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Impact of Light Winds on AERMOD A Case Study

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Even though AERMOD can process winds as low as 0.28m/s, it ignores calms. ... Set of hypothetical sources used by the AIWG Surface Characteristic Subgroup. ... – PowerPoint PPT presentation

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Title: Impact of Light Winds on AERMOD A Case Study


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 (Contd)
  • 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 wont like this but it could be a lot
    worse.
  • Using building downwash could significantly alter
    these results. James Thurmans 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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