Semi-parameterized street canyon models, as e.g. the Operational Street
Pollution Model (OSPM®), have been
frequently applied for the last two decades to analyse levels and
consequences of air pollution in streets. These models are popular due to
their speed and low input requirements. One often-used simplification is the
assumption that emissions are homogeneously distributed in the entire length
and width of the street canyon. It is thus the aim of the present study to
analyse the impact of this assumption by implementing an inhomogeneous
emission geometry scheme in OSPM. The homogeneous and the inhomogeneous
emission geometry schemes are validated against two real-world cases:
Hornsgatan, Stockholm, a sloping street canyon; and Jagtvej, Copenhagen;
where the morning rush hour has more traffic on one lane compared to the
other. The two cases are supplemented with a theoretical calculation of the
impact of street aspect (height
Semi-parameterized models as e.g. the Operational Street Pollution Model (OSPM®; Berkowicz et al., 1997) have been frequently applied in cities around the globe over the last 20 years (Assael et al., 2008; Berkowicz et al., 1996, 2006; Ghenu et al., 2008; Gokhale et al., 2005; Hertel et al., 2008; Kakosimos et al., 2010; Ketzel et al., 2012; Kukkonen et al., 2000; Vardoulakis et al., 2005). This type of model has the advantages of low input requirements and short execution times. This means that the model can cover many streets over long time periods due to its low computational demand.
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
Moreover, Kakosimos et al. (2010) and Vardoulakis et al. (2007) suggested that an improvement in the applicability of semi-empirical street level air quality models could be achieved by implementation of an inhomogeneous emission geometry scheme.
The present study is therefore based on the following research question:
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.
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.
Overview of the properties of the two street canyons used for validation of the dispersion schemes in the study. There is a measurement station (receptor) at each side of the street in Hornsgatan, but only one measurement station on the east side of Jagtvej.
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
The years 2007–2009 were chosen for Hornsgatan given that the use of studded tires has been banned on this street since 2010, 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 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 (Larssen et al., 2007; Moussiopoulos et al., 2005, 2004) and has thus been subject of a number of modelling studies (e.g. Denby et al., 2013a, b; Johansson et al., 2009; Ketzel et al., 2007; Olivares et al., 2007). 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., 2011, 2012; Silver et al., 2013).
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 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 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 exact technical reason for this factor is not known. However, comparisons between the Marksman counter and manual counts and between the Marksman counter and automatic camera recordings (Burman and Johansson, 2010) have confirmed the validity of this factor.
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
(
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
The traffic flow situation (called “level of service” in HBEFA) was modelled as a set of discrete categories. This was done by categorising the individual hour based on the total number of vehicles in the hour. The categorisation was performed based on the scheme from the ARTEMIS model reprinted in Table 2.
Level of service as a function of total number of vehicles per hour based on Vägverket and SMHI (2007).
In setting up OSPM, the street was divided into two emission segments of equal width, each segment covering two traffic lanes, although the inhomogeneous emission scheme described in Sect. 3.2 allows for any number of segments. 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 Hornsgatan 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” approach. The inhomogeneity thus only arises from the
slope of the street. This approach is useful if directional- or lane-divided
traffic counts do not 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
To analyse if the emissions distribution between the north side and the south
side of the street can be modelled as a constant ratio, an analysis of
measurements for near-parallel (
As seen in Fig. 1, the distribution of concentration ratios between the northern and southern sides of the street is skewed with the mode being around 1.2 and the mean value being 3.2. This result is not too far from the result presented by Gidhagen et al. (2004), where the emissions on the north side were 3 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 side and the south side of the street.
The hypothesis of a constant ratio distribution will be fortified if the ratio is not changing systematically with time.
The diurnal and weekly variation of the ratio 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.
Manual traffic counts next to the measurement station at Jagtvej were performed respectively in 2003 and in 2013. The traffic was counted in two directions on a weekday for 24 h in 2003 and between 07:00 and 19:00 LT 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 (COmputer Programme to calculate Emissions from Road Transport; 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 (
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.
Histogram of ratio between the north side and south side receptors for near-parallel wind directions for Hornsgatan, Stockholm.
Diurnal and weekly variation in the mean ratio between the
concentrations for the north side and south side receptors for near-parallel wind
directions with wind speeds above 2 m s
The NO
The resulting concentrations of inhomogeneous emissions as a function of street aspect ratio and emission inhomogeneity were calculated for 360 wind directions with wind speed and total emissions 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.
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.
To illustrate the modelling principles of OSPM, a typical street canyon
situation is illustrated in Fig. 3. OSPM calculates the concentrations
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 (1) side of the canyon is thus exposed both 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) and a recirculating contribution, and the windward receptor (2) is exposed to a direct contribution from emissions outside the recirculation zone (Berkowicz et al., 1997) and to diluted recirculating emissions from inside the recirculation zone (Ketzel et al., 2014). In the case where the recirculation zone occupies the whole street canyon, the leeward (marked with “1” in Fig. 5) side of the canyon will be exposed to both a direct and a recirculating contribution, whereas the windward receptor (marked with “2” in Fig. 5) will only be influenced by the recirculating contribution.
Cross section of a street canyon. The figure illustrates the governing flow patterns as modelled in OSPM. The two receptors are marked with red diamonds. In the figure the recirculation zone occupies the whole canyon although this need not be the case as e.g. shown in the following figures. Figure modified from Silver et al. (2013).
Schematic view of a street canyon seen from the top. The arrows represent the wind directions as modelled in OSPM. The length of the arrows are not proportional to the wind speed. The blue arrows are rooftop wind directions and the red arrows are street level wind directions. The receptors are marked with red diamonds.
The direct contribution can be written in integral form as (Hertel and
Berkowicz, 1989)
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
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
(
Illustration of the integration paths (red dotted lines) for an
arbitrary wind direction for the two receptors in the canyon. The upper blue
dotted line marks a critical wind direction (
Table of upper integration limits for respectively Eq. (3)
(
Table of the critical lengths along the integration path. These
lengths determine the upper and lower limits 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
For close to parallel wind directions the integration length ( In Hertel
and Berkowicz (1989)
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
Cross section of a street canyon with the dimensions of the recirculation zone illustrated. The red arrows represent the street level wind direction. Based on Hertel and Berkowicz (1989, p. 69).
For regular street canyons (height to width ratio close to one) the recirculation zone will occupy the majority of the canyon. This means that, for a large wind direction interval, the integration length for the leeward receptor will be significantly longer than the integration length for the windward receptor. Furthermore, the leeward receptor will be exposed to the full recirculating contribution, while the windward receptor only receives a further diluted recirculating contribution. These two effects mean that the leeward receptor will experience significantly higher concentrations than the windward receptor for a large wind direction interval.
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
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. At runtime the model calculates several distances
(e.g.
Illustration of the division of the street canyon into a number of
segments with accumulated widths
Statistical quantities for the south side receptor. Same definitions as in Table 5.
Mean NO
The exponentially decaying concentration contribution from segments further
away than
The recirculating contribution becomes
As seen from the lack of
The correlation coefficient (
Weekly variation in NO
Diurnal variation for weekdays in personal cars per hour and total
NO
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 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.
Diurnal variation in NO
The results are, however, confounded by the modelled street level contributions to the concentration's 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). It is most likely that 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 3 months where individual cars were identified and compared with register 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
Average NO
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 the daytime, 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 modelled as four discrete categories, whereas real traffic will behave like a continuum. This is a potential area of improvement for a future study.
Theoretical calculation of the concentration for the two receptors of a street canyon with two emission segments each covering half the street width and an aspect ratio of one as a function of the emission inhomogeneity and wind direction. Receptor 1 is marked in green and receptor 2 is marked in blue. The inhomogeneity is given as percentages of the total emission for the two segments and the inhomogeneous case is marked with dotted lines.
Theoretical calculation of the concentration of the two receptors for a street canyon with an emission inhomogeneity of 70 % (north going)/30 % (south going) as a function of aspect ratio (AR) and wind direction. Receptor 1 is marked in green and receptor 2 is marked in blue. The case with the high aspect ratio is marked with dotted lines.
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.
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 between the variations in emissions and number of cars can be explained by the diurnal variation in heavy duty vehicles. The data show the largest inhomogeneity between north and south directions in the morning rush hour. Moreover, the plots show that the traffic and the corresponding emissions have declined substantially from 2003 to 2013.
The diurnal variations in measured and modelled concentrations for weekdays for the 2 years are shown in Fig. 11. As expected, the change from homogeneous to inhomogeneous emissions only has an influence on the concentrations around rush hour from 08:00 to 09:00 LT, where also traffic is inhomogeneous. However, the difference between the homogeneous and the inhomogeneous emissions is relatively small, 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 has to do with the way the model has previously been calibrated to match the measurements. This means that the emissions used in the present study are markedly different from the emissions used when the model was designed. Adapting the model to the new emissions was deemed outside the scope of the present study and an area of improvement for a future study.
The average concentration as a function of wind direction for the morning
rush hour for the 2 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 2 years, the emission
biases equilibrate each other and give a clearer picture of the wind
direction dependency. When looking carefully at the graph it can be seen that
the difference in concentration between homogeneous and inhomogeneous
emissions is slightly larger for parallel compared to perpendicular
directions. The spike in the measurements around 100
A set of theoretical calculations were performed to clearer illuminate the
impact of inhomogeneous emissions without the confounding variables
influencing the results of the real street canyons. The calculations are
performed with a wind speed of 3.5 m s
The impact of the street canyon aspect ratio on the concentrations resulting from inhomogeneous emissions is shown in Fig. 14. The impact is largest for high aspect ratio (building heights larger than street width) canyons. This is expected, 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.
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 as a result of inhomogeneous directional traffic during rush hours. Moreover, the impacts of emission inhomogeneity and street aspect ratio were analysed theoretically.
The results showed that the model including inhomogeneous emissions was better able to reproduce the measured values on the two real-world streets. The impact of 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.
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 on the wall of the street. In
reality, measurement stations are often located several metres 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.
Name of the software: WinOSPM (Windows version of the Operational Street Pollution Model, OSPM).
Developer: Department of Environmental Science (ENVS), Aarhus University, Denmark.
Contact address: Aarhus University, Department of Environmental Science Frederiksborgvej 399, 4000 Roskilde, Denmark.
E-mail: ospm@au.dk
Operational System: Microsoft Windows 7 or later.
Software requirements: None.
Hardware requirements: At least 100 Mb free hard drive space and 1 Gb RAM.
Programming language: Visual Basic 6 combined with linked libraries written in Fortran 77.
Availability and cost: WinOSPM is a commercial software requiring licensing.
Information on the actual licensing conditions is given at
T.-B. Ottosen, M. Ketzel, K. E. Kakosimos, C. Johansson, R. Berkowicz, O. Hertel, and J. Brandt participated in setting up the study concept and the study design was done by T.-B. Ottosen, M. Ketzel, K. E. Kakosimos, C. Johansson, and R. Berkowicz. T.-B. Ottosen did the implementation of inhomogeneous emissions in OSPM with input from M. Ketzel and K. E. Kakosimos. T.-B. Ottosen conducted the data analysis with contributions to analysis and interpretation from M. Ketzel, K. E. Kakosimos, and C. Johansson. C. Johansson furthermore provided access to data from Hornsgatan and T. Ellermann provided access to data for Jagtvej. S. S. Jensen provided input on the traffic profile for Jagtvej. H. Skov and K. E. Kakosimos obtained funding for the study. T.-B. Ottosen wrote the article manuscript. All the co-authors participated in the interpretation of the results, provided critical comments to the manuscript, and read and approved the final manuscript.
This publication was made possible by a NPRP award [NPRP 7-674-2-252] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.
The HPC (High Performance Computing) resources and services used in this work
were provided by the IT Research Computing group in Texas A&M University
at Qatar. IT Research Computing is funded by the Qatar Foundation for
Education, Science and Community Development
(