In recent years, air quality and transportation modelers have focused on mobile source apportionment for better urban air quality management. Especially, the contribution from the truck activity to the urban air pollution (specifically NOx and PM2.5) is identified as a critical issue to be investigated. In the current practice, it is very difficult to determine the passenger travel related pollution and truck related pollution independently since we combine total activity for regional emission inventory estimation. On contrary, due to latest development in the field of freight modeling, the local and interregional through truck volumes can be predicted more reliably. So, application of vehicle specific activity (e.g. truck vs. auto) can be utilized to determine their respective contribution to urban air quality.

We have recently developed a spatial regression based truck model that can provide us improved Truck Miles Traveled (TMT) output for regional emission estimation purpose. In this paper, using the Spatial Regression Truck Model output, we have proposed a methodology to predict truck released PM2.5 concentrations in urban atmosphere using two most advanced models namely: the MOVES and AERMOD models. The MOVES model is mobile source emission estimation model and the AERMOD is an air dispersion model for all types of emission sources. To test the proposed methodology, we have used Hamilton County, Ohio as a study area.

The presentation briefly explains the truck modeling methodology used to develop improved regional emission inventories. Then it would provide the details about how the MOVES emission inventory output is processed for AERMOD model and the consecutive steps in pollutant dispersion modeling. Finally we show comparison results of using the default truck activity versus our proposed truck model’s activity data in the context of atmospheric dispersion for two different pollutants NOx and PM2.5. We also present different mobile source apportionments in regional air quality using proposed methodology compared with the defaults. This result is very useful for transportation modelers and policy makers, since it provides a decision support tool to test the implementation of appropriate transportation demand management strategy.