GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-11-611-2018Multi-scale modeling of urban air pollution: development and application
of a Street-in-Grid model (v1.0) by coupling MUNICH (v1.0) and
Polair3D (v1.8.1)KimYoungseobyoungseob.kim@enpc.frhttps://orcid.org/0000-0001-5963-5666WuYouSeigneurChristianRoustanYelvaCEREA, Joint Laboratory École des Ponts ParisTech / EDF R&D, Université Paris-Est, 77455 Champs-sur-Marne, FranceEDF R&D China, 100005 Beijing, ChinaYoungseob Kim (youngseob.kim@enpc.fr)15February201811261162911August20171September201722December20178January2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://gmd.copernicus.org/articles/11/611/2018/gmd-11-611-2018.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/11/611/2018/gmd-11-611-2018.pdf
A new multi-scale model of urban air pollution is presented. This model
combines a chemistry–transport model (CTM) that includes a comprehensive
treatment of atmospheric chemistry and transport on spatial scales down to
1 km and a street-network model that describes the atmospheric
concentrations of pollutants in an urban street network. The street-network
model is the Model of Urban Network of Intersecting Canyons and Highways
(MUNICH), which consists of two main components: a street-canyon component
and a street-intersection component. MUNICH is coupled to the Polair3D CTM
of the Polyphemus air quality modeling platform to constitute the
Street-in-Grid (SinG) model. MUNICH is used to simulate the concentrations of
the chemical species in the urban canopy, which is located in the lowest
layer of Polair3D, and the simulation of pollutant concentrations above
rooftops is performed with Polair3D. Interactions between MUNICH and
Polair3D occur at roof level and depend on a vertical mass transfer
coefficient that is a function of atmospheric turbulence. SinG is used to
simulate the concentrations of nitrogen oxides (NOx) and ozone (O3) in
a Paris suburb. Simulated concentrations are compared to NOx
concentrations measured at two monitoring stations within a street canyon.
SinG shows better performance than MUNICH for nitrogen dioxide (NO2)
concentrations. However, both SinG and MUNICH underestimate NOx. For the
case study considered, the model performance for NOx concentrations is not
sensitive to using a complex chemistry model in MUNICH and the Leighton
NO–NO2–O3 set of reactions is sufficient.
Introduction
Urban air pollution has been a public health issue for many decades.
Historically, the first urban air quality model with spatial and temporal
resolution was developed for the Los Angeles basin in California, USA
. This three-dimensional (3-D) gridded Eulerian model
used the atmospheric diffusion (mass-conserving) equation to calculate the
change with respect to time of the relevant air pollutant concentrations due
to emissions, transport, chemical transformation, and deposition. Because of
the urban design of western US cities, there was no need to explicitly take buildings
into account.
European cities differ from the Los Angeles basin because of the presence of
densely built districts with street-canyon configurations. Consequently,
although air quality models such as the one initially used for the Los
Angeles basin are commonly used to calculate urban background pollution,
different types of air quality models are needed to calculate air pollution
on the street scale. The conceptual approach of the Operational Street
Pollution Model (OSPM) has typically been used . The air
pollutant concentrations are calculated within a street canyon assuming
uniform traffic emissions across the street canyon, but air pollutant
concentrations can be calculated in ventilated and recirculated zones of the
street canyon. Mass transfer between the street and the urban background
atmosphere at the top of the street (i.e., roof level) is simulated.
This initial concept has been extended to calculate air pollutant
concentrations within a network of streets with the SIRANE model
. Although the SIRANE formulation does not distinguish
recirculation and ventilation zones and assumes a uniform concentration for
each street segment, it provides quite a better treatment of pollutant
transport across street intersections. The development of the SIRANE
formulation is based on a comprehensive investigation of airflow and mass
transfer via wind tunnel experiments and computational fluid dynamics (CFD)
simulations. SIRANE has been applied to various urban districts and has shown
satisfactory performance when compared to ambient air pollutant
concentrations e.g.,. However, the treatment of the
urban background above roof level in SIRANE is modeled using a Gaussian model
formulation, which prevents the use of a comprehensive atmospheric chemistry.
Consequently, it is not appropriate to simulate secondary air pollutants such
as ozone (O3) or fine particulate matter (PM2.5), which require
modeling the formation of secondary pollutants with a comprehensive chemical
kinetic mechanism.
Therefore, there is a dire need to combine the advantages of 3-D gridded
Eulerian models, which can simulate urban background concentrations of all
major air pollutants of interest, and those of street-network models, which
can simulate the concentrations of air pollutants in complex urban canopy
configurations. The multi-scale combination of Eulerian models with
near-source models was developed initially for the treatment of plumes from
tall stacks in the Los Angeles basin . Many other
“plume-in-grid” (PinG) models have been developed over the following 3
decades seefor an overview. Later PinG model
development efforts have included PinG models for line sources, area sources,
and volume sources using various modeling approaches
e.g.,
in order to treat aircraft emissions, ship emissions, traffic emissions from
roadways, and fugitive emissions from industrial sites. However, there is
currently no integrated model that dynamically combines a Eulerian model
with a street-network model. The objective of this work is to develop the
formulation of such a Street-in-Grid model (SinG), fully consistent with the
mass conservation principle, and present its initial application to an actual
urban case study. The Eulerian host model selected for this work is
Polair3D of the Polyphemus air quality modeling platform
, a 3-D chemistry–transport model (CTM) that has been
widely applied in Europe, North America, South America, Asia, and Africa
e.g.,. The Model of Urban Network of Intersecting
Canyons and Highways (MUNICH), which is used to simulate subgrid
concentrations in the urban canopy represented by the street network, is
presented in the next section. Then, the coupling of MUNICH to
Polair3D is described in Sect. . Finally, some
initial applications of MUNICH and the SinG model to a Paris suburb are
discussed.
Description of MUNICH
MUNICH is based conceptually on the SIRANE general formulation
. We can distinguish two main components of MUNICH:
(1) the street-canyon component, which represents the atmospheric processes
in the volume of the urban canopy, and (2) the street-intersection component,
which represents the processes in the volume of the intersection. These
components are connected to the Polair3D model at roof level and are
also interconnected. We describe each one of these components in turn.
Street-canyon component
For a street segment, which is defined as a street component bounded by
intersections with other streets at each end, the following assumptions are
used :
Air pollutant concentrations are uniform within a street segment.
The width of the street and the height of the buildings are uniform.
Emissions of air pollutants and deposition of air pollutants are uniform along
the street segment. However, deposition fluxes to different surfaces, including pavement,
building walls, and roofs are distinguished using the urban dry deposition model
of .
The wind direction follows the street segment direction.
The wind speed is uniform and is related to the wind speed at roof level,
the angle between the wind direction at roof level and the street segment direction,
and the street segment characteristics (width and height).
Steady state is assumed for a given time step.
Assuming steady state, the mass flux (Q in µg s-1) balance
is applied to calculate the concentration of an air pollutant in a street
segment.
Qs+Qinflow+Qchem=Qvert+Qoutflow+Qdep,
where Qs is the source emission rate, Qinflow is the
inflow rate of the air pollutant entering the street from upwind (typically
via an intersection), Qvert is the vertical flux by turbulent
diffusion at roof level (see Sect. ),
Qoutflow is the outflow rate of the air pollutant leaving the
street in the downwind direction, Qdep is the pollutant loss
rate due to atmospheric deposition, and Qchem is the air
pollutant chemical transformation rate (positive for formation and negative
for destruction). The emission term, Qs, is typically obtained from
a traffic emission model. The inflow term, Qinflow, is obtained
from the street-intersection component (see Sect. ).
The outflow rate, Qoutflow, is calculated as follows:
Qoutflow=HWustreetCstreet,
where H is the mean building height in the street segment and W is the
mean street width, ustreet is the mean horizontal wind velocity
in the street segment (see Sect. ), and
Cstreet is the air pollutant concentration in the street
segment.
Turbulent vertical mass transfer at the top of the street segment
The vertical flux, Qvert, as formulated in SIRANE does not
depend on the building height in the street segment and is therefore
defined by the external flow condition, based on .
Qvert=σWWL2πCstreet-Cbackground,
where Cbackground is the mean concentration above the street
segment, L is the street length, and σW is the standard
deviation of the vertical wind velocity at roof level, which depends on
atmospheric stability. One notes that this approach represents the turbulent
mass transfer rate using a mass transfer coefficient with units of
velocity. Such an approach in
which mass transfer coefficients are empirically defined and combined with
concentration gradients to calculate mass transfer rates is routinely used in
engineering. In air quality modeling, this approach is also used to model dry
deposition, and turbulent mass transfer in the surface layer is typically
approximated with a deposition velocity.
A slightly different parametrization was recently proposed by
, who used a turbulent dispersion coefficient defined as
follows:
Km=σWl,
where l is a characteristic mixing length within the street canyon. By
assuming that the size of the large turbulent eddies dominating vertical
mixing is limited by the smaller size of the street width and height, l is
proportional to the smaller of W and H as follows.
1l∼1W+1H
Then
l=β1WHW+H=β1H11+ar,
where β1 is a constant and ar is the aspect ratio (ratio
of building height to street width, H/W) .
Then, the vertical flux at roof level is expressed using the turbulent
dispersion coefficient as follows:
Qvert=β2KmWLHCstreet-Cbackground.
By combing Eq. () with Eqs. ()
and (), we obtain
Qvert=βσWWL11+arCstreet-Cbackground,
where β=β1β2.
Comparison of the turbulent transfer coefficients of the SIRANE
formulation (dotted line) and the formulation of (solid
line).
Comparison of the mean horizontal wind velocity (normalized with
respect to the wind speed at roof level) within the street canyon calculated
with the profiles of SIRANE (dotted lines) and MUNICH
(solid lines) as a function of the street aspect
ratio for three different angles between the wind direction and the street
direction: (a) 0∘, (b) 30∘, and
(c) 60∘.
The constant β can be estimated by comparison to
Eq.( ). Because the vertical flux in
Eq. () is estimated using the unity aspect ratio
(ar=1), we assume that the computed vertical fluxes with
Eqs. () and () are equal when
ar=1. We obtain β=0.45. Figure
compares the vertical transfer coefficient estimated with
Eqs. () and (). If ar<1,
i.e., in an area with low buildings, then the transfer coefficient is greater
with the formulation of than that of SIRANE. Conversely, if ar>1, i.e., in a street-canyon configuration, then
the vertical transfer is reduced compared to that of SIRANE.
Mean wind velocity within the street canyon
Here, we use the exponential wind vertical profile proposed by
and used by in their modeling
of dry deposition within street canyons. The corresponding formulas were
modified here to be specific to the angle between the wind direction and the
street-canyon direction (, and
, averaged the wind profile over all possible angles).
For narrow canyons, ar>2/3:
ustreet=2πuHcos(φ)expar2zH-1,
where φ is the angle between the wind direction above roof level and
the street direction. uH is the wind speed at the building
height and is a function of the friction velocity.
For the so-called intermediate case (i.e., moderate canyons),
1/3≤ar≤2/3:
ustreet=1+32π-1HW-13uHcos(φ)expar2zH-1.
For a wide configuration, ar<1/3:
ustreet=uHcos(φ)expar2zH-1.
An average wind speed can be derived from these empirical wind profiles by
integrating over the entire street-canyon height (0<z<H). These
empirical wind profiles are exponential functions and are therefore
qualitatively similar to the profile used in SIRANE to
derive the average wind velocity within the street canyon. The wind speeds
calculated using these wind profiles and those in SIRANE are compared in
Fig. . This figure illustrates the differences in the mean
wind speed obtained for different values of the aspect ratio ranging from 0.1
to 2. The largest differences are obtained when ar=2/3 and the
angle between the wind direction and the street direction is lower. For
φ=0, the average wind speed of MUNICH is about two-thirds that of
SIRANE.
Wind speed observations were not available to compare the results of the two
methods. However, due to the relatively low aspect ratio of the street
considered in this study (ar∼1/3 for Boulevard
Alsace-Lorraine), we do not expect strong sensitivity to the choice
of the formulation for the average wind speed. This point could become more
crucial for streets with higher aspect ratio and should be considered for
future applications.
Street-intersection component
The street-intersection component of MUNICH involves the following
assumptions, also used in SIRANE :
The air pollutant concentration is not uniform across the intersection
(as it has sometimes been assumed in earlier work).
The advective air flow in the street network is compensated for by inflow
or outflow at the top (roof level) of the intersection to ensure mass balance.
The mean air flow follows the wind direction at roof level.
The streamlines of the flow from a street to other streets across
the intersection cannot cross one another.
Fluctuations in wind direction are taken into account when constructing
the air flows from one street to others across the intersection.
Accordingly, the air mass fluxes (and the associated pollutant mass fluxes)
are computed for the streets that are connected to the intersection (entering
or leaving the intersection) using Eq. (). The air mass
fluxes for the streets are corrected by the computed vertical air flux in the
intersection at roof level.
If one considers only the mean air flow, the air flow rates for the streets
are determined solely based on the configuration of the streets, their
intersection, and the wind direction above roof level. However, experiments in
a wind tunnel and CFD simulations have shown that fluctuations in wind
direction importantly influence the air flow across an intersection
. Accordingly, one must take into account these
fluctuations to properly account for the transfer of air (and pollutant) mass
across the intersection. Then, the computation of the air fluxes depends not
only on the mean wind direction but also on the wind fluctuation. The wind
direction is assumed to follow a Gaussian distribution centered on its mean
value.
Chemical reactions
In MUNICH, the CB05 chemical kinetic mechanism is
implemented to ensure consistency with Polair3D in the SinG
configuration. CB05 consists of 53 species including volatile organic
compounds (VOCs) and inorganic species and 155 chemical reactions including 23
photolytic reactions. However, nitric oxide (NO) emissions in the urban
canopy are likely to scavenge O3 and other oxidants, thereby suppressing
VOC chemistry. Accordingly, a simple three-reaction mechanism involving
solely NO, nitrogen dioxide (NO2), and O3, known as the Leighton
photostationary state , was also implemented. However, the
Leighton photostationary state may not hold even in an urban environment when
VOC emissions are high . These two mechanisms
are compared below in terms of model performance and computational costs.
Dry and wet deposition
Dry deposition is computed using the approach developed for an urban canopy
. Surfaces available for dry deposition include
pavement (street and sidewalks), building walls, and building roofs. The dry
deposition fluxes (µg m-2s-1) are calculated by
multiplying the pollutant concentrations (µg m-3) and the
pollutant deposition velocities (m s-1). The estimation of the
deposition velocities depends on the atmospheric conditions and the surface
properties, which differ among the surface types. For the building roofs, the
background concentrations over the urban canopy are used, whereas the
concentrations within the street network are used for the pavement and
building walls.
Schematic diagram of the Street-in-Grid model.
Wet deposition consists of the scavenging by precipitation and deposition to
pavement and building roofs. Wet deposition to the building roofs is
estimated by the precipitation intensity and the background concentrations
over the urban canopy. The scavenging and deposition to the pavement is
computed for the entire atmospheric column and includes both the background
concentrations above rooftops and the concentrations within the urban
canopy:
Fstreet=ΛCstreetH+Cbackground(zc-H),
where Fstreet is the wet deposition flux to the pavement
(µg m-2s-1), Λ is the scavenging coefficient
(s-1), and zc is the cloud base height (m). The in-cloud wet
scavenging is supposed to have a weak impact for the species considered here.
Summary of MUNICH characteristics
The concept of the street-network model MUNICH is close to the one used in
SIRANE to represent concentration at the street level. We have introduced
several parametrizations for the vertical turbulent flux and the average wind
speed. It is however not possible to definitively advocate a specific choice
for these parametrizations with the set of observations available within the
framework of the TRAFIPOLLU project
(http://www.agence-nationale-recherche.fr/?Project=ANR-12-VBDU-0002).
MUNICH is then kept modular; the model can rely on the different
parametrizations following user choices. MUNICH is designed as a stand-alone
street-network model and does not aim to represent concentrations over the
urban canopy. Beyond its modularity the main strength of MUNICH over SIRANE
relies on the possibility of representing a complex chemistry in the street. It
also allows the interactive connection with a Eulerian chemistry transport
model.
Coupling of MUNICH with Polair3D: Street-in-Grid model
We describe here a new model, Street-in-Grid (SinG), which combines the
MUNICH street-network model and the Polair3D CTM. SinG is conceived
to conduct a multi-scale simulation, which estimates both grid-averaged
concentrations on the urban scale and concentrations within each street
segment. This combined model provides the following advantages.
It allows one to estimate the influence of the background concentrations
on the concentrations within the street network and vice versa.
There is no double counting of emissions originating within the urban
canopy: these emissions are input data to MUNICH and therefore they are
removed from the grid-averaged emission inventory of Polair3D.
There is consistency between the treatment of physical and chemical processes
on different scales. Transport and dispersion of pollutants on the urban and
street-network scales are calculated from the same meteorological data.
Similarly, the same chemical mechanism and the same formulations for dry and
wet atmospheric deposition are used on those different scales. There is, however,
the option to use a reduced form of the chemical mechanism within the street
network, following .
Figure schematically shows the concept of the SinG model.
As MUNICH is located within the lowest Polair3D layer,
meteorological variables in that layer, such as wind speed and direction, are
transferred to MUNICH via the SinG interface. Air pollutant concentrations in
the lowest Polair3D layer are also transferred since they are used
as the background concentrations for the street network. Then, MUNICH
computes the mass fluxes between the urban canopy (i.e., the street network)
and the urban atmosphere above roof level and the SinG interface transfers
them to Polair3D to compute new air pollutant concentrations in the
grid cells above the urban canopy. The interfacing between MUNICH and
Polair3D is conducted at fixed time steps, which were set at 10 min
in the following application, the integration time step of the Eulerian
model.
Application of MUNICH to a street network in a Paris suburbSimulation domain and setup
MUNICH was applied to simulate the concentrations of pollutants in a Paris
suburb (Le Perreux-sur-Marne, 13 km east of Paris).
Figure displays the location of the modeling
domain. The street network within the simulation domain consists of 577
street segments and is displayed in Fig. .
Simulations for gas-phase species including NOx, CO, and VOC emissions were
conducted during the period from 24 March to 14 June 2014. Here, we use the
parametrization proposed by for the vertical flux at
roof level and the exponential wind vertical profile proposed by
for the mean wind speed within the street canyon.
Four simulation domains are simulated from the continental scale to
the urban scale. (a) The largest domain 1 covers western Europe.
Domain 2 covers northern–central France. The red circles show the locations
of the background air monitoring stations. (b) Domains 3 and 4 cover
the Île-de-France region and the eastern Paris suburbs. The blue box
corresponds to the modeling area in suburban Paris for the MUNICH
simulations. The black stars and red circles show the locations of the urban
background air monitoring stations. Measured data at the stations with the
black stars are used for background concentrations in the MUNICH simulations.
SinG is only used for domain 4.
Traffic emissions
The traffic emission inventory used for the simulation domain was built for
the TRAFIPOLLU project. This emission inventory relies on the use of the
dynamic traffic model SymuVia and the COPERT 4 emission
factors (http://emisia.com/products/copert-4/versions). The dynamic
traffic model SymuVia calculates the vehicle trajectories, the number of
vehicles, and the averaged speed for a given time period for each street
segment of the simulated street network. Dynamic traffic models represent
vehicle flow on smaller spatial and temporal scales than static traffic models
and potentially allow an explicit representation of traffic congestion. A
discussion on the differences between dynamic and static traffic models in
link with water and air quality studies can be found in
. However, for the current work the SymuVia outputs were
averaged and combined with COPERT 4 emission factors to generate hourly
emission rates for each street segment. The emission rates depend on the
averaged vehicle speed and composition of the vehicle fleet. This latter was
determined through video monitoring . It is however
important to notice that the vehicle fleet composition appears to be a
sensitive input data .
Two typical days (25 March for a weekday and 30 March for the weekend) were chosen
to be simulated with the traffic model and used to represent the traffic
emission over the whole period. The traffic model estimates the vehicle flow
for each traffic direction of a two-way street. The traffic emissions of a
two-way street were then merged to obtain one emission rate for each
simulated street segment, the basic input data needed by MUNICH.
Surface areas of intersections are not taken explicitly into account in
MUNICH and streets are connected at the center of the intersection, i.e., an
intersection is represented by a point using a latitude–longitude coordinate
set. The geometry of the intersection can influence the mass exchange
. In particular, when intersections are large, vertical
mixing with the overlying atmosphere becomes more important. As this
phenomenon is not taken into account in the current version of the model, it
leads to underestimation of the exchanges through such open space in the street
network. There is a need here to extend the modeling framework to better
represent this type of urban space.
Figure shows the NOx traffic emissions that
were estimated for the 577 street segments of the simulation domain in the
Paris suburb. In Fig. 5a, NOx emission rates during nighttime are
presented. Very low emission rates are estimated for all the streets even
though those on the A86 highway are slightly higher. In Fig. 5b,
NOx emission rates during the morning rush hour increase more than
1400 µg m-1 s-1. Since the traffic model is calibrated
with flow observation and the vehicle fleet composition determined through
video monitoring, the remaining uncertainties in the emission data lie in the
use of only two typical days to represent the whole period and in COPERT 4
emission factors.
NOx emission rates (mg m-1 s-1) used in MUNICH
simulations for a weekday (a) during nighttime at 01:00 UTC and
(b) in the morning rush hour at 07:00 UTC on 25 March 2014.
Geographic data
Traffic lane widths and building heights were obtained from the BD TOPO
database (http://professionnels.ign.fr/bdtopo). Total street width
includes the lane width, the sidewalk width, or the highway shoulder width
(the A86 highway passes through the modeling domain). For minor surface
roads, a width of 3 m was used for sidewalks by default, which corresponds
to two sidewalks (the minimum sidewalk width in France is 1.4 m). For the
A86 highway, 20 m was added to the lane width including two shoulders
(4 m), a median strip (1.5 m), and two urban train lanes (4 m). Street
widths and building heights of the 15 major streets were explicitly
estimated. For the other streets, average street width (7.5 m) and building
height (6.9 m) estimated for the modeling domain were used.
Meteorological data
Meteorological data, including wind direction and speed, planetary boundary layer
(PBL) height, and friction velocity, were obtained from a Weather Research
and Forecasting (WRF) model version 3.6.1 simulation conducted
with a horizontal resolution of 1.5 × 1.5 km2. The simulated meteorological data were compared to
the measurements at three urban-background meteorological stations near the
simulation domain. The RMSE, the fractional bias
(FB), and the correlation coefficient (R) are the statistical indicators
used in to evaluate the meteorological fields. The WRF
simulation slightly overestimates the temperature (RMSE:
0.2–1.1 ∘C, FB: 0.02–0.07, and R: 0.9) and the wind
speed (RMSE: 0.8–1.1 m s-1, FB: 0.2–0.3, and R: 0.6–0.7). The
modeled wind direction is biased by an angular difference of about
15∘. An important error in the precipitation modeling is obtained
(RMSE: 0.04 mm h-1; FB: -0.6; R: 0.1) but this model error does
not have
a strong impact on the concentration of the poorly soluble species simulated.
Background concentrations
Background concentrations of NO, NO2, and O3 were obtained from two
urban background air monitoring stations near the modeling area (5–7 km
from the area; see Fig. ). Averaged values of the
hourly measured concentrations at the two stations were used to compute the
vertical mass transfer at the top of the street network in
Eqs. () and (). These stations are
operated by Airparif, the air quality agency of the Paris region
(http://www.airparif.asso.fr/).
Simulated NOx concentrations using MUNICH (a) during
nighttime at 01:00 UTC and (b) in the morning rush hour at 07:00 UTC
on 25 March 2014. The red rectangular box encompasses Boulevard
Alsace-Lorraine and the cross mark corresponds to the location of the air
monitoring stations on the sidewalks.
Temporal evolution of NO2 daily-averaged concentrations modeled
with MUNICH (blue line), Polair3D (green line), and the SinG model
(red line). They are compared to the measured concentrations (gray shaded
regions) at the stations nearby traffic on each sidewalk of the Boulevard
Alsace-Lorraine. If the measurement is available at only one station, a black
line is used instead.
Results
Figure shows that simulated concentrations in NOx are
high in the streets where the emission rates are high. The concentrations of
NOx during nighttime on 25 March reach 160 µg m-3 over the
major streets. During the morning rush hour on the same day, the
concentrations of NOx increase to 600 µg m-3. The modeled
high concentrations during the rush hour are due not only to high emission
rates but also to stable meteorological conditions with low PBL height
(520 m) and wind speed (2.5 m s-1). One notes that there is a clear
difference between the spatial patterns of the emission maps
(Fig. ) and concentration maps
(Fig. ). Streets with no or little NOx emissions
display non-negligible NOx concentrations, thereby highlighting the
importance of advective and turbulent transport in the street network.
Diurnal variation in NO2 concentrations modeled with MUNICH (blue
line), Polair3D (green line), and the SinG model (red line). They are
compared to the measured concentrations (black line) at the stations nearby
traffic on each sidewalk of Boulevard Alsace-Lorraine.
Statistical indicators of the comparison of simulated hourly
concentrations to the NO2 and NOx concentrations measured at the air
monitoring stations operated on the sidewalks of Boulevard Alsace-Lorraine.
a FB (fractional bias), NMSE (normal mean square
error), MFE (mean fractional error), VG (geometrical mean squared variance),
MG (mean geometrical bias), FAC2 (fraction in a factor of 2),
and R (correlation coefficient) . b For the simulation “MUNICH-s” a 25 % reduction of the
turbulent transfer coefficient, a one-third increase in NOx emissions from
traffic, and a reduction from 20 to 9 % of the
NO2/ NOx emissions ratio (in mass of NO2 equivalent) are applied. c For the simulation “SinG-s” a 25 % reduction of the
turbulent transfer coefficient, a 33 % reduction of the O3 boundary
conditions, a one-third increase in NOx emissions from traffic, and a
reduction from 20 to 9 % of the NO2/ NOx emissions ratio (in
mass of NO2 equivalent) are applied.
Figure compares the modeled 24 h averaged
concentrations of NO2 with the concentrations measured at the air
monitoring stations operated by Airparif during the TRAFIPOLLU project on the
two sidewalks of Boulevard Alsace-Lorraine for the period from 6 April to
15 June. Mean diurnal variations in NO2 concentrations over this period
are presented in Fig. . Statistical indicators defined
in Appendix for the comparison of hourly concentrations
are provided in Table . The NO2 modeled concentrations
using MUNICH generally underestimate the observations with a mean negative
bias of 32 %. Simulated morning and evening peaks are delayed compared to
the observation. The morning peak of emissions data for the street segment of
Boulevard Alsace-Lorraine corresponds in time to the peak of observed
concentrations. It is also important to note that, on average, over the street
network the morning peak of emissions data occurs 1 h later than in
Boulevard Alsace-Lorraine. It means that the delay in simulated
concentrations is introduced by a transport process (advection in the street
network or turbulent exchange with the background atmosphere).
In addition to NO2 concentrations, NOx concentrations (NO2
equivalent) were measured at the monitoring stations at Boulevard
Alsace-Lorraine. The comparison of the measured and simulated concentrations
with MUNICH shows a large underestimation in the NOx concentrations
(measurement: 148.5 µg m-3 and simulation with MUNICH:
50.3 µg m-3). Worse model performance for NOx than for
NO2 has also been reported in earlier studies
e.g.,, which suggests that NO2
model performance may actually benefit from some error compensation. Here for
example, the underestimation of NOx concentrations is partially
compensated for by an overestimation of the NO2/ NOx fraction.
Comparison of the daily-averaged measurements at the two air
monitoring stations for (a) NO2 and (b) NOx. The
first station is located 5 km from the modeling area (Champigny) and the
second station is located 7 km from the modeling area (Villemomble).
It is not obvious to attribute these discrepancies in NO2 and NOx
simulations to uncertainties in the model formulation or the input data
(background concentrations, meteorological data, and emission data).
Nevertheless, the sensitivity to the choice of the background concentration is
important. For the reference simulation the background concentrations are
estimated using the mean of concentrations measured at two urban background
stations (see Fig. ).
Figure shows similar temporal evolution in the measured
NO2 and NOx daily concentrations between the two stations. However,
large discrepancies in their peak values are observed (up to a maximum
difference of 300 % in the hourly concentrations). It implies that the
measured background concentrations certainly do not always correspond to the
concentration above a given street. Two additional simulations were conducted
to assess the relative contributions from the uncertainties in the background
concentrations derived from measurements. For NO2, NOx, and O3 the
standard deviations over the simulated period of the differences between the
measured concentrations at the two monitoring stations are calculated
(σNO: 8.1 µg m-3,
σNO2: 6.5 µg m-3, and
σO3: 5.1 µg m-3). The first simulation
was run with O3 concentrations increased by σO3 and NO
and NO2 concentrations lowered by σNO and
σNO2, respectively. In the second simulation reduced O3
concentration and increased NO and NO2 concentrations are used.
Differences between the averaged NO2 concentrations for these simulations
and the reference simulation are up to 30 %. This result points out the
difficulty of identifying measurements that are truly representative of the
“urban background” as needed in the street-network model. As shown in the
following the urban background concentrations can be estimated based on the
concentrations simulated with a Eulerian model. This does not ensure a
better representativity of the simulated background concentrations. However, a
dynamic coupling at least ensures a consistent treatment of the mass
conservation. Furthermore, it allows scenario analysis in a prospective
framework with a consistent evolution of background and local concentrations.
Beyond the urban background concentrations, the main remaining uncertainties
are related to the evaluation of the vertical transfer at rooftop and to the
traffic emissions data. A sensitivity test was conducted for further
investigation on the NOx underestimation and the NO2/ NOx ratio
overestimation with different configuration settings and input data set
(MUNICH-s in Table ). The aim is to propose a first
illustration of the uncertainties. A potential underestimation of the NOx
emissions from traffic and an overestimation of the vertical flux by
turbulent diffusion at roof level were considered to explain the deficit of
NOx concentrations within the street. The NO2/ NOx emission
ratio is also considered to explain the too high concentration ratio:
The turbulent transfer coefficient is decreased by 25 %.
A one-third increase in NOx emissions from traffic is applied in the street network.
A reduction from 20 to 9 % of the NO2/ NOx ratio
(in mass of NO2 equivalent) is applied in the emissions from traffic.
The magnitude of the turbulent transfer coefficient reduction is somewhat
arbitrary. It is however chosen to be consistent with the
difference between the two parametrizations considered for the
vertical turbulent transfer (Fig. ) for the aspect ratio
of Boulevard Alsace-Lorraine. It could account for the uncertainties in the
meteorological fields since the standard deviation of the vertical wind
velocity (σw) depends on the friction velocity, the
Monin–Obukhov length, and PBL height that also contribute to the global
uncertainty. This reduction can also be seen as a stopgap to deal with the
discrepancies due to the assumption of uniform concentration within each
street segment. For NOx, mainly emitted close to the street
surface, this latter assumption
certainly leads to overestimation of the concentration at the roof level
since the vertical profile of concentrations is rather supposed to
exponentially decrease with height (Vardoulakis et al. 2003; due to chemistry
this may be not the case for NO or NO2 taken separately). This last
assumption led to overestimation of the vertical turbulent flux computation
for NOx as a whole. It is interesting to note that beyond the limitation
of the NOx flux toward the background, the decrease in the turbulent
transfer coefficient also improves the NO2/ NOx concentration
ratio. It limits the O3 flux from the background and the mixing with an
air mass with a larger NO2/ NOx concentration ratio (observed
ratio ∼1/3 in the street against ∼4/5 in the background).
The increase in emissions is consistent with the uncertainties concerning
NOx emissions derived from COPERT 4 . The
value chosen initially for the NO2/ NOx ratio in the emissions
from traffic was determined from roadside concentration observed in
Île-de-France . However, this value may not
really be representative of the tailpipe ratio . The
9 % ratio (value applied for other emissions sectors;
) appears in the range of possible values reported
by .
These modifications of the reference simulation setup improve the NO2 and
NOx concentrations but the NOx concentrations remain largely
underestimated. The sensitivity of the model results to the turbulent
transfer coefficient implies that the choice between the
formulation and the one proposed in
can have an impact for streets with an aspect ratio far
from 1. More comprehensive studies need to be conducted for these conditions
of aspect ratio (e.g., in the center of Paris).
Application of SinG to a street network in a Paris suburbSimulation domains and input data
SinG is used to estimate the pollutant concentrations in both the 3-D gridded
domain and the street network. Four simulation domains are used from the
continental scale to the urban scale (see Fig. ).
Domain 1 covers western Europe with a horizontal resolution of 0.5∘.
Domains 2 and 3 cover northern–central France (0.15∘ resolution) and
the Île-de-France region (0.04∘ resolution), respectively. The
urban-scale domain 4 covers the eastern Paris suburbs (0.01∘
resolution) including the area where the street network is located. The
horizontal resolution of domain 4 corresponds to about 1 km. The street
network neighborhood is covered by 12 grid cells of domain 4 and corresponds
to about 1 % of the domain 4 area. The vertical resolution consists of 10
levels up to 6 km with the lowest level at 15 m.
For Polair3D, boundary conditions for the outer domain 1 were
obtained from data simulated by the MOZART 4 global CTM
. Meteorological data were obtained from WRF simulations
for all domains . Anthropogenic emissions were
calculated using the European Monitoring and Evaluation Programme (EMEP)
inventory for domains 1 and 2 (EMEP/CEIP 2014 present state of emissions as
used in EMEP models) and the Airparif inventory for domains 3 and 4. Biogenic
emissions were calculated with MEGAN v2.04 . For
MUNICH, which here is the urban canopy model embedded into Polair3D,
the input data presented in Sect. were used,
except for boundary conditions over rooftops, which were obtained from the
lowest layer of Polair3D in the SinG simulation.
Differences between SinG and Polair3D in the surface
concentrations (as a percentage for the means over the whole simulation period) of
(a) NOx and (b) O3. The red-boundary-enclosed area
corresponds to the grid cells where the street network is located. Grid cell
concentrations were calculated by combining the street-network and
above-rooftop concentrations weighted by the corresponding volumes. The
stars show the locations of the urban background air monitoring stations.
Evaluation of the simulated background concentrations
Two simulations were performed over domain 4 from 24 March to 14 June 2014.
Polair3D is used in the first simulation whereas SinG is used in the
second simulation to estimate the influence of the subgrid-scale treatment of
the urban canopy on the pollutant concentrations. The background
concentrations in the simulation with SinG are modeled by the Eulerian model
and updated every 10 min during the simulation to provide the needed upper
boundary condition for the urban canopy module. The simulated background
concentrations of O3 and NOx by Polair3D and SinG are compared
to the measured concentrations at the urban background air monitoring
stations (Champigny and Villemomble). Because these stations are relatively
far from the considered street network, the difference between the two models
is limited (see Fig. ). We obtained satisfactory
results in the NOx and NO2 concentrations but the O3 concentrations
are overestimated (∼ 25 µg m-3-∼ 45 %)
at both stations (see Appendix ). The
overestimation of ozone concentrations is partly related to an overestimation
of the boundary conditions. A comparison of simulated O3 concentrations
within domain 3 with the observations at six urban sites of the Airparif
network shows an overestimation of around
∼ 25–30 µg m-3 (∼ 33 %) (see
Appendix ).
Figure presents the differences between the two
simulations in the mean concentrations over the whole simulated period of
NOx and O3. Differences between Polair3D and SinG in the
NOx concentrations are at most 15 %. These differences are due to
different dispersion of NOx emitted within the urban canopy in SinG and
Polair3D. Since the wind speed is lower within the urban canopy than
above it, advection is slower on average in SinG than in Polair3D
for the grid cells that are treated with the urban canopy module. An increase
in the O3 concentrations occurs with SinG compared to Polair3D
(5 %). It is due to less O3 titration in SinG than in
Polair3D. In SinG, vertical dispersion of NOx is constrained by
the urban canopy. Therefore, O3 titration is less in SinG in comparison to
Polair3D due to lower NO concentrations above the urban canopy.
Evaluation of the simulated concentrations within the street
For the street segment in which measurements are available, the temporal
evolution of the modeled NO2 concentrations using SinG is compared to
that of MUNICH in Fig. and Table .
Statistical scores in Table show better performance for SinG
than MUNICH. The simulated background concentrations affect the
concentrations in the street canyon and lead to better performance with the
current configuration. A similar conclusion was reached by
, who compared a PinG model to a Gaussian model for
simulating NO2 concentrations near roadways. Simulating the background can
lead to better performance than using background concentrations from
monitoring stations that may not be representative for the considered
neighborhood. As expected, the concentrations simulated with the
Polair3D CTM underestimate the street-canyon NO2 and NOx
concentrations.
The comparison of the measured and simulated concentrations with SinG still
show a large underestimation in the NOx concentrations (measurement:
148.5 µg m-3 and simulation with SinG:
76.8 µg m-3). The NO2 concentrations are overestimated by
SinG during several time periods. Since the NO2/ NOx concentration
ratio in the street with MUNICH and SinG is very similar (0.75 and 0.78,
respectively), we conclude that the overestimation in NO2 concentrations
results from
the “same” error compensation as MUNICH but with higher NOx
concentrations.
A sensitivity test was conducted for further investigation on the NOx
underestimation with different configuration settings and input data set
(SinG-s in Table ). As the urban background concentrations of
NO2 and NOx appear simulated without any strong bias with SinG (see
Table ), the uncertainties at the street level
are supposed to be mainly related to the evaluation of the vertical transfer
coefficient at rooftop and to the traffic emissions data. The same
modifications concerning the emissions rates, the vertical turbulent
coefficient, and the NO2/ NOx ratio in the emissions from traffic
applied to MUNICH-s are considered for SinG-s. Additionally, a 33 %
reduction of the O3 boundary conditions is applied to reduce the
NO2/ NOx fraction in the simulated concentrations. The reduction
of the O3 boundary conditions is a pragmatic (and efficient) approach to
reduce the bias in simulated O3 background concentrations (see
Appendix ).
The NOx concentrations of the second SinG simulation remain
underestimated; however, the statistical indicators are clearly improved (see
Table ). The parameters investigated deserve a more
comprehensive sensitivity analysis that could be performed using a more
extended observation database.
Analysis of SinG computational burdens
Comparison of the computational times and model performance for the
simulated concentrations of NOx using SinG and Polair3D for the
period from 31 March to 6 April 2014. Statistical indicators are calculated
by the comparison of simulated hourly concentrations to the NOx
concentrations measured at the air monitoring stations operated on the
sidewalks of Boulevard Alsace-Lorraine.
aΔC= concentration at the current time step
(C1) - concentration at the previous time step (C0).
b Normalized time using Polair3D computational time as reference.
c FB (fractional bias), NMSE (normal mean square error),
MFE (mean fractional error), VG (geometrical mean squared variance), MG (mean geometrical bias),
FAC2 (fraction in a factor of 2), and R (correlation coefficient) .
The statistical indicators were calculated against the observations at the monitoring
stations at Boulevard Alsace-Lorraine.
Additional simulations were conducted to estimate the increase in
computational time using SinG compared to Polair3D. For the current
case study the increase in computational burden remains limited. This is
clearly due to the relatively limited fraction of the simulated domain
concerned with the street-network model. The time increase using SinG is partly
due to the number of iterations used to achieve steady state in MUNICH. The
number of iterations depends on the set error criterion, which differs among
the simulations listed as SinG-1 to SinG-5 (see
Table ). Steady state is assumed to be achieved when
the errors satisfy the error criterion. This error criterion can be
prescribed either in absolute terms (0.01 or 1 µg m-3) or in
relative terms (1 or 10 %), with respect to the concentrations at the
previous time step for all street segments of the urban canopy.
We examined the influence of the error criteria on the computational time and
model results. Five additional simulations using SinG are thus compared to
the one presented before using Polair3D as reference for the
computational time. The increases in the computational time vary from 2 %
(SinG-5) when no error criterion is imposed (i.e., a single calculation step
is conducted; for comparison it takes about 20 interactions to achieve steady
state in SinG-1) to 5 % (SinG-3) when a 1 % error criterion is
imposed. Model discrepancies are estimated by comparison with the observed
NOx street-canyon concentrations. Model results are not strongly
influenced by changing the error limit.
The influence of the chemical kinetic mechanism on the computational time and
model performance was also assessed (SinG-5 vs. SinG-6). The increase in the
computational time is halved when the Leighton photostationary state is used
instead of CB05. Model performance is not degraded with the Leighton
mechanism compared to CB05. Therefore, an operational version of SinG should
use the Leighton mechanism within the urban canopy with either the SinG-2,
SinG-4 or SinG-6 error criteria, depending on the accuracy desired.
Conclusions and implications
A new multi-scale model, Street-in-Grid (SinG), which combines a
street-network model, Model of Urban Network of Intersecting Canyons and
Highways (MUNICH), and a chemistry–transport model, Polair3D, was
developed to jointly represent the urban background and the local
street-level pollution. These models were used to simulate NO2 and NOx
air concentrations for a Paris suburb. The simulation results were compared
to background and street air concentration measurements.
Simulation results using the street-network model MUNICH indicate that the
temporal evolution of NO2 and NOx concentrations in the Boulevard
Alsace-Lorraine are well reproduced but NO2 and NOx concentrations are
underestimated. For this case study, the use of the multi-scale model leads
to a large reduction in the error and bias of the simulated concentrations in
the street. Providing the background concentrations modeled by
Polair3D to MUNICH improves the simulation results for NO2
concentrations. The NOx concentrations are also improved with SinG;
however, both MUNICH and SinG simulated NOx concentrations are largely
underestimated. This underestimation could be partly explained by
uncertainties in NOx emissions or an overestimation of NOx transport
into the overlying atmosphere at rooftop. For the latter it would be of
interest to further investigate, with the support of appropriate observation
data, the relative contribution of the uncertainties in the meteorological
data and of the model assumption. The impact of the horizontal resolution of
meteorological data on SinG simulations also needs to be studied.
For this case study, using a comprehensive chemistry within the street canyon
does not notably influence the NOx concentrations. Consequently,
computational costs can be reduced by using the Leighton photostationary
state within the urban canopy.
However, this test would need to be renewed for new applications. The
photostationary assumption cannot hold in condition with high VOC emissions.
Further studies are needed to extend the model to simulate primary and
secondary particulate matter in an urban canopy.
The observation database built within the framework of the TRAFIPOLLU project
was focused at the street level. We have not been able to evaluate the
ability of the new model to represent background concentrations in comparison
to a traditional Eulerian chemistry–transport model. An application of SinG to
larger urban domains would allow this type of analysis and would complete the
evaluation for street-level concentrations.
SinG is a useful tool to simulate both the concentrations of air pollutants
in complex urban canopy configurations and the
background concentrations in the overlying
atmosphere. Beyond the data usually needed for a CTM, traffic emissions data
for street segments and urban or building morphology data are mandatory for a
SinG simulation over an urban area. The urban or building morphology data are
available for many major cities in the world (for example, ESRI ArcGIS for
the US, EMU for the UK, or OpenStreetMap). The traffic emissions may be less
easily available than other data.
The source code of Street-in-Grid (v1.0) is available via
Zenodo with the following DOI 10.5281/zenodo.1025629.
Statistical indicators
Definitions of the statistical indicators.
IndicatorsDefinitionsRoot mean square error (RMSE)1n∑i=1n(ci-oi)2Fractional bias (FB)c‾-o‾(c‾+o‾)/2Mean fractional bias (MFB) and mean fractional error (MFE)1n∑i=1nci-oi(ci+oi)/2 and 1n∑i=1n|ci-oi|(ci+oi)/2Mean normalized bias (MNB) and mean normalized error (MNE)1n∑i=1nci-oioi and 1n∑i=1n|ci-oi|oiNormalized mean square error (NMSE)∑i=1n(ci-oi)2∑i=1ncioiCorrelation coefficient (R)∑i=1n(ci-c‾)(oi-o‾)∑i=1n(ci-c‾)2∑i=1n(oi-o‾)2Geometrical mean squared variance (VG)exp∑i=1n(ln(ci)-ln(oi)2nMean geometrical bias (MG)exp∑i=1nln(ci)-ln(oi)nFraction of modeled values within a factor of 2 of observations (FAC2)0.5≤ci/oi≤2
ci: modeled values; oi: observed values; n: number of data.
o‾=1n∑i=1noi and c‾=1n∑i=1nci.
Evaluation of simulated background concentrations
Statistical indicators of the comparison of simulated hourly concentrations
of O3 to the concentrations measured at the background air monitoring stations
within domain 2 (see Fig. ).
* Mean fractional bias (MFB), mean fractional error
(MFE),
and correlation coefficient (R).
Statistical indicators of the comparison of simulated hourly
concentrations of O3 to the concentrations measured at the urban
background air monitoring stations within domain 3 (see
Fig. ).
* Mean fractional bias (MFB), mean fractional error (MFE), and correlation coefficient
(R).
Statistical indicators of the comparison of simulated hourly
concentrations of NO2, NOx, and O3 in the SinG simulation to the
concentrations measured at the urban background air monitoring stations of
Villemomble and Champigny. The “O3 (SinG-s)” corresponds to the ozone
concentrations from the SinG-s simulation using the adjusted input data
including “corrected” O3 boundary conditions. MFB and MFE in the O3
concentration of the SinG simulation are strongly reduced using the corrected
boundary conditions. However, the correlation coefficients do not change
between the SinG and SinG-s simulations because the O3 concentrations in
the two simulations show very similar temporal evolutions.
* Mean fractional bias (MFB), mean fractional error (MFE), and correlation coefficient (R).
Simulated hourly concentrations of O3 are compared to the concentrations
measured at the background air monitoring stations in domains 2 and 3. For
domain 2, O3 concentrations are measured at four air monitoring stations
that are operated by EMEP (see Fig. a).
Table presents the comparison results. The
O3 concentrations are well estimated at a station that is located in
central France. However, the model largely overestimates the O3
concentrations at three other stations. This overestimation may be due to
uncertainties in long-range O3 transport. For domain 3, simulated O3
concentrations are compared to the concentrations measured at six urban
background monitoring stations (see Fig. b).
The modeled O3 concentrations are also overestimated (MFB: 42–48 %)
at those stations. These overestimations of O3 concentrations in domains 2
and 3 at the rural and urban background stations imply uncertainties in O3
boundary conditions for domain 4.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was funded by EDF R&D and EDF R&D China. The authors acknowledge
our colleagues Luc Musson-Genon, CEREA/EDF R&D, and Jiesheng Min, EDF R&D
China, for helpful discussions during the model development. We also thank
Airparif for providing the emission inventory and the measured concentration
data, Laëtitia Thouron for providing the WRF meteorological outputs, and
the TrafiPollu ANR project for making data available for the model
application and evaluation. Edited by: Jason
Williams Reviewed by: three anonymous referees
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