1. Identification of organic PM sources using the EMEP field campaigns
André Prévôt, Valentin Lanz
Paul Scherrer Institute, Switzerland
Eiko Nemitz
Centre for Ecology and Hydrology, Edinburgh, Great Britain
+ several institutions in Europe and the US
Thanks to Federal Office for Environment, Switzerland; EMEP Coordination; EUCAARI project members

2. Aerodyne Aerosol mass spectrometer

3. Positive Matrix Factorization (PMF) of full OM spectrum for source identification and attribution
Ulbrich et al., ACP 2009

4. Composition of PM1 (without BC) in Central Europe around the Alps
Lanz et al., ACPD, 2009

5. Organische Quellen von PM1 in Zentraleuropa
Lanz et al., ACPD, 2009

6. Illustrative example (Melpitz)
September-October 2008 campaign
Melpitz, Germany
HR-ToF-AMS (UMR) data
by Laurent Poulain, IFT, Leipzig
The illustrative example is about a dataset from Melpitz, autumn The meausrement site looks quite rural or agricultural...but in fact, I haven‘t been there.
The data was obtained with a HR-ToF-AMS by Laurent Poulain, who is here as well…and I hope he will agree to some extent with my analyses.
Basically, I will first present A. PMF applied to the organic mass spectral data and, then, B. the combination of backward trajectories with the PMF results.
Google maps © 2010 Tele Atlas
A. PMF analysis on OA data (FA-AMS)
B. Trajectories and OA components

7. PMF-solution: factor profiles44
(biomass burning)
p=4 factors
fpeak=-0.8
(opt. BBOA, HOA profiles, corr. with aux. data) 60 73 29 43 41
(hydrocarbon-like OA) 27 55 57 43 44
(less oxidized)
Shown here is the 4-factor PMF solution, the factor profiles for the Melpitz, fall-case. (note we exluded the very last episode of this campaign due to its elevated level in PMF scaled residuals)
Starting-off with the biomass burning factor, this one was identified based on marker fragments 60 and 73, the HOA factor based on the fragmentation pattern typical for hydrocarbons, while the two OOAs mostly consisted of mass fragments 43, 44, and copies. The optimum solution was found to be at an fpeak =-.8
The solution here was obtained with an fpeak = -.8 and it, first of all, optimizes the correlation of the corresponding time series with ancillary data.
And second, it makes the wood burning profile look reasonable. For example, with an fpeak of -.5, organic m/z 55 and 57 would not be visible on that linear scale. 44
(more oxidized) 43

8. PMF-solution: factor time series
17% OA
14% OA
33% OA
35% OA
Shown here are the corresponding time series. On average, in crude terms, one third of the OA mass was primary, while two thirds of the OA were secondary.
You can see the sulfate traced the time series of OOA-1 fairly well, and a somewhat lower coefficient of determination can be calculated between nitrogen oxides and HOA (hydrocarbon-like organic organic aerosol.) For most part of that campaign, HOA and NOx follow each other very nicely, but there are a few exception, e.g.

9. PMF-solution: factor time series
No CO data available
Only partially correlated with nitrate
OOA-2 = f(T-1, OOA-1)
here at this instance, which clearly lowers the overall coefficient of determination. A possible explanation for this mismatch would be that this HOA-plume was emitted somewhere remote from the receptor site (remote from Melpitz) and that NOx has reacted during transport while HOA is still there. Maybe it was emitted in a distant wood fire, as also the levoglucosan-like wood burning concentrationes are increased at +- the same time.
As far as I know there is, unfortunately, no CO data available to validate the biomass burning plumes. (Is this correct: no CO for Melpitz?)
OOA-2 was not very well correlated with nitrate, only partially. Therefore, I did not label it here as SV-OOA in the first instance. On the other hand, the diurnal cycles of OOA-2 (which I will show on the next slide) were inversely correlated with temperature and the OOA-2 level somewhat follows the level of OOA-1. This is an important observation, too, for several datasets I found that OOA-2 can be approximated as a function of OOA-1 and the inverse of the ambient temperature (and in most cases by ordinary least square)…and this seems to be the somewhat case here as well, approximately OOA-2 levels follow the OOA-1 levels, and deviations on a daily level can be corrected by the temperature.

10. A. PMF-solution: diurnal cycles of factors
late evening
morning rush-hour
night
The OOA-2 concentrations were in fact highest during the night (when ambient temperature is low) with no peaks during rush-hours.
In contrast, the OOA-1 concentrations were clearly enhanced during noon and after-noon.
BBOA peaks in the late evening while…
HOA was highest during the morning rush hours. At the moment, I do not fully undstand why there is no corresponding peak in the HOA cycle for the early evening. Either most of the primary emissions have reacted and the rush-hour peak is represented by OOA-1. Or maybe the dominant local wind direction is different in the evening compared to the morning.
after-noon

11. Trajectory clustering
N=2 clusters for Melpitz, fall (averages)
Cluster 1: „Continental“ air masses Cluster 2: „Atlantic“ air masses
(Eastern Europe) (Western Europe)
On the right, the trajectory mess on the is represented by average cluster trajectories: cluster 1, in violet, represents continental or Eastern air masses, while red stands for cluster 2, long-range transported air masses, Atlantic air masses and air from Western Europe…note also differences in height, although the clustering was based on a 2-dimensional criterion.
Question: are these clusters of the backward trajectoiry anyhow reasonable?

12. Trajectory clustering
OOA-1 (regionally formed, stable, aged SOA)
→ decrease with increasingly Atlantic air (‚clean‘) 1
Continental
Atlantic
cluster frequency
OOA-1
On the left-axis I plotted the modelled concentrations of OOA-1 representing a strongly aged, stable, regional background secondary component – on the right axis I plotted the cluster frequency of either so-called continental air or so called Atlantic air which however might also represent air masses from Western Europe. You can see that while continental air masses prevail in Melpitz, OOA-1 accumulates and steadily (with some small interruptions due to precipitation). Later, the regime changes and Western air is arriving at Melpitz. In Central Europe, Atlantic air masses are often associated with humid, and rather clean air, as well as rainfall, which was also observed here on the first of October explaining the rapid decrease in the OOA-1 concentration. After that, the OOA-1 components increases again, while some intrusions of continental air could be observed.
So, since trajectory points are influenced by both wind direction and speed, trajectory clustering can distinguish different groups of weather patterns. … in this sense I think this way of clustering is meaningful

13. Trajectory statistics (using FLEXTRA trajectories)
in collaboration with NILU
Results: Potential emission regions for primary OA (Melpitz)
(secondary OOA less suited)
HOA
BBOA
Here are the maps of potential POA emission sources: for HOA on the left this would be Northern Spain, France, Russia, Norway, UK, the Benelux,
…so virtually all countries…but Switzerland, of course, is as white as snow. No, in fact, grid cells are only included in these plots if they were hit by at minimum 10 trajectories at the height of the boundary layer or lower.
The highlight here might be a mismatch due to the low grid resolution and the redistribution algorithm …you might expect higher local contributions…that‘s not fully resolved yet.
As potential emission regions of BBOA we would identify, again Northern Spain, Russia and and local emissions.
Local, Spain/France,
Russia
Local, Spain/France, Norway,
Benelux/UK, Russia

14. Trajectory statistics using FLEXPART
in collaboration with NILU
CO: regional source contributions on
18 Sept, 28 Sept, 4 Oct 8-9 Oct (FLEXPART)
We identified CO sources in Russia, again, and also in Northern Spain, Southern France, but also some parts from the benelux countries…there is, again, some sort of agreements between the two approaches, which I think is quite promising…
When we do the intercomparison for the datasets that were simultaneously obtained within one EUCAARi campaign, we certainly further check the consistency of the approach by means of trajectory statistics.
BBOA source regions (trajectory statistics):
Local, Spain/France, Russia

15. Organic Components of PM1 in Central Europe
These are typical relative compositions as we expect them for both EMEP campaigns

16. Overview on PMF status (EMEP EUCAARI campaigns)
in prep. for
Puy de Dôme
Hyytiälä
Payerne
Mace Head
Chilbolton
etc

17. Mini-AMS ACTRIS-Sites: 1-year measurements in 2012
Possible additional sites
(there might be more)

18. The absorption exponent a
Absorption exponent a: a measure of the spectral variation in aerosol light absorption
babs  l-a
Example power law fit (l-2.0)
Wood burninga l-1.8 to -2.2
Traffic, diesel soota,b l-1.0 to -1.1
a Kirchstetter et al. 2004
b Schnaiter et al & 2005
Enhanced UV-absorption due to the presence of wood smoke

19. The aethalometer model
babs(470nm) = babs(470nm)traffic + babs(470nm)wb
babs(950nm) = babs(950nm)traffic + babs(950nm)wb
Model: OMAMS+BCAeth = c1*babs(950nm)traffic + c2*babs(470nm)wb
PMtrafiic PMwb
This two source assumption is applicable in Roveredo winter (stable meteorological conditions)
Modified approach from Allen et al. (2004)
Sandradewi et al., ES&T, 2008

20. Correlations of aethalometer model with 14C analyses and AMS data
Perron et al., in prep. for ACP

21. Use of Aethalometer for long-term analysis Herich et al. , in prep
Use of Aethalometer for long-term analysis Herich et al., in prep., Empa

22. Conclusions
EMEP campaigns: Source apportionment of organics will be available around the end of the year
Year 2012 will be an intensive year campaign with Mini-AMS and hopefully multi-wavelength aethalometers; Maybe consider pooled 14C analyses (e.g. monthly)
A summer and winter campaign could be added with more AMS .. Urban/rural pairs etc. (addition of trace gas measurements: VOCs, NH3, HNO3, SO2, CO, NOx)
Intensify collaboration with modelers for the last and the future campaigns

23. Real-Time Sites Site April/May ‘08 Sep/Oct ‘08 Feb/Mar ’09
Auchencorth/Bush UK IC
Q-AMS/IC
Harwell/Chilbolton
HR-AMS/IC
Hyytiala FI
C-AMS
C-AMS/NH3
Puijo
Q-AMS
Helsinki (urban)
HR-AMS
Vavihill SE
Cabauw NL
Melpitz DE
Payerne CH
Q-AMS/NH3
Jungfraujoch
Mace Head IR
Puy de Dome FR
Ispra IT
San Pietro
Q-AMS/HR-AMS
Finokalia GR
K-Puszta HU
Barcelona (urban) ES
Montseny

24. Sites of AMS Measurements during 2008/09 EMEP/EUCAARI Campaigns

25. CONCLUSIONS (1)
Nitrate very important in Feb/March campaign (strongly temperature dependent)
Sulfate important at remote sites
Organics mostly secondary at all sites

26. Thanks to all Participants!
Centre for Ecology and Hydrology: Chiara Di Marco, Gavin Phillips, Eiko Nemitz
Paul Scherrer Institute: Andre Prevot, Peter De Carlo, Claudia Mohr
Univ. Clermont-Ferrand: Karine Sellegri, Ralf Weigel, Paulo Laj
Univ. Kuopio: Petri Tiitta, Tomi Raatikainen, Ari Laaksonen
Institute for Tropospheric Research: Laurent Poulain
Juelich Research Centre: Amewu Mensah, Astrid Kiendler-Scharr
Univ. Gallway: Manuel Dall’Osto, Colin O’Dowd
Univ. Manchester: James Allan, Gerard Capes, Hugh Coe
Univ. Lund: Joakim Pagels, Axel Eriksson, Erik Swietlicki
Univ. Colorado: Jose Jimenez, Donna Sueper
Aerodyne Research Inc.: Doug Worsnop, Sally Ng
Carnegie Mellon Univ.: Lea Hildebrand, Spyros Pandis …

27. Chemical composition of PM1 with high time resolution in Zürich and Reiden (Kt. Luzern)
High time-resolution data (better than 10 minutes)

28. Die organischen Massenspektren können zur Quellenidentifikation genutzt werden
Diesel Exhaust Aerosol
New York, USA
Urban
Canada
Non-Urban
Canada
Switzerland
South Korea
McFiggans, Alfarra et al., Faraday Discussions, 2005

29. Aerosolmassenspektren von Holzfeuerungen
Levoglucosan
Holzfeueremissionen, Stückholzofen, relativ schlechte Verbrennung
Spezielle Nachtperioden in Roveredo
Mittel Nacht-Periode in Roveredo (Dezember)
Massenspektrum am Autobahnstandort Härkingen im Mai
Alfarra et al., ES&T, 2007