Assimilation of Meteorological Data from Mobile Sensors with AERMOD to Study Dispersion of Nonbuoyant Scalars during Convective Conditions

Prediction of ground concentrations and vertical profiles of pollutants or hazardous elements that are released into the atmosphere is crucial for health risk assessments. This study explores the mean plume characteristics (like centroid location, ground concentration and dispersion parameters) for a continuous release, as simulated by US EPA's Steady-State Gaussian Dispersion model AERMOD which is coupled with observed meteorological (MET) data obtained using mobile sensors. The 2D homogenous surface and vertical profiles of MET input variables required for AERMOD are provided by assimilation of measured MET fields using mobile sensors and simulated fields from high resolution WRF model runs using High Resolution Rapid Refresh (HRRR) dataset. Our approach bypasses the use of AERMET which is the standard pre-processor used to generate MET fields for AERMOD. Various convective conditions of the atmosphere are simulated and results are compared by using the bulk Richardson number (Ri_B). Results indicate that accurate representation of surface heat flux, wind speed and wind direction increased the accuracy of ground concentration predictions. This novel and new direction of using mobile sensors such as aerial drones is key for improving plume dispersion predictions by existing models.