This document compares the air dispersion models SCIPUFF and CALPUFF using complex terrain case studies. SCIPUFF consistently produced higher maximum pollutant concentrations than CALPUFF in simulations of SO2 releases in Colorado and Brazil using 1km and 4km weather grids. While the wind fields generated by the models were similar, the models responded differently to terrain effects. Higher weather grid resolution led to lower predicted concentrations. Future research could further evaluate how SCIPUFF and CALPUFF handle complex terrain modeling.

1. © 2018 – Lakes Environmental Software
Jesse Thé, Ph.D.
DMUG
Apr/19/2018
Evaluation of
SCIPUFF versus
CALPUFF in Complex
Terrain

2. © Copyright 2018 – Lakes Environmental Software
2
Objectives
1. CALPUFF and SCIPUFF/SCICHEM resolve
the transport of pollutants with different
approaches
2. To simplify evaluations, only passive gases
were evaluated (SCICHEM without
chemistry)
3. Does SCIPUFF produces significant
different concentrations than CALPUFF?

3. © Copyright 2018 – Lakes Environmental Software
3
SCIPUFF / SCICHEM CALPUFF
Outperforms AERMOD Outperforms AERMOD after 3km
Accurately models to 2000km Accurately models to 300km
Windfield calculations DO NOT
require the 1960’s radius of
interpolation
CALMET requires RMAX1, RMAX2,
R1, R2, and TERRAD. These
“factors” may work well for only a
portion of the modelling domain.
Handles dense gas dispersion Does not handle dense gas
Easier data input, similar to
AERMOD
Complex data input
Complete CMAQ photo-chemistry Empirical chemistry
Comparison

4. © Copyright 2018 – Lakes Environmental Software
4
Evaluation of the Comprehensive Air Quality Model with Extensions (CAMx) Against
Three Classical Mesoscale Tracer Experiments
by Bret A. Anderson, Kirk Baker, Chris Emery
9th CMAS Conference, Chapel Hill, NC, October 11, 2010
SCIPUFF outperforms CALPUFF by a significant margin
0
0.5
1
1.5
2
2.5
CALPUFF SCIPUFF HYSPLIT FLEXPART CAMx
RANK
RANK
“SCIPUFF performed
best over 46 ADM
models”

5. © Copyright 2018 – Lakes Environmental Software
5
CTEX3 Tracer LRT Model Evaluation
Higher values better

6. © 2018 – Lakes Environmental Software
Jesse Thé, Ph.D.
DMUG
Apr/19/2018
2nd Order Closure

7. © Copyright 2018 – Lakes Environmental Software
7
2nd Order Closure?
1. Turbulence
 What mixes pollutants in the atmosphere
 Not solved directly
 Gaussian Plume + Dispersion curves =
Simplest turbulence model
2. To represent mixing / dilution we must
model turbulence

8. © Copyright 2018 – Lakes Environmental Software
8
2nd Order Closure?
1. Navier Stokes Equations ‐ NS
2. Reynolds Averaged NS (RANS)
 Mean and fluctuating components
3. Produces more unknowns than Equations
4. Turbulence Closure: Creating “approximate”
equations to solve the unknowns
5. > 200 Closure schemes!
uuu 

9. © Copyright 2018 – Lakes Environmental Software
9
Momentum Conservation
gVP
Dt
VD
 


2
x
xxxx
z
x
y
x
x
x
g
z
V
y
V
x
V
x
P
z
V
V
y
V
V
x
V
V
t
V
 

































2
2
2
2
2
2
y
yyyy
z
y
y
y
x
y
g
z
V
y
V
x
V
y
P
z
V
V
y
V
V
x
V
V
t
V
 




































2
2
2
2
2
2
z
zzzz
z
z
y
z
x
z
g
z
V
y
V
x
V
z
P
z
V
V
y
V
V
x
V
V
t
V
 

































2
2
2
2
2
2

10. © Copyright 2018 – Lakes Environmental Software
10
Momentum Conservation
gVP
Dt
VD
 


2
zji
j
i
ii
guu
x
V
xx
P
Dt
VD
 
















 ''

''
jiuu ''
uu ''
vu ''
wu
''
vv ''
wv
''
ww

11. © Copyright 2018 – Lakes Environmental Software
11
Example of Closure
1st order ‐ Gradient on mean flow











i
j
eddyji
x
u
uu  ''
2nd order ‐ Variance of fields – velocity, etc.
u
u uuu 
Instantaneous = Mean + Fluctuating

12. © Copyright 2018 – Lakes Environmental Software
12
Closure Types
Closure Approximation Applied
0.5 Order Bulk Method
1st Order
• Gradient Transport
• Mixing Length
CALPUFF, AERMOD
2nd Oder
Variances of Wind,
Temp, Concentration
SCIPUFF
Non‐Local Schemes Large Eddy Simulation LES Models

13. © Copyright 2018 – Lakes Environmental Software
13
Puff vs. Plume
CALPUFF
AERMOD

14. © Copyright 2018 – Lakes Environmental Software
14
SCIPUFF
Lagrangian transport of
Gaussian puffs
Concentration field
represented by
collection of 3-D puffs
Q
+
Puffs characterized by
3-moments of the puff
concentration
– 0th Mass
– 1st Centroid
– 2nd Spread
Puff concentration

15. © 2018 – Lakes Environmental Software
Jesse Thé, Ph.D.
DMUG
Apr/19/2018
Case Studies

16. © Copyright 2018 – Lakes Environmental Software
16
Project Parameters
 July 15‐19, 2015 (5 days)
 POINT Source – SO2 – 100g/s
 Met Grid: 1km
 Receptor Spacing: 1km

17. © Copyright 2018 – Lakes Environmental Software
17
1700m to 3000m
Hayden, Colorado

18. © Copyright 2018 – Lakes Environmental Software
18
SCICHEM ‐ Hayden, CO
SCICHEM with MEDOC
WRF 1‐km ‐ Max: 506 μg/m3
July 17, hour 23 at ‐1.0, ‐2.0
WRF 4‐km ‐ Max: 792 μg/m3
July 15 , hour 9 at 0.0, 0.0

19. © Copyright 2018 – Lakes Environmental Software
19
CALPUFF ‐ Hayden, CO
WRF 4‐km ‐ Max: 296 μg/m3
July 18, hour 21 at 1.5, 5.5
CALPUFF with Prognostic Data
WRF 1‐km ‐ Max: 128 μg/m3
July 15, hour 21 at ‐5.5, 3.5

20. © Copyright 2018 – Lakes Environmental Software
20
CALPUFF ‐ Hayden, CO
WRF 1‐km ‐ MMIF CALPUFF‐Ready
Max: 159 μg/m3 – July 18, hour 22 at ‐4.5, 7.5
By-passing CALMET

21. © Copyright 2018 – Lakes Environmental Software
21
Comparisons ‐ Hayden, CO
SCICHEM Vs CALPUFF
Max 1-Hour SO2
Concentration
SCICHEM – 4-km WRF (MMIF MEDOC) 792 μg/m3
SCICHEM – 1-km WRF (MMIF MEDOC) 506 μg/m3
CALPUFF – 4-km WRF (3D.DAT) 296 μg/m3
CALPUFF – 1-km WRF (3D.DAT) 128 μg/m3
CALPUFF - 1-km WRF (MMIF CALMET.DAT) 159 μg/m3

22. © Copyright 2018 – Lakes Environmental Software
22
Comparisons ‐ Hayden, CO

23. © Copyright 2018 – Lakes Environmental Software
23
SCICHEM vs CALPUFF (1‐km)
SCICHEM 1‐KM WRF
Max: 506 μg/m3
CALPUFF 1‐KM WRF
Max: 128 μg/m3
Hayden, Colorado

24. © Copyright 2018 – Lakes Environmental Software
24
SCICHEM vs CALPUFF (4‐km)
Hayden, Colorado
SCICHEM 4‐KM WRF
Max: 792 μg/m3
CALPUFF 4‐KM WRF
Max: 296 μg/m3

25. © Copyright 2018 – Lakes Environmental Software
25
SCICHEM vs CALPUFF Wind Field
SCICHEM 4‐KM WRF CALPUFF 4‐KM WRF
Hayden, Colorado

26. © Copyright 2018 – Lakes Environmental Software
26
Sea level to 1500m
Rio, Brazil

27. © Copyright 2018 – Lakes Environmental Software
27
SCICHEM ‐ Rio, Brazil
SCICHEM with MEDOC
WRF 1‐km ‐ Max: 558 μg/m3
July 16, hour 9 at 0.0, 0.0
WRF 4‐km ‐ Max: 969 μg/m3
July 15, hour 9 at 0.0, 0.0

28. © Copyright 2018 – Lakes Environmental Software
28
CALPUFF – Rio, Brazil
WRF 4‐km ‐ Max: 327 μg/m3
July 16, hour 2 at ‐2.5, ‐3.5
CALPUFF with Prognostic Data
WRF 1‐km ‐ Max: 263 μg/m3
July 16, hour 3 at ‐1.5, ‐4.5

29. © Copyright 2018 – Lakes Environmental Software
29
CALPUFF – Rio, Brazil
WRF 1‐KM ‐ MMIF CALPUFF‐Ready
Max: 206 μg/m3 – July 17, hour 9 at ‐0.5, 0.5
By-passing CALMET

30. © Copyright 2018 – Lakes Environmental Software
30
Comparisons – Rio, Brazil
SCICHEM Vs CALPUFF
Max 1-Hour SO2
Concentration
SCICHEM – 4-km WRF (MMIF MEDOC) 969 μg/m3
SCICHEM – 1-km WRF (MMIF MEDOC) 558 μg/m3
CALPUFF – 4-km WRF (3D.DAT) 327 μg/m3
CALPUFF – 1-km WRF (3D.DAT) 263 μg/m3
CALPUFF - 1-km WRF (MMIF CALMET.DAT) 206 μg/m3

31. © Copyright 2018 – Lakes Environmental Software
31
Comparisons – Rio, Brazil

32. © Copyright 2018 – Lakes Environmental Software
32
SCICHEM vs CALPUFF (1‐km)
SCICHEM 1‐KM WRF
Max: 558 μg/m3
CALPUFF 1‐KM WRF
Max: 263 μg/m3
Rio, Brazil

33. © Copyright 2018 – Lakes Environmental Software
33
SCICHEM vs CALPUFF (4‐km)
Rio, Brazil
SCICHEM 4‐KM WRF
Max: 969 μg/m3
CALPUFF 4‐KM WRF
Max: 327 μg/m3

34. © Copyright 2018 – Lakes Environmental Software
34
SCICHEM vs CALPUFF Wind Field
CALPUFF 4‐KM WRFSCICHEM 4‐KM WRF
Rio, Brazil

35. © 2018 – Lakes Environmental Software
Jesse Thé, Ph.D.
DMUG
Apr/19/2018
Conclusions

36. © Copyright 2018 – Lakes Environmental Software
36
Conclusions
1. CALPUFF and SCICHEM (SCIPUFF) generate
similar windfields when using WRF.
2. CALPUFF and SCIPUFF respond with
substantial concentration differences when
using WRF 4‐km and WRF 1‐km resolutions.
3. Higher resolution grids, in this case WRF 1‐km,
consistently produced smaller 1‐hour
maximum concentrations.

37. © Copyright 2018 – Lakes Environmental Software
37
Future Research
1. Evaluation supports the Hypothesis that
SCIPUFF produces substantially different
results from CALPUFF for “non‐reacting”
pollutants.
2. Next step is to evaluate SCIPUFF against
CALPUFF at multiple sites over extended
periods.

38. © Copyright 2018 – Lakes Environmental Software
38
Questions ?

39. © Copyright 2018 – Lakes Environmental Software
39
uuu 
u
u
Instantaneous = Mean + Fluctuating
Turbulence

40. © Copyright 2018 – Lakes Environmental Software
40
IHOP Experiment – Windfield
SCIPUFF
Observed
MM5
WRF

41. © Copyright 2018 – Lakes Environmental Software
41
ETEX ‐ SCIPUFF
SCIPUFF performed
best over 46 ADM
models

42. © Copyright 2018 – Lakes Environmental Software
42
1. Develop prognostic
equations for each of the
moments
2. Assume that puff centroid
is representative of the
whole puff
3. Splitting and merging of
puffs
SPLIT
MERGE
Boundary
SCIPUFF