1. Chemical composition of submicron particles with an Aerosol Chemical Speciation Monitor at the JRC-Ispra site
M. Bressi, F. Cavalli, C. Belis, J-P. Putaud, S. Dos Santos, K. Douglas, R. Passarella
R. Fröhlich, A. Prévôt
E. Petralia, M. Berico, A. Malaguti, M. Stracquadanio

2. 1. Introduction
Why to study the chemical composition of submicron particles (PM1)?
« Aerosols: collection of airborne solid or liquid particles » (IPCC, 2007)
Aerosol Size
Aerosol Sources

3. Comparison of annual PM2.5 levels at different sites in Europe
Why to study the chemical composition of PM1 at Ispra?
Adapted from Putaud et al. (2010)
Decesari et al. (2001)
Po Valley region
2020 EU Annual PM2.5 limit value =
20 µg/m3
Comparison of annual PM2.5 levels at different sites in Europe

4. http://www.psi.ch/acsm-stations/ acsm-and-emep-stations
Why to use an Aerosol Chemical Speciation Monitor (ACSM)?
1. Scientific reasons 30 min time resolution
Apportionment of organic aerosols
2. Operational reasons Long-term, unattended and stable field measurements
3. Specificity of the JRC
European ACSM network
acsm-and-emep-stations

5. Objectives of this talk
Present the assessment of the precision and the accuracy of ACSM measurements
Document the levels and the chemical composition of PM1 at Ispra
Focus on the organic fraction of PM1 at Ispra

6. Continuous measurements since 20 December 2012
2. Material and methods
2.1 Sampling site
Continuous measurements since 20 December 2012
20/06/2013  On-going
PM10
20/12/2012  20/08/2013

7. 2.2 ACSM description
Q-MS
Aerodyne Research Inc.
Ng et al., 2011
Chemical composition of non-refractory (NR) PM1: Organics (Org), NO3, SO4, NH4, Cl
Time resolution: 30 min

8. 2.3 Calibration, tuning and correction factors
ACSMs
Ionization Efficiency (IE)
Ammonium Nitrate calibrations
4 calibrations
RFNO3=3.03E-11 amp/(µg/m3) RIENH4=5.3
1 calibration
RFNO3=4.46E-11 amp/(µg/m3) RIENH4=6.0
CE=0.5 for every compound.
RIE: Org, SO4, NO3, Cl from literature
(Canagaratna et al., 2007; Jimenez et al., 2003; Ng et al., 2011)
Collection Efficiency (CE)
Relative IE (RIE)
Time series correction
Very stable N2 signal
SEM voltage modified every
~2-3 days
Ion transmission correction (ITC)
Close to the default correction
Substantial discrepancies

9. 3. Results 3.1 Metrology ORGANICS AMMONIUM
Comparison between 2 ACSMs: 20/06/2013  18/08/2013
12h averaged data
(n=116)
3.1 Metrology
ORGANICS
AMMONIUM
Conclusion: Despite discrepancies between both instruments (ages, calibrations, etc.)
 Good agreement between both ACSMs
0.98<r2<0.99 and 0.8<slopes<1.2 for Org, SO4, NO3, NH4

10. Comparison between ACSM and independent analytical techniques:
ORGANICS
NITRATE
COMPOUNDS
ACSM
+ EC
Conclusion:  Good agreement between ACSM and other analytical techniques

11. 3.2 Chemistry Temporal variation of PM1 chemical composition
Strong daily variations 
atmospheric dilution and condensation
Example of an atmospheric process visible with the ACSM
Temporal variation of NR-PM1 during the winter season
(20/12/2012  21/03/2013)

12. Very high PM1 levels at Ispra Comparable NR-PM1 chemical composition
Average PM1 chemical composition during the winter season (20/12/2012  21/03/2013)
Zhang et al. (2007), Jimenez et al. (2009)
[PM1]ACSM+EC= 31.9 ± 18.1 µg/m3
Average (µg/m3, %)
Very high PM1 levels at Ispra
Comparable NR-PM1 chemical composition
PM1 levels during winter at Ispra >>
+ EU: PM2.5 Annual limit value 2020 = 20 µg/m3
+ WHO: PM hour mean daily max = 25 µg/m3
High proportion of Organics (54%)
SIA (37%)

13. Classical classes of OA in AMS/ACSM studies
3.3 Organic Aerosol (OA) apportionment
Organic compounds: Definition: any molecules made of C and H that can also contain O, N, P.
Low level of understanding regarding their effects on human health, biogeochemical cycles and Earth’s climate.
LV-OOA
Low-Volatility OOA
OOA
SV-OOA
Oxygenated OA
Semi-Volatile OOA OA
Organic Aerosol
HOA
COA
BBOA
Hydrocarbon-like OA
Cooking OA
Biomass Burning OA
Classical classes of OA in AMS/ACSM studies
Hallquist et al. (2009)

14. + E X = G * F = * + Residual matrix Mass spectral Matrix
Method: Positive Matrix Factorization (PMF)
+ E
Residual matrix X
= G * F
Mass spectral Matrix
Factor contribution
Factor profile
m/z m/z m/z 14 … n = * +
m/z 12 m/z 13 m/z 14 … n

15. Factor identification (1/2)
20/12/2012  21/04/2013
Factor identification (1/2)
m/z 44
m/z 57
m/z 60
Factor profiles

16. (field studies or chamber experiment)
Factor identification (2/2)
LV-OOA 1
BBOA 1
BBOA 2
HOA
LV-OOA 2
OOA other
Correlation between my factor profiles and factor profiles found in the literature
(field studies or chamber experiment)

17. Factor contribution (1/2)
Absolute contribution
(µg/m3)
Relative contribution
Period
20/12/2012  21/04/2013
Diurnal contribution
(µg/m3)
Averaged contribution
(µg/m3; %)
OOA other
LV-OOA 2
HOA
BBOA 2
BBOA 1
LV-OOA 1

18. Factor contribution (2/2)
Comparison of OA apportionment with other European studies
(adapted from Crippa et al., 2013)
Note: Sampling periods Ispra study: December 2012 – April 2013
Other studies: February/March 2009

19. 4. Conclusions & Perspectives
Metrology (Ispra)
ACSM vs ACSM  good agreement (2 months, 0.98<r2<0.99, 0.8<slopes<1.2)
ACSM vs independent techniques  good agreement (2 months, 0.8<r2<0.9, 0.9<slopes<1.1)
Chemistry (Ispra, Winter period)
PM1 levels  extremely high (> WHO recommendations for PM2.5)
PM1 chemical composition  information every 30 min
mainly Organics (54%) and SIA (27%)
Organic Aerosol (OA) apportionment (Ispra, Winter-Early Spring period)
Preliminary results…
High proportion of aged, oxidized OA (~40%?) and biomass burning OA (~40%?)

20. 4.2 Perspectives Metrology Chemistry OA apportionment
Comparison between 13 ACSMs in Paris in November (ENEA instrument + data treatment tool developed by Claudio Belis)
ACSM  ? Chemical composition of PM in the European air quality network ?
Chemistry
A European aerosol phenomenology: real-time measurements of PM1 chemical composition (JRC leadership under discussion)
OA apportionment
Use of external tracers, use of different methodologies (e.g. ME-2), etc.
Define a common methodology for OA apportionment at the European level
Source apportionment
Real sources behind this chemical composition??

21. APPENDICES

22. Why to study the chemical composition of submicron particles (PM1)?
Impacts of aerosols on:
human health
climate
economy
Forster et al. (2007)

23. Canagaratna et al. (2007)

24. AEROSOL CHEMISTRY GAS PHASE CHEMISTRY AEROSOL PHYSICS
Elemental Carbon (EC)
Organic Carbon (OC)
Organic Matter (OM):
OM=1.4*OC
(Putaud et al., 2010)
24-h daily sampling
of PM2.5
Sunset Lab. OCEC Analyzer
(EUSAAR2 protocol)
AEROSOL CHEMISTRY
20 Dec 2012
28 Feb 2013
Major ions including:
SO4
NO3
NH4 Cl
Partisol Plus
Ion Chromatography
20 Dec 2012  22 Feb 2013
20 Dec 2012  7 Feb 2013 CO
GAS PHASE CHEMISTRY
AEROSOL PHYSICS
Differential Mobility Analyzer
Size distribution
10 to 800 nm
NO, NO2 O3
SO2
Condensation Particle Counter

25. ENEA versus PSI Q-ACSM ORGANICS
Note: Org ENEA plotted with the default Ion Transmission Correction (ITC) on this graph
ORGANICS
Default ITC
12h averaged data
(n=116)
Experimental ITC

26. Note: SO4 ENEA plotted with the default Ion Transmission Correction (ITC) on this graph
SULFATE
Default ITC
12h averaged data
(n=116)
Experimental ITC

27. Including all data points
NITRATE
12h averaged data
(n=116)
Including all data points
Excluding 28 June 12pm

28. 12h averaged data
(n=116)
AMMONIUM
CHLORIDE

29. PSI Q-ACSM versus other analytical techniques
NITRATE
SULFATE
AMMONIUM
Good agreement for Secondary Inorganic Aerosol (SIA): r²= , slope=

30. Off-line Chloride  large uncertainties CHLORIDE
Surprisingly good despite expected filter sampling artifacts
ORGANICS
Good agreement
ACSM COMPOUNDS + EC

31. Zhang et al. (2007)

32. Jimenez et al. (2009)

33. Zhang et al. (2007)

34. Zhang et al. (2011)

35. Jimenez et al. (2009)

36. Kroll et al. (2011)

37. Kroll et al. (2011)

38. Courtesy of A. Prevot

39. Comparison between BBOA1 (Factor 5) and BBOA2 (Factor 4) factor profiles

40. Comparison between LVOOA1 (Factor 6) and LVOOA2 (Factor 2) factor profiles