied from 20 cm in the first 2 m to a few metres at thetop of the model domain. Buildings were modelled with rectangularblocks with flat rooftops. Vegetation in the surroundings was mod-elled as 10 m height trees with leafless base. More on the effect ofvegetation and its representation in ENVI-met® can be found inWania et al. (2011). In total 1.9 million cells covered the study area.For each cell a variety of parameters was calculated (wind field, tem-perature, pollutant concentration, turbulent kinetic energy and dissi-pation, radiation fluxes, etc.).

2.4. Effect of coagulation

Coagulation and deposition are removal processes that potentiallyreduce the number of UFP depending on the environment and thegoverning conditions. Ketzel and Berkowicz (2004) presented intheir study typical time scales for coagulation and deposition for dif-ferent environments (tunnels, kerbsides, urban area, near-city, etc.).They showed that for urban kerbside dilution is the fastest process(from seconds to few minutes) that acts on the UFP number concen-tration and thus suppressing the effect of coagulation and deposition.This conclusion is in line with other studies. For example Gidhagenet al. (2004) concluded that coagulation is of little importance forstreet scale. They also found that the removal processes are importantfor particles with diameter less than 0.01 μm, i.e. the smallest nucle-ation mode particles. Kumar et al. (2011) supports the view that co-agulation at street scale could be neglected especially when

Fig. 2. Share of different Euro norms for LDV and HDV for 2006. The

particles with diameter less than 0.010 μm are not considered. Inthis study the lower cut-off size is 0.02 μm, a size above which therole of coagulation is of no or negligible importance under ambientconditions in a street canyon environment. For that reason this pro-cess is neglected and not taken into consideration in the analysis.

3. Results and discussion

3.1. General discussion

During the measurement period, the weather was warm, calm anddry. The regional wind speed varied between 2 and 7 m/s and its di-rection was rather stable, with a strong southern component. In thestreet canyon, a similar southern wind field is observed in the mea-surements, especially in the middle of the canyon. However, this isnot surprising due to the north–south orientation of the canyon. At

number in parenthesis corresponds to the legislation for HDV.

Fig. 4. Measured wind direction at 2 m height on 05/08/2009. B, R and E stand for be-ginning, right and end respectively.

340 I. Nikolova et al. / Science of the Total Environment 412 (2011) 336–343

the beginning and by the end of the street the situation is more dy-namic. This is for example illustrated in Fig. 4 for August 5, 2009,showing the variation of the measured wind direction in point B (be-ginning), R (in the middle) and E (end) in the canyon.

Around the street ends the wind direction changes more drastical-ly, showing that in these locations the wind field is much more com-plex compared to the regional undisturbed flow. It is well known thatthis complex wind field due to the urban morphology has a direct ef-fect on the dispersion of UFP (Di Sabatino et al., 2003, 2007; Mazzeoand Venegas, 2005; Gromke et al., 2008; Narita et al., 2008; Kumaret al., 2008; Buccolieri et al., 2009; Abdulsaheb and Kumar, 2010).An illustration of the modelled wind speed at 2 m height with awind coming from south (August 5 time slot 15:04–15:31 h) is plot-ted in Fig. 5. The complex wind field is visualised by the streamlines.

Once the flow enters the canyon, it becomes parallel to the canyonaxis, i.e. flow with direction 180°–190°. There are also areas withprominent increase and decrease of the wind speed between thebuildings. As shown in the upper part of the figure the air flow is ac-celerated locally near the edge of a large building. Such variations in

Fig. 5. Modelled wind speed (colours) and direction (streamlines) for wind comingfrom 170°. (05/08/2009 15:04–15:31 h).

wind speed play a prominent role for the UFP dispersion and concen-tration levels. In Fig. 6 the modelled UFP concentration is plotted forAugust 5, 2009 with wind coming from 170°.

It is seen that the concentration in the canyon is lower than themodelled one on the busy boulevard (from 3 to 10 times dependingon the position on the boulevard— kerbside, centre line, etc.). The dif-ference is due to the higher traffic volume (respectively emissions) onthe boulevard. Large horizontal concentration gradients are simulatedat street junctions with intense versus no intense traffic. This is in linewith other studies (Wang et al., 2008; Hagler et al., 2009), showingthat such gradients are common when dealing with UFP.

For each of the time slots in the measurement campaign a compar-ison can be made between modelled and measured concentrations.Fig. 7a–g details those concentrations (mean measured concentra-tion, measured standard deviation and modelled concentration) at1.5 m height for the different selected time slots of the campaign.

Overall, the modelled UFP concentration in the canyon coincideswith the measured values, indicating an overall a good agreement.Table 2 gives statistical information such as correlation coefficient,relative root mean square error (RRMSE) and bias. In general themodelled results are within the standard deviation of the measure-ments although for some days, the model is not able to describe theobserved trends in the UFP concentrations levels. Some of theseanomalies (e.g. on July 28) will be further analysed in the nextsection.

The canyon is a street with an average of 3269 vehicle per day. Thebackground concentration is on average 8404 particles/cm3, which isalmost half of the observed concentration of UFP in the canyon. As aconsequence, the local contribution attributed to the moderate trafficflow in the canyon is as important as the background concentrationitself. If the background particle concentration is compared with theparticles concentration on the busy boulevard — the modelled con-centration is several times higher due to increased traffic emissions(see Fig. 6). This is in high contrast to mass concentrations of partic-ulate matter (e.g. PM10, PM2.5) where the local traffic contributionto the total mass observed at street level is small (Lefebvre et al.,2012).

In general the UFP concentration in the beginning of the canyon(point B, Fig. 1) is higher than that at other measurement points.The increased concentration in the beginning of the canyon couldbe due to several reasons — transport and accumulation of UFP by

Fig. 6. Modelled UFP concentration for wind coming from 170° (05/08/2009 15:04–15:31 h).

Table 2Statistical analysis giving the correlation coefficient R, the relative root mean squareerror (RRMSE) and the bias.

Date Time window R RRMSE [%] Bias [#/cm3]

17/07/2009 12:17–13:30 0.72 18.9 121424/07/2009 11:55–13:32 0.44 5.6 207626/07/2009 13:37–16:16 0.85 5.3 302428/07/2009 16:11–18:27 0.81 47.0 −36313/08/2009 11:32–15:39 0.93 8.3 28985/08/2009-1 15:04–15:31 0.82 5.6 33225/08/2009-2 15:40–18:03 0.83 0.6 2297

341I. Nikolova et al. / Science of the Total Environment 412 (2011) 336–343

the end of the canyon towards the beginning under the prevailingsouthern air flow; transport of UFP from the busy boulevard; idlingand acceleration of vehicles next to the instrument. The concentrationdecreases towards the middle of the street. For most of the time slots,this trend is captured well by the model. Additionally, in the middleof the street a clear difference is observed in the measured concentra-tion on both sides (leeward and windward side). In general the mod-elled leeward–windward asymmetry which is driven by the rooftopwind field and canyon vortex coincides well with the measuredvalues (see Fig. 7).

3.2. Analysis of anomalies

In the previous section, model results were compared with mea-surement data for the different time slots of the campaign. An overallacceptable agreement was obtained, although some striking deviationswere observed as well. Based on the available boundary conditions(traffic counts, rooftop wind field…), no further explanation of theseanomalies can be provided. However, for some events a further analysisseems relevant and plausible explanations can be put forward. Thisanalysis based on model simulations is presented in the next two sub-sections, first for the alternating left–right asymmetry in the canyon(anomaly on August 5), afterwards for the heavily increased concentra-tions in the beginning of the canyon (anomaly on July 28).

3.2.1. Alternating left–right asymmetry in the canyonOne day of the campaign (August 5, 2009) provides an interesting

example of a day with a fast change in the left–right asymmetry in themeasured levels of UFP. During the first half of the time slot (Fig. 7f),the concentration on the left side is lower as compared to the concen-tration on the right side. With a wind parallel to the street canyon,this asymmetry can be explained as a result of higher emissions onthe right side, i.e. more vehicles on the right lane. However duringthe second half of the time slot an inverse situation is observed —

higher UFP concentrations at the left as compared to the right side(Fig. 7g). In the first model setup, the wind direction reported bythe local airport is used as boundary condition of the model run. Dur-ing the first part, the simulated results show an excellent agreement

Fig. 7. Comparison between modelled and measured UFP concentrations in t

with the measured values (R=0.82). However, the modelled resultsfor the second part of the time slot were not resembling the measuredconcentration (inversed asymmetry in concentration levels in themiddle of the canyon). A plausible explanation for the changingleft–right asymmetry in the observed concentrations could be ashift in the rooftop wind field. However, for this period, no informa-tion about the regional wind field is available at the airport. Lookingat the measured wind direction in the canyon (Fig. 4) it is seen thatbetween 15:30–16:15 h the averaged wind direction is as the region-al wind. After 16:15 h there is a clear turn to south-west direction. Al-though this measured turn in the wind direction is observed at 2 mheight and does not fully represent the rooftop winds, this might bea confirmation of the hypothesis. To further test this hypothesis, themodel simulation of the UFP concentration was repeated for the sec-ond half of the time slot with a south-west wind (220°) as forcingcondition. The modelled results are compared with the measuredvalues in Fig. 7g. In this case the observed street canyon asymmetryin the UFP concentration is well resembled. Therefore, the turn inthe rooftop wind direction could logically explain the appearance ofa vortex in the canyon and the measured asymmetry in the UFPconcentration.

3.2.2. Increased concentrations at one locationAnother interesting day in the campaign is July 28, 2009 (refer

to Fig. 7d) where particles were measured during the traffic rushhour, between 16:11 and 18:27 h. This day is characterised by

he canyon for: beginning (B), left (L), right (R) and end (E) respectively.

Fig. 9. Modelled UFP concentration for different wind directions for all four points inthe canyon. The black horizontal line indicates the UFP concentration measured con-centration in the beginning of the street on 28/07/2009.

342 I. Nikolova et al. / Science of the Total Environment 412 (2011) 336–343

high UFP concentration at the beginning of the street canyon (B). Atthis location the averaged concentration (43,090 #/cm3) is accom-panied by a high standard deviation of the measurements(±43,959 #/cm3). The temporal variability of the UFP concentra-tions in the four measurement points in the canyon is given inFig. 8. As can be observed in this plot, the increased concentrationsin the beginning of the canyon are observed throughout the wholetime slot and are not related to a few isolated events. The modelledUFP concentration for the other points in the canyon is in very goodagreement with the measured values, while the concentration inthe beginning (B) is underestimated by the model. Based on theavailable information for the relevant boundary conditions (traffic,meteorology), no explanation for the deviation could be formulatedso far. In this section, two hypotheses for the observed anomaly willbe tested: a change in the local traffic emissions and a change in therooftop wind direction.

A possible reason for this high concentration could be temporalcongestion of vehicles on the right side of the canyon at the nearbytraffic light towards the boulevard. The measurement instrument isinstalled in the proximity of this traffic light. Notice that at the endof the canyon the road is open with a roundabout without trafficlights.

As mentioned by Nikolova et al. (2011) UFP traffic emission con-tain a substantial uncertainty, especially for non standard drivingconditions such as congestion or a stop and go regime in urban envi-ronments. To quantify the effect of this uncertainty on the ambientUFP concentrations, an additional simulation was setup as a sensitiv-ity test in which the traffic emissions in the first section of the canyonin front of the traffic lights were increased by a factor of two. Makinguse of these modified UFP emissions, the high measured peak value atthe beginning of the street can be explained by the model simulations(Fig. 7d, diamond marker). As can be observed in this figure, an in-crease in local emissions would be able to explain the observed highUFP levels. It has to be stressed that the factor of two applied in thissensitivity test is large but given the uncertainty in the UFP emissionmodelling it is not completely unrealistic.

Apart from increased emissions, another option was examined toexplain the behaviour of the observed data during that particularday. The UFP concentrations also increase when particles are trans-ported from the busy boulevard to the canyon. Therefore, the effectof different wind directions on the concentration was simulated.8 additional runs were performed where the wind direction waschanged from 0° to 360° with an increment of 45°. The windspeed was kept constant (3 m/s) during these 8 runs. The resultsare shown in Fig. 9.

Fig. 8. Measured UFP concentration on 28/07/2009 (5 min averages) between 16:15and 17:55.

As can be observed in this figure, northern wind can increase theUFP concentration in the canyon significantly. However, in thosecases the concentration will be higher in all four points in the canyonand not only in the beginning of the street. This behaviour is not ob-served in the measurement ruling out those northern wind hypothe-ses. Wind from the east increases the UFP concentration in thebeginning of the street as well, however without a significant increaseat the other points in the canyon. This typical behaviour is related tothe specific transport of UFP from the boulevard under these condi-tions. However the modelled concentrations at position B under east-ern wind remain lower than the measured value at that point. Basedon those findings, a local deviation of the wind direction could par-tially explain the anomalies of that time slot as well.

4. Conclusions

The aim of this study was to investigate the dispersion of ultrafineparticles and its spatial distribution in a street canyon, to evaluate theperformance of the 3D CFD model ENVI-met® at street scale and toshow the importance of the boundary conditions like wind directionand emissions on the UFP concentration. In order to compare themodelled results a short term campaign of measurements was setup in a street canyon in Antwerp, Belgium. The UFP concentrationwas measured using P-TRAK (TSI Model 8525) simultaneously atfour different locations in the canyon (one close to a busy boulevard,two opposite to each other in the middle and one at the other end).

The modelled UFP concentration in the canyon show qualitativeand quantitative good agreement with the measured concentrationin the canyon (R between 0.44 and 0.93) and model results are withinthe standard deviation of the measurements.

Based on further analysis of some anomalies observed in themodel validation, it is found that wind direction plays a role for thedispersion of UFP inside the canyon. Depending on the wind direc-tion, a vortex can be developed in the canyon and can increase or de-crease the UFP concentration at kerbsides. Data on regional windsmeasured every three hours is not sufficient to describe the variabilityin the wind field in the urban areas. Additionally, the concentrationcan drastically increase as a result of an increase in the traffic emis-sions (for example during rush hours). Direct source emissions dur-ing idle and acceleration may have a significant effect on the UFPconcentration.

343I. Nikolova et al. / Science of the Total Environment 412 (2011) 336–343

It is demonstrated that the concentration of UFP is dependent onvarious parameters: meteorology, emissions, building configuration.In order to improve UFP exposure estimates, all these effects have tobe taken into account. UFP dispersion models provide a valuable toolto participate in this debate. They can be beneficial for planning futuremeasurement campaigns (position of the instruments, height, etc.),support of decision makers in evaluating abatement strategies, urbanplanning (building constructions, ventilation systems, traffic manage-ment), etc. Since local traffic has a very large share in the total UFP con-centrations, UFP can be used as a sensitive parameter to evaluate long orshort term local air quality management strategies.

Acknowledgements

This work was sponsored by VITO N.V.

References

Abdulsaheb A, Kumar P. Influence of street canyon geometries on the dispersion ofnanoparticles. 18th International Symposium on Transport and Air Pollution,Dübendorf, Switzerland, 18–19 May; 2010. p. 1–6.

Albriet B, Sartelet KN, Lacour S, Carissimo B, Seigneur C. Modelling aerosol number dis-tributions from vehicle exhaust with an aerosol CFD model. Atmos Environ2010;44:1126–37.

Birmili W, Alaviippola B, Hinneburg D, Knoth O, Tuch T, Kleefeld-Borken J, et al. Disper-sion of traffic-related exhaust particles near the Berlin urban motorway: estima-tion of fleet emission factors. Atmos Chem Phys 2009;9:2355–74http://www.atmos-chem-phys.net/9/2355/2009/acp-9-2355-2009.pdf.

Bruse M, Fleer H. Simulating surface–plant–air interactions inside urban environmentswith a three dimensional numerical model. Environ Model Software 1998;13:373–84.

Buccolieri R, Gromke C, Di Sabatino S, Ruck B. Aerodynamic effects of trees on pollutantconcentration in street canyons. Sci Total Environ 2009;407:5247–56.

Crüts B, van Etten L, Törnqvist H, Blomberg A, Sandström T, Mills N. Exposure to dieselexhaust induces changes in EEG in human, volunteers. Part Fibre Toxicol 2008;5:4.doi:10.1186/1743-8977-5-4.

Di Sabatino S, Kastner-Klein P, Berkowicz R, Britter RE, Fedorovich E. The modelling ofturbulence from traffic in urban dispersion models — Part I: theoretical consider-ations. Environ Fluid Mech 2003;3(2):129–43.

Di Sabatino S, Buccolieri R, Pulvirenenti B, Britter R. Simulations of pollutant dispersionwithin idealised urban-type geometries with CFD and integral models. Atmos En-viron 2007;41:8316–29.

Donaldson K, Tran L, Albert Jimenez LA, Duffin R, Newby DE, Mills N, et al. Combustion-derived nanoparticles: a review of their toxicology following inhalation exposure.Part Fibre Toxicol 2005;5(6):553–60.

Emission Inventory Guidebook. (available online at:)http://www.eea.europa.eu/publi-cations/EMEPCORINAIR42006.

FOD–DIV. http://www.mobilit.fgov.be/nl/index.htm2006.Gidhagen L, Johansson C, Langner J, Olivares G. Simulation of NOx and ultrafine parti-

cles in a street canyon in Stockholm, Sweden. Atmos Environ 2004;38:2029–44.Gidhagen L, Johansson C, Langner J, Foltescu V. Urban scale modelling of particle num-

ber concentration in Stockholm. Atmos Environ 2005;39:1711–25.Gromke C, Buccolieri R, Di Sabatino S, Ruck B. Dispersion study in a street canyon with

tree planting by means of wind tunnel and numerical investigations — evaluationof CFD data with experimental data. Atmos Environ 2008;42:8640–50.

Hagler GSW, Baldauf RW, Thoma ED, Long TR, Snow RF, Kinsey JS, et al. Ultrafine par-ticles near a major roadway in Raleigh, North Carolina: downwind attenuation andcorrelation with traffic-related pollutants. Atmos Environ 2009;43:1229–34.

Holmes NS, Morawska L. A review of dispersion modelling and its application to thedispersion of particles: an overview of different dispersion models available.Atmos Enivron 2006;40(30):5902–28.

Ketzel M, Berkowicz R. Modelling the fate of ultrafine particles from exhaust pipe torural background: an analysis of time scales for dilution, coagulation and deposi-tion. Atmos Environ 2004;38:2639–52.

Kittelson DB, Watts WF, Johnson JP. Nanoparticle emissions on Minnesota highways.Atmos Environ 2004;38:9-19.

Kumar P, Fennell P, Britter R. Effect of wind direction and speed on the dispersion ofnucleation and accumulation mode particles in an urban street canyon. Sci TotalEnviron 2008;402:82–94.

Kumar P, Garmory A, Ketzel M, Berkowicz R, Britter R. Comparative study of measuredand modelled number concentrations of nanoparticles in an urban street canyon.Atmos Environ 2009;43:949–58.

Kumar P, Robins A, Vardoulakis S, Britter R. A review of the characteristics of nanopar-ticles in the urban atmosphere and the prospects for developing regulatory con-trols. Atmos Environ 2010;44:5035–52.

Kumar P, Ketzel M, Vardoulakis S, Pirjola L, Britter R. Dynamics and dispersion model-ling of nanoparticles from road traffic in the urban atmospheric environment — areview. J Aerosol Sci 2011;42:580–603.

Lefebvre W, Janssen S, Vankerkom J, Deutsch F, Veldeman N, Fierens F, Peelaerts W,Looy SV, Claeys N, Mierlo TV, Lefebre F. Making high resolution air quality mapsfor Flanders, Belgium. Air Pollut Model Appl XXI 2012;4:205–11.

Matson U, Ekberg LE, Afshari A. Measurement of ultrafine particles: a comparison oftwo handheld condensation particle counters. Aerosol Sci Technol 2004;38(5):487–95.

Mazzeo NA, Venegas LE. Evaluation of turbulence from traffic using experimental dataobtained in a street canyon. Int J Environ Pollut 2005;25(1–4).

Morawska L, Ristovski Z, Jayaratne ER, Keogh DU, Ling X. Ambient nano and ultrafineparticles from vehicle emissions: characteristics, ambient processing and implica-tions on human exposure. Atmos Environ 2008;42:8113–38.

Narita K, Sugawara H, Honjo T. Effects of roadside trees on thermal environment withinstreet canyon. Geographical Report of Tokyo Metropolitan University, 43. ; 2008.p. 41–8.

Nikolova I, Janssen S, Vrancken K, Vos P, Mishra V, Berghmans P. Size resolved ultrafineparticles emission model — a continuous size distribution approach. Sci Total Envi-ron 2011;409(18):3492–9.

Oberdorster G, Utell MJ. Ultrafine particles in the urban air: to the respiratory tract —and beyond? Environ Health Perspect 2002;110:A440–1.

Politis M, Pilinis C, Lekkas TD. Ultrafine particles (UFP) and health effects. Dangerous.Llike no other PM? Review and analysis. Glob NEST J 2008;10(3):439–52.

Regulation (EC) No 595/2009 http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:32009R0595:EN:NOT.

Regulation (EC) No 715/2007 http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:32007R0715:EN:NOT.

Samaras Z, Ntziachristos L, Thompson N, Hall D, Westerholm R, Boulter P. Characterisa-tion of exhaust particulate emissions from road vehicles (particulates) — final pub-lishable report; 2005. (Available online at)http://lat.eng.auth.gr/particulate.

Seindfeld J, Pandis S. Atmospheric chemistry and physics. New York: Wiley; 1998.Uhrner U, Von Lowis S, Vehkamaki H, Wehner, Brasel B, Hermann M, et al. Dilution and

aerosol dynamics within a diesel car exhaust plume — CFD simulations of on-roadmeasurement conditions. Atmos Environ 2007;41:7440–61.

Wang L, Morawska L, Jayaratne R. CFD modelling of ultrafine particle number concen-tration and dispersion at a bus station. Proceedings 11th International Conferenceon Indoor Air Quality and Climate Paper ID — 195, Copenhagen, Denmark; 2008.

Wania A, Bruse M, Blond N, Weber C. Analysis the influence of different street vegeta-tion on traffic-induced particle dispersion using microscale simulations. J EnvironManage 2011. doi:10.1016/j.jenvman.2011.06.036.

Wehner B, Birmili W, Gnauk T, Wiedensohler A. Particle number size distributions in astreet canyon and their transformation into the urban-air background: measure-ments and a simple model study. Atmos Environ 2002;36:2215–23.