(234p) Impact of Meteorology Datasets on Near Roadway Dispersion Model Estimates

More than 19% of the United States population lives near high traffic roadways where they are exposed to elevated near road pollutant concentrations. Vehicular emissions are one of the primary sources of air pollution in cities and associated with elevated morbidity and mortality rates in individuals living near roadways. Currently available near-roadway dispersion models use meteorological data from local weather stations, potentially limiting their implementation due to the unavailability of data in many areas. In order to overcome this limitation, process-based meteorological estimates from models such as the Weather Research and Forecasting (WRF) model are used as a substitute. This raises the question of how model estimates vary between process-based meteorological estimates and station data.

In this study we employ a Gaussian plume dispersion model, R-LINE, to simulate near road concentrations using both station data and Weather Research and Forecasting model (WRF) estimates. R-LINE simulates the dispersion from line sources by numerically integrating point source emissions along multiple road configurations. We evaluate the seasonal and diurnal variability of roadway dispersion for both of the meteorological input datasets and compare the model estimates. As we found fairly substantial differences in model estimates for the two input datasets, we also explore R-LINEâ??s sensitivity to different meteorological parameters in both stable and unstable atmospheric conditions for multiple locations in Connecticut. This study illustrates and quantifies how R-LINEâ??s estimates vary based on the use of different sources of meteorological inputs.