High-Emitting Vehicle Characterization Using Regression Tree Analysis

Abstract

A small fraction of motor vehicles on the roadway emit a disproportionate fraction of pollutant emissions, especially for carbon monoxide and hydrocarbons. Generally, these “high emitters” or “super emitters” exhibit higher emissions rates under all operating conditions than do “normal emitters.” Since the instantaneous emissions response between normal- and high-emitting vehicles can differ by one or more orders of magnitude, so do their average emissions over a “typical” trip. Identifying the proportion of normal- and high-emitting vehicles in an urban area and quantifying their emissions is vital for accurate emission inventory accounting. A methodology by which high and normal emitters can be classified is presented. Unlike previous emitter classification approaches, the approach is data driven and relies entirely on hot-stabilized emissions results. A statistical classification scheme, better known as hierarchical tree based regression, is used to separate vehicles into homogenous emitter categories. The approach is shown to have a number of advantages. First, it is flexible with respect to both the number of classes and types of variables used to identify classes. Second, it considers the influence of a large number of vehicle and technology attributes on emitter status. Third, it ensures that the highest emitters can be isolated from the normal emitters, so that separate emission rate models can be developed for these vehicles. Finally, the approach does not combine the effects of starts and hot-stabilized operations within the definition of high emitter, leading to a classification scheme whereby vehicles with poor start emissions characteristics will not be incorrectly classified as vehicles with poor hot-stabilized emission characteristics.