Improved Characterization of Vehicle Fuels and Emissions for Particulate Matter Estimations
- Author(s): Lichtenberg, William Max
- Advisor(s): Barsanti, Kelley
- et al.
In the study presented here, eight fuels and their emissions were characterized using 1-dimensional (1D) and 2-dimensional (2D) gas chromatography (GC); calculated particulate matter index (PMI) values were compared using compositional information from 1D vs. 2D GC, and PMI was evaluated as an estimation method for secondary organic aerosol (SOA) formation. Samples were sent to a commercial lab for 1D GC analyses and PMI determination. These eight fuels were also analyzed at University of California, Riverside’s College of Engineering Center for Environmental Research and Technology (CE-CERT) using 2D GC. The 2D GC provides improved characterization of the fuels over the 1D analysis. With the improved characterization, the composition of emissions could be refined. Part 1 of the research was to explore if this improved analyses by 2D GC significantly changes calculated PMI (used for estimating vehicle PM based on fuel composition). As part of that effort, a method for calculating PMI was developed for compounds that are solely identified by chemical classification and carbon number (i.e., structure is unknown). The new method allows more universal application. While the 2D GC allowed identification of up to 50 more compounds per fuel in the emissions as compared to the reported results using 1D GC, calculated PMI values were statistically similar.
Vehicle emissions are a major source of precursors for SOA, thus estimating the potential for SOA formation from vehicle emissions is desirable. Colleagues at CE-CERT conducted concurrent studies to measure the SOA formed from the eight fuels. A strong trend between the PMI and the SOA formed was observed. However, this trend was not sufficiently robust to use PMI alone as a predictor for SOA formation. A modification of the PMI was developed, the secondary PMI, by weighting the PMI factor for each of the compound classes based on the contribution of each class to potential SOA formation vs. PMI. Application of this secondary PMI demonstrated the potential for SOA formation to be estimated as a function of fuel composition. Future studies will be needed to refine development and define applicability limits of the secondary PMI.