This study examined the interactions of ethanol with aromatic compounds on aromatic species evolution during gasoline evaporation and the consequent impacts on particle emissions from a single-cylinder, gasoline direct injection engine. From a chemical kinetic standpoint, the dilution of the aromatic species in gasoline by ethanol blending should reduce particle emissions because aromatics are the primary species that form particles during gasoline combustion. However, increased evaporative cooling from the ethanol and ethanol's effect on when aromatics in the fuel droplet (or surface film or pool) evaporate can drive an increase in particle emissions. The objective of this study was to quantify these competing effects. The results show that a combination of ethanol's increased evaporative cooling and impact on aromatic compound vapor-liquid equilibrium can extend droplet lifetime and cause aromatics to evaporate later in the droplet evaporation process than would be the case without ethanol. When fuels were burned in a direct-injection single-cylinder engine at high speed (2500 rpm) and relatively high load, ethanol blending caused an increase in particle emissions for fuels containing a low vapor pressure aromatic. At a lower speed (1500 rpm), no statistically significant increase in particle emissions was observed, likely because more time was available for evaporation and mixing to occur. In contrast, particle emissions from a fuel blended with a high vapor pressure aromatic were either insensitive to or reduced by ethanol blending, likewise to engine operating conditions. Current models for predicting particle emissions from gasoline engines, such as particulate matter index (PMI), generally lack variables for non-linear interactions between ethanol and gasoline hydrocarbons. To identify nonlinear interactions between predictor variables that might better explain the observed particle emissions, the least absolute shrinkage and selection operator (LASSO) regularized regression approach was employed. Among the predictor variables evaluated was the Yield Sooting Index (YSI) which is as an improved measure to quantify the aromatic's chemical tendency to generate particle emissions. Following this analysis, two variables were selected based on their frequent appearance. The first was ethanol mole percent multiplied by aromatic mole percent divided by aromatic vapor pressure [(EtOH% * Aro %)/Aro VP@443 K]. The second was aromatic mole percent multiplied by aromatic YSI divided by aromatic molecular weight [(Aro% * Aro YSI)/Aro MW]. The results of linear regression including only these two combined explanatory variables achieved an r(2) = 0.959, substantially improved over the PMI model (r(2) = 0.688) and provides insight into how the PMI formalism might be modified to account for non-linear oxygenate-hydrocarbon coupling.