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Research poster (4 ft by 3 ft) created using a PowerPoint template, created by Kim Mercer for the many researchers funded by the TRC.
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On-Board Tailpipe Particulate Number Modeling AUTHORS: Chen Zhang, Lisa Aultman-Hall, Britt Holmén, Eric Jackson
ABSTRACT This study focused on assessing relationships between
tailpipe particulate numbers (PN) and second-by-second vehicle operating characteristics, including
speed, acceleration, vehicle specific power (VSP) etc. This study represents an advance in terms of detail over typical emission studies in that a PN prediction model is
estimated based on the continuous real-world PN data collected at a second-by-second level. The results of
this study contribute efforts for a new generation transportation emission models including movement towards inclusion of particle number in EPA's MOVES
model.
ACKNOWLEDGMENTS This study was funded by National Science Foundation
(NSF). It is a continuation of prior studies completed at the University of Connecticut by Eric Jackson, Lisa Aultman-Hall, and Britt Holmén.
DATA
ANALYSIS
UNIVERSITY OF VERMONT TRANSPORTATION RESEARCH CENTER BURLINGTON, VERMONT www.uvm.edu/~transctr
NormPN Speed bin VSP bin Mode
NormPN 10.40932 0.73721 0.08841
<.0001 <.0001 <.0001
Speed bin 10.25848 0.49907
<.0001 <.0001
VSP bin 10.08773
<.0001
Mode 1
ScanTool
Video Camera
Tailpipe Adapter
5-Gas
Analyzer
Pitot Tube
Mini-Diluter
Condensation
Particle
Counter
GPS Receivers
Accelerometer
Desktop
Computer
Thermocouples
4 Pressure Sensors to Calculate
Exhaust Flow rate
Ambient Temperature
and Relative Humidity
Sensor
Average normalized PN emissions by location
CONCLUSIONS • Vehicle specific power (VSP) is the most relevant factor when predicting PN rate
• Different modes (accelerating, decelerating, cruising, and idling) corresponds to distinctive relationships between PN rate and VSP
• Fixed effect ANOVA models seem to provide better prediction results than continuous models • Model diagnostics results show better model results are obtained by vehicle mode • Potential future research direction – 1) spatial analysis for PN prediction; 2) assessing the
relationship between instrumentation accuracy, time resolution and model accuracy
Figure 2 PN rate compared to VSP on a Loge scale
Figure 1 PN rate compared to speed and accelerations (for one randomly selected run)
Figure 3 Plot of Normalized PN Rate vs. VSP by Mode
Table 1 Spearman Correlation Mix Figure 4 Normalized PN estimates versus VSP Bin by mode
Figure 5 Residual vs. VSP plots a. Model of all modes together b. Model of accelerating mode
Figure 1 PN rate compared to speed and accelerations (for one randomly selected run)