A vehicle emission model is urgently needed in China to evaluate emissions under various traffic conditions. This paper summarized possible parameters of emission models. The correlations of emissions against several parameters were then analyzed by using the Beijing test data. This data is the first-hand gaseous and particulate matters (PM) emission data using on-board test instruments for heavy-duty diesel trucks in China. Several activity parameters were finally selected to set up the micro-emission model. Clusters and multivariable linear regression models were combined to develop a micro-emission model. Both the aggregated and instantaneous emission results showed that the micro-emission model tracked the measurements results well. This work suggests that this combined method can be used to finally establish an improved emission prediction model in China, based on the input of traffic information.
Samenvatting