Sonntag, Darrell2010-04-092015-04-092010-04-09bibid: 6890978https://hdl.handle.net/1813/14844Two particle number emission datasets were analyzed in detail. The first data set contained particle number emissions from four transit buses, including two hybrid diesel-electric buses, under a variety of driving conditions and technological/fuel treatments including: diesel oxidation catalysts, diesel particle filters and ultra-low sulfur diesel fuel. A linear mixed model was used to control for multiple sources of variability in real-world particle measurements, and identified significant factors influencing particle number emissions. Subsequently, link-level particle number emission models were developed for the DOC-equipped conventional buses, using different sets of available predictive data. Principle component analysis was used to reduce the variability of engine parameters to three interpretable parameters: percent engine load, engine speed and exhaust temperature. Time-resolved particle emissions from the diesel transit buses were evaluated in detail to understand the relationship of particle emissions, operating modes, and the relationship among multiple pollutants. Particle number and mass emissions are generally well-correlated during real-world behavior, however number are emissions are more influenced by the storage and subsequent release of particles evident during high engine speeds, while particle mass emission are more consistent with fuel events. Acceleration events on a stop-and-go urban route caused the maximum particle emission rates at resolved spatial scales, while over large spatial scales the highest emission rates occurred on the freeway. The concept of emission modes was introduced to understand the variability of gaseous and particle pollution during transient operation of the transit bus. Six repeatable emission modes were identified as being capable of explaining more than 75% of the total variability in emissions. Functional data analysis was introduced to analyze particle size distributions collected on a flex-fuel vehicle. A non-parametric smoothing technique can optimally smooth particle size distribution data without imposing prior distributional assumptions. The relationship among particle concentrations, operation conditions, and fuel type was estimated as a function of particle size using a functional linear model. Future paths of research are identified which take into account the smoothness of particle-size distributions. In summary, this dissertation contributes data, understanding, and quantitative concepts and methods to advance both research and practice-oriented particle emission models.en-USdissertation or thesis