Review
Line source emission modelling

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Abstract

Line source emission modelling is an important tool in control and management of vehicular exhaust emissions (VEEs) in urban environment. The US Environmental Protection Agency and many other research institutes have developed a number of line source models (LSMs) to describe temporal and spatial distribution of VEEs on roadways. Most of these models are either deterministic and/or statistical in nature.

This paper presents a review of LSMs used in carrying out dispersion studies of VEEs, based on deterministic, numerical, statistical and artificial neural network techniques. The limitations associated with deterministic and statistical approach are also discussed.

Introduction

In recent years, in most of the countries, the air pollution from industrial and domestic sources has markedly decreased due to passage of various acts by different governments. However, there has been a substantial increase of air pollution caused by the vehicular exhaust emissions (VEEs) due to addition of more and more vehicles on roadways to meet increase in transportation demand (Sharma and Khare 2001a; Mayer, 1999). Line source emission modelling (LSEM) is an important tool in screening of VEEs and helps in control and management of urban air quality. The US Environmental Protection Agency (EPA) and many other research institutes have developed a number of line source models (LSMs) for estimating vehicular pollutant concentrations. All these models involve deterministic and/or stochastic approach which, at present, are most widely used. In the recent past, artificial neural network (ANN) and in particular, multilayer perceptron (MLP) has also been applied in modelling the line source dispersion phenomena. Sharma and Khare (2001a) described various VEEs modelling studies in the domain of analytical modelling techniques—deterministic, numerical and statistical. The present review is aimed at readers with little or no understanding of LSM techniques. It is designed to act as a guide through the literature so that the readers may be able to appreciate these techniques. The review is divided into several sections, beginning with a brief introduction to the LSM approaches and followed by relevant LSM studies based on deterministic, numerical, statistical and ANN techniques. Some of the common practical problems and limitations associated with deterministic, numerical, statistical and ANN techniques have also been discussed.

Section snippets

Theoretical approaches of LSEM

The deterministic mathematical models (DMM) calculate the pollutant concentrations from emission inventory and meteorological variables according to the solutions of various equations that represent the relevant physical processes. In other words, differential equation is developed by relating the rate of change of pollutant concentration to average wind and turbulent diffusion which, in turn, is derived from the mass conservation principle. The common Gaussian LSM is based on the superposition

Line source deterministic models

Historically, as far as modelling of VEE is concerned, the work of Sutton (1932) may be regarded as the first of its kind. One of the early studies on deterministic vehicular pollution modelling was reported in Waller et al. (1965). The analytical method for estimating the pollution levels from motor vehicles in the vicinity of highways of common geometric configuration was developed by Chen and March (1971). The preliminary computational examples indicated that this method was capable of

Line source numerical models

Danard (1972) developed a two-dimensional Eularian model named as DANARD. It solved the mass conservation equation based on numerical methods outlined by Dufort and Frankel (1953). Using the boundary conditions imposed in DANARD, Ragland and Pierce (1975) derived the continuity equation for parallel and non-parallel diffusivity classes by an efficient matrix inversion technique. The model predicted concentrations for oblique and perpendicular cases by ignoring lateral diffusion. For parallel

Line source stochastic models

McGuire and Noll (1971) studied the relationship between maximum concentration and average time for CO, NOx and NO2 pollutants collected at 17 monitoring stations in California city. From the past studies on air pollution modelling, there existed substantial evidence that the series of pollution concentration and meteorological data were highly auto-correlated irrespective of time (Merz et al., 1972). McCollister and Wilson (1975) used the B–J type models for short-term forecast of oxidant and

ANN-based LSMs

Literature on application of ANN in line source modelling is found to be very scanty (Nagendra and Khare, 1999). Moseholm et al. (1996) studied the usefulness of neural network in understanding the relationships between traffic parameters and CO concentrations measured near an intersection, which was sheltered from wind by multi-storied buildings. In another work, Dorzdowicz et al. (1997) developed a line source neural network model for estimating hourly mean concentrations of CO in the urban

Limitations of LSMs

Deterministic LSEM approach is the most logical and traditional approach for the prediction of air pollution concentrations, yet it is not free from limitations. The prediction capability of deterministic models depends on the condition fulfilling the simplifying assumptions, which are made in the model formulation. For instance, when unit time interval is short (i.e., ⩽1 day) and ‘steady state’ assumptions required for the application of Gaussian type models are not met, deterministic models

Conclusion

The LSEM has been shown to be useful tool for prediction of urban air quality. Analytical modelling approaches including deterministic and statistical techniques, are commonly used for LSEM. Choosing the most suitable approach, depends on the complexity of the problem being addressed and the degree to which the problem is understood. Deterministic LSEM approach seems to be the most logical and traditional approach for modelling air pollution concentrations. The prediction capability of

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