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Effect of wind direction and speed on the dispersion ofnucleation and accumulation mode particles in an urbanstreet canyon
Prashant Kumara,⁎, Paul Fennellb, Rex Brittera
aDepartment of Engineering, University of Cambridge, CB2 1PZ Cambridge, UKbDepartment of Chemical Engineering and Chemical Technology, Imperial College London, SW72AZ UK
Article history:Received 2 January 2008Received in revised form31 March 2008Accepted 22 April 2008Available online 5 June 2008
There have been many studies concerning dispersion of gaseous pollutants from vehicleswithin street canyons; fewer address the dispersion of particulate matter, particularlyparticle number concentrations separated into the nucleation (10–30 nm or N10–30) oraccumulation (30–300 nm or N30–300) modes either separately or together (N10–300).This study aimed to determine the effect of wind direction and speed on particle dispersionin the above size ranges. Particle number distributions (PNDs) and concentrations (PNCs)were measured in the 5–2738 nm range continuously (and in real-time) for 17 days between7th and 23rd March 2007 in a regular (aspect ratio~unity) street canyon in Cambridge (UK),using a newly developed fast-response differential mobility spectrometer (samplingfrequency 0.5 Hz), at 1.60 m above the road level. The PNCs in each size range, during allwind directions, were better described by a proposed two regime model (traffic-dependentand wind-dependent mixing) than by simply assuming that the PNC was inverselyproportional to the wind speed or by fitting the data with a best-fit single power law. Thecritical cut-off wind speed (Ur,crit) for each size range of particles, distinguishing theboundary between these mixing regimes was also investigated. In the traffic-dependentPNC region (Ur≪Ur,crit), concentrations in each size range were approximately constant andindependent ofwind speed anddirection. In thewind speeddependent PNC region (Ur≫Ur,crit),concentrations were inversely proportional to Ur irrespective of any particle size range andwind directions. The wind speed demarcating the two regimes (Ur,crit) was 1.23±0.55 m s−1 forN10–300, (1.47±0.72 m s−1) for N10–30 but smaller (0.78±0.29 m s−1) for N30–300.
Keywords:Particle number distributionNucleation and accumulationmode particlesTraffic-produced turbulenceStreet canyonParticle dispersionWind-produced turbulence
1. Introduction
The impacts of ambient particulate pollution on public healthhave been longstanding concerns for the air quality manage-ment community and regulatory authorities (Pope, 2000; Seatonet al., 1995). Regulations controlling the emission of ambientparticulate matter (PM) have been based on limits for PM10
(Dp≤10 µm) and PM2.5 (Dp≤2.5 µm); these use particle mass
ry, Department of Engin4 1223 765311, +44 1223 33ar), [email protected]
ier B.V. All rights reserve
concentrations, not particle number concentrations (PNC).Recent toxicological studies have suggested that the ultrafinefraction (Dp≤100 nm), which is themain component of ambientparticles by number, are more toxic than coarser particles, perunit mass (Oberdorster, 2000). Furthermore, epidemiologicalstudies suggest correlation between exposure to ambientultrafine particles at high number concentration, and adversehealth effects (Davidson et al., 2005; Peters andWichmann, 2001).
eering, University of Cambridge, Trumpington Street, CB2 1PZ,2662.(R. Britter).
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The lack of standard methods and instrumentation for particlenumber measurements, and detailed understanding of theinfluence of ambient meteorology and traffic flows on particledispersion have been major concern to design effective mitiga-tion strategies for particulate pollution in urban areas.
Vehicles are the major source of ultrafine particles in urbanareas (Fenger, 1999; Schauer et al., 1996). Particles between10and300 nm diameter are those considered in detail here, since themajority (N99%) of the total number of particles in this studywasfound to be in this range. These particles were investigated as awhole (N10–300) and further separated into two broad size ranges,as nucleation (10–30 nm orN10–30) and accumulation (30–300 nmor N30–300) mode particles (Gouriou et al., 2004; Kumar et al.,2008a; Roth et al., 2008). The nucleation mode particles are notpresent in primary exhaust emissions, but are thought to be dueto condensation of the vapor phase present in the exhaust gases(CharronandHarrison, 2003;Kittelsonetal., 2006); theseparticlesare formedthroughnucleation (gas-to-particle conversion) in theatmosphere after the rapid cooling and dilution of exhaustemissions, when the saturation ratio of gaseous compounds oflow volatility (e.g., sulphuric acid) reaches amaximum (Rickeardet al., 1996; Charron and Harrison, 2003). Accumulation modeparticles are formed in the combustion chamber,with associatedcondensed organic matter; they are composed of carbonaceousagglomerates (soot particles) and ash. They are producedmainlyby diesel-engined or direct injection gasoline engined vehicles(Graskow et al., 1998).
Over the past two decades, several groups have studied thedispersion of vehicular emissions (gaseous pollutants andparticulates) in urban street canyons (Boddy et al., 2005;Kastner-Klein et al., 2004; Kim and Baik, 2004; Kumar et al.,2008a,b; Li et al., 2007; Weber et al., 2006; Wehner andWeidensohler, 2003), but the need for measurements of fineparticulates (those below 1000 nm) to aid the production andevaluation of dispersion models for regulatory purposes, isacute. For micro-scale numerical modelling of street canyon airpollution, the traffic-related component of ambient pollutantconcentration is generally assumed to be inversely dependenton above-roof wind speed, in particular when solar radiation isweak, stratification is neutral, and traffic-induced turbulence isignored (Berkowicz, 2000). The direction of the wind (cross-can-yon or along canyon) is also important in determining the flowand mixing processes in the street canyon and the consequentpollutant concentrations (Ketzel et al., 2002). At lowwind speedstraffic-produced turbulence and thermal effects become impor-tant. Investigation of the dependence of particle number con-centrations (PNCs) on wind speed andwind direction is vital forparticulate dispersion models. Unfortunately, no studies couldbe located in the literature which enabled the comprehensivetesting of dispersion models applied to particulates.
The effect of the above-roof wind speed and direction onthe particles in the nucleation and accumulation modes wasdetermined by measuring the particle number distributions(PNDs) in the 5–2738 nm size range, at 1.60 m above the roadlevel of an 11.60 m deep (H) street canyon in Cambridge (UK),between the 7th and 23rd of March 2007 for 17 days. Themeasurement height (z) of 1.60 m (i.e., z/H=0.14) was selectedwith the intention that the mixing effects of both traffic andwind-produced turbulence in the street canyon could beobserved (De Paul and Sheih, 1986; Di Sabatino et al., 2003;
Solazzo et al., 2007). In this study, a recently developed instru-ment, the ‘fast-response differential mobility spectrometer(DMS500)’ measured the PNDs in a broad range (5–2738 nm)with a high frequency (up to 10 Hz, though we used 0.5 Hz forour measurements), providing near real-time continuousmeasurements, unlike most other studies.
The main aims of this study were to determine the relativeeffects of the above-roof wind speed and wind direction on thedispersion of particles in the N10–300, N10–30 and N30–300 sizeranges, and to estimate the critical cut-off wind speed (Ur,crit) forthese particles, which distinguishes the boundary between thetraffic-dependent and the wind-dependent regimes. Ignoringthe traffic-dependent PNC regime may often lead to overprediction of concentrations. Therefore, a model providinginformation on Ur,crit, and reflecting the role of both traffic-produced (the PNCs that are independent of above-roof windspeed up to Ur,crit) and wind-produced turbulence (the PNCs areinversely dependent on the wind speed above Ur,crit) wasproposed and validated.
2. Methodology
2.1. Site description
Measurements were carried out in Pembroke Street (Cambridge,UK; 52°12′ N and 0°10′ E), just outside the Chemical EngineeringDepartment building. The studied section of street canyon (Fig. 1)is 167 m long, and runs approximately northeast to southwest.The Chemical Engineering Department is on the northwest (NW)sideof thestreetandPembrokeCollegeonthesoutheast (SE),withmeanbuildingheights (H) ofabout11.6monbothsides.Thestreetcanyon isnearlysymmetrical,withpitchedroofed (slopedparallelto the street) buildings on either side of the street. The streetcanyon is ~11.8mwide (Ws)with one lane (6.65mwide) travellingtowards the northeast (NE). The studied section has an aspectratio (H/Ws) about unity and has a length to height (L/H) about 14,making it a long length street canyon (Vardoulakis et al., 2003).The samplingwas carried out 66m from the SWend of the streetcanyon, 0.40mfromthewall of theChemical EngineeringDepart-ment buildingand set back 2.20mfrom thekerb. Pembroke Streetis close to a car park, whichwas closed during the studied period,and the city centre. Distinct peaks in traffic occurred duringmor-ning (07:00–09:00 h) and evening (18:00–20:00 h) office hours.Traffic flow at the NE end of the street was regulated by trafficsignals while the traffic flowwas free at the southwest (SW) end.
2.2. Instrumentation and data acquisition
A particle spectrometer (DMS500) measured the PNDs in the 5–2738 nm size range at 1.60 m. A sampling frequency of 0.5 Hz,rather than the maximal frequency of 10 Hz, was used toimprove the signal/noise ratio, and measurements were madecontinuously for 24 h a day, for 17 days between 7 and 23 March2007. The data from 20 March (16:00 h) to 21 March (16:00 h) arenot included in this analysis since pseudo-simultaneous mea-surements at four different heights were made to assess thevertical variation of PNCs during this time; results of theseexperiments are presented elsewhere (Kumar et al., 2008a). TheDMS500 was calibrated by the manufacturer (Cambustion Ltd.),
Fig. 1 –Schematic diagram of Pembroke Street, showing the street dimensions, sampling point and traffic flow as described inthe text.
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immediately before the study, by using polystyrene spheres ofknown diameter and also by comparing the results fromsampling an aerosol with those from a scanning mobilityparticle sizer. The calibration errors in particle diametermeasurements and sample flow rates were 3.4% and 2.3%respectively. A detailed description of the working principle(Biskosetal., 2005) of theDMS500, and its application indifferentscenarios and comparison with commonly deployed instru-ments (i.e., SMPS and Electrostatic Low Pressure Impactor)during road side measurements can be found in Collings et al.(2003) andSymonds et al. (2007). A cyclone,with a steel restrictorthat has a 0.52mmdiameter hole, was placed at the head of thesampling tube tomaintain a sample flow rate of 2.5 lmin−1, andto reduce the pressure within the sampling tube to 0.16 bar inorder to improve the time response of the instrument and toreduce particle agglomeration (Biskos et al., 2005).
An automatic, pole mounted, 3-cup vortex anemometer(Windware, UK) was used to record the above-roof (i.e., 16.60 mor z/H=1.43) wind speed (hereafter called as Ur). A wireless wea-ther station (Thermor, UK) was installed at 4.62 m (i.e., z/H=0.40)and recorded ambient temperature, humidity, atmosphericpressure,windspeedanddirection.Thewindspeedwas recordedevery minute during the entire sampling period by the anem-ometer which was set up above the roof (z/H=1.40). The windspeed was also measured on an approximately half-hourly basisat the street level (z/H=0.40), during working hours (0800:1900 h).Ambient temperature, relative humidity and pressure were alsorecorded with the same frequency. The half-hourly averagedreading from the Cambridge University operated AT&T weatherstation,whichwas approximately 500maway from the samplingsite (see Kumar et al., 2008b for details), were also collected andcorrelated with the local observations, whichwere found to be inreasonable (within 3%) agreement; these readings were used todetermine the wind direction above the rooftop.
Traffic volumes were sampled through the measurementperiod by a movement sensitive CCTV camera. Manual trafficcounts were also made for a few hours a day to ensure that thesamplingwas reliable. The traffic speed through the test sitewasmanuallymeasured tobeabout30±7kmh−1. Traffic volumewasconsistent through different hours of the day, for example, thiswas 229±92, 1142±71, 705±177, 1147±45 and 471±120 veh h−1
during 00:00–07:00, 07:00–09:00, 09:00–18:00, 18:00–20:00 and20:00–24:00 h, respectively.
2.3. Particle Losses in sampling tube
A thermally and electrically conductive sampling tube, made ofsilicon rubber to which carbon was has been added, 7.85 mminternal diameter and5.17m length (L1),wasused toobtain the airsamples. To quantify the particle losses in this tube, particlemeasurements were made with the same sampling frequencyfromastationarydiesel-enginedcar (approximately 500mmfromtheexhaust), andcomparedwith separately takenmeasurementsusing a reference tube of much shorter length (Lref=1.0 m). Weassumed that losses inLrefwouldbe equivalent to the losses in thefirstmeter of the sampling tubeswhichwere used in this, and thepreviously mentioned study (Kumar et al., 2008a), so thatapproximately the same number and distribution of particlesentered the 2nd meter of the sampling tube for each of samplingtubes, which was the size and number distribution measured forLref. Next, we correlated the size-dependent penetration throughthe “corrected” length of sampling tube (i.e. the total length of thetube minus Lref). This enabled us to correlate the losses in asampling tube as a function of its actual length. Fig. 2 shows thiscorrelation.Comparisonsof theexperimental resultswith laminarand turbulent flow regime models (Hinds, 1999) were also made;the results were better described by the turbulent flow model,even though the Reynolds number in the sample line lengthswaswithin the laminar regime. It is clear fromFig. 2 thatparticle lossesbelow10nmseemtobehighlysignificant, showing themaximumlossesashighas~90% for5nmparticles. Therefore thedatabelow10 nm are not considered in the analysis below, with particlelosses between 10 and 20 nm particles being corrected using theresults from Fig. 2. Further details of the correction methods aregiven in Kumar et al. (2008a).
3. Results and discussions
3.1. Above-roof wind speed
The dispersion of pollutants in a street canyon is closely re-lated to themixingmechanismswithin it.Wind-produced andtraffic-produced turbulence are considered to be the dominantmixingmechanismsof the particles in this study. Earlier studieshave shown that when the wind blows across a regular streetcanyon with Ur greater than around 1.2 m s−1, the mixing of
Fig. 2 –Size-dependent penetration of particles in samplingtube. For comparison, results of laminar and turbulentregime models for particle penetration in sampling tube arealso plotted with experimental results.
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gaseous pollutants is dominated by wind-produced turbulence,and below this wind speed the mixing of the pollutants in thelower part of the street canyon (up to z/H=0.20) is dominated bythe traffic-produced turbulence (De Paul and Sheih, 1986; DiSabatino et al., 2003; Kastner-Klein et al., 2003; Solazzo et al.,2007). For about 71% of the total duration of sampling, Ur wasgreater than 1.2 m s−1; otherwise it was ≤1.2 m s−1 (Fig. 3).
Mixing effects produced by differential heating of the wallsand road within the canyon, are considered to be negligible(Kim and Baik, 2001), especially since changes in temperaturewere modest (average 7.4 °C, standard deviation 4.6 °C) overthe entire measurements and the fact that thermal effects are
Fig. 3 –Wind rose diagram for half-hourly averaged wind speedwind directions cover different wind angles. The thick blue lineagainst each wind directions in parenthesis are the total frequenthis figure legend, the reader is referred to the web version of th
mainly from variations in solar heating of the street walls andthe ground during the day (Kovar-Panskus et al., 2002). In ourexperiments, solar radiation was weak (as evidenced by thelow temperatures) throughout the entire sampling period.
3.2. Wind direction
The flow within a street canyon can be characterised by theroof geometry, roughness elements, street canyon geometrysuch as aspect ratio and street orientation and with thesynoptic (above-roof) wind conditions (Kastner-Klein et al.,2004). The most important factor influencing the flow in thestreet canyon is wind direction. A single vortex can form in aregular (aspect ratio ~1) street canyonwhen the wind is acrossthe canyon (i.e., wind direction to the street axis exceeds 30°)and Ur is greater than 1.5 m s−1 (De Paul and Sheih, 1986).However, such vortices are less evident when the wind di-rection is more parallel to the canyon. The flow can also be acombination of an along-street flow and a re-circulating flow(Belcher, 2005). Our study covered wind flow from most di-rections (see Fig. 3), though the majority of the wind flow wasfrom the SW.
Experiments were undertaken during different wind direc-tions. The entire data set was half half-hourly averaged andwas divided into eight categories of wind directions. Thesewind directions were northwest (NW), north (N), northeast(NE), east (E), southeast (SE), south (S), southwest (SW) andwest (W), which represent the wind angles 292.5°–337.5°,337.5°–22.5°, 22.5°–67.5°, 67.5°–112.5°, 112.5°–157.5°, 157.5°–202.5°,202.5°–247.5°and 247.5°–292.5° respectively. As shown in Fig. 3,NW and SE represent cross-canyon flow for the leeward andwindward situations respectively whilst SW and NE representrespectively along canyon flowwith and against the direction oftraffic, with the other directions representing the conditions
over the entire sampling duration. As classified in the text,represents the orientation of street canyon. Values showncies of winds. (For interpretation of the references to colour inis article.)
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between cross- and along canyon flow. The frequencies of windsfrom NW, SE, NE, SW, S and W were 16, 5, 3, 38, 23 and 16%respectively; indicating that only a small data set was availablefor SEandNE, andnodatawasobtained for E andNwinds (Fig. 3).
3.3. Particle number distributions and concentrations
The PNDs can be described as consisting of different popula-tions in different size-modes, and further quantified by thetotal particle number concentrations in these modes. Themodes were categorized as nucleation mode (N10–30), accumu-lation mode (N30–300) and coarse mode (those between 300 and2738 nm or N300–2738). The average PNDs for each wind direc-tion are shown in Fig. 4(a–f). The PNDswere representative of apollution originating from typical urban traffic (Jones andHarrison, 2006; Roth et al., 2008), exhibiting a strong peaks at~15 nm and another peak at ~87 nm. The peak at ~15 nmwasattributed to the particles formed by nucleation and con-densation during the rapid cooling and dilution of semi-vola-tile species from the exhaust gaseswith ambient air whilst thepeak at ~87 nm was attributed to particles formed in thecombustion chamber, with associated condensed organicmatter. However, the magnitude of PNDs varied with winddirection (Fig. 4a–f). This variability is presumably due to thedifferent building geometries seen by the wind, the trafficvolume, the ambient meteorology (notably Ur and winddirection), and possibly the presence, strength and sense ofrotation of any street canyon vortex. What is most strikingabout these plots is that the magnitudes of the local maximaat 15 nm and 87 nm do not move in sympathy when consi-dering the various geometrical situations.
In further analysis we assume that the dependence of thePNDs on traffic volume and wind speed is as commonly ob-served; it increases (linearly) with increasing traffic volume anddecreases inverselywith increasingwind speed. Furthermore, ifwe assume that the number of particles at each peak diameter(15 and 87 nm) is proportional to the total number of particles intheN10–30 andN30–300 ranges, and normalise them for eachwinddirection bydividing by the traffic volume (T) andmultiplying byUr (assuming that thenumber count is proportional to the trafficvolume and the inverse wind speed law holds), before finallydividing through by the minimum value for the cross-canyonwind direction (from the SE) the differences in the normalisednumber of particles should indicate the effect of the variouswind directions. Comparison of normalised PNDs in the N30–300
range for different wind directions showed only modest (0.94±0.23 times theSE) variations.However, thenormalised PNDs inthe N10–30 range were larger by a factor of 5, 4, 3, 2 and 2 duringNE, NW, SW, S andWwinds respectively than those during theSEwinds. This variation is itself of interest but, possibly ofmoreinterest is why such a variation exists for the N10–30 range butnot for the N30–300 range.
The reason for this could be that the PNCs in the nucleationmode (N10–30) are affected differently by increased dilution thanthePNCs in theaccumulationmode (N30–300). Kittlesonet al. (2006)reported that nucleation mode particles are not present in thetailpipe and their formation is driven by the concentration ofnucleating species (mainly sulphuric acid and hydrocarbons) andits degree of super-saturation; dilution conditions such astemperature, residence time in the tail pipe, dilution ratio and
dilution rate may change the number concentrations of theseparticles by an order of magnitude or more. Conversely, accu-mulationmodeparticlesarecomposedprimarilyof carbonaceousagglomerates and ash, and are formed inside the engines of thevehicles during combustion or thereafter; these are less in-fluenced by sampling and dilution conditions (Kittelson et al.,2006). Our previous studies (Kumar et al., 2008b; 2007a,b) for streetcanyon measurements also showed that transformation pro-cesses for particles in the accumulation mode were generallycomplete by the time particles were measured, and their totalnumber can be assumed to be conserved (i.e., their concentrationonly changes when the air in which they are suspended in isdilutedby fresh, uncontaminated, air). This is discussed further inSection 3.6.
The PNCswere obtained in selected size ranges by integratingthe areas under PND curves over a given size range. The averagePNCs over the entire measurements in the N10–30, N30–300 andN300–2738 rangewereabout66±5, 32.5±5and0.5±0.03%of the total(N10–2738) PNCs, respectively. Similar resultswere reportedbyTuchet al. (1997) for European citieswhere they found that the PNCs inthe N10–30 range were dominant and that there was negligibleparticle number concentration of particles above 500 nm duringmeasurements of particles between 10 and 10,000 nm. Theseobservations were later confirmed byWehner andWiednesohler(2003) in their long term study (over 4 years) in Leipzig (Germany).As expected, the PNCs in theN300–2738 were found to be negligiblein this study. Therefore the overall range (N10–300) and the split ofthis into N10–30 (nucleation mode) and N30–300 (accumulationmode) only are considered in subsequent analysis.
3.4. Traffic-dependent and wind speed dependent particlenumber concentrations
There are two limiting cases for the dilution of the PNCs.Firstly, the traffic-dependent case, where dilution is domi-nated by traffic-produced turbulence occurring at smallerwind speeds. Secondly, the wind-dependent case, wheredilution is dominated by wind-produced turbulence occurringat higher wind speeds. For both cases, normalised numberconcentration of the traffic component of total concentrationscan be expressed as (Ketzel et al., 2002);
Ni�j � Cb;i�j
TmEi�j¼ aU�n
r OrNi�j � Cb;i�j
Tm ¼ awU�nr ð1Þ
where Ni−j is the PNC in any size range, Cb,i−j is the backgroundPNC in any size range, m and n are the exponents of T and Ur
respectively, a is a constant, Ei− j (taken to be constant in thisstudy) is theaverageparticlenumberemission factor (veh−1km−1)in any particle size range for all vehicles in the fleet, and aw is theproduct of a and Ei−j.
For the first casenmust be zero. For thesecondcase,n is oftentaken to be unity (the inversewind speed lawholds). The inversewind speed law arises if dilution of vehicle-produced particles isassumed to be proportional to above-roof wind speed (i.e., to theventilation rate of the canyon). As noted earlier, for thisassumption to hold, it is also important that stratifications areneutral, solar radiation isweak, and traffic-produced turbulenceis ignored. For both cases it is assumed that m=1, that is theparticulate emission is assumed to be proportional to the trafficvolume. Considering these assumptions, a model with two
Fig. 4 –Half Half-hourly averagedmeasured and corrected particle number distributions during winds from the (a) NW, (b) SE,(c) NE, (d) SW, (e) S and (f)W. AcronymsDp, T,Ur, RH and Ta stands for particle diameter, traffic volume, above-roofwind speed,relative humidity and ambient temperature respectively, and numbers against them show half half-hourly average valuesand their standard deviations over the entire sampling duration. Bars show the standard deviation of the half half-hourlyaveraged PNDs.
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distinct regimes, reflecting the role of traffic-produced andwind-produced turbulence, was proposed by modifying Eq.(1) thus:
Ni�j
Tm ¼ Ni�j
Tm
� �crit
ð2Þ
For Ur≪Ur,crit (n=0)
Ni�j
Tm ¼ Ni�j
Tm
� �crit
Ur;critU�nr ð3Þ
For Ur≫Ur,crit (n=1)
whereUr,crit is the critical cut-offwind speed atwhich the gradient(n) of the best-fit line changes, and (Ni−j/Tm)crit is the traffic-nor-malised PNC in any size range below Ur,crit. Eq. (2) represents theflat regions (i.e.,n=0)whereas Eq. (3) represents the regionswherethe inverse wind speed law holds (i.e., n=1). Eqs. (2) and (3) werecombined to give a continuous function spanning all experimen-talmeasurements.The fit of the function to theexperimentaldatawas optimised by varying the value ofUr,crit to achieve the lowestsumofsquarederrorsbetweenmodelandexperiment.Resultsareshown in Figs. 5–10. This appears to be the first time such amodelhas beenapplied to dispersion of fine particles. The values ofUr,crit
Fig. 5 –Half-hourly averaged normalised particle number concentrations against wind speed during winds from NW (i.e., crosscross-canyon winds, leeward situation) in (a) N10–300, (b) N10–30 and (c) N30–300 size ranges. For both size ranges, fit resultsshowing n=0 and 1 represent themodel Eqs. (2) and (3), and theUr on x-axis at which n changes from0 to 1 corresponds toUr,crit.Furthermore, to test the inverse wind speed law in the wind speed dependent PNC region, other fit results are drawn from thePNC data above Ur,crit which shows the deviations in n from assumed unity. Similar fit results are shown in Figs. 6–10.
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for all size rangesduringdifferentwinddirectionsarepresented inFig. 11, which are discussed in detail in Section 3.6. It should benoted that the above formulations (Eqs. (2) and (3)) do not includethe background PNCs (probably small) (Kumar et al., 2008b)because these were not directly measured. Therefore, this wasremoved fromEq. (1) and the PNCs in each size rangewere simplydivided through by the traffic volume, to obtain a traffic-normal-ised PNC in each size range, and plotted logarithmically againstUr
for all wind directions, as shown in Figs. 5–10.Figs. 5–10 clearly reveal that the normalised PNCs in all size
ranges are approximately independent of wind speed, up to acritical cut-off value (Ur,crit). AboveUr,crit, there is anapproximatelyinversely proportional decrease in normalised PNC with increas-ing wind speed. In this latter region, the normalised PNC dataaboveUr,crit was used to test whether n is really unity for all winddirections in each size range. The best-fit lineswere drawn to thisdata (shown in Figs. 5–10), and comparisons weremade betweenthe obtained values ofn andanassumedn=1. The average values
Fig. 6 –Half-hourly averaged normalised particle number concencross-canyon winds, windward situation) in (a) N10–300, (b) N10–30
of n over all wind directions for particles in theN10–300,N10–30 andN30–300 rangewere 1.00±0.25, 0.98±0.36 and 0.94±0.14 (seeTable 2or Figs. 5–10); these were close to the assumed value (unity),confirming an inverse wind speed law in each size range.Moreover, these observations also confirm that the proposedmodel (Eqs. (2) and (3), which provides simultaneous informationon Ur,crit by using two distinct regimes (n=0 and 1), better fits theentire PNCdata (shown in Figs. 5–10) than theothermodel simplyfitting the entire PNC data with a best-fit single power law (notshown in Figs. 5–10). The overall performanceof thesemodels areshown in Table 1 by comparing the commonly used followingstatistical indicators (Yadav and Sharan, 1996).
• Correlation coefficient (R) — this describes the degree ofassociation between the predicted and the observed values;its value lies between 0 and 1, and ideal value is 1.
• Mean fractional bias (FB) — this describes the tendency of themodel to overestimate (FBb0) or underestimate (FBN0) the
trations against wind speed during winds from SE (i.e., crossand (c) N30–300 size ranges.
Fig. 7 –Half-hourly averaged normalised particle number concentrations against wind speed during winds from NE (i.e., alongcanyon winds, and winds directed against the traffic movement) in (a) N10–300, (b) N10–30 and (c) N30–300 size ranges.
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observed values; its value lies between −2 and +2, and desiredvalue is zero.
• The fraction of predictions within a factor of 2 (FAC2)— thisdescribes the fraction of the data for which 0.5 ≤ (predictedconcentration/observed concentration) ≥2; ideal value 100%.
Table 1 clearly reveals that usinga singlepower law fit ratherthan a proposed two regimemodel on the PNC data in each sizerange over the entireUr range lead to significant over prediction(see difference in the values of FB) of concentrations.
After confirming from the above discussions that thenormalised PNCs are inversely proportional to the Ur in theregion where Ur≫Ur,crit, and that proposed model fit the entiredata set well for all wind directions, the next interesting aspectis to show the effect of wind directions on normalised PNCs inboth regions.
3.5. Role of traffic and wind-produced turbulence
Both traffic-produced and wind-produced turbulence influencethe normalised particle number concentrations within the
Fig. 8 –Half-hourly averaged normalised particle number concentcanyon winds, and winds directed with the traffic movement) in
street canyon. More precisely it is both the turbulence and anymean flow that might be set up by the traffic and the wind thatwill influence the magnitude and the spatial distribution of thenormalised PNC. In this paper wewill not consider any thermaleffects both for simplicity and because they were unlikely to beof significance over the measurement period (see Section 3.1).
Under low wind speed conditions the traffic-produced tur-bulence is the dominant process in the dilution of particlesemitted at street level (Di Sabatino et al., 2003; Solazzo et al.,2007; Vachon et al., 2002). This is the case for the left handside ofthe plots (Figs. 5–10) where the normalised PNCs are indepen-dent of thewind speed (n=0). This is the expected behaviour forUr≪Ur,crit but we will interpret this behaviour to be valid up toUr=Ur,crit. In this case we expect the same values of normalisedconcentrations (y-intercepts of Figs. 5–10) in each size rangeirrespectiveofwinddirection.Asexpected, thenormalisedPNCsin the N10–300 range were similar, with a mean of 161 and astandard deviation of 68 (Table 2). Similarly, the normalisedPNCs in N10–30 and N30–300 ranges were 90±25 and 87±24respectively (Table 2). Interestingly, the normalised PNCs ineach size range were the largest for the winds from the S,
rations against wind speed during winds from SW (i.e., along(a) N10–300, (b) N10–30 and (c) N30–300 size ranges.
Fig. 9 –Half-hourly averaged normalised particle number concentrations against wind speed duringwinds from S (i.e., betweenalong and cross cross-canyon winds, windward situation) in (a) N10–300, (b) N10–30 and (c) N30–300 size ranges.
90 S C I E N C E O F T H E T O T A L E N V I R O N M E N T 4 0 2 ( 2 0 0 8 ) 8 2 – 9 4
indicating a relatively smaller effect of traffic-produced turbu-lenceorpossibly a relatively larger emission rate per vehicle.Wecould not see any particular reason why these observationsshould be correlated with winds from the S. But overall ourobservations are generally as expected, that is the PNCs in eachsize range in the low wind speed regimes are independent ofwind speed. Under higher wind speed conditions the wind-produced turbulence is the dominant process in the dilution ofparticles emitted at street level (Britter and Hanna, 2003;Kastner-Klein et al., 2004).
For the righthandsideof theplots (Figs. 5–10)whereUrNUr,crit
the normalised PNCs decreasedwith increasedwind speed as isexpected. There are two interesting aspects to address in thisregion. Firstly, is there any deviation in the values of n from thatexpected of about unity for UrNUr,crit for each wind direction?Secondly, is there anyeffect ofwinddirection on thenormalisedPNC in each size range? These are discussed below.
• The fitting of the datawith a negative unity exponent is seento be not unreasonable. For N10–300, the best-fit values of n
Fig. 10 –Half-hourly averaged normalised particle number concebetween along and cross cross-canyon winds, leeward situation
were 0.64, 0.85, 1.15, 1.10 and 1.27 for winds from theNW, SE,SW, S and W respectively. Given the scatter of the originaldata it is argued here that these results are consistent with anegative unity exponent that is required by dimensionalarguments (Table 2). It is probably fortuitous that the averageof the 5 calculated exponents (omitting that for winds fromthe NE where very little data was available) was 1.00±0.25.Similarly, the average values of n over all wind directions forparticles in the N10–30 and N30–300 range were 0.98±0.36 and0.94±0.14, respectively (Table 2). This is particularly inter-esting given the findings in Section 3.6 (Fig. 11) that the Ur,crit
is affected by the relative orientation of the canyon and thewind. However, there were exceptions for N10–30 during thewinds from the NW and the SW where n was the smallest(0.4) and the largest (1.35), respectively. Similarly, for N30–300,n was smallest (0.69) during winds from the SW. The reasonfor the smallest n for N10–30 during NW can be that the datawas very sparse (Fig. 5b); no clear explanation for theremaining variation was found. However, the different flowconditions and levels of wind-produced turbulence during
ntrations against wind speed during winds from W (i.e.,) in (a) N10–300, (b) N10–30 and (c) N30–300 size ranges.
Fig. 11 –TheUr,crit for particles in theN10–300,N10–30 andN30–300
range (including and excluding the background) duringdifferent wind directions.
Table 1 – Overall performance of proposed model (Eqs. (2)and (3)) fitted on entire PNC data (shown in Figs. 5–10), andthe other model (best-fit single power law) fitted on entirePNC data (best-fit line not shown in Figs. 5–10)
Winddirections
Proposed model (Eqs.(2) and (3)) fitted onentire PND data
Other model (best-fitsinglepower law) fittedon entire PNC data
S R 0.79 0.68 0.79 0.59 0.50 0.61FAC2 84% 80% 79% 77% 79% 67%FB 0.03 0.00 0.04 0.11 0.01 0.37
W R 0.64 0.64 0.79 0.53 0.54 0.68FAC2 72% 73% 80% 66% 66% 78%FB 0.03 −0.01 −0.01 −0.19 −0.16 −0.27
R is the regressioncoefficient, FAC2 is the fractionofpredictionswithina factor of two and FB is the fractional bias.a Basedonvery little availabledata, therefore thesearenot consideredor estimated for analysis.
91S C I E N C E O F T H E T O T A L E N V I R O N M E N T 4 0 2 ( 2 0 0 8 ) 8 2 – 9 4
different wind directions, as described below, could be apossible reason.
• In the wind-produced turbulence regime, taken as UrNUr,crit,the magnitude of the normalised PNCs due to change inwind directions is directly measured by the coefficient (aw).The estimated values of aw are shown in Table 2 and theseclearly change significantly with wind direction. The change inaw during different wind directions can be due to two mainreasons; change in flow conditions (whichwill lead to a changein the level ofwind-produced turbulenceand to the transport ofparticles out of the canyon) and the presence of any organisedvortex structure within the canyon (whichwill lead to a spatialvariation of concentration within the canyon). Note that asmaller aw represents smaller concentrations and larger dilu-tion. For theN10–300, the results for theNWandSEwinds suggestthe presence of an organised vortex giving larger concentra-tionswhen themeasuring station is on the leeward side.Windsfrom the SW will be along the street and in the direction oftraffic flow. This would seem to be a case where there is littleturbulence generation due to flow separation from the canyonwallsor fromtheco-flowof thewindandthe traffic.Hence largenormalised PNCs are observed. A similar though weaker ar-gument could be made for winds from the S. However we areunable toexplain thesmallnormalisedPNCs forwinds fromtheW. Interestingly,when looking at the split range of particles theaw for N10–30 and N30–300 showed, in general, the same trend asexplained for N10–300 (Table 2), with an exception for N30–300
during the winds from the NW and the SE where these were incontrast to theexpected results. Themain reason for this seemsto be the small quantity of data set available for SE.
It can be concluded from this section that when UrbUr,crit
the normalised PNCs are nearly similar in each size rangeirrespective of any wind direction. Moreover, when UrNUr,crit
the particles are inversely proportional to the wind speedirrespective of any particle size range andwind directions, andthat effect of wind directions seems to be similar on the dis-persion of particles in each size range. However, the values ofUr,crit are different for all three size ranges, and change withchanges in wind direction, which is discussed in subsequentsection.
3.6. Critical cut-off wind speed
Fig. 11 shows the values of Ur,crit for each size range during dif-ferent wind directions. These are obtained from Figs. 5–10. Thevalue of Ur,crit was significantly influenced by the relative orien-tation of the canyon and thewind. The value ofUr,crit for particlesin the N10–300 range ranged from 0.70 to 1.98 m s−1, spanning theoften quoted value of 1.2 m s−1 (De Paul and Sheih, 1986; DiSabatino et al., 2003; Kastner-Klein et al., 2003; Solazzo et al., 2007;Vachon et al., 2002) for gaseous pollutants, with amean and stan-dard deviation of 1.23 m s−1 and 0.55 m s−1 respectively. ThederivedUr,critwasalways smaller (average0.78ms−1 andstandarddeviation 0.29m s−1, range 0.45–1.13m s−1) forN30–300 than forN10–30 (average 1.47 m s−1 and standard deviation 0.72 m s−1,range 0.70–2.22 m s−1) and the latter showed larger variationsfor all wind directions. These observations produced twointeresting questions. Why was the Ur,crit for each size rangedifferent for different wind directions?Whywas the Ur,crit notthe same for particles in N10–30 and N30–300 ranges? These areexplained as below.
• From our analysis it is apparent that theUr,crit as defined here,is a reflection of themagnitude of the coefficient aw (see Eq. (1)and Section 3.5 for details, and Table 2 for values). The Ur,crit
has been defined as the intersection of the traffic-related andwind-related correlations. The first of these has been assumedindependent of wind speed and direction while the secondvaries with the “turbulence-generating capacity” of the meanwind and a particular geometry. Thus when aw is large, Ur,crit
should also be, must depend upon the wind direction. Of
Table 2 – Y-Intercepts of the normalised PNCs for traffic-dependent PNC case and the values of n and aw for wind-dependentPNC case for particles in each size ranges; these are estimated from Figs. 5–10
a Based on very little available data, therefore these are not considered or estimated in analysis.
92 S C I E N C E O F T H E T O T A L E N V I R O N M E N T 4 0 2 ( 2 0 0 8 ) 8 2 – 9 4
course, it is also important to decidewhether this difference isoperationally important or not.
• Fig. 11 shows that the Ur,crit is always larger for N10–30 thanfor N30–300 for all wind directions, with larger variations forN10–30 than N30–300. These observations indicate two possi-bilities. The first is that particles in the N10–30 range (nuclea-tion mode) are relatively more affected than the particles inN30–300 (accumulation mode) range for the same level oftraffic-produced turbulence during any wind direction. Theother is that particles in the N10–30 range are relatively lessaffected than the particles in N30–300 range for same level ofwind-produced turbulence during any wind direction. Themost probable reason seems to be the first, since thenucleation mode particles are formed within the turbulentwake of a vehicle, so that traffic-induced turbulence plays amuch greater role in their measured number than does thewind. The gas-to-particle conversion leading to the nuclea-tion mode mainly depends on dilution ratio and amount ofsurface area available for volatile organics to adsorb to(Kittelson et al., 1999). The dilution ratio due to traffic-pro-duced turbulence in the wake of a moving vehicle can be ashigh as 1000 in the first 1–5 s, and can increase only afurther factor of ~10 in up to 10 min (Zhang and Wexler,2004). However, information on dilution in the near-vehiclewake could not be obtained with the sampling arrangementused.
It is important to note that the estimations of the above dis-cussedUr,crit didnot include thebackgroundconcentration (Cb,i− j).In order to show whether the inclusion of this parameter affecttheUr,crit an approximate estimates of theCb,i− j in each size rangeduring all wind directions were made by modifying Eq. (1) to:
Ni�j ¼ awTmU�nr þ Cb;i�j: ð4Þ
For all wind directions, the estimated Cb,i− j were found tobe relatively much smaller (b10%) than the total PNCs in anysize range. The incorporation of the estimated backgroundconcentrations into Eq. (1) did not lead to any significantchanges in Ur,crit as shown in Fig. 11, except during NE winds(though the small quantity of data from which the results arederived means that this results should be treated with care).
4. Summary and conclusions
This paper presents the results of a study performed in a regularstreet canyon (H/W~unity) in Cambridge (UK) continuously for17 days between 7th and 23rd March 2007 at 1.60 m above theroad level. Real-time continuous measurements of particlenumber distributions (PNDs) were made in 5–2738 nm sizerange using a fast-response particle spectrometer at a samplingfrequency of 0.5Hz. This study consideredparticles in theN10–300
range and split these into nucleation (N10–30) and accumulation(N30–300) mode particles to study the effect of above-roof windspeed and wind directions on the dispersion of these particles.Thestudytestedthe inversewindspeed lawforwind-dependentdispersion, the constancy of the PNC's for the traffic-dependentdispersion andamodel for distinguishing theboundarybetweenthese two processes
The average PNDs showed typical bi-modal distributionsduring each wind direction, with a strong nucleation modepeak at ~15 nm and an accumulation mode peak at ~87 nm.The magnitude of the PNDs varied according to the winddirection, and showedmuchhigher changes for the nucleationmode than for the accumulation mode. The main reasons forlarger changes were attributed to the larger effect of increaseddilution on particles in theN10–30 range than on particles in theN30–300 range. The average PNCs in the N10–30 range were thelargest (66±5%) fractionof the total (N10–2738) PNCs. ThePNCs inthe N30–300 and N300–2738 range were about 32.5±5 and 0.5±0.03%, respectively of the total. Broadly speaking, these resultswere in line with the literature (Longley et al., 2003; Roth et al.,2008; Tuch et al., 1997; Wehner and Weidensohler, 2003).
When the rooftopwind speedwas less than a critical value,Ur,crit, traffic-produced turbulence dominated the mixing inthe lower part of the canyon and the dilution of normalisedPNCs was independent of wind speed. However, when Ur wasgreater than Ur,crit, wind-produced turbulence dominated themixing in the canyon and the concentration of the normalisedPNCs was often found to be inversely proportional to Ur. Thisinverse dependence of concentrations on wind speed isrequired on dimensional grounds subject to some idealisa-tions. Initially we tested this inverse (n=1) dependence ofPNCs on Ur. The average values of n over all wind directions
93S C I E N C E O F T H E T O T A L E N V I R O N M E N T 4 0 2 ( 2 0 0 8 ) 8 2 – 9 4
for particles in the N10–300, N10–30 and N30–300 range were 1.00±0.25, 0.98±0.36 and 0.94±0.14 respectively, which were consi-dered to be reasonably close to unity.
This two regimemodel was shown to statistically provide abetter fit to the data than a single exponent power law modelapplied to the PNC data. In the wind speed dependent region,the magnitude of normalised (with respect to traffic volume)PNCs in each size range changed significantly with the changein wind directions (in fact, as did the value of Ur,crit). Thesechanges were characterised by the coefficient (aw) whichquantified the “turbulence-generating capacity” of the meanwind and the particular geometry. In general, the trend ofresults for all three size ranges in both traffic and wind speeddependent PNC regions were almost similar for all winddirections, except the change in Ur,crit.
Changes in Ur,crit with wind direction were because thenormalised PNCs for the traffic-produced turbulence case wasapproximately independent of wind direction and because thenormalised PNCs for the wind-produced turbulence case diddepend upon wind direction (all else held equal). Thus theintercept of these two cases (that is Ur,crit) must and doesdepend upon thewind direction. Of course, it is also importantto decide whether this difference is operationally important ornot. The value of Ur,crit for N10–300 range was 1.23±0.55 m s−1,with a similar value (1.47±0.72 m s−1) for N10–30 but smaller(0.78±0.29 m s−1) for N30–300. Interestingly, Ur,crit was alwayssmaller for N30–300 than for N10–30 for all wind directions. Thiswas attributed to a possible greater effect of dilution due totraffic-produced turbulence on particles in the nucleationmode than on particles in the accumulation mode since thenucleation mode particles are formed within the turbulentwake of a vehicle, so that traffic-induced turbulence may playa much greater role in their measured number than does thewind.
Operational dispersion models which do not include theeffects of traffic-produced turbulence may often lead to overprediction of concentrations, as is also shown in Table 1.While these results are preliminary, they clearly provide use-ful information on the dispersion of particles within streetcanyons and on the Ur,crit for particles in different size rangeswhich could be useful for micro-scale numerical modelling ofparticles in urban street canyons. Of course, our study is onlyfor one canyon geometry, for a limited time period and withone particular type of vehicle fleet. Clearly, the specificconditions within different canyons will affect dispersionmechanisms, meaning that a great deal of more work isrequired in this area, in street canyons of different geometricsand for different vehicle fleets.
Acknowledgements
Prashant Kumar acknowledges receipt of the CambridgeNehru Scholarship and the Overseas Research ScholarshipAward for his Ph.D. The authors thank Prof. A.N. Hayhurst andDr. J.S. Dennis for lending the DMS500 for the study. Thanksalso to Dr Jonathan Symonds from Cambustion Ltd. forlending the sampling heads and for the technical advice, andto Dr. David Langley for helping with some of the trafficmeasurements.
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