Modelling Collie power stations with TAPM - Ken Rayner Department of Environment and Conservation, Western Australia
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Modelling Collie power stations
with TAPM
Ken Rayner
Department of Environment and
Conservation, Western Australia
September 2009
Background • Collie is ~ 150 km SSW of Perth • existing and proposed coal fired power stations within the basin • other industries proposed for the basin • potential air quality concerns, notably SO2 and particulates
Bluewaters
Bluewaters PS
Power
stations Collie PS
and
monitoring
sites Collie Shotts
Muja PS
10 km
Air quality studies and TAPM • (selective summary for purposes of this talk) • monitoring program 1995‐2002 (SO2, met) • Hibberd and Physick (2003) (H&P) – data analysis – TAPM 2.0 testing, recommended parameters • EIA projects 2004 – 2009 based on H&P, using various versions of TAPM for 2001 met
Hibberd and Physick (2003) (H&P)
• hourly emissions data
• analysis of met and SO2 data ‐ dominant process was
strongly convective conditions:
– morning inversion break‐up with fumigation
– late morning to afternoon convective mixing with plume
ground‐strikes.
• TAPM tests to choose values for plume buoyancy
enhancement factors and surface roughness
• monthly deep soil moisture values recommended
• Eulerian dispersion for all sources
Collie
Hibberd,
Physick and
Park
(CASANZ Shotts
2003)
all Eulerian
Bluewaters
Hibberd and Physick conclusions • good data set • based on statistical analysis of results for three years, the third highest TAPM prediction provides a good estimate of the maximum observed concentrations
DEC concerns • EIA studies since 2003 have used evolving TAPM versions and different mixtures of Eulerian / Lagrangian dispersion while retaining H&P moisture, plume buoyancy; • Apparent tendency to increased over‐ estimation of concentrations (exacerbating tendency seen in H&P) • Loss of comparability to original H&P
TAPM grid for all modelling Meteorology 35 x 35 x 25 grid points 30, 10, 3, 1 km spacing Air quality 15, 5, 1.5, 0.5 km spacing
Muja reasonably clear of grid boundaries
Purpose of this investigation Consider relative differences due to: – model version change – Eulerian (E) vs. Lagrangian (L) dispersion while holding other model parameters unchanged. Note: TAPM 4 runs will use all recommended options, including V4 land use scheme. Hence soil moisture different.
TAPM bug
Collie monitoring station. Reproduction of Hibberd and Physick (2003) - Eulerian
Shotts monitoring station. Reproduction of Hibberd and Physick (2003) - Eulerian
Bluewaters monitoring station. Reproduction of Hibberd and Physick (2003) - Eulerian
Collie monitoring station. Comparing TAPM versions - Eulerian
Shotts monitoring station. Comparing TAPM versions - Eulerian
Bluewaters monitoring station. Comparing TAPM versions - Eulerian
Collie monitoring station. Comparing TAPM versions - Lagrangian (except Worsley
Shotts monitoring station. Comparing TAPM versions - Lagrangian (except Worsley
Bluewaters monitoring station. Comparing TAPM versions - Lagrangian (except Worsley
Summary • significantly greater over‐estimation in TAPM 3.0.7 and 4.0 results relative to TAPM 2.0, for both E and L dispersion • does not necessarily mean TAPM performance has degraded. (e.g. HP “tuned” Muja plume buoyancy parameters using TAPM 2 in E mode). CSIRO’s TAPM tests do not suggest consistent over‐estimation for 3.0.7, 4.0 • changing from Eulerian (E) to Lagrangian (L) dispersion produces some interesting results
Shotts monitoring station. Comparing E vs L dispersion - TAPM 3.0.7
Collie monitoring station. Comparing E vs L dispersion - TAPM 3.0.7
High E values dominated by
Muja which is 17 km from Collie
Collie
monitoring
station.
Comparing
E vs L
dispersion
Muja PS – E
Collie A – L
- TAPM 3.0.7Collie monitoring station. Comparing E vs L dispersion - daytime - TAPM 3.0.7
Collie monitoring station. Comparing E vs L dispersion - night-time - TAPM 3.0.7
Summary E results significantly over‐estimate observations at both sites, both day and night; L results are similar to E results during daytime, but are significantly lower than E and closer to observations during night‐time. Same day / night pattern for Shotts. Isn’t daytime when we would expect L and E to differ most? Other explanation?
Summary cont...
• Some folks think that Lagrangian modelling is
not required if the receptors of interest are
beyond 900 sec travel time (after the hybrid
LPM mode has switched to the EGM).
– i.e. “Eulerian dispersion will give the same results
as Lagrangian at distant receptors.”
• NOT CORRECT.Wind speed for top 30 concentrations at Collie
in 2001 (Eulerian and Lagrangian) - Night only
300
Concentration ( μ g/m )
3
250
Eulerian mostly < 4 m/sec. Travel
time > 4000 sec (>> 900 sec).
Lagrangian does not produce
200
matching results for low speed /
long travel times. CO TAPM 307 E
150
CO TAPM 307 L
100
50 17 km in
Every L has switched to E 900 sec
0
0 2 4 6 8 10 12 14 16 18 20
Wind speed at 150 metresCSIRO advice • Because the initial dispersion is different in the two modes, results can be different further downwind, even after the hybrid LPM mode has switched to the EGM mode. • we (CSIRO) recommend LPM for all point sources because we consider that this provides the better physical description of point source plume dispersion.
Uncertainty in extreme event
predictionsBluewaters
Bluewaters PS
Power
stations Collie PS
and
monitoring
sites Collie Shotts
Muja PS
10 kmTAPM 3.0.7 Max 1-hr SO2 Collie too close to grid boundary
TAPM 3.0.7 Max 1-hr SO2 Met grid shifted W slightly, pollution grid slightly expanded. Significant changes
TAPM 3.0.7 9th highest SO2 Collie too close to grid boundary
TAPM 3.0.7 9th highest SO2 Grid shifted W & expanded slightly. No major changes
TAPM 3.0.7 Max 1-hr SO2 Examine this “sausage
TAPM 3.0.7
Max 1-hr
SO2
Muja C&D
20 Aug only
11:00 peak
concentration
u < 1m/sConcentration profiles on 20 Aug 2001 at location
of highest 1-hour maximum from Muja PS
TAPM 3.0.7 600
Muja C&D
500
20 Aug
8:00 - 12:00 400
Height (m)
12:00
plume 300
11:00
10:00
fumigation 9:00
within 8:00
200
growing
mixed layer 100
0
0 500 1000 1500 2000 2500 3000 3500
Concentration (ug/m3)TAPM 3.0.7 Muja C&D 20 Aug u @ 250 m plume fumigation within growing mixed layer
TAPM 3.0.7 Muja C&D 20 Aug u @ 250 m plume fumigation within growing mixed layer
TAPM 3.0.7 Muja C&D 20 Aug u @ 250 m plume fumigation within growing mixed layer
TAPM 3.0.7 Muja C&D 20 Aug u @ 250 m plume fumigation within growing mixed layer
TAPM 3.0.7 Muja C&D 20 Aug u @ 250 m plume fumigation within growing mixed layer
TAPM 3.0.7 Muja C&D 20 Aug u @ 250 m plume fumigation within growing mixed layer
Summary • this “sausage” is caused by a credible fumigation event (analyses of wind and temperature profiles adds support). • But how much reliance should be placed on the prediction of this event (or similar events), noting that it appeared due to an innocuous change of grid centre and slight grid expansion?
Sensitivity tests The sausage reduced markedly in magnitude or disappeared when: • the grid was moved further south; • model was started on 18 and 16 August (2 and 4 spin‐up days) Also sensitive to the number of sources (which slightly affects meteorology – compiler optimisation issue?)
TAPM 3.0.7 Max 1-hr SO2 +Muja A and B Enhanced sausage
TAPM 3.0.7 Max 1-hr SO2 +Muja A and B +3 very small sources Sausage has vanished, other variations apparent
• variation also evident in TAPM 4 max • 9th highest hour results much less sensitive to model configuration changes than max hour. • strong argument to use 9th highest hour (or similar) as a stable model statistic for assessment purposes. • BUT you then need an assessment criterion for the 9th highest hour.
NZ modelling guide (2004) 99.9% value reported must be with reference to a specific receptor
• amounts to saying “model are always seriously biased high – concentrations are always significantly over‐estimated across the distribution of concentrations (not just the maximum)” • incorrect – not true in general • a skilful model does not exhibit uniform bias – plenty of examples.
TAPM 4 ‐ Kincaid (Hurley et al., 2008)
Aermod Kincaid (Paine et al., 1998)
2.1
4.0TAPM and DISPMOD re Kwinana
(Hurley et al., 2005)
measured
measured
99.9% 99.9%
Model matches measured very well at 99.9 percentile. Max
and RHC of measured are much higher than 99.9 percentile
at both sites.• for point sources, the 9th highest glc is typically 50 to 75% of the max glc at any given impacted location. • if using an unbiased model, the 9th highest prediction would be a serious underestimate of the actual highest (or second highest). • Kwinana: 350 μg/m3 99.9% is similar in stringency to the NEPM 570 μg/m3 2nd highest.
Recommendation • Don’t confuse the need for a stable model statistic with model bias. • Use something like 99.9 percentile 1‐hour average as a stable statistic for assessment against a 99.9 percentile 1‐hour criterion. • Correction of known model bias should be case‐by‐case via a clearly explained empirical fix. Might involve a case‐specific (not generally applicable) use of a different percentile,
• and if your model is always biased, perhaps it is time to fix it or get a new model.
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