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

Aerodyne Aerosol mass spectrometer

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

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

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

Illustrative example (Melpitz) September-October 2008 campaign Melpitz, Germany HR-ToF-AMS (UMR) data by Laurent Poulain, IFT, Leipzig www.tropos.de The illustrative example is about a dataset from Melpitz, autumn 2008. 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

PMF-solution: factor profiles 44 (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

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.

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.

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

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?

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

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

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

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

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

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

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. 2003 & 2005 Enhanced UV-absorption due to the presence of wood smoke

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

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

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

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

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

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

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

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 …

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

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

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