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When preparing inventory models, it is desirable to obtain representative distance-specific emissions factors that truthfully represent the vehicle activity on a particular road (facility) type. Unfortunately, emissions values are often measured using only one test schedule, which represents a single average speed and a specific type of activity. This paper investigated the accuracy of predicting the emissions for a test schedule based on measurements from a different test schedule for the case of a medium heavy-duty truck. First, the traditional Speed Correction Factor (SCF) approach was examined, followed by the use of a power-based model derived from continuous data, followed by an artificial neural network (ANN) approach. The SCF modeling used distance-averaged emissions and cycle-averaged vehicle speed to predict distance-averaged NOx. The power-based modeling was based on linear and polynomial correlations between continuous axle power and NOx.

Emissions from diesel engines, particularly NOx and TPM emissions are harmful to the environment. Reduction of NOx emissions from diesel engines is of increasing concern. In 1998, a novel approach called Selective NOx Recirculation (SNR) was used to reduce NOx emissions in diesel engines. The SNR concept relies on two major parts, one to collect the NOx emissions from the exhaust by an adsorber, and another to decompose NOx using the in-cylinder combustion process by injecting the collected NOx emissions into the intake manifold at an elevated concentration. This paper deals with the destruction rates during the combustion process. A 1992 DDC series 60, 350 hp, 12.7 liter engine was connected to a 500 hp DC dynamometer. A full-scale dilution tunnel and analyzers capable of measuring continuous NOx, CO2, CO, HC, and PM in the exhaust were used.