This research assessed the impact of USEPA’s AERMOD dispersion model (version of 19191) source types on predicted pollutant concentrations via a case study for the I-75/I-575 Northwest Corridor (NWC) in Atlanta, GA. Using MOVES-Matrix for MOVES 2014b, carbon monoxide (CO) emissions rates for every hour of a one-year study period were generated using traffic volumes and speed from Atlanta Regional Commission’s Activity-Based Model (ABM) 2020 and AERMET meteorological profiles provided by the state environmental agency (EPD). To develop concentration profiles to assess prediction differences across source types, consistent input datasets for hourly emissions by ABM link, hourly AERMET data, and gridded receptor placement (standard 20-meter grids and variable grids after link-screening) were run with the AERMOD source types: AREAPOLY (manually created and automatically generated), LINE, VOLUME, RLINE, and RLINEXT (with and without noise barriers). The team processed more than one trillion source-receptor pairs the study area using the cyberinfrastructure resources provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech. The results indicate that predictions from AREAPOLY and LINE are essentially identical. Predictions from RLINE and RLINEXT are almost the same, but these predictions are higher in most cases than any other source type. The VOLUME source type always yields the lowest concentrations and is less sensitive to wind directions and speed, due to the embedded wind meander dispersion parameters implemented only for VOLUME sources. Machine learning results indicate that wind speed, receptor ID (which accounts for adjacent roads and their and their mass flux emission rates in grams/meter2/second), and wind direction influence the results much more than source type selection. Introducing noise barriers to RLINEXT lowered concentration as expected, but modeling barrier effects was challenging due to the restrictive assumptions in AERMOD. Sensitivity analysis for RLINEXT suggests that barrier height, distance to the roadway, wind speed, and wind direction all affect morel predictions. Modelers need to exercise care in matching barriers to roadway link segments (i.e., barrier edge effects were observed).
