Development of an emissions inventory model for mobile sources

Transportation Research Part D 5 (2000) 77±101

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Development of an emissions inventory model for mobile sources A.W. Reynolds *, B.M. Broderick Department of Civil, Structural and Environmental Engineering, Trinity College, Dublin, Ireland

Abstract Trac represents one of the largest sources of primary air pollutants in urban areas. As a consequence, numerous abatement strategies are being pursued to decrease the ambient concentrations of a wide range of pollutants. A mutual characteristic of most of these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emissions inventory database. In the case of trac pollution, such an inventory must be compiled using activity statistics and emission factors for a wide range of vehicle types. The majority of inventories are compiled using ÔpassiveÕ data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. Current trends are towards integrating urban trac control systems and assessments of the environmental e€ects of motor vehicles. In this paper, a methodology for estimating emissions from mobile sources using real-time data is described. This methodology is used to calculate emissions of sulphur dioxide (SO2 ), oxides of nitrogen (NOx ), carbon monoxide (CO), volatile organic compounds (VOC), particulate matter less than 10 lm aerodynamic diameter (PM10 ), 1,3-butadiene (C4 H6 ) and benzene (C6 H6 ) at a test junction in Dublin. Trac data, which are required on a street-by-street basis, is obtained from induction loops and closed circuit televisions (CCTV) as well as statistical data. The observed trac data are compared to simulated data from a travel demand model. As a test case, an emissions inventory is compiled for a heavily tracked signalized junction in an urban environment using the measured data. In order that the model may be validated, the predicted emissions are employed in a dispersion model along with local meteorological conditions and site geometry. The resultant pollutant concentrations are compared to average ambient kerbside conditions measured simultaneously with on-line air quality monitoring equipment. Ó 2000 Elsevier Science Ltd. All rights reserved. Keywords: Trac emissions; Transportation modelling; Real-time data; Model validation; Integrated trac control

*

Corresponding author.

1361-9209/00/$ - see front matter Ó 2000 Elsevier Science Ltd. All rights reserved. PII: S 1 3 6 1 - 9 2 0 9 ( 9 9 ) 0 0 0 2 5 - 5

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1. Introduction The general approach used to calculate mobile source emission inventories is based on a twostep process (Bailey, 1995; Joumard, 1998a,b). The ®rst step involves the development of a set of emission factors, which represent the emission rate per unit of activity, but whose application is extended from a single engine to a whole ¯eet. These emission factors are determined under laboratory-controlled conditions for pre-determined driving cycles, for example FTP 75, which attempt to both capture and harmonise the actual conditions experienced by on-street vehicles. A description of these tests is provided by Adler (1997). Alternatively, emission factors are determined from Ôreal-worldÕ driving using on-board emissions measurement instrumentation (Journard et al., 1995; Kelly and Groblicki, 1993; Bishop and Stedman, 1990; Vanruymbeke et al., 1993). This has the obvious bene®t of re¯ecting exactly the speci®c local conditions and demands encountered in the study city being considered. In either case, however, an emissions inventory model will be dependent upon a limited sample of experimental data on which to base its calculations and it cannot be assured that this sample accurately re¯ects either local or contemporary conditions when applied elsewhere. The second step in the process involves the determination of an estimate of vehicle and/or trac activity. This activity data can be derived from either trac surveys/counters (Cardelino, 1998) or transportation models (Algers et al., 1998). As with on-board emissions measurement, data from trac surveys are more desirable, as they provide information on actual trac patterns on actual carriage-ways. However, such surveys have the major limitation that they only supply data pertaining to particular inspection times and locations, rather than a complete study area. In comparison, a transportation model represents a software simulation of the physical road network and trac environment contained therein (Warner, 1985). The current generation of these models can be very detailed and simulate entire urban domains (Algers et al., 1998). However, it is recognized that even the most complex transportation models still do not contain all the information needed for the compilation of a complete inventory of emissions of pollutants to the atmosphere. A number of studies have compared the available methodologies for calculating emission inventories. Sturm et al. (1997) describes three di€erent approaches to compiling emission inventories based on input data and area of application, namely Ôactual driving behaviourÕ, Ôspeci®c streetsÕ and Ôvehicle miles travelledÕ. Zachariadis and Samaras (1997) compared the results of four models used to calculate urban emission inventories, namely, HBEFA, DRIVE-MODEM, DGV and COPERT and found that the results were generally in good agreement. Shearn et al. (1996) identi®ed 22 pollution-modelling tools currently available. Algers et al. (1998) in their review of 58 micro-simulation models developed in Europe, USA, Canada, Australia and Japan found that 52% of them predicted exhaust emissions while 16% of them estimated roadside pollution levels. Samaras et al. (1995) compared the bottom-up and top-down approaches of compiling emission inventories and showed that, in principle, it is possible to reconcile both assessments methods, while Loibl et al. (1993) stated that the methodology for compiling an urban emissions inventory should be a combination of both approaches. Barth et al. (1996) identi®ed ®ve di€erent emission modelling methods used to estimate pollution from road transport in the US namely, EMFAC/ MOBILE, multiple driving cycles, velocity±acceleration matrix, emission mapping and physical models. Winther (1998) evaluated three models, COPERT II, German Workbook and the DTU

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model for passenger cars and found signi®cant di€erences between them. Seika et al. (1996) compared emission inventory compilation methods in the US and in Europe for the year 1990 and found that the di€erences between the inventories re¯ect not only true di€erences in emissions but also distinctions which are related to the way in which the inventory has been produced. Another comparative analysis of European and US methods found that although the same basic structure is used, considerable di€erences exist with regard to the inputs, assumptions and parameters taken into account (Longhurst and Power, 1998). Sturm (1996) showed that considerable errors of interpretation can arise when simple methods are applied in more complex areas. The major disadvantage of the above emission inventory calculation methods, is that they use passive trac data (derived from either transportation models or trac surveys). The UK-DETR (1997) discuss in detail the major disadvantages of employing data from either transportation models or sample counts/surveys for emission inventory calculations. Indeed, Chapin (1993) suggests that trac volumes, both overall and on local arterials, may be underestimated by as much as 10±20%. This in turn, may result in underestimating emissions by as much as 50% (Eisele et al., 1996). Any error in trac ¯ow rate will cause by itself a proportional error in emissions calculation, but if the vehicle velocities are derived from such a ¯ow rate, the error on emissions could increase dramatically due to the velocity e€ect on pollutants (Negrenti, 1998). Therefore, correct trac ¯ow is crucial for con®dence in any emission prediction model. In addition, mobile source emission inventories by their nature are often out-of-date by the time they are compiled. One approach to overcoming these disadvantages is to exploit the data readily available in many urban trac control systems. Ideally, a hybrid model, in which real-time data supplements rather than replaces passive methods, should be developed. This paper describes the development of one such mobile source emissions model employing real-time trac data obtained from an adaptive trac control system. These data de®ne vehicle type, ¯ows, delays and consequently velocities throughout Dublin city centre where the Sydney Co-ordinated Adaptive Trac System (SCATS) (Sim and Dobinson, 1980) is installed at approximately 500 junctions. SCATS gathers data on trac ¯ows in real-time at each intersection. These data are transferred via the trac control signal box to a central computer, which makes incremental adjustments to trac light timings based on minute-by-minute changes in trac ¯ow at each intersection. SCATS performs a vehicle count at each stop line, and also measures the gap between vehicles as they pass through each junction. Overall, it can be said that the primary function of SCATS, as with any area trac control system, is to control trac on an area basis rather than on an individual, uncoordinated intersection basis. SCATS is installed in 36 cities worldwide in countries such as Australia, Singapore, the United States, Ireland and Hong Kong. Closed circuit televisions (CCTV) at junctions provide additional information on vehicle classi®cation distributions and congestion. The information from SCATS and the CCTV is transmitted to a control centre where it is available, not only for trac signal control to reduce congestion and travel times, but also to estimate vehicular emissions. Traditionally, urban trac control systems have aspired to reduce congestion and their control strategies have re¯ected this. While trac congestion is normally quanti®ed in terms of travel times, delays and vehicle stops, more recently, increased emphasis has been placed on minimizing its negative environmental e€ects. For example, Okado (1997) described a project in Japan where NOx emissions were halved by co-ordinating signals to minimize delay. The Los Angeles Department of Transport installed an automated trac surveillance and control system and found

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that stops were consequently reduced by 35%, intersection delay by 20%, travel time by 13%, fuel consumption by 12.5% and air emissions by approximately 10% (Rowe, 1991). The capital investment in this system was returned in less than one year. To€olo et al. (1997) describe a system in Turin to redistribute trac away from hot spots. They estimate, using a model that CO concentrations decrease by 12% and the number of critical roadways with high NOx concentrations reduces to 17 from 72. In Orlando, the implementation of an integrated transport management system at 365 intersections produced a 56% drop in both vehicle stops and delays, a $2.2 million saving in annual fuel costs, and a 9±14% reduction in air pollutants (US-GAO, 1994). Murthy (1996) found that by applying co-ordination of signals across jurisdictional boundaries in the USA, NOx and Reactive Organic Gases decreased by 14% and 15%, respectively. The State of Washington analysed the bene®ts of upgrading and co-ordinating signal control equipment and re-timing existing signals for six signal systems. These studies showed annual reductions of 295,500 gallons in fuel usage and 145,000 h in vehicle delays (Siwek, 1995). Abbott et al. (1995) reported that a trac control system implemented in Copenhagen, designed to give undelayed progression at a velocity of 40 km-per-hour, gave a 5% reduction in NOx and a 20% reduction in CO and VOC emissions. In Virginia, a study showed that re-timing several signal systems reduced delays by 25.2%, vehicle stops by 25.5%, travel time by 10.2%, fuel consumption by 3.7%, and consequently air pollutants by 16±19% (Siwek, 1995). In many of these studies, changes in trac ¯ow variables are used in emissions calculation models, which are based on relationships between delay or speed and emissions. However, while most pollutants decrease as delay decreases, NOx emissions may actually increase (Wood and Harrison, 1998). Speci®cally, NOx emissions decrease with increasing mean vehicle velocity up to 25 km/h, but increase gradually in the 25±70 km/h range and increase rapidly above 70 km/h. Hence, the optimum trac conditions for emissions minimization may lie between the two extremes of congestion and free-¯ow. This is especially relevant in cities such as Dublin where, in comparison with prescribed limit values, ambient NO2 concentrations are of more concern than other criteria pollutants (Dublin Corporation, 1996). According to Siwek (1995), the environmental impacts of integrated transport management systems can be bene®cial, however the necessary data collection and analysis tools are weak. Abbott et al. (1995) evaluated the e€ects of trac management schemes such as urban trac control systems and signal co-ordination, in terms of fuel consumption and vehicle emissions. They found that generally both emissions and fuel consumption were reduced, however, the estimated reductions were both approximate and based on a combination of empirical and theoretical studies. Another similar study in the US concluded that the bene®ts of trac control signal systems are not being fully realized (US-GAO, 1994). The potential of the SCATS data acquisition technology allows an analytical model to be developed which would improve the evaluation of urban trac control policies. Further, because the emissions model and trac management systems are compatible, it will be feasible to seek to reduce emission levels while simultaneously considering trac ¯ow and congestion. Since similar type trac control systems are installed in many major cities, the methodology developed in Dublin is applicable to other urban areas. Clegg et al. (1999) found that by using di€erent transportation models to evaluate the e€ect of trac control policies implemented, di€erent results were obtained by each model. This system will allow the ÔactualÕ e€ect of trac control policies to be evaluated, by comparing before and after scenarios, rather than the ÔsimulatedÕ e€ect. However, real-time methods cannot be used to

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evaluate the impacts of proposed transport policies, trac management schemes or abatement strategies such as the use of alternative fuels. Therefore an integrated transportation and emissions prediction model is required for o€-line trac strategy assessment. Such a model has already been developed by the authors (Reynolds, 1998; Reynolds and Broderick, 1999a) and is useful in determining trac characteristics on parts of the network not included in the trac management system. The objectives of this study are threefold. Firstly, the identi®cation and feasibility of employing the raw data obtained from inductive loops and CCTV in emissions models is examined. Secondly, an emissions prediction model employing this raw data is validated through comparison with pollution data obtained from kerbside monitoring at a heavily tracked city-centre junction. Thirdly, the observed trac data are compared with the predictions of a transportation network model. In this manner, the accuracy of each stage in the compilation of an emission inventory may be assessed including the use of non-locally obtained emission factors. The focus of the project is on public exposure assessment on sidewalks rather than background concentrations. The type of results obtained are therefore useful in diminishing the requirement for extensive and expensive monitoring of pollution levels envisaged in the European Framework Directive on air quality assessment (Murley, 1998), and in the design and direction of indicative urban air monitoring campaigns.

2. Emissions inventory methodology Mobile source emission inventory calculations depend upon a large number of parameters. These include travel demand, trac conditions (congested or free-¯owing), vehicle operating mode (cruising, idling, accelerating or decelerating), vehicle operating conditions (cold or hot start, average velocity, load, trip length, frequency of trips), vehicle parameters (model and year, state of maintenance, engine type and size, emission reduction devices, accrued mileage, fuel delivery system), fuel characteristics (type, volatility, chemical composition), driver behaviour (gentle or aggressive), local climatic conditions (temperature, humidity), and local topography (road grade, altitude) amongst others. The determination of vehicle emissions is based upon the use of emission factors, which relate the quantity of pollutant emitted to individual and trip characteristics. While there exists a wealth of data on emission factors, their application is limited to the available information on vehicle movements and operating modes, which in this case is provided by SCATS and CCTV, supplemented with transportation modelling results. In general, trac-related exhaust emissions can be divided into three distinct groups: Ôhot emissionsÕ, Ôcold start extra emissionsÕ and Ôevaporative emissionsÕ (Andre et al., 1998). In addition, Salway et al. (1997) gives a detailed description of each of these types of emissions. 2.1. Emissions model The speed-related mass of pollutant emission from a hot engine, Ehot;p;v;y;l;g;s;f is calculated as follows:

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Ehot;p;v;y;l;g;s;f ˆ EFhot;p;v;y;g;s;f  Lsect  VTcruise;v;y;l;f ;

…1†

where · Ehot;p;v;y;l;g;s;f is the emission rate (in g/unit-time) of a particular pollutant p, emitted by vehicles of category v, manufactured in year y, driven on link type l, with an average grade g, at an average speed s, using fuel type f with hot engines; · EFhot;p;v;y;g;s;f the average representative emission factor (in g/km) for the pollutant p, relevant for vehicle category v, manufactured in year y, travelling on carriage-ways with an average grade g, using fuel type f with hot engines; · Lsect the length (in km) of the section of a carriage-way under consideration; · VTcruise;v;y;l;f the volume fraction of vehicles of category v, manufactured in year y, driven on link type l, using fuel type f; · hot refers to emissions from vehicles which have warmed up to their normal operating temperature; · p the pollutant (CO, NOx , PM10 , VOC, etc.); · v the vehicle type (cars, trucks, buses, motorcycles, etc.); · y the vehicle manufacture year; and · l the link type (urban, suburban, rural or motorway); · g the average grade of the carriage-way; · s the average speed of the vehicle; · f is the fuel type (petrol, diesel or LPG). 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 provided by the transportation model. The fraction of VKT with cold engines is called the Cold Mileage Percentage (CMP) and is de®ned by the CORINAIR group (Eggleston et al., 1993a) as CMP ˆ 0:647 ÿ 0:025  Ltrip ÿ …0:00974 ÿ 0:000385  Ltrip †Tamb ;

…2†

in which Ltrip is the average trip length (km) and Tamb is the ambient temperature (o C). The cold emission rate, Ecold;p;v;y;l;g;s;f is calculated as an extra emission rate to the hot emission rate using the following formula Ecold;p;v;y;l;g;s;f ˆ Ehot;p;v;y;l;g;s;f  REFp;v;y;f ;

…3†

where · Ecold;p;v;y;l;g;s;f is the emission rate (in g/unit-time) of a particular pollutant p, emitted by vehicles of category v, manufactured in year y, driven on link type l, with an average grade of g, at an average speed s, using fuel type f with cold engines; and · REFp;v;y;f is the relative emission factor for the pollutant p, relevant for the vehicle category v, manufactured in year y, using fuel type f. Cold start-up emission rates are determined independently by measuring exhaust emissions before the vehicle warms-up or before the catalyst Ôlight-o€ Õ. At signalized junctions and during heavy tracked periods, the trac is delayed due to primary and secondary queues or ÔstopsÕ. Primary stops occur at trac signals while secondary stops occur due to congestion away from signalized junctions. In these states, travel-distance independent emissions are produced. These extra emissions, due to queuing or idling, are added to the speed-

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related emissions determined above. The emission rate from a hot idling engine Eque;hot;p;v;y;l;f is calculated for each link as: Eque;hot;p;v;y;l;f ˆ EFidle;hot;p;v;y;f  Tdelay;l  VTque;v;y;l;f ;

…4†

where: · Eque;hot;p;v;y;l;f is the emission rate of pollutant p (in g/unit-time), caused by vehicles of category v, manufactured in year y, idling on link type l, using fuel type f with a hot engine; · EFidle;hot;p;v;y;f the average representative idling emission factor (in g/s) for the pollutant p, relevant for vehicle category v, manufactured in year y, using fuel type f for a hot engine; · Tdelay;l the average delay time per vehicle on a link l (in seconds); and · VTque;v;y;l;f is the volume of trac of category v, manufactured in year y, idling on link type l, using fuel type f. The cold idling emission rate Eque;cold;p;v;y;l;f is calculated in a similar manner to that employed in Eq. (4) with an average representative idling emission factor EFidle;cold;p;v;y;f for a cold engine being used. It is worth noting that some vehicles will pass through a junction without having to queue and consequently VTcruise;v;y;l;f does not necessarily equal VTque;v;y;l;f . All vehicles required to queue at a junction must decelerate when approaching the intersection and accelerate when leaving it. Unfortunately, their exact acceleration and deceleration rates cannot be determined without specialized equipment, nor are reliable emission factors for these conditions available. However, it has been shown that emission estimation based on the use of simple procedures employing trac ¯ow characteristics such as travel time on a link, delay and stops is often sucient (Chaudhary, 1995), because emissions due to accelerations and decelerations are intrinsically included in average velocity emission factors. The emissions calculated in the above equations originate at the vehicle tailpipe and are called exhaust emissions. They are caused primarily by incomplete combustion. 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. Diurnal emission occurs from evaporation in the fuel delivery system when the vehicle is stationary and the ambient temperature is rising. Hot-soak emissions are caused by high engine and fuel tank temperatures, and occur for one hour after the engine is switched-o€. Resting losses occur when the vehicle is stationary and ambient temperature is constant or decreasing, whereas running losses are caused by evaporation from the fuel delivery system while the engine running. Evaporative emissions are a very important contributor of VOCs, and are primarily associated with petrol-powered vehicles as octane is far more volatile than cetane. NAEI-UK (Salway et al., 1997) proposes using a mean emission rate of 5.9 g per kg of burned fuel. The evaporative emissions (Eevap ) are included as an extra emission from vehicles and are a function of the volatility of the fuel being used, the absolute ambient temperature and temperature changes, and vehicle design characteristics. For hot-soak emissions running losses the driving pattern is also of importance. Eevap;p;v;y;l;s;f ˆ EFevap;p;v;y;f  VKTv;y;l;f ;

…5†

where: · Eevap;p;v;y;l;f is the emission rate (in g/unit-time) of a particular pollutant p, emitted by vehicles of category v, manufactured in year y, driven on link type l, using fuel type f;

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· EFevap;p;v;y;f the average representative evaporative emission factor (in g/km) for the pollutant p, relevant for vehicle category v, manufactured in year y, using fuel type f; and · VKTv;y;l;f is the vehicle kilometers travelled by vehicles of category v, manufactured in year y, driven on link type l, using fuel type f. Road trac also gives rise to particulate matter (PM10 ) from brake-wear, tire-wear and reentrained carriage-way dust. The main source of information on these particulates is AP-42 (US EPA, 1994) and the validity of applying this data to an emission inventory for Dublin is open to question. It is likely however that any errors arising will be smaller than those incurred if these sources were ignored completely. The composite-emission (CE) for any pollutant emitted from an individual vehicle type and model year on a speci®c link using a particular fuel is calculated from the following formula CE ˆ …Ehot;p;v;y;l;g;s;f ‡ Eque;hot;p;v;y;l;f †…1 ÿ CMP† ‡ …Ecold;p;v;y;l;g;s;f ‡ Eque;cold;p;v;y;l;f †…CMP† ‡ Eevap;p;v;y;l;f :

…6†

The total CE of a particular pollutant for all trac on each link is calculated by summing the individual CEs for each vehicle type and year, allowing for the distribution of fuel type throughout the vehicle ¯eet under investigation. Since no emission factors are available speci®cally for Ireland, the emission factors used in this study are therefore derived from three main sources and are compiled in an emissions factor database (EFD): (a) Emission factors developed for urban areas in the UK, for example (Eggleston, 1993b; Gover et al., 1996; SEIPH, 1996; Buckingham et al., 1997; Hutchinson and Clewley, 1996); (b) The European Environment Agency's Atmospheric Emissions Inventory Guidebook (McInnes, 1996) and other European-wide initiatives such as COPERT II (Ahlvik et al., 1997), CORINAIR (Eggleston et al., 1993a), DRIVE-MODEM (Jost et al., 1992), HBEFA (INFRAS, 1995), MEET (H oglund, 1995) and the COST-319 action (Joumard, 1998a,b); and (c) The US EPAÕs manual on the Compilation of Air Pollution Emission Factors, AP-42 (US EPA, 1996). 2.2. Transportation model The transportation model adopted in this study comprises both SATURN ± Simulation and Assignment of Trac in Urban Road Networks (Van Vliet, 1995) and SATCHMO ± SATURN Travel CHoice MOdel (Hall, 1996) models. It provides a detailed representation of the Dublin transportation system for the weekday morning peak hour (8±9 a.m.) and the inter-peak hour (2±3 p.m.) as de®ned by Steer Davis Gleave (1993). The model comprises a substantial amount of data in the form of input, output and control ®les, many of which are speci®c to particular years (either the base year or various forecast years), time periods (peak and inter-peak) and market segments (car available and car not available). Each mechanized mode of both public and private transport is represented explicitly. For modelling purposes the city is divided into 367 zones which, together, cover the greater Dublin area. The model provides sucient data on which to base emission inventory calculations using the methodology outlined above, and the resultant emission predictions can be associated with

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speci®c locations in the trac network. However, when compared with trac information obtained through surveys or trac control systems, the modelled data contain inevitable local errors. Nevertheless, for parts of the urban area in which trac detection devices are not installed and when future year emission predictions are required, model results represent the only available data. As with most common transportation models, this model is built on four main steps: trip generation/attraction, trip distribution, modal choice/split, and trip assignment (Warner, 1985). The trip generation/attraction step determines the number of trips generated in each zone as a function of the socio-economic characteristics of that zone. The generation/attraction method used can be described as a zonally based local model, in that zones are addressed individually rather than being subject to uniform pre-determined growth rates. The trip generation/attraction predictions are grouped according to trip purpose, with the minimum distinction being work, school, business and others (for example social and recreational). Non-home based trips are estimated separately. This second step determines where the generated trip will go to and where the attracted trip comes from. Trips entering or leaving a particular zone are spread into trip origin/ destination (O/D) matrices of trac volume ¯ows for that zone. The modal choice/split step aims at assessing the share of di€erent modes of transport within mobility as a whole. The functional form used is the ratio of car trips to public transport trips as a function of the relative time, costs and zonal characteristics. Trip assignment is the process in which route choice is modelled. The trip assignment module assigns trips between pairs of zones to highway routes, based on capacity constraints and the minimum time and cost, or combinations of time and cost path between the zones. Mode choice and assignment in congested urban networks are co-dependant. The two inputs for SATURN are the Ôtrip O/D matrixÕ, which speci®es the number of trips from zone to zone and the ÔnetworkÕ, which speci®es the geographical location and connectivities of the roads upon which these trips take place. This physical network is composed of many sections of roads called links. A node at either end de®nes each of the links; trac entering the link at one node and exiting at the other. The network of major roads includes all motorways, main roads, secondary roads, and the more commonly used minor streets. These two inputs to SATURN may be thought of as demand and supply inputs. Both the matrix and network are input to a route choice model which allocates trips to routes throughout the network, from which total volume ¯ows along links may be summed and the corresponding network costs (for example, time delays and queues) calculated. The transportation model categorizes the motor vehicles into a three-category classi®cation system: light-duty-vehicles (LDVs) including passenger-cars and light-goods-vehicles (LGVs); heavy-goods-vehicles (HGVs); and buses. When combined with suitable laboratory data, this allows separate emissions to be determined for each vehicle type, as indicated in Eq. (6). 3. Assessment of methodology To assess the applicability and validity of the methodology proposed above, a heavy tracked junction within the centre of Dublin is investigated as a case-study. Previous studies have found that emissions are higher near junctions with the maximum occurring in the queuing area of each link (Calggett et al., 1981; Matzoros, 1990; Namdeo and Colls, 1995).

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An indirect means of assessing the accuracy of the vehicle emissions calculations is employed. This involves the application of an atmospheric pollutant dispersion model to the conditions measured and emissions predicted on site. The ambient concentrations predicted are compared with in situ and contemporary air quality measurements. The dispersion model used has been speci®cally developed for road-trac pollution assessment, but does not take photochemical reactions into account. However, as the air pollution is measured at the kerbside, the distance between the exhaust and the receptor is small and dilution is consequently low. Since the eventual urban area model can be considered as an amalgamation of similar junctions and links, such local assessments of methodology accuracy provide a platform for assessing the validity of the emissions inventory as a whole. Further local assessments, for example in a suburban area, would in turn increase con®dence in the utility of the model. 3.1. Survey site The survey site is at an arterial junction in the heart of Dublin City, which is characterized by high local congestion. It is one of the busiest signalized intersections in the city, with nearly forty thousand vehicles traversing the junction daily. The junction geometry is depicted in Fig. 1, in which the direction of trac ¯ow through the junction is indicated. Lombard Street consists of a one-way three-lane roadway, while Westland Row has one lane going in either direction. Pearse Street has four lanes travelling in the same direction.

Fig. 1. Intersection geometry at the survey site.

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3.2. Trac ¯ow monitoring As trac ¯ow represents one of the primary variables in this study, the ®rst step in the compilation of an emissions inventory is to determine the primary trac parameters. In total, six parameters are considered: volume, composition, average velocity, average queue length, link delay and turn delay. Selection of these parameters is based upon factors such as the availability of data from SCATS and CCTV, the computational eciency of the overall model and the robustness of the model to future enhancements. All trac sampling was carried out in accordance with procedures as laid out by the National Roads Authority (Holland and Kennedy, 1997). 3.3. Trac volume Automatic trac sensors measure the trac volume passing through the junction and the distance between each vehicle, which is a measure of the level of congestion. These sensors consist of induction loops embedded beneath each trac lane surface, which can detect vehicles passing above, but cannot distinguish between various types of vehicles. Hence, only total trac volume and level of congestion may be recorded, this data being stored as aggregate values, per lane, and is reported on a minute-by-minute basis. The diurnal variation of trac volume for a weekday is depicted in Fig. 2. The sharp fall in ¯ow close to 6 pm re¯ects severe trac congestion during the evening peak demand period.

Fig. 2. Diurnal variation of measured trac volume for a weekday.

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3.4. Trac composition A video camera was employed to record morning peak hours (8±9 a.m.) and inter-peak hours (2±3 p.m.) at the junction so that more speci®c trac parameters could be established. To this end, a ®ve-category motor-vehicle classi®cation system is employed: passenger-cars; light-goodsvehicles (LGVs); buses; heavy-goods-vehicles (HGVs); and motorcycles (MCs). The observed vehicle mix for peak and inter-peak hours, in terms of the percentage vehicle categorization at the junction in question, is given in Tables 1 and 2. It is worth noting that passenger cars are the populated vehicle category both for peak (i.e. 86.3%) and inter-peak (i.e. 73.5%) hours. 3.5. Average velocity and queue length In order that an accurate estimate of the space-mean-speed over one-hour periods could be evaluated, trac velocities were determined using camera recordings. It was not feasible in this study to measure the time-mean-speed, acceleration and deceleration of the vehicles at the trac junction. The required sample size for vehicle velocity and trac volume needs to consider errors resulting from velocity measurements. Errors can arise due to both the sample size and the precision of the measurement (Bennett, 1994). However, the required size of the sample is dependent on the distribution of velocities, the precision of the measurement, and the chosen con®dence intervals. The size of the sample can be estimated from the following equations (Pan, 1995). Table 1 Observed vehicle categorization of trac passing through the study junction during the peak hour (8±9 a.m.) Carriage-way (Dir'n wrt Junc.)

Motor vehicle type (%) MCs (%)

Cars (%)

LGVs (%)

HGVs (%)

Buses (%)

Total

Pearse St (to) Pearse St (from) Lombard St (to) Westland Row (to) Westland Row (from)

1.2 1.2 3.8 0.7 3.7

90.7 88.3 83.5 84.2 85.1

4.8 5.7 9.3 6.7 8.5

2.9 2.8 2.6 1.9 1.9

0.4 1.9 0.8 6.6 0.8

2057 2898 1024 701 884

Mean

2.1

86.3

7.0

2.4

2.1

Table 2 Observed vehicle categorization of trac passing through the study junction during the interpeak hour (2±3 p.m.) Carriage-way (Dir'n wrt Junc.)

MCs (%)

Motor vehicle type (%) Cars (%)

LGVs (%)

HGVs (%)

Buses (%)

Total

Pearse St (to) Pearse St (from) Lombard St (to) Westland Row (to) Westland Row (from)

4.0 3.8 5.0 3.3 5.4

73.8 73.2 73.1 73.2 74.3

17.0 15.8 15.7 12.2 14.8

4.3 4.5 4.1 4.1 2.8

1.0 2.7 2.1 7.2 2.7

1608 2477 813 691 635

Mean

4.3

73.5

15.1

4.0

3.1

A.W. Reynolds, B.M. Broderick / Transportation Research Part D 5 (2000) 77±101

 nˆ

sˆ Vavg

sxK E

89

2 ;

q
2 Pn ÿ V ÿ V i avg iˆ1 nÿ1  n  X Vi ; ˆ n iˆ1

…7†

;

…8† …9†

where n is the minimum acceptable sample size; s the standard deviation of the velocity measurements; K the normal distribution factor; E the error limits due to chance; Vavg the mean observed velocity for all trac on a link; and Vi is the individual vehicle mean-spacevelocity. If a con®dence interval of 95% is chosen and vehicle velocities are assumed to be normally distributed then the K factor has a value of 1.96. E has a value of 2 when the standard deviation of the sample is in the range of 8±12 km/h (Bennett, 1994). The minimum sample size is then in the range of 65±140. In the surveys, all vehicles passing through the junction were included. When the system is automated, it is envisaged that only the minimum sample will be taken into account to minimize processing times. Tables 3 and 4 present the observed average queues, link delay, turn delay and average velocities for the junction during peak and inter-peak hours. The average queues are speci®ed in terms of passenger carrying units (pcus). The daily trac volume can be determined using Table 3 Observed trac characteristics at the study junction during the peak hour (8±9 a.m.) Carriage-way (Dir'n wrt Junc.)

Average (pcu)

Link queue (s.)

Turn delay (s)

Average velocity (km/h)

Pearse St (to) Pearse St (from) Lombard St (to) Westland Row (to) Westland Row (from)

5 0 6 4 0

17 N/A 37 N/A N/A

12 N/A 28 39 N/A

9.5 9.4 12.7 9.6 11.5

Table 4 Observed trac characteristics at the study junction during the inter-peak hour (2±3 p.m.) Carriage-way (Dir'n wrt Junc.)

Average queue (pcu)

Link delay (s)

Turn delay (s)

Average velocity (km/h)

Pearse St (to) Pearse St (from) Lombard St (to) Westland Row (to) Westland Row (from)

3 0 4 3 0

10 0 27 N/A 0

8 N/A 31 37 N/A

11.3 10.7 9.1 8.5 11.7

90

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updated expansion factors for short period trac counts developed by the National Roads Authority (Devlin, 1998). For example, the daily trac volume for each link in the Dublin road network can be computed from the sum of 3.95 times the peak hour volume plus 11.35 times interpeak hour volume (Delvin, 1988). The trac volume for either Saturday or Sunday is the product of the inter-peak hour volume and a constant, 15.3 (Delvin, 1988). 3.6. Transportation model results Tables 5 and 6 present the results of the peak hour and inter-peak hour transportation model simulations in terms of the percentage vehicle classi®cation at the junction in question. The predicted time delays (both link and turn depending on which is applicable), average queue, and average vehicle velocity are shown in Tables 7 and 8. The transportation model speci®es the average queue lengths in pcus. 3.7. Comparison of modelled and observed trac variables The transportation model has been extensively calibrated and validated against independent trac surveys across Dublin. In this study, measured trac parameters are used to validate

Table 5 Predicted vehicle categorization of trac passing through the study junction during the peak hour (8±9 a.m.) Carriage-way (Dir'n wrt Junc.)

Motor vehicle type (%) LDVs (%)

HGVs (%)

Buses (%)

Total

Pearse St (to) Pearse St (from) Lombard St (to) Westland Row (to) Westland Row (from)

95.7 93.5 95.4 81.2 94.0

3.8 4.2 3.1 8.1 3.8

0.5 2.3 1.5 10.8 2.2

1311 2019 1072 372 733

Mean

91.9

4.6

3.5

Table 6 Predicted vehicle categorization of trac passing through the study junction during the inter-peak hour (2±3 p.m.) Carriage-way (Dir'n wrt Junc.)

Motor vehicle type (%) LDVs (%)

HGVs (%)

Buses (%)

Total

Pearse St (to) Pearse St (from) Lombard St (to) Westland Row (to) Westland Row (from)

99.3 91.9 87.2 73.2 76.3

0.1 6.1 10.1 18.6 12.4

0.6 2.0 2.7 8.3 11.3

772 1568 631 327 151

Mean

85.6

9.5

5.0

A.W. Reynolds, B.M. Broderick / Transportation Research Part D 5 (2000) 77±101

91

Table 7 Predicted trac characteristics at the study junction during the peak hour (8±9 a.m.) Carriage-way (Dir'n wrt Junc.)

Average queue (pcu)

Link delay (s)

Turn delay (s)

Average velocity (km/h)

Pearse St (to) Pearse St (from) Lombard St (to) Westland Row (to) Westland Row (from)

5 0 6 2 0

14 N/A 21 N/A N/A

17 N/A 20 18 N/A

13 11 15 10 24

Table 8 Predicted trac characteristics at the study junction during the inter-peak hour (2±3 p.m.) Carriage-way (Dir'n wrt Junc.)

Average queue (pcu)

Link delay (s)

Turn delay (s)

Average velocity (km/h)

Pearse St (to) Pearse St (from) Lombard St (to) Westland Row (to) Westland Row (from)

2 0 5 0 0

11 0 30 N/A 0

17 N/A 40 12 N/A

15 35 10 13 31

simulated parameters as an assurance that these trac variables are an adequate basis on which to base an emissions inventory whenever direct measurements are not available. Tables 1, 2, 5 and 6 show the modelled and measured trac volume and percentage mix for the junction. The observed vehicle ¯ow rates and percentage trac composition agree reasonably well with those predicted by the transportation model. At peak hour, the total predicted ¯ow through the junction is 73% of that observed. This di€erence is slightly greater for the o€-peak simulation. Overall, the model over-estimates the proportion of HGVs and buses within the trac stream. For example, 99 HGVs were observed to pass through the junction during the peak hour, while the model predicts 113. In addition, the transportation model does not take account of MCs, further a€ecting the volume composition. For both peak and o€-peak hours, the average queue length and link delay parameters are well predicted, but turning delays are not. Average vehicle velocities in the major ¯ows on Pearse St. were observed to be approximately 20% lower than predicted. Overall, the transportation model generally under-predicts trac volume, congestion and delays. This discrepancy may be due, in part, to the fact that certain journeys are not incorporated within the trip matrix (e.g. private buses, taxis, out-of-town visitors, etc.) and therefore are not included in the assignment module. The transportation model also over-predicts inter-peak hour velocities, delays and congestion for all streets except Westland Row because the junction trac signals are set to give a priority to trac at nearby junctions. It is felt that these discrepancies are indicative of the overall ability of the transportation model to predict actual on-street trac conditions and, as such, to act as the basis of an emissions inventory. The calculation of this inventory for the study junction and the assessment of its accuracy through comparison with ®eld data are described in the next section.

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4. Emissions inventory Trac data from video recordings and ®eld surveys as well as statistical information for the entire metropolitan area are combined with relevant emission factors to compile an emissions inventory for each of the carriage-ways leading to and from the junction. The inventory is developed for key pollutants such as SO2 , NOx , CO, VOC, PM10 , C4 H6 and C6 H6 . The trac in each lane is assumed to produce emissions at the centre of the carriage-way. Carriage-ways with more than one lane are modelled as separate roads running adjacent and parallel to each other. Each lane is divided into a number of segments re¯ecting the areas in which vehicles are more likely to be in steady-state velocity or queuing mode; all vehicles not queuing in a lane being assumed to be travelling at constant velocity. For these vehicles, an average velocity is assumed over the street length and an entirely velocity-dependent emission-rate is employed. For queuing vehicles, the mean queuing period at each location is used to calculate stationary emissions. As described previously, no distinction was made between acceleration, deceleration and cruise modes because only vehicle space-mean-speed was measured. Also, as link lengths around the study junction are relatively short, an estimate of acceleration and deceleration zones would not be very meaningful. However, as already stated, emissions due to acceleration and deceleration are intrinsically included in the average velocity emission factors The age pro®le of the trac, percentage of vehicles within each fuel category and proportion of vehicles ®tted with catalytic converters are estimated from the vehicle licensing records. In the greater Dublin area, approximately 21% of cars have petrol engines with a catalytic converter, 65% without a catalytic converter and the remaining 14% burn diesel. In the case of goods vehicles, both HGVs and LGVs, 93% have diesel engines with only about 6.5% using petrol (CSO, 1998). Vehicle emissions in Europe are governed by a series of EU Directives such as ECE, 91/441/ EEC and 94/12/EEC (Murley, 1998). Table 9 presents the proportion of cars by engine size, divided into groups that re¯ect these legislative steps (DOE, 1997). The age distribution of cars and combined goods vehicles, both HGV and LGV, is depicted in Fig. 3 (DOE, 1997). As an example of the emission factors stored in the EFD, Fig. 4 shows the velocity-related emission factors for CO, NOx , PM10 , and VOCs as applied to the car population in Dublin. The emission factors are normalized by the emission factors at 30 km/h. These are 14.79 g/km for CO, 1.71 g/km for NOx , 0.05 g/km for PM10 and 1.77 g/km for VOCs. By using an ambient temperature of 10o C and an average trip length of 16.9 km, it is estimated that 19% of all journey distances in the greater Dublin area are driven with cold-engined vehicles. The e€ects of ambient temperatures on the operating characteristics of vehicle engines can be assumed constant since a Table 9 Passenger car pro®le according to governing legislation and engine capacity (DOE, 1997) Legislation steps

Passenger cars (%) <1.4 L (%)

1.4±2.0 L (%)

>2.0 L (%)

94/12/EEC 91/441/EEC ECE

10.9 7.4 42.2

6.6 6.0 24.6

0.6 0.3 1.4

Total

60.5

37.2

2.3

A.W. Reynolds, B.M. Broderick / Transportation Research Part D 5 (2000) 77±101

93

Fig. 3. Age pro®le of trac ¯eet (DOE, 1997).

speci®c control period is much shorter (e.g. a hour) than a given season (e.g. spring, summer) (Chaudhary, 1995). The emissions inventory for all the carriage-ways leading to and from the study junction is computed in accordance with the methodology described earlier. Tables 10 and 11 presents the emissions inventories compiled for all trac passing through the study junction during the peak hour (8±9 a.m.) and inter-peak hour (2±3 p.m.). Some of the emission predictions are approximate or uncertain, and depend to a considerable degree on available statistical data. It is also worth noting that due to lack of baseline emission rates and correction factors for di€erent categories of vehicles, the methodology described above cannot be applied in full or in the same way for all pollutants considered. In general, emissions inventories are obsolete by virtue of the time taken to amass, compile and verify the diverse and elaborate set of data required. In this regard, the compilation of an emissions inventory is an ongoing process, and the most up-to-date emission factors available have been used. While these are certain to change with ongoing research and testing, new data can be readily incorporated into the proposed emissions model. 4.1. Sensitivity analysis and estimation of errors Motor vehicle emission inventory modelling is a scienti®c activity that requires abstraction and simpli®cation of complex real world processes. This simpli®cation leads to inherent model

94

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Fig. 4. Velocity-related emission factors for passenger cars in the UK (Buckingham et al., 1997).

Table 10 An emissions inventory compiled for all trac passing through the study junction during the peak hour (8±9 a.m.) Carriage-way (Dir'n wrt Junc.)

Emissions inventory (in g/h) SO2

Pearse St (to) Pearse St (from) Lombard St (to) Westland Row (to) Westland Row (from)

12.3 31.8 10.7 4.5 4.5

Total

64

NOx 301.6 815.8 259.4 129.8 106.8 1613

CO 1925.7 4651.5 1648.3 589.0 722.6 9537

VOC 307.7 751.0 316.5 93.3 137.4 1606

PM10 15.7 47.2 15.2 8.8 6.0 93

C4 H6 3.7 8.9 3.1 1.1 1.3 18

C6 H6 11.9 28.0 9.7 3.3 4.3 57

uncertainty and error. The sources of error can be grouped into two broad classes, namely internal and external (Negrenti, 1998). The internal sources of error involve the measurement and subsequent development of the emission factors. Due to the fact that con®dence intervals have not been reported for most experimental studies, an assessment of internal modelling errors is limited to a comparison of the average values of emission factors obtained in di€erent studies. On this basis, mean emission rates

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95

Table 11 An emissions inventory compiled for all trac passing through the study junction during the inter-peak hour (2±3 p.m.) Carriage-way (Dir'n wrt Junc.)

Emissions inventory (in g/h)

Pearse St (to) Pearse St (from) Lombard St (to) Westland Row (to) Westland Row (from)

10.8 30.5 9.7 5.0 3.7

Total

60

SO2

NOx 269.6 807.4 249.5 145.9 94.5 1567

CO 1392.1 3741.0 1259.1 568.2 506.1 7467

VOC 280.1 743.8 267.9 109.2 108.6 1510

PM10 18.4 56.9 17.4 10.8 6.5 110

C4 H6 2.7 7.1 2.3 1.0 0.9 14

C6 H6 8.0 20.9 7.0 3.0 2.8 42

appear to have an associated uncertainty factor of approximately two. It should be noted that these average values were not used in the emissions inventory calculations. The external sources of error involve the input variables such as trac ¯ow, sub-¯eet characterization, ambient temperature, and trip length. The major concern with many emission inventory models is the error inherent with the estimation of total trac volumes and subsequent e€ect on the output (Chapin, 1993; Negrenti, 1998). This problem is addressed here through the integration of real-time trac ¯ow measurements. A sensitivity analysis has been carried out on the methodology to determine the likely e€ects of other input errors, the results of which are fully documented elsewhere (Reynolds and Broderick, 1999a). All sensitivity analyses carried out displayed ÔreductionÕ e€ects, whereby changes in output are smaller than the corresponding changes in the input parameter value. For example, a 25% decrease in all trip lengths resulted in only a 4% increase in CO. Emissions of some pollutants are more sensitive to a particular input parameter than others. For example, NMVOC emissions appear to be relatively sensitive to any change in trip length, while CO2 emissions are not. 4.2. Air quality monitoring As no practical means of directly measuring emissions was available in the study, the validation of the above inventory is achieved through comparison with simultaneous ambient air quality measurements. Croxford and Penn (1998) suggest some guidelines for siting urban pollution monitors. An on-line air-quality monitoring unit was located at the kerbside (see Fig. 1). This unit was used to measure a wide variety of pollutants including CO, NOx , PM10 , SO2 and 23 di€erent VOCs. Individual VOCs are measured using the principle of gas chromatography. The measurement system consists of three main components; an ÔATD400Õ sample preconcentrator, an ÔAutosystem GCÕ gas chromatograph and ÔTurbochromÕ software used for system control, data collection and data analysis. NOx , SO2 and CO are measured using electrochemical sensors, based on fuel cell principles, with aqueous electrolytes. The tapered-element-oscillating-mass (TEOM) monitor measures PM10 concentrations. The measured data can be uploaded to either a data-logger or computer. These monitoring facilities have been described in greater detail elsewhere Keating et al., 1998; Marnane et al., 1998; Reynolds and Broderick, 1999b). A feature of all of the monitoring instruments used is that they record measurements at short time-intervals, allowing concentration variations to be assessed.

96

A.W. Reynolds, B.M. Broderick / Transportation Research Part D 5 (2000) 77±101

A meteorological station is also contained in the air quality monitoring unit. It consists of four sensors that measure relative humidity, temperature, wind direction and velocity. These sensors are attached to a telescopic pole and are connected to a data-logger located within the trailer. The temperature, wind velocity and direction will di€er from the regional meteorology, measured at the airport, due to the in¯uences of the local buildings. 4.3. Pollutant dispersion Air quality modelling was employed to provide a means by which ambient pollutant concentrations could be compared with the predicted emissions inventory. Emissions predicted using observed trac data were employed with site geometry and meteorological data to determine the resulting concentration ®eld using CALINE4, a steady-state line-source model speci®cally developed to predict air pollutant levels near highways and arterial streets (Benson, 1992). Although CALINE4 was developed by the US-EPA (1996), it is also used in several European countries. The model computes the e€ect on air quality of trac on carriage-ways in relatively ¯at terrain in an urban environment. Given that the maximum building height around the junction does not exceed ten meters, and the receptor is close to the emission source, the use of CALINE4 is appropriate. Although based on the gaussian plume approximation, the model also allows for deposition and sedimentation in order when computing particulate concentrations. Ambient concentrations of CO, NOx , SO2 , and particulate matter are predicted. The VOCs, C4 H6 and C6 H6 are modelled as inert gases. 4.4. Comparison of predicted and observed concentrations Observed kerbside pollution concentrations display large short-term (minute-by-minute) variations and are highly-sensitive to the exact monitoring location employed. This results in a signal to noise ratio so low that direct time-series comparison between pollution concentration and passing trac ¯ows is almost meaningless (Penn et al., 1996). However previous studies have shown that good correlations can be achieved between observed and predicted pollutant concentrations when these are averaged over longer periods of time e.g. hourly (Namdeo and Colls, 1996; Dracoulides and Duthiewicz, 1995; Karppinen et al., 1997; Bardeschi et al., 1991; Seika et al., 1998; Sokhi et al., 1998; Akeredolu et al., 1995; denBoeft et al., 1996). While more accurate methods, such as second-by-second micro-simulation of speci®c vehicles, are available to predict emissions, the trac data required are not directly available from SCATS. Table 12 compares the mean hourly measured and predicted kerbside concentrations of SO2 , CO, NOx , VOC, PM10 , C4 H6 and C6 H6 . The standard deviation of the measured pollutants is also given in Table 12. The variation in measured average hourly concentrations is also given as is the ratio of predicted to observed pollutant concentrations. In general, there is good agreement between the two values, as with the exception of SO2 these are all within 25% of each other (see last column in Table 12). This suggests that the time-averaged emission rates employed in the dispersion model agree reasonably well with those occurring on site. The model tends to over-predict VOC pollutant concentrations and under-predict non-VOC concentrations. The former observation may be attributed to the fact that the dispersion model does not incorporate any chemical reaction sinks. The relatively poor correlation between the measured and observed SO2

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97

Table 12 Mean hourly observed pollutant concentrations measured at the junction and the corresponding predicted pollutant concentrations determined using observed trac data (the standard deviations of the observed pollutant concentration values, and the ratio of the predicted to the observed (P/O) concentrations are given also) Pollutant

Predicted hourly concentration

Mean hourly observed concentration ‹ 1 std. dev. (lg/m3 )

P/O

SO2 CO NOx VOC PM10 C4 H6 C6 H6

8.29 2842.63 66.39 106.42 13.53 0.79 5.09

17.12 + 11.14 3586.21 + 758.62 75.47 + 37.74 99.17 + 82.60 18.31 + 12.11 1.02 + 0.74 4.97 + 4.73

0.48 0.79 0.88 1.07 0.74 0.77 1.03

concentrations may be due to the fact that the percentage of diesel engined motor vehicles is an estimate for all of Dublin and not just this intersection. It should be noted that dispersion modelling does not attempt to predict exact time-speci®c ambient concentrations. While signi®cant advances in techniques have been made over the last few decades, predictions remain critically dependent on a number of input variables such as emission rates and meteorological conditions. On account of this, time-averaged solutions are employed, and short-term variations are not re¯ected in the calculations. Therefore the mean hourly concentrations are given rather that individual or day-speci®c peak and inter-peak hours.

5. Conclusions The integration of urban trac control systems and the assessment of the environmental impacts of motor vehicles o€ers a realistic means of determining improved emission inventories. This is because many of the inherent errors associated with other methods of trac volume estimation are reduced signi®cantly. In this paper, real-time data from existing trac control instrumentation are employed, ensuring that the required information is provided without incurring additional resource requirements. This methodology is used in the development of an emissions model, the utility of which has been investigated through its application at an experimental site. This approach is to be extended to cover the entire domain governed by the SCATS trac control system. As this system is only installed on junctions in the city centre, transportation model results will be used to provide trac variables for the remainder of the urban area. Trac variables from the transportation model can also be used to evaluate any proposed changes in trac management schemes, public transport or infrastructure. A sample emissions inventory was compiled for all carriage-ways around one of the busiest intersections in the centre of Dublin. Trac variables obtained through video recordings and induction loop counts were used. Other information such as the number of cold-starts, the percentage of petrol, diesel or LPG engined-vehicles and the age pro®le of the ¯eet, not directly available from the trac control system, were derived from local statistical data. As an assurance that the Dublin transportation model was suciently accurate to support a reliable emissions inventory, observed and predicted trac data were compared. It was

98

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demonstrated, albeit for one site only, that there is reasonable agreement between the trac data from the transportation model and that measured in the ®eld. The accuracy of the emissions inventory model depends largely on the accuracy of the emission factors used. Emission inventories can be validated using vehicular on-board instrumentation or remote-sensing equipment (Stephens, 1994) to measure the components of exhaust gases from trac. It was not possible in this study to directly validate the inventory in this way. Therefore, an alternative approach was used which involved utilizing a recognized dispersion model to calculate the impact of the predicted emissions on local ambient air quality. The concentrations of pollutants dispersed from the intersection were measured experimentally and compared with the theoretical values; overall, reasonably close values were obtained. This comparison was performed on the basis of mean peak-and inter-peak hour trac and pollution concentrations, masking some errors and uncertainties which may be arise in short-term predictions. Moreover, although a wide range of pollutants were considered in the experimental data, the qualitative comparison relates only to total emissions and does not contain any information on the relative contribution of individual vehicle types. Nevertheless, the results demonstrate that the methodology employed to develop a trac emissions inventory is locally reliable if the trac ¯ow is properly quanti®ed and characterized, and time-averaged emissions are acceptable.

Acknowledgements The authors would like to acknowledge the support of the Dublin Transport Oce and ESB International, who both partially funded this study. Dublin Corporation, the National Roads Authority, Department of the Environment and the Central Statistics Oce provided trac ¯eet and vehicle data. Permission to employ the transportation model was granted by W.S. Atkins. Monitoring data were provided by I. Marnane and D. Keating, research students at Trinity College Dublin. References Abbott, P.G., Hartley, S., Hickman, A.J., Lay®eld, R.E., McCrae, I.S., Nelson, P.M., Phillips, S.M., Wilson, J.L., 1995. The Environmental Assessment of Trac Management Schemes. Transport Research Laboratory, Crowthorne, UK. Adler, U., 1997. Bosch Automotive Handbook, 4th ed. Robert Bosch GmbH, Postfach, Stuttgart, Germany. Akeredolu, F.A., Oluwole, A.F., Betiku, E.A., Ogunsola, O.J., 1995. Modelling Of Carbon Monoxide Concentrations from Motor Vehicles Travelling Near Roadway Intersections. Obafemi Awolowo University, Ile-Ife, Lagos, Nigeria. Aklvik, P., Eggleston, S., Gaudioso, D., Goriûen, N., Hassel, D., Hickman, A.J., Joumard, R., Ntziachristos, L., Rijkeboer, R.C., Samaras, Z., Zierock, K.H., 1997. COPERT II ± Computer Program To Calculate Emissions From Road Transport. European Topic Centre on Air Emission, EEA, Copenhagen, Denmark. Algers, S., Bernauer, E., Boero, M., Breheret, L., Di Taranto, C., Dougherty, M., 1998. Review of Micro-Simulation Models. Institute of Transport Studies, Leeds, UK. Andre, M., Kyriakis, N.A., Hammarstrom, U., Hickman, A.J., Samaras, Z., 1998. Trac characteristics. In: Proceedings of the Eighth CRC On-road Vehicle Emissions Workshop. San Diego, CA.

A.W. Reynolds, B.M. Broderick / Transportation Research Part D 5 (2000) 77±101

99

Bailey, B., 1995. Atmospheric Process Evaluation of Mobile Source Emissions. College of Engineering, Centre for Environmental Research and Technology (CE-CERT), University of California, Riverside, CA. Bardeschi, A., Colucci, A., Gianelle, V., Gnagnetti, M., Tamponi, M., Tebaldi, G., 1991. Analysis of the impact on airquality of motor-vehicle trac in the milan urban area. Atmospheric Environment Part B ± Urban Atmosphere 25 (3), 415±428. Barth, M., An, F., Norbeck, J., Ross, M., 1996. Modal emissions modelling: A physical approach. Transportation Research Record 50, 81±88. Bennett, C.R., 1994. A Speed Prediction Model For Rural Two-Lane Highways. School of Engineering, University of Auckland, New Zealand. Benson, P.E., 1992. A review of the development and application of the CALINE3 and CALINE4 models. Atmospheric Environment Part B ± Urban Atmosphere 26 (3), 379±390. Bishop, G.A, Stedman, D.H, 1990. On-road carbon-monoxide emission measurement comparisons for the 1988±1989 Colorado oxy-fuels program. Environmental Science and Technology 24 (6), 843±847. Buckingham, C., Forest, G., Sadler, L., Shah, S., Wickham, L., 1997. Atmospheric Emissions Inventory for Greater Manchester. London Research Centre, Black Prince Road, London, UK. Calggett, M., Shrock, J., Noll, K.E., 1981. Carbon monoxide near an urban intersection. Atmospheric Environment 15 (9), 1633±1642. Cardelino, C., 1998. Daily variability of motor vehicle emissions derived from trac counter data. Journal of the Air & Waste Management Association 48 (7), 637±645. Chapin, C.E., 1993. Mobile source CO modelling ± how good is it. In: Proceedings of 86th Annual Meeting of the Air & Waste Management Association. Chaudhary, N.A., 1995. Vehicle Emissions and Fuel Consumption Estimation in PASSER IV. Texas Transportation Institute, Texas A&M University System, College Station, Texas. Clegg, R., Clune, A., Routledge, I., Smith, M., 1999. MUSIC: beyond predict and provide; signal polices for trac demand management. In: Proceedings of the 31st Universities Transport Study Group. University of York, UK. Croxford, B., Penn, A., 1998. Siting considerations for urban pollution monitors. Atmospheric Environment 32 (6), 1049±1057. CSO, 1998. General Statistics. Central Statistics Oce, Dublin, Ireland. Delvin, J., 1988. Expansion Factors For Short Period Trac Counts. National Roads Authority, Dublin, Ireland. DenBoeft, J., Eerens, H.C., Den Tonkelaar, W.A.M., Zandveld, P.Y.J., 1996. CAR International: a simple model to determine city street air quality. Science of the Total Environment 190, 321±326. DOE, 1997. Irish Bulletin Of Vehicle And Driver Statistics. Department of Environment, Shannon, Clare, Ireland. Dracoulides, D.A., Duthiewicz, R.K., 1995. Evaluation of the ISCST2 Model with Measurements of SO2 Concentrations in the Greater Cape Town Region. Energy Research Institute, University of Cape Town, South Africa. Dublin Corporation, 1996. Atmospheric pollution and noise control unit Annual Report. Environmental Health Section, Dublin Corporation, Dublin, Ireland. Eggleston, H.S., Gaudioso, D., Goriûen, N., Joumard, R., Rijkeboer, R.C., Samaras, Z., Zierock, K.H., 1993. CORINAIR Working Group on Emissions Factors for Calculating 1990 Emissions from Road Trac. European Topic Centre on Air Emission, EEA, Copenhagen, Denmark. Eggleston, H.S., 1993. Pollution in the Atmosphere: Future Emissions from the UK. Warren Spring Laboratory, Stevenage, Hertfordshire, UK. Eisele, W.L., Turner, S.M., Benz, R.J., 1996. Using Acceleration Characteristics in Air Quality And Energy Consumption Analyis. Texas Transportation Institute, Texas A&M University System, College Station, Texas. Gover, M.P., Collings, S.A., Hitchcock, G.S., Moon, D.P., Wilkins, G.T., 1996. Alternative Road Transport Fuels ± A Preliminary Life-cycle Study for the UK. Energy Technology Support Unit, Harwell, Oxfordshire, UK. Hall, M., 1996. SATCHMO User Manual. WS Atkins Planning Consultants Ltd., Epson, Surrey, UK. H oglund, P.G., 1995. Estimating Trac Related Exhaust Emissions and Imissions at Road and Street Intersections. Esitelm a: KFB and VTI, Link oping, Sweden. Holland, T.J., Kennedy, M., 1997. Trac Station Counts And Road Travel For 1996. National Roads Authority, Dublin, Ireland.

100

A.W. Reynolds, B.M. Broderick / Transportation Research Part D 5 (2000) 77±101

Hutchinson, D., Clewley, L., 1996. West Midlands Atmospheric Emissions Inventory. London Research Centre, Black Prince Road, London, UK. INFRAS 1995. Workbook / Handbook On Emission Factors For Road Transport. INFRAS, Bern, Umweltbundesamt, Berlin, and Bundesamt f ur Umwelt, Wald und Landschaft, Bern, Germany. Jost, P., Hassel, D., Weber, F.J., 1992. Emission and Fuel Consumption Modelling Based on Continuous Measurements. TUV Rheinland, Cologne, Germany. Joumard, R., 1998a. European initiatives for modelling emissions from transport. In: Proceedings of the Eighth CRC On-road Vehicle Emissions Workshop. San Diego, CA. Joumard, R., 1998b. Methods of estimation of atmospheric emissions from transport: European scientist network /and scienti®c state-of-the-art action COST 319. Final Report. INRETS, Bron cedex, France. Joumard, R., Jost, P., Hickman, J., Hassel, D., 1995. Hot passenger car emissions modelling as a function of instantaneous speed and acceleration. Science of the Total Environment 169 (1-3), 167±174. Karppinen, A., Kukkonen, J., Konttinen, M., Rantakrans, E., Valkonen, E., Harkonen, J., Koskentalo, T., Elolahde, T., 1997. Comparison of Dispersion Model Predictions and the Results from an Urban Air Quality Measurement Network, Air Pollution ± Modelling, Monitoring and Management. Computational Mechanics, Billerica, MA, pp. 405±411. Keating, D., Marnane, I., Misstear, B., Broderick, B., 1998. An advanced air pollution monitoring system. In: Journal of the Institution of Engineers of Ireland. Dublin, Ireland. Kelly, N.A, Groblicki, P.J., 1993. Real-world emissions from a modern production vehicle driven in Los-Angeles. Journal of the Air & Waste Management Association 43 (10), 1351±1357. Loibl, W., Orthofer, R., Winiwarter, W., 1993. Spatially disaggregrated emission inventory for anthropogenic NMVOC in Austria. Atmospheric Environment 23, 37±48. Longhurst, J.W.S., Power, H., 1998. Air Pollution Emissions Inventory, Computational Mechanics Publications, Billericia, MA. Marnane, I., Keating, D., Broderick, B., Misstear, B., 1998. On-line monitoring of air pollution concentrations due to vehicular emissions in Dublin. In: Proceedings of the 30th Universities Transport Study Group (UTSG) Conference. Dublin, Ireland. Matzoros, A., 1990. Results from a model of road trac air pollution featuring junction e€ects and vehicle operating modes. Trac Engineering and Control 31 (1), 24±30. McInnes, G., 1996. Atmospheric Emissions Inventory Guidebook, 1st ed. European Environment Agency, Copenhagen, Denmark. Murley, L., 1998. NSCA Pollution Handbook. National Society for Clean Air and Environmental Protection, Brighton, UK. Murthy, A., 1996. Application Of ITS In Signal Synchronization. ITS Atlanta, Georgia. Namdeo, A.K., Colls, J.J., 1995. Automobile emission factors for straight roads and signalised intersection links. In : Proceedings of the 1995 East Midlands Region Environmental Conference ± Developers in Environmental Management Systems. Nottingham Trent University, Nottingham, UK. Namdeo, A.K., Colls, J.J., 1996. Development and evaluation of SBLINE, a suite of models for the prediction of pollution concentrations from vehicles in urban areas. Science of the Total Environment 190, 311±320. Negrenti, E., 1998. Modelli di Consumo ed Emissione: Risultati dall' azione COST 319. ENEA, Rome, Italy. Okado, T., 1997. Research on reducing auto exhaust nitrogen compounds (NOx ) through control of trac ¯ow. In: Proceedings of the Fourth World Congress on ITS. Berlin, Germany. Pan, Y., 1995. Development of speed and fuel consumption models for Chinese vehicles. Ph.D. Thesis, University of Birmingham, UK. Penn, A., Banister, D., O'Sullivan, P., 1996. E€ects of Street Grid Con®guration on Pedestrian Exposure to Vehicular Pollution: Civilising Urban Trac. University College, London, UK. Reynolds, A.W., 1998. An urban transportation and emissions model. Road Trac Impacts on Urban Air Quality Seminar, Trinity College, Dublin, Ireland. Reynolds, A.W., Broderick, B.M., 1999a. A GIS based transport and emissions model. In: Proceedings of the 31st Universities Transport Study Group (UTSG) Conference. University of York, UK.

A.W. Reynolds, B.M. Broderick / Transportation Research Part D 5 (2000) 77±101

101

Reynolds, A.W., Broderick, B.M., 1999b. Air pollution modelling and monitoring in Dublin. In: Proceedings of the Ninth Irish Environmental ResearchersÕ Colloquium. Johnstown Castle, Wexford, Ireland. Rowe, E., 1991. The Los-Angeles automated trac surveillance and control (ATSAC) system. IEEE Transactions on Vehicular Technology 40 (1), 16±20. Salway, A.G., Eggleston, H.S., Goodwin, J.W.L., Murrells, T.P., 1997. UK Emissions of Air Pollutants 1970±1995. National Environmental Technology Centre, Culham, Abingdon, Oxfordshire, UK. Samaras, Z., Kyriakis, N., Zachariadis, T., 1995. Reconciliation of macroscale and microscale motor-vehicle emission estimates. Science of the Total Environment 169 (1-3), 231±239. Seika, M., Harrison, R.M., Metz, N., 1998. Ambient Background Model ABM: development of an urban gaussian dispersion model and its application to London. Atmospheric Environment 32 (11), 1881±1891. Seika, M., Metz, N., Harrison, R.M, 1996. Characteristics of urban and state emission inventories-a comparison of examples from Europe and the United States. Science of the Total Environment 190, 221±234. SEIPH, 1996. The AIM Project and Air Quality in London. South East Institute of Public Health, Tunbridge Wells, Kent, UK. Shearn, S., Wood, K., Bowen, G.T., 1996. The Estimation of Vehicle Emissions Using SCOOT. Transport Research laboratory, Crowthorne, UK. Sim, A.G., Dobinson, K.W., 1980. Sydney Coordinated Adaptive Trac System SCAT system philosopy and bene®ts. IEEE Transactions on Vehicular Technology VT-29 (2), 130±137. Siwek, S.J., 1995. Integrated transportation management systems environmental considerations and issues. In: Proceedings of the Second National Symposium on Integrated Transportation Management Systems. Seattle, Washington. Sokhi, R.S., Fisher, B., Lester, A., McCrae, I., Bualert, S., Sootonstit, N., 1998. Modelling of air quality around roads. In: Proceedings of the Fifth International Conference on Harmonisation within Atmospheric Dispersion Modeling for Regulatory Purposes. Rhodes, Greece, pp. 492±487. Steer Savies Gleave, 1993. Dublin Transportation Initiative. Irish Government Stationery Oce. Dublin, Ireland. Stephens, R.D., 1994. Remote-sensing data as a potential model of vehicle exhaust emisions. Journal of the Air and Waste Management Association 44 (11), 1284±1292. Sturm, P., 1996. Determination of trac emissions-intercomparison of di€erent calculation methods. Science of the Total Environment 189/190, pp. 187±196. Sturm, P., Almbauer, R., Sudy, C., Pucher, K., 1997. Application of computational methods for the determination of trac emissions. Journal of the Air & Waste Management Association 47, 1204±1210. To€olo, S., Morello, E., Botta, C., 1997. Environmental control with urban ATT systems. In: Proceedings of the Fourth World Congress on ITS. Berlin, Germany. UK-DETR, 1997. Preparation And Use Of Atmospheric Emissions Inventories. Department of the Environment, Transport and the Regions, London, UK. US-EPA, 1996. Compilation of Air Pollutant Emission Factors (AP-42), Oce Of Air Quality Planning And Standards, Oce Of Air and Radiation, US EPA, Research Triangle Park, North Carolina. US-GAO, 1994. Bene®ts of Trac Control Signal Systems Are Not Being Fully Realized. US General Accounting Oce, Washington, DC. Van Vliet, D., 1995. Saturn 9.2 User Manual. Institute of Transport Studies, University of Leeds, UK. Vanruymbeke, C., Guitton, J.P., Vidon, R., Pruvost, C., 1993. Testing of an alternative method of pollutant emission measurement for passenger cars. Science of the Total Environment 134 (1±3), 197±209. Warner, C., 1985. Spatial Transportation Modelling. Sage, Beverley Hills, CA. Winther, M., 1998. Petrol passenger car emissions calculated with di€erent emission models. Science of the Total Environment 224 (1-3), 149±160. Wood, K., Harrison, J., 1998. Managing emissions. In: Proceedings of the Urban Trac Management and Control Annual Conference. DTI Conference Centre, London, UK. Zachariadis, T., Samaras, Z., 1997. Comparative assessment of European tools to estimate trac emissions. International Journal of Vehicle Design 18, 312±325.