Interactive comment on “ Analysis of the impact of inhomogeneous emissions in a semi-parameterized street canyon model ”

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Full the advantages of low input requirements and short execution times.This means that the model can cover many streets over long time periods.In order to retain the low calculation time of these models, a number of simplifying assumptions have to be made.One assumption, present in e.g.OSPM, is that the emissions are distributed homogeneously over the street canyon in the full length and width of the canyon.However, real streets have traffic lanes with finite width and varying traffic loads, either permanently or as a function of time as e.g.rush hours.Moreover, they might have sidewalks or cycle lanes with no emissions or wide central reserves likewise without emissions.Modelling these situations as homogeneous emission will potentially overestimate one side of the street and underestimate the other side of the street.This has an influence on e.g.limit values, where one side of the street can exceed the limit value while the other does not.
Sloping streets represent a natural case of inhomogeneous emissions in that vehicles driving uphill have a higher emission due to the increased engine load compared to vehicles driving downhill.Gidhagen et al. (2004) examined the measured NO x concentrations from a measurement campaign in Hornsgatan in Stockholm, Sweden; which has a slope of 2.3 %, using a Computational Fluid Dynamics (CFD) model.It was shown that the model representation of the wind direction dependence of the concentrations compared to the wind direction dependence of the measurements improved by assuming an emission relationship of 3 : 1 between the uphill and downhill side of the road.This followed along a marginal improvement in the correlation between the model and the measurements.In Gidhagen et al. (2004), Kean et al. (2003) is also quoted for reporting markedly higher emissions for vehicles going uphill compared to vehicles going downhill, a feature also implemented in emission models like the Handbook Emission Factors for Road Transport -HBEFA (www.hbefa.net).
Moreover, Kakosimos et al. (2010) and Vardoulakis et al. (2007) suggested that an improvement in the applicability of this type of model could be achieved by implementation of an inhomogeneous emission geometry scheme.
The present study is therefore based on the following research question: Introduction

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Full To what extend do the performance of street pollution models like OSPM improve as a result of moving from homogeneous emissions to inhomogeneous emissions, and how is this change influenced by the aspect ratio of the street and the inhomogeneity of the emissions?
The methods applied in the present study are explained in Sect. 2. This is followed by a description of how the concentrations are calculated based on respectively the homogeneous and the inhomogeneous emissions in Sect.3. The results and discussion are placed in Sect. 4 and the conclusions are presented in Sect. 5.

Methods
To analyse the impact of inhomogeneous emissions in OSPM two real-world cases were selected as being representative for inhomogeneous emission geometry streets as found in urban areas.The two real-world cases were supplemented by a set of theoretical calculations to analyse the impact of inhomogeneity and aspect ratio on the results.
The two street canyons chosen to analyse the impact of inhomogeneous emissions were respectively Hornsgatan in Stockholm, Sweden and Jagtvej in Copenhagen, Denmark.The main characteristics of the two street canyons are summed up in Table 1.
Hornsgatan is an example of a sloping street canyon with the average slope being 2.3 % (Gidhagen et al., 2004), and Jagtvej is diurnally inhomogeneous in that, depending on the time of day, there is more traffic in the northeast direction compared to the southwest direction.Both streets have two driving lanes in each direction (four lanes in total) plus non-emitting areas at the sides.The non-emitting areas are however not modelled explicitly in the present analysis, since including this would require the implementation of horizontal diffusion in the model cf. the discussion in Sect.3.2.This task remains for future work.
In the analysis, the NO x concentrations were used since in OSPM the concentration of NO 2 is calculated based on the concentration of NO x and O 3 .Thus in order not to 938 Introduction

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Full add the uncertainties from the chemistry in the analysis, the primary emitted tracer (NO x ) is used.Moreover, previous studies (Ketzel et al., 2011(Ketzel et al., , 2012) ) have shown that the emission and dispersion module implemented in OSPM have an acceptable performance for this species.The CO concentrations were not used since the accuracy of the COPERT 4 emission model, as implemented in OSPM, is not known for CO emissions in Denmark and the general levels are low, which means that the CO measurements are fraught with a large uncertainty (Ellermann et al., 2013).
The years 2007-2009 were chosen for Hornsgatan, since a ban on the use of studded tires was implemented in this street from 2010 and onwards, which probably affected the vehicle distribution.Modelling the influence of this was assessed to be complicated and outside the scope of the present study.For Jagtvej the two years 2003 and 2013 were chosen since traffic counts were performed next to the measurement station in these years.In order to assess the influence of inhomogeneous emissions, accurate traffic input is very important.
Both streets are part of routine air quality control monitoring programs and have been studied extensively in the past.One year of data from Hornsgatan were included in the Street Emission Ceiling Exercise (Moussiopoulos et al., 2004(Moussiopoulos et al., , 2005;;Larssen et al., 2007) and has thus been subject of a number of modelling studies (e.g.Denby et al., 2013a, b;Olivares et al., 2007;Ketzel et al., 2007;Johansson et al. 2009).The Jagtvej measurement station is part of the Danish air quality monitoring programme (Ellermann et al., 2013) and has likewise been the subject of extensive analysis (e.g.Ketzel et al., 2011Ketzel et al., , 2012;;Silver et al., 2013).

Emission modelling and measurements from Hornsgatan
The emission modelling for Hornsgatan uses the hourly automatic vehicle counts for the two driving directions on Hornsgatan.The vehicle counts were made using an automatic counts in the east inner lane were multiplied by 4.2 to compensate for a bias in the counting based on a manual counting check.
The vehicle distribution was modelled as the average weekly vehicle distribution based on vehicle classifications obtained by video number plate recognition in the fall of 2009 (Burman and Johansson, 2010).This ensured that the emission factors reflected the average weekly variation in vehicle distribution.All vehicle categories were modelled using HBEFA 3.2 (www.hbefa.net)except ethanol buses, which do not appear as vehicle category in HBEFA.These were instead modelled using the ARTEMIS emission model (Boulter and McCrae, 2007).The emission factors from ARTEMIS were harmonized to a different set of velocities compared to HBEFA.In order to harmonize the two emission models, the emissions from ARTEMIS were linearly interpolated to match the travel speeds from HBEFA.
The emission factors from HBEFA version 3.2, were used for the emission modelling since this emission model includes the effect of slope on the emissions.The emissions were exported from this model for slopes of ±2 and ±4 % and a linear interpolation to the slope of ±2.3 %, as given by Gidhagen et al. (2004), was performed.
The traffic flow situation (called "level of service" in HBEFA) was modelled by categorizing the individual hour based on the total number of vehicles in the hour as measured by the automatic vehicle counts.The categorization was performed based on the scheme from the ARTEMIS model reprinted in Table 2.
In setting up OSPM, the street was divided into two emission segments of equal width thus each segment covering two traffic lanes.The emissions were distributed over both the lanes and the sidewalk since the modelling of sidewalks is not yet a feature of the model, cf. the discussion in Sect.3.2.The vehicle speed, used for the calculation of traffic-produced turbulence, was assumed equal to the mean speed between the two lanes comprising the segment.
The emission modelling for this street was performed based on two approaches: -An approach based on the hypothesis that the traffic on the individual lane can be modelled as half the total traffic, subsequently referred to as the "proportional" Introduction

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Full approach.The inhomogeneity thus only arises from the slope of the street.This approach is useful if directional-or lane divided traffic counts don't exist for the street in question.
-An approach based on the modelling of inhomogeneous emissions based on traffic counts from the individual lane as described above.This approach is subsequently referred to as the "exact" approach.
The two approaches to emission modelling were subsequently compared.NO x was simultaneously monitored on the northern and southern sides of the road with a commercial NO x chemiluminescence analyser (model 31 M LCD, Environment SA, France).Urban background concentrations were taken from an identical instrument at a monitoring station located on the roof of a building approx.500 m east of the Hornsgatan street station.The roof level station is representative of the urban background and is not influenced by the emissions in any nearby street canyon.
To analyse if the emission distribution between the north side and the south side of the street can be modelled as a constant ratio, an analysis of measurements for nearparallel (±30 • ) wind directions for the conditions of a minimum wind speed of 2 m s −1 was performed.It was hypothesized that the ratio between the measured concentrations corresponds to the proportions between the emissions.This assumption is of course violated as a result of horizontal dispersion in the street canyon, but this effect was disregarded.
As seen from Fig. 1, the distribution of concentration ratios between the northern and southern side of the street is skewed with the mode being around 1.2 and the mean value being 2.27.This result is not too far from the result presented by Gidhagen et al. (2004), that the emissions at the north side were three times as large as on the south side.Moreover, the distribution is unimodal and has a relatively low standard deviation, which supports the assumption of an even traffic distribution between the north-and the south side of the street.Introduction

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Full The hypothesis of a constant ratio distribution will be fortified if the ratio is not changing systematically with time.
The diurnal and weekly variation is shown in Fig. 2. As can be seen the values show no clear diurnal or weekly variation and thus the assumption of an even distribution of traffic, but inhomogeneous emissions due to the slope in the two directions, between the two segments seems valid.

Emission modelling and measurements from Jagtvej
One manual traffic count next to the measurement station at Jagtvej was performed respectively in 2003 and in 2013.The traffic was counted in two directions on a weekday for 24 h in 2003 and between 7 a.m.-7 p.m. in 2013.The number of vehicles was split into a number of vehicle classes to provide the vehicle distribution.The emissions were modelled using the COPERT 4 model (EEA, 2009).
The diurnal vehicle speed profile for Jagtvej was based on a national study aiming to establish typical diurnal speed profiles for different types of urban streets (TetraPlan A/S, 2001) where the most representative for Jagtvej was chosen.Furthermore, average travel speed data were obtained from a recent national data set (http://speedmap.dk/portal) managed by the Danish Road Directorate.SpeedMap is based on GPS readings from vehicle fleets and provides travel speeds on all major roads in Denmark in a high spatial and temporal resolution.The average vehicle speed from 2011 was used to scale the diurnal profiles from the original study, and the velocity profile was assumed valid for both 2003 and 2013, since no information on the temporal development in vehicle speeds were available within the limits of the present study.
The emissions were subsequently distributed in two segments each covering half of the street width thus both covering the traffic lanes and the sidewalks.The choice of two segments was made since the traffic counts were only distributed into driving directions and not on the individual lane.Introduction

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Full The NO x measurements at the east side of Jagtvej were performed continuously by chemiluminescence using NO x Aerodyne API instruments.The urban background measurements were measured from a roof level measurement station approx.500 m from the street using similar instrumentation as the street level measurements.

Theoretical calculations
The resulting concentrations of inhomogeneous emissions as a function of street aspect ratio and emission inhomogeneity were calculated, using an Excel-version of OSPM, for 360 wind directions with wind speed and total emission approximately similar to the average conditions for Hornsgatan in order to generate comparable results.The calculations were performed on a hypothetical street canyon with two emission segments each covering half the width of the street.Subsequently the aspect ratio and the emission inhomogeneity were varied over a reasonable interval.

Model description
In the following sections the currently applied homogeneous and the tested inhomogeneous emission dispersion schemes will be described.This section does not contain a complete description of the OSPM model, for this the reader is referred to e.g.Berkowicz et al. (1997).However, sufficient details will be provided to understand the modifications in the model regarding handling the emission geometry.

The homogeneous emission dispersion scheme
To illustrate the modelling principles of OSPM, a typical street canyon situation is illustrated in Fig. 3. OSPM calculates the concentrations (C) at the wall side of the street canyon as a contribution from the street canyon (C street ) plus a contribution from urban background concentrations (C bg ).The contribution from the street canyon is subsequently a sum of a direct contribution (C dir ) plus a recirculating contribution (C rec ) 943 Introduction

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Full  (Berkowicz et al., 1997): It is a fundamental assumption of the model that when the wind blows over a rooftop in a street canyon an hourly averaged recirculation vortex is always formed inside the canyon as illustrated in Fig. 3.It is assumed that the ground level wind direction inside the recirculation zone is mirrored compared with the roof level wind direction, whereas outside the recirculation zone the wind direction follows the roof level wind direction as illustrated in Fig. 4.
The receptor at the leeward (marked with "1" in Fig. 3) side of the canyon is thus only exposed to a direct contribution from emissions inside the recirculation zone (unless the wind direction is close to parallel as described in Sect.3.1.1)plus a recirculating contribution, and the windward receptor (marked with "2" in Fig. 3.) is only exposed to a direct contribution from emissions outside the recirculation zone (Berkowicz et al., 1997) and from diluted recirculating emissions from inside the recirculation zone (Ketzel et al., 2014).

The direct contribution:
The direct contribution can be written on integral form as (Hertel and Berkowicz, 1989): Where C dir is the direct contribution, x start is the distance from the receptor where the plume has the same height as the receptor, which can also be zero in case h r ≤ h 0 , and x end is the upper integration limit as defined in Table 3, h 0 is the initial dispersion, h r is the height of the receptor (the height of the calculated concentration), Q is the Introduction

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Full emission flux (in g m −1 s −1 ), W is the width of the street, u street is the street level wind speed, and σ w is the vertical turbulence flux calculated as a function of the street level wind speed and the traffic produced turbulence.
The integration is performed along a straight line path against the wind direction as illustrated in Fig. 5. Equation ( 3) is used for calculating the direct contribution on both the leeward side and the windward side; however, the length of the integration paths can differ likewise as illustrated in Fig. 5.
In Fig. 5 it is assumed that x end = L rec , however, as shown in Table 3 this need not be the case.
For very long street canyons the plume will start dispersing out of the canyon at the top.In OSPM, this is assumed to happen when the plume height (σ z ) equals the general building height (H g ) (Ketzel et al., 2014) of the canyon.This point is called x esc and is defined as (Hertel and Berkowicz, 1989): Where H g is the general building height of the canyon.
Beyond the point x esc the contribution to the concentration at the receptor is assumed to decay exponentially with distance according to (Hertel and Berkowicz, 1989): Where σ wt is the roof level turbulence, and x end is the upper limit of the integral as defined in Table 3.The calculations and definitions of the critical lengths x start , x esc , L rec , and L max are summed up in Table 4.
For close to parallel wind directions the integration length (x end ) for the leeward side receptor (marked with "1" in Fig. 3.) is extended from L rec to L max to account for concentration resulting from emissions outside the recirculation zone.This is done when the Introduction

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Full factor f ext is greater than zero, and the contribution to the concentrations from the path outside the recirculation zone is then multiplied by f ext (Hertel and Berkowicz, 1989) 1 : where θ street is the angle between the street and the street level wind direction.

The recirculating contribution
The recirculating contribution is parameterized as a box model, where it is assumed that the inflow of pollutants equals the outflow of pollutants as illustrated in Fig. 6.The inflow of pollutants is the emission density in the street multiplied by the integration length L base (Berkowicz et al., 1997): Where L base = min(L rec , L max ).The recirculation zone is modelled as a trapezium with the upper length being half of the baseline length.The outflow from the box model is thus the ventilation at the top of the recirculation trapezium σ wt L top plus the ventilation at the hypotenuse of the trapezium (σ hyp L hyp ) as illustrated in Fig. 6 (Berkowicz et al., 1997): Where C rec is the recirculating concentration contribution and σ hyp is the average turbulence at the hypotenuse.Equations ( 7) and ( 8) can now be solved for the recirculating

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Full concentration by setting the inflow equal to the outflow:

The inhomogeneous emission dispersion scheme
In order to facilitate the modelling of streets with inhomogeneous emission distributions, the street was divided into a number of parallel segments as illustrated in Fig. 7.
The model user will define the width and the emission strength of each segment.This means that the above presented integrals become divided into a number of integrals and subsequently summed to yield the final concentration.The direct contribution thus becomes: Where n end is the segment number of the last segment influencing the receptor, n start is the first segment to influence the concentration at the receptor, W i is the accumulated width of the segment calculated from the receptor, and W i is the accumulated width of the segment calculated along the integration path from the receptor.The exponentially decaying concentration contribution from segments further away than x esc from the receptor becomes: The recirculating contribution becomes: In the homogeneous emission scheme the limits of the integrals are determined by the street geometry and the recirculation zone geometry.In the inhomogeneous scheme the limits of the integrals are always W i −1 and W i .Instead the limits of the sum determine which segments contribute to the concentration at the receptor.
As seen from the lack of y dependence in Eqs. ( 3) and ( 10), the model does not contain expressions for horizontal dispersion.In the original model this was unnecessary since the emissions were homogeneous in the entire canyon.In order to model sidewalks or similar segments with zero emission, horizontal dispersion has to be implemented in the model.This is the case due to the geometry of the canyon, meaning that as the wind direction approaches parallel, the integration length quickly approaches zero thus leading to zero concentration.Introducing horizontal dispersion in OSPM was however deemed outside the scope of the present study.In the following cases the streets are therefore divided into segments covering both the traffic lanes and the sidewalks.It would be possible to divide the street into more segments to model the individual traffic lanes.However, either the emission of the inner lane had to be distributed over the sidewalk as well, leading to a too low emission density, or the two lanes would have to be of equal width meaning that the segment division would not correspond to the traffic lane division.To avoid these methodological difficulties, it was decided to model the streets as two segments.Introduction

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Full  5 and for the South side receptor in Table 6.
As can be seen from Tables 5 and 6, there is a noticeable change in the performance of the model when moving from homogeneous emissions to inhomogeneous emissions, but only very little difference between the two approaches for modelling inhomogeneous emissions.This confirms the assumption made in Sect.2.1 that the emission distribution at Hornsgatan is not, to any significant extend, influenced by diurnal variations.It is also noticeable that the increase in performance is especially pronounced for the North side receptor where the FB is markedly improved and the NMSE is improved as well.For the South side receptor a smaller improvement is seen in FB.Conversely, moving from homogeneous emissions to inhomogeneous emissions has almost zero impact on the correlation coefficient on both sides and only a smaller effect on the NMSE on the north side.
The results are, however, confounded by the modelled street level contributions to the concentrations decline whereas the measured concentrations are almost stable.This effect is especially seen on the North side receptor and to a smaller extend on the South side receptor.This effect can most likely be ascribed to the emission model performance, since the effect is time dependent, and no interannual change in wind speed or direction is found (data not shown).Most likely the emission model is predicting too optimistic reductions for the modern EURO 5/6 vehicles that are not obtained under real-world driving conditions as reported in literature (Carslaw et al., 2011).This is also underlined by the fact that the traffic counts from the inductive loop technology matches fairly well with the camera recordings from 2009.The camera recordings were done over three months where individual cars were identified and compared with reg-949 Introduction

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Full ister data (Burman and Johansson, 2010).This means that the total traffic counts must be considered reasonably accurate.Since the vehicle distribution for the year 2009 is known very accurately from the camera recordings, this is probably not the explanation either.This leaves a change in traffic flow situation (levels of service) or a difference between the actual and modelled vehicle fleet; in terms of age composition, emissions as a function of slope, or other factors; over time as possible explanations for this discrepancy.
The wind direction dependency of the concentrations is shown in Fig. 8.As can be seen, the impact of moving from homogeneous emissions to inhomogeneous emissions is largest for parallel wind directions, where each receptor is only exposed to one emission segment.For perpendicular wind directions there is a small difference when the uphill emissions are close to the North side receptor and no difference when it is further away.A similar pattern is seen for the South side receptor with 180 • displacement.The wind direction plot shows a noticeable discrepancy between the model and the measurements around 200 • for both receptors.Gidhagen et al. (2004) states that horizontal dispersion is underestimated in the applied κ-ε CFD model, and that this is the cause of this discrepancy.If this is the case the underestimation will also appear in the present wind direction plots due to the lack of horizontal dispersion in OSPM.The weekly variation in concentrations is shown in Fig. 9.The general diurnal variation plus the difference between weekdays and weekends are reproduced well by the model.As can be seen, the two approaches to inhomogeneous emission modelling are almost indistinguishable.It can also be seen from the figure that the impact of inhomogeneous emissions is largest during day time where the concentrations are largest.Figure 9 shows as well that the diurnal variation is not reproduced in detail.On the North side, the morning rush hours and the evening hours are still underestimated, whereas the night time concentrations are underestimated.Moreover, the figure indicates a faster diurnal change in the modelled concentrations as compared to the measured concentrations.This probably has to do with the way the traffic flow situation is

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Full modelled as four discrete categories, whereas real traffic will behave like a continuum.This is a potential area of improvement for a future study.Certain times of the week are also clearly wrong most noticeably Saturday afternoon on the North side receptor and Saturday morning on the South side receptor.This is likewise a potential area of improvement in a future study.

Jagtvej
The diurnal variation in personal cars and emissions for the two driving directions is shown in Fig. 10.As can be seen the emissions follow the variation in personal cars fairly close.The deviations can be explained by the diurnal variation in heavy duty vehicles (not shown).As can be seen, the largest inhomogeneity arises in the morning rush hour.Moreover, it can be seen that the emissions have declined substantially from 2003 to 2013.
The diurnal variation in measured and modelled concentrations for weekdays for the two years is shown in Fig. 11.As can be seen, the change from homogeneous to inhomogeneous emissions only have a small influence on the concentrations around rush hour from 8-9 a.m.Here the difference between the homogeneous and the inhomogeneous emissions is approximately 6 ppb.As also seen from the graph, the model tends to overestimate the emissions in 2003, whereas the 2013 emissions seem fairly correct.The poor model performance for 2003 have to do with the way the model has previously been calibrated to match the measurements and is an area of improvement for a future study.
The average concentration as a function of wind direction for the morning rush hour for the two years is shown in Fig. 12.As can be seen, the difference between the homogeneous and the inhomogeneous emission is approximately homogeneously distributed among the different wind directions with difference up to 7 ppb.When averaging over the two years, the emission biases balance each other, and gives a clearer picture of the wind direction dependency.Introduction

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Theoretical calculations
The results of the theoretical analysis of the concentration dependency of the emission inhomogeneity are shown in Fig. 13.As can be seen, the larger the emission difference between the two segments, the larger the difference in concentration.As also shown for Hornsgatan, the largest difference is seen for near-parallel wind directions.However, bearing in mind the scale of the y axis, the differences are small.The inhomogeneity at Jagtvej corresponds to approximately 10 ppb and for Hornsgatan to approximately 20 ppb, orders of magnitude also confirmed by Figs. 8 and 12.The comparison with measurement will however give a smaller difference, since the real world data are averages of many different wind speeds and emissions.
The impact of the street canyon aspect ratio on the concentrations resulting from inhomogeneous emissions is shown in Fig. 14.As seen, the impact is largest for high aspect ratio (building heights larger than street width) canyons.This is natural since "the street canyon effect", where the impact of the recirculation zone means larger concentrations for the leeward side compared to the windward side, is larger for high aspect ratio canyons.As such, the impact of inhomogeneous emissions will also be larger for high aspect ratio canyons.

Conclusions
The present study presented an approach to, and analysed the impact of, implementation of inhomogeneous emissions in a semi-parameterized street canyon model (OSPM).The results were validated against two real world data-sets: one being inhomogeneous as a result of the slope of the street and the other being inhomogeneous as a result of rush hours.Moreover, the impact of emission inhomogeneity and street aspect ratio was analysed theoretically.
The results showed that the model including inhomogeneous emissions were better able to reproduce the measured values on the two real-world streets.The impact of Introduction

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Full the inhomogeneous emissions was largest for the sloping street and the largest effect was seen for near-parallel wind directions.The results for both streets were however influenced by other factors as well, most likely uncertainties in the emissions, which led to less clarity in the results.Overall the adoption of inhomogeneous emissions leads to a performance increase of up to 15 % in fractional bias at the north side receptor of Hornsgatan and a difference in street level contribution of up to 8 ppb.For Jagtvej the difference was shown to be up to 7 ppb in the morning rush hour.

Future work
The present study showed a potential for obtaining an improvement in model performance by introducing inhomogeneous emissions in models like OSPM.Two model elements are of immediate interest in relation to the present work: -At present the receptor is located at the wall of the street.In reality measurement stations are often located several meters from the wall leading to a shorter dilution of the emissions and thereby a higher concentration.Being able to move the receptor freely in the cross-canyon direction could potentially lead to a model performance improvement.
-At present the model does not facilitate the inclusion of zero emission segments such as pedestrian areas.As described in Sect.3.2, this means that an accurate description of a road like Hornsgatan, where traffic counts exist for all four lanes, is not yet possible.Introducing horizontal dispersion in the model will thus potentially make it possible to describe streets like Hornsgatan more accurately.Full   3) (x end ) and Eq. ( 5) (x end ).The definition and calculation of the lengths can be found in Table 4. Magnitude:

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Full  4. Table of the critical lengths along the integration path.These lengths determine the upper and lower limit of the integrals in the homogeneous emission dispersion scheme and of the sums in the inhomogeneous emission dispersion scheme.Moreover, they determine if the dispersion should be calculated according to Eqs. (3) or ( 5) plus whether the concentration should be multiplied with f ext as defined in Eq. ( 6).f red is the shortening function as defined in Eq. ( 6), H u is the upwind building height, θ street is the wind direction compared to the street direction, θ l is the critical wind direction as illustrated in Fig. 5, W is the street width, L b is the length from the receptor to the end of the street as illustrated in Fig. 5, and h r is the height of the inlet of the receptor above street level.
Name: Expression: Description: Length of the recirculation zone Length where the plume starts to disperse vertically out of the canyon.Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | inductive loop technology (Marksman 660 Traffic counter and Classifier, Golden River Traffic Ltd., UK).It provides hourly mean total traffic counts, classification of vehicles based on the length of the vehicle, plus mean speed on a lane by lane basis.The 939 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | the Fractional Bias (FB), and the Normalized Mean Square Error (NMSE) for the homogeneous and the exact-and proportional inhomogeneous schemes at Hornsgatan for the years 2007-2009 are shown for the North side receptor in Table Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Table 2 .
Level of service as a function of total number of vehicles per hour based on(Vägverket  and SMHI, 2007).

Table 3 .
Table of upper integration limits for respectively Eq. (

Table 5 .
Correlation coefficient, Fractional Bias, and Normalised Mean Square Error for the years 2007-2009 for the North side receptor."Exact" and "Proportional" refer to the emission modelling approaches described in Sect.2.1.Moreover, the measured and modelled annual mean NO x concentrations for the individual years are also shown.These are calculated as local street contribution only i.e. the background concentration subtracted from the measured/modelled street concentration to reflect the street contribution.