Year: 2025
Type: article
Abstract: Background. Rapid industrialization and urbanization have escalated air pollution, posing significant health and environmental threats. Precise quantification of air pollutant dispersion is critical for effective control and mitigation strategies. The Inverse Gaussian Plume Model (IGPM) is a robust analytical tool used for estimating pollutant concentration levels from point sources. Materials and Methods. This research utilized IGPM to estimate ground-level concentrations of airborne pollutants originating from specific point sources. The model's application was rigorously grounded in comprehensive datasets encompassing detailed meteorological parameters, essential source emission characteristics, and relevant topographical information. Meteorological inputs included hourly averaged wind speed and direction, atmospheric stability classifications, ambient temperature, and mixing layer height, which collectively govern pollutant transport and dilution. Source characteristics incorporated stack height, flue gas exit velocity, gas temperature, and pollutant emission rates specific to the investigated sources. Topographical data considered local terrain features that could influence plume trajectory and dispersion patterns. Results and Discussion. The model successfully predicted pollutant concentrations, demonstrating high correlation with observed data. Sensitivity analyses underscored the influence of atmospheric stability and wind speed on plume dispersion. The IGPM proved effective in diverse meteorological scenarios, emphasizing its adaptability for air quality assessments. Conclusion. The findings of this investigation affirm that IGPM serves as a reliable and accurate methodology for estimating air pollution levels stemming from point sources. Its demonstrated predictive capability makes it an asset for enhancing environmental monitoring programs, potentially supplementing fixed monitoring networks and identifying areas of concern. Furthermore, the model's utility extends significantly into the domain of regulatory compliance, facilitating environmental impact assessments for proposed industrial activities and evaluating the effectiveness of emission control measures. Keywords : air pollution, Inverse Gaussian Plume Model, forecasting, pollutant concentration, autonomous systems, control algorithms, machine learning.
DOI: 10.30970/eli.29.9
Authors: Volodymyr Hura, Liubomyr Monastyrskyi
Institution: Lviv University
Language: English
Topic: Wind and Air Flow Studies
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409616110