Parra 2010

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A methodology to urban air quality assessment during large time periodsof winter using computational uid dynamic models

Parra M.A.a, Santiago J.L.b,*, Martín F.b, Martilli A.b, Santamaría J.M.a

a Laboratorio Integrado de Calidad Ambiental (LICA), Departamento de Química y Edafología, Facultad de Ciencias, Universidad de Navarra, Irunlarrea s/n,

31080 Pamplona, Navarra, Spainb Atmospheric Pollution Unit, Environmental Department, CIEMAT, Av. Complutense 22, 28040 Madrid, Spain

a r t i c l e i n f o

Article history:

Received 18 December 2009

Received in revised form

8 March 2010

Accepted 10 March 2010

Keywords:

Ambient wind direction

Computational uid dynamics

Experimental measurements

Pamplona

Urban ow and dispersion

a b s t r a c t

The representativeness of point measurements in urban areas is limited due to the strong heterogeneity

of the atmospheric ows in cities. To get information on air quality in the gaps between measurement

points, and have a 3D eld of pollutant concentration, Computational Fluid Dynamic (CFD) models can be

used. However, unsteady simulations during time periods of the order of months, often required for

regulatory purposes, are not possible for computational reasons. The main objective of this study is to

develop a methodology to evaluate the air quality in a real urban area during large time periods by

means of steady CFD simulations. One steady simulation for each inlet wind direction was performed and

factors like the number of cars inside each street, the length of streets and the wind speed and direction

were taken into account to compute the pollutant concentration. This approach is only valid in winter

time when the pollutant concentrations are less affected by atmospheric chemistry. A model based on

the steady-state Reynolds-Averaged NaviereStokes equations (RANS) and standard k-3 turbulence model

was used to simulate a set of 16 different inlet wind directions over a real urban area (downtown

Pamplona, Spain). The temporal series of NOx and PM10 and the spatial differences in pollutant

concentration of NO2 and BTEX obtained were in agreement with experimental data. Inside urban

canopy, an important inuence of urban boundary layer dynamics on the pollutant concentrationpatterns was observed. Large concentration differences between different zones of the same square were

found. This showed that concentration levels measured by an automatic monitoring station depend on

its location in the street or square, and a modelling methodology like this is useful to complement

the experimental information. On the other hand, this methodology can also be applied to evaluate

abatement strategies by redistributing traf c emissions.

2010 Elsevier Ltd. All rights reserved.

1. Introduction

Citizens are exposed to atmospheric pollutants that have

a severe impact on health (WHO, 2000). To mitigate this effect, the

European Directive on ambient air quality (2008/50/CE) obliges theEuropean Union Countries to assess air quality and establish plans

to improve it where the standards are exceeded. Modelling is an

important tool both in complementing measurements to assess air

quality as well as in evaluating air pollution abatement plans.

Motor vehicles are the main pollutant sources in urban areas.

Toxic contaminants such as Nitrogen Oxides (NOx), Volatile

Organic Compounds (VOC) and Particulate Matter (PM) are emitted

by traf c, and they contribute to the formation of secondary

pollutants like ozone through photochemical reactions. The

dispersion of those hazardous materials in urban areas is deter-

mined by the interactions between meteorological conditions

(wind speed and direction, atmospheric stability), and building and

street congurations. Since these interactions are very complex,research to gain insight into the processes and features of ow and

pollutant dispersion in cities is necessary. To date, a signicant

amount of investigation has been carried out in two complemen-

tary directions:

Experimentally in wind tunnel (Meroney et al., 1996; Kastner-

Klein and Plate, 1999) and at eld scale (Dobre et al., 2005;

Klein et al., 2007) over different urban congurations.

With Computational Fluid Dynamics (CFD) models over

different idealized geometries (Sini et al., 1996; Assimakopoulos

et al., 2003; Kim and Baik, 2004; Santiago et al., 2007 ).* Corresponding author.

E-mail address: [email protected] (J.L. Santiago).

Contents lists available at ScienceDirect

Atmospheric Environment

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c om / l o c a t e / a t m o s e nv

1352-2310/$ e see front matter 2010 Elsevier Ltd. All rights reserved.

doi:10.1016/j.atmosenv.2010.03.009

Atmospheric Environment 44 (2010) 2089e2097

mailto:[email protected]://www.sciencedirect.com/science/journal/13522310http://www.elsevier.com/locate/atmosenvhttp://www.elsevier.com/locate/atmosenvhttp://www.sciencedirect.com/science/journal/13522310mailto:[email protected]

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The experimental information is the most faithful to the reality,

but it is partial because the strong heterogeneity of the atmospheric

ow in urban areas limits the representativeness of the point

measurements. On the other hand, numerical models are only an

approximation of the reality, but they can give a complete 3D

representation of the concentrationelds, that canbe used to assess

the air quality in the places where there are no measurements.

However, only a small number of CFD modelling studies have

been conducted for real cities: Flaherty et al. (2007) performed

steady-state Reynolds-Averaged NaviereStokes (RANS) computa-

tions with standard k-3 turbulence closure over the central district

of Oklahoma City, and recently, Xie and Castro (2009) used large-

eddy simulation (LES) to compute ow and dispersion within

a district of London.

The modelling studies over real cities mentioned above were

conducted for unsteady conditions (with time varying Boundary

Conditions) but the modelling periods simulated were few minutes

and the information required to assess air quality for regulatory

purposes (what is prescribed in the European directive, for

example) is air quality over weeks or months. To get this infor-

mation, the time period should be increased but simulations with

these conditions are too CPU expensive for today’s computers.

Hence, the aim of this study was to develop a methodologyto model large time periods using steady-state CFD simulations

(e. g. constant in time boundary conditions) and to apply it to an

area of a real city.

The paper is organised as follows: the model, experimental

set up and modelling methodology developed are described in

Section 2. Results and discussion are presented in Section 3:

rstly, temporal series of model concentrations and experimental

measurements of NOx and PM10 at an air quality station located in

a square are compared and the concentration patterns inside the

square are analysed; secondly, a comparison of model and

experimental 2-weeks averaged concentration of NO2 and BTEX at

three different sampling locations is carried out and the spatial

distributions of concentration are studied; thirdly, the application

of the methodology to air pollution abatement strategies is ana-lysed. Finally, concluding remarks are given in Section 4.

2. Model set up and methodology

2.1. Model description

The numerical model used in this study was described in

Santiago et al. (2007). The simulations of turbulent air ow and

pollutant dispersion were based on the steady-state Reynolds-

Averaged NaviereStokes equations (RANS) and the standard k-3

turbulence model. In addition, a transport equation for passive

scalars was solved to obtain pollutant concentration.

v

v x j

ru jC i

mt

Sc t vC iv x j

! ¼ S C (1)

where Sc t is the turbulent Schmidt number (¼0.9), S C is the source

term and mt is the turbulent viscosity computed as mt ¼ rC mðk2=3Þ.

RANS with standard k-3 turbulence models have shown some

deciencies in simulating the details of ow over arrays of cubes

(they do not capture accurately separation and reattachment

processes, Castro and Apsley, 1997). However, recently, these

turbulence models have been applied to simulate turbulent airow

over simplied urban congurations, in particular over arrays of

cubes (Lien and Yee, 2004; Santiago et al., 2007), and obtained

a good general agreement with experimental data. In addition, the

studies of Lien and Yee (2004) and Xie and Castro (2006) showed

that other modi

ed k-3 models give little difference in comparison

with standard k-3. Approaches such as large-eddy simulation (LES)

or direct numerical simulation (DNS) provide more accuracy but at

high computational expense (e.g. Cheng et al. (2003) found that the

computational cost for a LES simulation is about 100 times greater

than for a k-3 RANS simulation). Since in this study a wide range of

inlet wind directions over a complex geometry with a large number

of computational cells was analysed, we considered a standard k-3

RANS model as the best compromise between accuracy and

computational time.

2.2. Simulation set up

The study was carried out in downtown Pamplona (Navarra,

northern part of Spain, 42 490 N and 2 10 W), a medium size city

with about 200,000 inhabitants and a vehicle eet of 57 vehicles

for each 100 inhabitants. The simulations were focused on

a square named ‘Plaza de la Cruz’ (Fig. 1a), 120 m wide surrounded

Fig.1. a) Plan of studied zone of Pamplona.1: ‘Plaza de la Cruz’ Square (Location of the

automatic air quality station). 1 to 3: Location of passive samplers. Meteo: Location of

the automatic meteorological station. The white line represents the limits of the

numerical domain. b) Plan view of numerical domain.

M.A. Parra et al. / Atmospheric Environment 44 (2010) 2089e 2097 2090

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by buildings with homogenous height of about 15 m. There are

two traf c lines going around the square. The nearby streets were

also included in the simulations. The street width around the

square is 10 m approximately, so the street aspect ratio H/W ¼ 1.5.

A plan view of the numerical domain is shown in Fig. 1b. It

corresponds to a simplied representation of the studied area.

Following the recommendations of Franke et al. (2007), the top of

the domain was located at 5H (H ¼ 15 m, building height). In this

case all buildings have the same height. In horizontal directions,

the boundaries of the domain were located at more than 8H from

the buildings.

Domain discretization was performed by means of an irregular

grid with 3.5$106 cells approximately. Smaller grid cells were used

near the buildings to better resolve ow and dispersion. In the

horizontal directions (X and Y), the grid size was around 2 m close

to buildings and increased with a constant factor of 1.1 farther

away from the area of interest. In the vertical direction (Z), the

resolution used close to the buildings was 1.5 m (10 points), and

increased with a constant factor of 1.1 above the buildings. Grid

dependence was tested over a reduced domain for three hori-

zontal resolutions (1, 2 and 2.5 m). Velocity proles in different

zones of the square were analysed and no signicant differences

were found among them. It was concluded, then, that this gridresolution is good enough for the purpose of the study. At the

top, symmetry boundary conditions that enforce a parallel ow

and prescribe zero normal derivatives for all other ow variables

(included turbulent variables) were used. Solid boundaries

(buildings and ground) were simulated by means of standard wall

functions.

2.3. Methodology

The main motivation of this study was to develop a method-

ology to assess the air quality in a real urban area. Since the number

of monitoring stations is usually limited and each station is

representative of a small area, the numerical model was used to

evaluate the air quality in the locations without monitoringstations. In order to assess whether the model was suitable or not

for this purpose and to build condence in it, a comparison

between model results and measurements at the monitoring

stations was performed. The selected period chosen for the

comparison was January and February 2007, because in winter

pollutants are less affected by atmospheric chemistry (Sillman,

1999) which is not simulated by the model. Since, as explained,

unsteady CFD simulations are not affordable (the needed compu-

tational time would be several years), a methodology based on

several steady-state simulations was developed. The k-3 RANS

model was used to perform 16 steady-state simulations corre-

sponding to 16 different wind directions (sectors N, NNE, NE, ENE,

E, ESE, SE, SSE, S, SSW, SW, WSW, W, WNW, NW, and NNW). The

inlet wind direction varied from 0

to 360

with an increment of 22.5, i.e. sector N ¼ 0, NNE ¼ 22.5 and so on. The same inlet

proles for u, k, 3 were used in each simulation (equations (2)e(4))

(Richards and Hoxey, 1993).

uð z Þ ¼ u*k

ln

z þ z 0

z 0

(2)

k ¼u2* ffiffiffiffiffiffiC m

p (3)

3 ¼ u3

*

kð z þ z 0Þ (4)

where u* is the friction velocity, z 0 is the roughness length and k is

von Karman’s constant (k ¼ 0.4).

Measurements of wind speed and direction were recorded at

a meteorological station, managed by Government of Navarra

(http://meteo.navarra.es), located in an open area nearby the

simulated area (Fig. 1a). This site was used as reference because of

its close location to the simulated area, and the fact that

measurements were not inuenced by local building effects,

condition not guaranteed for other meteorological stations in the

simulated area (Klein et al., 2007).

Traf c was modelled by emitting passive tracers in rows of

computational cells close to the ground along each street. Four

different tracers were released in each simulation, one per type of

street (Fig. 1b). Since the pollutants were treated as passive, the

problem is linear, and the hourly nal concentration is the sum of

the concentrations of the four tracers. Emissions were estimated

based on S C ¼ N iðt ÞE F ½Li=speed1=V sourcei, where N iðt Þ is the

number of cars per unit time in street i, E F is the amount of

pollutant (in mass) emitted per unit time by a car, ½Li=speed is the

residence time of a car in the street, based on street length Liand car

speed, and V sourcei is the volume of the row of computational cells

where the emissions are located. It was assumed, then, that E F is

constant in the region considered (i. e. the composition of the careet is the same in each street), and that the speed of the cars isalso

the same in each street, which is reasonable in cities where speed

limits are enforced. Moreover, due to the linearity of the conser-

vation equation for the pollutant (Eq. (1)), the concentration is

proportional to the emissions. For this reason, the CFD simulations

were conducted using the same reference emission S C REFfor each

tracer. The nal concentration is then proportional to the concen-

tration obtained with the reference emissions, multiplied by

N iðt ÞðLi=V sourceiÞ. Note that here ðE F =speedÞis assumed constant,

but the numerical value is not known. The hourly traf c data were

obtained from local authorities and included hourly number of

vehicles in each street investigated in the simulations.

From eq. (1), it can also be shown that the concentration is

proportional to 1/wind speed. To explain this assumption, Eq. (1) isrewritten using the following non-dimensional variables (indicated

byw):

3 ¼ U 3ref $L1ref $

~3 (5)

k ¼ U 2ref $~k (6)

u ¼ U ref $~u (7)

x ¼ Lref $~ x (8)

Therefore; mt ¼ r$U ref Lref $~mt (9)

and Eq. (1) could be rewritten in terms of non-dimensional vari-

ables as follows,

L1ref v

v~ x j

U ref r~u jC i rU ref

~mt

Sc t

vC iv~ x j

! ¼ S C (10)

Hence, the concentration, C , could be normalised as,

C ¼ S C $Lref $U 1ref $r

1$~C . In this study, vinlet was used as U ref . The

assumption that the concentration is proportional to 1/wind speed

was veried simulating two cases with different inlet velocities but

keeping all the other inputs unchanged.

Using this approach, only one simulation was needed for each

wind direction sector, and the pollutant concentration at each hour

could be estimated as


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C Realðt ÞfC computedðSectorðt ÞÞ

¼X

i

C iðSectorðtÞÞ$ Li

V sourcei$N iðt Þ$

1

vinðt Þ ð11Þ

where i represents the different emissions within each street,

Sector(t ) is the wind direction sector at hour t , C i (Sector(t )) is the

concentration computed in the CFD simulation for the wind

direction sector corresponding to hour t for a given emission from

street i and for a given inlet wind velocity. As commented above, inthis case where the emissions were unknown, the emissions

considered in the CFD simulations inside each streets, were the

same for all tracers, and the value of C i, must be modulated with

the factors Li/V sourcei and N i (t) in order to take into account the

different emission inside each street in the real case. In addition,

the concentration computed was modulated by a factor 1/vin (t) (vin(t) is the real inlet wind speed at hour t ).

In conclusion, the concentration obtained using this approach

was only proportional to the real concentration because the exact

emission factor of vehicles and their speeds are unknown. For the

comparison with experimental data, then, normalised concentra-

tions were used. Several assumptions were made for this approach,

and it is important to clarify them. Among the others:

- It was assumed that the averaged pollutant concentration at

a certain hour was a function only of the emissions and mete-

orological conditions at that hour, and did not depend on the

previous hours values. No hour to hour accumulation effects

were considered. Given the small domain studied and the

proximity to pollution sources, this assumption is reasonable.

- The emissions factors were considered constant for every car

type and every car speed. We assume that all streets have

similar traf c features. Data concerning the car types were not

available and it was assumed the percentage of each car type

was the same in each street. In other words, we considered that

inside each street the mass of each tracer emitted per length

per time per vehicle was constant.

- Only dynamical effects determined the ow and dispersion. No

thermal effects were considered. This assumption could have

failed for low wind speeds.

Two kinds of data were used in the comparison with the

simulation results. Hourly data to compare temporal distribution in

a point inside the grid and 2-week averaged data in three different

points in order to compare the spatial distributions.

2.4. Experimental measurement

During January and February 2007, an air quality monitoring

station located in the urban square ‘Plaza de la Cruz’ (point 1,

Fig. 1a) measured every 2 min the concentrations of NO, NO2, NOxand PM10 and averaged them into hourly means. These measures

were taken at 3 m above ground level. The pollutants concentra-

tions data registered were kindly provided by the Government of

Navarra.

At the same time, an intensive sampling was performed in the

city with passive samplers (Parra et al., 2009). Three sampling

points were placed within this area. Their locations varied from

a high traf c density street (point 2), a very low traf c street (point

3) and a medium traf

c density street (point 1) (Fig.1a), close tothepreviously mentioned air quality automatic station located in an

urban square.

Radiello passive samplers (Fondazione Salvatore Maugeri-

IRCCS, Italy) were used to determine NO2 ambient concentrations

in each one of these three sampling points whereas Perkin

Elmer stainless steel tubes lled with Tenax TA were used for

BTEX (benzene, toluene, ethylbenzene, m,p-xylene and o-xylene)

sampling. These samplers were exposed to ambient air for

2-week periods, covering four sampling campaigns, placed at 3 m

above ground level and inside polypropylene mountable shelters

in order to protect them from direct sunlight and bad weather

conditions. Details of analysis methods are described in Parra

et al. (2006).

0,00

1,00

2,00

3,00

4,00

5,00

6,00

7,00

N O

x

0,00

2,00

4,00

6,00

8,00

10,00

12,00

W i n d S p e e d ( m s - 1 )

Modelled

Measurement

Wind Speed

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Fig. 2. Temporal series obtained by the experimental and simulated data of the NO x concentrations and the wind speed recorded at the meteorological monitoring station.

0,00

1,00

2,00

3,00

4,00

5,00

6,00

7,00

P M 1 0

Modelled

Measurement

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Fig. 3. Temporal series obtained by the experimental and simulated data of the PM10 concentrations.


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3. Results and discussion

3.1. Temporal distribution

The steady CFD simulations with k-3 RANS model were

compared against the experimental data recorded in the automatic

air quality station during the period of January and February 2007.

Some of the pollutants are photo-chemically reactive (e.g. NOx,

NO2,.), but in winter periods they are expected to be less affected

by atmospheric chemistry as their reactivity depends strongly on

sunlight and air temperature (Atkinson, 2000). Therefore we

assumed that compared with the urban boundary layer dynamics

(e.g. the effect of wind speed and wind direction inside the streets),

the chemical reactions for the pollutants investigated were

a secondary effect on the dispersion inside this urban area, at least

in winter time, and for this reason they were neglected. It will be

shown below that this assumption was supported by the simula-

tion results. Also, Baik et al. (2007) found that for NO (NO2)

concentration, the magnitude of the advection and turbulent

diffusion terms is much larger than that of the chemical reaction

term in their CFD simulation of reactive pollutant dispersion in

a single urban street canyon. For the comparison of the temporal

series, the experimental data from the air quality station in ‘Plazade la Cruz’ were used. These concentrations were normalised by

a reference concentration (C ref1) dened as the concentration

averaged over the selected time period (January and February

2007) in this station (eq. (12)).

C ref1 ¼ 1

T

Z T

C ðPlaza de laCruzÞ dt (12)

The temporal series obtained by the experimental and simulated

data of the NOx concentrations, as well as the wind speed recorded

at the meteorological monitoring station, are presented in Fig. 2.

For the NOx concentrations, model results followed closely the

hourly pattern of observation (r ¼ 0.535; p < 0.01, where r is the

correlation factor and p is the statistical signicance).Analysing in detail the results, higher differences were observed

in the cases with very low wind velocities. In some of these cases

the model overestimated the concentrations. Under these weak

wind conditions, the mean ow inside street canyons is strongly

related to unsteady turbulent recirculation (Louka et al., 2000; Li

et al., 2006) and RANS models can not reproduce these effects

properly. In fact, the assumption that the concentration is inversely

proportional (eq. (1)) to the inlet wind speed does not work when

the mean velocity is low and the turbulent transport is the most

important mechanism. In addition, it was assumed that the 1 h

averaged dispersion could be simulated by steady-state conditions.

This is reasonable, as long as the time scale of the phenomenon

studied (that can be estimatedas L/U , where L is thehorizontal scale

of the urban area considered, in this case of the order of 1 km, and U is the wind speed), is signicantlysmaller than 1 h. Under low wind

conditions (less than 0.5 m s1) this is no longer true, and the

assumption is questionable. Also, as it was discussed in previous

studies (Louka et al., 2002; Klein et al., 2007), differential heating of

the canyonwalls dueto the sunradiation in clear skydays can affect

the ow within street canyons and thermally induced ow motions

become important under low wind conditions. These thermal

effects were not considered in these simulations. Moreover, certain

chemical reactions for the most reactive pollutants (NO, NOx,.)

could become more important in relation to the atmospheric

dispersion for low winds. For wind velocities higher than

0.70 m s1 the results were very satisfactory (r ¼ 0.735; p < 0.01).

The main features of the PM10 concentrations were also in

agreement with the experimental data (Fig. 3), although the

deviation mentioned above for low wind velocities were also

observed (r ¼ 0.668; p < 0.01). The model tted the experimental

results better (r ¼ 0.783; p < 0.01) when wind velocities were

higher than 0.70 m s1.

On the other hand, by comparing the results obtained from the

simulations for the different wind directions, it could be observed

that ow and concentration map in the urban square, where the

eld measurements were registered, largely depended on ambient

Fig. 4. a) Plan view of ‘Plaza de la Cruz’ Square. b) Wind ow and concentration

patterns inside the square at z ¼ 3 m for WNW case. c) Same as b) but for NNE case.

These concentration patterns correspond to cases with the same characteristics except

for the wind direction.


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wind direction. The concentration of pollutants also depended on

the wind speed. In general, the highest levels of pollutants in the

automatic stations were recorded in those periods with wind from

sector WNW, whereas sectors N and NNE showed an opposite

behaviour. However, in other points of the urban square the

behaviour is different because of the large gradients of concentra-

tion inside this urban zone and the different concentration patterns

corresponding to each wind direction. Fig. 4 shows the concen-

trations (normalised by C ref1) inside the square at z ¼ 3 m for WNW

and NNE cases, and the inlet wind speed ¼ 3 m s1 and

hour ¼ 1900 h. In the WNW case, concentration is relatively high

near the western corner of the square and in the north-western

area, being lower in the south-eastern area. Some of the pollutants

emitted upwind were transported to the square through the two

north-western streets (streets 1 and 2). These pollutants and those

emitted in the traf c lines around the square were trapped in the

vortices created near the north and west corner of the square

producing high pollution zones. A different behaviour was

observed in NNE case. Here, the high pollution area was located

north-eastern of the square, with lower pollutant concentration in

the south. In this case, the pollutants emitted upwind entered

through the two streets that meet in the north corner of the square

(street 2 and 3) and through the north-eastern street in the eastcorner of the square (street 4). These pollutants and those emitted

in the traf c lines around the square were trapped in the vortices

created near the north and east corner producing the high pollution

area in the north-eastern of the square. Therefore, the value of

pollutant concentration measured by an automatic station for

a given case largely depends on its location inside the square. Thus

we should be cautious to analyse the air quality inside the square

with only one monitoring station. This can be extended to cases of

automatic stations located inside streets.

3.2. Spatial distributions

The model capability to solve the spatial variation of concen-

trations within the domain was also checked. In order to study the

concentration variations in this area, we used the NO2

and BTEX

concentrations in three sampling points located inside the studied

area. Here, 12 measurements of two-week averaged concentrations

were recorded (4 sampling periods at three sampling points). Two-

week averaged normalised concentrations were computed from

model results and compared against the experimental data for each

pollutant (Fig. 5). Unlike the temporal series, the reference

concentration for the normalization (C ref2) was dened as the

concentration averaged over the selected time period (January and

February 2007) in the three sampling points (i.e. the average of the

12 values). As in the previous section, pollutants considered in this

comparison (BTEX and NO2) are chemical reactive and the model

concentrations were computed for non-reactive pollutants. Linear

regressions between experimental and computed concentrations

are also shown in Fig. 5.

Overall, a good agreement was observed with regression coef-cients from 0.7018 to 0.9565. The worst correlation obtained was

for ethylbenzene (R2 ¼ 0.7018). This could be due to the fact that

ethylbenzene is known as one of the most reactive compounds

among those studied, and thereforeworse results were expected as

the model had no chemical considerations.

Based on reasonably good agreement in the comparison at these

3 locations during 4 periods of 2 weeks, we could conclude that the

Fig. 5. Comparison between experimental (C exp) and modelled (C mod) 2-week averaged concentrations of BTEX and NO 2 at the three sampling points. Lines represent the linear

regressions between experimental and modelled concentrations.


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performance of the model using this methodology was suitable tosimulate the concentration of non-reactive pollutants inside urban

areas during large time periods. However, for the reactive pollut-

ants studied in this work, this methodology may notbe valid during

summer time because in this season the atmospheric chemistry is

more intense, due to stronger solar radiation, and inuences

pollutant concentrations. Hereafter, the contour patterns of

concentration were analysed over this domain focusing on the

streets nearby the square. The same cases (ambient wind directions

NNE and WNW, inlet wind speed ¼ 3 m s1 and hour ¼ 1900 h) as

those used in the previous section were chosen to describe the

main features of ow and pollutant dispersion over this urban area.

Figs. 6 and 7 show the pollutant concentration in the streets nearby

the square and the wind ow at one street intersection at z ¼ 3 m

for both cases. Flow and dispersion in the street canyons aroundthesquare largely depended on ambient wind direction. In a simple

analysis, the ow could be considered as the interaction of chan-

nelling and recirculation ows (Dobre et al., 2005; Klein et al.,

2007). In these two cases the angle between ambient wind direc-

tion and street canyons around the square was 20 approximately.

In the WNW case, the angle was with the streets in north-eastern

direction (e.g. streets 3, 4, etc.) while in the NNE case, it was with

the street in north-western direction (e.g. streets 1, 2, etc.). This

oblique angle of the incidentow with respect to the streetcanyons

produced channelling ows in streets of both directions (i.e. north-

western streets and north-eastern streets) for the two cases.

However the channelling was more important in the direction

where the angle between the wind ow and street was closer to

0

(e.g. north-western streets in the case of WNW ambient wind

direction and north-eastern streets in the case of NNE ambient

wind direction). Therefore, part of the pollutants emitted upwind

and inside these streets was channelled in the street directions.

These ow patterns were similar to those obtained in wind tunnel

by Klein et al. (2007) near idealized intersections. However,

the present case is for a real conguration, and the irregularities

(e.g. the square, irregular buildings) have an inuence on these

patterns inside the street intersections, for example changing

the intensity of the channelling or creating more intense vortices

in the intersection. The horizontal vortices created in the street

intersection trapped part of the pollutants (Fig. 6). In addition,

recirculation ow in vertical direction was superposed to the

channelling ow. This fact can be observed in the reversed across-

canyon wind component in the lower part of the street (Fig. 7) andin the high values of pollutant concentrations at the leeward walls

of the canyons (Fig. 6).

3.3. Air pollution abatement strategies

Air pollution within a street is not only inuenced by the

emissions of pollutants within that street, but also by emissions

from nearby streets. These different contributions depend on

several factors, mentioned before, such as traf c intensity on each

street, street geometry and both wind speed and direction.

In order to perform an adequate planning of traf c distribution

in this area, it is necessary to identify the frequency of each inlet

wind direction and its mean wind speed and also to know its cor-

responding spatial distribution of pollutants. For example, ESE inlet

Fig. 6. Pollutant concentration in the streets nearby the square for a) WNW case and

b) NNE case. Fig. 7. Wind ow in one street intersection at z ¼ 3 m. Location of street intersection,

see Fig. 6.


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wind direction was frequent and wind speed during these periods

was relatively low resulting on elevated concentrations. When

studying in detail the distribution of pollutants under these

conditions, it was observed that emission 3 (Fig. 2) strongly affects

the concentrations in Point 2 (Fig. 1) and also causes high

concentration in those streets around ‘Plaza de la Cruz’. Thiszone is

where the highest peaks of concentration were found (Fig. 8).

Using the methodology described above, hypothetic situations

changing the traf c within each street can be evaluated modi-

fying the factor N i(t ) for each street in eq. (11). Note that we do

not have to repeat the CFD simulations (only repeat computations

of eq. (11)).

An example of the use of this methodology to establish an airpollution abatement strategy was carried out. The criteria to judge

the abatement strategies was based on the number of exceedances

of a threshold value, in this case it was set, for example as 2 times

the mean concentration during JanuaryeFebruary 2007 at ‘Plaza

de la Cruz’ air quality station. The results of this analysis (for

JanuaryeFebruary 2007) showed that there was a strong contri-

bution of emissions 3 and 2 over points 1 and 2 during those

periods with an elevated number of exceedances. As a conse-

quence, a traf c reduction in those streets (Emissions 2 and 3)

would have improved air quality within this area. New calcula-

tions with reorganizations in the traf c densities within the city

were performed. In this case, the principal aim was to reorganize

traf c and not to decrease it. The number of vehicles was the same

although the traf c density was modied, decreasing the numberof vehicles in Emission 2 and 3, and increasing it in Emissions 1

and 4. The results are shown in Table 1. As expected, the number

of exceedances decreased in point 1 and 2 and increased in point

3. The balance was considered positive because point 3 presented

the lowest concentrations and exceedances despite the increase in

traf c volume. Note that the concentration patterns changed after

the reorganization of traf c. Air pollution was reduced in some

places but it was increased in others. This is a simple example of

abatement strategy. More realistic scenarios, based on traf c

models, can also be studied.

4. Summary and conclusions

16 steady-state RANS simulations (one for each wind direction)

were performed in order to compute the evolution of pollutantconcentration inside an urban zone of Pamplona during January

and February 2007. In the simulations chemical reactions were

neglected. These results were compared with hourly averaged

concentration of NOx and PM10 from an air quality station of

Government of Navarra located in a square, and with 2-weeks

averaged concentration of NO2 and BTEX from an intensive exper-

imental campaign in the city of Pamplona using passive samplers.

The comparison of the steady CFD simulations with k-3 RANS

model explained correctly the hourly experimental data nding

good correlations, with the exception of those periods with low

wind velocities,when ourassumptions to neglect thermal effects or

chemical reactions were not accurate and RANS models had dif -

culties to simulate the mean ow inside street canyons. In addition,

the good agreement obtained seemed to indicate that chemicalreactions of primary pollutants as NOx, PM10, etc. were a secondary

effect on the dispersion inside urban environment in comparison

with the urban boundary layer dynamics (e.g. wind effect inside

urban canopy) in winter time. This analysis may be not valid during

summer because during this period pollutants are more affected by

atmospheric chemistry.

The pollutant concentrations were found to be strongly related

to both wind speed and direction. In addition, large concentration

differences were found between different zones in the same square

or in the same street. Therefore, the levels of pollutants measured

by an automatic air quality station largely depended on its location

inside streets or squares. A modelling approach as the one pre-

sented in this work could be an useful tool to make a complete air

quality assessment in an urban area.

Fig. 8. A, B, C and D) Spatial distribution of pollutants of partial contributions of Emission1, 2, 3 and 4 respectively for ESE inlet wind direction. The concentrations is computed for

inlet wind speed ¼ 3 m s1

and hour ¼ 1900 h and is normalised by C ref2.

Table 1

Normalised concentrations before and after the reorganization of traf c in the city

area, percentage of reduction and number of threshold exceedances in Point 1, 2

and 3.

Point 1 Point 2 Point 3

Before After Before After Before After

C on centra ti on 1. 00 0 .7 2

(27.8%)

2.20 1.60

(27,31%)

0.35 0.55

(þ56.2%)

No. of threshold

exceedances

77 26 326 223 6 10


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Moreover, the use of this methodology makes possible to study

the contribution of emissions inside each street (traf c intensity) to

the total pollutant concentration. Using this information it will be

possible to analyse the effect of traf c reduction in a street on the

pollutant concentration and evaluate the impact on air quality due

toa traf c reduction inside a streetor a reorganization of the traf c.

Acknowledgements

This research work was made possible by the permission of the

local authorities. Also, authors would like to thank them for the

transferring traf c data of Pamplona, the Government of Navarra

for the data of pollutants and meteorology and ‘Fundación CAN’ for

the concession of a research grant to M.A. Parra. The modelling

exercises of this study have been also partially supported by the

Spanish Ministry of Environment, Marine and Rural Affairs.

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