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M. Bressi, F. Cavalli, C. Belis, J-P. Putaud, S. Dos Santos, K. Douglas, R. Passarella

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. 1. Introduction.

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M. Bressi, F. Cavalli, C. Belis, J-P. Putaud, S. Dos Santos, K. Douglas, R. Passarella

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  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 Sources Aerosol Size

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

  4. Why to use an Aerosol Chemical Speciation Monitor (ACSM)? 1. Scientific reasons30 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 http://www.psi.ch/acsm-stations/ 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. 2.1 Sampling site 2. Material and methods 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 Ng et al., 2011 Aerodyne Research Inc. 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 4 calibrations RFNO3=3.03E-11 amp/(µg/m3) RIENH4=5.3 1 calibration RFNO3=4.46E-11 amp/(µg/m3) RIENH4=6.0 Ionization Efficiency (IE) Ammonium Nitrate calibrations 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. Comparison between 2 ACSMs: 20/06/2013  18/08/2013 3. Results 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: 20/12/2013  28/02/2013 ORGANICS NITRATE ACSM COMPOUNDS + 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. 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: PM2.5 24-hour mean daily max = 25 µg/m3 • High proportion of Organics (54%) • SIA (37%)

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

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

  16. 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 BBOA 1 BBOA 2 LV-OOA 1 LV-OOA 2 HOA

  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 4.1 Conclusions • 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 • 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. 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  7 Feb 2013 20 Dec 2012  22 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 Note: Org ENEA plotted with the default Ion Transmission Correction (ITC) on this graph ORGANICS 12h averaged data (n=116) Default ITC Experimental ITC

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

  27. 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=0.8-0.9, slope=0.9-1.0

  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

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