modellingorganicaerosol · 2008-07-29 · decomposition products droplet aerosol jang et al. 2002...
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Modelling Organic Aerosol
David Simpson
EMEP, MET.NO & Chalmers
– p.1/78
Overview
Introduction
Approaches to modelling
SOA models
Examples of model usage + evaluation
Conclusions
– p.2/78
What is EMEP?
Cooperative Programme for Monitoring and Evaluation ofthe Long-Range Transmission of Air Pollutants in Europe
(European Monitoring and Evaluation Programme)
– p.3/78
What is EMEP?
Cooperative Programme for Monitoring and Evaluation ofthe Long-Range Transmission of Air Pollutants in Europe
(European Monitoring and Evaluation Programme)
Aims: To provide sound scientific support for theConvention, in particular in the areas of:
Atmospheric monitoring and modelling
Emission inventories and emissions projections
Integrated assessment
– p.3/78
EMEP . . .
CLRTAP: Convention on Long Range Transboundary AirPollution
Adopted 1979
51 Parties
Eight Protocols
EMEP, 1984
Last one: Göteborg,1999
Contribution to EU NEC Directives + CAFE
– p.4/78
Air Pollution Modelling
– p.5/78
Purpose of Modelling
1. Policy - emissioncontrol
2. Scientificresearch
3. Both!
Example: d(Dep(N))/d(ENOx),Austria
– p.6/78
Complete approach
Detailed understanding might require:
size distributions
complex chemical pro-cesses
– p.7/78
Policy/Global Models
Typically require:
simpler - only masses (PM2.5, PM10)
Well evaluated (trustworthy) models
Concentrate on main processes:
– p.8/78
Box model
The simplest model:
em
Emissions
< >
><
dC
dt=
E
h− u.(C − C0)
– p.9/78
Box model
Adding Chemistry:
em
Emissions
CHEMISTRY
< >
><
dC
dt=
E
h− u.(C − C0) + P − L.C
– p.10/78
Box model, cont.
... and more terms
-Vg. C : dry deposition
-L . C : wet deposition
entrainment
. . .
Allow the box to move?
⇒ Lagrangian
– p.11/78
The Eulerian 3D model
Represents all mainphysical and chemicalprocesses
Numerical integration
Scientifically most sound method of calculating air pollution
– p.12/78
Eulerian model, cont.
3D models are CPU-expensive:
170 × 130 × 20 = 440 000 gridcells×100 species⇒ 44 million concentrations
Typically requires supercomputer for long simulations.
– p.13/78
Eulerian model, cont.
3D models are CPU-expensive:
170 × 130 × 20 = 440 000 gridcells×100 species⇒ 44 million concentrations
Typically requires supercomputer for long simulations.
(But, the times they are a changing.....)
– p.13/78
Aerosol Extras
⇒ Many other ‘effective’ species
– p.14/78
Aerosol Extras
⇒ Many other ‘effective’ species
Nucleation
– p.14/78
Aerosol Extras
⇒ Many other ‘effective’ species
Nucleation
Coagulation
– p.14/78
Aerosol Extras
⇒ Many other ‘effective’ species
Nucleation
Coagulation
Condensation
– p.14/78
Aerosol Extras
⇒ Many other ‘effective’ species
Nucleation
Coagulation
Condensation
Cloud-processes
– p.14/78
Aerosol Extras
⇒ Many other ‘effective’ species
Nucleation
Coagulation
Condensation
Cloud-processes
Size-resolved emissions
– p.14/78
Aside: Is complexity good?
Acc
ura
cy
Complexity– p.15/78
Aerosol modelling
Two main approaches:
1. Modal models
2. Sectional models
– p.16/78
Modal models
Make use of log-normal distribution
n(ln Dp) =N
√2π ln σg
exp
[
−1
2
(ln D − ln Dpg)2
ln2 σg
]
– p.17/78
Modal models, cont.
Break-down atmosphericdistribution in 2-3 modes
– p.18/78
Modal models, cont.
The kth moment is defined as:
Mk =
∫
+∞
−∞
Dkpn(ln Dp)d(ln Dp)
with solution
Mk = N.Dkpg
exp
[
k2
2ln2 σg
]
M0 = total particle number concentration
M2 ∝ surface area
M3 ∝ volume, mass
– p.19/78
Modal models, cont.
Advantages
Requires very few parameters (σ, Dpg)
Computationally inexpensive
Dis-Advantages
Has no explicit size-distribution, therefore conditionsassumed uniform within a mode
– p.20/78
Sectional models
Divide aerosol distribution into ‘bins’ or ‘sections’.Typically 4-100, e.g.
– p.21/78
Sectional models, cont.
Advantages
State-of-the-art description (with many sections)
Allows different chemical mixtures at different sizes
Flexible
Dis-Advantages
Computationally expensive
Physics/chemical basis not always known
– p.22/78
Belief in models?
The basic rule:
– p.23/78
Belief in models?
The basic rule:
Garbage in ⇒ ⇒ Garbage out:
– p.23/78
Belief in models?
The basic rule:
Garbage in ⇒ ⇒ Garbage out:
SOA twist:
– p.23/78
Belief in models?
The basic rule:
Garbage in ⇒ ⇒ Garbage out:
SOA twist:
Garbage in the middle!
– p.23/78
Sources of OC
– p.24/78
Organic Aerosol
OA: Subject=Horrendous!! 1000s of compounds, mainlyunknown. Formation mechanisms complex and unkown!
– p.25/78
Partitioning
Gas-Particlepartitioning:
Ai
Gi
=COA
C∗
i
where
C∗
i is saturationconcentration,= f (Vapourpressure)
– p.26/78
G/P cont.
A multitude of eqns found, e.g.
Ai
Gi
= Ki.COA =RT
MWζip0L,i
.COA
Ai
Gi
=COA
C∗
i
=RT
MWiζ′
ip0L,i
.COA
Smog-chambers:
Y = COA
∑ αiKi
1 + αiKiCOA
– p.27/78
Chemistry < − > SOA?
Stolen from Neil...
– p.28/78
SOA: α-K approaches
Smog-chamber data could be explained with:
VOC + Ox ⇒ α1 P1 + α2 P2
– p.29/78
G/P approaches
COA(µg/m3) ⇒
Pi = Ai + Gi
Ai
Gi
= Ki.COA
Ai
Gi
=COA
C∗
i
– p.30/78
α-K approaches, cont.
Pros:
Easy-to-use
Available for many compounds
– p.31/78
α-K approaches, cont.
Pros:
Easy-to-use
Available for many compounds
Cons:
Derived from smog-chambers, often 40◦C, 100s ppb,v.low RH
Not flexible/mechanistic
– p.31/78
α-K approaches, cont.
Pros:
Easy-to-use
Available for many compounds
Cons:
Derived from smog-chambers, often 40◦C, 100s ppb,v.low RH
Not flexible/mechanistic
Coefficients used so far, wrong?
– p.31/78
New Evaluations
m-xylene
(Griffin, 1999)
a-pinene
(Griffin, 1999)
Isoprene
(Pre-2005)m-xylene
(Ng, 2007)(high-NOx)
a-pinene
(Chan2007/,Ng2006)
Isoprene
(Henze,2006)m-xylene
(Ng, 2007)
(low-NOx)
Isoprene
(Chan, 2007)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
Yie
ld (
Fra
cti
on
)
Yield (Y) at M0 = 5 ug/m3
’OLD’ ’NEW’
Changes in Yield Estimates
e.g. Ng et al. (2006, 2007), Chan et al. (2007)– p.32/78
Volatilty Approach
Donahue, Robinson....
The Volatility Basis Set
!! !" !# $ # "$
$%#
$%"
$%!
$%&
$%'
$%(
$%)
$%*
$%+
#
,-.# $/01/2!./3
!!4
!/2567-8-,/97:;<=->4
!"#$%$&'($!)$*
!+
C∗i =
{
0.01,0.1,1,10,100,1000,104,105,106}
µgm
α-Pinene + Ozone Mass Balance
!"!#
!""
!"#
!"$
!"%
"
" '#
" '$
" '%
" '&
!
!'#
!'$
!'%
()*+,!-+.
!/0
!"+,123.456789+*832:25+;4::+<34=>62?0
C10=1
C10O4 = 1.4
O3+
O
O
OOH
100 ppt
10-7 torr
• Mass yields α′i = {.004,0, .05,.09, .12, .18,...}
• Only around 0.055 SOA formation from α-pinene in the LVOC range at low NO
• Mass balance for ‘nominal product’ demands ξmax =∑
i αi ≃ 1.4
– p.33/78
Volatility methods?
Pros:
Flexible framework
Maps more of parameter space
Easier to link new data/experiments
Efficient for global models
Cons:
Still not mechanistic
– p.34/78
EMEP approach
EMEP Kam-2 Method: ‘Explicit’, extended from Kamens et al.:
21 reactions, 15 products, dimer, .... Andersson-Sköld andSimpson, JGR, 2001
– p.35/78
EMEP BSOA, Kam-2
Evaluated against smog-chamber:
0 50 100 150 200 250
Time (mins)
0
10
20
30
40
50
Ae
roso
l m
ass (
ug
/m3
)
Hoffman H6
T=43-48 C, light, +NOx
0 50 100 150 200
Time (mins)
0
500
1000
1500
2000
2500
3000
Ae
roso
l m
ass (
ug
/m3
)
Kamens K3
T= 15-6 C
– p.36/78
BSOA; Kam-2 Method
Comparison with Smog-Chambers good(Andersson-Sköld and Simpson, 2001):9-820 ppb α-pinene, 0-240 ppb NOx
– p.37/78
EMEP Kam-2(X)
Pros:
Flexible framework
‘Real’ species (surrogates anyway)
Linked with gas-phase chemistry
Evaluated against several smog-chamber exps.
Cons:
No aqueous/heterogenous chemistry
One (α-pinene!) species
Old
– p.38/78
The Big Stuff
MCM, 1000s reactions, 200 SVOC species (Jenkin et al.,JGR, 2004)
(or CACM, Griffin et al.) – p.39/78
MCM-type
Pros:
Explicit framework
‘Real’ species
1000s of reactions - as realistic as possible
Cons:
No aqueous/heterogenous chemistry
Two (α, β-pinene) species
Needs very large (100-500) correction factors forvolatility
– p.40/78
MCM-type, cont.
Cons:
Heavy!
– p.41/78
MCM-type, cont.
Cons:
Heavy!Pro:
Attempt to incorporate best-understanding
– p.41/78
New issues....
HULIS
H2SO4
isoprene
carbonyls
OH, H2O2
Lignin pyrolysis products
co
nd
en
sati
on
Decomposition
products
droplet
aerosol
Jan
g e
t a
l. 2
00
2
Lim
be
ck e
t a
l. 2
003
Za
ppo
li e
t a
l. 1
99
9,
Ma
yo
l-B
race
ro e
t a
l. 2
00
2
Gel
encs
ér e
t al.,
200
3
Gelencsér et al., 2002
heterogeneous
direct emission multiphase
Working hypotheses for HULIS formation
Still changing - e.g. Warneck, Ervens, Jang, Griffin,suggest aqueous/heterogenous pathways as source ofSOA. Isoprene, glyoxal, oxalic acid, ....
Do we know which pathway to follow?
– p.42/78
Sensitivity
All models sensitive to:
Vapour pressure
∆H assumptions
Activity coefficients
Deposition assumptions
Emissions
– p.43/78
Sensitivty: ∆H
Tsigaridis+Kanakidou, ACP, 2003
– p.44/78
Emissions – IIASA
Fine-particle emissions - Kupiainen, and Klimont, 2007
– p.45/78
Dry Deposition
Problems of Theory vs. Measurements:
From PhD Thesis, Rick Thomas
– p.46/78
Summary of Models
Many models
Little basis for choosing!
Little basis for evaluation!
– p.47/78
Summary of Models
Many models
Little basis for choosing!
Little basis for evaluation!
Unconstrained!Need Observations!
– p.47/78
Results
Results: Annual Average OC, year 2002 (ugC/m3)
Kam2X
– p.48/78
BSOA contribution
BSOA/OC (%)
ASOA/OC (%)
Kam2X– p.49/78
OC, take 2
OC with alternative vapour pressures
Kam-2X
Kam-2
– p.50/78
Sensitivity of OC: Birkenes
Jul02 Oct02 Jan03 Apr03 Jul03 Oct030
1
2
3
4
5
6
7
8
9
BSOAASOAWOODFFUELBGND
Jul02 Oct02 Jan03 Apr03 Jul03 Oct030
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
BSOAASOAWOODFFUELBGND
– p.51/78
Birkenes EC, TC
Jul02 Oct02 Jan03 Apr03 Jul030
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Site: Birkenes EC
EC−modEC−obs
Jul02 Oct02 Jan03 Apr03 Jul030
1
2
3
4
5
6
7
8
9
10
Site: Birkenes TC
TC−modTC−obs
Model performance - quite good at all Northern Europeansites
– p.52/78
Aveiro SO−2−4
, TC
Jul02 Oct02 Jan03 Apr03 Jul03 Oct03 Jan04 Apr04 Jul040
2
4
6
8
10
12
14
Site: Aveiro SO4
SO4−obsSO4−mod
Jul02 Oct02 Jan03 Apr03 Jul03 Oct03 Jan04 Apr04 Jul040
2
4
6
8
10
12
14
16
18
20
Site: Aveiro TC
TC−obsTC−mod
Model performance - quite bad for TC at all southernEuropean sites
– p.53/78
CARBOSOL Project
1-week filters (PM2), analysed for:
cellulose ⇒ biological parti-cles
levo-glucosan
⇒ biomass-burning
OC/EC ⇒ primary emis-sions
14C ⇒ modern/fossil
16 papers: Present and Retrospective State of Or-ganic Aerosol Over Europe, J. Geophysical Research,VOL. 112, D23, 2007
– p.54/78
Aveiro revisited...
Use levoglucosan to ‘correct’ WOOD
Jul02 Oct02 Jan03 Apr03 Jul03 Oct03 Jan04 Apr04 Jul040
0.5
1
1.5
Site: Aveiro LEVO
Jul02 Oct02 Jan03 Apr03 Jul03 Oct03 Jan04 Apr04 Jul040
5
10
15
20
25
30
35
Site: Aveiro TCxwood
TCxwood−obsTCxwood−mod
Promising :-)
– p.55/78
cf CARBOSOL
K-Puszta (Hungary), Summer
Obs.-Derived EMEP Model
(5–95th %ile) (Kam2 - Kam2X)
TC 5.2 1.6 – 2.7
WOOD 0.3 – 0.5 0.05
EC 0.4 – 0.7 0.4
FFUEL 0.2 – 0.5 0.4
BSOA 2.9 – 3.6 0.2 - 1.4
ASOA 0.05 – 0.7 0.03 - 0.04
Units: µg C m−3
Simpson et al., JGR, 2007
– p.56/78
Use of tracers, cont.
SORGA: Norwegian project
Tove Svendby, Karl-Espen Yttri, ...
David Simpson, MET.NO
Hans Puxbaum + co. (TUV)
Kristina Stenström, Lund Univ.
– p.57/78
SORGA
– p.58/78
Source-Apportionment, cont
Other tracers:14C ⇒ modern/fossil
cellulose ⇒ plant matter, ...
sugars/alcohols ⇒ fungi, ...
OC/EC ⇒ primary emissions
levoglucosan ⇒ biomass-burning
- all factors approximate.
- some ‘traps’, e.g. some modern 14C could be fromcooking oils, tyres, etc.
(e.g. Gelencsér et al., JGR, 2007)
– p.59/78
SORGA
Sources of PM1, Summer:
(K.E. Yttri et al., 2008, Prelim)– p.60/78
SORGA
aKT-EMEP model, Hurdal:
– p.61/78
SORGA
Kam-2X-EMEP Model, Hurdal:
– p.62/78
The Target
Do we know how much OC we want?
Key words:Artifacts (EC/OC, -ve, +ve, ...)– can be of order 50% ?
Representativity - what does e.g.[OC] mean?
– p.63/78
The Target
Do we know how much OC we want?
Key words:Artifacts (EC/OC, -ve, +ve, ...)– can be of order 50% ?
Representativity - what does e.g.[OC] mean?
– p.63/78
Artifacts
EUSAAR result:
: Fig. from Jean-Philippe Putaud
– p.64/78
Other studies
Volkamer et al., GRL, 2006
– p.65/78
Other studies
Volkamer et al., GRL, 2006
– p.66/78
Conclusions
State of OC science ‘in infancy’ (Donahue et al., 2005)
– p.67/78
Conclusions
State of OC science ‘in infancy’ (Donahue et al., 2005)
. . . because as we know, there are known knowns;there are things we know we know. We also knowthere are known unknowns; that is to say we knowthere are some things we do not know. But there arealso unknown unknowns - the ones we don’t know wedon’t know.
- (Donald Rumsfeld, 2005)
– p.67/78
Conclusions
Modellers have no way to ‘solve’ SOA modelling untilchemists have understood the basics.
But, model’s can serve to test theories and emissions
Emissions? Primary OC/BC + precursor (terpenes!)emissions need verification (near-sourcemeasurements?)
– p.68/78
Conclusions 2
Measurements are required to develop and constrainmodels and validate emissions
Needs chemical speciation, tracers, many locations
Long-term field data + campaigns+supersites ideal
AMS, C14, ......
– p.69/78
Wishes...
Would be good to specify:
Emissions (AVOC, BVOC, volatility)
Volatile E(PM)? Or Condensible E(VOC)?
Source of Atmos. Aerosol:
How much is modern/fossil
How much is biomass/BSOA
How much is through aqueous pathway
Acidity/S ?
Mixing polar/nonpolar/liquid/other??
Which smog-chamber data are relevent?
Link smog/flow-chambers – atmosphere
– p.70/78
Garbage Avoidance
Strategies:
Check basics - does the model work for anything?
Check other pollutants - SO2, SO4, NOx, NOy,
Check emissions!
Check PCM tracers - EC, levoglucosan, C14
Check measurements - what do they mean?!
Be humble.....
– p.71/78
The End...
– p.72/78
Other studies
The simplest result – all PM from forests:
See: Tunved et al., Science, 2006
– p.73/78
Other studies
The simplest result – all PM from forests:
See: Tunved et al., Science, 2006
NB: Applies to clean air, selected air masses
– p.73/78
Evaluation
Mainly by comparison with:
More Complex models (e.g. for chemical schemes)
Measurements - the main test!!
– p.74/78
Ozone
– p.75/78
Model vs. Model
EMEP vs IVL (HCHO):
Andersson-Skold & Simpson, Atmos.Env., 1999
– p.76/78
HCHO Cont. Field Comp:
Donon, France:
– p.77/78
Isoprene
(Nice result for precursor too:)
Lessons?
Combination - lab (via. MCM) + field data verypowerful – tests kinetics, emissions and chemistry
(New comparisons in progress, Tack SCARP, Tellus,FZJ!)
– p.78/78
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