Post on 24-Dec-2015
Modeling Elemental Composition of Organic Aerosol: Exploiting Laboratory and Ambient Measurement and the Implications of the Gap Between ThemQi Chen* (1), Colette L. Heald (1), Jose L. Jimenez (2), Manjula R.
Canagaratna (3), Qi Zhang (4), Ling-Yan He (5), Xiao-Feng Huang (5), Pedro Campuzano-Jost (2), Brett B. Palm (2), Douglas Day (2), Laurent Poulain (6), Scot T. Martin (7), Jonathan P. D. Abbatt (8), Alex K.Y. Lee (8), John Liggio (9)
*Now at Peking University, China
Funded by NSF
Insufficient Understanding of Organic Aerosol (OA)
[Heald et al., ACP, 2011]
Models have difficulty in reproducing the concentration and the variability of organic aerosol.
transportation
processing
chemically constrained by H/C and O/Care variable for different sources
vary while agingdictate hygroscopicity and particle density
? ? ?
Exploiting Ambient and Laboratory Measurement
[Heald et al., GRL, 2010]
[Ng et al., ACP, 2011]
Need to re-visit: (1) more real-time data
(2) corrected AMS elemental ratios an increase of 14-45% in O:C and of 7-20% in H:C for ambient OA (Canagaratna et al., 2014)
[Simon and Bhave, et al, EST, 2012]
The New Dataset of OA Elemental Composition
MILAGROIMPEX
DC3
We synthesize a dataset of both laboratory and ambient observations of the OA elemental ratios, including unpublished results. This dataset contains a total of 56 surface observations (rural/remote, pollution/fire, and downwind conditions are all represented), three aircraft measurements, and chamber/flow-tube results.Comparisons between ambient and laboratory
data are made.
Ambient Observations
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
H:C
1.61.20.80.40.0
O:C
-1.0 -0.5 0.0 0.5 1.0OSc
(a)
Mexico City
Whistler Peak
Mace Head
Ground Urban Downwind Remote/Rural
Aircraft MILAGRO (2006) DC-3 (2012)
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
H:C
1.61.20.80.40.0
O:C
-1.0 -0.5 0.0 0.5 1.0OSc
(a)
Mexico City
Whistler Peak
Mace Head
campaign-average
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
H:C
1.61.20.80.40.0
O:C
-1.0 -0.5 0.0 0.5 1.0OSc
(a)
Mexico City
Whistler Peak
Mace Head
Slope=-0.6Intercept=2.0
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
H:C
1.61.20.80.40.0
O:C
(b)
Riverside
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
H:C
1.61.20.80.40.0
O:C
(b)
Riverside
Mexico City (T0)
Fresno
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
H:C
1.61.20.80.40.0
O:C
(b)
Riverside
Mexico City (T0)
Fresno
Cool
Davis
SPC
Upton
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
H:C
1.61.20.80.40.0
O:C
(b)
Riverside
Mexico City (T0)
Fresno
Borneo
AmazonSGP
BEACHON
Melpitz
Cool
Davis
SPC
UptonWhistler Mtn.
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
H:C
1.61.20.80.40.0
O:C
(b)
Riverside
Mexico City (T0)
Fresno
Borneo
DC-3
AmazonSGP
BEACHON
Melpitz
Cool
Davis
SPC
UptonMILAGROWhistler Mtn.
Fitted to invididual datasets(shown for the data range) — Urban — Downwind — Remote/Rural — Aircraft
Individual slopes are steeper (−0.7 to −1.0), suggesting that the mean fit is compensating for various intercepts.
diversity
Laboratory Measurements
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
H:C
1.61.20.80.40
O:C
n nn nn
n
nnn
ppp
ooooxxx
x
aaaaaaaaaa
aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
aaaaaaaaaaaaaa
aaaaaaaaa aaaaaaaaa
aaaaaaaaa aaaaaX X
X
X
X
TTT
ZZZ
rrr
r
r
rr
rrrrr
b
bbb
b
b
b
bb
bI
III IIIIIIII
InInInInSSSSSSSS
S
SMMM
MMMM MMLLLLLLL
dg
d
d
d
ttcc
c cccGGG
e
e
e
ee
R
E
(c)
Biomass burning OA (b)Anthropogenic POABiogenic SOA Aromatic SOAFresh IVOC SOAGlyoxal aqueous uptake (G)Marine Emissions (R)IEPOX-OA monoterpene ELVOC
Biomass burning OA (b)Anthropogenic POABiogenic SOA Aromatic SOAFresh IVOC SOAGlyoxal aqueous uptake (G)Marine Emissions (R)IEPOX-OA monoterpene ELVOC
1.61.20.80.40
O:C
Anthropogenic (POA+SVOC/IVOC)
(d)
SOA (gas + particle)
1086420
Biomass burning (POA+SVOC/IVOC) (day)
Heterogeneous Oxidation squalane OA Lubricating oil particles glyoxal OA (aquesous)
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
1.61.20.80.40
O:C
Anthropogenic (POA+SVOC/IVOC)
(d)
SOA (gas + particle)
1086420
Biomass burning (POA+SVOC/IVOC) (day)
Heterogeneous Oxidation squalane OA Lubricating oil particles glyoxal OA (aquesous)
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
1.61.20.80.40
O:C
Anthropogenic (POA+SVOC/IVOC)
(d)
SOA (gas + particle)
1086420
Biomass burning (POA+SVOC/IVOC) (day)
Heterogeneous Oxidation squalane OA Lubricating oil particles glyoxal OA (aquesous)
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
H:C
1.61.20.80.40
O:C
n nn nn
n
nnn
ppp
ooooxxx
x
aaaaaaaaaa
aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
aaaaaaaaaaaaaa
aaaaaaaaa aaaaaaaaa
aaaaaaaaa aaaaaX X
X
X
X
TTT
ZZZ
rrr
r
r
rr
rrrrr
b
bbb
b
b
b
bb
bI
III IIIIIIII
InInInInSSSSSSSS
S
SMMM
MMMM MMLLLLLLL
dg
d
d
d
ttcc
c cccGGG
e
e
e
ee
R
E
(c) 2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
1.61.20.80.40
O:C
Anthropogenic (POA+SVOC/IVOC)
(d)
SOA (gas + particle)
1086420
Biomass burning (POA+SVOC/IVOC) (day)
Heterogeneous Oxidation squalane OA Lubricating oil particles glyoxal OA (aquesous)
(d) downwind TSOA & Aging(g/p)ISOA(NOx)
ASOA & Aging(g/p)BBOA & Aging(g/p)APOA & Aging(g/p)
(c) Mexico City TSOAISOA(NOx)
ASOABBOA & Aging(g/p)APOA & Aging(g/p)
2.4
2.0
1.6
1.2
0.8
H:C
1.20.80.40.0
(e) Monoterpene-dominant TSOA & Aging(g/p)
Add ELVOCAdd APOA, BBOA & Aging
ManitouWhistlerMountain
2.4
2.0
1.6
1.2
0.8
H:C
(a) Riverside TSOAISOA(NOx)
ASOA
-1.0
-0.5
Add APOA
Lab vs. Field #1: Statistical Mixtures Compared to Ambient
Consistencies
missing sources and pathways which maintain high H:C in areas polluted areas
1.20.80.40.0
(g) Rainforest TSOA & Aging(g/p)ISOA & Aging(g/p)IEPOXp, ELVOC
Amazon
Borneo
Add APOA, Aged BBOAAdd GSOA & Aging
Marine OA
1.61.20.80.40.0
AircraftAmbient
Add GSOA & Aging
TSOA & Aging(g/p)ISOA(NOx)
ASOA & Aging(g/p)BBOA & Aging(g/p)APOA & Aging(g/p)
ELVOC
(h)
Low NOx isoprene chemistry and glyoxal-type of aqueous-phase chemistry can drive the match
lab experiments do not adequately mimic ambient (mixtures? extend of aging?)
Lab vs. Field #2: Observationally-Based Model Simulation
Step 1: SOA yields to reflect recent measurementsStep 2: Account for semi-volatile POA emissionsStep 3: Assign elemental ratios to POA/SOA types simulated in model based on lab dataStep 4: Age gas-phase organics based on flow-tube data but end point constrained by field obs. (50% increase in burden)
Emissions FromFossil Fuel
BiofuelBiomass Burning
VOC
HydrophobicO-POAn
Oxidation Products
SOGi
Gas-phase Particle-phase
*,i iCSOAi
HydrophilicI-POAn
Marine Emissions
Biogenic Emissions
×0.5
1.15d
IsopreneMonoterpenesSesquiterpenes
Aromatics
×0.5
OH, O3
NO3
OH, O3
NO3
kage, jSVOCj SVOC-SOA2, j
SOG-SOA1, i
kcarbon, j×85%
×15%
SVOC-SOA1, j
SOG-SOA2, i kage, ikcarbon, i
Marine POA
End point:O:C=1.1H:C=1.4(defined by field obs)
(GEOS-Chem v9-01-03)
Aging Dramatically Alters Simulation of OA Elemental Composition
Aging leads to
- more pronounced spatial variability (a wider range)
- more pronounced seasonality over continents
Surface distributions
O:C
H:C
Model Simulations Compared to Surface Observations1.2
1.0
0.8
0.6
0.4
0.2
0.0
Ba
se
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Ag
ing
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Ag
ing
O:C
foss
il fu
el o
f 0.
03
inst
ead
of
0.1
1.21.00.80.60.40.20.0
Observed O:C
2.2
2.0
1.8
1.6
1.4
1.22.2
2.0
1.8
1.6
1.4
1.22.2
2.0
1.8
1.6
1.4
1.2
2.22.01.81.61.41.2
Observed H:C
0.1
1
10
0.1
1
10
0.1
1
10
0.1 1 10
Observed OA [µg m-3
]
UrbanDownwindRemote/Rural
Urban (JJA)
1.2
1.0
0.8
0.6
0.4
0.2
0.0B
ase
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Ag
ing
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Ag
ing
O:C
foss
il fu
el o
f 0.
03
inst
ead
of
0.1
1.21.00.80.60.40.20.0
Observed O:C
2.2
2.0
1.8
1.6
1.4
1.22.2
2.0
1.8
1.6
1.4
1.22.2
2.0
1.8
1.6
1.4
1.2
2.22.01.81.61.41.2
Observed H:C
0.1
1
10
0.1
1
10
0.1
1
10
0.1 1 10
Observed OA [µg m-3
]
UrbanDownwindRemote/Rural
Urban (JJA)
The model performance in remote regions is largely improved by aging.H:C are underestimated, consistent with missing sources or pathways for high H:C.
Heterogeneous oxidation effectively helps to reproduce the vertical gradient.
10
8
6
4
2
0
Alti
tude
(km
)
1.21.00.80.60.4
O:C
Observation Base Aging Aging w/. SOA heterogeneous aging Aging w/. 5xSOG -> SOA Aging w/. 25 KJ/mol enthalpy Aging w/. 2xEpoa
1086420
OA
(b) DC-3
4.03.02.01.00.0
OA
10
8
6
4
2
0
Alti
tude
(km
)
1.21.00.80.60.4
O:C
(a) IMPEX
10
8
6
4
2
0
Alti
tude
(km
)
1.21.00.80.60.4
O:C
Observation Base Aging Aging w/. SOA heterogeneous aging Aging w/. 5xSOG -> SOA Aging w/. 25 KJ/mol enthalpy Aging w/. 2xEpoa
O:C OA1.701.601.501.401.30
H:CH:C
Cannot reproduce variability in observed H:C.
1×10-13 cm3 molecule−1 s−1
Conclusions
The disconnect between laboratory and ambient OA elemental composition, especially for areas influenced by pollution and/or fires -> missing sources and/or pathways which maintain high H:C -> linked to missing OA mass in those regions
Simple, measurement-based aging scheme largely improves simulation of elemental composition. Including heterogeneous oxidation helps reproduce the vertical profile.
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
H:C
1.61.20.80.40
O:C
n nn nn
n
nnn
ppp
ooooxxx
x
aaaaaaaaaa
aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
aaaaaaaaaaaaaa
aaaaaaaaa aaaaaaaaa
aaaaaaaaa aaaaaX X
X
X
X
TTT
ZZZ
rrr
r
r
rr
rrrrr
b
bbb
b
b
b
bb
b
dg
d
d
d
ttcc
c cccGGG
R
E
Biomass burning OA (b)Anthropogenic POABiogenic SOA Aromatic SOAFresh IVOC SOAGlyoxal aqueous uptake (G)Marine Emissions (R)IEPOX-OA monoterpene ELVOC
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
H:C
1.61.20.80.40
O:C
n nn nn
n
nnn
ppp
ooooxxx
x
aaaaaaaaaa
aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
aaaaaaaaaaaaaa
aaaaaaaaa aaaaaaaaa
aaaaaaaaa aaaaaX X
X
X
X
TTT
ZZZ
rrr
r
r
rr
rrrrr
b
bbb
b
b
b
bb
b
dg
d
d
d
ttcc
c cccGGG
R
E
(c)
+ alcohol/peroxide
+ carboxylic acid
Thank you!