A new method to compare vehicle emissions measured by remote sensing and laboratory testing: High-emitters and potential implications for emission inventories

Abstract

A new method is presented which is designed to investigate whether laboratory test data used in the development of vehicle emission models adequately reflects emission distributions, and in particular the influence of high-emitting vehicles. The method includes the computation of a ‘high-emitter’ or ‘emission distribution’ correction factor for use in emission inventories. In order to make a valid comparison we control for a number of factors such as vehicle technology, measurement technique and driving conditions and use a variable called ‘Pollution Index’ (g/kg). Our investigation into one vehicle class has shown that laboratory and remote sensing data are substantially different for CO, HC and NO x emissions, both in terms of their distributions as well as in their mean and 99-percentile values. Given that the remote sensing data has larger mean values for these pollutants, the analysis suggests that high-emitting vehicles may not be adequately captured in the laboratory test data. The paper presents two different methods for the computation of weighted correction factors for use in emission inventories based on laboratory test data: one using mean values for six ‘power bins’ and one using multivariate regression functions. The computed correction factors are substantial leading to an increase for laboratory-based emission factors with a factor of 1.7–1.9 for CO, 1.3–1.6 for HC and 1.4–1.7 for NO x (actual value depending on the method). However, it also clear that there are points that require further examination before these correction factors should be applied. One important step will be to include a comparison with other types of validation studies such as tunnel studies and near-road air quality assessments to examine if these correction factors are confirmed. If so, we would recommend using the correction factors in emission inventories for motor vehicles.