Particulate matter (PM) emitted from gasoline combustion continues to be a subject of research and regulatory interest. This is particularly true as new technology gasoline direct injection (GDI) engines can produce significantly higher levels of PM compared to older technology port fuel injection (PFI) engines. The goal of this study was to conduct a comprehensive literature search and subsequent statistical analysis related to the effects of gasoline properties, such as aromatics, octane indices, and fuel volatility, on PM (mass and number) emissions from PFI and GDI vehicles/engines.
The statistical analyses showed a range of positive and negative correlations between different fuel properties and PM mass, total particle number (PN) and solid particle number (SPN) for different engine types (GDI, PFI, and for subdivisions of these engine types), numbers of engine cylinders and driving cycles. For GDI vehicles, total aromatic content, T70, T90 (the temperature when 70% and 90% of a fuel by volume boils away during a distillation test), and distillation end point (EP) [(the highest temperature achieved during a distillation test)] were positively correlated with PM mass emissions, PN emissions, or both. Anti-Knock index (AKI), research octane number (RON), and motor octane number (MON), and T10 (the temperature when 10% of a fuel by volume boils away during a distillation test) were negatively correlated with PM mass emissions, PN emissions, or both. For PFI vehicles for the Federal Test Procedure (FTP), LA92 and US06 cycles, T50, T70, T90, AKI and MON showed more mixed results, with both positive and negative correlations, while distillation EP and RON showed a negative correlation with PM mass emissions. Many of these analyses also showed statistically significant interactions, which indicates that the magnitude and direction of the regression coefficient (slope) estimated between the fuel property and PM emissions component varied as of function of at least one of the categorical variables (i.e., vehicle engine technology or model year, number of cylinders, and/or drive cycle). The presence of such statistical interactions demonstrates the underlying complexity in the data set. The details related to the interactions can provide valuable information to researchers for interpreting data sets that include combinations of different vehicle technologies. The information can also be used in the design of test programs, where a better understanding of how the effects of different fuel properties can vary as a function of different vehicle technologies and drive cycles can aid in study planning.