In air quality impact analysis, screening models are often applied to assess whether more detailed and labor-intensive modeling is required. The screening model estimates the impacts of a “worst case” analysis and if no potential air quality impacts are estimated, it can be assumed that a more refined analysis with detailed data is not needed. This reduces the need to obtain high-resolution meteorology data, fleet composition data, on-road operating condition data, etc. This study presents a user-friendly and computationally efficient methodology for regional-scale air pollutant concentration screening modeling based on the MOVES-Matrix and the dispersion model AERMOD, thus allowing users to quickly evaluate multiple alternatives. The methodology allows the entire region to be quickly evaluated. During the development of this air quality impact assessment screening tool, Atlanta, Georgia was used as the case study. The team connected MOVES-Matrix directly with the Atlanta Regional Commission’s (ARC) Activity-Based Model 2015 (ABM15) outputs (for traffic volumes) and a simplified AERMOD dispersion model approach to predict pollutant concentration profiles at a high-spatial resolution (15 meters) using the PACE supercomputing cluster. The research team utilized AERMOD creating the equivalent of a line-source model for the entire metropolitan area. From the results potential carbon monoxide and particulate matter hot spots were easily identified using the conservative to identify problem areas approach where more complex modeling may be required. The regional screening tool speed and efficiency should be of interest to a broad readership including those interested in vehicle emission modeling, near-road air quality modeling, transportation conformity analysis, state implementation plan development, and transportation and air quality regulatory analysis.