Ammonia (NH3) emissions from fertilizer application is a highly uncertain input to chemical transport models (CTMs). Reducing such uncertainty is important for improving predictions of ambient NH3 and PM2.5 concentrations, for regulatory and policy purposes and for exploring linkages of air pollution to human health and ecosystem services. Here, we implement a spatially and temporally resolved inventory of NH3 emissions from fertilizers, based on high-resolution crop maps, crop nitrogen demand and a process model, as input to the Comprehensive Air Quality Model with Extensions (CAMx). We also examine sensitivity to grid resolution, by developing inputs at 12 km x 12 kmand 4 km x 4 km, for the Corn Belt region in the Midwest United States, where NH3 emissions from chemical fertilizer application contributes to approximately 50% of anthropogenic emissions. Resulting predictions of ambient NH3 and PM2.5 concentrations were compared to predictions developed using the baseline 2011 National Emissions Inventory, and evaluated for closure with ground observations for May 2011. While CAMx consistently underpredicted NH3 concentrations for all scenarios, the new emissions inventory reduced bias in ambient NH3 concentration by 33% at 4 km x 4 km, and modestly improved predictions of PM2.5, at 12 km x 12 km(correlation coefficients r = 0.57 for PM2.5, 0.88 for PM-NH4, 0.71 for PM-SO4, 0.52 for PM-NO3). Our findings indicate that in spite of controlling for total magnitude of emissions and for meteorology, representation of NH3 emissions and choice of grid resolution within CAMx impacts the total magnitude and spatial patterns of predicted ambient NH3 and PM2.5 concentrations. This further underlines the need for improvements in NH3 emission inventories. For future research, our results also point to the need for better understanding of the effect of model spatial resolution with regard to both meteorology and chemistry in CTMs, as grid size becomes finer.