This study uses a Gaussian plume model of airborne contaminants and artificial intelligence (AI) tools to evaluate the environmental impact of industrial emissions. It focuses on determining the major variables affecting the dispersion of airborne pollutants and calculating their concentrations downwind from the source of emissions. The study made use of hypothetical data from the NOAA website, which included a variety of physical and atmospheric variables like temperature, wind speed, emission rate, and chimney features. The Pasquill-Gifford classification was combined with a rule-based artificial intelligence tool to compute the dispersion coefficients (σy, σz) and assess atmospheric stability. The results showed that wind speed, distance from the emission source, and atmospheric stability all had a major impact on pollutant concentration. Additionally, the findings of the AI tool and well-established conventional methodologies were shown to be in good agreement. This work is important to be used as a training program for students.
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