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Impact of Light Winds on AERMOD A Case Study

Impact of Light Winds on AERMOD A Case Study. Joe Sims AL Department of Environmental Management Region 4 Modelers Workshop March 18, 2009. The Issue. Introduction of 1-minute averaged ASOS winds will generally result in lighter wind speeds than currently available.

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Impact of Light Winds on AERMOD A Case Study

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  1. Impact of Light Winds on AERMODA Case Study Joe Sims AL Department of Environmental Management Region 4 Modelers WorkshopMarch 18, 2009

  2. The Issue • Introduction of 1-minute averaged ASOS winds will generally result in lighter wind speeds than currently available. • Recent (since mid-90s) archived met data at NCDC comes from METAR observations. • Observed winds less than 3 knots reported as calm in the METAR code. • Winds 2-6 knots with variable direction reported as variable (and ignored by AERMOD). • Even though AERMOD can process winds as low as 0.28m/s, it ignores calms. How will use of light winds impact AERMOD predicted concentrations?

  3. The Study • Find a met data set with known higher incidence of light winds • For the same location, obtain a standard ASOS data set. • Obtain/create a source set with a wide variety of stack heights, buoyancy characteristics, emission types, etc. • Run AERMOD and compare results.

  4. Met Data • Met Data (Test) (“Hybrid”) • Birmingham Airport (BHM) 1-minute ASOS data used to create hourly average winds for 2002. • This data set created by OAQPS for use in the Birmingham Annual PM2.5 SIP. • Met data treated in AERMET as On-site data and augmented as necessary by regular ASOS reports from BHM. • Upper air data from the Shelby County Airport (EET) used. • Surface characteristics developed from AERSURFACE. • Met Data (Control) (“Std ASOS”) • Standard BHM ASOS data.

  5. Wind Speed Frequency Distribution

  6. Receptor Grid, Etc. • Receptors • Polar grid, centered at BHM. • 36 radials. • Rings 25m apart out 200m, 50m apart from 200m to 1000m and 100m apart from 1000m to 5000m. • RURAL/URBAN – Ran both conditions. • Terrain - Flat.

  7. Sources • Set of hypothetical sources used by the AIWG Surface Characteristic Subgroup. • 12 non-buoyant point sources • 10 buoyant point sources • 5 volume sources • 4 area sources

  8. Point Sources

  9. Volume Sources

  10. Area Sources

  11. Results - Point Sources – Ratio of H1H (RURAL)

  12. Results – Volume/Area Sources - Ratio of H1H (RURAL)

  13. Results - Point Sources - Ratio of H1H (URBAN)

  14. Results – Volume/Area Sources - Ratio of H1H (URBAN)

  15. Comments • 25% of the winds were in the 0-3kt category, which were not available for use in the “Std ASOS” AERMOD runs. In other words, this experiment gives a reasonable test of AERMOD performance using light winds. • Significant differences in the results between rural and urban. This is seen especially for taller stacks, where concentrations are generally greater than 4 times higher (and as much as 13 times) for the hybrid wind set. The opposite is seen with the lowest release height area sources. • Point source B2-35 was not included in the charts for the short term averages because it was an outlier. B2-35 is a 35m tall stack with some buoyancy (293K). Other stacks near the same height with more buoyancy behaved better. Apparently B2-35 did not have enough buoyancy to penetrate the inversion.

  16. Comments (Cont’d) • In almost all cases, the predicted concentrations using lighter winds were higher than when using standard ASOS – as expected. • I was especially interested in point sources in a rural environment. This combination constitutes the vast majority of PSD applications we see in Alabama. Except for the 3hr averages for low-level releases (and the troublesome B2-35), the ratios are pretty much in the 1 to 2 range. Applicants won’t like this but it could be a lot worse. • Using building downwash could significantly alter these results. James Thurman’s results (presented at the Denver workshop) demonstrated this. • Using terrain could also alter the results. • This was only a one year sample. James’ runs showed a significant year-to-year variability.

  17. Conclusions • Using the hourly averaged 1-minute ASOS data to better represent dispersion potential from a source makes a lot of sense. • It generally seems to give more conservative results than standard ASOS, therefore is more protective of human health. • We as regulators must be prepared for challenges (and complaints) from the regulated community.

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