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

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

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 http://www.psi.ch/acsm-stations/ acsm-and-emep-stations

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

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

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

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

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

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

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)

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: PM2.5 24-hour mean daily max = 25 µg/m3 High proportion of Organics (54%) SIA (37%)

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)

+ 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 12 m/z 13 m/z 14 … n = * + m/z 12 m/z 13 m/z 14 … n  

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

(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)

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

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

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%?)

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??

APPENDICES

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

Canagaratna et al. (2007)

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

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

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

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

12h averaged data (n=116) AMMONIUM CHLORIDE

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

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

Zhang et al. (2007)

Jimenez et al. (2009)

Zhang et al. (2007)

Zhang et al. (2011)

Jimenez et al. (2009)

Kroll et al. (2011)

Kroll et al. (2011)

Courtesy of A. Prevot

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

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