Source apportionment of the particulate organic mass by the Swiss groups

Transcription

1 Source apportionment of the particulate organic mass by the Swiss groups P. DeCarlo, C. Mohr, R. Chirico, M. Heringa, S. Weimer, N. Perron, J. Sandradewi, A.S.H. Prevot, U. Baltensperger Paul Scherrer Institut, Switzerland V. Lanz, C. Hueglin EMPA, Switzerland S. Szidat University of Berne, Switzerland M.R. Alfarra University of Manchester, UK Thanks to BAFU, Kantons Lucerne, Graubünden, Tessin, Zürich, Oekoscience

2 Positive Matrix Factorization of OM in Zürich summer 8% 8% 15% 69% Org NO3 SO4 NH4 Lanz et al., ACP (27)

3 Temporal evolutions of OOA I and OOA II AMS particle-sulphate [µg m -3 ] OOA, type I [µg m -3 ] temperature [ C] AMS particle-nitrate [µg m -3 ] OOA, type II [µg m -3 ] dat C- analysis indicates that most SOA is non-fossil (probably a combination from oxidation of terpenes, sesquiterpenes, isoprene, and possibly from biomass burning emissions Lanz et al., ACP, 27

4 IMPORTANT: PMF does not always work!!!! - PMF might not always provide precise results - Other statistical methods should still be developed - Combination of AMS with other data can provide validation of the PMF results but also additional information

5 A solution could be to use chemical mass balance (CMB) methods or hybrid versions of CMB and PMF ij = gipfpj + p p' x g f + e ip' q ij x CMB (G unknown) unknown sources/ free factors (PMF) x known sources/ fixed factors (CMB) x PMF (G, F unknown) F jm= F ' jm ± a j ' F jm solved by the multilinear engine, ME (Paatero, 1999) a = { 1} - In Zurich winter we used HOA as the known source, OOA and wood burning could be additionally identified Lanz et al., ES&T,

6 Härkingen (Mai) Reiden (Februar) Zürich (Sommer und Winter) Rheintal (erste mobile Messungen im Februar 27) Payerne (Juni, Januar 7) JFJ (Winter, Sommer) Massongex (Dezember 6) Roveredo (März, Dezember) Carbon apportionment using 14 C analysis Estimation of fossil and non-fossil SOA contribution OM EC OCnonfossil 56% 19% Z_meanPM1 Zürich (mean) 38% Wood burning 17% 38% Traffic ECbb (biomass Secondary burning) 4% ECfossil (fossil) Secondary 17% (non-fossil) 7% OCfossil 23% EC (fossil) EC (non-fossil) Use of AMS analysis : - wood burning 38% - HOA 7% Assumptions : - only SOA, HOA and wood burning present - OM/OC=2 for wood burning and SOA and OM/OC=1.2 for HOA 81% Lanz et al., ES&T, - RESULT : SOAnonfossil: 69% SOAfossil: 31%

7 PSI mobile laboratory

8 PMF from 1-minute q-ams resolution data (6 seconds possible) versus simple approximation a =.16 (Weimer et al.) ; b = 1/12 (Canagaratna et al.) c =.3 (DeCarlo et al.) ; d = 36 (Alfarra et al.) Weimer et al., ES&T, in preparation

9 Wood burning experiments in log-wood ovens flaming 3% smoldering 28% 18% Weimer et al., J. Geophys. Res.,

10 Set up for experiments Ambient air Excess air 6 L/min 6 L/min Aethalometer PAS CPC Clean air generator Pressurised air heater (15 C) Heated line (15 C) Heated Diluter (15 C) Heated line (15 C) 7 L/min 1:84 1:8 7 L/min Smog Chamber AMS Tailpipe DC Diesel car CO 2 Analyzer CO 2 Analyzer CO, NO, NOx, O 3 Analyzer SMPS Idle - 5 km/h - 6 km/h

11 4 3 Org BC µg/m Time after lights on (hrs) 2

12 Organic matter (OM)/Black carbon (BC) time series ( hrs) 3. OM/BC (23 hrs) Time after lights on (hrs) 2 5 km/h - gear 4 Low OA concentration

13 m/z57 / BC 1x1-3 8 Org57overBC OrgC3H5OoverBC OrgC4H9overBC (OrgC3H5O+OrgC4H9)/BC Org/BC Time after lights on (hrs) 2

14 Aethalometer model to derive the contribution of woodburning and traffic to PM1 Carbonaceous material (CM) Sandradewi et al., ES&T,

15 Average diurnal cycle of the carbonaceous material, OC/EC and wood burning versus traffic contributions Sandradewi et al., ES&T,

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17 Conclusion and Offers SOA from full exhaust (traffic, biomass burning, trees) are important to study! W-ToF-Data from diesel car will not change but refine existing analyses by Carnegie Mellon. OOA not always SOA : partially wood burning, and processed primary OA It would be cool to see more aethalometer / AMS data analyses.. Aethalometer can be used very easily for long-term measurements maybe along with the ACSM in future New analyses methods like ME-2, and others (see Valentin Lanz..) should still be developed. PSI would like to offer to take the lead in the overall PMF, ME-2 et al. analysis of the EMEP and EUCAARI datasets in collaboration with Jimenez, et al. We are still offering 14 C analyses from Hi-Vol-Filters (price negotiable between Euros and 65 Euros)