ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-2013-2016Volatility of organic aerosol and its components in the megacity of ParisPacigaAndreaKarneziEleniKostenidouEvangeliaHildebrandtLeahttps://orcid.org/0000-0001-8378-1882PsichoudakiMagdaEngelhartGabriella J.LeeByong-HyoekCrippaMonicaPrévôtAndré S. H.BaltenspergerUrsPandisSpyros N.spyros@chemeng.upatras.grDepartment of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USACenter for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, USAInst. of Chemical Engineering Sciences, FORTH/ICEHT, Patras, GreeceMcKetta Department of Chemical Engineering, University of Texas, Austin, TX, USADepartment of Chemical Engineering, University of Patras, Patras, GreeceLaboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen PSI, 5232, SwitzerlandEuropean Commission, Joint Research Centre, Institute for Environment and Sustainability, Air and Climate Unit, Via Fermi, 2749, 21027 Ispra, ItalySpyros N. Pandis (spyros@chemeng.upatras.gr)23February20161642013202315June201520August201521January201631January2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/2013/2016/acp-16-2013-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/2013/2016/acp-16-2013-2016.pdf
Using a mass transfer model and the volatility basis set, we estimate the
volatility distribution for the organic aerosol (OA) components during summer
and winter in Paris, France as part of the collaborative project MEGAPOLI.
The concentrations of the OA components as a function of temperature were
measured combining data from a thermodenuder and an aerosol mass spectrometer
(AMS) with Positive Matrix Factorization (PMF) analysis. The hydrocarbon-like
organic aerosol (HOA) had similar volatility distributions for the summer and
winter campaigns with half of the material in the saturation concentration
bin of 10 µg m-3 and another 35–40 % consisting of low
and extremely low volatility organic compounds (LVOCs with effective
saturation concentrations C* of 10-3–0.1 µg m-3 and
ELVOCs C* less or equal than 10-4µg m-3,
respectively). The winter cooking OA (COA) was more than an order of
magnitude less volatile than the summer COA. The low-volatility oxygenated OA
(LV-OOA) factor detected in the summer had the lowest volatility of all the
derived factors and consisted almost exclusively of ELVOCs. The volatility
for the semi-volatile oxygenated OA (SV-OOA) was significantly higher than
that of the LV-OOA, containing both semi-volatile organic components (SVOCs
with C* in the 1–100 µg m-3 range) and LVOCs. The
oxygenated OA (OOA) factor in winter consisted of SVOCs (45 %), LVOCs
(25 %) and ELVOCs (30 %). The volatility of marine OA (MOA) was
higher than that of the other factors containing around 60 % SVOCs. The
biomass burning OA (BBOA) factor contained components with a wide range of
volatilities with significant contributions from both SVOCs (50 %) and
LVOCs (30 %). Finally, combining the bulk average O : C ratios and
volatility distributions of the various factors, our results are placed into
the two-dimensional volatility basis set (2D-VBS) framework. The OA factors
cover a broad spectrum of volatilities with no direct link between the
average volatility and average O : C of the OA components.
Introduction
Atmospheric aerosols have adverse effects on human health (Caiazzo et al.,
2013; Pope et al., 2009) and contribute to climate change (IPCC, 2013).
Over 50 % of the submicron particulate mass is often comprised of organic
compounds (Zhang et al., 2007). OA (organic aerosol) originates from many
different natural and anthropogenic sources and processes. It can be emitted
directly, e.g., from fossil fuels and biomass combustion (so-called primary
organic aerosol, POA) or can be formed by atmospheric oxidation of volatile,
intermediate volatility and semi-volatile organic compounds (secondary
organic aerosol, SOA). Since the oxidation pathways of organic vapors are
complex and the corresponding reactions lead to hundreds or even thousands of
oxygenated products for each precursor, our understanding of OA formation
mechanisms and the OA chemical and physical properties remains incomplete.
Furthermore, a lack of information regarding the sources along with the
physical and chemical properties, and lifetime of organic aerosol (OA) has
made predictions of OA concentrations by chemical transport models uncertain.
The volatility of atmospheric OA is one of its most important physical
properties. It determines the partitioning of these organic compounds between
the gas and particulate phases, the OA concentration, and the atmospheric
fate of the corresponding compounds. Measurement of the OA volatility
distribution has been recognized as one of the major challenges in our
efforts to quantify the rates of formation of secondary organic particulate
matter (Donahue et al., 2012). Thermodenuders (TD) have been developed to
measure the volatility of ambient aerosol (Burtscher et al., 2001; Wehner et
al., 2002, 2004; Kalberer et al., 2004; An et al., 2007). Most TDs consist of
two basic parts: a heated tube where the more volatile particle components
evaporate, leaving less volatile species behind, and the denuder tube, usually
containing activated carbon where the evaporated material is adsorbed thus
avoiding potential recondensation when the sample is cooled to room
temperature. The aerosol mass fraction remaining (MFR) at a given
temperature, after passing through the TD, is the most common way of
reporting the TD measurements. The MFR, though an indirect metric of
volatility for a specific TD operation, also depends on the aerosol
concentration, size, enthalpy of vaporization, potential resistances to mass
transfer, etc. (Riipinen et al., 2010).
The two-dimensional volatility basis set (2D-VBS) framework from Donahue et
al. (2012) has been used in order to describe atmospheric OA formation and
evolution by lumping all organic compounds (with the exception of VOCs) into
surrogates along two axes of volatility and the oxygen content (expressed as
the O : C ratio or carbon oxidation state). Using the 2D-VBS requires the
ability to measure the OA distribution as a function of volatility and
O : C ratio (or carbon oxidation state).
Positive Matrix Factorization (PMF), aims to deconvolve the bulk OA mass
spectra obtained by the aerosol mass spectrometer (AMS) into individual
“factors” that give information about the sources or processing of organic
aerosol (Lanz et al., 2007; Ulbrich et al., 2009; Huffman et al., 2009; Zhang
et al., 2011). Typical factors correspond to either primary sources including
HOA (hydrocarbon-like OA), BBOA (biomass burning OA) and COA (cooking OA) or
secondary OA like SV-OOA (semi-volatile oxygenated OA) and LV-OOA (low
volatility oxygenated OA). Although there have been numerous studies that
have identified PMF factors in ambient data sets, there have been few studies
that have attempted to estimate the corresponding factor volatility (Huffman
et al., 2009; Cappa and Jimenez, 2010). Huffman et al. (2009) characterized
the volatility of PMF factors derived for the MILAGRO campaign in Mexico City
and for the SOAR-1 campaign in Riverside, CA. They concluded that BBOA was
the most volatile and OOA was the least volatile component. HOA was more
volatile than OOA in almost all cases. Cappa and Jimenez (2010), using a
kinetic evaporation model, estimated the volatility distributions for the
various PMF OA factors for the MILAGRO campaign. Here we extend this work
focusing on another megacity, Paris.
In this study, we estimate the volatility distributions of PMF factors
derived from two month-long summer and winter campaigns in a suburban
background site in Paris. The data analysis approach is first outlined and
the corresponding challenges are discussed. We use the mass transfer model of
Riipinen et al. (2010), together with the approach introduced by Karnezi et
al. (2014) to estimate the volatility distributions for all PMF factors. We
finally synthesize the corresponding OA findings using the 2D-VBS framework.
MethodsMeasurement site and sampling
Two comprehensive field campaigns were performed during July of 2009 and
January/February of 2010 at an urban background sampling site, SIRTA (Site
Instrumental de Recherche par Teledetection Atmospherique) (Haeffelin et al.,
2005) located about 20 km southwest of Paris' city center. The data sets were
collected as part of a collaborative project known as MEGAPOLI (Megacities:
Emissions, urban, regional, and Global Atmospheric POLution and climate
effects, and Integrated tools for assessment and mitigation) (Baklanov et
al., 2008; Beekmann et al., 2015). A suite of instruments were used including
a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) from
Aerodyne research, Inc. (DeCarlo et al., 2006) for particle mass and
composition, a scanning mobility particle sizer (SMPS) from TSI, Inc. for
particle size and number distributions and the Carnegie Mellon University
thermodenuder (TD) for volatility measurements.
The TD design was similar to that described in An et al. (2007), consisting
of a heated tube followed by a denuding section, which uses activated
charcoal to prevent recondensation of organic vapors. The TD was operated at
temperatures ranging from about 20 to 200 ∘C during both campaigns,
yielding thermograms of the organic aerosol mass remaining as a function of
TD temperature. The TD scanned this temperature range using different
temperatures each day. A centerline residence time of 25 s at 298 K was used
for all measurements (Lee et al., 2010). This corresponds to a mean residence
time of approximately 50 s at 298 K.
Changes in composition, mass, and size as a result of aerosol evaporation
were quantified by both the SMPS and the HR-ToF-AMS by alternate sampling
between the TD and the ambient sample line, every 5 min. The SMPS was
operated with a sheath flow of 5 L min-1 and a sample flow rate of
0.5 L min-1. The HR-ToF-AMS, which measures the aerosol
size-composition distribution of the submicron non-refractory material, was
operated in both the higher sensitivity mode (V-mode) and the higher
resolution mode (W-mode) (DeCarlo et al., 2006). The V-mode data are used in
this study. The AMS collection efficiency was estimated at 0.38 during the
summer (Crippa et al., 2013a) and 0.5 during the winter (Crippa et al.,
2013b).
Ambient (blue dots) and thermodenuder (red dots) organic mass
concentration measurements for Paris during summer 2009.
Data analysis
TD raw measurements need to be corrected for particle losses due to diffusion
of small particles, sedimentation of larger particles, and thermophoretic
losses (Burtscher et al., 2001). To account for these losses, which depend on
particle size, TD temperature, and sample flow rate, Lee et al. (2010) have developed
size and temperature-dependent corrections for this particular TD. The
organic aerosol concentrations measured after the TD were corrected for
losses corresponding to the operating conditions during the campaign. The OA
mass fraction remaining (MFR) was calculated dividing the loss-corrected OA
concentration after the TD at time period i with that of the by-pass line
at time period i+1. The fact that the two measurements correspond to two
different 5 min time intervals introduces some uncertainty in the calculated
MFR values because of the variability of the atmospheric concentrations. Some
of this variability is averaged out when average MFR values are calculated
for each temperature.
The preparation of these large data sets for analysis required careful
examination of the ambient OA variability in order to determine the
appropriate averaging intervals. The OA mass concentration data for the
summer campaign are shown in Fig. 1. Overall, the particulate matter mass
concentration was surprisingly low during this period in Paris, with a
campaign average PM1 OA for SIRTA of only 0.83 µg m-3.
As expected, there were several periods during which the OA concentration was
much higher than 1 µg m-3 reaching levels up to
6 µg m-3. To evaluate whether the OA during these higher
concentration periods had different MFR values than the rest of the samples,
we separated the data in two groups using an OA concentration cutoff of
1.5 µg m-3. Figure S1 in the Supplement shows the
corresponding MFR measurements for both low and high concentration periods.
Given the experimental variability, there is no discernable difference in
evaporation between the higher and the lower concentration periods and,
therefore, these were averaged together for the analysis. The similarity of
the average MFR values during these low and high concentration periods (the
latter were often characterized by higher OA variability) also suggests that
our calculation of the MFR using measurement pairs did not introduce
significant bias in the average estimated MFR.
We performed a similar analysis for the winter campaign. Paris during winter,
unlike the summer, was characterized by higher fine PM concentrations with an
average PM1 OA concentration of 3.1 µg m-3 (Fig. 2). The
OA threshold concentration was chosen to be 4.5 µg m-3 and
again there was no evidence of effects of concentration (in the observed
range) on volatility (Supplement, Fig. S2) and the corresponding MFRs were
averaged together. Finally, the data points were averaged into temperature
bins of 5 ∘C. The calculation of one MFR value every 5 ∘C
is a compromise between the need to average more data points at similar
temperatures and maintaining the dynamic behavior of the thermogram.
Averaging over wider temperature ranges (e.g. 10 ∘C) did not result
in any essential differences in our analysis and conclusions.
Ambient (blue dots) and thermodenuder (red dots) OA mass concentration time
series for the winter 2010 campaign.
Along with the bulk organic measurements, additional information can be
derived from the HR-ToF-AMS V-mode mass spectra using the PMF analysis
technique. The deconvolved spectra yielded several organic aerosol
“factors” for each campaign. A complete discussion of the PMF analysis of
the ambient measurements and the resulting factors can be found in Crippa et
al. (2013a, b). The PMF analysis was repeated, combining both ambient and
thermodenuded spectra with guidance from the original analysis of the
ambient-only data (e.g., the same number of factors was used). This second
analysis produced for all practical purposes the same results for the ambient
data set as that of the ambient measurements only and can be found in the
corresponding publications.
Average and threshold ambient concentrations for each PMF factor.
The low OA concentrations especially during the summer resulted in very low
concentrations of the corresponding factors and thus high MFR uncertainty.
The MFRs of the various factors were, as expected, extremely variable when
the factor concentrations were close to zero. Therefore, to minimize these
problems, a minimum ambient mass concentration was determined for each PMF
factor, based on the concentration range for which MFR measurements exceeded
1.5. The average ambient concentration and threshold concentration with
corresponding statistical information for each PMF factor is shown in
Table 1. The corresponding factor concentration thresholds during the summer
were in the 0.05–0.1 µg m-3 range. MFR measurements of PMF
factors with ambient levels less than 0.1 µg m-3 are clearly
quite uncertain. All the corresponding MFR values from these low factor
concentration periods were excluded from the analysis. Few MFR measurements
were excluded during the winter period, while 20–50 % of the
measurements for the various factors were excluded during the summer.
Volatility distribution estimation
To estimate the volatility distributions from the corrected thermograms we
employed the dynamic mass transfer model of Riipinen et al. (2010). The model
simulates particle evaporation using experimental inputs including TD
temperature and residence time, initial particle size, and ambient OA
concentration. The volatility of these complex mixtures is defined using the
corresponding effective saturation concentration, C∗, at 298 K.
Along with saturation concentration, two parameters that can affect the
evaporation rate and the corresponding volatility estimation are the enthalpy
of vaporization and the mass accommodation coefficient. Unfortunately, these
values are currently unknown for these complex multi-component systems.
Often, a mass accommodation coefficient of unity is assumed. However, mass
transfer limitations to evaporation have been observed in some experimental
systems, leading to mass accommodation coefficient values of much less than
one (Saleh et al., 2013). Typical values of 100 kJ mol-1 and 1.0 are
assumed for the enthalpy of vaporization and accommodation coefficient,
respectively. Use of lower accommodation coefficient values results in
shifting of the estimated volatility distributions to higher values. Lee et
al. (2010) explored this sensitivity and estimated that an order of magnitude
change in the mass accommodation coefficient was “equivalent” to a
corresponding change in the volatility distribution. Similar conclusions
about the sensitivity of the estimated volatility to the accommodation
coefficient were reached by Cappa and Jimenez (2010) as well as Riipinen et
al. (2010).
Loss-corrected average OA thermograms for summer (red circles) and
winter (blue squares) campaigns. The error bars correspond to ±2 standard deviations of the mean. Points with no error bars correspond to a
single measurement.
Estimated volatility distributions for summer (left panel) and
winter total OA (right panel). The error bars correspond to the fitting
uncertainties according to the algorithm of Karnezi et al. (2014).
As described in Donahue et al. (2006), the volatility distribution is
represented by surrogate species with a saturation concentration of
Ci∗. The Ci∗ bins are logarithmically spaced,
allowing for extremely low and high volatility species to be compared in a
single framework. The analysis here was limited to a 6-consecutive C∗ bin solution with a variable mass fraction value for each bin. Different
volatility ranges were tested and the best range was selected for each
factor. The “goodness of fit” was quantified using the error analysis
outlined in Karnezi et al. (2014). The standard error was calculated for all
C∗ bin-mass fraction combinations. For a given 6-bin solution, the
top 2 % of mass fraction combinations with the lowest error was used to
find the average mass fraction in each bin and the corresponding standard
deviation.
The OA components are described as semi-volatile (SVOCs with C* of 1, 10,
and 100 µg m-3), low volatility (LVOCs with C* of
10-3, 10-2, and 0.1 µg m-3), and extremely low
volatility (ELVOCs with C*≤ 10-4µg m-3) in
the rest of the paper (Murphy et al., 2014).
Results and discussionOrganic aerosol volatility
The average loss-corrected OA thermograms for the two seasons are shown in
Fig. 3. The two thermograms seem very similar while differences are mostly
noticeable at the high temperatures. In the winter thermogram an approximate
30 % of the OA remained at 180 ∘C while in the summer thermogram less than
10 % was present at the same temperature. This might suggest more ELVOCs
being present at winter. However, the summer thermogram shows that nearly
50 % of the mass evaporated at a thermodenuder temperature of
83 ∘C (T50). The winter measurements suggested a similar
T50 value of 88 ∘C. This crude comparison of volatility through
the corresponding thermograms suggests that the OA in the two seasons could
have similar average volatility distributions. It is surprising that the
seasonal differences in emissions are not reflected in the corresponding
thermograms. We will examine the reasons for this similarity in the
subsequent section by analyzing the volatility of the corresponding factors.
The volatility distributions for the total OA for the two seasons are
depicted in Fig. 4. They are quite similar to each other especially
considering the corresponding uncertainties and they are characterized by
higher concentrations of components with C*= 10-4 and
10 µg m-3.
Volatility of organic aerosol components
Five PMF factors were determined for the summer data set by Crippa et
al. (2013a). Hydrocarbon-like OA (HOA) most closely resembles fresh vehicle
emissions in that the mass spectrum resembles that of transportation sources.
Cooking OA (COA) was also observed in the summer campaign, peaking during
noon and evening meal times. Marine OA (MOA) was identified based on
relatively high levels of organic sulfur and a strong correlation with
methanesulfonic acid (MSA), which is a product of continued oxidation of
phytoplankton decomposition products. Two SOA factors were also reported:
semi-volatile oxygenated OA (SV-OOA) and low-volatility oxygenated OA
(LV-OOA). These two factors were differentiated based on their O : C ratio. The
two secondary OA factors made up 57 % of the total OA mass. The remaining
factors contributed fairly similar average fractions of 18 % for COA,
12 % for HOA, and 13 % for MOA. Detailed discussion of the PMF
factors along with verification analysis were provided by Crippa et
al. (2013a).
Estimated volatility distributions for summer PMF factors (left
panel) and winter PMF factors (right panel). The error bars correspond to
the fitting uncertainties according to the algorithm of Karnezi et al. (2014).
The PMF analysis for the winter campaign yielded four factors. The HOA and
COA factors were again present. There was also a single secondary OA factor
which was termed oxygenated OA (OOA). This factor could not be further
separated into SV-OOA and LV-OOA. The final factor reported was biomass
burning OA (BBOA), correlating with known molecular markers for residential
wood burning (e.g., levoglucosan). The OOA factor was found to dominate the
organic aerosol mass, contributing nearly 65 % on average. The complete
analysis and description of these factors can be found in Crippa et
al. (2013b).
Using the mass transfer model from Riipinen et al. (2010) and the approach of
Karnezi et al. (2014) we fitted the corresponding thermograms (Fig. S3),
using a C∗ bin solution with a variable mass fraction value for each
bin. Specifically for each factor we used an individual consecutive 6-bin
solution (chosen as the 6-bin solution with the best fits) resulting in the
volatility distributions, shown in Fig. 5. The modeled thermograms for all
factors from both summer and winter campaigns are shown in Fig. 6. Finally,
the volatility distributions for each factor are summarized in Table S1 in
the supplementary information. The fitting of individual factor thermograms
implicitly assumes that each factor had the same size distribution as the
total OA and that the factors were present as an external mixture. To test
the uncertainty introduced by this assumption we compared the volatility
distribution of the total OA with the composition weighted sum of the
volatility distributions of the individual OA factors for both summer and
winter. The two distributions (total and sum of factors) agreed within a few
percent for both seasons suggesting that the uncertainty is modest and within
the uncertainty limits shown in the corresponding figures.
The HOA factors for the summer and winter campaigns had very similar
thermograms and volatility distributions with half of the material in the
10 µg m-3 bin (Fig. 5). Roughly 40 % of the HOA in both
seasons consisted of LVOCs and ELVOCs. This volatility similarity is
consistent with the similarity in mass spectra derived by the PMF analysis
(Fig. 7a). The angle θ between the corresponding vectors (treating
the AMS spectra as vectors according to Kostenidou et al., 2009) was
14∘ suggesting similar chemical fingerprints. This is not surprising
for a megacity where the transportation and any industrial sources are
expected to have chemically similar emissions in both summer and winter.
Similar were also the T50 for the HOA factors with values of
49 and 54 ∘C for the summer and winter campaign,
respectively. Cappa and Jimenez (2010) also estimated that the HOA in Mexico
City had a wide volatility distribution with approximately 35 % of its
mass consisting of LVOCs and ELVOCs while the remaining 65 % was SVOCs.
Almost 40 % of the HOA had C*≥ 10 µg m-3 which
compares very well with the 50 % estimated here.
Estimated best-fit thermograms for all PMF factors. The solid
lines represent the thermograms for the summer campaign and the dashed lines
the thermograms for the winter campaign.
The situation was quite different for the cooking OA factor. Here the
seasonal differences were more pronounced for the thermograms (Fig. 6), the
estimated volatility distributions (Fig. 5) and the corresponding mass
spectra (Fig. 7b). The winter COA was substantially less volatile than the
summer COA, more than an order of magnitude based on average logC* values,
weighted by the mass fraction of each bin (average C∗= 10-2µg m-3 for the summer campaign and average
C∗= 4 × 10-4µg m-3 for the
winter campaign). The COA factor during the winter campaign did not contain
semi-volatile components while 37 % of the summer COA was semi-volatile.
The COA winter factor consisted of ELVOCs (37 %) and LVOCs (63 %).
The COA mass spectra in Fig. 7b show that the winter COA was characterized
by a higher fraction of molecular fragments at higher mass-to-charge (m/z)
ratio. This is consistent with organic components of longer carbon chain
which, for the same level of oxidation, are expected to have lower
volatility. The angle θ between the COA spectra was 26∘,
suggesting a significant chemical difference. One explanation is that the
cooking habits are different in the two seasons with outdoor cooking (e.g.,
barbecue) dominating in the summer and indoor cooking relying more on oil and
butter, being more significant in the winter. We also cannot rule out some
imperfect unmixing of OA sources and components. The T50 for the COA
factors were different as well, with values of 91 and 148 ∘C for the
summer and winter campaign, respectively.
The LV-OOA factor detected in the summer had the lowest volatility (Fig. 5)
of all the derived factors. There was no sign of evaporation until the TD
temperature reached nearly 150 ∘C (Fig. 6). We estimate that this
factor consisted almost exclusively of OA with effective saturation
concentrations equal to or lower than 10-3µg m-3, which
are almost exclusively ELVOCs. The average ambient concentration of this
factor during the summer was 0.12 µg m-3 and its average
C∗ was equal to 5 × 10-6µg m-3. Very
low volatilities (practically all the OA had C*≤ 10-3µg m-3) were also estimated for LV-OOA by Cappa
and Jimenez (2010) in Mexico City during the MILAGRO campaign.
Seasonal mass spectra comparison for (a) HOA and (b) COA in Paris.
Red lines correspond to the summer measurements while blue symbols
correspond to the winter data.
The estimated volatility for the SV-OOA factor is consistent with its naming
by Crippa et al. (2013a) as it was significantly higher than that of the
LV-OOA (Fig. 5). We estimated that roughly half of the SV-OOA was SVOCs while
it contained also LVOCs (42 %) and a small amount of ELVOCs (6 %).
Its T50 was 61 ∘C and its average C∗ was roughly
0.2 µg m-3. These values are once more generally consistent
with the estimates of Cappa and Jimenez (2010) showing that SVOCs dominated
the SV-OOA during MILAGRO (approximately 40 %) with LVOCs contributing
another 35 %.
The OOA factor determined in the winter had a volatility distribution
(Fig. 5), containing SVOCs (45 %), LVOCs (25 %) and ELVOCs
(30 %). The winter OOA and the summer SV-OOA spectra had a θ
angle of 34∘, while there was an even larger discrepancy between the
winter OOA and the summer LV-OOA with an angle of 37∘. The T50
was equal to 85 ∘C. These differences in mass spectra and T50
are consistent with the differences in volatility. The average volatility of
OOA was much higher than LVOOA in summer but lower than SVOOA.
The marine OA (MOA) factor was only detected during the summer campaign at an
average concentration of 0.17 µg m-3. Its volatility was
relatively high (Fig. 6), and almost all the MOA had evaporated at
100 ∘C. The MOA factor consisted mainly of SVOCs (61 %) and some
LVOCs (36 %). Its T50 was equal to 58 ∘C and its average
C∗ was approximately 0.4 µg m-3.
The BBOA factor was present in the winter data set with an average ambient
concentration of 0.6 µg m-3. The corresponding estimated
volatility distribution (Fig. 5) shows that half of the BBOA factor consisted
of SVOCs (with most material in the 10 µg m-3 bin) and the
other half of LVOCs and ELVOCs. A similar bimodal distribution was also found
by May et al. (2013) with a peak at 0.01 and one at
100 µg m-3 for controlled biomass burning in the laboratory.
The difference in the location of the high-volatility peak can potentially be
explained by the wider range of concentrations in the experiments analyzed by
May et al. (2013) compared to the limited range in the ambient Paris
measurements. The more volatile BBOA components were never in the particulate
phase in our data set so their abundance cannot be determined. The BBOA
T50 was 70 ∘C, higher than that of HOA and less than those of
COA and OOA. Finally, its average C∗ was approximately
0.1 µg m-3. The BBOA in Mexico City was approximately half
LVOCs and half SVOCs (Cappa and Jimenez, 2010) and had a much lower ELVOC
fraction than the wintertime Paris BBOA in the present study.
Synthesis of results in the 2D-VBS
We employed the 2D-VBS framework in order to synthesize the above results,
combining the bulk average O : C ratio and volatility distributions of the
various factors. Each of the different factors had a distribution of O : C
values, but this distribution cannot be determined from the AMS measurements.
The HOA, BBOA, and COA factors all had relatively low O : C values but they
covered a wide range of average volatilities (Fig. 8). The MOA and secondary
OA factors for both seasons had much higher O : C values but they also
covered a wide range of volatilities, with LV-OOA having the lowest one. The
HOA during summer had higher O : C than HOA during winter, suggesting
incomplete separation from aged HOA or difference in the sources, while their
volatility distribution was similar, as discussed earlier. The COA factor
during the summer campaign had slightly higher O : C and a higher
volatility than the COA from the winter campaign. The OOA during the winter
had the highest O : C ratio but compared to the less oxidized SVOOA, it had
lower average volatility and higher volatility compared to LVOOA. These
results indicate that there was not a direct link between the average
volatility and the bulk average O : C for these OA components. This is
actually the reason for the introduction of the 2D-VBS: the second dimension
is needed to capture at least some of the chemical complexity of the
multitude of organic compounds in atmospheric particulate matter.
2D-VBS representation of the PMF factors for the summer and
winter campaigns. With the red color of the bars we represent the HOA
factors, with the pink color the COA factors, the green the SVOOA and OOA,
the blue is for the MOA factor, the brown for the BBOA factor and the black
for the LVOOA factor. The darker shading of the colored bars denotes a
larger mass fraction for a given C* bin. The diamond represents the average
log10(C*) value for a given PMF factor.
The broad spectrum of volatilities and extent of oxidation are not
surprising. Donahue et al. (2012) extrapolated from the few available ambient
measurements to provide rough estimates of the factor locations on the
2D-VBS. Superimposition of our factors and those estimated by Donahue et
al. (2012) (Fig. S4) indicates that the factor locations agree surprisingly
well. This is quite encouraging both for our results and our current
understanding of the evolution of atmospheric OA.
Conclusions
Two month-long field campaigns were conducted at an urban background sampling
site, SIRTA in Paris, France as part of the collaborative project MEGAPOLI.
The particulate matter mass concentration was surprisingly low during summer
in Paris, with a campaign average PM1 OA for SIRTA of only
0.83 µg m-3, while during winter it was characterized by
higher fine PM concentrations, with an average PM1 OA concentration of
3.1 µg m-3.
The volatility distributions of PMF factors derived during both campaigns
were estimated. Five factors were determined for the summer data set.
Hydrocarbon-like OA (HOA), cooking OA (COA), marine OA (MOA) and two
Secondary OA (SOA) factors were also identified: semi-volatile oxygenated OA
(SV-OOA) and low-volatility oxygenated OA (LV-OOA). The PMF analysis for the
winter campaign determined four factors. The HOA and COA factors were again
identified. There was also a single secondary OA factor that was termed
oxygenated OA (OOA). The final factor observed was biomass burning OA
(BBOA).
The HOA factors for both campaigns had similar volatility distributions with
half material in the 10 µg m-3 bin. Both factors contained
also LVOCs and ELVOCs with a total contribution of around 40 % to the HOA
mass. This similarity was consistent with the corresponding mass spectra
derived by the PMF analysis.
The summer COA was significantly more volatile than the winter COA. The
weighted-average COA C* during the summer was more than an order of magnitude
higher than that in the winter. The winter COA did not contain any
semi-volatile organic components (SVOCs) whereas 37 % of the summer COA
was semi-volatile. LVOCs were significant components of the COA, representing
37 % of the COA in the summer and 63 % in the winter. These
differences in volatility were consistent with the differences in AMS spectra
and could be due to different seasonal cooking habits. Also, imperfect
separation of the OA components by PMF cannot be excluded.
The LV-OOA factor detected in the summer had the lowest volatility of all the
derived factors. There was no sign of LV-OOA evaporation until the TD
temperature reached 150 ∘C. The LV-OOA factor consisted nearly
exclusively of ELVOCs (97 %). Roughly half of the SV-OOA mass consisted
of SVOCs while the rest was mainly LVOCs (42 %). The OOA factor
determined in the winter had a volatility distribution containing SVOCs
(45 %), ELVOCs (30 %) and LVOCs (25 %).
The marine OA (MOA) factor, only detected during the summer campaign, was
relatively volatile with an average C∗ of approximately
0.4 µg m-3. The MOA factor consisted mainly of SVOCs
(61 %) and LVOCs (36 %).
The BBOA factor was present in winter with an average ambient concentration
of 0.6 µg m-3. Half of the BBOA consisted of SVOCs and the
other half of extremely low-volatile and low-volatile organic components. The
BBOA was less volatile than the HOA factors but more volatile than COA and
OOA.
Finally, combining the O : C ratio and volatility distributions of the
various factors, we integrated our results into the 2D-VBS synthesizing the
corresponding OA findings. The factor locations agreed well with the location
of factors proposed by Donahue et al. (2012). The HOA, BBOA, and COA factors
had all relatively low O : C but their average volatilities were different
by orders of magnitude. The MOA for summer and secondary OA factors for both
seasons had much higher O : C with a wide variety of volatilities, where
MOA had the highest one and LV-OOA had the lowest one. The results suggest
that the average O : C of each factor was not directly linked to its average
volatility, underlining the importance of measuring both properties, and that
all factors include compounds with a wide range of volatilities.
The estimated volatility distributions by the use of just TD measurements
are characterized by considerable uncertainties (Karnezi et al., 2014).
However, the relative volatilities of the various factors discussed above
should be more robust. The absolute volatility distributions do depend on
the assumed enthalpy of vaporization and accommodation coefficient
(parameterization of mass transfer resistances). They also depend on the
assumptions of similar size distributions and external mixing of the OA
components corresponding to each factor.
The Supplement related to this article is available online at doi:10.5194/acp-16-2013-2016-supplement.
Acknowledgements
This research was supported by the FP7 project MEGAPOLI, the FP7 IDEAS
project ATMOPACS, and the ESF-NRSF ARISTEIA grant ROMANDE. Lea Hildebrandt
was supported by a Graduate Research Fellowship from the United States
National Science Foundation. Edited by:
M. Beekmann
ReferencesAn, W. J., Pathak, R. K., Lee, B.-H., and Pandis, S. N.: Aerosol volatility
measurement using an improved thermodenuder: Application to secondary organic
aerosol, J. Aerosol Sci., 38, 305–314, 10.1016/j.jaerosci.2006.12.002,
2007.Baklanov, A., Lawrence, M. G., and Pandis, S. N.: Description of work
document for the European Collaborative Project “Megacities: Emissions,
urban, regional and Global Atmospheric POLlution and climate effects, and
Integrated tools for assessment and mitigation” (MEGAPOLI) for the Seventh
Framework Programme of the European Commission, available at:
http://megapoli.info (last access: 1 February 2016), 2008.Beekmann, M., Prévôt, A. S. H., Drewnick, F., Sciare, J., Pandis, S.
N., Denier van der Gon, H. A. C., Crippa, M., Freutel, F., Poulain, L.,
Ghersi, V., Rodriguez, E., Beirle, S., Zotter, P., von der Weiden-Reinmüller,
S.-L., Bressi, M., Fountoukis, C., Petetin, H., Szidat, S., Schneider, J.,
Rosso, A., El Haddad, I., Megaritis, A., Zhang, Q. J., Michoud, V., Slowik,
J. G., Moukhtar, S., Kolmonen, P., Stohl, A., Eckhardt, S., Borbon, A., Gros,
V., Marchand, N., Jaffrezo, J. L., Schwarzenboeck, A., Colomb, A.,
Wiedensohler, A., Borrmann, S., Lawrence, M., Baklanov, A., and
Baltensperger, U.: In situ, satellite measurement and model evidence on the
dominant regional contribution to fine particulate matter levels in the Paris
megacity, Atmos. Chem. Phys., 15, 9577–9591, 10.5194/acp-15-9577-2015,
2015.
Burtscher, H., Baltensperger, U., Bukowiecki, N., Cohn, P., Hüglin, C.,
Mohr, M., Matter, U., Nyeki, S., Schmatloch, V., and Streit, N.: Separation
of volatile and non-volatile aerosol fractions by thermodesorption:
instrumental development and applications, J. Aerosol Sci., 32, 427–442,
2001.Caiazzo, F., Ashok, A., Waitz, I. A., Yim, S. H. L., and Barrett, S. R. H.:
Air pollution and early deaths in the United States. Part I: Quantifying the
impact of major sectors in 2005, Atmos. Environ., 79, 198–208,
10.1016/j.atmosenv.2013.05.081, 2013.Cappa, C. D. and Jimenez, J. L.: Quantitative estimates of the volatility of
ambient organic aerosol, Atmos. Chem. Phys., 10, 5409–5424,
10.5194/acp-10-5409-2010, 2010.Crippa, M., El Haddad, I., Slowik, J. G., DeCarlo, P. F., Mohr, C., Heringa,
M. F., Chirico, R., Marchand, N., Sciare, J., Baltensperger, U., and
Prévôt, A. S. H.: Identification of marine and continental aerosol
sources in Paris using high resolution aerosol mass spectrometry, J. Geophys.
Res.-Atmos., 118, 1950–1963, 10.1002/jgrd.50151, 2013a.Crippa, M., DeCarlo, P. F., Slowik, J. G., Mohr, C., Heringa, M. F., Chirico,
R., Poulain, L., Freutel, F., Sciare, J., Cozic, J., Di Marco, C. F.,
Elsasser, M., Nicolas, J. B., Marchand, N., Abidi, E., Wiedensohler, A.,
Drewnick, F., Schneider, J., Borrmann, S., Nemitz, E., Zimmermann, R.,
Jaffrezo, J.-L., Prévôt, A. S. H., and Baltensperger, U.: Wintertime
aerosol chemical composition and source apportionment of the organic fraction
in the metropolitan area of Paris, Atmos. Chem. Phys., 13, 961–981,
10.5194/acp-13-961-2013, 2013b.DeCarlo, P. F., Kimmel, J. R., Trimborn, A., Northway, M. J., Jayne, J. T.,
Aiken, A. C., Gonin, M., Fuhrer, K., Horvath, T., Docherty, K. S., Worsnop,
D. R., and Jimenez, J. L.: Field-deployable, high-resolution, time-of-flight
aerosol mass spectrometer, Anal. Chem., 78, 8281–8289,
10.1021/ac061249n, 2006.Donahue, N. M., Robinson, A. L., Stanier, C. O., and Pandis, S. N.: Coupled
partitioning, dilution, and chemical aging of semivolatile organics, Environ.
Sci. Technol., 40, 2635–2643, 10.1021/es052297c, 2006.Donahue, N. M., Kroll, J. H., Pandis, S. N., and Robinson, A. L.: A
two-dimensional volatility basis set – Part 2: Diagnostics of
organic-aerosol evolution, Atmos. Chem. Phys., 12, 615–634,
10.5194/acp-12-615-2012, 2012.Haeffelin, M., Barthès, L., Bock, O., Boitel, C., Bony, S., Bouniol, D.,
Chepfer, H., Chiriaco, M., Cuesta, J., Delanoë, J., Drobinski, P.,
Dufresne, J.-L., Flamant, C., Grall, M., Hodzic, A., Hourdin, F., Lapouge,
F., Lemaître, Y., Mathieu, A., Morille, Y., Naud, C., Noël, V.,
O'Hirok, W., Pelon, J., Pietras, C., Protat, A., Romand, B., Scialom, G., and
Vautard, R.: SIRTA, a ground-based atmospheric observatory for cloud and
aerosol research, Ann. Geophys., 23, 253–275, 10.5194/angeo-23-253-2005,
2005.Huffman, J. A., Docherty, K. S., Aiken, A. C., Cubison, M. J., Ulbrich, I.
M., DeCarlo, P. F., Sueper, D., Jayne, J. T., Worsnop, D. R., Ziemann, P. J.,
and Jimenez, J. L.: Chemically-resolved aerosol volatility measurements from
two megacity field studies, Atmos. Chem. Phys., 9, 7161–7182,
10.5194/acp-9-7161-2009, 2009.
IPCC: Climate Change: The Physical Science Basis – Contribution of Working
Group I to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G. K., Tignor,
M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.
M., Cambridge University Press, Cambridge, United Kingdom and New York, NY,
USA, 1535 pp., 2013.
Kalberer, M., Paulsen, D., Sax, M., Steinbacher, M., Dommen, J., Prevot, A.
S. H., Fisseha, R., Weingartner, E., Frankevich, V., Zenobi, R., and
Baltensperger, U.: Identification of polymers as major components of
atmospheric organic aerosols, Science, 303, 1659–1662, 2004.Karnezi, E., Riipinen, I., and Pandis, S. N.: Measuring the atmospheric
organic aerosol volatility distribution: a theoretical analysis, Atmos. Meas.
Tech., 7, 2953–2965, 10.5194/amt-7-2953-2014, 2014.
Kostenidou, E., Lee, B. H., Engelhart, G. J., Pierce, J. R., and Pandis, S.
N.: Mass spectra deconvolution of low, medium and high volatility biogenic
secondary organic aerosol, Environ. Sci. Technol., 43, 4884–4889, 2009.Lanz, V. A., Alfarra, M. R., Baltensperger, U., Buchmann, B., Hueglin, C.,
and Prévôt, A. S. H.: Source apportionment of submicron organic
aerosols at an urban site by factor analytical modelling of aerosol mass
spectra, Atmos. Chem. Phys., 7, 1503–1522, 10.5194/acp-7-1503-2007,
2007.Lee, B. H., Kostenidou, E., Hildebrandt, L., Riipinen, I., Engelhart, G. J.,
Mohr, C., DeCarlo, P. F., Mihalopoulos, N., Prevot, A. S. H., Baltensperger,
U., and Pandis, S. N.: Measurement of the ambient organic aerosol volatility
distribution: application during the Finokalia Aerosol Measurement Experiment
(FAME-2008), Atmos. Chem. Phys., 10, 12149–12160,
10.5194/acp-10-12149-2010, 2010.May, A. A., Levin, E. J. T., Hennigan, C. J., Riipinen, I., Lee, T., Collett,
J. L., Jr., Jimenez, J. L., Kreidenweis, S. M., and Robinson, A. L.:
Gas-particle partitioning of primary organic aerosol emissions: 3. Biomass
burning, J. Geophys. Res.-Atmos., 118, 11327–11338, 10.1002/jgrd.50828,
2013.Murphy, B. N., Donahue, N. M., Robinson, A. L., and Pandis, S. N.: A naming
convention for atmospheric organic aerosol, Atmos. Chem. Phys., 14,
5825–5839, 10.5194/acp-14-5825-2014, 2014.
Pope, C. A., III, Ezzati, M., and Dockery, D. W.: Fine-particulate air
pollution and life expectancy in the United States, New England Journal of
Medicine, 360, 376–386, 2009.Riipinen, I., Pierce, J. R., Donahue, N. M., and Pandis, S. N.: Equilibration
time scales of organic aerosol inside thermodenuders: Evaporation kinetics
versus thermodynamics, Atmos. Environ., 44, 597–607,
10.1016/j.atmosenv.2009.11.022, 2010.Saleh, R., Donahue, N. M., and Robinson, A. L.: Time scales for gas-particle
partitioning equilibration of secondary organic aerosol formed from
alpha-pinene ozonolysis, Environ. Sci. Technol., 47, 5588–5594,
10.1021/es400078d, 2013.
Wehner, B., Philippin, S., and Wiedensohler, A.: Design and calibration of a
thermodenuder with an improved heating unit to measure the size-dependent
volatile fraction of aerosol particles, J. Aerosol Sci., 33, 1087–1093,
2002.
Wehner, B., Philippin, S., Wiedensohler, A., Scheer, V., and Vogt, R.:
Variability of non-volatile fractions of atmospheric aerosol particles with
traffic influence, Atmos. Environ., 38, 6081–6090, 2004.Ulbrich, I. M., Canagaratna, M. R., Zhang, Q., Worsnop, D. R., and Jimenez,
J. L.: Interpretation of organic components from Positive Matrix
Factorization of aerosol mass spectrometric data, Atmos. Chem. Phys., 9,
2891–2918, 10.5194/acp-9-2891-2009, 2009.Zhang, Q., Jimenez, J. L., Canagaratna, M. R., Allan, J. D., Coe, H.,
Ulbrich, I., Alfarra, M. R., Takami, A., Middlebrook, A. M., Sun, Y. L.,
Dzepina, K., Dunlea, E., Docherty, K., DeCarlo, P. F., Salcedo, D., Onasch,
T., Jayne, J. T., Miyoshi, T., Shimono, A., Hatakeyama, S., Takegawa, N.,
Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian,
K., Williams, P., Bower, K., Bahreini, R., Cottrell, L., Griffin, R. J.,
Rautiainen, J., Sun, J. Y., Zhang, Y. M., and Worsnop, D. R.: Ubiquity and
dominance of oxygenated species in organic aerosols in
anthropogenically-influenced Northern Hemisphere midlatitudes, Geophys. Res.
Lett., 34, L13801, 10.1029/2007GL029979, 2007.
Zhang, Q., Jimenez, J. L., Canagaratna, M. R., Ulbrich, I. M., Ng, S. N.,
Worsnop, D. R., and Sun, Y.: Understanding atmospheric organic aerosols via
factor analysis of aerosol mass spectrometry: a review, Anal. Bioanal. Chem.,
401, 3045–3067, 2011.