This document summarizes a study of PM2.5 formation during a photochemical episode in the Po Valley in Italy. Measurements were taken in Limito di Pioltello near Milan from June 17-20 and July 8-17, 2009. The study found that (1) secondary inorganic aerosols like nitrates, sulfates and ammonium dominated PM2.5 mass during the episode, forming through aqueous phase photochemistry, (2) fine particles peaked at night while coarse particles peaked during the afternoon due to different sources, and (3) source apportionment models like the Multilinear Engine ME-2 helped identify local traffic, nitrates from the Po Valley, and sulfates from Milan as major sources

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F. KARAGULIAN1, L. Clarisse2,
F. Lagler1, M. Barbiere1, R. Connolly1 and, C. A. Belis1
PM formation during a photochemical
episode in the Po Valley: measurements,
mechanisms and source apportionment
European Commission - JRC Institute for Environment and Sustainability, Ispra, Italy
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Université Libre de Bruxelles, Brussels, Belgium
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Is there any influence of photochemistry
on PM2.5 concentration in the Po valley?
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3. Limito di Pioltello and the Po valley, Italy
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Site of the field campaign:
Limito di Pioltello
Milano
Limito di Pioltello
Date: 17-20 Jun 2009
08-17 July 2009
Po Valley
Limito di Pioltello is located in the Po valley
near the city of Milan
 Large agricultural areas
 Industrial area
 Large residential area
~ 30.000 inhabitants

4. 4-hour experimental time resolution
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 Sequential samplers (FAI Instruments and Leckel SEQ47/50) for gravimetric
measurements of PM1 (24h) PM2.5 (4h, 24h) and PM10 (24h), respectively
 Sampling: quartz filter for ion chromatography and offline carbon analysis,
Teflon® filter, (PTFE) for gravimetric and elemental analysis,
on-line semi-continuous OC/EC analyzer Sunset (on hourly basis)
 VOCs sampled on air for 24 hours with CARBOPACK tubes made of activated
charcoal cartridges and analyzed with GC-MS
 Carbonyls groups sampled with 2,4-Dinitrophenylhydrazine-coated (DNPH)
cartridges and analyzed with HPLC
 Size distribution of particles were also collected with an optical particle
counter (OPC, Grimm)

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Results

6. What causes the PM2.5, PM10 summer EPISODE?
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EPISODE
15-16 July 2009
less polluted
NO input ?
increase of O3 levels
EPISODE

7. Ions (PM2.5) correlated with absolute humidity
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EPISODE
 Night-early morning: NO3- and NH4+
 Evening:SO4- -

8. NO3- and SO4-- are balanced by NH4+
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EPISODE
less polluted
SO 24
3
NO
NH 4+
particles
(secondary inorganic aerosols, SIA)
Seinfeld, J. H., and S. N. Pandis (2006), John Wiley & Sons, Inc.
Vecchi, R., et al. (2009), Environ. Monit. and Assess., 154(1-4), 283-300
Zhang, K., et al. (2007), Atmos. Res., 84, 67-77

9. Secondary Inorganic Aerosols (SIA)
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Aqueous phase photochemistry of SO2 and NO2
photochemistry
SO2(g)
NO2(g) + NO3( g )
nighttime
OH •
SO3(g)
N 2O5( g )
H 2O(l )
H 2O(l )
H 2 SO4
HNO3
Aerosol
formation
Heterogeneous
processes
NH 3(g)
Seinfeld, J. H., and S. N. Pandis (2006), John Wiley & Sons, Inc.
Zhang, K., et al. (2007), Atmos. Res., 84, 67-77
Dickerson, R. R. (2003), Nat. Atmos. Dep Progr. Ammonia Workshop
Finlayson-Pitts, B. J., and J. N. Pitts, (2000), Academic Press: San Diego
Karagulian, F., and M. J. Rossi, (2005), 7(17), Phys. Chem. Chem. Phys. 3150-3162
SIA
(NH 4 )2 SO4
(NH 4 )HSO4
NH 4 NO3

10. NH3 in the Po Valley: 7-19 July 2009
satellite observation in the boundary layer
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Large reservoir of NH3
in the Po valley (Italy)
Milano
major NH3 sources:
 fertilizers
 animal waste
Limito di Pioltello
Po Valley
major NH3 sinks:
 wet and dry deposition
volatilization:
 temperature
 relative humidity - - - > nucleation
Averaged NH3 total column
from 7 to 17 July 2009
(data are provided by IASI on the MetOp-A
Meteorological payload)
Clarisse, L., et al. (2009), Nature Geoscience, 2(7), 479-483
The aerial background photographs are © 2008 Google-Imagery © 2008 Terrametrics (http://maps.google.com)

11. PM2.5 dominated by SIA during the EPISODE
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 Si, Fe, Al, Zn, Na, contributed for
less than 1% to the total PM2.5 mass
OM = 1.6 x OC
EPISODE
16th July 2009
Less OM = 1.4 x OC
polluted
8th July 2009
OM = 2.0 x OC
Turpin, B. J., and H. J. Lim (2001), Aerosol Science and Technology, 35(1), 602-610
Vecchi, R., et al. (2009), Environ. Monit. and Assess., 154(1-4), 283-300

12. Fine particles dominate the EPISODE
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Fine particles
Coarse-crustal
(transport, re-suspension)
(2.0-15.0 µm)
ev
en
ing
n ig
ht
(0.25-0.58 µm)
* Data are from ARPA Lombardia (Italy)

13. Wind direction coherent with trends for wind speed
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Coarse particles during
afternoon
(advection + traffic)
Fine particles during
nighttime (local)
00-04h
04-08h
08-12h
12-16h
16-20h
20-24h

14. Photochemical formation of carbonyls
during the EPISODE
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O3
O3
OH •
formation
Sampling on air!
O3 + VOCs → Aldehydes
 vehicle exhausts
 gasoline vapor
•
VOCs + OH → R + H 2O
Lau, A. K. H. et al. (2010), 408(19), Science of the Total Environment 4138-4149.
Photochemical smog
daytime
Atkinson, R., et al. (1984), Journal of Physical Chemistry, 88(6), 1210-1215
Donahue, N. M., et al. (2009), Atmos. Environ., 43(1), 94-106

15. The most volatile OC fraction of PM2.5 correlates with
photochemical oxidation products
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EPISODE
Oxygenated organic
compounds
RT Quartz temperature protocol
for OC/EC thermal desorption
(OC1)
oxygenated
organic compounds
Correlation: r2 ~ 0.7
Matuschek, G., et al. (2007), Environmental Science and Technology, 41(24), 8406
Streibel, T., et al. (2006), Analytical Chemistry, 78(15), 5354

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Comparison between EPA PMF 3.0 and
Multilinear Engine (ME-2)
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17. Source apportionment and receptor models
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Contribution of the kth source
in the jth sample
Mass conservation principle….
p
xij =
∑g
ik f kj
two way model
k =1
Concentration of the jth specie
in the ith sample
i = 1,2,….m (samples)
Concentration of the ith specie
in the kth source
j = 1,2,….n (species) uij is the measured uncertainty
When both gik and fkj are unknown, the Positive matrix Factorization (PMF) model is used (EPA PMF 3.0)
Q
main
=
n
∑∑
i =1
object function
p


 xij −
g ik f kj 
m


k =1


u ij
j =1 





∑
2
min Q main (x, f)
f
best fit

18. Multilinear Engine ME-2
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We can also use a PMF which includes constraints
and pulling equations
 Introducing the Multilinear Engine ME-2
Q = Q main + Q aux
Q
aux
V
=
∑
v =l
aux
Qv
V
=
∑
2
(av − f pj )2 /sv
v =l
softness of the pulling
pulling value

19. Source Contribution Estimation to PM2.5
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Factors
Measured Local Source Profiles
Autopull ME-2 2-way model with
constraints and pulling equations
Paatero and Hopke, PMF-CD, 2010. YP-Tekniikka Ky Rikalantie 6 FI-00970 Helsinki FINLAND
EU Report, Regione Lombardia (under revision)
Amato, F. et al., (2009), 43(17), Atmospheric Environment, 2770-2780
Viana, M., et al. (2008), Journal of Aerosol Science, 39(10), 827-849

20. Wind direction factors for each source
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Nitrate
Sulfate
Traffic
Predominant
from Po Valley
Predominant
from Milano area
Limito
Milano
Enhanced ME-2 2-way model with
constraints
and pulling equations
Po Valley
Uniformly distributed
around the receptor site

21. Higlights
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Diurnal Trend Analysis
Night:
 Low wind speed from East with high RH
 High concentration of sec. nitrate
 Local nucleation and heterogeneous reactions
Afternoon-evening:
 Breeze mainly form South-East
 Traffic re-suspension
 PM likely advected from rural areas
Traffic:
 Maximum values during rush hours

22. PM2.5 diurnal profiles
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23. Conlusions
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 Photochemistry mainly occurs during the EPISODE
 Multilinear Engine with constraints, pulling equation and,
wind direction analysis resulted to be an efficient approach for
data processing
 ME-2 factors resemble source profile more
than PMF factors
 The constraints did not lead to a significant
increase in Q
 ME-2 factors more coherent with advection
and particle properties (size distribution)

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Thanks for your attention
Acknowledgment
Data: Michel Gerboles, Sinan Yatkin, Bo Larsen
Programming: Luca Spano’
General Support: all my friends
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