There is no statistically significant difference in total PM2.5 concentration, city population, GDP or area among cities that have different top ranking emission sources (ANOVA p= 0.29, 0.69, 0.10, and 0.68, respectively). However, there is a statistical difference in fraction of withincity generated PM2.5 among cities that have different top sources (p= 0.00), where cities with high fraction of within-city generated PM2.5 have top PM2.5 sources as residential and commercial, industrial and energy, indicating these sources are likely generated locally. Additionally, there is a statistical difference in population buffer fraction among cities having different top sources (p= 0.03), where residential and commercial, dust, industrial and energy sources tend to be the top PM2.5 sources among cities with higher population buffer fractions, while agricultural, international shipping and surface transportation tend to be the top PM2.5 sources among low population buffer fraction cities. Along with the results shown in Figure , our findings suggest if a city does not have other densely-populated areas nearby, it tends to have the most locally-generated PM2.5, whereas if a city has other densely populated areas nearby, it tends to have a smaller proportion of PM2.5 generated locally. The level of population buffer fraction is strongly associated with certain sources, as well as with total and secondary PM2.5 concentration. None of the urban parameters we investigated are well-correlated with the top PM2.5 source for a city. This finding suggests that atmospheric modeling holds value for understanding the local context of which sources contribute the most to local pollution.