Combustion of hydrocarbons produces both particulate- and gas-phase emissions responsible for major impacts on atmospheric chemistry and human health. Ascertaining the impact of these emissions, especially on human health, is not straightforward because of our relatively poor knowledge of how chemical compounds are partitioned between the particle and gas phases. Accordingly, we propose coupling a two-filter sampling method with a multi-technique analytical approach to fully characterize the particulate- and gas-phase compositions of combustion by-products. The two-filter sampling method is designed to retain particulate matter (elemental carbon possibly covered in a surface layer of adsorbed molecules) on a first quartz fiber filter while letting the gas phase pass through and then trap the most volatile components on a second black-carbon-covered filter. All samples thus collected are subsequently subjected to a multi-technique analytical protocol involving two-step laser mass spectrometry (L2MS), secondary ion mass spectrometry (SIMS), and micro-Raman spectroscopy. Using the combination of this two-filter sampling–multi-technique approach in conjunction with advanced statistical methods, we are able to unravel distinct surface chemical compositions of aerosols generated with different set points of a miniCAST burner. Specifically, we successfully discriminate samples by their volatile, semi-volatile, and non-volatile polycyclic aromatic hydrocarbon (PAH) contents and reveal how subtle changes in combustion parameters affect particle surface chemistry.
Particulate matter (PM) produced by incomplete combustion of
hydrocarbon-based fuels is often found associated with gas-phase compounds
that include carbon and nitrogen oxides (CO,
Several methods allowing the concomitant sampling of airborne PAHs in both the gas and particulate phases have been developed in recent decades (see, for example, the reviews by Pandey et al., 2011; Szulejko et al., 2014; Munyeza et al., 2019). The sampling protocol starts with the choice of a suitable sorbent material to either solely capture the vapor phase or solely retain PM. The former sorbent material mostly consists of polyurethane foam, resins, or graphitized carbon black mesh, whereas the latter is made of glass fiber, quartz fiber, or Teflon. The sorbents are placed in series, i.e., one after the other in the exhaust line. The soluble organic fraction is then extracted off-line from the sorbent (filter and/or resin) for subsequent gas chromatography–mass spectrometry (GC–MS) analyses (An et al., 2016; Elghawi et al., 2010; Sun et al., 2006). However, such solvent extraction methods exhibit recovery rates that are highly dependent upon the technique applied and the nature of PAHs a priori present. Accordingly, the GC–MS method, which relies on solvent extraction methods and calibration standards, is a time-consuming technique which is inherently more sensitive towards compounds having the greatest solubility. To circumvent this limitation, solvent-free methods have been recently developed based on thermal desorption (e.g., Villanueva et al., 2018), microwave-assisted desorption, or solid-phase micro-extraction (Szulejko et al., 2014). However, because sampling substrates may differ for PM and gas trapping, and often necessitate extraction techniques before characterization whose efficiencies are substrate-dependent, results obtained for the two phases may be difficult to compare and do not necessarily represent the whole PAH family making up either filter.
The CAST (Combustion Aerosol Standard) generator is often chosen to produce combustion-generated particles as it is easy to implement for systematic laboratory experiments, with the fuel and oxidation air flows being easily modifiable, enabling the investigation of a variety of chemistries. A miniCAST soot generator operated with different parameters as a source of combustion by-products can mimic some of the physicochemical properties of aircraft emissions, for example (Bescond et al., 2014; Marhaba et al., 2019; Moore et al., 2014). The observations derived from soot produced with this generator hence allows for potential real-world extrapolations, especially for combustion devices not equipped with after-treatment systems. Concomitantly sampling and characterizing the particulate and gas phases can thus be extremely useful when evaluating the impact of various sources (aircraft jet engines, wood combustion stoves, biomass burning) on the environment, as the gas–particulate partitioning conditions the overall reactivity. The two-filter method presented here can therefore be utilized to assess the efficiency of after-treatment systems, which are known to successfully remove the majority of particle-bound organic species from the surface, which results in increasing the elemental carbon (EC) to organic carbon (OC) contribution (Focsa et al., 2019). Time-of-flight aerosol mass spectrometry (ToF-AMS) has been used in the past by Ferge et al. (2006) and Mueller et al. (2015) to study PAH formation in a CAST generator at different oxidation flows. However, because only the particle phase has been analyzed, no information about the gas-phase composition can be derived in these experiments, which provide only an incomplete picture of the PAH family emitted in the exhausts.
In this work, we coupled a two-filter sampling method with a multi-technique analytical approach to fully characterize the particulate- and gas-phase compositions of combustion by-products. The two-filter collection method is intended to separate the particulate phase (front filter) from the gas phase (back filter) using fibrous filtration media (quartz fiber filters – QFFs). Once collected, the filters are analyzed using a multi-technique approach encompassing two-step laser mass spectrometry (L2MS), secondary ion mass spectrometry (SIMS), and micro-Raman spectroscopy. The L2MS technique has been extensively developed in our group (at the University of Lille, PhLAM laboratory) over the last decade to specifically probe the chemical composition of combustion by-products (Delhaye et al., 2017; Faccinetto et al., 2011, 2015; Moldanová et al., 2009; Popovicheva et al., 2017). Its high sensitivity and selectivity towards specific classes of compounds owing to different ionization schemes makes it an extremely valuable analytical tool that can be adapted to various samples. Using three different ionization wavelengths, it is possible to target various classes of compounds such as aromatic and aliphatic compounds. In addition, it is possible to reach a sub-femtomole limit of detection for PAHs upon specific desorption and ionization conditions (Faccinetto et al., 2011, 2015). The laser desorption process along with its coupling with the subsequent ionization step have been optimized over the years (Faccinetto et al., 2008; Mihesan et al., 2006, 2008) and ensure a soft removal (with minimum internal energy excess) of molecules adsorbed on the particle surface, while avoiding/limiting both their fragmentation and the in-depth damaging of the underlying carbon matrix (Faccinetto et al., 2015). L2MS spectra obtained in this work are additionally reinforced with the SIMS spectra of deposited miniCAST PM, with no sample preparation prior to the analyses since the particulate matter is preferentially trapped on the front filter. Subtle differences and similarities between front and back filters are revealed using mass spectrometry measurements (L2MS and SIMS) and the recently developed advanced statistical methodologies (Duca et al., 2019; Irimiea et al., 2018, 2019) based on principal component analysis (PCA).
PM was sampled from the exhaust of a miniCAST generator (5201c) from Jing
Ltd., as described previously in Yon
et al. (2018), for example. Briefly, the miniCAST contains a propane–nitrogen flame with
operating conditions controlled by the flow rates of propane, nitrogen,
oxidation air (
Samples were analyzed using a two-step laser mass spectrometry (L2MS)
technique built in-house (Mihesan et al., 2008). Briefly, the
soot sample is introduced into the analysis chamber (10
Schematic of the sampling line and photos of collected samples. The combustion parameters for the miniCAST burner are presented in the table.
The desorbed plume propagates normally from the sample surface in between
the extraction plates of the ToF-MS. The molecules from the desorbed plume
are then ionized by either a resonant two-photon ionization R2PI
(Haefliger and
Zenobi, 1998; Mihesan et al., 2006; Zimmermann et al., 2001) process at
Time-of-flight secondary ion mass spectrometry analysis was conducted with
the TOF.SIMS
Raman analyses were performed with an inVia reflex spectrometer (Renishaw)
equipped with an Olympus microscope (BXFM)
(Chazallon et al., 2014). The spectra presented in
this work were obtained by irradiation with a 514 nm laser with a nominal
power of 150 mW. The laser power was reduced to avoid thermal effects at the
sample surface. Using a lens with
PCA is a technique used to highlight variation and patterns in a dataset,
and in this case it was used to reveal the differences in chemical composition
of the samples, and in particular between (i) front and back filters and
(ii) miniCAST set points. PCA is very convenient to outline the subtle
differences between datasets, since it reduces the dimensionality of
complex data while preserving most of the information. PCA was applied to
each of the five datasets (three L2MS ionization wavelengths and two SIMS
polarities) following the procedure detailed in
Popovicheva et al. (2017),
Irimiea et al. (2018), and
Duca et al. (2019). Further
information can also be found in Sect. S1 in the Supplement.
Briefly, each mass spectrum was represented by the integrated areas of a
selected number of mass peaks in the spectrum. The number of selected mass
peaks was 66, 105, and 60 in L2MS mass spectra recorded at
L2MS mass spectra of samples SP1, SP2, SP3, and SP4 (for both front and back filters) produced at three different ionization wavelengths (266, 157, and 118 nm) are discussed in this section. Mass spectra obtained with the 266 nm ionization wavelength are presented in Fig. 2, whereas results obtained for 157 and 118 nm are both presented in Fig. S3.
Upon 266 nm ionization, all mass spectra are dominated by signals attributed
to aromatic species, and more specifically to PAHs (Fig. 2). An important
advantage of L2MS is to generate, for the most part, fragment-free mass
spectra while maintaining a high signal-to-noise ratio, due to the
controlled desorption and ionization fluences
(Faccinetto et al., 2011). On
all mass spectra generated with the 266 nm ionization wavelength, the lightest
detected PAH is naphthalene (
On front filters (
Comparison between mass spectra for SP1, SP2, SP3, and SP4 samples
recorded with
On back filters (
The various miniCAST set points exhibit different PAH mass distributions on
their front and back filters, which likely relates to the different
volatility properties of PAHs and probably affects their subsequent trapping
on front and back filters. Distinct volatility properties have been observed
in the past on particles originating from wood combustion by
Bari et al. (2010), who classified the PAHs on the basis
of their number of aromatic rings resulting in the detection of three
different PAH categories. The authors classified the PAHs consisting of two
aromatic rings as volatiles as they were mostly found in the gas phase,
while those made of three and four rings were classified as semi-volatiles.
PAHs comprising more than four rings were classified as non-volatile as they
were observed in the PM in their study. Note that slightly different classes
have also been defined elsewhere in the literature
(An
et al., 2016; Elghawi et al., 2010; Sun et al., 2006). In our study, we
largely found compounds consisting of one and two aromatic rings on back
filters, while PAHs of
“Contrast plot” representing the variation in PAH signal
detected with L2MS at
The total PAH signal derived from L2MS measurements (
Variation in total PAH signal detected with L2MS at
In order to access other classes of molecules, the miniCAST set points have
been also analyzed using 157 and 118 nm ionization wavelengths. The majority
of PAHs were also detected with SPI at 157 nm for both front and back
filters, albeit at a lower signal intensity (as can be seen by comparing the
two sets of spectra in Figs. 2 and S3). The overall shape changes due to
the different ionization efficiencies of PAHs from R2PI at 266 nm and SPI at
157 nm. At lower masses, additional peaks with prominent features at
Mass spectra obtained with SPI at 118 nm (Fig. S3) show a high degree of
fragmentation. In all cases, peaks at
In conclusion, our L2MS results for the three ionization wavelengths
converge to show that heavy PAHs (
In order to better discriminate the chemical composition of the various samples, particularly (i) the front and back filters and (ii) the miniCAST set points, principal component analysis (PCA) was applied to mass spectra recorded for all three individual ionization wavelengths. A full description of this statistical method is provided in Sect. S1. Here, the covariance matrix was built from the integrated areas of all the detected peaks with a signal-to-noise ratio (SNR) > 3. The physical meaning of all derived principal components can be inferred from the contribution of the various molecular species to the loadings (see Sect. S1 and Figs. 5b and S2). By identifying the molecular families contributing to this variance, we can interpret the PCA score plots (Fig. 5) and grasp the nature of the subtle chemical differences between the samples.
The loading and scree plots corresponding to the L2MS data generated with
the 266 nm ionization wavelength are presented in Figs. 5b and S1a,
respectively. They show that PC1 expresses the largest variance (58.86 %)
in the dataset and differentiates samples having a large number of high-mass
PAHs (positive contribution:
Score
SIMS measurements are complementary to L2MS analysis as they can provide
insights into the compounds that preferentially produce negative ions. For
the sake of comparison with L2MS results, SIMS measurements were first
obtained in positive mode. Positive spectra of SP1, SP2, SP3, and SP4
samples are presented in Fig. 6 (
ToF-SIMS mass spectra of samples SP1, SP2, SP3, and SP4 obtained
in positive polarity for front filters (lower spectra) and back filters
(upper spectra). For visualization purposes, we focus on the
As to the PAH distribution observed on back filters, it is distinct from
that of front filters in that the highest mass detected is
Total PAH signal detected with SIMS on front (orange) and back
(blue) filters plotted as a function of oxidation air flow, along with
Negative polarity mass spectra obtained for front and back filters are
presented in Fig. S4.
SIMS results confirm L2MS measurements regarding the organic carbon and more specifically the PAH contents and mass distributions for the various miniCAST set points. In contrast to L2MS, specific SIMS fragmentation patterns provide additional information about the presence of elemental carbon and outline the distinct elemental carbon vs. organic carbon contents for the different miniCAST set points.
Variation in the signal of various markers, as derived from SIMS
spectra. The panels represent the total peak areas of the following
families:
PCA was applied to the positive-mode SIMS spectra. All hydrocarbon fragments
and the most representative peaks for PAHs were chosen for the analysis (see
Sect. S3). The PCA score plot for the first two components (PC1 and PC2,
responsible for 92 % of the variance) is presented in Fig. 9a, and their
corresponding loadings are shown in Fig. 9c. PC1 represents 73 % of the variance
and is associated with small fragment ions with
PCA was also applied to the negative-mode SIMS spectra for selected mass
peaks, including carbon clusters
To sum up, PCA on SIMS results confirms the existence of various families of carbon clusters on the PM that can be associated either with the soot matrix or with the surface PAH coating.
Score plots of PC1 and PC2 derived from positive
The two-filter system provides a unique opportunity to perform Raman
spectroscopy on either the gas phase trapped on the back filter or the PM
collected on the front filter. Raman spectra measured for each sample are
presented in Fig. 10. All spectra for PM deposited on front filters are in
very good agreement with those already measured for the same miniCAST set
points (e.g., Ess et al.,
2016), while back filter spectra are dominated by the absorption of the
pre-deposited black carbon. Soot particles often exhibit distinct Raman
signatures that can be used to distinguish samples mostly by their
hybridization and nanostructure (e.g., stacking properties) compared to that
obtained for a perfect graphite crystal, i.e., a crystal made of
sp
Raman spectra of SP1 (blue), SP2 (green), SP3 (red), and SP4
(brown) samples on front filter
Both the fluorescence background (FB) and the soot Raman feature are
observed to vary significantly with the set point (Fig. 10). The former
refers to the baseline, whereas the latter refers to the two broadbands
centered at 1356 and 1598 cm
Two conclusions can be drawn from these observations. First, when comparing
fluorescence signals of back and front filter samples to PAH content, we can
further refine our definition of organic content. Fluorescence is not just
related to the total PAH signal, although this is a good marker of organic
content. If it were, fluorescence would also be observed for back filter
samples, in accordance with their relatively high gas-phase PAH contents.
The lack of fluorescence signal on back filters, whose chemical composition
is dominated by small PAHs, suggests that the fluorescence can be attributed
mainly to non-volatile PAHs in the particulate phase, even though the
heaviest mass detected in L2MS (
Information on soot nanostructure ordering can be derived from the
Combustion by-products (PM and gas phase) produced by a miniCAST generator
are first separated and then characterized using a two-filter collection
method and a multi-technique analytical–statistical protocol. Front and back
filters thus generated are representative of the exhaust stream and are
subsequently analyzed through first an original L2MS technique featuring
three ionization schemes, followed by SIMS, and last micro-Raman
spectroscopy. The three-wavelength L2MS scheme is employed in our study to
target specific classes of compounds. We evidence the presence of aliphatic compounds and specific fragment ions using a 118 nm ionization source and we can focus on aromatic species using instead a 266 nm laser. Aromatic species
were detected in all mass spectra (L2MS and SIMS). When combined with
advanced statistical methods (PCA), mass spectrometry datasets revealed how
different all samples were. Based on the PAH classification of Bari et al. (2010), we were able to discuss aromatics distribution across front and back
filters in terms of volatile (one to two rings), semi-volatile (three to four rings), and
non-volatile PAHs (larger than four rings). We determined that PM is
essentially sampled on front filters, whereas the dominant compounds trapped
on all back filters were volatile PAHs regardless of the combustion
conditions. The good separation between the two phases confirmed the high
particle collection capability of QFF front filters. PCA revealed that
distinct amounts of volatile compounds were present in samples produced with
different combustion parameters. Specifically, changes in oxidation air flow
conditions in the miniCAST resulted in notable changes in the mass
distribution for both front and back filters. L2MS results at 266 nm
indicated that low oxidation air flow conditions (SP2 and SP3) produced more
semi-volatile and non-volatile compounds in the exhaust stream. The addition
of quenching gas (
The data presented here can be provided on request to the contact author.
The supplement related to this article is available online at:
YC, CP, AF, CI, and CF conceptualized and built the sampling system and defined the methodology; LDN, YC, RI, CI, GL, and CP performed the sample collection; LDN and JAN (SIMS), DD and MV (L2MS), RI, and JAN and CP (Raman) performed the analysis and data reduction; LDN, YC, DD, MV, JAN, CP, and CF interpreted the results and wrote the original draft with additional contributions from other co-authors: IKO, AF, CI, MZ, and BC. JY, ET, CI, YC, and CF provided funding and access to experimental infrastructure and organized the sampling campaign. All co-authors reviewed and approved the manuscript.
The authors declare that they have no conflict of interest.
This work was supported by the French National Research Agency (ANR) through the PIA (Programme d'Investissement d'Avenir) under contract ANR-10-LABX-005 (CaPPA – Chemical and Physical Properties of the Atmosphere), the MERMOSE project sponsored by DGAC (French national funds), and the European Commission Horizon 2020 project PEMs4Nano (H2020 Grant Agreement no. 724145). In addition, the authors thank the Région Hauts-de-France, the Ministère de l’Enseignement Supérieur et de la Recherche (CPER Climibio), and the European Fund for Regional Economic Development for their financial support. Jennifer A. Noble acknowledges the financial support of Horiba Scientific. The collection campaign was financed by GDR Suie (GDR CNRS 3622). The authors acknowledge Nicolas Nuns for his support in acquiring the SIMS data.
This research has been supported by the Agence Nationale de la Recherche (grant no. ANR-10-LABX-005), the Région Hauts-de-France (grant CPER CLIMIBIO), the European Union H2020 Research and Innovation programme (grant no. 724145), and the Centre National de la Recherche Scientifique (grant GDR Suie 3622).
This paper was edited by Hartmut Herrmann and reviewed by two anonymous referees.