Abstract
Abstract Background Rate models for predicting vehicular emissions of nitrogen oxides (NO X ) are insensitive to the vehicle modes of operation, such as cruise, acceleration, deceleration and idle, because these models are usually based on the average trip speed. This study demonstrates the feasibility of using other variables such as vehicle speed, acceleration, load, power and ambient temperature to predict (NO X ) emissions to ensure that the emission inventory is accurate and hence the air quality modelling and management plans are designed and implemented appropriately. Methods We propose to use the non-parametric Boosting-Multivariate Adaptive Regression Splines (B-MARS) algorithm to improve the accuracy of the Multivariate Adaptive Regression Splines (MARS) modelling to effectively predict NO X emissions of vehicles in accordance with on-board measurements and the chassis dynamometer testing. The B-MARS methodology is then applied to the NO X emission estimation. Results The model approach provides more reliable results of the estimation and offers better predictions of NO X emissions. Conclusion The results therefore suggest that the B-MARS methodology is a useful and fairly accurate tool for predicting NO X emissions and it may be adopted by regulatory agencies.
Highlights
Rate models for predicting vehicular emissions of nitrogen oxides (NOX ) are insensitive to the vehicle modes of operation, such as cruise, acceleration, deceleration and idle, because these models are usually based on the average trip speed
Among air pollutants coming from natural effects (Duc et al 2013), man-made emissions have been the main concern in air-quality modelling and control
To enhance the prediction performance for the NOX emissions, the boosting Multivariate Adaptive Regression Splines (MARS) (B-MARS) modelling approach is proposed in this paper
Summary
Rate models for predicting vehicular emissions of nitrogen oxides (NOX ) are insensitive to the vehicle modes of operation, such as cruise, acceleration, deceleration and idle, because these models are usually based on the average trip speed. Among air pollutants coming from natural effects (Duc et al 2013), man-made emissions have been the main concern in air-quality modelling and control. Vehicular emissions, in this context, can bring serious impacts on air quality and have received increasing research attention (Sharma et al 2010). Road transport is likely to remain a large contributor to air pollution, especially in urban areas. For this reason, major efforts are being made for the reduction of polluting emissions from road transport.
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