The ChArMEx (Chemistry and Aerosols
Mediterranean Experiments) SOP2 (special observation period 2) field campaign
took place from 15 July to 5 August 2013 in the western Mediterranean Basin
at Ersa, a remote site in Cape Corse. During the campaign more than 80
volatile organic compounds (VOCs), including oxygenated species, were
measured by different online and offline techniques. At the same time, an
exhaustive description of the chemical composition of fine aerosols was
performed with an aerosol chemical speciation monitor (ACSM). Low levels of
anthropogenic VOCs (typically tens to hundreds of parts per trillion for
individual species) and black carbon (0.1–0.9
Positive matrix factorization (PMF) and concentration field (CF) analyses
were performed on a database containing 42 VOCs (or grouped VOCs), including
OVOCs, to identify the covariation factors of compounds that are representative
of primary emissions or chemical transformation processes. A six-factor
solution was found for the PMF analysis, including a primary and secondary
biogenic factor correlated with temperature and exhibiting a clear
diurnal profile. In addition, three anthropogenic factors characterized by
compounds with various lifetimes and/or sources have been identified
(long-lived, medium-lived and short-lived anthropogenic factors). The
anthropogenic nature of these factors was confirmed by the CF analysis, which
identified potential source areas known for intense anthropogenic emissions
(north of Italy and southeast of France). Finally, a factor characterized
by OVOCs of both biogenic and anthropogenic origin was found. This factor
was well correlated with submicron organic aerosol (OA) measured by an
aerosol chemical speciation monitor (ACSM), highlighting the close link
between OVOCs and organic aerosols; the latter is mainly associated
(96 %) with the secondary OA fraction. The source apportionment of OA
measured by ACSM led to a three-factor solution identified as hydrogen-like OA (HOA),
semi-volatile oxygenated OA (SV-OOA) and low volatility OOA (LV-OOA)
for averaged mass concentrations of 0.13, 1.59 and 1.92
A combined analysis of gaseous PMF factors with inorganic and organic fractions of aerosols helped distinguish between anthropogenic continental and biogenic influences on the aerosol- and gas-phase compositions.
Organic matter is directly emitted into the atmosphere both in the gas phase
as volatile organic compounds (VOCs) and in the aerosol phase as primary
organic aerosol (POA). The sources can be of biogenic (from land or marine
ecosystems) or anthropogenic (from traffic, industrial activities or
residential heating) origins. Once emitted, it can be transported over long
distances and undergo chemical transformations due to atmospheric
photo-oxidants, such as ozone (O
Positive matrix factorization (PMF) models (Paatero and Tapper, 1994;
Paatero, 1997) have been widely used to identify and quantify sources of
VOCs, generally in urban environments (e.g., Latella et al., 2005; Leuchner
and Rappenglück, 2010; Gaimoz et al., 2011; Yuan et al., 2012). This type
of analysis allows for the separation of different sources (e.g., vehicular
exhaust, fuel evaporation and residential heating) and the apportionment of
those sources to the VOC budget. PMF was also used at remote sites (Lanz et
al., 2009; Sauvage et al., 2009; Leuchner et al., 2015), despite the need to
assume mass conservation between the source location and the measurement site
in this approach (Hopke, 2003). In such environments, PMF can be used as a
tool to identify aged primary sources and the photochemical formation of
organic trace gases. This approach can therefore be useful to gain insight
into the sources and processes involved in the evolution of organic trace
gases measured at remote locations. For example, Leuchner et al. (2015)
applied PMF to 24 C
Similar PMF approaches have also been conducted on the organic fraction of
aerosols measured mostly by an aerosol mass spectrometer (AMS) to identify
different components characterized by their sources, their formation
and/or their chemical composition (Ng et al., 2010a; Zhang et al., 2011).
For example, aerosol factors such as HOA (hydrocarbon-like organic aerosol),
and OOA (oxygenated-like organic aerosol) are commonly extracted from AMS
spectra using PMF analysis and are attributed to POA and SOA, respectively
(Zhang et al., 2011). The latter can also be separated into several factors
as a function of volatility: low volatility OOA (LV-OOA) and
semi-volatile OOA (SV-OOA) (Zhang et al., 2011). For example, Hildebrandt et
al. (2010) detected two types of OOA with low volatility using PMF on AMS
data recorded at Finokalia, an eastern Mediterranean remote site; no
HOA was present in detectable amounts. In contrast, PMF analysis applied
to aerosol measurements taken at an urban background site in Barcelona
in spring, in the western Mediterranean Basin, revealed a significant impact
by local primary emissions from HOA, cooking organic aerosol (COA) and
biomass burning organic aerosol (BBOA) factors accounting for 44 % of OA;
regional and local secondary sources (LV-OOA and SV-OOA) dominated
the OA burden (Mohr et al., 2012). Another study combining ACSM
measurements and
More recently, combined source apportionments of organic aerosol and VOCs were performed in urban environments (Slowik et al., 2010; Crippa et al., 2013a), allowing a better classification of organic aerosol (OA) from the PMF analysis. This type of analysis also provided insight into OA sources, such as the identification of gaseous precursors.
Residential time analysis identifies the geographical location of potential source areas by combining measured or estimated variables at a receptor site with back-trajectory analyses (Ashbaugh et al., 1985; Seibert et al., 1994; Stohl, 1996). Combined with PMF results, these models have been used to locate source regions of PMF factors (Hwang and Hopke, 2007; Lanz et al., 2009; Tian et al., 2013). This association of receptor-oriented models can be powerful in identifying the nature of the source or the chemical processes characterizing PMF factors. The concentration field (CF) is one of these source-receptor inverse models, which was developed by Seibert et al. (1994). It consists of a redistribution of the measured or estimated variables in grid cells along estimated back trajectories.
The Mediterranean Basin is an ideal location to study the sources and the fate of organic carbon during long-range transport since it is impacted by strong natural and anthropogenic emissions and undergoes intense photochemical events (Lelieveld et al., 2002). The ChArMEx project (Chemistry and Aerosols Mediterranean Experiments) aims at assessing the present and future state of the atmospheric environment and its impacts in the Mediterranean Basin. This initiative proposes setting up a coordinated experimental effort for an assessment of the regional budgets of tropospheric trace species, their trends and their impacts on air quality, marine biogeochemistry and regional climate. For that purpose an intensive field campaign was performed during summer 2013 at Cape Corse (north of the island of Corsica) where a full suite of trace gases and aerosol species were measured for 3 weeks. In the framework of ChArMEx, the CARBO-SOR (CARBOn within continental pollution plumes: SOurces and Reactivity) project aimed more specifically at investigating the sources of primary and secondary organic trace gases and the composition of continental plumes reaching Cape Corse, with the goal of assessing their impacts on the photo-oxidants and/or SOA sources and levels.
As part of the ChArMEx and CARBO-SOR projects, this study investigates the
sources and the chemistry of atmospheric organic matter by combining
different statistical tools, i.e., the PMF and ME-2 (multilinear engine-2)
models and the concentration field method. This approach was used to
(i) identify the covariation factors of measured VOCs that are representative of
primary emissions at various stages of aging and chemical transformation
occurring during long-range transport and to (ii) better characterize the
different fractions of organic aerosol. The PMF factors were then used to
assess the origin of non-refractive organic species in PM
The ChArMEx SOP2 (short observation period 2) field campaign took place from
15 July to 5 August 2013. The measurement site is located in Ersa at Cape
Corse (42.969
Summary of VOC measurements performed at Cape Corse during the ChArMEx SOP2 field campaign. DL is detection limit.
Localization and geographical configuration of the measurement site in Ersa at Cape Corse (source: Google Maps). The white solid square in the insert (top left) represents the localization of the city of Bastia.
During the ChArMEx SOP2 field campaign, more than 80 VOCs, including non-methane hydrocarbons (NMHCs) and oxygenated (O) VOCs, were measured using complementary online and offline techniques with sampling inlets located approximately 1.5 m above the roof of a trailer in which the instruments were housed. Table 1 summarizes the VOC measurements performed during the campaign.
Sixteen protonated masses were extracted from a proton transfer reaction
time-of-flight mass spectrometer (PTR-TOF-MS; KORE
Technology®, second generation), leading to
the measurements of OVOCs (alcohols, such as methanol
An automated zero procedure was performed every hour for 10 min. Humid zero
air was generated by passing ambient air through a catalytic converter
(stainless steel tubing filled with Pt wool held at 350
The signal of every unit mass is accumulated over 10 min and normalized by the
signals of H
Forty-three C
Sixteen C
Thirty-five C
Finally, sixteen C
The detection limits for each species measured by all five techniques were
determined as 3
During the campaign, measurements of other trace gases (NO, NO
NO and NO
O
Online measurements of organic aerosols (PILS-IC, PILS-TOC, OCEC Sunset field instruments and Q-ACSM) have been available since the beginning of June 2013, but the data reported here are restricted to the ChArMEx SOP2 period (15 July to 5 August 2013) for which VOC measurements have been performed.
In addition, black carbon (BC) was continuously monitored during the same extended period using a seven-wavelength Aethalometer (model AE-31; Magee Scientific®) at a time resolution of 15 min.
Measurements of major anions (Cl
Measurements of water-soluble organic compounds (WSOCs) in PM
Semicontinuous (2 h time resolution) concentrations of elemental carbon (EC)
and organic carbon (OC) in PM
These online EC and OC measurements were also intercompared with an analysis from an offline filter sampling to check their reliability, leading to satisfactory agreement between the two methods (see Fig. S3a in the Supplement). EC online measurements were also compared to BC measurements from an Aethalometer, leading to satisfactory agreement (see Fig. S3b).
Since summer 2012, measurements of the chemical composition of
non-refractory submicron aerosol (NR-PM
The Q-ACSM used here participated in the large intercomparison
study of 13 Q-ACSMs that took place at the ACMCC (Aerosol Chemical Monitor
Calibration Centre;
A study of back trajectories was performed to identify and classify the
origin and typology of the different air masses reaching Cape Corse during
the campaign and to support interpretation of the results. Back trajectories
of 48 h were calculated every 6 h during the whole campaign with an ending
point at the measurement site (42.969
Five back-trajectory clusters identified for the ChArMEx SOP2 field campaign at Cape Corse. This classification was conducted using back trajectories calculated by the HYSPLIT model (NOAA-ARL). The five clusters are illustrated by example maps for four trajectories (interval of 6 h between each; time of arrival indicated by different colors of trajectory) for five single days representative of an isolated cluster (25, 21, 28, 30 and 18 July for marine west, Europe northeast, Corsica south, France west and calm low wind, respectively).
A visual classification of these back trajectories was performed as a function of their origin, altitude and wind speed and segregated into five clusters (Fig. 2). A description of the five clusters is provided in Table 2. Four clusters correspond to different wind sectors defined by the origin of the air masses reaching the measurement site (west, northeast, south and northwest). These clusters are characterized by different transit times since the last potential anthropogenic contamination (i.e., since the air mass left the continental coasts). The air masses from the “marine west” cluster have spent 36 to more than 48 h above the sea, while they have spent 10–20 and 12–18 h for the “Europe northeast” and the “France northwest” clusters, respectively (Table 2). For the “Corsica south” cluster, the indicated transit time (12–24 h) considers the time spent by air masses above land (the islands of Corsica and Sardinia) before passing over the sea. These different transit times potentially indicate different atmospheric processing times for the air masses, the longest being for the “marine west” cluster.
The last cluster gathers air masses transported over short distances over 48 h and therefore during calm situations with low wind speeds (Fig. 2). The “calm low wind” cluster and the “marine west” cluster are the two most representative clusters, each representing 30 % of the air mass origin. They are followed by the “Europe northeast” cluster representing 26 %, and then by the “Corsica south” and “France northwest” clusters representing 8 and 6 % of the air mass origins, respectively.
Regarding the relatively long transit time of air masses traveling from
continental source areas to the measurement site (from 10 to more than 48 h;
see Sect. 2.5), an assessment of the photochemical age using field
observations can be performed with specific ratios of long-lived VOCs
measured at significant levels at the site. The use of graphic
representations of the ratios for three different alkanes, such as
ln(butane / ethane) versus ln(propane / ethane), is well suited to assessing the
photochemical age of air masses that experienced long-range transport
(Rudolph and Johnen, 1990; Jobson et al., 1994; Parrish et al., 2007).
Considering an air parcel isolated from any new emissions or mixing with
other air parcels and also considering that the main loss of alkanes is their
oxidation by the OH radical, the relation of the three alkanes can be
estimated
as described by Eq. (2) (Jobson et al., 1994):
Since ethane is the least reactive of these compounds, the ratios will tend to decrease with increasing photochemical age. The evolution of ln(butane / ethane) as a function of ln(propane / ethane) during the ChArMEx SOP2 field campaign in Cape Corse is presented in Fig. 3. The points in Fig. 3 have been color coded as a function of the back-trajectory clusters described in the previous section.
Back-trajectory clusters for the ChArMEx SOP2 field campaign in Cape Corse. The averaged transport time corresponds to the time spent since the last anthropogenic contamination, i.e., since the air masses left the continental coasts.
Figure 3 reveals that the air masses of the marine west (light blue) cluster present higher photochemical ages (lower alkane ratios) relative to the air masses of the Europe northeast (purple) cluster, which is consistent with the analysis of back trajectories (Sect. 2.5). Moreover, the good linearity observed in the evolution of the ratios allows for a qualitative comparison of the photochemical age of air masses from the different wind clusters.
These ratios have been compared to ratios observed at measurement sites of different types (see Fig. S4). The ratios obtained during the campaign cover a large range of values with particularly low values for the marine west cluster, which is typical of relatively aged air masses sampled at very remote sites. It indicates that air masses can spend several days over the sea before reaching the measurement site, especially for the marine west cluster. In general, ratios representative of remote locations are observed all along the campaign, confirming the remote nature of the Cape Corse station.
It is noteworthy that the slope observed for our dataset (0.65; see Fig. 3) is significantly lower than the theoretical ones calculated for an isolated air mass experiencing selective oxidation by OH (2.50) or Cl (1.97). The lack of concordance with theoretical slopes has often been observed (e.g., Parrish et al., 1992; McKeen et al., 1996) and has been attributed to the mixing between air parcels of different histories and origins during long-range transport (Parrish et al., 2007 and references therein). A deviation from the theoretical slope could also occur if the sampled air masses were enriched in new emissions from different sources, such as ship or marine emissions, during transport.
Evolution of ln(butane / ethane) as a function of ln(propane / ethane) during the ChArMEx SOP2 field campaign. The data were color coded as a function of the back-trajectory clusters (light blue, purple, yellow, red and brown for the marine west, Europe northeast, Corsica south, France northwest and calm low wind clusters, respectively). The red line corresponds to the linear regression. The black lines correspond to the theoretical kinetic evolution of the ratios of alkanes due to oxidation by OH only (solid line) or Cl only (dashed line).
In this study, US EPA PMF 3.0 was used to perform the factor analysis. For
a detailed presentation of the PMF principle, the reader can refer to the
first description made by Paatero and Tapper (1994) and to the user guide
written by Hopke (2000). A specific dataset at a receptor site can
be viewed as a data matrix
The PMF analysis was conducted on a dataset of 42 species, including NMHCs
and OVOCs measured by the two online GCs and the PTR-TOF-MS (see
Sect. S5), and 329 observations with a time of
1 h 30 min (time resolution of the GCs). Measurements taken by active sampling on
sorbent and DNPH cartridges were not included in this dataset due to their
low time resolution (3 h), which would have resulted in too few observations.
Furthermore, compounds were not considered when missing, when more than half
of the observations were below the detection limit or when associated with a low
signal-to-noise ratio (
Ethane, methanol and acetone are characterized by high background
concentrations at the measurement site. To minimize the weight of these
three species in the PMF results, their estimated background concentrations
(500, 1000 and 1200 ppt for ethane, methanol and acetone, respectively) were
subtracted from the measured concentrations in the data matrix
The PMF was run following the protocol proposed by Sauvage et al. (2009) and
relying on several statistical indicators (unexplained part for each factor,
correlation between the sum of the factor contributions and the sum of the
measured concentration, the parameter
Moreover, the homogeneity of the database built using measurements from
different techniques was studied to ensure that all instruments are
well represented in the solutions. This was done by ensuring that no
substantial differences are observed between the scaled residuals of the
different instruments. We therefore calculated the mean of the absolute
values of the scaled residuals for the three instruments
Furthermore, 100 bootstrap runs were performed for the six-factor solution to estimate the stability and uncertainty of this solution. This operation consisted of performing additional PMF runs using new input data files built by randomly selecting nonoverlapping blocks of the original data matrix; the contribution of each factor was derived from these runs and then compared to the original solution. The lowest correlation coefficient between bootstrap solutions and base run solutions was 0.6. The six-factor solution appeared to be well mapped in the base run with a mapping of bootstrap factors to base run factors higher than 86 % for all factors (see Sect. S6).
The source apportionment of organic aerosol components from Q-ACSM was performed
using positive matrix factorization (PMF; Paatero, 1997; Paatero and Tapper,
1994) via the ME-2 solver (Paatero, 1999). An extended Q-ACSM dataset of
2 months (from 5 June to 5 August 2013) was used here in order to obtain
a wider range of atmospheric variability and improve the PMF output results. The
extraction of OA data and error matrices as mass concentrations in
In this study, we therefore applied separate factorization analysis to both VOCs and aerosol databases. Another approach consists of a factorization analysis of combined aerosol and gaseous databases (Slowik et al., 2010; Crippa et al., 2013a). Thus, an attempt to perform such PMF analysis was conducted using the gaseous database (42 VOCs) described above and full ACSM spectra as inputs; the homogeneity of the different inputs was taken into account by applying a scaling procedure as proposed by Slowik et al. (2010) and Crippa et al. (2013a). However, it did not allow for the satisfactory apportionment of aerosol measurements and led to weaker solutions than the ME-2 analysis. It was therefore decided to keep separate solutions for gas- and aerosol-phase organics.
Receptor-oriented models have been developed to identify, localize and
quantify potential source areas that impact the concentrations of a
variable measured at a receptor site in the form of a contribution
quantity map. In this study we have used the concentration field (CF) approach
developed by Seibert et al. (1994). This method consists of redistributing
concentrations of a variable observed at a receptor site along the
back trajectories, ending at this site inside a predefined grid
(0.5
The 3-day back trajectories (selected to account for distant potential source areas of species with long lifetimes) used in the CF analysis were calculated by the British Atmospheric Data Centre (BADC) model every hour. This model uses the wind fields calculated by the European Centre for Medium-Range Weather Forecasts (ECMWF) to determine the trajectories of air masses. This model was selected here instead of HYSPLIT for convenience, since the format of the output files matches that needed for our CF model. Comparisons of randomly selected back trajectories in each identified cluster (see Sect. 2.5) calculated by both models (BADC and HYSPLIT) have revealed satisfactory agreement in terms of origin and areas overflown. The BADC back trajectories were interrupted when the altitude of the air mass exceeded 1500 m a.s.l. to get rid of the important dilution affecting air masses in the free troposphere (the boundary layer height has been arbitrarily set here to 1500 m a.s.l. for all trajectories). Furthermore, the grid cells containing fewer than five trajectory points were not considered for robustness purposes.
To take into account the uncertainties associated with the back trajectories,
a smoothing of concentrations was applied to all the grid cell values as
recommended by Charron et al. (2000) and using Eq. (6):
Time series of selected trace gases and wind direction at Cape Corse during the ChArMEx SOP2 field campaign. The colored areas correspond to back-trajectory clusters (light blue, purple, yellow, pink and orange-brown for the marine west, Europe northeast, Corsica south, France northwest and calm low wind clusters, respectively).
The measured mixing ratios of some organics (acetylene, isoprene, sum of
monoterpenes and acetone), inorganic trace gases (CO, NO,
NO
In contrast, significant levels of primary biogenic compounds were observed and could reach up to 1.2 and 2.0 ppb for isoprene and the sum of monoterpenes, respectively (Fig. 4). These compounds were locally emitted by the typical vegetation in the Mediterranean region (“maquis” shrubland) surrounding the measurement site. The mixing ratios for these compounds present a clear diurnal cycle with the highest values coinciding with maxima of temperature and solar radiation. Two periods characterized by high mixing ratios of biogenic VOCs were observed (27–28 July and 2–4 August), which correspond to the warmest periods of the campaign.
Oxygenated VOCs, such as acetone, were also present at significant levels of up to 3.8 ppb (Fig. 4). This compound has primary and secondary sources from the oxidation of both biogenic and anthropogenic VOCs (see discussion in Sect. 4.2.3). Therefore, acetone levels increase both when anthropogenic VOC concentrations increase (first part of the campaign) and when intense biogenic emissions are observed (27–28 July and 2–4 August).
NO
Oxygenated VOCs (including primary and secondary OVOCs from anthropogenic and biogenic origins) largely dominate the speciation of the measured VOCs (78–80 %; see Fig. S7). OVOCs are dominated by methanol, acetone and formic acid, which represent 28, 23 and 14 % of total OVOCs, respectively. The weak contribution of biogenic hydrocarbons to the total VOC composition (4–5 %; see Fig. S7) is due to the fact that these contributions are calculated on a 24 h basis and not only during daytime when their concentrations are more elevated.
Finally, anthropogenic NMHCs represent only 15–18 % of the measured VOCs
(see Fig. S7), which is consistent with the remote location
of the site. This VOC family is dominated by ethane, propane and ethylene,
which represent 34, 7 and 7 % of total A-NMHCs, respectively.
However, it is worth noting that this apportionment is only valuable for the
measured species. The difference between the measured OH reactivity
(total sink of OH) and the calculated one using all measured compounds
reported for this campaign indicates that approximately 56
The chemical composition derived from the Q-ACSM measurements is reported in
Fig. 5a for the period of study (15 July to 5 August) and
shows a clear and permanent dominance of OM, which represents 55 % of the
total mass of NR-PM
The overall OA concentrations during the campaign vary within 2 orders of
magnitude (ranging from 0.13 to 9.77
Temporal variability at Cape Corse in
The temporal variability in OC and WSOC is reported in Fig. 5b and shows very close patterns with
a few periods of noticeable discrepancies (17 and 28–30 July).
There is a clear correlation between the two datasets (
Real-time observations of two light organic tracers (MSA and oxalate) are
reported in Fig. 5c. MSA (methanesulfonic acid,
CH
Source-receptor models, such as PMF, usually aim at identifying and quantifying the contributions of sources of pollutants impacting a measurement site. In our case, the remote location of the site combined with the reactivity of the selected species does not allow for the proper identification and quantification of primary sources. Our main objective here is the identification of covariation factors of species that could be representative of aged or fresh primary emission and also of photochemical processes occurring during long-range transport or occurring locally. For this purpose, PMF was applied to a large dataset (42 different species), including primary VOCs from anthropogenic or biogenic origins, and also secondary products measured by three different techniques (PTR-TOF-MS, GC-FID-FID and GC-FID-MS; see Sect. 2.2).
Figure 6 shows the time series of the six factors obtained by the PMF analysis. Figure 7 shows the contributions of each factor to the species selected as inputs for the PMF model (in %) and the absolute averaged contribution of each species to the six factors determined by the PMF analysis (in ppt). Finally, Fig. 8 presents the maps of simulated contributions (in ppt) using the CF model for four of the six PMF factors. The relative contributions of the different PMF factors to the sum of species used as inputs are presented in Fig. S8.
Among the six PMF factors, three different factors were attributed to primary
anthropogenic sources (factors 2, 3 and 5) and are characterized by
compounds with various lifetimes (Figs. 6 and 7). The lifetimes reported below are
estimated from kinetic rate constants of the reactions between the species
of interest and OH, assuming an averaged OH concentration of 2.0
Factor 2 is composed of long-lived primary anthropogenic species, such as
ethane (58 % explained by factor 2), acetylene (44 % explained), propane
(30 % explained) and benzene (45 % explained; see
Fig. 7), with lifetimes ranging from 5 to 25 days
and typically emitted by natural gas use and combustion processes. In
addition to these long-lived primary anthropogenic species, other
anthropogenic NMHCs with shorter lifetimes compose this factor, such as
ethylene (35 % explained) or 2-methyl-2-butene co-eluted with 1-pentene
(42 % explained). It tends to indicate that in addition to the lifetime,
the nature of the sources (e.g., combustion processes) also partly influences
the profile of this factor. Furthermore, factor 2 exhibits behavior
similar to CO (see Sect. S9), a long-lived compound
(lifetime of
Factor 3 is composed of medium-lived primary anthropogenic species, such as
Factor 5 is composed of short-lived primary anthropogenic VOCs, such as
ethylene (38 % explained by factor 5), propene (44 % explained) and
toluene (38 % explained), with lifetimes ranging from 5 to 23 h and
typically emitted by combustion processes. This factor exhibits higher
levels for air masses coming from the Corsica south sector (see
Fig. 6). Likewise, areas in the south of Corsica
are identified as potential source areas for this factor
(Fig. 8). Emissions from these areas could be due
to intense ship emissions, for which speciation is dominated by alkenes
(ethene, propene), aromatics and heavy alkanes (
Time series for the contribution of the six gas-phase PMF factors together with temperature, CO, the measured organic fraction of aerosols and wind speed. The colored areas correspond to back-trajectory clusters: light blue, purple, yellow, pink and orange-brown for the marine west (M-W), Europe northeast (Eu-N-E), Corsica south (Co-S), France northwest (Fr-N-W) and calm low wind (calm-low) clusters, respectively.
Profiles of the six gas-phase PMF factors with contributions of the
factors to each species (black histograms; left axis in %) and
contributions of the species to each factor (red circles; right axis in ppt).
The “prod terpenes” 1, 2, 3 and 4 correspond to the
Source identification for the six gas-phase PMF factors using the CF model. Contributions are in parts per trillion (ppt).
The total contribution of anthropogenic-like factors to the sum of species used as inputs in the PMF model is in the range of 49–52 %. This is higher than the contributions of anthropogenic NMHCs relative to measured VOCs (15 %, see Fig. S7). This can be explained by the fact that anthropogenic NMHCs not only contribute to these anthropogenic factors, but some OVOCs are also part of them. For example, methanol and acetone both contribute to a non-negligible extent to these anthropogenic factors. Methanol contributes to 7 and 39 % of LL anthropogenic factors and ML anthropogenic factors, respectively; acetone contributes to 14 and 11 % of LL anthropogenic factors and SL anthropogenic factors, respectively. Therefore, higher contributions of these factors to the gas-phase composition are expected. Considering the primary anthropogenic part of OVOCs determined based on the anthropogenic factor contribution to OVOCs, the contribution of anthropogenic VOCs to measured VOCs rises to 42 % (see Fig. 9), which is much closer to the PMF results.
Among the six factors, two biogenic factors are also clearly identified (factors 1 and 6). They are respectively composed of primary biogenic species (factor 1) and oxidation products of primary biogenic hydrocarbons (factor 6). Therefore, they have been classified and will be respectively reported in the following as the “primary biogenic factor” (factor 1) and “secondary biogenic factor” (factor 6).
Factor 1 is composed of primary biogenic species with very short lifetimes emitted locally by the vegetation surrounding the measurement site, such as isoprene (68 % explained by factor 1), the sum of monoterpenes (83 % explained) and camphor co-eluted with undecane (38 % explained; see Fig. 7). This factor exhibits clear diurnal cycles (Figs. 6 and S9) and is correlated, as expected, with temperature (see Sect. S9), which is known to influence biogenic emissions together with solar radiation (Guenther et al., 1995, 2000).
Distribution of the different VOC groups (ANMHC is anthropogenic NMHCs, blue; BNMHC is biogenic NMHCs, green; OVOC is oxygenated VOCs, pink) calculated from the database used for PMF analysis (same as bottom panel of Fig. S7). The OVOC group is divided into three subclasses to account for their different origins: primary anthropogenic (primary A-OVOC, diagonal stripes), primary biogenic (primary B-OVOC, grid pattern) and secondary origin from the oxidation of both anthropogenic and biogenic VOCs (secondary OVOC, horizontal stripes). The partitioning of these OVOCs into the three subclasses is described in Sect. 4.2.4.
This factor represents 14 % of the sum of species used as inputs in the PMF model (Fig. S8). This is higher than the contributions of biogenic NMHCs to measured VOCs (4–5 %; see Fig. S7). As already proposed for anthropogenic factors, this can be explained by the fact that biogenic NMHCs not only contribute to these primary biogenic factors, but some biogenic OVOCs can also be part of them. For example, carboxylic acids, methanol and acetone also contribute 13, 15 and 11 % on average, respectively (explained by factor 1). Taking into account the primary biogenic part of OVOCs, the contribution of biogenic VOCs to measured VOCs rises to 15 % (see Fig. 9), which is closer to the PMF results.
Factor 6 is composed of oxidation products of primary biogenic VOCs, such as
methyl vinyl ketone (MVK) and methacrolein (MACR; 67 % explained by
factor 6), which are measured as a sum by PTR-TOF-MS (
The last factor (factor 4) has been interpreted as an “oxygenated factor” since it is mainly characterized by OVOCs, such as carboxylic acids (54 % formic acid, 43 % acetic acid, 28 % propionic acid and 14 % butyric acid), alcohols (49 % methanol and 21 % isopropyl alcohol) and carbonyls (57 % acetone, 18 % acetaldehyde and 21 % methyl ethyl ketone). Most of these species are formed by the oxidation of both anthropogenic and biogenic compounds, although some of them can also be directly emitted into the atmosphere and can therefore be of both primary and secondary origin. For example, methanol (the highest contributor to factor 4) can be emitted by vegetation (MacDonald and Fall, 1993), biomass burning (Holzinger et al., 1999) or urban and industrial activities (Hu et al., 2011). It can also be formed by photochemistry (mainly the photooxidation of methane; Tyndall et al., 2001). The same is true for acetone (the second-highest contributor to factor 4). Acetone can be directly emitted from vegetation (Goldstein and Schade, 2000; Hu et al., 2013), biomass burning (Simpson et al., 2011) and anthropogenic sources (Hu et al., 2013), and it can also be formed via the photochemical oxidation of anthropogenic VOCs, such as alkanes (Goldstein and Schade, 2000), and biogenic VOCs, such as monoterpenes (Reissell et al., 1999). Note that the same is true for carboxylic acids, which also have multiple sources (de Angelis et al., 2012 and references therein).
The multisource pattern for this factor is highlighted by its time series. Factor 4 exhibits similar behavior as anthropogenic factors (factors 2 and 3) at the beginning of the campaign with an increase to reach a maximum around 21 July and then a decrease. This factor rises again during the intense biogenic-influenced warm period (26–28 July) as observed for the secondary biogenic factor (factor 6).
The CF analysis for this factor leads to the identification of northern Italy and a large area in southern Corsica as potential source regions. The north of Italy may contribute to the anthropogenic continental influence of this factor, while the large regions in the south of Corsica may contribute to the biogenic influence since the highest biogenic signature also corresponds to air masses coming from the Corsica south sector. This could be explained by both potential biogenic emissions from the vegetation in Corsica (the site being at the extreme north of the island) and/or warmer and more stagnant conditions arising when air masses come from the Corsica south sector, favoring local biogenic emissions and low dispersion of oxidation products. It could also be due to local anthropogenic emissions from Corsican cities erroneously attributed to more distant regions, as already observed for the CF analysis of factor 5. Finally, one cannot rule out the possibility of a primary or secondary influence of ship emissions to factor 4 for this potential source area. This is also in accordance with the non-negligible contribution of this factor to the acetylene variability (29 % explained by this factor). This factor represents 28–31 % of the sum of species used as inputs in the PMF model (Fig. S8) and is therefore the most important one. Combined with the secondary biogenic factor, it leads to a contribution of 34–37 % for the oxygenated factors. This is significantly lower than the OVOC contribution to the actual measured VOCs (80 %; see Fig. S7) and can be explained by the contribution of most OVOCs, such as acetone, methanol and carboxylic acids, to other PMF factors. When only considering the secondary part of measured OVOCs, their contribution to measured VOCs decreases to 42 % (see Fig. 9), which is closer to the PMF results.
Mass spectra profile obtained for the three-factor-constrained PMF
solution (factor 1
From the six PMF factors, it is possible to apportion the measured OVOCs among their potentially different origins (primary anthropogenic or biogenic emissions, photochemical production from the oxidation of anthropogenic or biogenic hydrocarbons). Therefore, factor 1 is attributed to a primary biogenic origin, while factors 2, 3 and 5 are attributed to a primary anthropogenic origin, and factors 4 and 6 are attributed to a secondary origin (photochemical oxidation of primary VOCs from both biogenic and anthropogenic origins). The contributions of each OVOC to a specific PMF factor are summed up and ascribed to the corresponding origin. The subtracted backgrounds of acetone and methanol are redistributed to each PMF factor according to the relative contribution of these species to each factor. The apportionment of anthropogenic, biogenic and secondary origins for OVOCs can be seen in Fig. 9. Primary anthropogenic sources, primary biogenic sources and secondary processes account for 34, 13 and 53 % of the measured OVOCs, respectively. Therefore, the measured OVOCs at Cape Corse are approximately half oxidation products of VOCs and half primary VOCs.
To our best knowledge, only three studies have been conducted that applied PMF for gas-phase species in remote environments (Sauvage et al., 2009; Lanz et al., 2009; Leuchner et al., 2015). These studies were only based on NMHC measurements and chlorinated organic species in one case (Lanz et al., 2009). No oxygenated VOCs were considered. Consequently, these three studies only identified factors representative of primary sources.
Leuchner et al. (2015) identified six PMF factors at a remote site at
Hohenpeissenberg over a period of 7 years (2003–2009), including primary
biogenic, short-lived combustion, short-lived evaporative, residential
heating, long-lived evaporative and background factors. The
classification of factors was linked to the difference in the source
typology (biogenic versus anthropogenic, combustion versus evaporative) and/or the
lifetime of compounds (short-lived versus long-lived). Lanz et al. (2009) found
only four PMF factors at a continental mountain site at Jungfraujoch
(Switzerland) over 8 years (2000–2007), including a highly aged
combustive emission factor correlated with CO, a fresh emission and
solvent-use factor correlated with NO
Therefore, we incorporated OVOCs for the first time in a database used for PMF analysis at a remote environment. It allows for the first identification of the PMF factors representative of secondary processes in addition to factors related to primary sources. As has been found in previous studies performed in such environments, we also found that primary anthropogenic PMF factors were separated according to the lifetime of the compounds that composed them. As in the three studies described above, a clear primary biogenic factor is identified in our study. Furthermore, our analysis allowed for the apportionment of the anthropogenic, biogenic and secondary parts of OVOCs.
Based on the two available months of ACSM data, a three-factor solution was
selected here, corresponding to a minimum of the quality parameter
The consistency of the different OA factors was further checked with the
external tracers in Fig. 11; HOA with BC (fossil
fuel tracer), SV-OOA with WSOC and LV-OOA with oxalate. The good agreement
of SV-OOA with WSOC is consistent with freshly formed SOA being
semi-volatile and water soluble as reported, for instance, by Hennigan et al. (2008a)
who observed strong similarities between semi-volatile
NH
The different OA factors obtained here are mainly of continental origin, and therefore their temporal variability is mostly related to the amount and frequency of continental air masses reaching the sampling site. Nevertheless, the diurnal variation in SV-OOA and LV-OOA (Fig. S11) suggests that local photochemical processes have also occurred, with local formation of fresh SV-OOA in the morning followed by rapid oxidation, which could explain the enhancement of LV-OAA in the afternoon.
Time-series of
Stacked time series of aerosol fractions (top panel), VOC PMF factors (middle panel) and ln(propane / ethane) as a proxy for photochemical age (bottom panel). F-LL, F-ML and F-SL anthropogenic refer to the long-lived, medium-lived and short-lived anthropogenic factors, respectively. The colored areas at the top correspond to back-trajectory clusters: light blue, purple, yellow, pink and orange-brown for the marine west (M-W), Europe northeast (Eu-N-E), Corsica south (Co-S), France northwest (Fr-N-W) and calm low wind (calm-low) clusters, respectively.
Average mass concentrations are 0.13, 1.59 and 1.92
First, the gas-phase “oxygenated factor” (factor 4) is correlated with the
organic fraction of the aerosol measured by ACSM (
Figure 12 shows stacked time series of the different fractions (inorganic and organic) of aerosol measured by ACSM (top panel) and stacked time series of contributions of PMF factors (middle panel) for the VOCs (see Sect. 4.2). This figure aims to draw a parallel between aerosol- and gas-phase compositions to highlight the link between the two phases.
From these graphs and from the back-trajectory clusters (also shown in
Fig. 12), it is possible to distinguish two
periods during which processed anthropogenic continental air masses reached
the site (between 19 and 24 July and between 30 July and 3 August 2013).
The first period is characterized by high contributions of anthropogenic and
oxygenated gas PMF factors (middle panel of Fig. 12)
and an aerosol with inorganic (ammonium sulfate) and organic
fractions in approximately similar proportions (top panel of
Fig. 12). This period also corresponds to the
highest values of ln(propane / ethane) of
The second period of long-range transported anthropogenic continental emissions is characterized by less intense anthropogenic gas-phase PMF factors, especially for the long-lived anthropogenic factor, and a clear predominance of the organic fraction for aerosols. Aerosol mass concentrations are also lower by approximately 50 % compared to the first period. During both periods, a non-negligible biogenic influence is also observed from primary and secondary biogenic PMF VOC factors. This is even more pronounced for the second “anthropogenic” period. During these periods, it is therefore likely that oxygenated VOCs and OOAs have both biogenic and anthropogenic origins in variable proportions.
A period of intense biogenic influence without significant long-range
transport of anthropogenic continental emissions can also be distinguished
(between 26 and 28 July) with elevated contributions of the primary and
secondary biogenic gas-phase PMF factors (Fig. 12).
The oxygenated gas-phase PMF factor also rose during this period, and
the aerosol composition is dominated by OA with low levels of inorganic
aerosols. The inorganic fraction of aerosols decreases to reach less
than 10 % of the aerosol composition on 27 July. This strong decrease
occurred at the same time as a change in air mass origin from marine west
to Corsica south. This is consistent with the lack of anthropogenic
influence during this period, confirmed by lower ln(propane / ethane) of
Finally, very low contributions of HOA were observed during the whole
campaign from the PMF analysis of ACSM measurements (typically below
0.3
An analysis of the isotopic ratio of
Given the good correlation observed between OA and the gas-phase oxygenated
factor (
The ChArMEx SOP2 field campaign provided a unique opportunity for insight into the various sources and fates of organic carbon in the Mediterranean atmosphere, thanks to the measurement of a large panel of gaseous and aerosol species at a remote site located at Cape Corse in the western Mediterranean Basin. The combination of gaseous and particulate organic databases, as collected during this campaign, is not common and has the potential to help improve our understanding of SOA formation. Moreover, the Mediterranean basin is an ideal location to characterize organics in the atmosphere since it is impacted by strong natural and anthropogenic sources and undergoes intense photochemical aging, especially during summer. The measurement site (Cape Corse) offered ideal experimental conditions since it is surrounded by the sea and is located at various distances from regional anthropogenic emission hot spots (such as north of Italy, southeast of France, northeast of Spain or north of Africa). These characteristics coupled with extremely low local anthropogenic sources allowed for the study of anthropogenic plumes after several days of atmospheric processing. In addition, intense local biogenic emissions permitted the investigation of biogenic and anthropogenic interactions in air mass composition.
These specific conditions led to the observation of contrasting situations,
i.e., highly variable photochemical ages of processed anthropogenic air
masses coupled with intense and local biogenic emissions. Low levels of
anthropogenic VOCs (
The aerosol chemical composition derived from Q-ACSM measurements shows a
clear predominance of OM, which represents 55 % of the total mass of
NR-PM
PMF was conducted to identify covariation factors of VOCs that are representative of primary emissions and secondary photochemical transformations occurring during the transport of air masses. This analysis was performed using a gas-phase database of 42 VOCs (or sum of VOCs) of anthropogenic and biogenic origins, including NMHCs and OVOCs for the first time. A six-factor solution turned out to be optimal for this PMF analysis. In parallel, a concentration field (CF) analysis was conducted on four PMF factors to help in their identification through the localization of potential source areas. This combination of CF and PMF was particularly helpful in interpreting the factors associated with the long-range transport of anthropogenic compounds.
Three anthropogenic factors characterized by primary anthropogenic VOCs with various lifetimes were found. The CF analysis confirmed the anthropogenic nature of these factors by an identification of potential source areas in regions experiencing intense anthropogenic activities (e.g., the Po Valley and southeast of France).
Two biogenic factors were also identified. Both factors exhibited clear diurnal cycles and were correlated with temperature. In addition to a primary biogenic factor usually observed in VOC source apportionment studies, we also clearly identified, for the first time in PMF analysis, a secondary biogenic factor made up of first-generation oxidation products of biogenic VOCs.
A last oxygenated factor characterized by OVOCs of both biogenic and
anthropogenic origins was also derived from the PMF analysis. The
identification of this unusual factor was made possible by the extension of
the input database to secondary oxygenated VOCs. This factor was influenced
by anthropogenic and biogenic sources, showing elevated levels during
periods of intense local biogenic influence (e.g., 26–28 July) and periods of
long-range transport of anthropogenic continental emissions (e.g., 21–23 July).
This factor was also correlated with submicron OA measured by ACSM
(
The source apportionment of OA measured by ACSM led to a three-factor solution
identified as hydrogen-like OA, semi-volatile oxygenated OA and
low volatility oxygenated OA. These three factors accounted for an averaged mass
concentration of 0.13, 1.59 and 1.92
A coupled analysis of VOC and OA sources was conducted. During biogenic
periods, the aerosol composition was dominated by (secondary) OA,
indicating a substantial impact of BVOCs on aerosol composition. During periods of long-range transport of anthropogenic continental
emissions, the inorganic and organic fractions of submicron aerosols were
similar. During the whole campaign, low levels of hydrogen-like OA (HOA)
were observed (
Access to the data
used for this publication is restricted to
registered users following the data and publication policy of the
ChArMEx program
(
The authors declare that they have no conflict of interest.
This article is part of the special issue “CHemistry and AeRosols Mediterranean EXperiments (ChArMEx; ACP/AMT inter-journal SI)”. It does not belong to a conference.
This study received financial support from the MISTRALS and ChArMEx programs, ADEME, the French Environmental Ministry, the CaPPA projects and the Communauté Territoriale de Corse (CORSiCA project). The CaPPA project (Chemical and Physical Properties of the Atmosphere) is funded by the French National Research Agency (ANR) through the PIA (Programme d'Investissement d'Avenir) under contract ANR-11-LABX-0005-01 and by the Regional Council Nord-Pas de Calais and the European Funds for Regional Economic Development (FEDER). This research was also funded by the European Union Seventh Framework Programme under grant agreement number 293897, the DEFI-VOC project, CARBO-SOR/Primequal and SAF-MED (ANR grant number ANR-12-BS06-0013-02). Greenhouse gas data were provided by the ICOS France monitoring network.
The authors also want to thank Eric Hamonou and François Dulac for logistical help during the campaign and all the participants of the ChArMEx SOP2 field campaign. Edited by: Nikolaos Mihalopoulos Reviewed by: three anonymous referees