Atmospheric Evolution and Chemical Aging of Organic Particulate Matter
Spyros Pandis and Neil Donahue
Center for Atmospheric Particle Studies (CAPS)
Carnegie Mellon University
Some Questions
• Do the biogenic and anthropogenic SOA components get along (mixing)?• Does the biogenic SOA age gracefully (chemical aging)?• Effects of SOA on climate relevant particle like absorption• Can our updated models reproduce the observations of OA and particle number in areas with both biogenic and anthropogenic sources?
Mixing of Different OA Components
OA for Different Mixing Assumptions
PMCAMx-Summer 2001
Mixing Experiments- Donahue
Correlation of the mass spectra of individual particles with the average initial composition of one of the two populations as a function of time.
Chemical Aging of SOA
β-Caryophyllene SOA Formation
1 10 100 1000
0
10
20
30
40
50
60
70
SO
A Y
ield
(%
)
SOA Mass (μg m-3)
Chen et al. (2011)
Winterhalter et al. (2009)
Lee et al. (2006)
Jaoui et al. (2013)
Ng et al. (2006)
Experimental setup
Smog Chamber(Temperature controlled room)
AMS
SMPS
8
Teflon bag12 m3
Ozone+NOx monitors
ozone generator
O2
tank
300 ppb
HONO or H2O2
Air
2-butanol
Air
β-caryophylleneInjection point
Air
3-30 ppb
0 50 100 150 200 250 300
0
10
20
30
40
50
60
wall losses corrected
raw measurement
time (min)
SO
A M
ass (
μg
m-3)
Temperature increase 20oC →40oC
Formation-Evaporation of the SOA
(28 ppb β-caryophyllene + 300 ppb Ozone + 2-butanol)
β-Caryophyllene ozonolysis SOA yield
0.1 1 10 100 1000
0
20
40
60
80
100
120
SO
A y
ield
(%
)
SOA mass ( ìg m-3)
Chen et al. (2012) (excess of ozone)
This study (no seeds)
This study (with seeds)
SOA (mg m-3)
0 20 40 60 80 100 120 140 160 180 200 220 240 260 2800
5
10
15
20
25
30
raw measurement
wall losses corrected
UV light onUV light offHONO
Time (min)
SO
A M
as
s (
mg
m-3)
Aging of β-caryophyllene SOA by OH
(3 ppb β-caryophyllene + 300 ppb ozone)11
0 50 100 150 200 250
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
UV light onUV light off
HONO
O
:C
Time (min)
O:C during aging of SOA
12
Aging of a-pinene SOA
PM Mass Concentration
14
-1 0 1 2 3 4 5 6
0
10
20
30
40
50
60
70
802nd HNO
2
A
eroso
l M
ass
Conce
ntr
atio
n (m
g m
-3)
Time (h)
1st HNO2
COA,1 COA,2
MBTCA Production and Aging
(3-methyl-1,2,3-butanetricarboxylic acid)
Ambient Perturbation Experiments
Dual Mobile Chamber System
Dual Mobile Smog Chamber System
Instrumentation in Mobile Laboratory
Mobile Smog Chambers
Baseline Characterization
20x103
15
10
5
0
Nu
mb
er
(# c
m-3
)
14:00 15:00 16:00 17:00
Time (h)
Chamber 1 Chamber 2
a-Pinene Addition to Chamber 1
20000
17500
15000
12500
10000
7500
5000
2500
0
Nu
mb
er
co
nce
ntr
atio
n (
# c
m-3
)
16:00 17:00 18:00 19:00 20:00 21:00 22:00
Time (h)
Chamber 1 Chamber 2
UV 'ON'
OA Mass Concentration(a-pinene addition)
2.50
2.25
2.00
1.75
1.50
1.25
1.00
0.75
0.50
0.25
0.00
ma
ss c
on
ce
ntr
atio
n (
μg
m-3
)
16:00 17:00 18:00 19:00 20:00 21:00 22:00
Time (h)
Chamber 1 Chamber 2
UV 'ON'
Wall-Loss Corrected OA Concentrations
Corrected
Absorption by SOA+BC
The D-toluene SOA - soot particles have core shell morphology and their absorption is consistent with Mie theory predictions.
Comparison of Mie theory with measurements
SOA formation
2 x SOA formation
PMCAMx Evaluation Summer 2012, Italy
25
OA in a Polluted Area
(Po Valley, Italy)
• Central site of the PEGASOS 2012 campaign
• Rural area in Po Valley
• Agricultural sources
• Industrial sources
Extensive AMS measurements
from 6 June until 8 July 2012
Ground measurements
Zeppelin measurements
PMCAMx
Org
an
ics
(μg
m-3
)
Application of PMCAMx over Europe
Europe
5400 × 5832 km2 region
36 × 36 km2 grid resolution
14 vertical layers (up to 6 km)
MeteorologyWRF oo
EmissionsAnthropogenic (TNO – GEMS)
Biogenic (MEGAN)
SAPRC-99
Volatility basis-set27
OA (μg m-3), June – July 2012
Predicted PM2.5 concentrations – Summer
2012
28
OA (μg m-3) Nitrate (μg m-3)
Sulfate (μg m-3) Ammonium (μg m-3)
0
5
10
15
20
25
30
0
5
10
15
20
25
30
11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8
PM
1O
rgan
ics
(μg
m-3
)
Date (2012)
PM1 OA - Bosco Fontana
Mean predicted: 6.07 μg m-3
Mean observed(AMS): 8.10 μg m-3
June July
observedpredicted
PM1 Organics (μg m-3) – Bosco Fontana
Comparisons with zeppelin observations - OA
30
20/6
0
200
400
600
800
1000
1200
0
2
4
6
8
10
12
14
16
18
20
4:33 5:45 6:57 8:09 9:21 10:33 11:45
Org
anic
s (μ
g m
-3)
Time of day (hrs)
Predicted Observed Altitude
Altitu
de
4/7
0
100
200
300
400
500
600
700
800
0
2
4
6
8
10
12
14
5:16 6:28 7:40 8:52 10:04
Org
anic
s (μ
g m
-3)
Time of day (hrs)
Predicted Observed Altitude
Altitu
de
PMCAMx - Evaluation Spring 2013, Finland
31
PM1 Organics (μg m-3) - Finland
32
April May
0
2
4
6
8
10
12
14
0
2
4
6
8
10
12
14
24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
PM
1O
A (
μg
m-3
)
Date(2013)
Predicted : 1.9 μg m-3
Observed : 2.1 μg m-3
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48 19:12 21:36
PM
1O
A (
μg
m-3
)
Time of day (hrs)
Predicted
Observed
Predicted
Observed
Diurnal OA Sources - Finland
33
April May
0
1
2
3
4
5
6
Co
nce
ntr
atio
n (
μg
m-3
)
Date (2013)
OPOA bSOA aSOA fresh POA
aSOA mean predicted = 0.3 μg m-3
bSOA mean predicted =1.2 μg m-3
Fresh POA mean predicted = 0.1 μg m-3
Oxygenated POA mean predicted = 0.3 μg m-3
PMCAMx-Evaluation
1-D Trajectory 2-D VBS Version
Homogeneous chemical aging (with OH)
Simple Functionalization- Base case (Murphy et al., ACP, 2011)
+ O
ato
ms
C*C*/10C*/102
0
1
2
Reactant Species
50%
50%
Reaction constants:
• kOH = 1∙10-11 cm3 molec-1s-1 negligible bSOA aging
for aSOA and bSOA
• kOH = 4∙10-11 cm3 molec-1s-1
for SOA-sv, SOA-iv
Average Diurnal O:C
(San Pietro Capofiume, Italy)
Average predicted O:C = 0.62
Average measured O:C = 0.58
Ground AMS Measurements
Average Diurnal OA Concentration
(San Pietro Capofiume, Italy)
Average predicted OA = 3.4 μg m-3
Average measured OA = 2.8 μg m-3
2 4 6 8 10 12 14 16 18 20 22 240
2
4
6
8
10
OA
(mg
m-3
)
Local Time (Hour)
Ground Measurements
Model
Average Vertical O:C Profile
(All Zeppelin flights)
Average measured O:C = 0.58
Average predicted O:C = 0.59
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
200
400
600
800
1000A
ltit
ud
e (
m)
O:C
Model
Zeppelin
Homogeneous chemical aging (with OH)
Functionalization- Fragmentation
+ O
ato
ms
C*C*/10C*/102
0
1
2
Reactant Species
50%
50%
b
SOA Effects on Particle Number
Particle number concentration fields
(June 2012)
N10 N100(cm-3) (cm-3)
Without organic condensation
Changes due to condensation of organic
vapors (no chemical aging)
N10 N100
Comparison with field observations
• Ground stations:
BIRkenes - Norway
MACe Head - Ireland
HYYtiala - Finland
K-PUszta - Hungary
CORsica - France
ASPvreten
VAVhill
ISPra
San Pietro Capofiume
BOLogna
PATra
FINokalia
THEssaloniki
DREsden
HOHenpeissenberg
MELpitz
SCHneefernerhaus
WALdhof
- Sweden
- Italy
- Germany
- Greece
Evaluation of PMCAMx-UF
Measured With organics and aging
Nucleation Frequency (June 2012)
Location
Measured Organics/Aging
Conclusions
• Rapid mixing of aromatic and biogenic SOA for RH exceeding 20%• Reductions of anthropogenic SOA will help reduce biogenic SOA
• Small change in biogenic SOA produced under low NOx
conditions as it keeps reacting with OH
• Significant later generation production of SOA if the first generation of reactions has taken place under high NOx
conditions.
• Results of MBTCA oxidation by OH and ambient perturbation experiments consistent with the above conclusions.
• Condensation of SOA on BC containing particles increases absorption by as much as a factor of two.• Core-shell Mie theory model reproduces this effect
Conclusions
• Updated PMCAMx predictions for a polluted area with both anthropogenic and biogenic influences consistent with the simple VBS parameterizations of SOA formation and chemical aging.
• A number of 2-D VBS schemes can explain OA and O:C observations.
• Additional constraining expected from ongoing application of PMCAMx to the SOAS campaign.
• SOA condensation leads often to reduction of particle number but to increases of the CCN concentrations.• Significant progress in simulating particle number concentrations
• Future work: Estimation of effects of controls of anthropogenic emissions on biogenic and anthropogenic OA in the Eastern US.
Acknowledgements
Graduate Students and Post-docs
M. Day, L. Hildebrandt, C. Kaltsonoudis, E. Karnezi, E. Kostenidou, B. Murphy, D. Patoulias, E. Robinson, A. Tasoglou, N. Wang, Q. Ye.
Colleagues
I. Riipinen, R. Subramanian, PEGASOS team.
Financial Support
EPA STAR and EU FP7 PEGASOS.
Publications Donahue N. M., W. Chuang, S. A. Epstein, J. H. Kroll, D. R. Worsnop, A. L. Robinson, P. J. Adams,
and S. N. Pandis (2014) Why do organic aerosols exist? Understanding aerosol lifetimes using the 2D VBS, Environ. Chem., 10, 151-157.
Murphy B. N., N. M. Donahue, A. L. Robinson, and S. N. Pandis (2014) A naming convention for atmospheric organic aerosol, Atmos. Chem. Phys., 14, 5825-5839.
Tasoglou A. and S. N. Pandis (2014) Formation and chemical aging of secondary organic aerosol during the b-caryophyllene oxidation, Atmos. Chem. Phys., 15, 6035-6046.
Hildebrandt-Ruiz L., A. Paciga, K. Cerully, A. Nenes, N. M. Donahue, and S. N. Pandis (2014) Formation and aging of secondary organic aerosol from toluene: changes in chemical composition, volatility, and hygroscopicity, Atmos. Chem. Phys., 15, 8301-8313.
Day M. C., M. Zhang, and S. N. Pandis (2015) Evaluation of the ability of the EC tracer method to estimate secondary organic aerosol carbon, Atmos. Environ., 112, 317-325.
Pandis S. N., K. Skyllakou, K. Florou, E. Kostenidou, E. Hasa, and A. A. Presto (2015) Urban particulate matter pollution: A tale of five cities, Faraday Discussions, in press.
Ye Q., E. S. Robinson, X. Ding, P. Ye, R. C. Sullivan, and N. M. Donahue (2016) Secondary organic aerosols are crunchy when dry but runny when wet, submitted.
Tasoglou A., G. Saliba, R. Subramanian, and S. N. Pandis (2016) Absorption of chemically aging biomass burning carbonaceous aerosol, to be submitted.
Wang N., E. Kostenidou, N. M. Donahue, and S. N. Pandis (2015) Chemical aging of a-pinene first-generation ozonolysis products by reactions with OH: I: Low NOx conditions, in prep.
Kaltsonoudis C., E. Kostenidou, E and S. N. Pandis (2015) Development and evaluation of a mobile dual two-chamber system for atmospheric field perturbation experiments, in prep.
Karnezi E. and S. N. Pandis (2015) Simulation of organic aerosol formation in a polluted area with the 2-D Volatility Basis Set, in preparation.