Performance evaluation of vehicles emissions prediction models

Abstract

Road traffic is a dominant source of urban air pollution. Therefore, it is necessary to quantify emission levels as accurately as possible to evaluate their impacts on the public health and the environment. Several models were developed to predict these emissions. These models can be grouped into three categories, namely, emission factors models, average speed models, and modal models. The prediction capability of most developed models is relatively poor. Therefore, there is a pressing need to improve the predictability of the existing models or to develop new ones with better accuracy. The main focus of this paper is to review different traffic emissions modeling efforts and to describe the effect of different factors on emission levels and modeling accuracy, so as to get reliable emission estimates. In addition, different models were evaluated for the prediction capability of certain emissions such as carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide (NO2) and hydrocarbons (HC). These models are mainly based on traffic volume, composition, and flow. The predicted values by one of the models were compared to measured values based on field surveys. The result of comparison indicated that there is a significant difference between the measured and predicted values. These differences ranged from 17% for NO2 to 72% in the case of CO, which suggests that the NO2 model has better predictability. This deviation in prediction may be attributed to the fact that prediction models ignored some of the parameters affecting vehicle emissions such as the type of fuel used, air–fuel ratio, engine compression ratio, spark timing, surrounding environment, wind effect, regional characteristics and high pollutants emitters effect.