Statistical Analysis of Diesel Vehicle Exhaust Emission: Development of Empirical Equations to Calculate Particulate Emission

2004-01-3066

10/25/2004

Event
2004 Powertrain & Fluid Systems Conference & Exhibition
Authors Abstract
Content
Many studies have been completed over the past thirty years in search of a correlation between particulate emissions and exhaust smoke opacity for diesel fuel vehicles. A simple, direct correlation between opacity and particulate emissions does not exist due to the wide variety of factors that affect the production of diesel particulates. In an attempt to address this deficiency, data from the Australian National Environmental Protection Council for 72 vehicles deemed representative in terms of vehicle make, age and mass were analysed. The data included opacity and particulate measurements as well as a breakdown of emissions in terms of O2, CO, CO2, NOx, and HC readings for each vehicle as it was tested according to a comprehensive drive cycle. These drive cycles were developed to be representative of driving in a range of urban traffic conditions on Australian roads. The best model for predicting particulate matter was identified to have a linear relationship with the average opacity, vehicle mass, fuel consumption and O2, CO and NOx readings. As diesel vehicles are classified into six categories by the Australian Design Rules (ADR), correlations for particulate matter were also developed for each vehicle category. The best model for predicting particulate matter across all six ADR categories (with different numerical coefficients for each category) was found to linearly depend on the average opacity, vehicle mass, cumulative power and O2, CO, CO2 and NOx readings. Excellent results were found in four of the six ADR categories examined.
Meta TagsDetails
DOI
https://doi.org/10.4271/2004-01-3066
Pages
13
Citation
Hessami, M., and Child, C., "Statistical Analysis of Diesel Vehicle Exhaust Emission: Development of Empirical Equations to Calculate Particulate Emission," SAE Technical Paper 2004-01-3066, 2004, https://doi.org/10.4271/2004-01-3066.
Additional Details
Publisher
Published
Oct 25, 2004
Product Code
2004-01-3066
Content Type
Technical Paper
Language
English