Evaluating effects of engine operating variables on particle number emission rates using robust regression models

This study examines the dynamics of high-frequency diesel particulate number (PN) emissions and their dependence on engine operating variables. Data collected from a heavy-duty diesel bus during a real-world experiment are analyzed with L1-regression models that are robust to outliers and free from distributional assumptions. The model-building procedure reveals the relative statistical significance of six engine operating variables and their interactions. Different models are estimated for accumulation mode PN (which dominates emitted particle mass) and for the ratio of ultrafine PN (which accounts for more than 90% of total emitted PN) to accumulation mode PN. A major implication from the study results is that interactions between engine operating variables should be thoroughly examined in instantaneous emission models. Specifically, the interaction between fuel-to-air ratio (or engine load) and engine speed exerts a significant effect on the above mentioned PN ratio, while the effect of exhaust temperature becomes statistically significant above 250°C. Boost and injection pressure were not statistically significant in explaining observed PN variability. Models for accumulation mode particles were stronger in terms of predictive power compared with models estimated for PN ratios.