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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]
8/20/2019 Parra 2010
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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
M.A. Parra et al. / Atmospheric Environment 44 (2010) 2089e 2097 2091
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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
82/20/7051/20/7010/20/7051/10/7010/10/70
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
82/20/7051/20/7010/20/7051/10/7010/10/70
Fig. 3. Temporal series obtained by the experimental and simulated data of the PM10 concentrations.
M.A. Parra et al. / Atmospheric Environment 44 (2010) 2089e 2097 2092
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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.
M.A. Parra et al. / Atmospheric Environment 44 (2010) 2089e 2097 2093
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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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