On-road motor vehicle emission inventory model A.W. Reynolds,B. Broderick Department of Civil Structural and Environmental Engineering, Trinity College, Dublin, Ireland Email: anthony.reynolds@ucg.ie Email: bbrodrck@tcd.ie Abstract The atmosphere of the world's cities is increasingly affected by transport-related air pollution. Hence, poor air quality is fast becoming one of the most pressing and unrelenting environmental problems of our times. In response, a research project is underway to develop and build an integrated air quality model for transport-related pollution management in the greater Dublin area - DAQMS (Dublin Air Quality Modelling System). An integral part of transport-related pollution management and air quality assessment is the estimation of emissions from motor vehicles. To-date, no suitable methodology exists for determining motor vehicle emission factors in Ireland, taking into account the variations due to factors such as fuel, engine type, age and motor vehicle operating mode. Current computer models lack the sufficient detail required to properly predict an emissions inventory. This paper describes the development of a computer model to reliably calculate mobile source emissions. The model employs traffic information from the SATURN model, allowing the emissions produced on a given link or road to be identified from transportation planning data. Any subsequent changes in emission levels due to alterations in the road network can then be automatically assessed. In addition to this emission data, a dispersion model is used to predict variations in the ambient concentrations of pollutants by combining site-specific geometry and meteorology with the results from the emissions.
388 Urban Transport and the Environment for the 21st Century 1 Introduction Pollutant emissions from vehicles depend on a wide variety of factors including vehicle (characteristics, age, fuel and engine type, tuning condition), climatic conditions (temperature), traffic conditions (congested or free-flowing), operating mode (idling, accelerating, cruising or deaccelerating), vehicle use (short or long trips), and driver behaviour (gentle or aggressive). This paper describes the development of an emission model for an urban area taking the above into account. The overall objective of the study is to obtain emission estimates for SO], NOx, CO, VOCs, 1,3-butadiene, benzene, and PMio from road traffic in the greater Dublin area. Traffic data is obtained from a combination of network traffic model results and traffic surveys at particular locations as well as statistical data. To develop an applicable methodology and to test its accuracy, the model is applied initially to a busy intersection in Dublin. Ambient pollutant concentrations are measured and compared to those predicted on the basis of the calculated emissions. 2 Survey Site Characteristics The study area is at a heavy trafficked signalised intersection of Westland Row, Pearse St and Lombard St in Dublin city centre. The intersection geometry is shown in Figure 1. Pearse St consists of a one-way four-lane carriageway. Westland Row has one lane going in either direction, while Lombard St has three lanes travelling in the same direction. The arrows in the diagram show the direction of traffic flow. Figure 1. Intersection Geometry at Survey Site in Dublin
Urban Transport and the Environment for the 21st Century 389 A mobile monitoring station is located close to the kerbside of the junction. The surrounding buildings tend to reduce the influences at the monitoring intake of traffic on other streets and stationary sources. Therefore, it can be assumed that emissions from the local environment will have a dominant effect on the ambient conditions obtained. The unit can measure both meteorological conditions (temperature, relative humidity, wind speed and direction) and pollutants (CO, SO], NO%, PMio, and 23 different VOCs). A detailed description of the measuring facilities in the monitoring unit is given elsewhere [1]. 3 Traffic Flow Monitoring Automatic traffic monitors measure the total volume of traffic passing over the survey site. These monitors consist of induction loops embedded below the surface of each traffic lane. The loops count all vehicles that pass over them, but cannot distinguish between different types of vehicles. The diurnal variation of traffic volume is given in Figure 2. Pearse St. (to) -Pearse St. (from ) Lombard St. (to) Westland Row (to) Westland Row (from) Figure 2. Diurnal Variation of Traffic Volume at Survey Site In order that the traffic mix could be established, a video camera was used to record the morning peak hour traffic flow (Sam to 9am) and a typical inter-peak hour flow (2pm to 3pm). A trafficfleetcomposition for a 24-hour period is assigned to each link in the road network using the following relationship: Daily flow = 3.75 x peak hour flow + 11.5 x inter-peak hour flow (1)
390 Urban Transport and the Environment for the 21st Century The 24-hour weekend (Saturday and Sunday) traffic volumes are calculated by multiplying the inter-peak hour flow by 15.25. The traffic is divided into cars, light goods vehicles (LGV), buses, heavy goods vehicles (HGV) and motorcycles. The vehicle composition is given in Table 1. Street (Dir'n wrt June.) Pearse St (to) Pearse St (from) Lombard St (to) Westland Row (to) Westland Row (from) IVI -cycles 3.31% 3.16% 4.70% 2.66% 4.98% Cars 77.88% 76.93% 75.66% 75.88% 76.96% Vehicle Type (%) LGV 14.00% 13.32% 14.13% 10.85% 13.25% HGV 3.96% 4.08% 3.73% 3.56% 2.58% Table 1. Observed Vehicle Composition for a typical day Buses 0.85% 2.50% 1.78% 7.05% 2.23% Traffic velocities were observed (see Table 3) so that an accurate estimate of the space mean speed could be evaluated. However, it was not feasible in this study to measure the time mean speed, acceleration and deacceleration of the vehicles at the traffic junction. 4 Traffic Network Model The traffic network model, SATURN, (Simulation and Assignment of Traffic in Urban Road Networks) [2] provides a representation of the Dublin transportation network system for an average weekday morning peak hour (08.00 to 09.00), and an average inter-peak hour (14:00 to 15:00). The classification system in SATURN categorises traffic into light duty vehicles (LDV) including cars and LGV's, heavy duty vehicles (HDV) and buses. Table 2 presents the vehicle composition. Street (Dir'n wrt June.) Pearse St (to) Pearse St (from) Lombard St (to) Westland Row (to) Westland Row (from) LDV 98.41% 92.29% 89.22% 75.17% 80.65% Vehicle Type (%) HDV Buses 1.01% 0.58% 5.63% 2.07% 8.38% 2.40% 16.02% 8.91% 10.29% 9.06% TOTAL 13794 25603 11276 5155 4485 Table 2. Predicted Vehicle Composition for a typical day This model also provides average velocity (see Table 3) and queuing data (average queue length, link time delay, turn time delay, free flow time, etc.) for every link in the network.
Urban Transport and the Environment for the 21st Century 391 5 Comparison of Traffic Data The SATURN model has been both calibrated and validated against previous traffic surveys across Dublin. In this study, the modelled traffic speeds and flows are validated against observed vehicle data as an assurance that the traffic volume flows and velocities are realistic, from an atmospheric emission viewpoint. Tables 1 and 2 show the measured and predicted traffic volume and mix data for the junction. Measured total volume flows, percentage vehicle mix agrees reasonably well with those predicted by the SATURN model. However, the SATURN model does not take account of motorcycles and this affects the volume mix. The network model generally under-predicts traffic volume and over-predicts average velocities. This may be due, in part, to the fact that it was validated in 1996 whereas the traffic surveys were carried out in 1997. The model is currently being validated and calibrated for 1997. SATURN also over-predicts inter-peak hour average velocities due to the fact that the traffic signals at the junction are timed to give a priority to traffic at nearby junctions. Street (Dir'n wrt June.) Pearse St (to) Pearse St (from) Lombard St (to) Westland Row (to) Westland Row (from) Velocity (km/hr) (peak) 9.5(13) 9.4(11) 12.7(15) 9.6(10) 11.5(24) Velocity (km/hr) (inter-peak) 11.3(15) 10.7(35) 9.1 (10) 8.5(13) 11.7(31) Table 3. Observed (and Predicted) Average Velocities at Survey Site 6 Emission Estimation Methodology Traffic-related emissions can be divided into two groups: hot and cold. When a vehicle is started for thefirsttime in a day, it will be cold (known as a cold-start). During this period, the emissions are substantially higher than when the engine is hot. All other starts during the day are not truly cold-starts, as the vehicle engine will normally cool down very little, unless it is left unused for many hours during cold weather. The total vehicle kilometres travelled on each link (VKTtotai) are calculated by multiplying the length of the link (L) by the total traffic volume flowrate per unit time (VFtotai) This VKTtotai is then subdivided
392 Urban Transport and the Environment for the 21st Century into the vehicle kilometres travelled (VKTy) for each category of vehicle. The total VKTv for each category can then be combined with emission factors in order to calculate the total emissions for each link. The hot emission rate for a vehicle category is calculated by multiplying an emission factor for a particular pollutant by the VKT travelled by each type of vehicle. Ehot,p,v,l,f = EFhot,p,v,l,f * VKTv,l,f * Tmodel (2) where: Ehot,p,v,i,f is the emission rate (g/unit time)of pollutant p caused by vehicles of category v, driven on link type 1, using fuel type f with hot engines EFhot,p,v,i,f is the average representative emission factor (g/km) for the pollutant p, relevant for the vehicle category v, operated on roads of type 1, using fuel type f with hot engines; VKTv,i,f is the vehicle kilometres travelled by vehicles of category v, driven on link type 1, using fuel type f; and Tmodei is the modelling period (hours, days, months or years); and: p is the pollutant (CO, NO%, PMw, VOC, etc.); v is the vehicle type (cars, LDV, HDV, buses or motorcycles); 1 is the link type (urban, suburban, rural or motorway); and f is the fuel type (petrol, diesel or LPG). The total emission rate of a particular pollutant for all traffic on each link is calculated by summing up the individual emission rates for each vehicle type, using the various fuels. The percentage of vehicles within each category of petrol, diesel and LPG engined, and the proportion of vehicles fitted with catalytic converters, are estimated from the vehicle licensing records. Approximately 21% of cars have petrol engines with a catalytic converter, 65% burn petrol with no catalytic converter and the remaining 14% burn diesel. In the case of goods vehicles (HGVs and LGVs) 93% have diesel engines with only 7% using petrol. [3] The Emission Factors (EF) are calculated from a Baseline Emission Rate (BER) for each vehicle. The BERs only reflect emissions from one set of driving parameters. Correction factors are needed to account for emissions outside this set to provide a more accurate estimate of emissions under a broader range of operating conditions. These correction factors include temperature (TCP), velocity (VCF), operating modes (OMCF), and high emitters (HECF). EFp,v/,vy = BERp,v,f,vy * TCFp.v.f * VCFp,v.f * OMCFp,v,f * HECF^f (3)
Urban Transport and the Environment for the 21st Century 393 BERs are calculated at standard temperature and pressure. If the ambient temperature varies, this can affect the emission rates. The TCP adjusts the BERs to non-standard temperatures. Similarly, the VCFs and OMCFs were developed to account for vehicles travelling at different velocities and operating modes. The HECFs were developed to account for the small portion of vehicles that cause most of the pollution. At signalised junctions, the traffic is delayed due to congestion. These extra emissions due to queuing are added to the hot-start emissions. The queuing emission, Eque,p,v,r,vy, in (g/unit time) is calculated for each link: que,p,v,f,vy ~ k* que,p,v,f,vy A delay Tmodel *Tque (4) where: EFque,p,v,f,vy is the average representative emission factor in (g/sec) for the pollutant p, relevant for the vehicle category v, using fuel type f with hot engines; is the average delay time in seconds per unit time; and que is the volume of traffic queuing at the junction. A proportion of the vehicles in each link is assumed to be started with the engine cold. This proportion is estimated from the average trip length statistic. The fraction of VKT with cold engines is called the Cold Mileage Percentage (CMP) and is defined in CORINAIR [4]. The Composite Emission Rate (CER) for each link is then calculated from the following formula: CER = Eho,p,v.l,f * (1-CKP) + Ecold,p,v,l,f * CKP, + Eque,p,v,f,vy (5) The emissions calculated in the above equation originate at the vehicle tailpipe and are called exhaust emissions. Fuel also evaporates from the fuel storage and delivery system. These are known as evaporative emissions and include diurnal, hot-soak, resting and running losses. Traffic also gives rise to particulate matter (PMio) from wear on brakes, tyres and the street surface. Since no emission factors are available specifically for Ireland, the emission factors used in this study are derived from three different sources: a) Emission factors developed in the UK [5], [6], [7]; b) US EPA's Emission Factors M;mual (AP-42) [8]; and c) European EA's Atmospheric Emissions Inventory Guidebook [9].
394 Urban Transport and the Environment for the 21st Century Some of the emission factors are approximate or uncertain, and depend heavily on available statistical data. The development of emission factors is an ongoing process, and the most up-to-date factors available at the time of preparation of this paper have been used. They may well change with further research into this area. 7 Emission Rates Total estimates of emissions for SO:, CO, NOx, PMm, VOC, 1,3- butadiene and benzene are calculated for each of the links around the study site using traffic information from the sample surveys and video recordings as well as statistical data [3], [10]. Table 4 gives the combined inventories for each street leading to and from the junction. Street (Dir'n wrt June.) Pearse St (to) Pearse St (from) Lombard St (to) Westland Row (to) West land Row (from) SO: 7.1 19.6 6.3 3.1 2.5 CO 967.9 2519 860.9 364.3 355.4 Emissions (g/hour) PMio VOC 11.3 182.3 34.6 473.8 10.7 177.8 6.6 66.9 4.1 73.5 NOx 176.3 514.4 160.1 90.2 62.1 1,3-But. 1.9 4.8 1.6 0.7 0.6 Benzene 5.7 14.4 4.9 2.0 2.1 Table 4. Estimation of Pollutant Emissions (g/hr) at Survey Site 8 Pollutant Dispersion The accuracy of the emission calculations was verified by employing them, along with site meteorological data, in an adapted version of the US EPA's CALINE model version 4 [11]. This model can predict the ambient concentrations of CO, NO%, inert gases and particulate matter due to local road traffic. The VOC's are modelled as inert gases. Table 5 shows a comparison between the mean observed and predicted one-hour pollutant concentrations (in ig/m^) for SO], NO%, CO, VOC, 1,3-butadiene, Benzene and PMio. In general, the model underpredicts non-voc pollutant concentrations and over-predicts VOC concentrations. To a large extent, this latter observation may be attributed to the fact that the dispersion model does not allow for any decay in the VOC concentrations with time. The poor correlation between the observed and predicted SO: concentrations may be due to the fact that the percentage of diesel cars is an estimate for all of Dublin and not just this
Urban Transport and the Environment for the 21st Century 395 junction. present. Alternatively, some other non-traffic source of SO: may be Pollutant Observed Predicted P/O SO: CO NOx PMio voc 1,3-Butadiene Benzene 17.12 3586.21 75.47 18.31 99.17 0.79 4.97 8.29 2842.63 66.39 13.53 106.42 1.02 5.09 0.48 0.79 0.88 0.74 1.07 1.29 1.03 Table 5. Observed and Predicted Pollutant Concentrations 9 Conclusions A methodology for determining atmospheric vehicular emissions based on transportation modelling data has been presented and applied at a demonstration site. Emission rates were calculated for all links around this site using information obtained through sample surveys and video recordings. This traffic information was compared to output results from the traffic network model. Other data (age profile of traffic, trip length, percentage of diesel, petrol or LPG vehicles, etc.) not directly available was estimated from statistical data for all of Dublin. The study has shown that there is good agreement between the traffic data available from the SATURN model and that measured in the field. The concentrations of pollutants dispersed from the study junction have also been measured experimentally and modelled theoretically. In general, the observed and predicted concentrations are in reasonable agreement. Acknowledgements The authors would like to acknowledge the support of ESB International and the Dublin Transport Office, who both partially funded this study. Thanks also to the CSO, Dublin Corporation and the NRA for the provision of traffic statistics. Special thanks to D Keating and I. Marnane, research students at TCD, for the provision of monitoring data.
396 Urban Transport and the Environment for the 21st Century References [1] Marnane, I., Keating, D, Broderick, B & Misstear, B, On-line monitoring of air pollution concentrations due to vehicular emissions in Dublin, Proc. of the 30th UTSG conference, Dublin, 1998. [2] Van Vliet, D, SATURN 9.2 User Manual, ITS, University of Leeds, UK, 1995 [3] Central Statistics Office (CSO), Dublin, Ireland, 1998 [4] Eggleston S., Gaudioso, D, GoriBen, N., Joumard, R, Rijkeboer, R.C., Samaras, Z, & Zierock, KH, CORINAIR Working Group on Emissions Factors for Calculating 1990 Emissions from Road Traffic, ISBN 92-826-5571-X, 1993 [5] Air Quality Assessment, Design Manual for Roads and Bridges, 11,3,1, Dept. of Transport, UK, 1993 [6] Salway, AG, Eggleston, H.S., Goodwin, J.W.L., & Murrells, T.P, UK Emissions of Air Pollutants. 1970-1994, NAEI, AEA Technology/Dept of the Environment, UK, 1996. [7] Eggleston, H.S., Pollution in the atmosphere: Future Emissions from the UK, Warren Spring Laboratory, ISBN 0-85624-748-0, 1993. [8] Compilation ofair Pollutant Emission Factors (AP-42), Office Of Air Quality Planning And Standards, Office Of Air And Radiation, US EPA, 1996. [9] Mclnnes, G, Atmospheric Emissions Inventory Guidebook, produced jointly by EMEP/CORINAIR, prepared by the EMEP Task Force on Emission Inventories, EEA, 1996. [10] National Roads Authority (NRA), Dublin, Ireland, 1998 [11] Benson, P.E., CALINE4 - A dispersion model for predicting air pollutant concentrations near roadways, Caltrans, USA, 1989