A review of deterministic, stochastic and hybrid vehicular exhaust emission models

Abstract

Vehicles spend more time near junctions and intersections in different driving modes, i.e., queuing, decelerating or accelerating and thus generating more pollutants than at road links [Claggett, M., Shrock, J., Noll, K.E., 1981. Carbon monoxide near an urban intersection. Atmos. Environ. 15, 1633–1642]. As a result, the receptors in these urban corridors are prone to frequent exposures of high pollutant concentrations (episodic conditions). In order to predict such ‘episodes’, an air quality model, capable of estimating the entire range (middle and extremes) of pollutant concentration distribution is needed. Hybrid models (combining deterministic and statistical distribution models) have demonstrated the ability to predict the entire range of pollutant concentrations in such co mplex dispersion situations with reasonable accuracy [Jakeman, A., Simpson, R.W., Taylor, J.A., 1988. Modelling distributions of air pollutant concentrations-III: Hybrid modelling deterministic-statistical distributions. Atmos. Environ. 22 (1) 163–174]. The present paper reviews the relevant deterministic and stochastic based vehicular exhaust emission models that may be hybridized and thus generate a hybrid model with improved prediction accuracy. The paper also describes the implications of hybrid models in formulating the Episodic-Urban Air Quality Management Plan (e-UAQMP).