Latitudinal and Seasonal Distribution of Particulate. MSA over the Atlantic using a Validated. Quantification Method with HR-ToF-AMS

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1 Supporting Information: Latitudinal and Seasonal Distribution of Particulate MSA over the Atlantic using a Validated Quantification Method with HR-ToF-AMS Shan Huang, Laurent Poulain *, Dominik van Pinxteren, Manuela van Pinxteren, Zhijun Wu, Hartmut Herrmann, and Alfred Wiedensohler Leibniz Institute for Tropospheric Research, Leipzig, Sachsen, 04318, Germany Institute for Environmental and Climate Research, Jinan University, Guangzhou, Guangdong, , China College of Environmental Sciences and Engineering, Peking University, Beijing, , China *Corresponding author: Laurent Poulain, Leibniz Institute for Tropospheric Research, Permoserstr. 15, Leipzig, Germany. poulain@tropos.de; Phone: ; fax: Pages, 5 Figures, 2 Tables, 3 Equations. S1

2 SI-1 AMS Collection Efficiency (CE) A constant collection efficient (CE) of 0.7, rather than the Composition Dependent Collection Efficiency (CDCE) from Middlebrook et al., 1 was applied in AMS data analysis during all Polarstern measurements. An important reason is that the possible contribution of the sea salt that is not properly detected by the AMS is not considered on the CDCE approach. The CDCE is a function of acidity, relative humidity (RH), and ammonium nitrate mass fraction (ANMF). 1 Ignoring pronounced Na + ion from the sea salt may lead to the overestimation of the particle acidity, consequently disturbing the determination of the CE. Also, the sampling aerosols went through a Nafion dryer prior to entering the AMS, so the shape of the particles (RH < 40%) may be not as expected to be spherical despite of the high estimated acidity. Besides, the ANMF during Polarstern measurements was always much lower (average: 0.05 ± 0.03, min: 0.01, max: 0.33) than the effective range of the CDCE function (ANMF> 0.4). However, so far there is no uniform CE for ambient measurements in the marine boundary layer. Quinn et al. 2 started examining and applying the component dependent CE (mainly considering particle bounce) from 0.45 for ammonium bisulfate to 1 for acidic sulfate during the measurements along the Northeast US coast and across the Gulf of Maine. Ovadnevaite et al. 2 used the CDCE (ranging from 0.45 to 0.97) calculated by the method from Middlebrook et al. 1 for measurements in the Mace Head. Also, a constant CE of 1 has been used in measurements over the Northeast Pacific 3 and over the South Atlantic 4 due to high mass fraction of sulphuric acid under humid sampling conditions (without dryer). For a campaign in Paris where aerosol particles were influenced by both marine and continental air masses, Crippa et al. 5 derived a constant CE of 0.38 for dry particles according to the good agreement between AMS data and S2

3 external measurements. Therefore, for AMS measurements in Polarstern cruises, the CE was determined by the inter-comparison between AMS and external measurements during all 4 cruises. Based on several tests, a constant CE of 0.7 can make AMS total/speciation mass concentration agree well with the total particle mass concentration derived from particle number size distribution (PNSD) measured by mobility particle size spectrometer (MPSS; Figure S1a) and with the speciation mass concentration, e.g., sulfate, from offline measurements (Figure S1b) at the same time. Both the underestimation (-19%) comparing to MPSS measurements and the overestimation (+16%) comparing to sulfate offline measurements are within the AMS measurements uncertainties (±30%). 6-8 Note that the ship contamination has been excluded from the current data analysis based on the relative wind direction. Figure S1 Inter-comparison (a) between total particle mass concentration from AMS and that calculated by particle number size distribution (PNSD) from MPSS; (b) between AMS sulfate mass concentration and that from filter measurements. S3

4 SI-2 MSA mass spectral pattern and the key ion Figure S2 provides the mass spectra of pure MSA from standard calibrations (a) during Polarstern cruises using HR-ToF-AMS, (b) reported by Huang et al. 9 for HKUST measurements using HR-ToF-AMS, and (c) reported by Phinney et al. 3 for Northeast Pacific campaigns using Q-AMS. Although these three mass spectra contain almost the same MSA fragments (e.g. CH + 3, sulfate ions, C x S y ions), the relation between them is different. For instance, the intensities of two sulfate ions, i.e. SO + (m/z 48), SO + 2 (m/z 64) are over twice (2.68 and 3.66) higher than that of + the ion CH 3 SO 2 (m/z 79) based on this study, but lower than or nearly the same as that (from 0.46 to 1.12) reported by Phinney et al. 3 and Huang et al. 9 Given that the quantification of MSA is mainly based on the key ion and the ratios between other ions and the key ion, the differences of the mass spectral pattern directly affect the calculated MSA mass concentration, which is illustrated in Method Validation part in the manuscript. S4

5 Fraction of signal C x C x H y C x H y O 1 C x H y O z C x S y SO x Standard deviation (a) Standard MSA by HR-ToF-AMS (this study) (b) Standard MSA by HR-ToF-AMS (Huang et al., 2015) (c) Standard MSA by Q-AMS (Phinney et al., 2006) m/z Figure S2 Mass spectral profiles of MSA obtained from (a) this study, (b) HKUST study 9 (redrawn according to the supplemental table in the article), and (c) Phinney et al. study 3 (obtained from AMS mass spectral database ) Figure S3 shows the high resolution spectrum for MSA main fragments (in V mode) during a selected period (around 3 days) with remarkable MSA signals in the forth Polarstern cruise. Peaks were fitted automatically based on the raw mass spectra data by software Pika1.15D and examined by users. Ions with pronounced peak shape (height) were chosen to fit the raw data. Also, those ions with possibility to appear in the ambient atmosphere were also used to explain the raw data (e.g. Br +, m/z ; C 5 H 5 N +, m/z ). Figure S3 illustrates that the S5

6 characteristic MSA ions CH 2 SO 2 + (m/z ), CH 3 SO 2 + (m/z ), and CH 4 SO 3 + (m/z ) could be clearly identified from their neighbour ions using HR-ToF-AMS. Figure S3 High resolution spectrum for MSA main fragments in Polarstern measurements: (a) CH 2 SO 2 + (m/z ), (b) CH 3 SO 2 + (m/z ), and (c) CH 4 SO 3 + (m/z ). S6

SI-3 Relative Ionization Efficiency (RIE) of MSA Figure S4 shows the comparison between MSA mass concentration derived from HR-ToF-AMS by applying RIE of 1.

7 SI-3 Relative Ionization Efficiency (RIE) of MSA Figure S4 shows the comparison between MSA mass concentration derived from HR-ToF-AMS by applying RIE of 1.2 and PNC-derived MSA mass concentration with error bars. Figure S4 The comparison between MSA mass concentration derived from HR-ToF-AMS using a RIE of 1.2 and PNC-derived MSA mass concentration with error bars. Points with different color related to measured MSA particles with different diameter: 200nm (light green), 225nm (dark green), 250nm (brown), 300nm (dark red), and 350nm (purple). The collection efficiency (CE) of 1 was applied to AMS data considering that atomized MSA particles were liquid droplets. 10 S7

8 SI-4 Reported MSA quantification methods (1) Ge method 11 is shown in Equation S1. [MSA], [CH 2 SO 2 + ], [CH 3 SO 2 + ], and [CH 4 SO 3 + ] are mass concentrations for MSA and its characteristic ions is the ratio between 3 ions and total MSA intensity according to the standard calibrations [MSA] = [CH 2SO 2 + ] + [CH 3 SO 2 + ] + [CH 4 SO 3 + ] [eq S1] (2) HKUST method 9 is described by Equation S2 and Equation S3. [MSA], [Org], [SO 4 ] = mass concentration of MSA, organics and sulfate; k I MSA,k, n I Org,n, t I SO4,t = total ions intensities of MSA, organics and sulfate; I MSA,CH3SO2 = signal intensity of the key ion CH 3 SO 2 + ; k = fragments of MSA; n = fragments of organics; t = fragments of sulfate; RIE S is relative ionization efficiency for species S, including MSA, organics, and sulfate. I MSA,k k = I MSA,CH3SO2 9.7% [eq S2] [MSA] = n I Org,n RIE Org k I MSA,k RIE MSA + I SO4,t t RIE SO4 ([Org] + [SO 4 ]) [eq S3] S8

SI-5 Non-sea-salt sulfate (nssso 4 ) estimation Non-sea-salt sulfate has been estimated by total sulfate minus sea-salt sulfate which is equal to intensity of Na

Figure S5 provides the comparison between calculated nssso 4 and total SO 4, indicating that 96% of the detected sulfate was of nonsea-salt origin.

9 SI-5 Non-sea-salt sulfate (nssso 4 ) estimation Non-sea-salt sulfate has been estimated by total sulfate minus sea-salt sulfate which is equal to intensity of Na + times 0.25 (0.25 is the ratio between sulfate and Na + in the sea water) 12 according to offline measurements during Polarstern cruises. Figure S5 provides the comparison between calculated nssso 4 and total SO 4, indicating that 96% of the detected sulfate was of nonsea-salt origin. The contribution of sea salt to the total sulfate is negligible. Figure S5 The comparison between non-sea-salt (nss) sulfate and total sulfate from offline measurements during the Polarstern cruises. S9

10 Table S1 MSA mass concentration in different seasons and regions (unit: µg m -3 ) Median Average σ Max Spring North Atlantic (> 5 ) South Atlantic (< - 5 ) Near equator (-5 ~ 5 ) Total Autumn North Atlantic (> 5 ) South Atlantic (< - 5 ) Near equator (-5 ~ 5 ) Total (Min = below detection limit; σ = standard deviation) S10

11 Table S2 Modified fragmentation table for MSA quantification mz frag_msa frag_org frag_so4 frag_no *frag_MSA[79] 12,-frag_MSA[12] *frag_MSA[79] 13,-frag_MSA[13] *frag_MSA[79] 0.04*frag_NO3[30], 0.04* frag_no3[46] *frag_MSA[79] 15,-frag_NH4[15],- frag_air[15],- frag_msa[15] *frag_Org[18] frag_so3[16] *frag_Org[18] frag_so3[17] *frag_Org[44] frag_so3[18] *frag_Org[1 8], 0.002*frag_Org[17] frag_so3[19] *frag_Org[18] frag_so3[20] 24 24,-frag_SO4[24] frag_so3[24],frag_h 2SO4[24] *frag_MSA[79] frag_org[44],- frag_msa[28] *frag_MSA[79] 29,-frag_air[29],- frag_msa[29] *frag_MSA[79] 0.022*frag_Org[29] 30,-frag_air_m[30],- frag_org[30],- frag_msa[30] *frag_MSA[79] 31,-frag_NO3[31],- frag_msa[31] *frag_MSA[79] frag_so3[32], frag_h2so4[32], * frag_no3[30] 0.002* frag_no3[30] S11

12 frag_msa[32] *frag_MSA[79] frag_so3[33], frag_h2so4[33] - frag_msa[33] *frag_MSA[79] frag_so3[34], frag_h2so4[34] - frag_msa[34] ,-frag_chloride[37] 38 38,- frag_chloride[38],- frag_air[38] ,-frag_K[41] *frag_MSA[79] 44,-frag_air_m[44],- frag_msa[44] *frag_MSA[79] 45,-frag_MSA[45] *frag_MSA[79] 46, -frag_msa[46] *frag_MSA[79] 47,-frag_NO3[47],- frag_msa[47] *frag_MSA[79] 0.5*frag_Org[62] frag_so3[48], frag_h2so4[48],- frag_msa[48] * frag_no3[46] 0.004* frag_no3[46] *frag_MSA[79] 49,-frag_SO4[49],- frag_msa[49] *frag_MSA[79] 50,-frag_SO4[50],- frag_msa[50] frag_so3[49], frag_h2so4[49] frag_so3[50], frag_h2so4[50] ,-frag_SO4[52] frag_so3[52], S12

13 frag_h2so4[52] *frag_MSA[79] 62,-frag_MSA[62] *frag_MSA[79] 63,-frag_NO3[63],- frag_msa[63] 1.5*0.002*frag_NO3 [30], 0.002* frag_no3[46] *frag_MSA[79] 0.5*frag_Org[50], 0.5*frag_Org[78] *frag_MSA[79] 0.5*frag_Org[51], 0.5*frag_Org[79] *frag_MSA[79] 66,-frag_SO4[66],- frag_msa[66] frag_so3[64], frag_h2so4[64],- frag_msa[64] frag_so3[65],frag_h 2SO4 [65],- frag_msa[65] frag_so3[66], frag_h2so4[66] *frag_MSA[79] 67,-frag_MSA[67] S13

14 *frag_MSA[79] 78,-frag_MSA[78] 79 f(ch 3 SO 2 + )*79 79,-frag_MSA[79] *frag_MSA[79] 0.75*frag_Org[94] frag_so3[80], frag_h2so4[80],- frag_msa[80] *frag_MSA[79] 0.5*frag_Org[67], 0.5*frag_Org[95] frag_h2so4[81],- frag_msa[81] ,-frag_SO4[82] frag_so3[82], frag_h2so4[82] *frag_MSA[79] 83,-frag_SO4[83],- frag_msa[83] frag_h2so4[83] 84 84,-frag_SO4[84] frag_so3[84], frag_h2so4[84] 85 85,-frag_SO4[85] frag_h2so4[85] *frag_MSA[79] 96,-frag_MSA[96] *frag_MSA[79] 97,-frag_MSA[97] S14

15 *frag_MSA[79] 0.5*frag_Org[84], 0.5*frag_Org[112] frag_h2so4[98],- frag_msa[98] 99 99,-frag_SO4[99] frag_h2so4[99] ,-frag_SO4[100] frag_h2so4 [100] References (1) Middlebrook, A. M.; Bahreini, R.; Jimenez, J. L.; Canagaratna, M. R., Evaluation of Composition-Dependent Collection Efficiencies for the Aerodyne Aerosol Mass Spectrometer using Field Data. Aerosol Sci. Tech. 2012, 46, (3), (2) Ovadnevaite, J.; Ceburnis, D.; Leinert, S.; Dall'Osto, M.; Canagaratna, M.; O'Doherty, S.; Berresheim, H.; O'Dowd, C., Submicron NE Atlantic marine aerosol chemical composition and S15

16 abundance: Seasonal trends and air mass categorization. J. Geophys. Res. - Atmos. 2014, 119, (20), 2013JD (3) Phinney, L.; Richard Leaitch, W.; Lohmann, U.; Boudries, H.; Worsnop, D. R.; Jayne, J. T.; Toom-Sauntry, D.; Wadleigh, M.; Sharma, S.; Shantz, N., Characterization of the aerosol over the sub-arctic north east Pacific Ocean. Deep Sea Res. Pt. II 2006, 53, (20 22), (4) Zorn, S. R.; Drewnick, F.; Schott, M.; Hoffmann, T.; Borrmann, S., Characterization of the South Atlantic marine boundary layer aerosol using an aerodyne aerosol mass spectrometer. Atmos. Chem. Phys. 2008, 8, (16), (5) Crippa, M.; El Haddad, I.; Slowik, J. G.; DeCarlo, P. F.; Mohr, C.; Heringa, M. F.; Chirico, R.; Marchand, N.; Sciare, J.; Baltensperger, U.; Prévôt, A. S. H., Identification of marine and continental aerosol sources in Paris using high resolution aerosol mass spectrometry. J. Geophys. Res. - Atmos. 2013, 118, (4), (6) Poulain, L.; Birmili, W.; Canonaco, F.; Crippa, M.; Wu, Z. J.; Nordmann, S.; Spindler, G.; Prévôt, A. S. H.; Wiedensohler, A.; Herrmann, H., Chemical mass balance of 300 C nonvolatile particles at the tropospheric research site Melpitz, Germany. Atmos. Chem. Phys. 2014, 14, (18), (7) Crippa, M.; DeCarlo, P. F.; Slowik, J. G.; Mohr, C.; Heringa, M. F.; Chirico, R.; Poulain, L.; Freutel, F.; Sciare, J.; Cozic, J.; Di Marco, C. F.; Elsasser, M.; Nicolas, J. B.; Marchand, N.; Abidi, E.; Wiedensohler, A.; Drewnick, F.; Schneider, J.; Borrmann, S.; Nemitz, E.; Zimmermann, R.; Jaffrezo, J. L.; Prévôt, A. S. H.; Baltensperger, U., Wintertime aerosol chemical composition and source apportionment of the organic fraction in the metropolitan area of Paris. Atmos. Chem. Phys. 2013, 13, (2), S16

17 (8) Freutel, F.; Schneider, J.; Drewnick, F.; von der Weiden-Reinmüller, S. L.; Crippa, M.; Prévôt, A. S. H.; Baltensperger, U.; Poulain, L.; Wiedensohler, A.; Sciare, J.; Sarda-Estève, R.; Burkhart, J. F.; Eckhardt, S.; Stohl, A.; Gros, V.; Colomb, A.; Michoud, V.; Doussin, J. F.; Borbon, A.; Haeffelin, M.; Morille, Y.; Beekmann, M.; Borrmann, S., Aerosol particle measurements at three stationary sites in the megacity of Paris during summer 2009: meteorology and air mass origin dominate aerosol particle composition and size distribution. Atmos. Chem. Phys. 2013, 13, (2), (9) Huang, D. D.; Li, Y. J.; Lee, B. P.; Chan, C. K., Analysis of Organic Sulfur Compounds in Atmospheric Aerosols at the HKUST Supersite in Hong Kong Using HR-ToF-AMS. Environ. Sci. Technol. 2015, 49, (6), (10) Matthew, B. M.; Middlebrook, A. M.; Onasch, T. B., Collection Efficiencies in an Aerodyne Aerosol Mass Spectrometer as a Function of Particle Phase for Laboratory Generated Aerosols. Aerosol Sci. Tech. 2008, 42, (11), (11) Ge, X.; Zhang, Q.; Sun, Y.; Ruehl, C. R.; Setyan, A., Effect of aqueous-phase processing on aerosol chemistry and size distributions in Fresno, California, during wintertime. Environ. Chem. 2012, 9, (3), (12) Bates, T. S.; Quinn, P. K.; Coffman, D. J.; Johnson, J. E.; Miller, T. L.; Covert, D. S.; Wiedensohler, A.; Leinert, S.; Nowak, A.; Neusüss, C., Regional physical and chemical properties of the marine boundary layer aerosol across the Atlantic during Aerosols99: An overview. J. Geophys. Res. - Atmos. 2001, 106, (D18), S17

Primary and secondary organic aerosol origin by combined gas-particle phase source apportionment

Supplementary information for manuscript Primary and secondary organic aerosol origin by combined gas-particle phase source apportionment Crippa Monica 1, Canonaco Francesco 1, Slowik Jay G. 1, El Haddad