comparison of three secondary organic aerosol algorithms implemented in cmaq weimin jiang*, Éric...
TRANSCRIPT
Comparison of Three Secondary Organic Aerosol Algorithms
Implemented in CMAQ
Weimin Jiang*, Éric Giroux, Dazhong Yin, and Helmut Roth
National Research Council of Canada
2
Outline
• SOA calculation in CMAQ
• The three CMAQ SOA algorithms
• Model set-up
• Impact on organic aerosol modelling results:
spatial, temporal, SOA/fine ratios, algorithm correlations
• Impact on organic aerosol modelling performance:
comparison with measurements
• Conclusions and discussion
3
SOA calculation in CMAQ
• Three major steps
• Steps 1 and 3: Binkowski and Roselle (2003); Binkowski and Shankar (1995); US EPA (1999)
• Implementation details: Jiang and Roth (2003)
• Step 2: SOA algorithm to calculate SOA mass formation rate.
4
Three CMAQ SOA algorithms
• Pandis: constant AYs for 6 pseudo SOA precursor species
• Odum: AYs for 4 pseudo species from
• Schell: system of equations for 10 condensable species derived from 6 pseudo species, with T correction for gas phase saturation concentrations
02
2,2
01,
1,10 1
α
1
α
MK
K
MK
KMAY
om
om
om
om
10,,2,1,
//
/
1,
,*,,,
i
mCmC
mCCCC
n
linitinitllaer
iiaerisatitotiaer
5
Model set-up: the model
• Base model: CMAQ 4.1
• Modularized AERO2 by NRC (Jiang and Roth, 2002)
• Schell extracted from AERO3 in CMAQ 4.2 and converted to a submodule in AERO2
• Three CMAQ executables: different only in SOA submodule; all other science and code the same
6
Modularized aerosol module
n u cle a t io n _ K L P2 /n u cle a t io n _ K L P3 /n u cle a t io n _ H K 9 8 /n u cle a t io n _ n o o p
a e ro pro c
a e ro _ driv e r
a e ro s te p m o de _ m e rg in g /m o de _ m e rg in g _ n o o p
PM e m is /PM e m is _ n o o p
1
1
aero _ d ata
1
c o n s t_ d ata
5
air_ d ata
4
aero em is _ d a ta
3
p r ec u r s r_ d ata
2
in o re ql_ e ql3 /in o re ql_ e ql3 _ n o o p
co n d_ n u cl/co n d_ n u cl_ n o o p
S O A _ Pa n dis /S O A _ O du m /S O A _ S ch e ll/
S O A _ n o o p
co a g u la t io n /co a g u la t io n _ n o o p
o de _ s o lv e r s ize v a r
s ize v a r
1
1
1
1 1
1
1
1
1
2 3 4
5
2
2
2
5
3 5
2
4
4
44
4
5
5
5
5
5
5
5
2
5
5
7
Model set-up: domain, period, inputs
• Nested LFV domain, Pacific ’93 episode (July 31 – August 7, 1993): see H. Roth’s presentation
• All model inputs are the same except for organic aerosol species:
– clean IC and BC for the study of algorithm impact on modeling results
– observation-base IC and BC for the study of algorithm impact on model performance
9
Impact on temporal variation
0
1
2
3
0:00 7/31
0:00 8/1
0:00 8/2
0:00 8/3
0:00 8/4
0:00 8/5
0:00 8/6
0:00 8/7
0:00 8/8
Co
nce
ntr
atio
n (m g
m-3
)
Anthropogenic SOA
0
1
2
3
4
5
0:00 7/31
0:00 8/1
0:00 8/2
0:00 8/3
0:00 8/4
0:00 8/5
0:00 8/6
0:00 8/7
0:00 8/8
Co
nce
ntr
atio
n (m g
m-3
)
Biogenic SOA
0
2
4
6
8
0:00 7/31
0:00 8/1
0:00 8/2
0:00 8/3
0:00 8/4
0:00 8/5
0:00 8/6
0:00 8/7
0:00 8/8
Co
nce
ntr
atio
n (m g
m-3
)
Total SOA
10
Impact on model performance
0
10
20
30
40
50
0:00 7/31
0:00 8/1
0:00 8/2
0:00 8/3
0:00 8/4
0:00 8/5
0:00 8/6
0:00 8/7
0:00 8/8
Co
nce
ntr
atio
n (m g
m-3
)
PIME3
0
4
8
12
16
7/31 8/1 8/2 8/3 8/4 8/5 8/6 Avg.
Co
nce
ntr
atio
n (m g
m-3
)
PIME3
11
Conclusions and discussion
Schell Pandis Odum
Science best among three simplified not usable
SOA-generation n x Pandis 10n x Odum very low
performance good on average underestimate dramatic
underestimate
Note wide range of norm.bias
Deficiency/problem no partitioning of org. OAY, not
IAY
aerosol to gas phase
overestimate SOA
(corrected in CMAQ 4.3?)
12
Odum algorithm problem: OAY vs. IAY
OAY = Overall AY
= average AY
from ROG=0 and M0=0
to ROG= ROG* and M0=M0*
IAY = Instantaneous AY
= AY at ROG* and M0*
R O G
M 0
S lo p e = IA Y
S lo p e = OAY
R O G *0
M 0*
1
2
13
OAY equation vs. IAY equation
i i
ii
MK
KMOAY
0om,
om,0 1
α
i i
ii
i i
ii
MK
K
MK
K
IAY
20om,
2om,
2
0om,
om,
1
α
1
α
edMcM
baMIAY
0
20
20 )(
.αα
,αα2
,αα
,αα
,αα
22om,2
21om,1
2om,21om,12om,1om,
22om,
21om,21
2om,21om,1
2om,1om,21
KKe
KKKKd
KKc
KKb
KKa
• Jiang (2003), Atmos. Environ. (in press)
14
OAY or IAY: A big deal?
0 .1 1 1 0 1 0 0 1 ,0 0 0M 0 (mg /m 3)
0 .0 0
0 .0 4
0 .0 8
0 .1 2
0 .1 6
0 .2 0
Aer
osol
Yie
ld
1 0
1 0 0
1 ,0 0 0
1 0 ,0 0 0
1 0 0 ,0 0 0
% D
iffe
renc
e
A n th ro p o g en ic
IAY
OAY
IAY - OAY 100 OAY
0 .1 1 1 0 1 0 0 1 ,0 0 0M 0 (mg /m 3)
0 .0 0
0 .1 0
0 .2 0
0 .3 0
0 .4 0
Aer
osol
Yie
ld
1 0 0
1 ,0 0 0
1 0 ,0 0 0
% D
iffe
renc
e
B io g en ic
IAY - OAY 100 OAY
IAY
OAY
Yes, a big deal both conceptually and quantitatively.
15
Acknowledgment
• US EPA: Original Models–3/CMAQ
• Environment Canada Pollution Data Branch, Air Quality Research Branch, Pacific & Yukon Region:
Raw emissions and ambient measurement data
• Dr. D. G. Steyn of the University of British Columbia: Pacific ’93 data set
• Program of Energy Research and Development (PERD) in Canada:
Funding support