Multi-scale modeling of urban air pollution: development and application of a Street-in-Grid model by coupling MUNICH and Polair3D

A new multi-scale model of urban air pollution is presented. This model combines a chemical-transport model (CTM) that includes a comprehensive treatment of atmospheric chemistry and transport at spatial scales down to 1 km and a street-network model that describes the atmospheric concentrations of pollutants in an urban street network. The streetnetwork 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 5 of the Polyphemus air quality modeling platform to constitute a 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 roof-tops is performed by 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 10 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. 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 (Reynolds et al., 1973).This threedimensional (3D) 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 U.S. cities, there was no need to take buildings into account explicitly.
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 at the 1 Geosci.Model Dev.Discuss., https://doi.org/10.5194/gmd-2017-189Manuscript under review for journal Geosci.Model Dev. Discussion started: 1 September 2017 c Author(s) 2017.CC BY 4.0 License.street scale.The conceptual approach of the Operational Street Pollution Model (OSPM) has typically been used (Berkowicz, 2000).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 (Soulhac et al., 2011).Although the SIRANE formulation does not distinguish recirculation and ventilation zones and assumes a uniform concentration for each street segment, it provides a significantly 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.Soulhac et al., 2012).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 (O 3 ) or fine particulate matter (PM 2.5 ), 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 3D 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 (Seigneur et al., 1983).Many other "Plume-in-Grid" (PinG) models have been developed over the following three decades (see Karamchandani et al., 2011, for 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., Cariolle et al., 2009;Karamchandani et al., 2009;Huszar et al., 2010;Jacobson et al., 2011;Briant and Seigneur, 2013;Holmes et al., 2014;Kim et al., 2014) 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 an 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 (Mallet et al., 2007), a 3D chemical-transport model (CTM), which has been widely applied in Europe, North America, South America, Asia, and Africa (e.g., Sartelet et al., 2012).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 Section 3. 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 (Soulhac et al., 2011).We can distinguish two main components to 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 (Soulhac et al., 2011): -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 Cherin et al. (2015).
-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.
where Q s is the source emission rate, Q inflow is the inflow rate of the air pollutant entering the street from upwind (typically via an intersection), Q vert is the vertical flux by turbulent diffusion at roof level (see Section 2.1.1),Q outflow is the outflow rate of the air pollutant leaving the street in the downwind direction, Q dep is the pollutant loss rate due to atmospheric deposition, and Q chem is the air pollutant chemical transformation rate (positive for formation and negative for destruction).The emission term, Q s , is obtained typically from a traffic emission model.The inflow term, Q inflow , is obtained from the street-intersection component (see Section 2.2).The outflow rate, Q outflow is calculated as follows: where H is the mean building height in the street segment and W is the mean street width, u street is the mean horizontal wind velocity in the street segment (see Section 2.1.2),and C street is the air pollutant concentration in the street segment.The vertical flux, Q vert , 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 Salizzoni et al. (2009).
where C background is the mean concentration above the street segment, L is the street length, and σ W 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 unit of a velocity.Such an approach is routinely used in engineering where mass transfer coefficients are empirically defined and combined with concentration gradients to calculate mass transfer rates.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 Schulte et al. (2015) who used a turbulent dispersion coefficient defined as follows: 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.
where β 1 is a constant and a r is the aspect ratio (ratio of building height to street width, H/W ) (Landsberg, 1981).
Then, the vertical flux at roof level is expressed using the turbulent dispersion coefficient as follows: By combing Equation 7with Equations 4 and 6, we obtain where β = β 1 β 2 .
The constant β can be estimated by comparison to Equation 3.Because the vertical flux in Equation 3 is estimated using the unity aspect ratio (a r = 1), we assume that the computed vertical fluxes with Equations 3 and 8 are equal when a r = 1.
We obtain β = 0.45. Figure 1 compares the vertical transfer coefficient estimated with Equations 3 and 8.If a r < 1, i.e., in an area with low buildings, then the transfer coefficient is greater with the formulation of Schulte et al. (2015) than that of SIRANE.On the contrary, if a r > 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 Lemonsu et al. (2004) and used by Cherin et al. (2015) 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 (Lemonsu et al., 2004 andCherin et al., 2015 averaged the wind profile over all possible angles).
-For narrow canyons, a r > 2/3: where ϕ is the angle between the wind direction above roof level and the street direction.u H 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 ≤ a r ≤ 2/3: -For a wide configuration, a r < 1/3: 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 (Soulhac et al., 2008) to derive the average wind velocity within the street-canyon.The wind speeds calculated

Street-intersection component
The street-intersection component of MUNICH involves the following assumptions, also used in SIRANE (Soulhac et al., 2009): -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 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 Equation 1.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 influence significantly the air flow across an intersection (Soulhac et al., 2009).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 (Yarwood et al., 2005) is implemented to ensure consistency with Polair3D in the SinG configuration.CB05 consists of 53 species including volatile organic compounds (VOC) and inorganic species and 155 chemical reactions including 23 photolyses.However, nitric oxide (NO) emissions in the urban canopy are likely to scavenge O 3 and other oxidants, thereby suppressing VOC chemistry.Accordingly, a simple three-reaction mechanism involving solely NO, nitrogen dioxide (NO 2 ) and O 3 , known as the Leighton photostationary state (Leighton, 1961), was also implemented.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 (Cherin et al., 2015).Surfaces available for dry deposition include pavement (street and sidewalks), building walls, and building roofs.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 roof tops and the concentrations within the urban canopy: where F street is the wet deposition flux to the pavement (µg m −2 s −1 ), Λ is the scavenging coefficient (s −1 ), and z c is the cloud base height (m).The in-cloud wet scavenging is supposed to have a weak impact for the species considered here.
3 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 at 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 at different scales.Transport and dispersion of pollutants at 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 at those different scales.There is, however, the option to use a reduced form of the chemical mechanism within the street network, following Karamchandani et al. (1998).
Figure 3 shows schematically 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 Polair3D lowest 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.Figure 4 displays the location of the modeling domain.

Traffic emissions
The traffic emissions for the simulation domain were estimated using the dynamic traffic model, Symuvia (Leclercq et al., 2007) with the COPERT 4 emission factors (http://emisia.com/products/copert-4/versions),as part of the TrafiPollu project (http://www.agence-nationale-recherche.fr/?Project=ANR-12-VBDU-0002).The emission rates depend on the vehicle speed and composition of the fleet.Two typical days (March 25 for weekday and March 30 for weekend) were chosen for the traffic simulation.
The dynamic traffic model estimates the emission rates for each traffic direction of a two-way street.The traffic emissions of a two-way street were merged to obtain one emission rate for the street segment.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.In this work, the traffic emissions were prepared for 577 street segments The obtained emission data for the street network are presented in Figure 5.

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 2 sidewalks (the minimum sidewalk width in France is 1.4 m).For the A86 highway, 20 m were added to the lane width including 2 shoulders (4 m), a median strip (1.5 m), and 2 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/speed, planetary boundary layer (PBL) height, and friction velocity, were ob-10 tained from a Weather Research and Forecasting (WRF) model (Skamarock et al., 2008) simulation counducted with a horizontal resolution of 1.5×1.5 km 2 (Thouron et al., 2017).The simulated meteorological data were compared to the measurements at urban-background meteorological stations near the simulation domain and showed satisfactory results.

Background concentrations
Background concentrations of NO, NO 2 , and O 3 were obtained from two urban background air monitoring stations near the modeling area (5 to 7 km from the area, see Figure 4).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 Equations 3 and 8.These stations are operated by AIRPARIF, the air quality agency of the Paris region (http://www.airparif.asso.fr/).

Results
Figure 6 shows that simulated concentrations of NO x are high in the streets where the emission rates are high (see Figure 5).The concentrations of NO x during nighttime on March 25 reach 160 µg m −3 over the major streets.During the morning rush-hour on the same day, the concentrations of NO x 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 10 speed (2.5 m/s).One notes that there is a clear difference between the spatial patterns of the emission maps (Figure 5) and concentration maps (Figure 6).Streets with no or little NO x emissions display non-negligible NO x concentrations, thereby highlighting the importance of advective and turbulent transport in the street network.(Mean geometrical bias), FAC2 (Fraction in a factor of 2), R (Correlation coefficient) (Chang and Hanna, 2004;Yu et al., 2006).For Polair3D, boundary conditions for the outer domain 1 were obtained from data simulated by the MOZART 4 global CTM (Emmons et al., 2010).Meteorological data were obtained from WRF simulations for all domains (Thouron et al., 2017).
Anthropogenic emissions were calculated using the 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 (Guenther et al., 2006).For MUNICH, which here is the urban canopy model embedded into Polair3D, the input data presented in Section 4 were used, except for boundary conditions over roof top, which were obtained from the lowest layer of Polair3D in the SinG simulation.

Evaluation of the simulated background concentrations
Two simulations were performed over domain 4 from March 24 to June 14, 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 to the urban canopy module.The simulated background concentrations of O 3 and NO x 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 are not significant (see Figure 9).We obtained satisfactory results in the NO x and NO 2 concentrations but the O 3 concentrations are overestimated (∼ 45%) at both stations (see Appendix B).The overestimation of ozone concentrations is partly related to an overestimation of the boundary conditions.A comparison of simulated O 3 concentrations within domain 3 with the observations at six urban sites of the AIRPARIF network shows an overestimation of around 33% (see Appendix B).   Figure 9 presents the differences between the two simulations in the mean concentrations over the whole simulated period of NO x and O 3 .Differences between Polair3D and SinG in the NO x concentrations are at most 15%.These differences are due to different dispersion of NO x 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 cell, that are treated with the urban canopy module.An increase in the O 3 concentrations occurs with SinG compared to Polair3D (5%).It is due to a more limited O 3 titration in SinG than in Polair3D, because in SinG, there is a quasi-total O 3 titration within the urban canopy, but little titration above due to much lower NO levels.

Evaluation of the simulated concentrations within the street
For the street segment where measurements are available, the temporal evolution of the modeled NO 2 concentrations using SinG is compared to those of MUNICH in Figure 7 and Table 1.Statistical scores in Table 1 show better performance for SinG than MUNICH using the statistical indicators.The simulated background concentrations significantly affect the concentrations in the street-canyon and lead to better performance with the current configuration.A similar conclusion was reached by Briant and Seigneur (2013) who compared a PinG model to a gaussian model for simulating NO 2 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 significantly underestimates the street-canyon NO 2 concentrations.
In addition to NO 2 concentrations, NO x concentrations (NO 2 equivalent) were measured at the monitoring stations at Boulevard Alsace-Lorraine.The comparison of the measured and simulated concentrations with SinG shows a significant underestimation in the NO x concentrations (148.5 µg m −3 vs 76.8 µg m −3 ).Worse model performance for NO x than for NO 2 has also been reported in earlier studies (e.g.Ketzel et al., 2012;Soulhac et al., 2012), which suggests that NO 2 model performance may actually benefit from some error compensation.Here for example, the underestimation of NO x concentrations is partially compensated by an overestimation of the NO 2 /NO x fraction.A sensitivity test was conducted for further investigation on the NO underestimation with a different configuration settings and input data set (SinG-s in Table 1).The aim is to propose a first illustration of the main uncertainties.
A potential underestimation of the NO x emissions from traffic and an overestimation of the the vertical flux by turbulent diffusion at roof level were considered to explain the deficit of NO x concentrations within the street.A one-third increase of NO x emissions from traffic is applied in the street network.This increase is consistent with the uncertainties concerning NO x emissions derived from COPERT 4 (Kouridis et al., 2010).The turbulent transfer coefficient is decreased by 25%.Beyond the uncertainties on the value itself, this reduction can be seen as a stopgap to deal with the discrepancies due to the assumption of uniform concentration within each street segment.For NO x , mainly emitted near from the street ground, this latter assumption certainly leads to overestimate the concentration at the roof level since the vertical profile of concentrations is rather supposed to be exponentially decreasing with height (Vardoulakis et al., 2003, due to chemistry this may be not the case for NO or NO 2 taken separately).The vertical turbulent flux computation is then probably overestimated for NO x as a whole.A 33% reduction of the O 3 boundary conditions and a reduction from 20% to 9% of the NO 2 /NO x ratio (in mass of NO 2 equivalent) in the emissions from traffic are also considered to reduce the NO 2 /NO x fraction in the simulated concentrations.
The reduction of the O 3 boundary conditions is a pragmatic (and efficient) approach to reduce the bias in O 3 simulated background concentrations (see Appendix B).The value chosen initially for the NO 2 /NO x ratio in the emissions from traffic was determined from roadside concentration observed in Île-de-France (AIRPARIF, 2015).However this value may be not really representative of the tailpipe ratio (Kimbrough et al., 2017).The 9% ratio (value applied for others emissions sectors, Sartelet et al., 2007) appears in the range of possible values reported by Carslaw and Rhys-Tyler (2013).
The NO x concentrations of the second SinG simulation remain underestimated, however the statistical indicators are clearly improved (see Table 1).The parameters investigated deserve a more comprehensive sensitivity analysis that could be performed using a more extended observation database.

Analysis of SinG computational burdens
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 by 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 2).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 of 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 interations to achieve steady state in SinG-1) to 5% (SinG-3) when a 1% error criterion is imposed.Model discrepencies are estimated by comparison with the observed NO x street-canyon concentrations.Model results are not influenced significantly by changing the error limit.
The influence of the chemical kinetic mechanism on the computational time and model performance were also assessed for NO 2 concentrations.The NO x concentrations are also improved with SinG, however both MUNICH and SinG simulated NO x concentrations are significantly underestimated.This underestimation could be partly explained by uncertainties in NO x emissions or an overestimation of NO x transport into the overlying atmosphere at roof top.
Using a comprehensive chemistry within the street-canyon does not influence the NO x concentrations notably.Consequently, computational costs can be reduced significantly by using the Leighton photostationary state within the urban canopy.However further studies are needed to extend the model to simulate primary and secondary particulate matter in an urban canopy.
The observation database build within the framework of the TrafiPollu project was focused at the street level.We 2.1.1Turbulent vertical mass transfer at the top of the street segment

Figure 1 .
Figure 1.Comparison of the turbulent transfer coefficients of the SIRANE formulation (dotted line) and the formulation of Schulte et al. (2015) (solid line).

Figure 2 .
Figure2.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(Soulhac et al., 2008) (dotted lines) and MUNICH(Lemonsu et al., 2004) (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 • , (c) 60 • The dry deposition fluxes (in µg m −2 s −1 ) are calculated by multiplying the pollutant concentrations (in µg m −3 ) and the pollutant deposition velocities (in m s −1 ).The estimation of the deposition velocities depends on the atmospheric conditions and the surface properties, which Geosci.Model Dev.Discuss., https://doi.org/10.5194/gmd-2017-189Manuscript under review for journal Geosci.Model Dev. Discussion started: 1 September 2017 c Author(s) 2017.CC BY 4.0 License.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.

Figure 3 .
Figure 3. Schematic diagram of the Street-in-Grid model.

Figure 4 .
Figure 4. Modeling domains for the Street-in-Grid simulations.In the right panel, 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.SinG is only used for domain 4. Measured data at the stations with the black stars are used for background concentrations in the MUNICH simulations 5

Figure 5 .
Figure 5. NOx emission rates (µg m −1 s −1 ) used in MUNICH simulations for a week day (a) during nighttime at 1 AM (UTC) (b) in the morning rush-hour at 7 AM (UTC) on March 25, 2014.

Figure 7 Figure 6 .
Figure 7 compares the modeled 24-h averaged concentrations of NO 2 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 April 6 to June 15.Statistical indicators defined in Appendix A for the comparison of hourly concentrations are provided in Table1.The NO 2 modeled concentrations using MUNICH generally underestimate the observations with a mean negative bias of 32%.It is not obvious to attribute these discrepancies in NO 2 simulations to the model formulation or the input data (background concentrations, meteorological data and emission data from the dynamic traffic model).Nevertheless the sensitivity to the choice of the background concentration is important.The background concentrations are estimated using the mean of concentrations measured at two urban background stations (see Figure4).Figure8shows similar temporal evolution in the measured NO 2 and NO x daily concentrations between the two stations.However significant discrepancies in their pick 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.This result points out the difficulty of identifying measurements that are truly representative of the "urban background" as wished in the street-

Figure 7 .
Figure 7. Temporal evolution of NO2 daily-averaged concentrations modeled with MUNICH (blue line) and the SinG model (red line).They are compared to the measured concentrations (black shaded regions) at the stations nearby traffic on each sidewalks of the Boulevard Alsace-Lorraine.If the measurement is available only one station, black line is used instead.

Figure 8 .
Figure 8.Comparison of the daily-averaged measurements at the two air monitoring stations for (a) NO2 and (b) NOx.The first station is located at 5 km from the modeling area (Champigny) and the second station is located at 7 km from the modeling area (Villemomble).

Figure 9 .
Figure 9. Differences between SinG and Polair3D in the surface concentrations (in % 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-roof-top concentrations weighted by the corresponding volumes.The stars show the locations of the urban background air monitoring stations.

(
. The increase of 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 of the accuracy desired.6 Conclusions 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 chemical-transport model, Polair3D, was developed to represent jointly the urban background and the local street-level pollution.These models were used to simulate NO 2 and NO x air concentrations for a Paris suburb.The simulation results were compared to background and street air concentrations measurements.Simulation results using the street-network model MUNICH indicate that the temporal evolution of NO 2 and NO x concentrations in the Boulevard Alsace-Lorraine are well reproduced but NO 2 and NO x concentrations are underestimated.For this case study, the use of the multi-scale model leads to a significant 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

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have not been able to evaluate the ability of the new model to represent background concentrations in comparison to traditional Eulerian chemical-transport model.An application of SinG to larger urban domains would allow this type of analyse and would complete the evaluation for street level concentrations.Geosci.Model Dev.Discuss., https://doi.org/10.5194/gmd-2017-189Manuscript under review for journal Geosci.Model Dev. Discussion started: 1 September 2017 c Author(s) 2017.CC BY 4.0 License.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 those data available for the model application and evaluation.19 Geosci.Model Dev.Discuss., https://doi.org/10.5194/gmd-2017-189Manuscript under review for journal Geosci.Model Dev. Discussion started: 1 September 2017 c Author(s) 2017.CC BY 4.0 License.ci − oi | (ci + oi)/2 Normalized mean square error (NMSE) Fraction of modeled values within a factor of two of observations (FAC2) 0.5 ≤ ci/oi ≤ 2 ci: modeled values, oi: observed values, n: number of data.Dev.Discuss., https://doi.org/10.5194/gmd-2017-189Manuscript under review for journal Geosci.Model Dev. Discussion started: 1 September 2017 c Author(s) 2017.CC BY 4.0 License.B Evaluation of simulated background concentrations Table B1.Statistical indicators of the comparison of simulated hourly concentrations of NO2, NOx and O3 to the concentrations measured at the urban background air monitoring stations of Villemomble and Champigny.The "O3 cor." correspond to the ozone concentrations from the second simulation using "corrected" boundary conditions.

Table 1 .
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.

Table 2 .
Comparison of the computational times and model performance for the simulated concentrations of NOx using SinG and Polair3D for the period from March 31 to April 6, 2014.∆C = concentration at the current time step (C1) -concentration at the previous time step (C0).† : normalized time using Polair3D computational time as reference.

Table B2 .
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 Figure4).: Mean fractional bias (MFB), mean fractional error (MFE) and correlation coefficient (R) * : Mean fractional bias (MFB), mean fractional error (MFE) and correlation coefficient (R) *