The American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) has been widely used in regulatory modeling analyses over the past 15 years, and was proved to be the state-of-the-art model for ambient air quality predictions.
In a traditional modeling analysis, permit applicant compiles front-end data including building structures, emission point locations and release parameters, operating schedules, and emission rates. Combining with terrain, meteorological, land use, and population data, applicant runs AERMOD and obtains the predicted concentrations of air contaminants at any locations of interest. This process works perfectly when all required information is available. However, with many current-day fast-paced projects, especially those green-field sites still undergoing design processes, it is typically difficult to have all confirmed puzzle pieces to put together the whole picture. Applicant’s desire to permit maximum production and allow various operating scenarios also adds additional complexities to any modeling analyses.
With the advancement of computing powers and technologies, it is now feasible to address all possible operating scenarios together with ambiguous source locations and parameters, to provide a defendable modeling demonstration to support the air permit applications for most complicated projects. It is also possible to target a rear-end result and backtrack the acceptable input parameters and operating scenarios from a set of model inputs.
With Python programming language, this presentation introduces a few customizable tools for pre-processing required input data, creating AERMOD input files, and summarizing the modeling results from hundreds or thousands of model runs. Attributing to the nature of Python codes, automation tools can be further customized to meet any specific needs. This presentation will share and discuss a few case studies for the application of customized automation tools for AERMOD model setup.
