The source-specific linkage between ambient particulate matter smaller than 2.5 ?m (PM2.5) and asthma is limited. In this study, we analyzed the impact of PM2.5 sources on daily asthma hospital utilization in Cincinnati, Ohio, USA. We used Poisson regression models to estimate the daily number of asthma ED visits zero, one, and two days after a 10?g/m^3 increase in PM2.5, adjusting for temporal trends, holidays, temperature, and humidity. The contributions of nine PM2.5 sources were estimated using a chemical mass balance method. We used a model-based clustering method to group together days with similar source-specific contributions into six distinct clusters. We found significant effect modification of the asthma-related effects of PM2.5 by cluster membership one day later (ANOVA p-value = 0.004). Specifically, elevated PM2.5 concentrations on a cluster of days characterized by low contributions of coal combustion showed a significantly reduced risk of hospital utilization for asthma (rate ratio: 0.86, 95% CI: [0.77, 0.95]) compared to other clusters. Targeting reduction in air pollution to days with high contributions of coal burning may decrease asthma hospital utilization.