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Evaluation of low wind modeling approaches for two tall-stack databases.

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This study evaluated the AERMOD air dispersion model's performance under low wind conditions. Special low wind options and sub-hourly data improved model accuracy, especially in elevated terrain, reducing over-prediction biases.

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Area of Science:

  • Environmental science
  • Atmospheric science
  • Air quality modeling

Background:

  • The AERMOD air dispersion model is widely used for regulatory purposes.
  • Accurate modeling of air pollutant dispersion under low wind speed conditions is challenging.
  • Limited meteorological data and lack of turbulence measurements can impact model performance.

Purpose of the Study:

  • To evaluate the performance of the AERMOD model under low wind speed conditions.
  • To assess the impact of specific low wind speed options and sub-hourly meteorological data on model accuracy.
  • To investigate AERMOD's performance in both flat and elevated terrain for tall stack releases.

Main Methods:

  • Tested AERMOD using two field-study databases with SO2 monitors and hourly emissions data.
  • Evaluated AERMOD in default mode, with low wind speed beta options, and using sub-hourly meteorological data.
  • Utilized data from North Dakota (Mercer County) and Indiana (Gibson Generating Station) sites.

Main Results:

  • Low wind speed beta options improved AERMOD performance, reducing over-prediction biases, particularly in elevated terrain.
  • The Sub-Hourly AERMOD Run Procedure (SHARP) using sub-hourly data yielded relatively unbiased results.
  • AERMOD's low wind speed options had a minor effect in flat terrain but a significant effect in elevated terrain.

Conclusions:

  • AERMOD's low wind speed options enhance model consistency for high concentration events.
  • The Sub-Hourly AERMOD Run Procedure (SHARP) is a viable alternative for low-wind meander conditions.
  • Model adjustments are recommended for improved accuracy in complex terrain and low wind scenarios.