Abstract

This study explores the correlation in measured hot running NO/CO2 ratios by a remote sensing device (RSD) and dynamometer testing. Two large diesel cars (E4/E5) are tested on the dynamometer in hot running conditions using a new drive cycle developed for this study and then driven multiple times past the RSD. A number of verification and correction steps are conducted for both the dynamometer and RSD data. A new time resolution adjustment of RSD acceleration values proves important. Comparison of RSD and dynamometer data consistently shows a strong weighted correlation varying from +0.89 to +0.95, despite the high level of variability observed in the RSD measurements. This provides further evidence that relative changes in mean NO/CO2 ratios as measured with the RSD should provide robust emissions data for trend analysis studies and as inputs for regional emissions models. However, a positive bias of approximately 25 ppm NO/% CO2 is observed for the RSD, and bias correction of RSD measurements should be considered pending further testing.

Highlights

  • Motor vehicles are a major source of air pollution and greenhouse gas (GHG) emissions in urban areas around the world

  • This study explores the correlation between a remote sensing device (RSD) and dynamometer testing

  • Two diesel cars were tested on the dynamometer and driven multiple times past the RSD in hot running conditions

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Summary

Introduction

Motor vehicles are a major source of air pollution and greenhouse gas (GHG) emissions in urban areas around the world. Comprehensive measurement of vehicle emissions in urban networks is cost prohibitive due to the large number of on-road vehicles with different emission profiles, large spatial and temporal variability in vehicle activity and many real-world factors that influence emission levels. Emission measurements are used to develop and validate vehicle emission (factor) models [2,3]. Of particular interest is accurate quantification of on-road and real-world emissions. This information is essential for implementation of cost-effective measures to improve air quality in urban areas [4]

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