megapoli scientific report 11-16 evaluation of links...

MEGAPOLI Scientific Report 11-16

Evaluation of Links between Secondary VOCs and Secondary Organic Aerosols of Anthropogenic and Biogenic Origin MEGAPOLI Deliverable D3.5 Borbon A., E. Freney, N. Marchand, M. Beekmann, E. Abidi, W. Ait-Helal, A. Colomb, J. Cozic, N. Locoge, S. Sauvage, K. Sellegri, B. Temine-Roussel, J. Sciare, V. Gros, J.L. Jaffrezo, U. Baltensperger and the MEGAPOLI Campaign Team

Footprint emission sensitivity in nested domain for the SIRTA site (15-16 July 2009)

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Paris, 2011

FP7 EC MEGAPOLI Project

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Colophon Serial title: MEGAPOLI Scientific Report 11-16 Title:

Evaluation of links between secondary VOCs and secondary organic aerosols of anthropogenic and biogenic origin Subtitle: MEGAPOLI Deliverable D3.5 Link between organic aerosol formation and secondary VOC build-up from urban and peri-urban sites Main Author(s): A. Borbon (LISA/CNRS), E. Freney (LaMP/CNRS), N. Marchand (LCP-IRA), M. Beekmann (LISA/CNRS) Contributing Author(s): E. Abidi3, W. Ait-Helal1,4, A. Colomb2, J. Cozic6, N. Locoge4, S. Sauvage4, K. Sellegri2, B. Temine-Roussel3, J. Sciare5, V. Gros5, J.L. Jaffrezo6, U. Baltensperger7

(1) Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA), Créteil, France.

(2) Laboratoire de Météorologie Physique (LaMP), Clermont-Ferrand, France. (3) Laboratoire Chimie de Provence (LCP-IRA), Marseille, France

(4) Ecole de Mines de Douai (EMD), Département de Chimie, France (5) Laboratoire des Sciences et du Climat (LSCE), Orme-les Merisiers, France

(6) Laboratoire de Glaciologie et de Géophysique de l’Environnement (LGGE), Grenoble, France (7) Paul Scherrer Institut (Villigen), Switzerland

Responsible institution(s): Centre National de Recherche, Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA) Paul Scherrer Institute (PSI), Switzerland Language: English Keywords: Source apportionment, chemical composition, urban, suburban, variability Url: http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-16.pdf Digital ISBN: 978-87-92731-20-3 MEGAPOLI: MEGAPOLI-42-REP-2011-09 Website: www.megapoli.info Copyright: FP7 EC MEGAPOLI Project


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Content: Abstract ................................................................................................................................................4 1. Introduction....................................................................................................................................5 2. Characterization of VOC and SOA Relationships at a Suburban Site in the Paris Agglomeration5

2.1 Instrumentation for VOC measurements and general results ..................................................5 2.2 Emission and chemistry in Paris suburban area.......................................................................6 2.3 ΔOA/ΔCO evolution in Paris suburban area...........................................................................7 2.4 Identification of SOA formation events...................................................................................8 2.5 Characterization of SOA formation events..............................................................................8 2.6 Analysis of air mass origins and time series ............................................................................9 2.7 Correlation analysis................................................................................................................11 2.8 Conclusion and perspectives..................................................................................................12

3. Characterization of VOC and SOA Relationships at an Urban Site in the Paris Agglomeration 13 3.1 Presentation of PTRMS measurements..................................................................................13 3.2 General presentation of AMS + PTRMS time series.............................................................14

3.2.1 Primary fraction ..............................................................................................................14 3.2.2 Secondary fraction .........................................................................................................16

3. 3 Principal Components Analysis (PCA).................................................................................18 3.4 Conclusions............................................................................................................................19

4. Formation of Organic Aerosol and Relation to Precursor Gases in the Paris Agglomeration Pollution Plume..................................................................................................................................20

4.1 General presentation of ATR42 flights and AMS, PTRMS measurements ..........................20 4.2 General results chemical composition from AMS for plumes heading to N and E, outflow vs. inflow........................................................................................................................................20 4.3 PMF analysis..........................................................................................................................25 4.4 Analysis of OC build-up in and outside plume - how much more OA, OOA, HOA inside than outside the plume ...................................................................................................................26 4.5 Relationships between OA and the photochemical age of the air mass calculated from gaseous compounds........................................................................................................................27 4.6 SV-OOA analysis against VOC’s inside the plume .............................................................28 4.7 Conclusions/ Discussion ........................................................................................................29

5. Concluding Remarks......................................................................................................................30 Acknowledgements............................................................................................................................32 References..........................................................................................................................................33

Appendix 1: Emission weighted air mass origins calculated with the Lagrangian particle model FLEXPART during events 3 and 4 ................................................................................................34 Appendix 2: Correlation plots........................................................................................................35 Appendix 3: Selected Nucleation Events Observed at LHVP Site during Summer 2009.............37

Previous MEGAPOLI reports............................................................................................................38


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Abstract A major aim of the Megapoli Paris campaign was to better understand the link between the forma-tion of secondary organic aerosol and gaseous precursors, in particular VOC species. This deliver-able describes analysis relating different organic aerosol components (obtained from AMS meas-urements) to a large range of VOC measurements. VOC’s can be used as tracers for primary emis-sions of anthropogenic and biogenic origin, and for secondary pollutant build-up. This analysis is performed for two urban and sub-urban primary sites (LHVP in Paris 13 arrondissement, SIRTA / Palaiseau near the south-western edge of the agglomeration, see MEGAPOLI Deliverable 3.1) and aircraft measurements in and around the Paris pollution plume (see MEGAPOLI Deliverable 3.4). The analysis is performed for the summer campaign in July 2009 when the formation of secondary compounds was strongest. At the urban site, a clear correlation between primary traffic emissions related VOC species and black carbon was observed, while the primary fraction derived from AMS measurements by PMF (positive matrix formulation) showed a different behavior, indicating the presence of non-traffic related sources. For secondary organic aerosol (SOA), the correlation with biogenic precursors, par-ticularly isoprene and its oxidation products, is highlighted, even in a large megacity city like the Paris agglomeration. Large SOA peaks seem to be linked to long range transport more than to local emissions. At the suburban site at the south-western edge of the agglomeration, the correlation of SOA with biogenic precursors is confirmed. During events with the largest SOA peaks are analysed here, La-grangian retro-plume calculations showed an air mass origin over South-Western France and Spain, with a large probability of elevated biogenic VOC emissions. In addition to advection, also local SOA formation is evident at both sites because of an afternoon increase in SOA and a decrease in VOC precursor species. Last, at the suburban site a correlation between SOA and VOC species of intermediate volatility (C14, C15) is made evident and needs to be further elucidated. These species are believed to be efficient aerosol precursors, but have been rarely observed in the field. Measurements made onboard the ATR-42 showed a production of secondary organic aerosol parti-cles within the pollution plume, manifested in an increase of the SOA concentrations, normalised to black carbon and primary VOC’s as a primary pollution tracer, with distance from the agglomera-tion. The correlation with anthropogenic VOC’s is stronger than that with biogenic ones. This sug-gests that anthropogenic VOCs are the dominant source for the formation of additional SOA within the pollution plume. On the contrary, aerosol sulfate levels were larger outside the plume, which suggests both important SO2 sources outside of the agglomeration and possibly competition be-tween SO2 and organic material within the pollution plume. Combining results from the urban and sub-urban sites with airborne observations within the plume suggests that (1) that background aerosol advected to the agglomeration is mainly of biogenic ori-gin, that (2) SOA formation within the urban agglomeration due to anthropogenic VOC emissions is still limited, but becomes important within the pollution plume.


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1. Introduction Correlation analysis of different organic aerosol fractions with VOC species which can be used as tracers for primary emissions of anthropogenic and biogenic origin, and for secondary pollutant build-up has given interesting results on the sources of organic aerosol during different campaigns. The MEGAPOLI Paris campaign allowed concurrent sampling of a large range of VOC species and detailed analysis of organic aerosol at two urban and sub-urban primary sites, LHVP in Paris 13 ar-rondissement, SIRTA / Palaiseau near the south-western edge of the agglomeration (Figure 1 and see MEGAPOLI Deliverable 3.1) and aircraft measurements in and around the Paris pollution plume (see MEGAPOLI Deliverable 3.4). Taking these sites together should allow obtaining a geo-graphically complete picture about organic aerosol source within the agglomeration and in its plume. Organic aerosol – VOC relationships are analysed subsequently for these different sites (Chapters 2, 3, 4) and final conclusions drawn in Chapter 5.

Figure 1: Location of the urban LHVP and sub-urban SIRTA measurement sites within the MEGAPOLI summer campaign set- up.

2. Characterization of VOC and SOA Relationships at a Subur-ban Site in the Paris Agglomeration

2.1 Instrumentation for VOC measurements and general results A large suite of VOC collocated with aerosol measurements were measured at the SIRTA site in July 2009. These VOCs includes 13 alkanes (C4-C16), 5 aromatics (C6-C9), 16 aldehydes and ketones (C2-C10), 1 alkene (isoprene) and 4 monoterpenes (C10). Medium-chain and long-chain alkanes, aromatic and biogenic VOCs (isoprene and monoterpenes) are potential precursors of Secondary Organic Aerosol formation, with for some of them high measured SOA yields in the laboratory. Measuring techniques encompassed on-line GC-FID (Airmovoc) for C6-C9 hydrocarbons (Gros et al., 2010) and off-line tube sampling at 3h-time resolution (Across automatic sampler) followed by GC-FID/MS analysis at the laboratory (Detournay et al., 2010). All the data have been averaged on a 3-hour time basis. Table 1 reports the statistical distribution of the VOC mixing ratios during the summer campaign. Data show a high variability with a relative standard


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deviation greater than 80%, on average, highlighting the various influences impacting the distribution of VOC at the SIRTA site.

Table 1: VOC distribution in ppt during the MEGAPOLI summertime campaign at the SIRTA site. NMHC Mean CV min max Oxygenated VOC Mean CV min max

Toluene 206.57 87% 11.92 900.94 Acetaldehyde 627.85 46% 223.07 1 837.73Ethylbenzene 36.6 86% 2.94 155.44 Acetone 1358.9 37% 166.01 3 187.79mp‐xylene 80.42 95% 6.93 425.26 Acroleine ‐ ‐ ‐o‐xylene 39.88 79% 3.38 154.03 Propanal 85.03 50% 24.11 252.27i‐butane 383.13 93% 108.01 2 279.64 Methylvinylketone 289.27 94% 17.11 1 600.23n‐butane 501.62 113% 111.55 4 881.30 Butenal ‐ ‐ ‐i‐pentane 444.6 111% 75.02 3 034.78 Butan‐2‐one (MEK) 1633.37 92% 50.16 8 775.42n‐pentane 258.52 91% 67.61 2 022.83 Methacroleine 60.43 104% 10.55 402.88Hexane 117.42 57% 50.36 638.36 i‐butanal + n‐butanal 51.86 59% 19.42 207.69Nonane 14.4 92% 1.72 66.55 Benzaldehyde 26.99 66% 9.08 115.19Decane 24.21 93% 1.11 110.68 Glyoxal 47.62 52% 17.82 163.1Undecane 19.43 85% 1.46 96.4 i‐pentanal 33.49 44% 15.82 64.59Dodecane 22.28 96% 1.35 116 n‐pentanal + o‐tolualdehyde 29.61 45% 14.76 73.69Tridecane 12.77 94% 1.12 70.47 Methylglyoxal 56.11 69% 15.67 350.87

Tetradecane 27.44 86% 1.2 166.27Pentadecane 23.33 77% 1.08 133.18Hexadecane 21.63 84% 1.7 125.62Hexanal 53.64 93% 6.24 314.49Heptanal 66.51 102% 6.47 364.13Octanal 81.63 81% 11.7 374.92Nonanal 52.65 78% 11.89 236.84Decanal 38.51 58% 5.95 142.77a‐pinene 48.01 94% 2.21 233.1b‐pinene 9.59 59% 2.07 27.84Limonene 16.15 102% 1.29 94.81Isoprene 411.18 97% 76.74 5 076.32

2.2 Emission and chemistry in Paris suburban area VOCs measured at SIRTA are the results of the combined effects of emission, dilution and chemistry. Here we have represented the diurnal profiles normalized to toluene of some representative primary and secondary VOCs and the OOA (oxygenated organic aeorosol) factor (derived from the PMF analysis provided by the MPI group, and corresponding to secondary organic aerosol) (Figure 2). Normalization to toluene only takes into account the relative importance of emission and chemistry.

Figure 2: Diurnal profiles of some VOCs and OOA normalized to toluene.


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The majority of primary VOCs (toluene and a-pinene) exhibits a midday minimum as a result of their chemical removal principally by the OH radical. The minimum is even more pronounced for the most reactive VOCs (here, a-pinene). On the contrary, isoprene shows a late afternoon maximum due to its temperature and light dependent biogenic emissions. It should be mentioned that the median values of isoprene are greater than its two oxidation products (sum of mvk+macr). As their atmospheric lifetime (OH) (0.5 days) is longer than the one of isoprene (1.5 hours), one can conclude on a large contribution of locally emitted isoprene to observed concentrations. The secondary products (ethanal, mvk+macr and OOA) smoothly build-up together in the afternoon as a results of their chemical productions. The profile of ethanal is rather constant as a result of its secondary formation combined to its chemical removal. Finally, the normalized profiles to toluene shows the build-up of secondary OOA in the afternoon at the SIRTA site, together with oxidized VOCs, as a result of the photochemical removal of primary anthropogenic and biogenic VOCs.

2.3 ΔOA/ΔCO evolution in Paris suburban area The organic matter OM (OA from the AMS data) from the entire period of measurements are correlated with a linear regression coefficient r2 of 0.36 (Figure 3 – left panel) to CO . The slope of a linear fit to all data is 55 μg.m-3/ppm. Part of the scatter can be explained by the secondary formation of organic matter. To illustrate this point, the data points are color-coded by the ratio O3/CO which is used here as a proxy of the degree of photochemical processing (left panel – Figure 3). On the one hand, fresh air masses with low photochemical processing show the lowest level of ozone and the highest levels of CO; the ratio O3/CO tends to zero. On the other hand, processed air masses show the highest level of ozone and the lowest levels of CO; the ratio O3/CO increases. It is seen that the data with a higher photochemical processing tend to follow a steeper line (red points) than the points with a lower photochemical processing (blue points). The ΔOA/ΔCO observed for primary urban emissions derived from other field studies in urban areas in the United States is shown on figure 2 (grey area (5 to 10 μg.m-3/ppm) (Aiken et al., 2007; Zhang et al., 2005). The suburban Paris data do not lie along this primary emission area; that is, few air masses encountered at the SIRTA site have a low degree of photochemical processing. The scatterplots are also color-coded by the wind direction (right panel). Here the picture is less clear, but air masses with larger degree of pollution and lower photochemical processing originate more from a 0-150° deg. (continental) sector. On the contrary, cleaner air masses with often higher photochemical processing originate from a 200-350° deg. sector which is associated with oceanic wind regimes.

Figure 3: Scatterplots of OA vs CO color-coded by the ratio O3/CO used as a proxy of photochemical processing (left panel) and wind direction (right panel). Also indicated the ΔOA/ΔCO estimated from

primary urban emissions in previous urban studies.


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2.4 Identification of SOA formation events Six SOA formation events were identified (Figure 4). They are associated with an increase of the OOA factor to at least 2 times above its background level which is below 0.5 µg.m-3 (western wind regimes corresponding to clean oceanic air masses and a wind speed > 4-5 m.sec-1). Two of these events are preceded by Paris urban outflow (July 4th and July 20th-21st) but for a very short period of time of a few hours. SOA formation events are also generally associated with stagnant conditions: an increase of the ambient temperatures and a decrease of the wind speed to 2 m.sec-1 and below.

Figure 4: General characteristics of the six events of SOA formation (grey areas).

2.5 Characterization of SOA formation events The characterization of SOA formation event includes : - an analysis of meteorology air mass origin (Flexpart outputs provided by Stohl et al., 2011) - an analysis of time series of trace gases mixing ratios (including VOC precursors). - a correlation analysis between OOA and VOCs


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The Flexpart products used here are the PES integrated-column and the PES footprint which reveals the major geographical pathway(s) along which the air has traveled to the receptor and the major source regions that potentially contribute to the concentrations at the receptor.

2.6 Analysis of air mass origins and time series The time series of OOA and some representative VOCs are reported on Figure 5. During events 1, 2, 5 and 6, the build-up of SOA is collocated with the increase of most anthropogenic and biogenic VOCs even if no clear covariation is depicted. On the contrary, during event 3 and 4, a clear covariation of OOA with isoprene and pinenes is seen. In the following we will only describe in details these two events as they are also consistent with what was observed in Paris urban centre at the LHVP site. Focused time series for these two events are reported on Figure 6 and 7.

Figure 5: Time series of OOA and some representative VOCs.


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Before these two events SIRTA is under a well-established western-regime (see also Figure 4). When looking at the FLEXPART products (cf. Appendix 1), both events could be also influenced by long range transport of air masses from Western Spain and North of Portugal. These regions ap-pear as potential source contributors. During the events, temperature increases compared to previous days reaching a +30°C maximum and wind speed decreases (1 to 3 m.sec-1). During these events with southerly winds, SIRTA is located on the upwind edge of the agglomeration.

Figure 6: Time series of OOA, HOA and primary VOCs during events 3 and 4.

During these two events, all species exhibit their typical diurnal pattern. Traffic-emission-related compounds (CO, NOx, aromatics, isopentane) show a peak in the morning (06:00-09:00) and in the evening (only for for event 3 (18:00-21:00)) which precedes the SOA formation. While the mixing ratios of biogenic isoprene and pinenes start to increase together around 11:00 and reach a plateau around 16:30 during event 3, only isoprene clearly increases during event 4. OOA builds-up from 12:00 PM and reaches its maximum in the late evening. During event 3, intermediate volatility VOCs (alkanes >C12) increase in the late morning and also covariate with OOA. These compounds



do not show the typical traffic-related compound profile compared to high volatility alkanes (ie. isopentane). They seem to have a temperature dependent profile.

Figure 7: Time series of OOA, HOA and oxygenated VOCs during events 3 and 4.

2.7 Correlation analysis

The correlations between OOA and all measured VOCs were examined. When considering all data, scatterplots are diffuse and a poor correlation is observed with an r2 coefficient lower than 0.15 as could be expected from the analysis of the time series Some examples are provided in Figure 8 for some SOA precursors. When isolating scatterplots event by event, the correlation can be improved for biogenic VOC precursors and intermediate volatility compounds. An example is given for event 3 and 4. More than 50% of the variability of OOA can be explained by some VOC precursors. These compounds include biogenic VOCs: isoprene during both events and only a-pinene during event 4, which are well established SOA precursors. During event 3, another significant relationship is also depicted between OOA and one intermediate volatility alkane (tetradecane); 68% of OOA variability could be also explained by tetradecane. On the contrary, aromatics do not explain SOA formation during these two events.



Figure 8: Correlation analysis between OOA and some VOC precursor for all data (grey markers) and for

event 3 and 4 (colored markers); correlations are camlculated for event 3. *

2.8 Conclusion and perspectives A large suite of VOCs together with aerosol characterization were collected at the SIRTA site in Paris suburban area during the summer megapoli campaign. The presence of OA in Paris suburban area is largely due to SOA formation (within air masses travelling to SIRTA in the vicinity to SIRTA and at larger distance) as it increases with a proxy of photochemical ageing. On average, the SOA levels are increased in the afternoon, correlated with a photochemical removal of primary anthropogenic and biogenic VOCs. This indicates the importance of local photochemical processes. On the contrary, SOA peaks are related to long range transport processes in addition to local build-up : during the summertime campaign, six SOA events were depicted. SOA formation seems to be favored under South to South-Eastern wind regimes, while south-west to westery winds advect cleaner oceanic air masses. While it appears difficult to study separately the potential effects of individual (or group) of VOC precursors on SOA formation during most of the events, two events (July 16th and 21st) have revealed the biogenic origin of SOA formation which seems to be consistent with the LHVP observations. In addition, intermediate volatility alkanes are more correlated with SOA than traffic related alkanes; this relationship needs to be further investigated.



3. Characterization of VOC and SOA Relationships at an Ur-ban Site in the Paris Agglomeration

The relation between organic aerosol (OA) formation (primary and secondary fractions) and VOCs at LHVP was established, by studying the correlations between VOCs measured by HS-PTRMS (High Sensitivity- Proton transfer mass spectrometer) and OA fractions derived from AMS meas-urements (see WP3.2). Ancillary data such as Black Carbon (BC, MAAP 5012), O3, NOx and HULIS concentrations are also used. In the present report this analysis focuses on the summer intensive field campaign (from 7/7 to 8/1 20091). All the data have been averaged on 15 min time basis.

3.1 Presentation of PTRMS measurements HS-PTRMS enables the detection and the quantification in real time of VOCs that present a higher proton affinity than that of water. Briefly, HS-PTRMS consists of a discharge ion source that pro-duces H3O+ ions, a drift-tube reactor, in which the proton-transfer reactions between H3O+ and VOC take place, and a quadrupole mass spectrometer that detects the resulting product ions. In gen-eral, the product ion of a given VOC having a molecular mass M will be detected by the HS-PTRMS at M+1 uma, corresponding to VOCH+. Because of the quadrupole resolution, VOC iso-mers cannot be distinguished. During the field campaign, HS-PTRMS has been used in the SIM mode (Single Ion Monitoring mode). 17 ions of interest have been monitored all along the cam-paign (table 2). The quantification of the corresponding VOC has been performed using calibration gases, when available, or default proton-transfer rate coefficients.

Table 2: Main ions monitored by PTR-MS. 1. quantification using calibration gases , 2. quantification us-ing the default protonation rate coefficient, 3. quantification using the protonation rate coefficients in-

ferred by Zhao and Zhang, 2004. ions Quantification Most probable VOC (possible interference)m/z 33 Calibration1 methanol m/z 41 Ionicon2 alkanes & alkenes fragments��m/z 42 Calibration acetonitrilem/z 45 Calibration acetaldehyde m/z 59 Calibration acetone (+ propanal + glyoxal)�m/z 69 Calibration isoprene (furan + C5-C9 n-aldehydes) m/z 71 Calibration methacrolein+methyl vinyl ketone ( + alkanes & alkenes fragments)�m/z 73 Calibration methyl ethyl ketone ( butanal, Methyl glyoxal + C5-C9 n-aldehydes)m/z 79 Calibration benzene�m/z 93 Calibration toluenem/z 97 Ionicon furan derivatives + C5-C9 n-aldehydes�m/z 105 Ionicon styrene (PiBN��m/z 107 Ionicon C8 aromatics + benzaldehyde�m/z 121 Ionicon C9 aromatics + tolualdehyde��m/z 129 Ionicon naphthalenem/z 137 Zhao and Zhang, 20043 monoterpenes�m/z 151 Ionicon pinonaldehyde (?)��

1 HS‐PTRMS data from 7/1 to 7/7 are not available due to a contamination of the sampling line



3.2 General presentation of AMS + PTRMS time series

3.2.1 Primary fraction Figures 9 and 10 illustrate the correlations between primary anthropogenic aerosol fractions (BC, HOA – Hydrocarbon like Organic Aerosol) and selected primary VOCs. Correlation plots between all parameters taken under consideration in this study are presented in appendix 2. During the summer campaign strong correlations are observed between aromatic VOCs (benzene, toluene, C8 aromatics, styrene, naphtalene,..) and BC, tracer of primary aerosol emitted by com-bustion processes (mainly vehicular exhaust aerosol considering the abundance of diesel engines in the French vehicles fleet).

Figure 9: Time series of BC, HOA, OA concentrations and selected primary anthropogenic VOCs.

The HOA fraction exhibits a particular behavior. In most urban area, the HOA fraction is extremely well correlated to BC and thus mainly related to primary vehicular emissions. In Paris, during this campaign, we observe a slight discrepancy between these two variables (figure 10). This discrep-ancy is well illustrated by the diurnal profile of the HOA/BC ratio (figure 11). It shows two marked maxima ; one between 12 and 14 h (local time) and a second between 20 and 22h. During these two periods one can suspect the addition of at least one unidentified source containing a significant amount of non-oxidized organic materials and no or few BC. This implies that the HOA fraction can not be considered here as univocally originating from traffic. Here a 4th urban factor could be



probably identified but the attribution of the various factors to the corresponding sources is still on-going. It should be noted that the 2 factors solution (HOA and LV-OOA) is statistically the most satisfactory.

Figure 10: Correlation plots between BC, tracer of primary aerosol emitted by combustion processes, and

selected VOCs. Among all VOCs quantified by PTRMS, none exhibits a similar diurnal profile to that observed for the HOA/BC ratio (figure 11). The diurnal profile of the ion mz 137, basically associated with monoterpenes, is also a little surprising (figure 11). A maximum of concentration is observed early morning (between 4 and 10 h) while no significant increase is observed during the afternoon. The identification of these nocturnal peaks is still ongoing, but two assumptions can already be made. First, monoterpenes observed over Paris downtown are most probably transported from outside the agglomeration and not produced locally. Second, interferences (presence of isomers detected at m/z 137) could also explain a part of this behaviour, especially considering the abundance of mz137 during the morning traffic peak (7-10h). However, up to date, no significant isomers of monoter-penes have been identified in vehicular exhaust. For isoprene the diurnal profile appears also com-plex. Morning peaks (7h-10h) can be explained by measurement interferences (mostly furan) and also direct emission of isoprene by vehicles. The increase during the afternoon is consistent with larger emissions due to larger temperatures and radiation.



Figure 11: Diurnal profiles of selected variables (primary compounds/fractions).

3.2.2 Secondary fraction Figure 12 presents the time series of the OOA fraction (corresponding to SOA), HULIS and se-lected VOCs. Total OA concentration and the ratio between mz71 and mz69 corresponding to the (MACR+MVK)/isoprene ratio are also represented. Diurnal profiles of selected variables are pre-sented in figure 13. While primary anthropogenic variables exhibit relatively clear diurnal patterns associated mainly with traffic, secondary compounds/fractions variability is more related to long range transport events than local oxidation processes. It is interesting to note that these events are associated with isoprene and its most abundant gas phase oxidation products (MACR and MVK). We also observed a significant correlation between the abundance of the OOA fraction and methanol concentration. One of the most interesting features regarding figure 12 is the excellent agreement between OOA and HULIS concentrations. This confirms the physico-chemical properties of the OOA fraction found within this study (ie. highly oxidized and low volatility) and supports the assumption of long range transport events. The large peaks on July 16 and 21 have been also identified at SIRTA and are discussed in Chapter 2 as event 3 and event 4 (with the same conclusion of correlation with bio-genic precursors).



Figure 12: Time-series of OOA, OA, HULIS and selected VOCs.



Figure 13: Diurnal profiles of secondary variables (LHVP, Summer 2009).

3. 3 Principal Components Analysis (PCA) In order to get a clearer and global picture of the relationships between gas and particulate phases principal component analysis has been performed. PCA performed with absolute concentrations is mostly driven by the dynamical ranges of concentration levels. Therefore relative concentrations normalized by maximal values are used here (figure 14). VOCs have been normalized by the total concentration of VOCs (including those not represented in figure 14). In fig. 14, all mz correspond thus to the contribution of each VOC to the total concentration of VOCs. In the same way aerosol fractions have been normalized by PM1 or OA concentrations. Two diagnostic ratios of air mass age have also been injected into the data set: (MACR+MVK)/Isop and Benz/Tol for the biogenic and anthropogenic fractions, respectively. An increase of these two ratios implies an increase of the oxidation state of the air mass. A last variable has been injected in the input matrix: the relative contribution of particles smaller than 20nm to the total number of particles. As nucleation events have been observed during the campaign at LHVP site (see Appendix 3), the relative contribution of ultra-fine particles is used to tentatively trace these nucleation events.



Projection of the variables on the factor-plane ( 1 x 2)Active and Supplementary variables

*Supplementary variable

ActiveSuppl.

mz33

mz41mz45

mz69

mz71

mz79mz93mz137

mz151

mz59

mz73

%NO3

%Sulfate

%NH4

%Org

%BC

%HOA

HOA/BC%LVOOA

(MACR+MVK)/Isop

Benz_Tol

*%N20nm

-1.0 -0.5 0.0 0.5 1.0

Factor 1 : 24.79%

-1.0

-0.5

0.0

0.5

1.0

Factor2 :18.85%

mz33

mz41

mz71

mz79mz93mz137

mz151

mz59

mz73

%NH4

%Org

%BC

%HOA

HOA/BC%LVOOA

Benz_Tol

Figure 14: Principal component analysis (F1xF2) - normalized data set (N=2449, time resolution 15 min)

(LHVP, Summer 2009). As results of PCA analysis, it appears that the input data set is rather noisy. The two main principal components explain less than 50% of the total variance of the system. Only few variables are close to the correlation circle. The correlation between variables can thus not be considered as significant. However this projection suggests several interesting points:

• Factor 1 appears as linked to the organic content of the aerosol while the second factor re-lates to the oxidation state of the OA;

• Large contributions of OA to the total sub micrometer aerosol mass seem to be associated with large contributions of isoprene and its major oxidation products and aged air masses;

• More OA is oxidized more the relative contributions of methanol, acetone ( and glyoxal) and acetaldehyde increase;

• Relative contributions of primary traffic related compounds/fraction, are consistent with what was previously highlighted in section 3.2.1 (correlations between BC and traffic re-lated VOC’s). In addition primary vehicular emissions do not appear as a major source of OA during the period under study;

• HOA/BC and the relative contribution of ultrafine particles are not correlated with any other variables used here (even considering the 6 first factors).

3.4 Conclusions From this preliminary study, the most important point to be highlighted is the great influence of biogenic precursors, particularly isoprene, on SOA formation and on the total OA concentrations even in a city like Paris. Oxidation processes of OA appear also linked to an increase of the relative concentration of VOC such as methanol, acetone (+glyoxal) and acetaldehyde. A better quantifica-tion of these processes is ongoing. The same work with the data set obtained during the winter campaign is also ongoing.



4. Formation of Organic Aerosol and Relation to Precursor Gases in the Paris Agglomeration Pollution Plume

4.1 General presentation of ATR42 flights and AMS, PTRMS measure-ments

A total of 11 research flights (RF) during the MEGAPOLI experiment were performed aboard the ATR-42 from July 1 to 29, 2009. These flights were devoted to measurements in the Paris agglom-eration pollution plume. The ATR-42 was equipped with a Time-of-Flight Aerosol Mass Spec-trometer (ToF-AMS), a Proton Transfer Mass Spectrometer (PTRMS), as well as measurements of ozone (O3), carbon monoxide (CO), and nitrogen oxides (NO, NOy, and NOx). Meteorology meas-urements of temperature, wind speed, relative humidity, and liquid water content were also per-formed for each flight. Flight patterns and measurements have been extensively described in Deliv-erable 3.4. In this chapter, we present the results of chemistry measurements of the non-refractory submicron aerosol from the ToF-AMS and from black carbon (BC) measurements and compare them with available gas-phase measurements from the PTR-MS and NOx measurements. The first three RF were not included in the analysis since the heater temperature of the ToF-AMS was incorrectly calibrated. Of the remaining RF the PTR-MS was operational for only four RF. Of the eight RF when the AMS was operational, four were flying north of the Paris metropolitan area, two were fly-ing east of the Paris metropolitan area, and two were flying northeast. Northern sector flights were performed when wind direction was arriving from the south traveling north, and easterly flights were performed when wind was arriving from the west going in an easterly direction. The aim of this flight plan was to follow the pollution plume as it was leaving the Paris metropolitan area. Flight altitudes were typically less than 700 m above sea level. Flights took place during afternoon, so within a well developed convective boundary layer. During all these RF the overall source of the air mass was from a south-westerly to westerly direction.

4.2 General results chemical composition from AMS for plumes head-ing to N and E, outflow vs. inflow

In general, northern sector flights had highest average mass concentrations (8.5 ± 2.3 µg m-3) and were dominated by organic particle species (> 50 %). Easterly flights had lower average mass con-centrations (6.1 ± 2.1 µg m-3) and had equal contributions from both sulfate and organic bearing particles (Fig. 15). Nitrate and ammonia had similar contributions in both easterly and northern sec-tor flights. BC provided information on the level of pollution and the boundaries of the pollution plume for each flight (Fig. 16). Using this information we can colour flight tracks by particle com-position and investigate which particle compositions are measured within the plume and which measured outside of the plume. This is illustrated in Figure 17 for each RF for the Org and sulfate mass concentrations.



Figure 15: Flight summary for the eight RF where the AMS was operational. On the right of the image the top pie chart illustrates the average particle composition for the easterly sector flights and the bottom shows

the average particle composition for the northerly sector flights.

Figure 16: An example of two time series of AMS and BC measurements taken during RF 29 (15/07/2009) (Top) and during RF 33 (25/07/11) (Bottom).



Figure 17: The flight tracks of the ATR-42 coloured by particle composition Org and sulfate particle mass

concentration for RF 28 through to 31.



Figure 18: The flight tracks of the ATR-42 coloured by particle composition Org and sulfate particle mass concentration for RF 32 through to 36.

In general, we observe an increase in organic and nitrate bearing material for all flights when the ATR-42 is within the pollution plume, however, the absolute mass concentrations of sulfate is con-sistently decreased within the pollution plume (Figures 17 and 18). Organic and nitrate bearing par-ticles are associated with emissions directly from the Paris pollution plume, however sulfate bearing particles only show increases outside of the plume. This suggests that sulfate bearing particles are more efficiently formed outside of the plume, while organic and nitrate bearing species have larger sources of precursors in the plume.



Figure 19: The average particle composition measured during each flight both inside and outside of the pol-

lution plume. Strongest mass concentrations of sulfate are measured during easterly sector flights when air masses are arriving from the west. Similarly, highest mass concentrations of sulfate are observed on the northerly section of the flight tracks around Paris (Fig. 17). A possible source of increased SO4-particle numbers northwest of Paris is the shipping port ‘Le Havre’ as well as shipping SO2 emis-sions in the Chanel. There have been several studies that have documented the impact of the ship-ping industry on the atmospheric particle composition. Commercial shipping is estimated to con-tribute 5 to 8% of global anthropogenic SO2 emissions (Eyring et al., 2005). Gaseous oxides of sul-fur produced during combustion of fossil fuels can be oxidized to particulate sulfate (SO4

2-) through gas-phase reactions. In addition to SO4

2_ formed from SO2, particulate emissions from shipping can also include BC, organic matter (OM) and ash (Petzold and Schönlinner, 2004). However, it is clear from Fig. 17 and 18 that the sulfate mass concentration does not remain con-stant across the plume and do not increase within the plume boundaries. In almost all RF, there is a decrease in the mass concentration of sulfate bearing particles when we sample in the pollution plume and at the same time a corresponding increase in the mass concentration of the organic, BC, and nitrate bearing particles (Figure 2 and 5). One hypothesis to explain this trend from inside to outside the pollution plume between sulfate and organics is that there is competition between organic matter gaseous precursors and sulfate matter precursors (SO2) for given amount of oxidants in the pollution plume. If the oxidants are limited in the plume, the presence of larger amounts of organic gaseous compounds would lead to a lack of oxidant available for oxidizing SO2. In support of this we observe high concentrations of NOX and slight decreases in the concentrations of O3 within the plume close to the center of Paris. As we move further away from the center of Paris there is a decrease in NOX and a corresponding increase in O3. Although O3 is correlated with increases in BC (identifying the plume boundaries), highest



concentrations of O3 are often measured on the borders of the plume with decreases in O3 mass concentrations within the center suggesting that the O3 is quickly reacted with NO, in the reaction NO+ O3= NO2 + O2. Similarly, increasing O3 is correlated with increasing organic mass concentra-tion, suggesting that it plays a strong role in the oxidation of organics.

4.3 PMF analysis The organic mass spectra from the C-TOF-AMS instrument are analysed using positive matrix fac-torisation (PMF). PMF is a statistical technique in which the organic mass spectral time series (X) are represented as a linear combination of a set of characteristic factor profiles (F) and their time-dependant intensities (G) (Paatero, 1997; Paatero and Tapper, 1994). PMF is therefore described by the matrix equation X = GF + E, where E is the residual matrix, defined as the difference between the data matrix X and the fitted solution GF. The PMF analysis was conducted using PMF2 soft-ware package (P.Paatero, University of Helsinki, Finland), together with the PMF analysis and evaluation tool (Ulbrich et al., 2009). The error matrix calculated in the Squirrel software (version 1.51) was modified following the recommendations of Ulbrich et al. (2009) and references therein. A minimum error of 1 ion was applied throughout the X matrix, and the organic peaks calculated as the fraction of the CO2

+ ion (m/z 44, 18, 17, and 16) were down weighted as described in Ulbrich et al. (2009). Matrix rotations were explored by varying the fpeak parameter from -3 to +3. The fpeak parameter allows the user to explore different PMF solutions that give the same fit to the data (Ul-brich et al. 2009). The number of PMF factors and fpeak values were determined by minimising the scaled residuals and factor correlations with external tracers (including CO, O3, NOx, SO2, NO3

-, and SO4

2-) and comparing the component mass spectra with reference mass spectra available on the AMS mass spectral database (Ulbrich et al., 2009;Lanz et al., 2007). An optimum fpeak value of 0 was applied for each dataset. Analysis of all RF showed that three different types of organic species were resolved from the total organic mass concentrations; however in each separate RF only two different types of organic mass spectra were resolved. Only one flight (RF 36 (29/7/11)) contained contributions from a hydrocar-bon organic aerosol (HOA). All other flights contained a semi-volatile and a low-volatile oxidized organic aerosol (SV- and LV-OOA). The LV-OOA is characterized by a significant contribution of m/z 44 (CO2

+) this type of organic aerosol is thought to be representative of aged organic aerosol and has been shown to have a low volatility. SV-OOA is in general characterized by dominant peaks at m/z 41, 43, 55, 57 with smaller contributions from m/z 44. These values can vary depend-ing on the age of the organic aerosols. SV-OOA particles are in general formed during warm sum-mer periods or in fresh pollution plumes and tend to be more volatile than LV-OOA. SV-OOA can include contributions from cooking organic aerosol, biomass burning aerosol, or primary organic aerosol particles. HOA had significant contributions from m/z 41, 43, 55, 57 but no contribution from m/z 44. All HOA and SV-OOA organic aerosol particles will be oxidized to LV-OOA. These characteristics have to be understood as average characteristics of an ensemble of individually dif-ferent particles. In the PMF analysis performed, only two factors out of the three types listed above are resolved. In contrast to AMS instruments measuring at ground based sites which are closer to a number of aero-sol sources, airborne studies the sampled air mass tend to be well mixed after being transported to higher altitudes and are most often oxidized to form secondary organic aerosol. For instance, when no HOA factor is found, the presence of HOA type aerosol can not be excluded; simply it could be mixed with more oxidized aerosol and is NOT discerned by PMF analysis analysis. DeCarlo et al (2008) noted that above Mexico City the O/C ratio was 0.4 and above, representing highly oxygen-ated aerosol. It was suggested that this was a result of flights through the mixed layer above the city



occurring in the late afternoon when photochemical SOA formation has already been active for sev-eral hours (Kleinman et al 2008; Salcedo et al., 2006; Volkamer et al., 2006). Similar conclusions could be made for the MEGAPOLI dataset since all flights took place between 11:30 and 15:30 UTC. In general, the SV-OOA or HOA mass concentrations increase when measured within the pollution plume compared to outside the pollution plume (Figure 20), whereas the aged LV-OOA had less variable concentrations but often showed increases in mass concentrations outside or on the edge of the plume boundaries, in a similar way to sulfate. In general the HOA and SV-OOA are well corre-lated with nitrate bearing particles and BC.

Figure 20: Time series of LV-OOA and SV-OOA for four flights during the MEGAPOLI experiment. In-

creases in SV-OOA correspond to periods when the AMS was sampling from the plume.

4.4 Analysis of OC build-up in and outside plume - how much more OA, OOA, HOA inside than outside the plume

Using the defined pollution plume boundaries we characterized the organic particle composition inside the pollution plume to that outside the pollution plume and measured its increase as a func-tion of distance from the city. In almost all RF the organic aerosol mass concentration increased with distance within the pollution plume. These increases in the mass concentrations of organic mass concentrations were only observed between 0 km and 120 km from the city, after which parti-cle mass concentrations were diluted. In order to investigate if the increase in LV-OOA is a result of formation within the plume it is necessary to determine a reference point to define the plume boundaries. Since the BC concentration is being used to define the level of pollution and to define the boundaries of the pollution plume within a given flight, we use the BC mass concentration as a reference point to the pollution plume. We therefore normalize the mass concentrations of LV-OOA and SV-OOA using the mass concentrations BC. Plotting this ratio of mass concentration of LV-



OOA against BC as a function of distance from the city (Fig. 21) we observe that this ratio in-creases with distance from the city inside the plume up to 200 km. Similar trends are observed for SV-OOA. Outside of the pollution plume the measured LV-OOA/BC ratio is much higher (4 to 6) than inside the plume (2 to 4), and the overall change with distance is less apparent than within the plume (Fig 8). This shows that there is a significant amount of photochemical processing occurring within the plume resulting in the production of LV-OOA. This is similar to studies showing that the formation of secondary organic aerosol (SOA) from urban sources is produced rapidly (Volkamer et al., 2006).

Figure 21: The mass concentrations (±1 σ) of LV-OOA (Top) and the ratio of LV-OOA to BC (Bottom)

within the pollution plume as a function of distance from the city.

Figure 22: The ratio of LV-OOA to BC (±1 σ) outside of the pollution plume as a function of distance from

the city.

4.5 Relationships between OA and the photochemical age of the air mass calculated from gaseous compounds

In order to compare the formation of SOA with the photochemical age of the pollution plume, we used the –Log(NOX/NOY) term (both inside and outside of the plume), as an indicator of the photo-chemical age of the air mass. However, this indicator of air mass age is only valid if there are no new sources of aerosol such as biomass burning or industrial sources that reset the age of the air mass. The significant contribution of aerosol particles from the nearby shipping port (at 200 km dis-tance) might perturb these measurements. For the flights in which NOX and NOY measurements are available we observe that there is an increase in LV-OOA mass concentrations with increasing -Log(NOX/NOY) (Figure 23). In the example shown, this increase in mass concentration of organic



aerosol is more pronounced within the plume for LV-OOA and more pronounced outside of the pol-lution plume for SV-OOA. In Fig. 23 the points are colored by distance from the city where red points correspond to distances greater than 100 km and blue points correspond to distances less than 40 km.

Figure 23: The -log (NOx/NOy) against LV-OOA and SV-OOA concentrations both inside and outside of the plume for RF 32 (21/07/11). Points are colored by distance from the city where red points correspond to dis-

tance greater than 100 km and blue points correspond to distances less than 40 km.

4.6 SV-OOA analysis against VOC’s inside the plume Of the eight RF with reliable AMS data there were only four flights in which VOC measurements were available. In the four RF flights, m/z 33 (methanol) was the most dominant VOC measured by the PTR-MS. After m/z 33, the measured VOC’s with the highest mass concentrations were m/z 59 (acetone), m/z 69 (Isoprene), and m/z 45 (acetaldehyde). In general, the anthropogenic marker peaks (m/z 93 (toluene), m/z 79 (benzene), and m/z 59 (acetone)) showed significant increases in mass concentrations in the plume whereas biogenic emissions remained constant showing no in-creases in the plume. Despite having relatively small contributions the measured AMS organic spectra showed highest correlations with m/z 73 (methylketone) and m/z 59 (acetone). However during RF 36, which was considered a high pollution day, the HOA particles also had similar time series as that of m/z 45 (acetaldehyde). In conclusion, correlation of organic aerosol with secondary VOC’s with anthropogenic origin appears to be largest, which would points to an anthropogenic origin of aerosol within the plume. In order to investigate the relationship between SOA formation and VOC emissions we can com-bine the plots of the fraction of SV-OOA to the total aerosol mass against the – Log(NOX/NOY) ratio (Figures 23 and 24). As illustrated in Fig. 23, increases in the photochemical age of the air mass correspond to increases in the mass concentration of the SOA. Colouring these plots by con-centrations of a primary VOC emission tracer (benzene) we observe that, within the plume bounda-ries, high benzene concentrations are associated with lower SV-OOA mass fractions, and lower



photochemical age. As the fraction of the oxidized organics increase with photochemical age there is a corresponding decrease in the benzene concentrations (Fig. 24). Similarly, high concentrations of HOA are associated with high benzene concentrations and as the mass fraction of HOA de-creases with photochemical age there is a decrease in benzene concentration (Fig. 24). These obser-vations suggest that anthropogenic VOC play a dominant role in the formation of LV and SV-OOA within the Paris pollution plume. We do not observe these same trends between the fraction of sec-ondary organic aerosol, photochemical age, and VOC concentration outside of the plume (Figure 24).

Figure 24: The -log (NOx/NOy) against the fraction of SV-OOA to the total mass concentration both inside and outside of the plume for RF 36 (29/07/11). Points are colored by the mass concentrations of Benzene for

measurements made inside the plume (right hand side) and outside the plume (left hand side).

4.7 Conclusions/ Discussion Measurements made in the ATR-42 during the MEGAPOLI experiment in June 2009 have offered an insight into the different sources that have a large effect on the overall particle chemical compo-sition. This is the first time that airborne measurements have been made in a small geographical area allowing the formation of SOA to be observed over short timescales. The production of secondary organic aerosol particles is evident within the pollution plume, with LV-OOA and SV-OOA organic aerosol increasing in mass concentration with increasing distance from the city, specifically when LV-OOA and SV-OOA concentrations are normalized to primary pollutants such as BC. Indeed, primary pollutants and in particular HOA organic aerosols decrease as expected with increasing distance from the city, by plume dilution and by oxidation. Based on the measurements available the mass concentrations of VOC, such as benzene and toluene, suggest that these are the most important precursor gas phase species to the formation of SV-OOA. How-



ever, it is not possible to quantify the contribution from the oxidation and evaporation from primary organic particulate species. Although biogenic emissions, such as isoprene, are measured in high quantities they showed no correlation to the formation of SOA both inside and outside of the pollu-tion plume. This suggests that, in Paris during the period of this study, anthropogenic VOCs are dominant in the formation of SOA within the pollution plume, adding to a background of SOA with major biogenic origin, as made evident in Chapters 2 and 3. The contribution of sulfate particles to the aerosol population in and around Paris is likely from the oxidation of SO2. During the MEGA-POLI experiment we observe that outside of the pollution plume when particle concentrations are low there are high concentrations of sulfate bearing particles but as soon as we enter into the pollu-tion plume there is a sharp decrease in the sulfate production and an increase in organics. This sug-gests that the production of secondary organic aerosol particles is favored over the production of sulfate particles. Results from the AMS show that using high-time resolution measurements we can better understand the different aerosol sources in the atmosphere, The advantage of airborne measurements over ground based measurements is that we have a unique view of the evolution of both the organic and inorganic aerosol particle composition over a large geographical area.

5. Concluding Remarks In conclusion, this deliverable has described the link between the formation of secondary organic aerosol and gaseous precursors, in particular VOC species at two suburban sites within the agglom-eration and in the pollution plume during the MEGAPOLI summer campaign in July 2009. At the urban site, a clear correlation between primary traffic emissions related VOC species and black carbon was observed, while the primary fraction derived from AMS measurements by PMF (positive matrix formulation) showed a different behavior, indicating the presence of non-traffic related sources. For secondary organic aerosol (SOA), the correlation with biogenic precursors, par-ticularly isoprene and its oxidation products, is highlighted, even in a large megacity city like the Paris agglomeration. Large SOA peaks are seem to be linked to long range transport more than to local emissions. At the suburban site at the south-western edge of the agglomeration, the correlation of SOA with biogenic precursors is confirmed. During the events with the largest SOA peaks which have been analysed here, this site has sampled air masses entering the agglomeration. Lagrangian retro-plume calculations show an air mass origin over South-Western France and Spain, with a large probability that biogenic VOC emissions have strongly affected the air masses. On the contrary, more westerly wind regimes advecting oceanic air masses to the agglomeration result as expected in lower level organic aerosol and VOC levels at both sites. In addition to advection, also local SOA formation is evident at both sites because of an afternoon increase in SOA and a decrease in VOC precursor spe-cies. Last, at the suburban site a correlation between SOA and VOC species of intermediate volatil-ity (C14, C15) is made evident and needs to be further elucidated. These species are believed to be efficient aerosol precursors, but have field observations are rare. Measurements made onboard the ATR-42 showed a production of secondary organic aerosol parti-cles within the pollution plume, manifested in an increase of the SOA concentrations, normalised to black carbon and primary VOC’s as a primary pollution tracer, with distance from the agglomera-tion and with increased photochemical age. The correlation with anthropogenic VOC’s is stronger than that with biogenic ones. This suggests that anthropogenic VOCs are the dominant source for the formation of additional SOA within the pollution plume. On the contrary, aerosol sulfate levels were larger outside the plume, which suggests both important SO2 sources outside of the agglom-



eration and possibly less efficient oxidation processes inside the plume (due to a larger primary pol-lutant burden and completion for oxidants). Combining results from the urban and sub-urban sites with airborne observations within the plume suggests that (1) that background aerosol advected to the agglomeration is mainly of biogenic ori-gin, that (2) SOA formation within the urban agglomeration due to anthropogenic VOC emissions is still limited, but becomes important within the pollution plume, when photochemical processing of air masses occurs. This is different to Mexico-city, where significant SOA production within the agglomeration has been made evident. Further analysis needs to elucidate these differences which are likely due to stronger anthropogenic emissions, enhanced residence times, and enhanced photo-chemical activity over Mexico City as compared to the Paris agglomeration.



Acknowledgements The MEGAPOLI project is very thankful and acknowledges the Laboratoire d’Hygiène de Paris (LHVP), The SIRTA/IPSL, le Golf de la Poudrérie à Livry-Gargan for hosting campaign sites. Without this help, the campaign would not have been possible. Also University Paris-Est, Créteil, INRA/Gignon, Météo-France, Roissy and ENPC, Marne la Vallée are thanked to host lidar instruments. It is also grateful to all voluntary participants who made this campaign a great success. The research leading to these results has received funding from the European Union’s Seventh Framework Programme FP/2007-2011 within the project MEGAPOLI, grant agreement n°212520. In addi-tions the project has been supported through the French ANR MEGAPARIS and LEFE- CHAT MEGA-POLI-FRANCE projects, and through an Ile de France SEPPE project. All contributions from the MEGAPOLI campaign team are acknowledged :

• M. Beekmann1, U. Baltensperger2, A. Borbon1, J. Sciare3, V. Gros3, A. Baklanov4, M. Lawrence5, S. Pandis6, V.Kostenidou6, M.Psichoudaki6, L. Gomes7, P. Tulet7, A. Wiedensohler8

, A. Held*, L. Pou-lain8, K.Kamilli8, W. Birmli8, A. Schwarzenboeck9, K. Sellegri9, A. Colomb9, J.M. Pichon9, E.Fernay9

, J.L. Jaffrezo10, P. Laj10, C. Afif1, V. Ait-Helal1*, B. Aumont1, S. Chevailler1, P. Chelin1, I. Coll1, J.F. Doussin1, R. Durand-Jolibois1, H. Mac Leod1, V. Michoud1, K. Miet1, N. Grand1, S. Perrier1, H. Petetin1, T. Raventos1, C. Schmechtig1, G. Siour1, C. Viatte1, Q. Zhang1**, P. Chazette3, M. Bressi3, M. Lopez5, P. Royer3, R. Sarda-Esteve3, F. Drewnick5, J. Schneider5, M. Brands5, S. Bormann5, K. Dzepina5, F. Freutel5, S. Gallavardin5, T. Klimach5, T. Marbach5, R. Shaiganfar5, S.L. Von der Wei-den5, T. Wagner5, S.Zorn5, P. De Carlo2, A. Prevot2, M. Crippa2, C. Mohr2, Marie Laborde2, M. Gysel2, Roberto Chirico2, Maarten Heringa2, A. Butet11, A. Bourdon11, E. Mathieu11, T. Perrin11, SAFIRE team, J.Wenger12, R. Healy12, I.O. Connor12, E. Mc Gillicuddy12, P. Alto13, J.P.Jalkanen13, M. Kulmala13, P Lameloise14, V. Ghersi14, O. Sanchez14, A. Kauffman14, H. Marfaing14, C. Honoré14, L. Chiappini15, O. Favez15, F. Melleux15, G. Aymoz15, B. Bessagnet15, L. Rouil15, S. Rossignol15, M. Haeffelin16, C. Pietras16, J. C. Dupont16, and the SIRTA team, S. Kukui17, E. Dieudonné17, F. Rav-etta17, J.C.Raut17,G. Ancellet17, F. Goutail17, J.L Besombes18, N. Marchand19, Y. Le Moullec20, J. Cuesta21, Y. Té22, N. Laccoge23, S. Lolli24, L. Sauvage24, S.Loannec24, D. Ptak25, A. Schmidt25, S. Conil26, M. Boquet27,

1Laboratoire InterUniversitaire des Systèmes Atmosphériques (LISA), Université Paris Est et 7, CNRS, Cré-teil, France, 2 Paul Scherrer Institut, Villigen, Switzerland, 3Laboratoire des Sciences du Climat et de l’Environnement (LSCE), Gif sur Yvette, France, 4 Danish Meteorological Institute, Copenhagen, Denmark, 5Max-Planck-Institute for Chemistry, Mainz, Germany, 6Foundation for Research and Technology, Hellas, University of Patras, Greece, 7Game,Centre National de Recherche Météorologique, Toulouse , France , 8Institut für Troposphärenforschung, Leipzig, Germany, 9Laboratoire de Météorologie Physique, Clermont-Ferrand, France, 10Laboratoire de Glaciologie et Géophysique de l’Environnement, Grenoble, France , 11SAFIRE, Toulouse, France, 12University College Cork, Ireland, 13University of Helsinki, Finland , 14AIRPARIF, Paris, France, 15INERIS, France, ,16SIRTA/IPSL, Palaiseau, France, 17Laboratoire Atmosphè-res, Milieux, Observations Spatiales, Paris, France, ,18Laboratoire de Chimie Moléculaire et Environnement, Chambery, France, 19Laboratoire de Chimie Provence, Marseille, France, 20Laboratoire de l’Hygiène de la Ville de Paris, France,, 21Laboratorie de Météorologie Dynamique, Palaiseau, France , 22Laboratorie de Phy-sique Moléculaire pour l'Atmosphère et l'Astrophysique, 23Département Environnement et Chimie, Ecole de Mines de Douais, France, , 24LEOSPHERE, France, 25Universität Duisburg-Essen), Germany, 26ANDRA, Châtenay-Malabry, France, 27CEREA, Marne La Vallée, France ,**also ARIA-Technologie. France



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Appendix 1: Emission weighted air mass origins calculated with the Lagrangian particle model FLEXPART during events 3 and 4

Figure A1: Particle air mass origins for July 16 (event 3) and July 21 (event 4) 2009 (in Chapter 2).



Appendix 2: Correlation plots

Figure A2.1: Correlation plots - Gas phase (LHVP, Summer 2009 campaign).



Figure A2.2: Correlation plots – Gas and particulate phases (LHVP, Summer 2009 campaign).



Appendix 3: Selected Nucleation Events Observed at LHVP Site during Summer 2009

Figure A3: time series of the particle size distribution and of several pollutants during two nucleation

events observed at LHVP during summer 2009; on July 22 (left side, on July 28 and 29, right side.



Previous MEGAPOLI reports Previous reports from the FP7 EC MEGAPOLI Project can be found at:

http://www.megapoli.info/

Collins W.J. (2009): Global radiative forcing from megacity emissions of long-lived greenhouse gases. Deliverable 6.1, MEGAPOLI Scientific Report 09-01, 17p, MEGAPOLI-01-REP-2009-10, ISBN: 978-87-992924-1-7

http://megapoli.dmi.dk/publ/MEGAPOLI_sr09-01.pdf

Denier van der Gon, HAC, AJH Visschedijk, H. van der Brugh, R. Dröge, J. Kuenen (2009): A base year (2005) MEGAPOLI European gridded emission inventory (1st version). Deliverable 1.2, MEGAPOLI Scientific Report 09-02, 17p, MEGAPOLI-02-REP-2009-10, ISBN: 978-87-992924-2-4


Baklanov A., Mahura A. (Eds) (2009): First Year MEGAPOLI Dissemination Report. Deliverable 9.4.1, MEGAPOLI Scientific Report 09-03, 57p, MEGAPOLI-03-REP-2009-12, ISBN: 978-87-992924-3-1


Allen L., S Beevers, F Lindberg, Mario Iamarino, N Kitiwiroon, CSB Grimmond (2010): Global to City Scale Urban Anthropogenic Heat Flux: Model and Variability. Deliverable 1.4, MEGA-POLI Scientific Report 10-01, MEGAPOLI-04-REP-2010-03, 87p, ISBN: 978-87-992924-4-8 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-01.pdf

Pauli Sievinen, Antti Hellsten, Jaan Praks, Jarkko Koskinen, Jaakko Kukkonen (2010): Urban Mor-phological Database for Paris, France. Deliverable D2.1, MEGAPOLI Scientific Report 10-02, MEGAPOLI-05-REP-2010-03, 13p, ISBN: 978-87-992924-5-5 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-02.pdf

Moussiopoulos N., Douros J., Tsegas G. (Eds.) (2010): Evaluation of Zooming Approaches De-scribing Multiscale Physical Processes. Deliverable D4.1, MEGAPOLI Scientific Report 10-03, MEGAPOLI-06-REP-2010-01, 41p, ISBN: 978-87-992924-6-2 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-03.pdf

Mahura A., Baklanov A. (Eds.) (2010): Hierarchy of Urban Canopy Parameterisations for Different Scale Models. Deliverable D2.2, MEGAPOLI Scientific Report 10-04, MEGAPOLI-07-REP-2010-03, 50p, ISBN: 978-87-992924-7-9 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-04.pdf

Dhurata Koraj, Spyros N. Pandis (2010): Evaluation of Zooming Approaches Describing Multi-scale Chemical Transformations. Deliverable D4.2, MEGAPOLI Scientific Report 10-05, MEGAPOLI-08-REP-2010-01, 29p, ISBN: 978-87-992924-8-6 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-05.pdf

Igor Esau (2010): Urbanized Turbulence-Resolving Model and Evaluation for Paris. Deliverable D2.4.1, MEGAPOLI Scientific Report 10-06, MEGAPOLI-09-REP-2010-03, 20p, ISBN: 978-87-992924-9-3 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-06.pdf

Grimmond CSB., M. Blackett, M.J. Best, et al. (2010): Urban Energy Balance Models Comparison. Deliverable D2.3, MEGAPOLI Scientific Report 10-07, MEGAPOLI-10-REP-2010-03, 72p, ISBN: 978-87-993898-0-3 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-07.pdf

Gerd A. Folberth, Steve Rumbold, William J. Collins, Tim Butler (2010): Determination of Radia-tive Forcing from Megacity Emissions on the Global Scale. Deliverable D6.2, MEGAPOLI



Scientific Report 10-08, MEGAPOLI-11-REP-2010-03, 19p, ISBN: 978-87-993898-1-0 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-08.pdf

Thomas Wagner, Steffen Beirle, Reza Shaiganfar (2010): Characterization of Megacity Impact on Regional and Global Scales Using Satellite Data. Deliverable D5.1, MEGAPOLI Scientific Report 10-09, MEGAPOLI-12-REP-2010-03, 25p, ISBN: 978-87-993898-2-7 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-09.pdf

Baklanov A., Mahura A. (Eds.) (2010): Interactions between Air Quality and Meteorology, Deliv-erable D4.3, MEGAPOLI Scientific Report 10-10, MEGAPOLI-13-REP-2010-03, 48p, ISBN: 978-87-993898-3-4 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-10.pdf

Baklanov A. (Ed.) (2010): Framework for Integrating Tools. Deliverable D7.1, MEGAPOLI Scien-tific Report 10-11, MEGAPOLI-14-REP-2010-03, 68p, ISBN: 978-87-993898-4-1 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-11.pdf

Sofiev M., Prank M., Vira J., and MEGAPOLI Modelling Teams (2010): Provision of global and regional concentrations fields from initial baseline runs. Deliverable D5.2, MEGAPOLI Tech-nical Note 10-12, MEGAPOLI-15-REP-2010-03, 10p. http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-12.pdf

H.A.C. Denier van der Gon, J. Kuenen, T. Butler (2010): A Base Year (2005) MEGAPOLI Global Gridded Emission Inventory (1st Version). Deliverable D1.1, MEGAPOLI Scientific Report 10-13, MEGAPOLI-16-REP-2010-06, 20p, ISBN: 978-87-993898-5-8 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-13.pdf

Lawrence M. G., Butler T. M., Collins W., Folberth G., Zakey A., Giorgi F. (2010): Meteorological Fields for Present and Future Climate Conditions. Deliverable D6.5, MEGAPOLI Technical Note 10-14, MEGAPOLI-17-REP-2010-09, 9p. http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-14.pdf

Beekmann M., Baltensperger U., and the MEGAPOLI campaign team (2010): Database of Chemi-cal Composition, Size Distribution and Optical Parameters of Urban and Suburban PM and its Temporal Variability (Hourly to Seasonal). Deliverable D3.1, MEGAPOLI Scientific Report 10-15, MEGAPOLI-18-REP-2010-10, 21p, ISBN: 978-87-993898-6-5 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-15.pdf

Beekmann M., Baltensperger U., and the MEGAPOLI campaign team (2010): Database of the Im-pact of Megacity Emissions on Regional Scale PM Levels. Deliverable D3.4, MEGAPOLI Scientific Report 10-16, MEGAPOLI-19-REP-2010-10, 29p, ISBN: 978-87-993898-7-2 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-16.pdf

Kuenen J., H. Denier van der Gon, A. Visschedijk, H. van der Brugh, S. Finardi, P. Radice, A. d’Allura, S. Beevers, J. Theloke, M. Uz-basich, C. Honoré, O. Perrussel (2010): A Base Year (2005) MEGAPOLI European Gridded Emission Inventory (Final Version). Deliverable D1.6, MEGAPOLI Scientific Report 10-17, MEGAPOLI-20-REP-2010-10, 37p, ISBN: 978-87-993898-8-9 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-17.pdf

Karppinen A., Kangas L., Riikonen K., Kukkonen J., Soares J., Denby B., Cassiani M., Finardi S., Radice P., (2010): Evaluation of Methodologies for Exposure Analysis in Urban Areas and Application to Selected Megacities. Deliverable D4.4, MEGAPOLI Scientific Report 10-18, MEGAPOLI-21-REP-2010-11, 29p, ISBN: 978-87-993898-9-6 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-18.pdf

Soares J., A. Karppinen, B. Denby, S. Finardi, J. Kukkonen, M. Cassiani, P. Radice, M.Williams (2010): Exposure Maps for Selected Megacities. Deliverable D4.5, MEGAPOLI Scientific Re-



port 10-19, MEGAPOLI-22-REP-2010-11, 26p, ISBN: 978-87-92731-00-5 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-19.pdf

Rumbold S.T., W.J. Collins, G.A. Folberth (2010): Comparison of Coupled and Uncoupled Models. Deliverable D6.4, MEGAPOLI Scientific Report 10-20, MEGAPOLI-23-REP-2010-11, 15p, ISBN: 978-87-92731-01-2 http://megapoli.dmi.dk/publ/MEGAPOLI_sr10-20.pdf

Baklanov A., Mahura A. (Eds) (2010): Second Year MEGAPOLI Dissemination Report. Deliver-able D9.4.2, MEGAPOLI Scientific Report 10-21, MEGAPOLI-24-REP-2010-12, 89p, ISBN: 978-87-92731-02-9


Moussiopoulos N., Douros J., Tsegas G. (Eds) (2010): Evaluation of Source Apportionment Meth-ods. Deliverable D4.6, MEGAPOLI Scientific Report 10-22, MEGAPOLI-25-REP-2010-12, 54p, ISBN: 978-87-92731-03-6


Theloke J., M.Blesl, D. Bruchhof, T.Kampffmeyer, U. Kugler, M. Uzbasich, K. Schenk, H. Denier van der Gon, S. Finardi, P. Radice, R. S. Sokhi, K. Ravindra, S. Beevers, S. Grimmond, I. Coll, R. Frie-drich, D. van den Hout (2010): European and megacity baseline scenarios for 2020, 2030 and 2050. Deliverable D1.3, MEGAPOLI Scientific Report 10-23, MEGAPOLI-26-REP-2010-12, 57p, ISBN: 978-87-92731-04-3


Galmarini S., Vinuesa J.F., Cassiani M., Denby B., Martilli A., (2011): Evaluation of Sub-Grid Models with Interactions between Turbulence and Urban Chemistry. Recommendations for Emission Inventories Improvement. Deliverable D2.6, MEGAPOLI Scientific Report 11-01, MEGAPOLI-27-REP-2011-01, 41p, ISBN: 978-87-92731-05-0


Butler T., H.A.C. Denier van der Gon, J. Kuenen (2011): The Base Year (2005) Global Gridded Emission Inventory used in the EU FP7 Project MEGAPOLI (Final Version). Deliverable D1.5, MEGAPOLI Scientific Report 11-02, MEGAPOLI-28-REP-2011-01, 27p, 978-87-92731-06-7


Schlünzen K.H., M. Haller (Eds) (2011): Evaluation of Integrated Tools. Deliverable D7.2 MEGA-POLI Scientific Report 11-03, MEGAPOLI-29-REP-2011-03, 50p, 978-87-92731-07-4


Sofiev M., M. Prank, J. Kukkonen (Eds) (2011): Evaluation and Improvement of Regional Model Simulations for Megacity Plumes. Deliverable D5.3, MEGAPOLI Scientific Report 11-04, MEGAPOLI-30-REP-2011-03, 88p, 978-87-92731-08-1

http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-04.pdf Baltensperger U., Beekmann M., and the MEGAPOLI campaign team (2011): Source Apportion-

ment of Major Urban Aerosol Components Including Primary and Secondary PM Sources. Deliverable D3.2, MEGAPOLI Scientific Report 11-05, MEGAPOLI-31-REP-2011-05, 20p, ISBN: 978-87-92731-09-8 http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-05.pdf

Kampffmeyer T., U. Kugler, M. Uzbasich, J. Theloke, R. Friedrich, D. van den Hout (2011): Short, Medium and Long Term Abatement and Mitigation Strategies for Megacities. Deliverable D8.1, MEGAPOLI Scientific Report 11-06, MEGAPOLI-32-REP-2011-05, 40p, ISBN: 978-87-92731-10-4 http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-06.pdf

Folberth G.A., S. Rumbold, W.J. Collins, T. Butler (2011): Regional and Global Climate Changes



due to Megacities using Coupled and Uncoupled Models. Deliverable D6.6, MEGAPOLI Sci-entific Report 11-07, MEGAPOLI-33-REP-2011-06, 18p, ISBN: 978-87-92731-11-1 http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-07.pdf

Petetin H., M. Beekmann, V. Michoud, A. Borbon, J.-F. Doussin, A. Colomb, A. Schwarzenboeck, H. Denier van der Gon, C. Honore, A. Wiedensohler, U. Baltensperger, and the MEGAPOLI Campaign Team (2011): Effective Emission Factors for OC and BC for Urban Type Emis-sions. Deliverable D3.3, MEGAPOLI Scientific Report 11-08, MEGAPOLI-34-REP-2011-06, 30p, ISBN: 978-87-92731-12-8 http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-08.pdf

Folberth G.A., S. Rumbold, W.J. Collins, T. Butler (2011): Estimate of Megacity Impacts in a Fu-ture Climate. Deliverable D5.7, MEGAPOLI Scientific Report 11-09, MEGAPOLI-35-REP-2011-06, 21p, ISBN: 978-87-92731-13-5 http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-09.pdf

Eckhardt S., M. Cassiani, A. Stohl (2011): Influence of North American Megacities on European Atmospheric Composition. Deliverable D5.6, MEGAPOLI Scientific Report 11-10, MEGA-POLI-36-REP-2011-06, 28p, ISBN: 978-87-92731-14-2 http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-10.pdf

Cassiani M., Stohl S., Eckhardt S., Sovief M., Prank M., Butler T., Lawrence M., Collins W.J., Fol-berth G.A., Rumbold S., Pyle J.A., Russo M.R., Stock Z., Siour G., Coll I., D’Allura A., Fi-nardi S., Radice P., Silibello C. (2011): Prediction of Megacities Impact on Regional and Global Atmospheric Composition. Deliverable D5.4, MEGAPOLI Scientific Report 11-11, MEGAPOLI-37-REP-2011-06, 55p, ISBN: 978-87-92731-15-9 http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-11.pdf

Sofiev M., M. Prank, A. Baklanov (Eds) (2011): Influence of Regional Scale Emissions on Megacity Air Quality. Deliverable D5.5, MEGAPOLI Scientific Report 11-12, MEGAPOLI-38-REP-2011-06, 60p, ISBN: 978-87-92731-16-6 http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-12.pdf

Hannukainen M., de Leeuw G., Collins W.J. (2011): Comparison of Measured and Modeled Radia-tive Effects. Deliverable D6.3, MEGAPOLI Scientific Report 11-13, MEGAPOLI-39-REP-2011-08, 22p, ISBN: 978-87-92731-17-3 http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-13.pdf

Esau I., Baklanov A., Zilitinkevich S. (2011): Improved Urban Parameterizations Based on Prog-nostic Equations, Utilizing LES Results. Deliverable D2.5, MEGAPOLI Scientific Report 11-14, MEGAPOLI-40-REP-2011-09, 61p, ISBN: 978-87-92731-18-0 http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-14.pdf

Esau I. (2011): Urbanized Turbulence-Resolving Model and Evaluation Against WP3 Paris Plume Data. Deliverable D2.4.2. MEGAPOLI Scientific Report 11-15, MEGAPOLI-41-REP-2011-09, 49p, ISBN: 978-87-92731-19-7 http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-15.pdf

Borbon A., E. Freney, N. Marchand, M. Beekmann, E. Abidi, W. Ait-Helal, A. Colomb, J. Cozic, N. Locoge, S. Sauvage, K. Sellegri, B. Temine-Roussel, J. Sciare, V. Gros, J.L. Jaffrezo, U. Baltensperger, and the MEGAPOLI campaign team (2011): Evaluation of Links between Sec-ondary VOCs and Secondary Organic Aerosols of Anthropogenic and Biogenic Origin. Deliv-erable D3.5, MEGAPOLI Scientific Report 11-16, MEGAPOLI-42-REP-2011-09, 42p, ISBN: 978-87-92731-20-3 http://megapoli.dmi.dk/publ/MEGAPOLI_sr11-16.pdf



MEGAPOLI

Megacities: Emissions, urban, regional and Global Atmos-

pheric POLlution and climate effects, and Integrated tools for

assessment and mitigation

EC FP7 Collaborative Project

2008-2011

Theme 6: Environment (including climate change) Sub-Area: ENV-2007.1.1.2.1:

Megacities and regional hot-spots air quality and cli-mate

MEGAPOLI Project web-site http://www.megapoli.info

MEGAPOLI Project Office Danish Meteorological Institute (DMI) Lyngbyvej 100, DK-2100 Copenhagen, Denmark

E-mail: [email protected] Phone: +45-3915-7441 Fax: +45-3915-7400

MEGAPOLI Project Partners

• DMI - Danish Meteorological Institute (Denmark) - Contact Persons: Prof. Alexander Baklanov (co-ordinator), Dr. Alexander Mahura (manager)

• FORTH - Foundation for Research and Technol-ogy, Hellas and University of Patras (Greece) - Prof. Spyros Pandis (vice-coordinator)

• MPIC - Max Planck Institute for Chemistry (Ger-many) - Dr. Mark Lawrence (vice-coordinator)

• ARIANET Consulting (Italy) – Dr. Sandro Finardi • AUTH - Aristotle University Thessaloniki (Greece)

- Prof. Nicolas Moussiopoulos • CNRS - Centre National de Recherche Scientifique

(incl. LISA, LaMP, LSCE, GAME, LGGE) (France) – Dr. Matthias Beekmann

• FMI - Finnish Meteorological Institute (Finland) – Prof. Jaakko Kukkonen

• JRC - Joint Research Center (Italy) – Dr. Stefano Galmarini

• ICTP - International Centre for Theoretical Phys-ics (Italy) - Prof. Filippo Giorgi

• KCL - King's College London (UK) – Prof. Sue Grimmond

• NERSC - Nansen Environmental and Remote Sensing Center (Norway) – Dr. Igor Esau

• NILU - Norwegian Institute for Air Research (Norway) – Dr. Andreas Stohl

• PSI - Paul Scherrer Institute (Switzerland) – Prof. Urs Baltensperger

• TNO-Built Environment and Geosciences (The Netherlands) – Prof. Peter Builtjes

• MetO - UK MetOffice (UK) – Dr. Bill Collins • UHam - University of Hamburg (Germany) – Prof.

Heinke Schluenzen • UHel - University of Helsinki (Finland) – Prof.

Markku Kulmala • UH-CAIR - University of Hertfordshire, Centre for

Atmospheric and Instrumentation Research (UK) – Prof. Ranjeet Sokhi

• USTUTT - University of Stuttgart (Germany) – Prof. Rainer Friedrich

• WMO - World Meteorological Organization (Swit-zerland) – Dr. Liisa Jalkanen

• CUNI - Charles University Prague (Czech Repub-lic) – Dr. Tomas Halenka

• IfT - Institute of Tropospheric Research (Ger-many) – Prof. Alfred Wiedensohler

• UCam - Centre for Atmospheric Science, Univer-sity of Cambridge (UK) – Prof. John Pyle

Work Packages

WP1: Emissions (H. Denier van der Gon, P. Builtjes)

WP2: Megacity features (S. Grimmond, I. Esau)

WP3: Megacity plume case study (M. Beekmann, U. Baltensperger)

WP4: Megacity air quality (N. Moussiopoulos)

WP5: Regional and global atmospheric composition (J. Kukkonen, A. Stohl)

WP6: Regional and global climate impacts (W. Collins, F. Giorgii)

WP7: Integrated tools and implementation (R. Sokhi, H. Schlünzen)

WP8: Mitigation, policy options and impact assessment (R. Friedrich, D. van den Hout)

WP9: Dissemination and Coordination (A. Baklanov, M. Lawrence, S. Pandis)

megapoli scientific report 11-16 evaluation of links...

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