Preliminary evaluation of alternative wind profile characterizations on AERMOD dispersion modeling results

Authors: HAMPTON, KYLA - Texas A&M University; BUSER, MICHAES - Texas A&M University; Whitelock, Derek

Submitted to: National Cotton Council Beltwide Cotton Conference
Publication Type: Proceedings
Publication Acceptance Date: 1/15/2025
Publication Date: 4/9/2025
Citation: Hampton, K., Buser, M., Whitelock, D.P. 2025. Preliminary evaluation of alternative wind profile characterizations on AERMOD dispersion modeling results. National Cotton Council Beltwide Cotton Conference, New Orleans, LA, January 14-16, 2025. Presentation only.

Interpretive Summary: AERMOD is the preferred EPA dispersion model used to regulate air pollution from a wide range of sources from industrial facilities to agricultural operations. AERMOD was developed for tall stacks, like those at power plants, and past research has shown that it overpredicts pollutant concentrations from low-level sources, like cotton gins. This can lead to biased regulation. The model is heavily reliant on weather data, but issues can arise due to ill-fitting representation of the weather, such as differences in factors such as wind speed and direction, terrain, surrounding land use, temperature, and atmospheric pressure variations. The nearest weather station may not always be the most appropriate for modeling. This preliminary evaluation assessed the impact of on-site weather data versus standard airport weather station data on AERMOD concentration results. It was found that the predicted concentrations utilizing on-site data performed better than the predicted concentrations utilizing off-site weather data. However, both predicted concentrations varied in correlation with experimentally measured concentrations, demonstrating inconsistencies in weather data used for air dispersion modeling. Identification of causes of errors from AERMOD should lead to more equitable regulation of agricultural operations and provide more post-harvest processing options for U.S. producers.

Technical Abstract: AERMOD is a steady-state Gaussian air dispersion model used to predict and assess air pollution from a wide range of sources such as industrial facilities to agricultural operations like cotton ginning. As the preferred regulatory model, it ensures compliance with the National Ambient Air Quality Standards (NAAQS). This Gaussian-based model is heavily reliant on meteorological factors, wind speed, and direction. These core components determine how pollutants spread from the source and affect the surrounding environment. Generally, AERMOD dispersion modeling utilizes meteorological data from the nearest common source facility, such as an airport. When using airport meteorological data, issues can arise due to ill-fitting representation of the meteorological conditions. Difficulties range from differences in factors such as wind speed and direction, terrain, surrounding land use, temperature, and atmospheric pressure variations. In addition, there may be discrepancies in data quality from measurement variability and temporal resolution. These issues indicate that the nearest meteorological facility may not always be the most appropriate representation for modeling. The goal of this research was to conduct a preliminary evaluation to assess the impact of on-site meteorological data versus standard airport data on AERMOD concentration results. It was found that the predicted concentrations utilizing on-site metrological data performed better than the predicted concentrations utilizing off-site meteorological data. However, both predicted concentrations varied in correlation demonstrating inconsistencies in meteorological data used for air dispersion modeling.