Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions

Abstract

Artificial neural networks (ANNs) are useful alternative techniques in modelling the complex vehicular exhaust emission (VEE) dispersion phenomena. This paper describes a step-by-step procedure to model the nitrogen dioxide (NO 2) dispersion phenomena using the ANN technique. The ANN-based NO 2 models are developed at two air-quality-control regions (AQCRs), one, representing, a traffic intersection (AQCR1) and the other, an arterial road (AQCR2) in the Delhi city. The models are unique in the sense that they are developed for ‘heterogeneous 1 1 It consists of light, heavy vehicles, three-wheelers: auto rickshaws and two-wheelers: scooter and motorcycles. ’ traffic conditions and tropical meteorology. The inputs to the model consist of 10 meteorological and 6 traffic characteristic variables. Two-year data, from 1 January 1997 to 31 December 1998 has been used for model training and data from 1 January to 31 December 1999, for model testing and evaluation purposes. The results show satisfactory performance of the ANN-based NO 2 models on the evaluation data set at both the AQCRs ( d = 0.76 for AQCR1, and d = 0. 59 for AQCR2).