Abstract Description: The trend of digital transformation has been on the rise in environmental permitting and compliance for industries. As a critical part of air quality permitting efforts, air dispersion modeling analysis itself is already a digital transformation of the old-fashioned air quality impact analysis. However, with industrial applicants’ desire for maximum flexibility on design and production, together with the advancement of computing powers and technologies, further digitalization is being sought for air dispersion modeling analysis.
Programmed automation is a very helpful approach for model setup and post-processing when a great number of combinations on modeling scenarios, or a great number of model output results files are involved. This presentation will discuss a few case studies including an automated AERMOD model setup for scenario analysis, a post-processor for frequencies of various levels of exceedances, an assistive approach to address hourly varying stack parameters, etc.
Artificial Intelligence (AI) and Machine Learning is a further step beyond programmed automation in the journey of digital transformation. This presentation will give an introduction on what is AI and machine learning and explore how they can be utilized in air dispersion modeling practices. Challenges to applying AI and machine learning will be discussed, and current attempts and future outlooks will be shared.
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