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Assessing the environmental impact associated with different trip purposes
Transportation Research Part D 18 (2013) 110–116
Contents lists available at SciVerse ScienceDirect
Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Assessing the environmental impact associated with different trip purposes C. Beckx a,⇑, W. Lefebvre a, B. Degraeuwe a, M. Vanhulsel a, B. Kochan b, T. Bellemans b, S. Dhondt c, L. Int Panis a,b a
Flemish Institute for Technological Research, Boeretang 200, 2400 Mol, Belgium Transportation Research Institute, Faculty of Applied Economics, Hasselt University, Wetenschapspark 5 Box 6, B-3590 Diepenbeek, Belgium c Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussel, Belgium b
a r t i c l e
i n f o
Keywords: Trip purpose Vehicle emissions Vehicular environmental impacts Timing of trips
a b s t r a c t We examine how trips with diverse motivations vary in their spatio-temporal characteristics and result in different impacts on the environment. An integrated model chain is used includes an activity-based traffic demand model, an emission model and a pollutant concentration module. The model setup is applied to the northern region of Belgium and analysed for NO2, an important transport-related air pollutant. The results demonstrate that not every vehicle kilometer has the same impact on the environment in terms of emissions and concentration increases. We find that the highest concentration increase per kilometer is produced by work-related trips. Trips for shopping purposes and services produced the lowest marginal concentration increase. The difference between the highest and lowest marginal concentration increases is about 40%. Important explanatory factors include the time of day when a trip is made as well as vehicle type and speed. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Source-related measures in reducing the environmental impact of transport are important; unlike technological measures, they intervene in the problem at an earlier stage. In the context of road traffic air pollution, a focus on the source of the problem involves examining people’s travel behaviour. The environmental impacts of car use depend not only on the vehicle type and its speed, but also on trip timing. The timing of a trip determines the impact of emissions on the resulting pollutant concentration levels. Pollutants emitted during trips at night or early in the morning have a different impact on ground-level concentrations than daytime exhaust emissions due to the atmospheric conditions.1 Since trips with different purposes may differ in aspects such as vehicle speed and trip timing, it is also reasonable to assume that the marginal environmental impact of one kilometer travelled may differ depending on trip purpose. This paper examines the marginal environmental impact of different vehicle trips in terms of emissions and concentration increases. 2. Model chain We apply a model chain to assess the spatial–temporal aspects of travel behaviour and its related environmental impacts, expressed in terms of emissions and pollutant concentrations. The chain includes an activity-based model for assessing tra⇑ Corresponding author. Fax: +32 14 32 11 85. E-mail address: [email protected] (C. Beckx). Int Panis and Beckx (2007) also find that emissions at night cause a larger increase in pollutant concentrations than those during the day due to varying meteorological parameters and their related conditions for dispersion. 1
1361-9209/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.trd.2012.10.002
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vel behaviour, a traffic emission model for converting trips into emissions, a concentration interpolation tool that employs air quality data and a bi-Gaussian plume dispersion model that produces detailed pollutant concentration maps and subsequently enables assess of the impact of each trip on the resulting concentration levels. We used model input data for 2007 and delineated a study area that includes the Flanders region and Brussels, a densely populated area of 13,522 km2 with about 7.4 million inhabitants. Trips are classified according to trip purpose. A complete technical description of all models applied is beyond the scope of this paper and can be found elsewhere. The following sections therefore provide only a brief overview of the most important modelling aspects. More detailed information on the technical aspects of each model can be found in Lefebvre et al. (2011) and/or in selected references (as indicated in the sections below). An evaluation of the air quality results produced by this model chain indicated positive validation, which means that the simulated concentration levels correspond to measured concentration values with sufficient accuracy. 2.1. Simulating travel behaviour The activity-based model ‘Forecasting Evolutionary Activity-Travel of Households and their Environmental RepercussionS’ (FEATHERS) is a microscopic, agent-based simulation framework (Bellemans et al., 2010). It simulates origin–destination matrices for all agents in a synthetic population based on activity diary data and demographic and socio-economic inputs. Here, the origin–destination matrices covering 1145 population zones are simulated for a synthetic population representing about five million adults living in Flanders and Brussels. The matrices are estimated per hour of the day for an average week. Using a traffic assignment algorithm from the Transcad transport model (Caliper), they are the assigned to the geographic road network following Beckx et al. (2009). The purpose for each trip is defined according to the activity performed at the trip’s end using the following:
work: going to work outside of the home; home: returning home from activities outside of the home; bring/get: bringing or getting other people, for instance, taking children to school; shopping: shopping for everyday and other goods; services: going to the post office, bank, etc.; leisure: going to the gym, taking music lessons, etc.; social: going out with friends, visiting family, going to a concert, etc.; touring: leisure based on the transport itself, e.g. taking the car for a drive; transit: traffic travelling to or from destinations outside of Brussels or Flanders; other: all traffic that does not fall into one of the categories above, e.g. going to church; freight: Information on freight transport is provided by the freight model for Flanders (Borremans et al., 2008).
2.2. Converting trips into emissions The traffic loads provided by the Feathers-Transcad modelling step are converted into vehicle emissions by the MIMOSA4 emission model; the most recent version of the traffic emission model MIMOSA (Mensink et al., 2000) and relies on the COPERT4 methodology (Gkatzoflias et al., 2012) for the emission functions and calculating energy consumption. MIMOSA requires information on vehicle fleet composition, traffic intensity and average speeds at a resolution of 1 h for each road segment in region. It then generates hourly emission output for emissions, such as NOx, PM10 and PM2.5. The model calculates not only total emission values but also geographically distributed emissions over time. 2.3. Calculating pollutant concentration levels This step, converts the spatially and temporally distributed emissions from the MIMOSA emission model into detailed pollutant concentration levels. Because of the need not only to simulate air pollution with sufficient spatial detail to account for the steep concentration gradients along motorways and major point sources, but also to take into account regional air pollution gradients, the land use model (RIO) and the bi-Gaussian plume model immission frequency distribution model (IFDM) is used. RIO is a validated land-use model for Flanders and the Netherlands (Janssen et al., 2008), estimating hourly pollutant concentrations in a 3 3 km2 area on the basis of data collected by the official fixed-site monitoring network and a land usederived covariate. RIO produces hourly concentration maps for PM10, PM2.5, NO2, O3 and SO2. Based on these, annual pollutant concentration statistics can be derived. RIO has proved to be highly accurate in estimating pollution over Belgium. Here, background concentrations are provided by this method. The IFDM model is a bi-Gaussian plume model, designed to simulate non-reactive pollutant dispersion on a local scale. An additional chemistry module within the model describes the chemical equilibrium of nitrogen oxides and ozone. As IFDM is a receptor model, it can be used for both regular and irregular
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grids. In addition to the standard 1 1 km2 grid an irregular line source-following grid is defined to account for the steep concentration gradients found alongside motorways (25 800 m2 close to roads).2 RIO and IFDM, are combined to embrace both the regional aspects of air pollution and the local concentration gradients near major line and point sources. A combination procedure, which eliminates the double counting of traffic emission sources, is generally carried out by eliminating the marginal concentration increase that results from the traffic emissions, at a lower resolution, before adding the concentrations caused by these emissions. To discern the implications of a trip purpose, the RIO–IFDM combination procedure is adapted. The subtraction step (eliminating the marginal concentration increase caused by the traffic emissions) is initially carried out, but then, rather than adding the effect of all traffic emissions to the concentrations, only those emissions not caused by the trip purpose we wished to investigate are added. Then, the effect of this purpose on pollutant concentrations can be discerned in the difference between the model run with the emissions from all trip purposes and the run with one purpose eliminated. The advantage of this methodology is that the chemical equilibrium remains comparable (which would not be the case if we eliminated all purposes except the one we wished to investigate). The small deviation from the chemical equilibrium can be compensated for using the chemistry model incorporated in IFDM.
3. Results 3.1. Transportation data Hourly origin–destination matrices are predicted using the FEATHERS travel demand model. Distinctions are made between trips with different trip purposes. Kilometers travelled are obtained by assigning these matrices to a road network using an equilibrium traffic assignment. Results are analyzed per trip purpose, both temporally and geographically. Trips to home represent the largest portion of kilometers travelled, followed by work-based trips, transit traffic and freight transport. This distribution can vary significantly depending on whether it is a weekday or weekend and on the time of day. Fig. 1 presents the distribution of vehicle kilometers per trip motive per time of day, and includes daily patterns of vehicle kilometers for an average weekday and an average weekend. The number of vehicle kilometers travelled is displayed per hour and per trip purpose. On the weekday graph, the morning and evening traffic peaks are clearly visible. The morning traffic peak constitutes mainly of work-related traffic and the evening peak largely represents people returning home. Unsurprisingly, trips for shopping are made during opening hours: mainly between 10 a.m. and 8 p.m. in Belgium. Furthermore, freight traffic is more pronounced between 6 a.m. and 8 p.m. At weekends, the number of vehicle kilometers travelled is less than during the week. Fig. 1 shows how the distribution of trip purposes can change during the day and from day to day. Certain types of trip take place mainly during peak hours, while others occur in a more scattered way throughout the day. There are also reasons to believe that people use road types depending on the trip purpose (e.g. local, urban roads for performing nearby activities versus motorways for long-distance trips). Since both aspects (timing and road type) will influence the driving speed (which has implications for the resulting vehicle emissions), another interesting aspect to explore is the distribution of vehicle speed per trip purpose. In Fig. 2 the percentage of kilometers travelled per trip purpose is therefore presented using speed intervals. Vehicle speeds are classified into four categories: under 51 kph, between 51 and 70 kph, between 71 and 90 kph, and over 90 kph (corresponding to the most common speed limits in Belgium). We see significant differences between trip purposes. For instance, in the highest speed class, where a large portion of the kilometers are driven, we see that freight transport and transit trips clearly dominate. Compared to the other trip motivations, on the other hand, kilometers driven for services or shopping are represented to a much lesser degree in this highest speed class.
3.2. Vehicle emissions Regards emissions of NOx, total emission values per trip purpose will depend mainly on the number of kilometers travelled. Since we focus on the marginal impact that each trip purpose has on the environment, however, Fig. 3 shows NOx emissions per kilometer for the trip purposes. The results per trip purpose are expressed in relation to the average NOx exhaust emissions for non-freight trips (distance-weighted), which is 0.722 g/km. Evidently, much higher emissions per kilometer are found for freight transport than for other trip purposes, which are mainly associated with light-duty vehicles. Due to the large difference between emissions from freight transport and passenger cars, the value for freight transport (6.282 g/ km) is not shown on the graph. Fig. 3 shows differences by comparing emissions for the other trip purposes. The highest emissions per kilometer are for transit trips, followed by trips for touring purposes. The lowest emission values are found for shopping trips and service-related trips. 2 This approach is similar to the methodology used by Lefebvre et al. (2011) and ensures that more receptor points would be available at locations where the largest gradients are expected.
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Fig. 1. Vehicle kilometers travelled per trip purpose per time of day: distribution for an average weekday (top) and distribution for an average weekend day (bottom).
Fig. 2. Speed distribution by trip purpose as a percentage of kilometers travelled by trip purpose over speed intervals.
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Fig. 3. NOx emissions per trip purpose per travelled kilometer. Note: Results are expressed in relation to the average exhaust emissions per kilometer for non-freight trips.
3.3. Pollutant concentrations Every motorised vehicle trip producing exhaust emissions impacts on the concentration of pollutants in the air. The impacts, however, depend not only on the amount of emissions but also on factors such as wind speed and direction, meteorology, and background concentrations. Consequently, it is useful to analyse both the concentration increase per trip purpose and the marginal increase per kilometer per purpose. The absolute concentration increase in the annual mean NO2 concentration caused by road traffic in 2007 in the Northern part of Belgium is estimated at 3.76 lg/m3. Fig. 4 shows that a large percentage of the concentration increase is caused by freight transport. Furthermore, trips to work and home are also responsible for a large share of the concentration increase because of the large number of vehicle kilometers travelled for these purposes. In Fig. 5, the NO2 concentration increase per travelled kilometer is seen for the various trip purposes. The concentration change per travelled kilometer reflects the marginal impact of a vehicle kilometer on the yearly averaged pollutant concentration level. The results per trip purpose are expressed in relation to the average distance-weighted concentration increase for non-freight trips, which is 0.043 lg/m3 per billion vehicle kilometers on a yearly basis. Trips for freight transport have a larger impact on the concentration than light-duty trips due to the higher emission rates. The value for freight transport, not seen in the figure, is 0.385 lg/m3 per billion freight kilometers. The highest concentration increase per kilometer is for workrelated trips, followed by trips to home and transit traffic. Trips for shopping purposes and services produced the lowest marginal concentration increase.
Fig. 4. Distribution of the mean absolute concentration increase in NO2 caused by road traffic, valid for the yearly averaged NO2 concentration in 2007.
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Fig. 5. NO2 concentration changes per trip purpose per travelled kilometer. Note: Results are expressed in relation to the average concentration increase per billion vehicle kilometers on a yearly basis.
4. Discussion To examine the impact of trip purposes on the environment, we analysed the marginal impact of a vehicle kilometer in terms of emissions and concentration increases. As regards the impact on emissions, results show significant differences between NOx emissions caused by light-duty vehicles and those caused by heavy-duty freight vehicles because of the latter’s higher fuel consumption rates. When we compare emission associated with the various trip purposes by passenger car users, however, we find that factors other than vehicle type also result in differences; they vary from 0.704 g/km for shopping and service-related trips to 0.756 g/km for transit trips. Touring trips are also found associated with increased exhaust emissions per kilometer, about 0.735 g/km. When the distribution of vehicle kilometers over speed intervals is examined we see that vehicle speed, and specifically the number of kilometers driven in the highest speed interval may explain the differences in emissions. At the highest speed interval, at which a large share of driving takes place, differences emerge between trip purposes. Transit trips and trips for touring, for instance, are mainly driven at high speeds, whereas trips for shopping and services are seldom at speeds above 90 kph. Furthermore, in line with COPERT’s U-shaped emission functions (Gkatzoflias et al., 2012), these higher vehicle speeds will also result in higher emissions. We do not, however, take into account two issues related to emission calculation. First, emissions for non-freight trips are based on the characteristics of the average vehicle fleet. We considered relevant statistics on passenger car data such as mileage per fuel type, age, and Euro standard, but fleet composition may vary according to journey purpose; the type of vehicle used for shopping may differ from that used for work trips or when picking up children, but no data is available on this. Secondly, the calculations assume that every vehicle kilometer is driven with the same proportion of hot and cold starts. The MIMOSA emission model takes into account average trip length to assess the additional amount of cold-start emissions in the area and then distributes the additional exhaust emissions uniformly over the kilometers travelled. No distinction is thus made between various trips purposes even though, for example, trips to work may involve more cold starts after car spending a night standing on the driveway. Analyses of the impact of vehicle kilometers on NO2 concentration indicates that, in terms of annual average concentrations for the area, kilometers done are responsible for an absolute increase of 3.76 lg/m3, with large part of the increase caused by freight traffic. Regarding non-freight trips, Fig. 5 shows that trip purposes produce varying marginal impacts on NO2. For instance, trips to work produce the greatest marginal impacts per travelled kilometer; 40% higher than for shopping trips. In contrast to emissions, where speed distribution is an important explanatory factor, concentrations can mainly be explained by other factors. The impact of emissions on concentrations in fact depends on a range of factors including wind speed and direction, meteorology and background concentration. The results therefore suggest that trips with different purposes are rarely made under the same conditions. When we look at the distribution of vehicle kilometers travelled during the day (Fig. 1), we see that trip purposes differ from each other regarding the time of day when they are made. Trips to work, for example, mainly occur in the morning, while trips for shopping purposes and for services usually take place in a time period spread across the day, mainly between 10 a.m. and 6 p.m. In the morning, however, dispersion conditions are generally very limited (Ahrens, 2002). As a result, emissions produced during a morning trip to work usually produce a greater concentration increase, but during the day, dispersion conditions generally are better. Meteorological conditions vary daily and hourly. Since we simulate an entire year, all seasons and meteorological conditions are taken into account. Meteorology, however, varies not only by time but also to location. This is not taken into account in the current study since the model is currently only capable of using data from one meteorological mast. The impact of this limitation should be minor, however, as northern Belgium is a relatively small region.
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The results produced by this framework are presented for NO2 since these concentration levels closely reflect the spatial distribution of traffic-related air pollution, and are therefore a good indicator for traffic-related health issues. Future studies that focus on human exposure to air pollution and the related health impacts might therefore build on these results. 5. Conclusions We find that for emissions of NOx, the type of vehicle used (passenger car versus heavy-duty vehicle) is the most important explanatory factor. Furthermore, the speed at which the trip is made also determined the emissions produced per kilometer travelled: the more kilometers driven in the highest speed interval, the higher the emissions per kilometer. The marginal impact on emissions is therefore higher for transit trips than for shopping trips. As regards the impact on NO2 concentrations, our results demonstrate that a large share of the absolute concentration increase caused by road transport can be attributed to freight transport, mainly because of heavy-duty vehicles’ higher fuel consumption rates and emissions per travelled kilometer. We also found that concentration changes are influenced by the time of day when the emissions are produced. Trips to work that occur in the early morning, for example, result in much larger concentration changes because of higher atmospheric stability than those that occur during the day when dispersion conditions are better. Acknowledgment This research was supported by the Institute for the Promotion of Innovation by Science and Technology in Flanders. References Ahrens, D.C., 2002. Meteorology Today: An Introduction to Weather, Climate and the Environment, seventh ed. Brooks Cole, Stamford. Beckx, C., Arentze, T., Int Panis, L., Janssens, D., Vankerkom, J., Wets, G., 2009. An integrated activity-based modelling framework to assess vehicle emissions: approach and application. Environment and Planning B 36, 1086–1102. Bellemans, T., Kochan, B., Janssens, D., Wets, G., Arentze, T., Timmermans, H., 2010. Implementation framework and development trajectory of FEATHERS activity-based simulation platform. Transportation Research Record 2175, 111–119. Borremans, D., Grispen, R., Kienzler, H.P., Organe, K., Peetermans, E., Van houwe, P., Zillhardt, D., 2008. Multimodaal goederenmodel brengt goederenstromen in kaart. Het Ingenieursblad 6–7, 14–18. Gkatzoflias, D., Kouridis, C., Ntziachristos, L., Samaras, Z., 2012. COPERT IV: Computer Programme to Calculate Emissions from Road Transport. Emisia.. Int Panis, L., Beckx, C., 2007. Trucks driving at night and their effect on local air pollution. In: European Transport Conference. Noordwijkerhout, The Netherlands. Janssen, S., Dumont, G., Fierens, F., Mensink, C., 2008. Spatial interpolation of air pollution measurements using CORINE land cover data. Atmospheric Environment 42, 4884–4903. Lefebvre, W., Fierens, F., Trimpeneers, E., Janssen, S., Van de Vel, K., Deutsch, F., Viaene, P., Vankerkom, J., Dumont, G., Vanpoucke, C., Mensink, C., Peelaerts, W., Vliegen, J., 2011. Modeling the effects of a speed limit reduction on traffic-related elemental carbon (EC) concentrations and population exposure to EC. Atmospheric Environment 45, 197–207. Mensink, C., De Vlieger, I., Nys, J., 2000. An urban transport emission model for the Antwerp area. Atmospheric Environment 34, 4595–4602.
Contents lists available at SciVerse ScienceDirect
Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Assessing the environmental impact associated with different trip purposes C. Beckx a,⇑, W. Lefebvre a, B. Degraeuwe a, M. Vanhulsel a, B. Kochan b, T. Bellemans b, S. Dhondt c, L. Int Panis a,b a
Flemish Institute for Technological Research, Boeretang 200, 2400 Mol, Belgium Transportation Research Institute, Faculty of Applied Economics, Hasselt University, Wetenschapspark 5 Box 6, B-3590 Diepenbeek, Belgium c Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussel, Belgium b
a r t i c l e
i n f o
Keywords: Trip purpose Vehicle emissions Vehicular environmental impacts Timing of trips
a b s t r a c t We examine how trips with diverse motivations vary in their spatio-temporal characteristics and result in different impacts on the environment. An integrated model chain is used includes an activity-based traffic demand model, an emission model and a pollutant concentration module. The model setup is applied to the northern region of Belgium and analysed for NO2, an important transport-related air pollutant. The results demonstrate that not every vehicle kilometer has the same impact on the environment in terms of emissions and concentration increases. We find that the highest concentration increase per kilometer is produced by work-related trips. Trips for shopping purposes and services produced the lowest marginal concentration increase. The difference between the highest and lowest marginal concentration increases is about 40%. Important explanatory factors include the time of day when a trip is made as well as vehicle type and speed. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Source-related measures in reducing the environmental impact of transport are important; unlike technological measures, they intervene in the problem at an earlier stage. In the context of road traffic air pollution, a focus on the source of the problem involves examining people’s travel behaviour. The environmental impacts of car use depend not only on the vehicle type and its speed, but also on trip timing. The timing of a trip determines the impact of emissions on the resulting pollutant concentration levels. Pollutants emitted during trips at night or early in the morning have a different impact on ground-level concentrations than daytime exhaust emissions due to the atmospheric conditions.1 Since trips with different purposes may differ in aspects such as vehicle speed and trip timing, it is also reasonable to assume that the marginal environmental impact of one kilometer travelled may differ depending on trip purpose. This paper examines the marginal environmental impact of different vehicle trips in terms of emissions and concentration increases. 2. Model chain We apply a model chain to assess the spatial–temporal aspects of travel behaviour and its related environmental impacts, expressed in terms of emissions and pollutant concentrations. The chain includes an activity-based model for assessing tra⇑ Corresponding author. Fax: +32 14 32 11 85. E-mail address: [email protected] (C. Beckx). Int Panis and Beckx (2007) also find that emissions at night cause a larger increase in pollutant concentrations than those during the day due to varying meteorological parameters and their related conditions for dispersion. 1
1361-9209/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.trd.2012.10.002
C. Beckx et al. / Transportation Research Part D 18 (2013) 110–116
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vel behaviour, a traffic emission model for converting trips into emissions, a concentration interpolation tool that employs air quality data and a bi-Gaussian plume dispersion model that produces detailed pollutant concentration maps and subsequently enables assess of the impact of each trip on the resulting concentration levels. We used model input data for 2007 and delineated a study area that includes the Flanders region and Brussels, a densely populated area of 13,522 km2 with about 7.4 million inhabitants. Trips are classified according to trip purpose. A complete technical description of all models applied is beyond the scope of this paper and can be found elsewhere. The following sections therefore provide only a brief overview of the most important modelling aspects. More detailed information on the technical aspects of each model can be found in Lefebvre et al. (2011) and/or in selected references (as indicated in the sections below). An evaluation of the air quality results produced by this model chain indicated positive validation, which means that the simulated concentration levels correspond to measured concentration values with sufficient accuracy. 2.1. Simulating travel behaviour The activity-based model ‘Forecasting Evolutionary Activity-Travel of Households and their Environmental RepercussionS’ (FEATHERS) is a microscopic, agent-based simulation framework (Bellemans et al., 2010). It simulates origin–destination matrices for all agents in a synthetic population based on activity diary data and demographic and socio-economic inputs. Here, the origin–destination matrices covering 1145 population zones are simulated for a synthetic population representing about five million adults living in Flanders and Brussels. The matrices are estimated per hour of the day for an average week. Using a traffic assignment algorithm from the Transcad transport model (Caliper), they are the assigned to the geographic road network following Beckx et al. (2009). The purpose for each trip is defined according to the activity performed at the trip’s end using the following:
work: going to work outside of the home; home: returning home from activities outside of the home; bring/get: bringing or getting other people, for instance, taking children to school; shopping: shopping for everyday and other goods; services: going to the post office, bank, etc.; leisure: going to the gym, taking music lessons, etc.; social: going out with friends, visiting family, going to a concert, etc.; touring: leisure based on the transport itself, e.g. taking the car for a drive; transit: traffic travelling to or from destinations outside of Brussels or Flanders; other: all traffic that does not fall into one of the categories above, e.g. going to church; freight: Information on freight transport is provided by the freight model for Flanders (Borremans et al., 2008).
2.2. Converting trips into emissions The traffic loads provided by the Feathers-Transcad modelling step are converted into vehicle emissions by the MIMOSA4 emission model; the most recent version of the traffic emission model MIMOSA (Mensink et al., 2000) and relies on the COPERT4 methodology (Gkatzoflias et al., 2012) for the emission functions and calculating energy consumption. MIMOSA requires information on vehicle fleet composition, traffic intensity and average speeds at a resolution of 1 h for each road segment in region. It then generates hourly emission output for emissions, such as NOx, PM10 and PM2.5. The model calculates not only total emission values but also geographically distributed emissions over time. 2.3. Calculating pollutant concentration levels This step, converts the spatially and temporally distributed emissions from the MIMOSA emission model into detailed pollutant concentration levels. Because of the need not only to simulate air pollution with sufficient spatial detail to account for the steep concentration gradients along motorways and major point sources, but also to take into account regional air pollution gradients, the land use model (RIO) and the bi-Gaussian plume model immission frequency distribution model (IFDM) is used. RIO is a validated land-use model for Flanders and the Netherlands (Janssen et al., 2008), estimating hourly pollutant concentrations in a 3 3 km2 area on the basis of data collected by the official fixed-site monitoring network and a land usederived covariate. RIO produces hourly concentration maps for PM10, PM2.5, NO2, O3 and SO2. Based on these, annual pollutant concentration statistics can be derived. RIO has proved to be highly accurate in estimating pollution over Belgium. Here, background concentrations are provided by this method. The IFDM model is a bi-Gaussian plume model, designed to simulate non-reactive pollutant dispersion on a local scale. An additional chemistry module within the model describes the chemical equilibrium of nitrogen oxides and ozone. As IFDM is a receptor model, it can be used for both regular and irregular
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grids. In addition to the standard 1 1 km2 grid an irregular line source-following grid is defined to account for the steep concentration gradients found alongside motorways (25 800 m2 close to roads).2 RIO and IFDM, are combined to embrace both the regional aspects of air pollution and the local concentration gradients near major line and point sources. A combination procedure, which eliminates the double counting of traffic emission sources, is generally carried out by eliminating the marginal concentration increase that results from the traffic emissions, at a lower resolution, before adding the concentrations caused by these emissions. To discern the implications of a trip purpose, the RIO–IFDM combination procedure is adapted. The subtraction step (eliminating the marginal concentration increase caused by the traffic emissions) is initially carried out, but then, rather than adding the effect of all traffic emissions to the concentrations, only those emissions not caused by the trip purpose we wished to investigate are added. Then, the effect of this purpose on pollutant concentrations can be discerned in the difference between the model run with the emissions from all trip purposes and the run with one purpose eliminated. The advantage of this methodology is that the chemical equilibrium remains comparable (which would not be the case if we eliminated all purposes except the one we wished to investigate). The small deviation from the chemical equilibrium can be compensated for using the chemistry model incorporated in IFDM.
3. Results 3.1. Transportation data Hourly origin–destination matrices are predicted using the FEATHERS travel demand model. Distinctions are made between trips with different trip purposes. Kilometers travelled are obtained by assigning these matrices to a road network using an equilibrium traffic assignment. Results are analyzed per trip purpose, both temporally and geographically. Trips to home represent the largest portion of kilometers travelled, followed by work-based trips, transit traffic and freight transport. This distribution can vary significantly depending on whether it is a weekday or weekend and on the time of day. Fig. 1 presents the distribution of vehicle kilometers per trip motive per time of day, and includes daily patterns of vehicle kilometers for an average weekday and an average weekend. The number of vehicle kilometers travelled is displayed per hour and per trip purpose. On the weekday graph, the morning and evening traffic peaks are clearly visible. The morning traffic peak constitutes mainly of work-related traffic and the evening peak largely represents people returning home. Unsurprisingly, trips for shopping are made during opening hours: mainly between 10 a.m. and 8 p.m. in Belgium. Furthermore, freight traffic is more pronounced between 6 a.m. and 8 p.m. At weekends, the number of vehicle kilometers travelled is less than during the week. Fig. 1 shows how the distribution of trip purposes can change during the day and from day to day. Certain types of trip take place mainly during peak hours, while others occur in a more scattered way throughout the day. There are also reasons to believe that people use road types depending on the trip purpose (e.g. local, urban roads for performing nearby activities versus motorways for long-distance trips). Since both aspects (timing and road type) will influence the driving speed (which has implications for the resulting vehicle emissions), another interesting aspect to explore is the distribution of vehicle speed per trip purpose. In Fig. 2 the percentage of kilometers travelled per trip purpose is therefore presented using speed intervals. Vehicle speeds are classified into four categories: under 51 kph, between 51 and 70 kph, between 71 and 90 kph, and over 90 kph (corresponding to the most common speed limits in Belgium). We see significant differences between trip purposes. For instance, in the highest speed class, where a large portion of the kilometers are driven, we see that freight transport and transit trips clearly dominate. Compared to the other trip motivations, on the other hand, kilometers driven for services or shopping are represented to a much lesser degree in this highest speed class.
3.2. Vehicle emissions Regards emissions of NOx, total emission values per trip purpose will depend mainly on the number of kilometers travelled. Since we focus on the marginal impact that each trip purpose has on the environment, however, Fig. 3 shows NOx emissions per kilometer for the trip purposes. The results per trip purpose are expressed in relation to the average NOx exhaust emissions for non-freight trips (distance-weighted), which is 0.722 g/km. Evidently, much higher emissions per kilometer are found for freight transport than for other trip purposes, which are mainly associated with light-duty vehicles. Due to the large difference between emissions from freight transport and passenger cars, the value for freight transport (6.282 g/ km) is not shown on the graph. Fig. 3 shows differences by comparing emissions for the other trip purposes. The highest emissions per kilometer are for transit trips, followed by trips for touring purposes. The lowest emission values are found for shopping trips and service-related trips. 2 This approach is similar to the methodology used by Lefebvre et al. (2011) and ensures that more receptor points would be available at locations where the largest gradients are expected.
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Fig. 1. Vehicle kilometers travelled per trip purpose per time of day: distribution for an average weekday (top) and distribution for an average weekend day (bottom).
Fig. 2. Speed distribution by trip purpose as a percentage of kilometers travelled by trip purpose over speed intervals.
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Fig. 3. NOx emissions per trip purpose per travelled kilometer. Note: Results are expressed in relation to the average exhaust emissions per kilometer for non-freight trips.
3.3. Pollutant concentrations Every motorised vehicle trip producing exhaust emissions impacts on the concentration of pollutants in the air. The impacts, however, depend not only on the amount of emissions but also on factors such as wind speed and direction, meteorology, and background concentrations. Consequently, it is useful to analyse both the concentration increase per trip purpose and the marginal increase per kilometer per purpose. The absolute concentration increase in the annual mean NO2 concentration caused by road traffic in 2007 in the Northern part of Belgium is estimated at 3.76 lg/m3. Fig. 4 shows that a large percentage of the concentration increase is caused by freight transport. Furthermore, trips to work and home are also responsible for a large share of the concentration increase because of the large number of vehicle kilometers travelled for these purposes. In Fig. 5, the NO2 concentration increase per travelled kilometer is seen for the various trip purposes. The concentration change per travelled kilometer reflects the marginal impact of a vehicle kilometer on the yearly averaged pollutant concentration level. The results per trip purpose are expressed in relation to the average distance-weighted concentration increase for non-freight trips, which is 0.043 lg/m3 per billion vehicle kilometers on a yearly basis. Trips for freight transport have a larger impact on the concentration than light-duty trips due to the higher emission rates. The value for freight transport, not seen in the figure, is 0.385 lg/m3 per billion freight kilometers. The highest concentration increase per kilometer is for workrelated trips, followed by trips to home and transit traffic. Trips for shopping purposes and services produced the lowest marginal concentration increase.
Fig. 4. Distribution of the mean absolute concentration increase in NO2 caused by road traffic, valid for the yearly averaged NO2 concentration in 2007.
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Fig. 5. NO2 concentration changes per trip purpose per travelled kilometer. Note: Results are expressed in relation to the average concentration increase per billion vehicle kilometers on a yearly basis.
4. Discussion To examine the impact of trip purposes on the environment, we analysed the marginal impact of a vehicle kilometer in terms of emissions and concentration increases. As regards the impact on emissions, results show significant differences between NOx emissions caused by light-duty vehicles and those caused by heavy-duty freight vehicles because of the latter’s higher fuel consumption rates. When we compare emission associated with the various trip purposes by passenger car users, however, we find that factors other than vehicle type also result in differences; they vary from 0.704 g/km for shopping and service-related trips to 0.756 g/km for transit trips. Touring trips are also found associated with increased exhaust emissions per kilometer, about 0.735 g/km. When the distribution of vehicle kilometers over speed intervals is examined we see that vehicle speed, and specifically the number of kilometers driven in the highest speed interval may explain the differences in emissions. At the highest speed interval, at which a large share of driving takes place, differences emerge between trip purposes. Transit trips and trips for touring, for instance, are mainly driven at high speeds, whereas trips for shopping and services are seldom at speeds above 90 kph. Furthermore, in line with COPERT’s U-shaped emission functions (Gkatzoflias et al., 2012), these higher vehicle speeds will also result in higher emissions. We do not, however, take into account two issues related to emission calculation. First, emissions for non-freight trips are based on the characteristics of the average vehicle fleet. We considered relevant statistics on passenger car data such as mileage per fuel type, age, and Euro standard, but fleet composition may vary according to journey purpose; the type of vehicle used for shopping may differ from that used for work trips or when picking up children, but no data is available on this. Secondly, the calculations assume that every vehicle kilometer is driven with the same proportion of hot and cold starts. The MIMOSA emission model takes into account average trip length to assess the additional amount of cold-start emissions in the area and then distributes the additional exhaust emissions uniformly over the kilometers travelled. No distinction is thus made between various trips purposes even though, for example, trips to work may involve more cold starts after car spending a night standing on the driveway. Analyses of the impact of vehicle kilometers on NO2 concentration indicates that, in terms of annual average concentrations for the area, kilometers done are responsible for an absolute increase of 3.76 lg/m3, with large part of the increase caused by freight traffic. Regarding non-freight trips, Fig. 5 shows that trip purposes produce varying marginal impacts on NO2. For instance, trips to work produce the greatest marginal impacts per travelled kilometer; 40% higher than for shopping trips. In contrast to emissions, where speed distribution is an important explanatory factor, concentrations can mainly be explained by other factors. The impact of emissions on concentrations in fact depends on a range of factors including wind speed and direction, meteorology and background concentration. The results therefore suggest that trips with different purposes are rarely made under the same conditions. When we look at the distribution of vehicle kilometers travelled during the day (Fig. 1), we see that trip purposes differ from each other regarding the time of day when they are made. Trips to work, for example, mainly occur in the morning, while trips for shopping purposes and for services usually take place in a time period spread across the day, mainly between 10 a.m. and 6 p.m. In the morning, however, dispersion conditions are generally very limited (Ahrens, 2002). As a result, emissions produced during a morning trip to work usually produce a greater concentration increase, but during the day, dispersion conditions generally are better. Meteorological conditions vary daily and hourly. Since we simulate an entire year, all seasons and meteorological conditions are taken into account. Meteorology, however, varies not only by time but also to location. This is not taken into account in the current study since the model is currently only capable of using data from one meteorological mast. The impact of this limitation should be minor, however, as northern Belgium is a relatively small region.
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The results produced by this framework are presented for NO2 since these concentration levels closely reflect the spatial distribution of traffic-related air pollution, and are therefore a good indicator for traffic-related health issues. Future studies that focus on human exposure to air pollution and the related health impacts might therefore build on these results. 5. Conclusions We find that for emissions of NOx, the type of vehicle used (passenger car versus heavy-duty vehicle) is the most important explanatory factor. Furthermore, the speed at which the trip is made also determined the emissions produced per kilometer travelled: the more kilometers driven in the highest speed interval, the higher the emissions per kilometer. The marginal impact on emissions is therefore higher for transit trips than for shopping trips. As regards the impact on NO2 concentrations, our results demonstrate that a large share of the absolute concentration increase caused by road transport can be attributed to freight transport, mainly because of heavy-duty vehicles’ higher fuel consumption rates and emissions per travelled kilometer. We also found that concentration changes are influenced by the time of day when the emissions are produced. Trips to work that occur in the early morning, for example, result in much larger concentration changes because of higher atmospheric stability than those that occur during the day when dispersion conditions are better. Acknowledgment This research was supported by the Institute for the Promotion of Innovation by Science and Technology in Flanders. References Ahrens, D.C., 2002. Meteorology Today: An Introduction to Weather, Climate and the Environment, seventh ed. Brooks Cole, Stamford. Beckx, C., Arentze, T., Int Panis, L., Janssens, D., Vankerkom, J., Wets, G., 2009. An integrated activity-based modelling framework to assess vehicle emissions: approach and application. Environment and Planning B 36, 1086–1102. Bellemans, T., Kochan, B., Janssens, D., Wets, G., Arentze, T., Timmermans, H., 2010. Implementation framework and development trajectory of FEATHERS activity-based simulation platform. Transportation Research Record 2175, 111–119. Borremans, D., Grispen, R., Kienzler, H.P., Organe, K., Peetermans, E., Van houwe, P., Zillhardt, D., 2008. Multimodaal goederenmodel brengt goederenstromen in kaart. Het Ingenieursblad 6–7, 14–18. Gkatzoflias, D., Kouridis, C., Ntziachristos, L., Samaras, Z., 2012. COPERT IV: Computer Programme to Calculate Emissions from Road Transport. Emisia.