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Project Status: complete

Title: Correlation of the real-time particulate matter emissions measurements of a ESP remote sensing device (RSD) and a dekati electronic tailpipe sensor (ETaPS) with gravimetrically measured PM from a total exhaust dilution tunnel system

Principal Investigator / Author(s): Gautam, Mridul and Donald Stedman

Contractor: West Virginia University & University of Denver

Contract Number: ICAT 06-02

Research Program Area: Emissions Monitoring & Control

Topic Areas: ICAT Grants / Technology, Mobile Sources & Fuels


The University of Denver spent six weeks in West Virginia measuring diesel particles from 15 heavy duty diesel trucks with an Electrical Tailpipe Particle Sensor (ETaPS) and correlating the results with a RSD 4600 (supplied by ESP) and a gravimetric filter from a chassis dynamometer. The ETaPS is an electrical charger, placed directly in raw exhaust, and measures particles based on their active surface area. The use of a diesel particle filter (DPF) was implemented in three different ways. Five trucks were installed with a functioning DPF, five trucks were installed with bypassed DPF to simulate a failing DPF, and the last five trucks had no DPF. The University of Denver found that ETaPS (volt*sec/mile also Vs/mi) correlates well with to gravimetric readings (gm/mile) except for one truck. Probably the expected disagreement with semi-volatiles indicated by high HC readings from said truck. Functioning DPFs produce typical ETaPS readings of 2volt*sec/mile while readings of 20volt*sec/mile, or more, indicate either a failing DPF or an absent DPF. An ETaPS can be mounted on a real truck and be ready to drive in real-world conditions in ten minutes or less. Any outdoors testing with an ETaPS must be in a shielded environment as there is significant interference from other power sources (i.e. power lines, and transformers). RSD smoke factor readings above 0.15 (1.5gm of soot/kg of fuel) certainly indicate a malfunctioning DPF.


For questions regarding this research project, including available data and progress status, contact: Research Division staff at (916) 445-0753

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