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Despite progressive implementation of stringent emission regulations, vehicle tailpipe emissions remain the major source of air pollution problems in most urban areas. To control and reduce tailpipe pollutants, it is critical to understand in-use emissions as a basis for any future emission controls. At present, emission factors are mainly studied by chassis dynamometer methods. However, concerns have been raised about the extent to which emissions produced by on-road vehicles can be predicted using emission factors developed based on standardized dynamometer test procedures. This paper describes an on-board, in-use vehicle emissions measurement system which measures tailpipe emission rates while the vehicle is in real service experiencing complex traffic conditions, driver behavior and weather.

Motorized transport has become an essential part of our world economic system with an ever-increasing number of vehicles on the road. However, considering the depletion of energy resources and the aggravation of greenhouse gas issues, it is critical to improve vehicle fuel consumption. These demands are moving us toward advanced engine and powertrain technologies. However, understanding our progress also requires improvements in the way we measure and certify vehicle emissions and fuel economy performance. This paper describes the use of an on-board fuel consumption and emissions measurement system to develop on-road fuel consumption functions that can be used to quantify the fuel economy impact of vehicle, road and traffic control changes. The system uses an ECM OBD-II scanner, a Mass Air Flow meter and an emissions analyzer to monitor fuel consumption and exhaust CO2 emission rates (in g/s) as well as vehicle speed and other parameters.

The advanced development of a previous in-use emissions measurement system developed at the University of Alberta, has been used to illustrate the challenges in accurately measuring real-time mass emissions of NOx, with specific attention given to the issue of sensor time resolution. An analysis of the alignment of vehicle and emissions data has shown constant value time shifting of remote emissions sensor data, to match vehicle data, as the most accurate method for synchronization. Although variable time shifting routines theoretically determine alignment time more accurately, the variable shifting of slow response sensor data has shown an added smearing effect to time shifted remote analyzer data. The effect of sensor response time on accuracy of mass emission rates, has shown that slow response remote emissions sensors are under predicting the total emissions produced by vehicles.