Modelling of vehicular exhausts – a review

Transportation Research Part D 6 (2001) 179±198

www.elsevier.com/locate/trd

Modelling of vehicular exhausts ± a review Prateek Sharma a, Mukesh Khare b,* a

b

School of Environmental Management, GGS Indraprastha University, Delhi 110 006, India Department of Civil Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110 016, India

Abstract A review of the air pollution studies carried out to study the dispersion of vehicular exhaust emissions (VEEs) has been made. The review includes the modelling studies in the domain, primarily, of analytical modelling ± deterministic mathematical models and numerical models, and statistical models. Various model performance evaluation and comparative assessment studies have also been discussed. Further, the studies conducted to model VEEs at the urban road intersection and urban street canyons have also been reviewed. Ó 2001 Elsevier Science Ltd. All rights reserved. Keywords: Line sources; Vehicular exhaust emissions; Analytical models; Numerical models; Statistical models; Street canyon; Urban road intersection

1. Introduction A comprehensive review of the existing status of the general atmospheric dispersion modelling of the pollutants emitted from various point, line and area sources was presented by Turner (1979). The air pollution from industrial and domestic sources, generally speaking, has markedly decreased due to passage of various Acts by di€erent governments in most of the countries. However, there has been a substantial growth of road trac over the years and consequently, of air pollution caused by the vehicular exhausts, which is now been considered as one of the primary source of urban air pollution. Many studies have been conducted in the past to model the vehicular exhaust emission (VEE). Considering the magnitude of problem and, the importance of these studies, it becomes quite relevant to have an assessment/review of the existing modelling studies carried out in this area. Thus, the present study gives a review of the existing literature in the area of modelling of VEEs.

*

Corresponding author. E-mail address: [email protected] (M. Khare).

1361-9209/01/$ - see front matter Ó 2001 Elsevier Science Ltd. All rights reserved. PII: S 1 3 6 1 - 9 2 0 9 ( 0 0 ) 0 0 0 2 2 - 5

180

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

2. Studies on modelling of vehicular exhausts 2.1. General The motor vehicle exhaust is the most predominant source of air pollution in the urban centres, the world over. As such many studies, ranging from simple measurements and reporting to sophisticated rigorous modelling exercise in complex urban environments, have been reported in literature. For instance, Waller et al. (1965) estimated air pollution in a city street; Chovin (1967) did the exhaust gas (CO) investigation in Paris; Clayton et al. (1960) studied the relationship of street level CO concentration to trac accidents; Fussel (1970) reported atmospheric pollution from petrol and diesel vehicles; the relationship between the highway and the ambient air quality has been reported by Noll and Miller (1975a,b), Noll et al. (1974, 1975). Kondo (1973) presented the e€orts of the Japan Society of Mechanical Engineering (JSME) to develop an air pollution prediction system (APPS) around a trunk road or an intersection; Ellis et al. (1978) determined the vehicle cold and hot operating conditions for estimating highway emissions; Eccleston and Hurn (1974), Heinmillar (1978), Ostrouchov (1978), Chang et al. (1980) carried out studies to investigate the impact of ambient temperature on VEEs; Noll et al. (1977) reported an air monitoring programme to determine the impact of highways on ambient air quality; the characteristics of turbulence and dispersion of pollutants near major roads was studied by Rao et al. (1979a,b), Middelton et al. (1979), Brennan and McCrae (1988). McCrae and Hickman (1989) reported vehicular air pollution studies in complex motorway interchange and topographically complex locations; Ashdown Environment Limited (1989), a TRRL report gives a literature review of the perceived nuisances associated with VEEs; Eskridge and Hunt (1979), Eskridge and Rao (1983) discuss the prediction of trac induced turbulence and velocity ®elds near roadways. A number of case studies for various regions in di€erent countries related to impact of vehicular emissions to the air quality have been reported by Longhurst et al. (1994), Heida et al. (1994), Suleiman et al. (1994). 3. Modelling studies The mathematical models are widely used to evaluate the air quality near the roadways. Many models have been devised to describe the pollutants from roadways (e.g. Beaton et al., 1972; Dabberdt et al., 1973; Ward, 1975; Darling et al., 1975). These models provide theoretical estimates of air pollution levels as well as temporal and spatial variations for present and proposed conditions. The estimates are a function of meteorology, highway geometry, and downwind receptor location (Noll et al., 1975). The models vary in complexity, namely from a simple Gaussian line source approach to numerical solutions of ¯uid dynamics equations (Rao et al., 1980). In the US, the passage of the National Environmental Policy Act of 1969 initiated modelling of pollution due to vehicles. A number of highway air pollution models were developed in the early 1970s, such as CALINE (Beaton et al., 1972), EGAMA (Egan et al., 1973), and HIWAY (Zimmerman and Thompson, 1975). However, historically as far as modelling of vehicular air pollutants is concerned, the work of Sutton (1932) may be regarded as ®rst of its kind. Sutton (1932) presented an expression for the case where the wind is perpendicular to the in®nite line

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

181

source (when a receptor is located close to a line source, then this source can be treated as an in®nite line source and the resultant di€usion equation is greatly simpli®ed). Turner (1970) extended this relation to oblique winds when the angle between the wind and the line source is greater than 45°. Calder (1973) showed that Turner's equation gave incorrect results for very oblique winds. He derived an approximation formula, which he showed, gave accurate results for wind angles down to 15°. Prior to that, a model was developed by Csanady (1972) for ®nite line source but it is applicable only when the wind is perpendicular to the roadway. However, for ®nite line sources one must account for edge e€ects. Sutton (1932) and Mikkelsen et al. (1982) have done this for a wind-oriented perpendicular to a ®nite line source. But for a ®nite oblique line source (the general case) no simple solution to the di€usion equation exists (Esplin, 1995). Since an explicit solution to the general ®nite line source (GFLS) problem is not available, it has to be approximated as a series of point sources. This point source approximation signi®cantly increases the amount of time required to run an atmospheric dispersion model for those cases where there are large number of line sources and/or area sources. Esplin (1995) presented a computationally ecient approximate solution to the GFLS problem of ®nite length and oblique wind angle, based on the work of Calder (1973) who derived the solution for the in®nite, oblique line-source problem. The solution is reported to be valid for wind angles greater than 15°; for wind angles below this value a point± source approximation is presented. Prior to that Luhar and Patil (1989) presented a general ®nite line source model (GFLSM), wherein by adopting a suitable co-ordinate transformation, the model could be used for all wind angles. Khare and Sharma (1999) evaluated this GFLSM for Delhi trac and meteorological conditions and suggested some modi®cations in the model. One of the popular models for estimating pollutant concentrations due to vehicles is a Gaussian dispersion model ± the HIWAY (Zimmerman and Thompson, 1975), developed by the US EPA. For an ``at grade'' road con®guration, highway emissions, according to this model, are considered to be equivalent to a series of ®nite line sources; each lane of trac is modelled as if it were a straight continuous, ®nite line source with uniform rate. However, investigations by Chock (1977a,b), Noll et al. (1978), Sistla et al. (1979)) and Rao et al. (1980) indicated that the EPA± HIWAY model overestimates pollutant concentrations adjacent to the highway. This over-estimation was found to be more signi®cant under stable atmospheric conditions and for parallel wind-road orientation angles with low wind speeds (Rao and Keenan, 1980). Peterson (1978) used the wind ¯uctuation data in a modi®ed version of the original HIWAY model, which speci®ed the dispersion parameters as a function of wind ¯uctuation statistics and found that there was signi®cant improvement in the model performance over the existing version of the HIWAY model. Rao and Keenan (1980) made some suggestions for improvement of the EPA±HIWAY model and presented a new set of dispersion curves applicable for pollutant dispersion estimation near roadways based on the data collected in the general motors (GM) study (Cadle et al., 1976) and in the New York State (NYS) study (Rao et al., 1978a,b). The study found that when the original Pasquill±Gi€ord (P±G) curves used in the HIWAY model are replaced by these new dispersion curves and the aerodynamics drag factor is included, the performance of the HIWAY model improved signi®cantly. Later, Petersen (1980) presented an updated version of the HIWAY model ± the HIWAY-2, released by the EPA in May of 1980. The di€erence between the original and improved version is that the latter model gives more realistic (lower) concentration estimates, due to updated dispersion algorithm. Instead of using the six P±G dispersion curves presented by

182

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

Turner (1970), HIWAY-2 uses only three stability regimes (unstable, neutral and stable) for which new distance dispersion curves have been developed. HIWAY-2 allows the user to simulate multiple highways and multiple hours, thereby facilitating the modelling of intersection situations and the performance of sensitivity analysis. Chock (1978) developed a Gaussian based highway dispersion model ± the GM model, based on the experimental data obtained in the GM dispersion study on a test track (Cadle et al., 1976). This model avoids the cumbersome integration necessary for conventional Gaussian model that makes point source assumption but uses an in®nite line source approach and speci®es one dispersion parameter as a function of wind road orientation from the source. Another Gaussian based line source model is the California model ± CALINE (Beaton et al., 1972), that uses separate equations for calculating pollutant concentration under crosswind and parallel wind conditions. Later a series of improved versions of CALINE model were developed. CALINE-2 model, developed from the California model based on the Gaussian plume theory together with concepts of a box model, is described by Ward et al. (1977). The model assumes that pollutants are well mixed over the roadway upto ®xed height ± mixing cell concept, as assumed in the original version, but calculates concentrations di€erently. CALINE 3 (Benson, 1979) is a state-of-the-art Gaussian Plume line source model that allows for the speci®cation upto 10 ®nite length line source and upto 20 receptors; it automatically sums the contribution to each receptor from each link. This model has features not found in the HIWAY models (Cohn and McRoy, 1982). Among these are explicit considerations of averaging time, surface roughness, deposition velocity, elevated highway sections, and mixing cell volume. CALINE 4 on the other hand uses semi-empirical solution to the Gaussian di€usion equation (Benson et al., 1986; Benson, 1989). It is a computer-based model developed from tables and nomographs used to predict CO concentrations. However, it can now be used for several pollutants (CO, NOx , inert gases, and particulates) and in various road conditions including intersection, bridge and depression. The Gaussian equation used is based on two assumptions: a uniform horizontal wind ¯ow and steady-state meteorological conditions. These assumptions mean, its use in complex terrain, where wind channelling e€ects can occur, should be approached with care (Benson et al., 1986; Benson, 1989). The model requires meteorological data and mixing height. Pasquill stability classes are used with spreading coecients derived from a series of nomographs. It is primarily designed as a short-term model for calculating 1-h average pollutant concentration and thus requires hourly emissions and meteorological data. The development and application of these two later versions of CALINE model viz. CALINE-3 and ± 4 has been reviewed by Benson (1992). Carpenter and Cleman~a(1975) developed AIRPOL-4 model based on the techniques of segmentation in conjunction with an appropriate numerical scheme to evaluate the Gaussian integral. The roadway co-ordinate, according to this model, is translated onto a receptor co-ordinate system. The two co-ordinate system and this transformation have the advantage that they permit the Gaussian equation to be directly applied to each roadway point to determine the contribution of that point source to the pollution at the receptor. The model is capable of predicting concentrations for both upwind and downwind of a roadway at any desired sampling interval. The intersection midblock model (IMM) (EPA, 1978) is an operational regulatory model in the US that can predict CO concentrations at selected receptor locations which are based on line emission rates and hourly unit data. Extra CO emissions that are generated due to acceleration, deceleration or idling are also taken into account for various kinds of vehicles at the intersection. The dispersion parameters used in the IMM codes consist of the ambient dispersion

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

183

and dispersion due to movement of trac which is obtained from the GM experiments (Rao and Keenan, 1980; Cadle et al., 1976). Hickman and Colwill (1982) of the Transport and Road Research Laboratory (TRRL), UK describe a simple and e€ective method of estimating pollutant concentrations around highways, which uses the Gaussian dispersion theory with empirical modi®cations so that it accurately represents the roadside situation. The inputs for the model have been con®ned to information readily available to the highway engineer, namely trac ¯ow, trac speed and road layout with simple meteorological data viz. wind direction and speed. This work followed the work of Hickman et al. (1979). Another method developed at TRRL uses a set of graphs developed from computer model and takes into account vehicle ¯ow, vehicle speed and distance of receptor from the roads (Water®eld and Hickman, 1982). Meteorological and other variables, which also have a large e€ect on pollutant concentrations, are not considered independently in this method because it provides an estimate of the maximum concentration likely to occur. Another screening model derived from a Gaussian dispersion model developed at the TRRL is the design manual for roads and bridges (DMRB) model (DMRB, 1994). It was developed to determine approximate pollutant concentrations for current and new road schemes. The model thus, indicates as to whether further air quality assessments are required or not. Initially it was developed to cover other pollutants with the assumptions that their dispersion will be equivalent to CO. Recently the DMRB and CALINE 4 model have been reportedly used for predicting concentrations of NOx at an interchange in UK (Burden et al., 1997). However, according to the study both the models are unlikely to provide satisfactory predictions of NOx levels due to trac volumes at the interchange. This is because of the speculative nature of future trac and meteorological scenarios and the uncertainties in predictions of known levels from known input data identi®ed in the study. Although a number of models exist for predicting concentrations under di€erent atmospheric conditions and roadway con®gurations, there have been only a few experimental studies to provide a valid data base for model veri®cation (Sistla et al., 1979; Rao et al., 1980). In the ®eld experiments conducted by Stanford Research Institute (SRI), GM Corporation and the New York State Department of Environmental Conservation (NYS), suciently detailed pollutant, trac, and meteorological data were collected to be used for model validation. Of particular value in these studies, has been the inclusion of tracer release experiments (Sistla et al., 1979). All three experiments were conducted on ``at-grade'' roadway in relatively ¯at terrain. A major contribution to the highway dispersion modelling came through the tracer experiments conducted by the NYS study along the long Island Expressway near Huntington, New York, reported by Sistla et al. (1979), wherein the tracer gas concentrations observed downwind of the line source have been compared to those predicted by numerical models (numerical models, commonly used for modelling vehicular air pollutants have been discussed below). The study also analysed the wind ¯ow characteristics adjacent to the roadway to identify how trac modi®es the ¯ow due to turbulent eddy generation and changes in e€ective roughness. The trace data collected from a well-conceived and controlled highway experiment conducted at the GM test facility (Cadle et al., 1976) in 1975 by the GMs with co-operation of Ford Motor, the Chryster and the US EPA are, however, regarded to furnish, for the ®rst time, an outstanding data set for model evaluation and validation (Rao et al., 1986). Many numerical models have also been used for modelling highway dispersion. Some of them have been discussed here.

184

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

Danard (1972) developed a two-dimensional Eulerian model ± DANARD, which solves the mass conservation equation according to numerical methods outlined by Dufort and Frankel (1953). RAGLAND (Ragland and Pierce, 1975) solves the continuity equation for either of two classes (parallel or non-parallel) using an ecient matrix inversion technique using the same boundary conditions as imposed by Danard (1972) except that Danard's cross-road di€usivity value is replaced by the more appropriate cross-wind di€usivity. The model predicts concentrations for oblique and perpendicular cases by ignoring lateral di€usion; for the parallel case, the model solves the equation in three-dimensions including lateral di€usion. Kirsch and Mason (1975) developed the MROAD-2 model, which is also an Eulerian two-dimensional grid model that numerically solves the mass conservation equation. The size of the grid can be speci®ed by the user and model allows the existence of several line sources (all assumed to be perpendicular to the plane of the model), including elevated roadways. Pitter (1976) describes ROADS model, which is a two-dimensional conservation model. The model determines the steady-state concentrations of pollutants by numerically solving the equation (using the Lax±Wendro€ ®nite di€erence scheme) governing atmospheric advection±di€usion and chemical reactions. Another important model is ROADWAY (Eskridge and Thompson, 1982). This is a ®nite di€erence model, which predicts pollutant concentration near a roadway. It assumes a surface layer describable by surface layer similarity theory with the superposition of the e€ects of vehicle wakes. The unique part of the ROADWAY model is the vehicle wake theory, which was originally developed by Eskridge and Hunt, and modi®ed by Eskridge and Thompson (1982), and Eskridge and Rao (1983, 1986). The model can also be used to predict velocity and turbulence along the roadway. ROADCHEM (Eskridge and Thompson, 1982) is a version of ROADWAY, which incorporates the chemical reactions involving NO, NO2 and O3 as well as advection and dispersion. It uses surface-layer similarity theory to produce vertical angle turbulence pro®les. Other relevant models are PAL (Peterson, 1978) and PALDS (Rao, 1982; Rao and Snodgrass, 1982). CAR±FMI (H ark onen et al., 1995, 1996a,b) is a road network dispersion model that models the vehicular sources and has been developed by the Finnish Meteorological Institute. A recent study (Karppinen et al., 1997) describes the application of this model in estimating the contributions from mobile sources in predicting the emissions, dispersion and chemical transformation of NOx in an urban area. A number of studies based on dispersion model calculations were carried out in the streets of Amsterdam by Heida et al. (1989, 1992). These studies used the Calculation of Air pollution from Road trac (CAR) model developed by the Dutch National Institute of Environmental Health (RIVM) and Dutch Institute of Applied Scienti®c Research (TNO) (Van den Hout et al., 1989) for estimating the CO and NOx concentrations in a number of streets in downtown Amsterdam. The CAR model has been extensively described and analysed by Eerens et al. (1993). The model has been tested calibrated on many occasions, not only in the wind tunnel experiments (Van den Hout et al., 1989) but also under street conditions (Elskamp, 1989; Heida et al., 1989). Corresponding to the US EPA HIWAY-2 model Petersen (1980), the Norwegian Institute of Air Research (NILU) has developed ROADAIR and CONTILENK models for open roads and NERI OSRM (Hertel and Berkowicz, 1989a,b,c) for street canyon, respectively, which are used to estimate sub-grid concentration close to roads within square grid. Thus, several dispersion models for motor vehicle exhaust emissions have been reported in the literature. However, only few dispersion models are applicable to urban streets (Kono and Ito, 1990a,b), and almost all recent extensive automobile air pollution studies including a large-scale ®eld programme have

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

185

concentrated on highways outside highly built-up urban areas (Okamato et al., 1996). Studies carried out by Karim and Matsui (1995) and Karim et al. (1996) indicated that in an urban street canyon, the turbulent property of wind, which a€ects the trac pollutant concentrations, was very intricate to simulate. With the result that pollutant concentration became signi®cantly elevated under low wind speeds (worst case scenario). This might be due to the fact that wind data in real world were being collected at the meteorological stations which did not represent the street canyons or moving vehicle e€ects. In an another study, Karim and Matsui (1998) and Karim et al. (1998) developed a computer model consisting of wind distributions, emission dispersion and modi®ed Gaussian equation to identify street canyon and vehicle wake e€ects on the transport of air pollution from urban road microenvironments. The computer model simulates and analyses the wind ¯ow and their components in the street canyon considering a two-dimensional street canyon ¯ow pattern. Vehicle wake turbulence were also estimated in microenvironments. Subsequently, the turbulent parameters were integrated in Gaussian equations to estimate CO and NOx concentrations. Later in another study, Karim (1999) developed a trac pollution inventory and modelled dispersion of vehicular pollutants in an urban environment. An alternate category of models that are quite useful, especially in real-time, short-term forecasting are the statistical models. An attempt at predicting hourly and daily maximum CO concentration in the Los Angeles basin was undertaken by McCollister and Wilson (1975) using antecedent CO concentrations as predictors in a linear stochastic model. This one-dimensional model predicted extreme events poorly. Tiao et al. (1975) modelled the variations in CO in downtown Los Angeles by examining trac densities, wind speeds and inversion heights. Aron and Aron (1978) forecasted daily maximum CO concentrations throughout the Los Angeles area and found that preceding days' CO concentration, pressure di€erences between nearby stations, surface temperature, day of the week, length of daylight, solar radiation and inversion height, were the most important variables. Jakeman et al. (1991)used a hybrid deterministic/stochastic model to predict seasonal extremes of one-hour average urban CO concentrations. Miles et al. (1991) discovered that the stochastic models are accurate in predicting high-percentile extreme events of vehicular pollutants in an urban area in comparison to the deterministic models. Bardeschi et al. (1991) noted the importance of times series of concentrations, emissions, and meteorological conditions during the hours prior to the high CO concentrations. Liu et al. (1994) used Monte Carlo simulation method to predict personal exposure levels to CO in Taipei. Glen et al. (1996) developed an empirical model of monthly CO for long-term trend assessment. The reviewed literature, however, reveals that the statistical models such as time-series analysis have scantily been applied for modelling air pollution where the primary source is vehicular exhaust. Karim and Matsui (1998) developed a stochastic method in an urban street canyon for predicting maximum concentration of CO and NOx in road microenvironments. The model takes trac data, emission rates, meteorological data, vehicle dimensions and canyon geometry. Fomunung et al. (1999) developed a statistical model for forecasting NOx emissions from light duty motor vehicles. The tree regression technique is used as a tool for determining relationship among variables in the data, with main focus on identifying useful interactions among discrete variables. Comrie and Diem (1999) developed multivariate regression models using variables and interaction terms related to anticipated nocturnal stability as well as time series of CO. The models were evaluated using a range of error statistics. It was found that the models provide accurate daily forecasts of CO with explained variances approaching 0±9 and errors under 1 ppm. Sharma et al.

186

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

(1999) used the extreme value theory for predicting the number violations of the national ambient air quality standards (NAAQS) for an urban road intersection where the primary source of pollution was VEEs. For the same intersection Sharma and Khare (1999, 2000a,b) used the BoxJenkins modelling techniques to, respectively, investigate the impact of a legislation to control vehicular pollution (intervention analysis); and provide short-term, real-time forecast of the ambient air pollution levels due to vehicular sources. Recently, statistical tools such as arti®cial neural networks (ANN), fuzzy logic theory (FLT) etc. are reportedly being used as an alternative tool in modelling the pollutants from vehicular trac. Some of the relevant studies are reported here. Raimondi et al. (1997) report an APM based on FLT, which allows to take into account model uncertainties and describes daily dynamics of a variable-dosage area product (DAP) ± representative of ground level pollution produced by vehicular trac in urban areas of complex topography. Air pollution being an imprecise and variable event, the application of FLT seems to be a valid tool for improving the description of air pollution and is an area worth venturing in for future research. Other applications of FLT in air pollution have been reported by Tanaka et al. (1992) and Bacci et al. (1981). Moseholm et al. (1996) argue that neural networks are an e€ective and ecient method for exploring complex relationships between trac, wind, and short-term CO concentrations near intersections. A neural network-based model for the analysis of CO contamination in the urban areas of Rosario due to vehicular trac has been reported to be developed by Drozdowicz et al. (1997). Gualtieri and Tartaglia (1997) have used a street canyon model for estimating NOx concentration due to trac. Garner and Dorling (1998, 1999) developed multilayer perceptron model for forecasting hourly NOx and NO2 concentrations in an urban area of London city. Tao and Xinmiao (1998) considered trac environment quality assessment methods in the context of their application as a part of the Anshan City's comprehensive transportation planning project. It involved applying multistage fuzzy clustering analysis, wherein after an initial setting up of an assessment system, assessment criteria, formulae for the sub-ordination function, allocating weights and modelling design programme have been established. 4. Model performance evaluation and comparative assessment Many models have been devised to describe the dispersion of pollutants from roadways. The degree of accuracy in simulating the temporal and spatial distributions of pollutant concentration depends, in general, on many factors, which have been elaborated in Benarie (1980), Juda (1989), and Zannetti (1990). A number of studies involving review, evaluation, assessment and upgradation of existing models have been reported in the literature. These have been discussed here. Noll et al. (1978) compared three idealised line source models ± EPA±HIWAY (Zimmerman and Thompson, 1975), the original CALINE (Beaton et al., 1972), and CALINE 2 (Ward, 1975) ± that predict CO concentrations near highways. The sensitivity analysis indicated overprediction by EPA-HIWAY model than the two CALINE models for oblique and crosswind conditions; for parallel wind conditions, the CALINE model was found to predict higher pollutant concentrations. A comparison of predicted and measured concentrations reportedly showed that all models overestimated concentrations for parallel wind conditions and underestimated concentrations for oblique and crosswind conditions. A good comparative account of Gaussian and numerical

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

187

models can be found in Sistla et al. (1979) wherein the results obtained in the tracer experiments conducted by the NYS study (Rao et al., 1978b) are reported. The concentration predicted by four Gaussian models, viz. HIWAY (Zimmerman and Thompson, 1975), GM (Chock, 1978), AIRPOL-4 (Carpenter and Cleman~ a, 1975), CALINE-2 (Ward et al., 1977) and four numerical models, viz., DANARD (Danard, 1972), MROAD 2 (Kirsch and Mason, 1975), RAGLAND (Ragland and Pierce, 1975), and ROADS (Pitter, 1976), were compared with the tracer gas concentrations downwind of the line source. The simulation capability of each model has been discussed and the displacement parameters in two of the Gaussian models (GM and HIWAY) were compared to those obtained by solving the Gaussian equation for known source strength, meteorological conditions and measured concentrations of tracer gas. Of the models tested, GM and HIWAY reportedly performed better compared to other Gaussian models. The numerical models performed about the same as the above two Gaussian models. However, the GM model was reported to provide a better simulation for parallel cases than any of the other models tested. Rao et al. (1980) evaluated some of the commonly used highway dispersion models. The models evaluated were four Gaussian models, viz., GM (Chock, 1978), HIWAY (Zimmerman and Thompson, 1975), AIRPOL-5 (Carpenter and Cleman~a, 1975) and CALINE-2 (Ward et al., 1977) and three numerical models, viz. DANARD (Danard, 1972), MROAD-2 (Kirsch and Mason, 1975) and ROADS (Pitter, 1976). The data used in the analysis were the tracer gas experiment data collected by the GM Corporation, the experimental details of which are given in Cadle et al. (1977). The study revealed that for the given data set the model with the best performance in accurately predicting the measured concentration was the GM model, followed in order by AIRPOL-2, HIWAY, CALINE, DANARD, MROAD-2 and ROADS. The GM model however, was found to be skewed towards underprediction; HIWAY model was suggested to be useful as screening tool for regulating purposes since it had the highest percentage in the category of overprediction if the concentration data in the range of 50th percentile through 100th percentile are included in the analysis. Further, some suggestions for improvement were made in the then version of HIWAY model for the stable and parallel wind road conditions. Later, Rao et al. (1986) presented a statistical evaluation of the improved version of CALINE and HIWAY namely CALINE-3 (Benson, 1979) and HIWAY-2 (Rao and Keenan, 1980) and the ROADWAY model (Eskridge and Thompson, 1982) using GM tracer data. Comparison of the model predictions, paired and unpaired in time with measurements, suggested that HIWAY-2 and ROADWAY perform best, while the performance of CALINE-3 was reported to be acceptable. The application of extreme value theory (EVT) and the bootstrap re-sampling procedure to the modelled and measured data (unpaired) showed all three models capable of predicting extreme concentrations within the model performance criteria. Prior to that Rao and Keenan (1980) made suggestions for improvement of the EPA±HIWAY model, wherein the overestimation in the pollutant concentration for stable atmospheric conditions, especially under parallel wind-road orientation angles with low wind speed by the HIWAY model (Zimmerman and Thompson, 1975) was reported to be due to the trac induced turbulence near roadways, which the model's dispersion parameters did take into account. A use of modi®ed P±G curves that quanti®ed the nature of the trac induced turbulence and its in¯uence on the pollutant dispersion in the near ®eld was suggested. The results showed that the model performance signi®cantly improved when these new dispersion curves in conjunction with an aerodynamic drag factor, which in a rough way accounts for the change in the mean wind ®eld due to the moving vehicles are used in the HIWAY model. The

188

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

performance of EPA HIWAY model was also assessed by Chock (1977a), Chock (1977b) and its various limitations and de®ciencies have been reported in Chock (1978). Yamartino and Wiegand (1986) developed simple models for the ¯ow, turbulence and pollutant concentration ®elds within urban street canyons. These models for ¯ow and turbulence were then used as input to a comprehensive urban canyon pollutant dispersion model ± Canyon Plume-Box Model (CPBM). The CPBMs performance was compared for CO concentration data with SRI street canyon APRAC sub-model (STREET) of Johnson et al. (1973) and MAPS model of Sobottka and Leisen (1980a,b) under the full range of meteorological conditions, and also with STREET under the full range of meteorological conditions for which the STREET model was speci®ed and designed. A comparison between SF6 concentrations and values estimated using the OMG VOLUME SOURCE model (Kono and Ito, 1990a) and three line source dispersion models ± JEA model (Japan Environmental Agency), Tokyo model (Tokyo Metropolitan Goverment, 1983) and EPA HIWAY-2 model Petersen (1980) ± was presented by Kono and Ito (1990b). The OMG VOLUME-SOURCE model was reported to provide the most precise and accurate representation of the actual gas dispersion. Okamato et al. (1990) undertook a comparative study of various estimation methods for NOx emissions from roadway. Chan et al. (1995) compared and evaluated some simple and popular air dispersion models for street canyons. The models were: the empirical model used in APRAC (Johnson et al., 1973), Guangzhou empirical model (GZE) (Qin and Kot, 1993), CALINE-4 (Benson, 1989), and the Parallel Wind and In®nite Line source Gaussian model (PWILG) (Qin and Kot, 1993). The study revealed that the models, in general, were comparatively accurate in predicting maximum ground concentrations. The accuracy of CO prediction was much in¯uenced by the assumption of vehicular composition, while the uncertainty of emission sources other than vehicular emissions was an important error source in predicting NOx concentration. 5. Air quality near urban intersection The air quality near urban intersections has been a subject of a number of publications (Kunzelman et al., 1974; Dabberdt and Sandys, 1978; Dabberdt et al., 1995). It has been observed that pollution concentrations are higher near trac junctions, where queuing occurs, than at the links (Claggett et al., 1981). This is because vehicles spend longer periods of time near junctions, in driving modes that generate more pollutants viz. queuing, decelerating or accelerating, than the steady cruise. Dabberdt and Sandys (1978) published a procedure for calculating CO concentrations near congested intersections. In 1985, Transport Research Board (TRB) developed a hybrid methodology (CAL3Q) based upon the signalised intersection analysis and the deterministic queuing theory (Nevell, 1982; Schattanek et al., 1990). Schattanek et al. (1990) revised CAL3Q to adequately model under- and over- saturation roadway trac scenarios, i.e. CAL3QHC. In 1989, the USEPA commissioned a performance evaluation of several methodologies that combined emission, trac, and dispersion models to identify the modelling approach that best estimated CO concentrations near congested intersections. The result of the evaluation showed that of the eight models tested, CAL3QHC performed well in predicting CO concentrations in the vicinity of a congested intersection (Schattanek et al., 1990). Claggett et al. (1981) presented a methodology for identi®cation of the air quality levels near intersections based on

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

189

di€usion model predictions with little supporting measurements of model validation. This study provided an analysis of CO data collected near a signalised urban arterial intersection. The model used in the study was the HIWAY model (Zimmerman and Thompson, 1975). Claggett and Miller (1979) reported CO monitoring and line source model evaluation study for an urban freeway and intersection prepared for the Illinois EPA. 6. Air quality in urban street canyons Some of the most severe air pollution caused by automobile emissions may occur along the road surrounded by tall buildings. This type of con®guration is called ``street canyon''. Air dispersion in street canyon is di€erent from that in open ¯at region or a complex terrain region. The vertical and horizontal turbulence intensities, for instance, have similar values in the street canyon and are much weaker than those in open ¯at land (Qin and Kot, 1993). The scale of turbulence in¯uencing concentration ¯uctuation is limited in street canyons (Csanady, 1973). The ®eld studies by De Paul and Sheih (1986) provided a major leap in the understanding of the mechanisms by which pollutants are transported from street canyons under ambient winds perpendicular to the street. A research group from Stanford Research Institute (SRI) has been involved in studying the air pollution in this situation; Johnson et al. (1973) has proposed the SRI street canyon model for predicting air quality within a street canyon. Kondo (1973) presented the e€orts of the JSME to develop an air pollution prediction system (APPS) around a trunk road or intersection (Okamato et al., 1996). Models for predicting pollutant concentrations within a street canyon have been developed by Johnson et al. (1971), Ludwig and Dabberdt (1972), Hotchkiss and Harlow (1973) and Nicholson (1975). The modelling of air quality at street, by Johnson et al. (1971) and Ludwig and Dabberdt (1972) was done by assuming that the wind speed at street level can be extrapolated linearly from the wind speed at the roof level, that the concentration for the downwind side of the canyon is linearly related to height and is proportional to wind speed and street width, and that the bulk concentration can be determined by dividing the source term by the product of street width and wind speed. The model developed by Nicholson (1975) calculates the mean concentration for the street level air as a function of the mean updraft velocity at roof level when a secondary ¯ow pattern develops within the street canyon (Hotchkiss and Harlow, 1973; Hoydysh et al., 1974). In the past two decades, simple models have been developed to simulate dispersion of vehicular emissions in street canyons, for example, empirical models (Johnson et al., 1973; Simmon, 1981), box models (Nicholson, 1975) and Gaussian dispersion models (Benson, 1989). However, vehicular exhaust emission is not a stable and continual source. Controlled by trac lights, trac ¯ow in urban streets exhibits platoon movements; vehicle emissions vary with trac and ambient conditions, which these simple models have not considered. More complicated models (EPA, 1975 EMFAC, California Air Resources Board, 1986; COPERT, Eggleston et al., 1989; Trinity Consutants, 1991) were developed taking into account more parameters such as ambient temperature, vehicle speed, mileage accrual rates, calendar year, and operating cycle. Chan et al. (1995) compared and evaluated some simple and popular APMs for street canyons. Other important air pollution studies in street canyons include Sobottka and Leisen (1980a,b), Potenta et al. (1982), Hov and Larssen (1984), Yamartino and Wiegand (1986), Hoydysh and Dabberdt

190

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

(1988, 1994), Dabberdt and Hoydysh (1991),Kono and Ito (1990a,b),Qin and Chan (1993),Qin and Kot (1993), Eerens et al. (1993), Lee and Park (1994), Okamato et al. (1994, 1996). Yamartino and Wiegand (1986) developed simple models for the ¯ow and turbulent ®elds within an urban street canyon, which served as input to a comprehensive urban canyon pollutant dispersion model, named the Canyon Plume-Box Model (CPBM). The CPBM was evaluated using trac and pollutant data from an extensive monitoring programme; the paper also presents a comparison of CPBMs performance for CO with the SRI street canyon APRAC sub-model (STREET) of Johnson et al. (1973) and the MAPS model of Sobottka and Leisen (1980a,b). CPBM is reported to perform better than the predecessor models and contains no canyon speci®c tuning parameters that would inhibit applying it to variety of street canyon geometry. Hoydysh and Dabberdt (1988) studied the kinematics and dispersion characteristics of ¯ows in asymmetric street canyons. Based on the concepts developed previously by Yamartino and Wiegand (1986), Hertel and Berkowicz (1989a,b,c), Hertel et al. (1990) and Berkowicz et al. (1997) and Berkowicz (1998) developed a robust and simple operational model called operational street pollution model (OSPM). The OSPM model was successfully tested with hourly concentration of vehicular pollutants in selected cities of Denmark, Norway and Netherlands. A version formulated by Buckland (1998) was applied satisfactorily to two canyon sites in the United Kingdom. Many model runs were then made where street geometry, trac speed and trac ¯ow were varied. Subsequent to this Buckland and Middleton (1999) successfully used the results of the previous models runs in developing a street canyon nomogram, called AEOLIUS. Kono and Ito (1990a) presented the development of the OMG VOLUME-SOURCE model for use in concentration estimates from roadway emissions in the vicinity of urban street canyon situations. In the followup paper (Kono and Ito, 1990b), the performance of OMG VOLUME-SOURCE model was compared with three line source dispersion models viz. JEA model (Japan Environmental Agency, 1982), TOKYO model (Tokyo Metropolitan Goverment, 1983), and EPA HIWAY-2 model Petersen (1980). The performance of the OMG VOLUME SOURCE model was reported to be better than the other three models. Lee and Park (1994) developed a sound parameterisation scheme whereby the pollutant concentration in a city street canyon can be estimated from source term, meteorological conditions, and street canyon geometry using a two-dimensional, timedependent ¯ow model. Recently, a dispersion model aimed at forecasting NOx concentration due to vehicular trac within a street canyon has been reported (Gualtieri and Tartaglia, 1997) using the semi-empirical approach. The NOx model performance showed a precision degree comparable with respect to analogue semi-empirical models currently used to forecast inert pollutant levels. Prior to this, Tartaglia et al. (1995) developed and validated an urban street canyon model based on CO experimental data. Almost all recent extreme automobile air pollution studies including a large-scale ®eld programme have concentrated on highways outside built-up urban areas. HIWAY-2, CALINE-3 and many other roadway pollutant models were developed, not for road surrounded by buildings but for a highway of at-grade or cut-o€ sections (Rao et al., 1980; Rao and Visalli, 1981; Okamato et al., 1996). Liu et al. (1993) developed a practical air quality simulation model, which can be applied to a street canyon; the model contains two sub-models: one is a wind-®eld model, the other is a di€usion model based on a Monte Carlo particle scheme. However, this model, a two-dimensional numerical model can be applied only to the cross-section of street canyon under perpendicular wind conditions. For complicated street canyon, two-step procedure is adopted, the details of which have been reported by Okamato et al. (1994). Okamato

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

191

et al. (1996) have evaluated this two-dimensional model and compared its performance with SRI model and the APPs three-dimensional model; the predictive performance of this model is reported to be comparable to the SRI model and superior to APPS three-dimensional model.

7. Conclusions The passage of various Government Acts to control the source emissions from various industrial sources and the increase in the vehicular trac in the urban areas have brought an important shift with respect to the contribution to the urban pollution of various sources. Vehicular sources are now considered to be the predominant sources of urban air pollution. Air quality models can provide signi®cant insight to study the impact of the vehicular sources on the urban air quality. The role that APMs play in air pollution abatement strategies makes it all the more imperative that the models must give, as far as possible, reliable and accurate estimates of air quality in the vicinity of roads. A review of the existing modelling studies relevant to the VEEs was discussed in the present paper. The reviewed literature suggests that there have been very few applications of the stochastic modelling to model the vehicular exhausts. These techniques provide a useful tool, especially where the short-term real-time forecast is required and where the impact of certain ``intervention'', say in the form pollution control legislation is required to be studied. Further, statistical tools such as ANN and FLT provide an alternative to the conventional and classical dispersion models. Not much work has been done in this area and it provides an interesting proposition in the ®eld of vehicular exhaust modelling. Thus, for better model predictions a consistent model evaluation and further improvement by collection and critical assessment of various model comparisons with measured air quality are warranted.

References Aron, R.H., Aron, I., 1978. Statistical forecasting models: carbon monoxide concentrations in the Los Angeles Basin. J. Air Pollut. Control Assoc. 28, 681±684. Ashdown Environment Limited, 1989. Perceived Nuisances of Vehicle Exhaust Emissions: A Literature Review. Transport and Road Research Laboratory, Crowthorne, Berkshire. Bacci, P., Bolzen, P., Fronza, G.A., 1981. Stochastic predictor of air pollution based on short-term meteorological forecast. J. Appl. Meteorol. 20. Bardeschi, A., Colucci, A., Gianelle, V., Gnagnetti, M., Tamponi, M., Tebaldi, G., 1991. Analysis of the impact on air quality of motor vehicle trac in the Milan urban area. Atmos. Environ. 25B, 415±428. Beaton, J.L., Ranzieri, A.J., Shirley, E.C., Skog, J.B., 1972. Mathematical approach to estimating highway impact on air quality. Fed. Highway Administration Report No. FHWA-RD-72-36, Washington, DC. Benarie, M.M., 1980. Urban Air Pollution Modelling. MIT Press, London. Benson, P.E., 1979. CALINE -3: A versatile dispersion model for predicting air pollutant levels near highways and arterial roads. Final Report. FHWA/CA/TL-79/23. California Department of Transportation, Sacramento, CA. Benson, P.E., Nokes, W., Cramer, R.L., 1986. Evaluation of the CALINE-4 line source dispersion model for complex terrain application. Transp. Res. Rec. 1058, 7±13. Benson, P.E., 1989. CALINE 4 ± a dispersion model for predicting air pollution concentrations near roadways. Final Report, FHWA/CA/TL-84/15. California Department of Transportation, Sacramento, CA.

192

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

Benson, P.E., 1992. A review of the development and application of the CALINE 3 and 4 models. Atmos. Environ. 26B (3), 379±390. Berkowicz, R., Hertel, O., Sorensen, N.N., Michelsen, J.A., 1997. Modelling air pollution from trac in urban areas. In: Perkins, R.J., Belcher, S.E. (Eds.), 1994, Proceedings from IMA meeting on Flow and Dispersion through Obstacles, Cambridge, England, 29±30 March. Clarendon Press, Oxford, pp. 121±142. Berkowicz, R., 1998. Street scale models. In: Fenger, J., Hertel, O., Palmgren, F. (Eds.), Urnban Air Pollution ± European Aspects. Kluwer Academic Publishers, Dordrecht (Chapter 12). Brennan, V.H., McCrae, I.S., 1988. Pollution levels near a motorway in cutting. In: Grefen, Lobel (Eds.), Environmental Meteorology. Buckland, A.T., 1998. Validation of a street canyon model in two cities. Presented at IOP Conference on Urban Air Quality ± Monitoring and modelling. Hat®eld 11±12 July 1996; Environmental Monitoring and Assessment 52, 255±267. Buckland, A.T., Middleton, D.R., 1999. Nomograms for calculating pollution within street canyons. Atmos. Environ. 33, 1017±1036. Burden, N.C., Whitewell, I., Longhurst, J.W.S., 1997. Testing the tools of air quality management: a comparison of predictions of nitrogen dioxide concentrations by CALINE 4 and the DMRB models with monitored levels at the M4/M5 interchange in Bristol, UK. In: Power, H. Tirabassis, T., Brebbia, C.A. (Eds.), Air Pollution V. Fifth International Conference on Air Pollution Modelling, Monitoring and Management, Air Pollution V. Computational Mechanics Publications, Southampton, Boston, pp. 335±346. Cadle, S.H., Chock, D.P., Heuss, J.M., Monson, P.R., 1976. Results of the General Motors sulfate dispersion experiments. General Motors Research Publication, GMR-2107. General Motors Corporation, Warren. Cadle, S.H., Chock, D.P., Mouson, P.R., Heuss, J.M., 1977. General Motors sulfate dispersion experiment: experimental procedures and results. J. Air Pollut. Control Assoc. 27, 33±38. Calder, K.L., 1973. On estimating air pollution concentrations from a highway in an oblique wind. Atmos. Environ. 7, 863±868. California Air Resources Board. 1986. Methodology to Calculate Emission Factors for On-Road Motor Vehicles. Technical Support Section, Emissions Inventory Branch, Motor Vehicle Emissions and Projections Section, November 1986. Carpenter, W.A., Cleman~a, G.G., 1975. Analysis and comparative evaluation of AIRPOL-4. Virginia Highway and transportation Research Council Report, VHTRC 75-R55, Charlottesville, VA. Chan, L.Y., Hung, W.T., Qin, Y., 1995. Assessment of vehicular emission dispersion models applied in street canyons in Guangzhouh, PRC. Environ. Internat. 21, 39±46. Chang, T.Y., Norbeck, J.M., Weinstock, B., 1980. Ambient temperature e€ect on urban CO air quality. Atmos. Environ. 14, 603±608. Chock, D.P., 1977a. General motors sulfate dispersion experiments: an overview of the wind, temperature and concentration ®elds. Atmos. Environ. 11, 553±559. Chock, D.P., 1977b. General motors sulfate dispersion experiments: assessment of the EPA HIWAY model. J. Air Pollut. Control Assoc. 27, 39±45. Chock, D.P., 1978. A simple line-source model for dispersion near roadways. Atmos. Environ. 12, 823±829. Chovin, P., 1967. Carbon monoxide: analysis of exhaust gas investigations in Paris. Environ. Res. 1, 198±216. Claggett, M., Shrock, J., Noll, K.E., 1981. Carbon monoxide near an urban intersection. Atmos. Environ. 15, 1633±1642. Claggett, M., Miller, T.L., 1979. Final Report: Carbon monoxide monitoring and line source model evaluation for an urban freeway and urban intersection, prepared for the Illinois Environmental Protection Agency, Spring®eld, IL. Clayton, G.D., Cook, W.A., Fredrick, W.G., 1960. A study of the relationship of street level carbon monoxide concentrations to trac accidents. J. Am. Ind. Hyg. Ass. 21, 46±54. Cohn, L.F., McRoy, G.R., 1982. Environmental Analysis of Transportation Systems. Wiley, New York. Comrie, A.C., Diem, J.E., 1999. Climatology and forecast modeling of ambient carbon monoxide in Phoenix, Arizona. Atmos. Environ. 33, 5023±5036. Csanady, G.T., 1972. Crosswind shear e€ects on atmospheric di€usion. Atmos. Environ. 6, 221±232. Csanady, G.T., 1973. Turbulent Di€usion in the Environment. D. Reidel Publication Company, Dordecht, Boston. Dabberdt, W.F., Hoydysh, W.G., 1991. Street canyon dispersion: sensitivity to block shape and entrainment. Atmos. Environ. 25A, 1143±1153.

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

193

Dabberdt, W.F., Sandys, R.C., 1978. Guidelines for Air Quality Maintenance Planning and Analysis, vol. 9 (revised): Evaluating Indirect Sources, EPA-450/4-78-001, Research Triangle Park, NC. Dabberdt, W.F., Hoydysh, W.G., Schorling, M., Yang, F., Holynskij, O., 1995. Dispersion modelling at urban intersection. Sci. Total Environ. 169, 93±102. Dabberdt, W.F., Ludwig, F.L., Johnson Jr., W.B., 1973. Validation and applications of an urban di€usion model for vehicular pollutants. Atmos. Environ. 7, 603. Danard, M.B., 1972. Numerical modeling of carbon monoxide concentrations near a highway. J. Appl. Met. 11, 947±957. Darling, E.M. Jr., Prerau, D.S., Downey, P.J., Mengert, P.H., 1975. Highway Air Pollution Dispersion Modelling: A Preliminary Evaluation of Thirteen Models, US Department of Transportation. Paul, De, Sheih, F.T., 1986. Measurements of wind velocities in a street canyon. Atmos. Environ. 20, 455±459. Design Manual for Roads and Bridges, vol. 11, Environmental Assessment, Section 3, Part 1 ± Air Quality. Department of Transport HMSO, London. Drozdowicz, B, Benz, S.J., Santa Cruz, A.S.M., Scenna, N.J., 1997. A neural network based model for the analysis of carbon monoxide contamination in the urban area of Rosario. In: Power, H. Tirabassis, T., Brebbia, C.A. (Eds.), Air Pollution V. Fifth International Conference on Air Pollution Modelling, Monitoring and Management, Air Pollution V. Computational Mechanics Publications, Southampton, Boston, pp. 677±684. Dufort, E.C., Frankel, S.P., 1953. Stability conditions in the numerical treatment of parabolic di€erential equations. Math. Tab. Nat. Coun. Wash. 7, 135. Eccleston, B.H., Hurn, R.W., 1974. Ambient Temperature and Vehicle Emissions. Bureau of Mines, Bartlesville, OK, EPA-460/3-74-028, PB 247692. Eerens, H.C., Sliggers, C.J., van den Hout, K.D., 1993. The CAR model: the Dutch method to determine city street air quality. Atmos. Environ. 27B, 389±400. Egan, B.A., Epstein, B.A., Keefe, M., League, J., Lavery, T., 1973. Development of procedure to simulate motor vehicle pollution levels. Environment Research and technology, Inc., Report No., ERT-p-343f, Lexington, MA. Eggleston, H.S., Goribtaen, N., Joumard, R., Rijkeboer, R.C., Samras, Z., Zierock, K.H., 1989. Environment and quality of life. CORINAIR working group on emission factors for calculating 1985 emission from road trac. Commission of the European Communities, CD-NA-12260-EN-C, Brussels. Ellis, G.W. et al. 1978. The Determination of Vehicular Cold and Hot Operating Conditions for Estimating Highway Emissions, DOT, FHWA, September 1978. Elskamp, H.J., 1989. National air quality monitoring network ± technical description. RIVM Report 228702017, Bilthoven, The Netherlands. EPA, 1975. Compilation of air pollutant emission factors. Oce of Air and radiation, Oce Air Quality Planning and Standards, Research Triangle Park, NC. EPA, 1978. Carbon monoxide hot spot guidelines, vol. V: User's Manual for Intersection Midblock Model, EPA-450/378-037. Eskridge, R.E., Hunt, J.C.R., 1979. Highway modelling Part I: prediction of velocity and turbulence ®elds in the wake of vehicles. J. Appl. Met. 18, 387±412. Eskridge, R.E., Rao, S.T., 1983. Measurement and prediction of trac-induced turbulence and velocity ®elds near roadways. J. Appl. Met. 22, 1431. Eskridge, R.E., Rao, S.T., 1986. Turbulent di€usion behind vehicles: experimentally determined turbulence mixing parameters. Atmos. Environ. 20, 851±860. Eskridge, R.E., Thompson, R.S., 1982. Experimental and theoretical study of the wake of a block-shaped vehicle in a shear-free boundary ¯ow. Atmos. Environ. 16, 2821±2836. Esplin, G.J., 1995. Approximate explicit solution to the general line source problem. Atmos. Environ. 29, 1459±1463. Fomunung, I., Washington, S., Duensler, R., 1999. A statistical model for estimating oxides of nitrogen emissions from light duty motor vehicles. Transp. Res. D4, 333±352. Fussel, D.R., 1970. Atmospheric pollution from petrol and diesel engine vehicles. Petrol. Rev., 192±202. Garner, M.W., Dorling, S.R., 1998. Arti®cial neural networks: the multilayer perceptron ± a review of applications in the atmospheric sciences. Atmos. Environ. 32, 2627±2636. Garner, M.W., Dorling, S.R., 1999. Neural networks modelling and prediction of hourly NOx and NO2 concentrations in urban London. Atmos. Environ. 33, 709±719.

194

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

Glen, W.G., Zelenka, M.P., Graham, R.C., 1996. Relating meteorological variables and trends in motor vehicle emissions to monthly urban carbon monoxide concentrations. Atmos. Environ. 30, 4225±4232. Gualtieri, G., Tartaglia, M., 1997. A street canyon model for estimating NOx concentrations due to road trac. In: Jose, R.S., Brebbia, C.A. (Eds.), Measurements and Modelling in Environmental Pollution. First International Conference on Environmental Pollution MMMEP 97. Computational Mechanics Publications, Southampton, Boston, 1997, pp. 211±220. H ark onen, J., Valkonen, E., Kukkonen, J., Rantakrans, E., Jalkanen, L., Lahtinen, K., 1995. An operational dispersion model for predicting pollution from a road. Int. J. Environ. Pollut. 4±6, 602±610. H ark onen, J., Walden, J., Kukkonen, J., 1996a. Comparison of model predictions and measurements near a major road in an urban area. In: Kretzchmar, J., Cosemans, G. (Eds.), Proceedings of the Fourth Workshop on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, vol. 2. Oostende, Belgium, 1996 Vlaamse Instelling voor Technologisch Onderzoek, Mol, Belgium, pp. 453±460. H ark onen, J., Valkonen, E., Kukkonen, J., Rantakrans, E., Lahtinen, K., Karppinen, A., Jalkanen, L., 1996b. A Model for the Dispersion of Pollution from a Road Network. Finnish Meteorological Institute, Publications on Air Quality 23 Helsinki, 34. Heida, H., Jong, A.L., Huygen, C., Model calculations of street air concentrations for carbon monoxide and nitrogen dioxide in Amsterdam. In: Brasser, L.J., Mulder, W.C. (Eds.), Proceedings of the Eighth World Clean Air Congress, vol. 3. The Hague, The Netherlands, Elsevier, Amsterdam, 1989, pp. 233±238. Heida, H., Jong, A.L., Huygen, C., 1992. Monitoring and model calculations of automotive exhaust related air pollution in an Amsterdam urban street canyon. In: Buonicore, A.J., Breda, N.P.E. (Eds.), Papers from the Ninth World Clean Air Congress, vol. 5, Montreal, Canada, 1992. Air Waste Management Association, Pittsburgh, PA, pp. J.U. -30.06. Heida, H., Jong, A.L., Muchall, R.C., 1994. Trac related environmental impact mapping in downtown Amsterdam. In: Baldasano, J.M., Brebbia, C.A., Power, H., Zannetti, P. (Eds.), Air Pollution II Vol. 1: Computer Simulation. Second International Conference on Air Pollution, Computational Mechanics Publications, Southampton, Boston, pp. 385±392. Heinmiller, W., 1978. E€ect of low temperature on the exhaust emissions and fuel economy of 84 automobiles in Chicago. Oce of mobile Source Air Pollution Control, US EPA, Technical Report 78-3. Hertel, O., Berkowicz, R., 1989a. Modelling air pollution from trac in street canyon. Evaluation of data and model development. Roskilde, Denmark Report DMU Luft-A129. National Environment Research Institute. Hertel, O., Berkowicz, R., 1989b. Operational street pollution model (OSPM). Evaluation of the model data from St. Olavs Street in Osla. Report DMU Luft-A135. National Environment Research Institute, Roskilde, Denmark. Hertel, O., Berkowicz, R., 1989c. Modelling NO2 concentrations in a street canyon. Report DMU Luft-A131. National Environment Research Institute, Roskilde, Denmark. Hertel, O., Berkowicz, R., Larssen, S., The operational street pollution model (OSPM) Paper. In: van Dop, H., Steyn, D.G. (Eds.), Proceedings of the 18th International meeting of NATO/CCMS on Air Pollution Modelling and its Application, vol. 15. Vancouver, Canada, 13±17 May 1990, Plenum Press, New York, pp. 741±749. Hickman, A.J., Colwill, D.M., 1982. The estimation of air pollution concentrations from road trac. TRRL Report LR 1052. Transport and Road Research Laboratory Assessment Division, Transport System Department, Crowthorne, Berkshire. Hickman, A.J., Colwill, D.M., Hughes, M.R., 1979. Predicting air pollutant levels from trac near roads. Department of the Environment Department of Transport, TRRL Report SR 501. Transport and Road Research Laboratory Assessment Division, Transport System Department, Crowthorne, Berkshire. Hotchkiss, R.S., Harlow, F.H., 1973. Air pollution transport in street canyons. EPA-R4-73-029, NTIS PB- 233252. Prepared by Los Alamos National Laboratory for US Environmental Protection Agency. Hov, O., Larssen, S., 1984. Street canyon measurements of nitrogen dioxide in Oslo. Measurements and model calculations. Environ. Sci. Technol. 18, 82±87. Hoydysh, W.G., Dabberdt, W.F., 1988. Kinematics and dispersion characteristics of ¯ows in asymmetric street canyons. Atmos. Environ. 22, 2677±2689. Hoydysh, W.G., Dabberdt, W.F., 1994. Concentration ®elds at urban intersections: ¯uid modelling studies. Atmos. Environ. 28, 1849±1860.

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

195

Jakeman, A.J., Bai, J., Miles, G.H., 1991. Prediction of seasonal extremes of 1-h average urban CO concentrations. Atmos. Environ. 25B, 219±229. Japan Environmental Agency. 1982. Manual for the Nitrogen Oxides Total Pollution Load Control (in Japanese). Johnson, W.B., Dabberdt, W.F., Ludwig, F.L., Allen, R.J., 1971. Field study for an initial evaluation of an urban di€usion model for carbon monoxide. Comprehensive Report for Coordinating Research Council and EPA, Contract CAPA-3-68 (1-69), Stanford Research Institute, Menlo Park, CA, 240 pp. NTIS PB203469. Johnson, W.B., Ludwig, F.L., Dabberdt, W.F., Allen, R.J., 1973. An urban di€usion simulation model for carbon monoxide. J. Air Pollut. Control Assoc. 23, 490. Juda, K., 1989. Air Pollution Modelling. In: Cheremisino€, P.N., (Ed.), Encyclopaedia of Environmental Control Technology, vol. 2, Air Pollution Control. Gulf Publishing Company, Houston, Texas, USA, pp. 83±134. Karim, M.M., 1999. Trac pollution inventories and modeling in metropolitan Dhaka, Bangladesh. Transp. Res. D4, 291±312. Karim, M.M., Matsui, H., 1995. E€ects of wind speed on the dispersion of pollutant concentration in a road of Nagoya City. In: Proceedings of the Fourth International Conference in Urban Planning and Urban Management. Melbourne, Australia, 11±14 July 1995. Karim, M.M., Matsui, H., 1998. A mathematical model of wind ¯ow, vehicle wake, and pollution concentration in urban road microenvironments. Part I: Model description. Transp. Res. D3, 81±92. Karim, M.M., Matsui, H., Guensler, R., 1996. Achievements of dispersion model for predicting micro-environmental pollution from trac emissions. In: Proceedings of the 89th Annual Meeting of A&WMA. Nashville, Tennessee, 23± 28 June 1996. Karim, M.M., Matsui, H., Guensler, R., 1998. A mathematical model of wind ¯ow, vehicle wake, and pollution concentration in urban road microenvironments. Part I: Model results. Transp. Res. D3, 171±191. Karppinen, A., Kukkonen, J., Konttinen, M., Rantakrans, E., Valkonen, E., H ark onen, Koskentalo, T., Elol ahde, T., 1997. Comparison of dispersion model predictions and the results from an urban air quality measurement network. In: Power, H. Tirabassis, T. Brebbia, C.A. (Eds.), Air Pollution V., Fifth International Conference on Air Pollution Modelling, Monitoring and Management, Air Pollution V. Computational Mechanics Publications, Southampton, Boston, pp. 405±411. Khare, M., Sharma, P., 1999. Performance evaluation of general ®nite line source model for Delhi trac conditions. Transp. Res.: Part D 4, 65±70. Kirsch, J.W., Mason, B.F., 1975. Mathematical models for air pollution studies involving the Oregon I205 highway project. Systems, Science and software Report, SSS-R-76-2744, La Jolla, CA. Kondo, J., 1973. A study on the air pollution by automobile emissions. J. Japan Soc. Mech. Eng. 76, 619±627 (in Japanese). Kono, H., Ito, S., 1990a. A micro-scale dispersion model for motor vehicle exhaust gas in urban areas- OMG volumesource model. Atmos. Environ. 24B, 243±251. Kono, H., Ito, S., 1990b. A comparison of concentration estimate by the OMG volume ± source dispersion model with three line source dispersion models. Atmos. Environ. 24B, 253±260. Kunzelman, P., McAdams, H.T., Domke, C.J., Williams, M., 1974. Automobile Exhaust Emissions Modal Analysis Model, EPA-460/3-74-005, Ann Arbor, MI. Lee, I.Y., Park, H.M., 1994. Parameterization of the pollutant transport and dispersion in urban street canyons. Atmos. Environ. 28, 2343±2349. Liu, F.C., Okamato, S., Yamada, H., Shiozaiva, K., 1993. Development of an atmospheric di€usion model for automobile emission in urban area. In: Proceedings of the 34th Annual Meeting Japan Society of Air Pollution, vol. 384. Chiba, Japan (in Japanese). Liu, J.J., Chan, C.C., Jeng, F.T., 1994. Predicting personal exposure levels to carbon monoxide (CO) in Taipei, based on actual CO measurements in microenvironments and a Monte Carlo simulation method. Atmos. Environ. 28, 2361±2368. Longhurst, J.W.S., Lindley, S.J., Conlan, D.E., 1994. Towards local air quality management in the United Kingdom. In: Baldasano, J.M., Brebbia, C.A., Power, H., Zannetti, P. (Eds.), Air Pollution II, vol. 2, Pollution Control and Monitoring. Computational Mechanics Publications, Southampton, Boston, pp. 525± 532.

196

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

Ludwig, F.L., Dabberdt, W.F., 1972. Evaluation of the APRAC-1A urban di€usion model for carbon monoxide. Final report. Coordinating Research Council contract CAPA-3-68 (1-69). NTIS No. PD 210819. Luhar, A.K., Patil, R.S., 1989. A general ®nite line source model for vehicular pollution prediction. Atmos. Environ. 23, 555±562. McCollister, G.M., Wilson, K.R., 1975. Linear stochastic models for forecasting daily maxima and hourly concentrations of air pollutants. Atmos. Environ. 9, 417±423. McCrae, I.S., Hickman, A.J., 1989. Air pollution from trac in topographically complex locations. In: Proceedings of the Third International Symposium on Highway Pollution. Munich, FRG, September. Middleton, D.R., Butler, J.D., Colwill, D.M., 1979. Gaussian plume dispersion model applicable to a complex motorway interchange. Atmos. Environ. 13, 1039±1049. Mikkelson, T., Troen, I., Larsen, S.E., 1982. On the ®nite line source problem in di€usion theory. Atmos. Environ. 16, 2591±2594. Miles, G.H., Jakeman, A.J., Bai, J., 1991. A method of predicting the frequency distribution of air pollution from vehicle trac, basic meteorology, and historical concentrations to assist urban planning. Environ. Int. 17, 575±580. Moseholm, L., Silva, J., Larson, T., 1996. Forecasting carbon monoxide concentration near a sheltered intersection using video surveillance and neural networks. Transp. Res. D1, 15±28. Nevell, G.F., 1982. Applications of Queuing Theory, second ed. Nicholson, S.E., 1975. A pollution model for street-level air. Atmos. Environ. 9, 9±31. Noll, K.E., Miller, T.L., 1975a. Design of Highway Air Monitoring Surveys, vol. I. Publication of the Federal Highway Administration. Noll, K.E., Miller, T.L., 1975b. Highway Air Quality Monitoring Manual, vol. II. Publication of the Federal Highway Administration. Noll, K.E. et al., 1975. Air monitoring program to determine the impact of highways on ambient air quality. Final Report, Tennessee Department of Transportation Department of Civil Engineering, The University of Tennessee, Knoxville, Tennessee. Noll, K.E., Burdick, J.C.III, Miller, T.L., Gilbert, J., Rainey, R.H.Jr., Jones, K., 1974. Highway Air Monitoring Data Summary and General Solutions to the Highway Line Source Model. Department of Civil Engineering, The University of Tennessee. Noll, K.E., Miller, T.L., Claggett, M., 1978. A comparison of three highway line source dispersion models. Atmos. Environ. 12, 1323±1329. Noll, K.E., Miller, T.L., Rainey, R.H., May, R.C., 1977. Air monitoring program to determine the impact of highways on ambient air quality, NTIS-PB-253-046. Okamato, S., Kobayashi, K., Ouo, N., Kitabayashi, K., Katatani, N., 1990. Comparative study on estimation methods for NOx emissions from a roadway. Atmos. Environ. 24A, 1535±1544. Okamato, S., Lin, F.C., Yamada, H., Shiozaiva, K., 1994. Development and application of an air quality simulation model for automobile emissions in a street canyon. In: Proceedings of the Seventh IUAPPA Regional Conference on Air Pollution and waste Issues. Taipei, 2±4 November 1994, pp. 97±106. Okamato, S., Lin, F.C., Yamada, H., Shiozawa, K., 1996. Evaluation of a two-dimensional numerical model for air quality simulation in a street canyon. Atmos. Environ. 30, 3909±3915. Ostrouchov, N., 1978. E€ect of Cold Weather on Motor Vehicle Emissions and Fuel Economy. Fishers and Environment, SAE-780084. Petersen, W.B., 1978. A Pilot Study on Dispersion Near Roadways. EPA-600/4-78-044. Petersen, W.B., 1980. User's Guide for HIWAY-2, highway air pollution model, 3±17, EPA-600/8-80-018. Environmental Science Research Laboratory, Oce of Research and Development US Environmental Protection Agency, Research Triangle Park, NC, 27711. Pitter, R.L., 1976. User's Manual ROADS, PSMG, VISI. Oregon Graduate Center, Beaverton, Oregon. Potenta, E.J., Fahrer, V.S., Tai, R.C., 1982. State-of-the art approach to predicting pollutant concentrations in urban street canyons. APCA Paper No. 82-50.6, Presented at the 75th Annual Meeting, 20±25 June, New Orleans, LA. Qin, Y., Chan, L.Y., 1993. Trac source emission and street level air pollution in urban area of Guangzhou, South China (PRC). Atmos. Environ. 27B, 275±285.

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

197

Qin, Y., Kot, S.C., 1993. Dispersion of vehicular emissions in street canyon, Guangzhou city, South China (PRC). Atmos. Environ. 27B, 286±295. Ragland, K.W., Pierce, J.J., 1975. Boundary layer model for air pollutant concentrations due to highway trac. J. Air Pollut. Control Assoc. 25, 48±51. Raimondi, F.M., Roland, F., Vitale, M.C., Calcara, A.M.V., 1997a. Short-time fuzzy DAP predictor for air pollution due to vehicular trac. In: Jose, R.S., Brebbia, C.A. (Eds.), Measurements and Modelling in Environmental Pollution. pp. 189±200; First International Conference on Environmental Pollution MMMEP 97, Computational Mechanics Publications, Southampton, Boston. Rao, K.S., 1982. Analytical solutions of a gradient-transfer model for plume deposition and sedimentation. EPA-600/882-079, NTIS PB-82-215153, US Environmental Protection Agency, Research Triangle Park, NC. Rao, S.T., Keenan, M.T., 1980. Suggestions for improvement of the EPA-HIWAY model. J. Air Pollut. Control Assoc. 30, 247±256. Rao, K.S., Snodgrass, H.F., 1982. PAL-Ds Model: the PAL model including deposition and sedimentation -user's guide. EPA-600/8-82-023, NTIS PB-83-117739, US Environmental Protection Agency, Research Triangle Park, NC. Rao, S.T., Visalli, J.R., 1981. On the comparative assessment of the performance of air quality models. J. Air Pollut. Control Assoc. 31, 851. Rao, S.T., Chen, M., Keenan, M., Sistla, G., Peddada. R., Wotzak, G., Kolak, N., 1978a. Dispersion of pollutants near highways: experimental design and data acquisition procedures. EPA-600-4-78-037 NTIS PB-284-866, US Environmental Protection Agency, Research Triangle Park, NC. Rao, S.T., Chen, M., Keenan, M.T., Sistla, G., Samson, P.J., Romana, D.J., 1978b. Overview of the NYS- Long Island Expressway dispersion experiment. In: Proceedings of the 57th Annual Meeting Transportation Research Board, Session 25. Rao, S.T., Keenan, M.T., Sistla, G., 1979. Dispersion of Pollutants Near Highways, Data Analysis and Model Evaluation, EPA-600/4-79-011. Rao, S.T., Sede®an, L., Czapski, U., 1979b. Characteristics of turbulence and dispersion of pollutants near major highways. J. Appl. Met. 18, 283±293. Rao, S.T., Sistla, G., Wilson, J.S., 1980. An evaluation of some commonly used highway dispersion models. J. Air Pollut. Control Assoc. 30, 239±246. Rao, S.T., Sistla, G., Eskridge, R.E., Petersen, W.B., 1986. Turbulent di€usion behind vehicles: evaluation of roadway models. Atmos. Environ. 20, 1095±1103. Schattanek, G., Kahng, J., Stratou, T. Braverman, T.N. (Eds.), 1990. EPA User's Guide to CAL3QHC ± A modelling methodology for predicting pollutant concentrations near roadway intersections. USEPA. Sharma, P., Khare, M., Chakrabarti, S.P., 1999. Application of extreme value theory for predicting violations of air quality standards for an urban road intersection. Transp. Res. D 4, 201±216. Sharma, P., Khare, M., 1999. Application of intervention analysis for assessing the e€ectiveness of CO pollution control legislation in India. Transp. Res. D 4, 427±432. Sharma, P., Khare, M., 2000a. Real-time prediction of extreme ambient carbon monoxide concentrations due to vehicular exhaust emissions using univariate linear stochastic models. Transp. Res. D5, 59±69. Sharma, P., Khare, M., 2000b. Short-term, real-time prediction of the extreme ambient carbon monoxide concentrations due to vehicular exhaust emissions using transfer function-noise model. Transp. Res. D (accepted). Simmon, P.B. (1981). User's manual for the APRAC-3/MOBILE emission and di€usion modelling package. EPA 9099-81-002.PB02-103763. Atmospheric Science Center, Menlo Park, CA. Sistla, G., Samson, P., Keenan, M., Rao, S.T., 1979. A study of pollutant dispersion near highways. Atmos. Environ. 13, 669±685. Sobottka, H., Leisen, P., 1980a. Vehicle exhaust gas emissions in city streets and their distributions; comparison of measurements and model aspects. IMA Conference on Modeling of Dispersion in Transport Pollution, Southendon-Sea, England, 17±18 March. Sobottka, H., Leisen, P., 1980b. Pollutant dispersion of vehicle exhaust gases in street canyons. In: Proceedings of the Fifth International Clean Air Congress. Buenos, Aires. Suleiman, T.I.A., Khateeb, G.G.A., Rabi, A.Z., Awad, A.R., 1994. E€ect of trac on air pollution areas-case study in Jordan. In: Baldasano, J.M., Brebbia, C.A., Power, H., Zannetti, P. (Eds.), Air Pollution II vol. 1: Computer

198

P. Sharma, M. Khare / Transportation Research Part D 6 (2001) 179±198

Simulation. Second International Conference on Air Pollution, Computational Mechanics Publications, Southampton, Boston, pp. 367±376. Sutton, O.G., 1932. A theory of eddy di€usion in the atmosphere. Proc. R. Soc. London A 135 (826), 143. Tanaka, K., Sano, M., Watanabe, H., 1992. Identi®cation and analysis of fuzzy model for air pollution ± an approach to self-learning control of CO concentration. IEEE, 261±266. Tao, Y., Xinmiao, Y., 1998. Fuzzy comprehensive assessment, fuzzy clustering analysis and its application for urban trac environment quality evaluation. Transp. Res. D3, 51±57. Tartaglia, M., Giannone, A., Gualtieri, G., Barbaro, A., 1995. Development and validation of an urban street canyon model based on carbon-monoxide experimental data measured in Firenze. In: Sucharov, L.J. (Ed.), Urban Transport and the Environment for the 21st Century. Computational Mechanics Publications, Southampton. Tiao, G.C., Box, G.E.P., Hamming, W.J., 1975. A statistical analysis of the Los Angeles ambient carbon monoxide data 1955-172. J. Air Pollut. Control Assoc. 24, 1129±1136. Tokyo Metropolitan Government, 1983. Study on the Air Quality Models for the Prediction on the Vicinity of Streets. Tokyo Metropolitan Government, Environmental Protection Bureau, pp. 22±25, 35±40 (in Japanese). Trinity Consultants, 1991. MOBILE 4.1 software, Dallas, TX. Turner, D.B., 1970. Workbook of atmospheric dispersion estimates. Oce of air programs publication No. AP-26. US Environmental Protection Agency, Research Triangle Park, NC. Turner, D.B., 1979. Atmospheric dispersion modeling: a critical review. J. Air Pollut. Control Assoc. 29, 502±519. Van den Hout, K.D., Baars, H.P., Duijn, N.J., 1989. E€ects of Buildings and trees on air pollution by road trac. In: Brasser, L.J., Mulder, W.C. (Eds.), Proceedings of the Eighth World Clean Air Congress, vol. 4. The Hague, The Netherlands, 1989. Elsevier, Amsterdam. Waller, R.E., Commins, B.T., Lawther, P.J., 1965. Air pollution in a city street. Br. J. Ind. Med. 22, 128±138. Ward, C.E., 1975. Air Quality Manual Modi®cation. Environmental Improvement Branch, Transportation Laboratory of CALTRANS. Ward, C.E., Ranzieri, A.J., Shirley, E.C., 1977. CALINE 2 - An improved microscale model for the dispersion of air pollutants from a line source. Federal Highway Administration Report, FHWA-RD-77-74, Washington, DC. Water®eld, V.H., Hickman, A.J., 1982. Estimating air pollution from road trac: a graphical screening method. TRRL Report SR 752. Transport and Road Research Laboratory Assessment Division, Transport System Department, Crowthorne, Berkshire. Yamartino, R.J., Wiegand, G., 1986. Development and evaluation of simple models for the ¯ow, turbulence and pollutant concentration ®elds within an urban street canyon. Atmos. Environ. 27B, 389±400. Zannetti, P., 1990. Air Pollution Modelling, Theories, Computational Methods and available Software. Van Nostrand Reinhold, New York. Zimmerman, J.R., Thompson, R.S., 1975. User's guide for HIWAY, a highway air pollution model. EPA-650/4-74-008.