ArticleLine source model for vehicular pollution prediction near roadways and model evaluation through statistical analysis
Introduction
In most of the metropolitan areas of the world, transportation facilities are improving/increasing every year in order to meet increased demand. As a result, more and more vehicles are added to the roadways. Vehicular traffic has become a major source of air pollution in urban areas. In the early years of transportation development in India, the primary concern was the ability to move men and material effectively and rapidly. Not enough attention has been directed to the ill-effects of transportation on the environment or on the depletion of natural resources. Past goals and objectives were much simpler and the constraints on the system were less complex. But now there is a tremendous change in the transportation project development system everywhere in the world. The primary objective nowadays is to provide a safer and better environment for future generations. In order to meet the above objectives it is necessary to predict the concentration of pollutants due to automobiles from roadways. Mathematical models are one of the best tools available for this purpose. This paper discusses the modelling approach of roadways and the efficiency of these models used for prediction.
Section snippets
Mathematical models
A mathematical model is an assembly of concepts or phenomena in the form of one or more mathematical equations which approximate the behaviour of a natural system or phenomenon. Mathematical models are usually employed to predict the desired concept or parameters for different types of current or future conditions using readily available or measured input data. In air pollution problems, mathematical models are used to predict concentrations of one or more species in space and time as related
Field study and data collection procedures
Madras is one of the major metropolitan cities of India. Transportation facilities are improving every year in the city to meet increases in demand due to excessive population growth. This leads to a high increase in traffic volume. The four selected line source models have been evaluated against the database consisting of carbon monoxide (CO) concentrations, traffic counts and meteorological parameters collected on the major highway at Madras during the period of May 1989 to January 1990 (
Model tested and its characteristics
The model used in the present work is a Gaussian-based GFLSM. The basic methodology used in developing this model includes co-ordinate transformation between the wind co-ordinate system (x1, y1, z1) and the line source co-ordinate system (x, y, z). The middle point of the line source can be assumed as the origin for both co-ordinate systems, which also have the same z-axis. The line source is along the y-axis and the wind vector is in the x1 direction. In the line source co-ordinate system all
Determination of input parameters to the model
Input parameters to the model can be determined based on the following information:
- 1.
mobile source emissions;
- 2.
meteorological conditions;
- 3.
geometry of the road.
Air quality prediction and results
The model was run with estimated emission, meteorological conditions, geometry of the road and receptors. The model predictions were carried out for 1 h averaging time, since the CO monitoring was done in the field for 1 h averaging time. Comparisons between model outputs and measurements were performed using both quantitative data analysis techniques and statistical methods to evaluate model performance. The predicted pollutant concentration and isopleths of CO are given in Fig. 1 and Fig. 2
Model evaluation
The evaluation of model performance is a matter of great interest and it becomes particularly important in all those fields in which modelling is used as a decision-making tool. There is a need for a comprehensive discussion on the evaluation of air-quality models as well as on the development of general evaluation methods, that can easily be implemented. Studies in this direction have been carried out by many investigators; however, standard evaluation procedures as well as performance
Results and discussion
Quantitative data analysis of the model output is given in Table 3. GFLSM predicts concentration within a factor of 2, about 82%, with less overprediction (25%). It seems that for almost half of the time GFLSM (46%) underpredicts concentration. The O and P summary measures indicate that GFLSM underpredicts the value. A comparison of o and p with regard to how close the two deviation quantities approach each other gives a relative indication of how well a model is reproducing the observed
Conclusions
It can be observed from the analysis presented that GFLSM performance is good and can be conveniently applied when the road is finite in length. It can also handle all orientations of wind direction in relation to the road. However, there is a slight overprediction when wind direction is parallel to the roadway. The other error may be due to the fact that accurate input data were not available for estimation of emission factors for Indian vehicles.
Acknowledgements
This is a part of the M.E. dissertation work of the first author carried out at the Centre for Environmental Studies (CES), Anna University, Madras. The authors are grateful to the staff of Anna University for the facilities made available for this work.
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