Knowledge of particulate matter concentrations <2.5 μm in diameter (PM2.5) across the United States is limited due to sparse monitoring across space and time. Epidemiological studies need accurate exposure estimates in order to properly investigate potential morbidity and mortality. Previous works have used geostatistics and land use regression (LUR) separately to quantify exposure. This work combines both methods by incorporating a large area variability LUR model that accounts for on road mobile emissions and stationary source emissions along with data that take into account incompleteness of PM2.5 monitors into the modern geostatistical Bayesian Maximum Entropy (BME) framework to estimate PM2.5 across the United States from 1999 to 2009. A cross-validation was done to determine the improvement of the estimate due to the LUR incorporation into BME. These results were applied to known diseases to determine predicted mortality coming from total PM2.5 as well as PM2.5 explained by major contributing sources. This method showed a mean squared error reduction of over 21.89% oversimple kriging. PM2.5 explained by on road mobile emissions and stationary emissions contributed to nearly 568,090 and 306,316 deaths, respectively, across the United States from 1999 to 2007.