Introduction
Methylglyoxal (MGLY, CH3C(O)CHO) is an important α-dicarbonyl
species in the atmosphere. It is mainly produced during the oxidation of
volatile organic compounds (VOCs) of which isoprene and acetone are the
main contributors. Fu et al. (2008) calculated production rates of 110 and
10 Tg year-1 from the oxidation of isoprene and acetone, respectively.
Other precursors of MGLY are C3-C5 isoalkanes (Jacob et al.,
2002), aromatic compounds (Volkamer et al., 2001; Pan and Wang, 2014; Wu et
al., 2014), and monoterpenes (Fick et al., 2003; Nunes et al., 2005). Due
to the anthropogenic and biogenic natures of MGLY precursors, this compound
can therefore be found at significant levels (low tens to hundreds of pptv)
in urban, rural or even remote and marine environments (Henry et al., 2012
and references therein).
The principal sink of MGLY is thought to be photolysis (Fu et al., 2008),
which can significantly contribute to the formation of ROx
(OH+HO2+RO2) radicals in the troposphere (Dusanter et al.,
2009), which in turn can enhance the formation rates of secondary
pollutants, including ozone and secondary organic aerosol (SOA). In
addition, MGLY has been identified as a direct precursor of SOA (Altieri et
al., 2008; Hallquist et al., 2009) due to aqueous reactions in clouds
leading to the formation of oligomers and oxalic acids, which can then form
SOA upon cloud droplet evaporation (Altieri et al., 2008).
Despite the important role of MGLY in the atmosphere, there are only a few
measurement techniques, most of which are expensive, requiring highly skilled
operators or suffering from low time resolution. A common method relies on
chemical derivatization and chromatographic analysis. Several derivatization
agents can be used to trap carbonyl compounds such as
2,4-dinitrophenylhydrazine (DNPH) (Lee et al., 1998; K. F. Ho et al., 2014;
Lawson et al., 2015), o-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine (PFBHA)
(Spaulding et al., 1999; Ho and Yu, 2002; Ortiz et al., 2006, 2013; Temime et
al., 2007) and pentafluorophenylhydrazine (PFPH) (Ho and Yu, 2004; Pang and
Lewis, 2011; Pang et al., 2011; Dai et al., 2012). Methods relying on
chemical derivatization imply active sampling through cartridges or liquid
solutions containing the selected reagent and a subsequent offline analysis
using gas chromatography – mass spectrometry (GC–MS) or high performance
liquid chromatography with ultraviolet detection (HPLC-UV). Low detection
limits are reached for these techniques with the advantages of monitoring
several carbonyl compounds simultaneously. Indeed, Ho and Yu (2004) reported
detection limits below 0.3 ppbv for a large range of carbonyl compounds,
including formaldehyde, acetaldehyde, propanal, acrolein, glyoxal, MGLY and
others. These authors used cartridges loaded with PFPH on a Tenax sorbent
tube, a sampling time of 4 h and a sampling flow rate of
100 mL min-1. Ait-Helal et al. (2014) even reported lower detection
limits ranging from 10 to 60 pptv for C1-C9 aldehydes and ketones, including
MGLY, using a sampling duration of 3 h for DNPH cartridges and a sampling
flow rate of 1.5 L min-1. The cartridges were analyzed by HPLC-UV.
However, the main drawback of these methods is the low time resolution of
typically 3–4 h, which is too long to investigate photochemical processes.
Alternative techniques based on mist chambers and derivatization solutions
such as PFBHA (Spaulding et al., 2002) or DNPH (Munger et al., 1995) were
used to measure MGLY with a faster time resolution of approximately 10 min
and low limits of detection (LOD). Spaulding et al. (2002) reported a LOD of
7.7 pptv at a sampling flow rate of 25–70 L min-1 (Spaulding et al.,
2002). More recently, a microfluidic derivatization approach using PFBHA and
a planar glass micro-reactor was developed to measure glyoxal and MGLY at a
time resolution of 30 min and sampling flow rate ranging from 100 to
600 mL min-1 (Pang et al., 2014). This setup exhibits LODs of 76 and
185 pptv (3σ) for glyoxal and MGLY, respectively. The authors also
report the use of a solid-phase microextraction (SPME) method, previously
described by Gomez Alvarez et al. (2012), capable of measuring MGLY with a
LOD of 150 pptv (3σ) and a measurement time of 25 min. The SPME
technique relies on a derivatization of aldehyde species into oximes on a
fibre loaded with PFBHA and a subsequent analysis by gas
chromatography flame ionization detection (GC–FID). Gomez Alvarez et al. (2007) also mentioned the possibility to measure MGLY using a SPME
instrument as well as a gas chromatography – electron capture detector
(GC–ECD), both calibrated against Fourier transform infrared spectroscopy
(FTIR).
In addition to these chemical derivatization methods, optical/spectroscopic
approaches have also been employed to measure MGLY. Henry et al. (2012)
reported a laser-induced phosphorescence (LIP) instrument capable of
simultaneous measurements of glyoxal and MGLY with a time resolution of
5 min and LODs of 4.4 and 243 pptv (3σ), respectively. Thalman and
Volkamer (2010) developed a blue LED (light-emitting diodes) cavity-enhanced
differential optical absorption spectroscopy (CE-DOAS) instrument for
in situ measurements of MGLY among other compounds (nitrogen dioxide,
glyoxal, iodine oxide and water vapor). This instrument exhibits a LOD of
170 pptv (2σ) at a time resolution of 1 min. Incoherent broadband
cavity-enhanced absorption spectroscopy (IBBCEAS) has also been used to
measure both glyoxal (Washenfelder et al., 2011) and MGLY (Pang et al.,
2014), with a LOD of 1 ppbv (3σ) for a measurement time of 20 s for
the latter. FTIR is another spectroscopic method capable of measuring MGLY
(Talukdar et al., 2011). However, FTIR exhibits a LOD in the ppbv range,
which is not low enough for ambient measurements, even with a long path
length of hundreds of meters (Pang et al., 2014). Overall, while these
spectroscopic techniques usually exhibit performances that are suitable for
atmospheric measurements, they also require highly skilled operators and the
use of fragile pieces of equipment (light sources, mirrors, etc.).
The use of proton transfer reaction time-of-flight mass spectrometry
(PTR-ToF-MS) has been attempted for Glyoxal measurements by Stönner et al. (2017). However, these authors showed that the sensitivity of PTR-ToF-MS
instruments was too low to monitor ambient concentrations. MGLY measurements
by PTR-ToF-MS have been reported by Pang et al. (2014) and Thalman et al. (2015) during intercomparison experiments. Pang et al. (2014) observed a
significant disagreement between PTR-ToF-MS measurements and results from
other techniques (Microfluidic derivatization, IBBCEAS, FTIR, SPME) during
photo-oxidation experiments of isoprene under low NOx conditions in the
EUPHORE chamber. According to the authors, this disagreement was due to
interferences from (H2O)3 H3O+ at m/z 73 (no
deconvolution of peaks within this mass unit). Thalman et al. (2015) also
reported interferences from the (H2O)3 H3O+ cluster and
the fragmentation of larger compounds upon protonation. However, blank
measurements made at the same relative humidity than in ambient air should
contain the contribution of (H2O)3 H3O+ and frequent
blank measurements, as usually done during field campaigns, could easily be
subtracted to reduce the impact of (H2O)3 H3O+ on the
MGLY measurements. De Gouw and Warneke (2007) reported measurements of
methyl ethyl ketone (MEK) at the same unit mass using a PTR-MS equipped with a
quadrupole. However, time-of-flight mass spectrometers provide the
opportunity to deconvolve signals of MGLY (m/z 73.029) and MEK (m/z 73.065),
which are separated by 0.036 Daltons. Thus, if the mass resolution of the
PTR-ToF-MS instrument is sufficient, an adequate peak-fitting procedure and
frequent blank measurements should allow a selective detection of
methylglyoxal. While PTR-ToF-MS instruments also require highly skilled
operators and are more expensive than other techniques allowing MGLY
measurements, a growing number of research groups are deploying this type of
instrumentation during intensive field campaigns, making it of great
interest for MGLY measurements. It is expected that PTR-MS should allow
a lower LOD than any other technique reported in the literature so
far.
In this study, we present online measurements of MGLY using proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS). This study describes
a procedure to conduct measurements of MGLY using PTR-ToF-MS, reports a
comparison of PTR-ToF-MS and DNPH–HPLC measurements performed during an
intensive field campaign in the Mediterranean basin, and presents an
investigation of the MGLY loss rate during this campaign.
Experimental section
The Chemistry-Aerosol Mediterranean Experiment (ChArMEx)
The ChArMEx SOP2 (special observation period 2) field campaign took place from
15 July to 5 August at Corsica Cape (France) on a hilltop (altitude of 533 m)
within a wind farm (42.969∘ N, 9.380∘ E). It is a
coastal site surrounded by the sea a few kilometers away in all directions
(2.5–6 km) (Zannoni et al., 2015). The site was covered by typical
Mediterranean vegetation (maquis shrubland) (Zannoni et al., 2015),
leading to large emissions of biogenic VOCs and elevated concentrations of
isoprene (up to 1.3 ppbv) and monoterpenes (up to 2.2 ppbv) (Michoud et al.,
2017). Since MGLY is an oxidation product of isoprene (first, second
and third generation), this site is of interest for performing and
investigating its budget. On the contrary, a small anthropogenic influence was
observed at the measurement site, since the closest city, Bastia, is located
∼30 km away (Michoud et al., 2017).
PTR-ToF-MS measurements
Measurements of MGLY, among other species (Michoud et al., 2017), were
conducted using a PTR-ToF-MS instrument from KORE Inc™ (second generation). Ambient air was sampled through a 5 m long line
made of PFA (perfluoroalkoxy). The line was held at 50 ∘C and the
flow rate was set at 1.2 L min-1 to reduce the residence time below
4 s. The PTR-ToF-MS was sampled from this line at a constant flow rate of
150 mL min-1. Reactor pressure and temperature were set at 1.33 mbar
and 40 ∘C, respectively, leading to an E/N value of 135 Td. The
PTR-ToF-MS spectra were integrated over 10 min, leading to six measurements
per hour.
An automatized zero procedure was performed for 10 min every hour to
subtract potential contaminations from the lines and to suppress
interferences from water clusters and other ions formed inside the glow
discharge. Zero air was generated by passing ambient air through a catalytic
converter (1/2′′ stainless steel tubing filled with 2 g of Pt wool held
at 350 ∘C) allowing the instrument to be zeroed at the same relative
humidity as in ambient air. In order to test the efficiency of the
catalytic converter, mixtures of several tens of hydrocarbons at the ppb
level were passed through the converter and the remaining VOCs were measured
by GC analyzers. Levels lower than the detection limits of the GCs (5–10 pptv) were observed, indicating efficient removal of the VOCs.
VOC signals were extracted from the 10 min mass spectra by summing the
number of counts detected within m/z windows centered on the exact masses of
the VOCs of interest (m/zVOC±0.21). These signals were
normalized by the signals of H3O+ and the ionic water cluster
H3O+(H2O) as proposed by de Gouw and Warneke (2007). VOC
concentrations were then calculated using Eq. (1).
[RH]=iRH_net(iH3O+×500+Xr⋅iH3O+(H2O)⋅250)⋅150000Rf,
where iRH_net is the net VOC signal (difference of
signals recorded when sampling ambient and zero air), iH3O+ the
signal from H3O+ ions at m/z 21, iH3O+(H2O) the
signal from H3O+(H2O) at m/z 39, Xr a factor to account
for the effect of humidity on the PTR-ToF-MS sensitivity (de Gouw and
Warneke, 2007), Rf the sensitivity determined by calibration (in
ncps ppb-1) and 150 000 the corresponding number of primary
H3O+ ions in the PTR-ToF-MS reactor (in counts per second). The instrument was
calibrated every 3 days during the campaign using a gas calibration unit
(IONICON®) and various standards (RESTEK,
PRAXAIR) made of hydrocarbons (isoprene, benzene, toluene, o-xylene,
ethylbenzene, α-Pinene) and monofunctional oxygenated VOCs
(methanol, acetaldehyde, acetone, methyl ethyl ketone). Information about
individual mixing ratios of VOCs in the calibration gases can be found in
Michoud et al. (2017; Supplement Sect. S1). Mixing ratios were in the
range 0.9–4.5 ppm for the abovementioned species and ranged from 3 to 15 ppb
after dilution with zero air. Uncertainties associated with these mixing
ratios range from 5 % to 10 % (1σ). These calibrations were
performed at a relative humidity of 50 % at 20 ∘C without
passing the entire 5 m long heated sampling line. XR was determined
by conducting additional calibrations at various relative humidity values
before and after the campaign. The calibration factor, Rf in Eq. (1), was
normalized to 150 000 cps of reagent ions. Specific calibrations performed
for methylglyoxal are described in Sect. 3.1.
As mentioned in the introduction, MGLY and MEK are detected at m/z 73.029
and 73.065, respectively. A Gaussian peak-fitting operation was performed to
deconvolve the two peaks observed in the m/z window 72.95–73.15 during
ambient sampling using the curvefit tools from Grams™ software (Thermo Scientific™) (see
Supplement Fig. S1). The signals recorded in this window were
accumulated over 1 h to reduce the time needed for this procedure, which was
done manually. An automatic peak-fitting operation is planned in the future
via software development. The MEK-to-methylglyoxal ratio of areas
observed for the 1 h cumulated signals was then applied to each 10 min
recorded signals (total number of counts recorded within the m/z window
72.95–73.15) providing measurements of methylglyoxal and MEK at a 10 min
time resolution. It is worth noting that the MGLY lifetime of at least 1 h
and the longer lifetime of MEK ensure that the MGLY-to-MEK ratio do not
change significantly over an hour. Once the signals were deconvolved for
each compound, the procedure described in the previous paragraph was applied
to calculate their ambient concentrations using sensitivity and humidity
dependence factors determined during calibration (Xr=0.5 and 0.49 for
MGLY and MEK, respectively).
The 3σ detection limits were calculated from the hourly blank
measurements. The average detection limit for methylglyoxal during the whole
campaign is 22 pptv (3σ) at the time resolution of 10 min. The total
uncertainty was estimated following the “Aerosols, Clouds, and Trace gases
Research InfraStructure network” guidelines (ACTRIS Measurement Guideline
VOC, 2012), taking into account precision and systematic errors. The
repeatability of MGLY measurements was calculated as the square root of the
net signal (iRH_net) since the statistic for PTR-ToF-MS
signals follows a Poisson distribution (de Gouw and Warneke, 2007) and was
on average 9±3%. The systematic errors concerned the calibration
factor (Rf) and the peak-fitting procedure and are estimated to be 22 %
for methylglyoxal (19 % and 10 % respectively for the individual errors
associated with the calibration factor (Rf) and the peak-fitting procedure).
Active sampling on DNPH cartridges
Measurements of carbonyl compounds from C1 to C8, including MGLY
and MEK, were performed using DNPH cartridges (Waters™) and an automatic sampler (ACROSS-TERA
Environment™), based on the US EPA TO-11A method.
The analysis of the cartridges was performed in the laboratory using HPLC-UV
(Waters 2695 & 2487). This deployment has already been described by
Ait-Helal et al. (2014) and Michoud et al. (2017). Ambient air was sampled
through a 3 m long PFA line (1/4′′) at a height of 1.5 m above the roof of
the trailer next to the PTR-ToF-MS sampling line. This air was collected for
3 h on each cartridge at a flow rate of 1.5 L min-1. A potassium iodide
(KI) ozone scrubber and a stainless steel particle filter (porosity:
2 µm) were set up on the sampling line before the automatic sampler.
The 3σ detection limit was determined to be 6 pptv for MGLY from
blank cartridges (unused cartridges stored under similar conditions than
exposed cartridges). The systematic error is estimated to be 25 % for
these measurements.
The HPLC-UV instrument used to analyze the DNPH samples was calibrated using
a standard solution of hydrazone compounds (TO11/IP-6A) commercialized by
Supelco. However, MGLY-DNPH is not present in this solution and a hydrazone
standard was made by mixing a known volume of an aqueous solution of MGLY
(40 % in water, Acros Organics™) into an excess
of acidified DNPH solution. It is worth noting that calibrating the HPLC-UV
using a liquid standard of hydrazones is based on the assumption that the
collection efficiency of carbonyl compounds through DNPH cartridges is
100 %.
Investigation of the methylglyoxal loss rate
Two sinks were considered in the steady-state loss calculations: reaction of
MGLY with OH and MGLY photolysis. The loss from the reaction with OH was
calculated using concentrations of both MGLY and OH, the latter being
measured by chemical ionization mass spectrometry (Kukui et al., 2008), and
the recommended rate constant of
1.50×1011 cm3 molecule-1 s-1 (Atkinson et al., 2006). J(NO2), and the photolysis frequencies
for some other species were derived from the actinic flux measured with an
actinic flux spectroradiometer METCON 6007 (Meteorologie Consult GmbH).
However, photolysis frequencies for MGLY were not derived from these
measurements. The approach described in Dusanter et al. (2009) was therefore
employed to calculate J(MGLY) and J(NO2) as a function of the solar
zenith angle for the measurement site (lat: 42.969∘ N, long:
9.380∘ E) using the master chemical mechanism (MCM)
parameterization (Jenkin et al., 1997; Saunders et al., 2003). This
parameterization was derived for an ozone column of 345 Dobson, an altitude
of 500 m and clear-sky conditions. Calculated values of J(MGLY) were then
corrected for differences in altitude, cloud covering, aerosol and
O3
column densities using a scaling factor derived from the
measured-to-calculated J(NO2) ratio. The photolytic loss of MGLY was
calculated using these scaled photolysis frequencies and PTR-ToF-MS
measurements of MGLY.
Results and discussion
PTR-ToF-MS calibrations for MGLY
These calibrations were not performed during the field campaign but a few
months later using a liquid calibration unit (LCU,
IONICON™) and an aqueous solution of MGLY (40 %,
Acros Organics™) (see
Fig. 1). The LCU allows a standard
mixture containing the targeted compounds to be generated at known mixing ratios by
evaporating an aqueous solution of these compounds into a large flow of zero
air (1.0 L min-1 in our case). The standard solution flow was varied
between 1 and 20 µL min-1 to generate MGLY concentrations
ranging from 0.6 to 11 ppbv.
MGLY (black circles) and MEK (blue circles) calibration plot for PTR-ToF-MS
measurements: normalized net signals at m/z 73.029 (MGLY, ncps) and 73.065
(MEK, ncps) vs. generated mixing ratio (ppbv).
Figure 1 shows that the PTR-ToF-MS response is linear with the MGLY
concentration over the tested range, with no significant offset. While the
lower limit of the tested range is larger than observed ambient
concentrations (0.05–0.3 ppbv, Fig. 2), it has to be noted that the PTR-MS
response has always been observed to be linear with the analyte
concentration and a linear response is expected for MGLY for mixing ratios
below 0.6 ppbv. These calibration experiments indicate an averaged
calibration factor of 2.54±0.49 ncps ppb-1 when normalized to
150 000 cps of reagent ions and using a Xr factor set to 0.5. It is worth
noting that changing the flow rate of the liquid standard solution to
generate various MGLY concentrations leads to a change in humidity in the
gas exiting the LCU, tracked by the m/z 37–m/z 19 ratio (varying from 0.1
to 0.5) during these calibration experiments. The good linearity observed in
Fig. 1 gives confidence in the Xr factor value used to determine the
calibration factor. Therefore, the same Xr value of 0.5 was used for ambient
measurements of MGLY.
Time series of MGLY measured by PTR-ToF-MS (red) and active
sampling on DNPH cartridges (black) (a); sum of monoterpenes (black)
and isoprene (red) measured by PTR-ToF-MS (b); and estimated
J(MGLY) (black) (c). Error bars for MGLY measurements (a)
correspond to systematic errors of 22 % and 25 % for PTR-ToF-MS and
DNPH cartridge measurements, respectively.
To account for a potential drift in sensitivity between the field
measurements and the calibration experiments performed later in the
laboratory, calibrations of MEK were also performed during the laboratory
experiments using a gas calibration unit (GCU,
IONICON™) and a standard mixture provided by
IONICON (Restek™). A comparison between MEK
response factors observed during field measurements and laboratory
experiments allows a drift of the PTRMS sensitivity to be accounted for as
further discussed below. The Restek mixture contains 15 compounds including
0.99±0.05 (2σ) ppmv of MEK. Calibrations of MEK were
performed every 3 days during the field experiments using the GCU and the
same Restek mixture. Since MEK is detected at the same mass unit than MGLY,
a change in sensitivity for MGLY between the field measurements and the
laboratory calibrations due to a change in ion transmission inside the mass
spectrometer would also be observed for MEK. A ratio of the calibration
factors measured for MGLY and MEK during the laboratory experiments was used
to calculate the MGLY calibration factor from the calibration factor
measured for MEK during the field campaign. The laboratory calibrations led
to an averaged sensitivity factor of 6.60±0.16 ncps ppb-1 for
MEK when normalized to 150 000 cps of reagent ions, leading to a MGLY-to-MEK
sensitivity ratio of 0.38. During the ChArMEx field campaign, an averaged
calibration factor of 7.57±0.52 ncps ppb-1 was observed for MEK
when normalized to 150 000 cps of reagent ions, indicating a decrease of
approximately 13 % between the field and laboratory measurements. However,
as mentioned above, using the MGLY-to-MEK sensitivity ratio determined in
the laboratory allows this change to be corrected.
Time series of MGLY
Concurrent measurements of MGLY by PTR-ToF-MS and the DNPH–HPLC–UV method in
an environment characterized by intense biogenic emissions represent a good
opportunity to test how the two techniques compare for this compound.
Figure 2 presents time series of MGLY measurements
from PTR-ToF-MS (red) and active sampling on DNPH cartridges (black) from 15 July to 6 August. The PTR-ToF-MS measurements performed at a time resolution
of 10 min were averaged over 3 h around the sampling middle time of each
cartridge measurement to allow a direct comparison between the two
techniques. This figure shows that significant levels of MGLY were observed
during the campaign, with concentrations ranging from 30 to 370 pptv. In
addition, these measurements indicate clear diurnal variations, which are
consistent with similar variations of MGLY precursors of biogenic origin
observed during the campaign, e.g., isoprene and monoterpenes (see
Fig. 2, middle panel). Figure 3 displays campaign-averaged diurnal profiles for MGLY and indicates daily maxima observed
around 13:45 LT (Central European Summer Time +02:00 UTC) when the
photochemistry is the most intense (see Fig. 2, bottom panel).
Diurnal profiles (box plots) of MGLY measured by both PTR-ToF-MS
(b) and active sampling on DNPH cartridges (a) for the
campaign average. Purple bars represent the maxima, green bars the minima,
red bars the medians, blue crosses the averages, and the sides of the boxes
are the first (b) and the third (a) quartiles.
Comparison of MGLY measurements
Overall, reasonable agreement is observed between the two techniques (see
Fig. 2), except for 17 July, 25 July and the last
4 days on which the measured PTR-ToF-MS concentrations were higher by
16 %–148 %. A close look at Fig. 2 also indicates that PTR-ToF-MS
measurements are usually higher at night and the concentrations do not
decrease as low as that observed for the cartridges. For example, 3 h
averaged PTR-ToF-MS concentrations measured at 01:30, 04:30 and 22:30 LT for the overall campaign (Fig. 3) are 127,
144 and 136 pptv, respectively, which are approximately 14, 21 and 30 pptv
higher (11 %–22 %) than cartridge measurements, respectively.
Figure 4 displays a scatter plot of PTR-ToF-MS vs.
DNPH–HPLC–UV measurements, with a coefficient of determination of 0.48
(R2). A significant intercept of 88±16 pptv (1σ)
confirms the higher concentrations observed by PTR-ToF-MS at night,
suggesting a positive offset on the PTR-ToF-MS measurements, a negative
offset on the cartridge measurements or both. In contrast, a slope
significantly lower than unity (0.58±0.10, 1σ) seems to
indicate a negative bias in the response of the PTR-ToF-MS measurements, a
positive bias for the cartridge measurements or both.
Scatter plot of MGLY concentrations measured by PTR-ToF-MS vs.
concentrations measured by active sampling on DNPH cartridges. Black line and
insert represent the linear regression. Systematic errors associated with the
PTR-ToF-MS (22 %) and DNPH cartridge (25 %) measurements are accounted
for in the regression analysis. The scatter plot has been color coded
according to the relative humidity.
A calibration issue cannot explain an intercept in the scatter plot but
could explain part of the disagreement observed for the slope. Three
potential reasons may lead to a calibration issue: (i) the generation of
unreliable calibration standards, a humidity dependence of (ii) the
PTR-ToF-MS response or (iii) the DNPH derivatization. The procedures used to
calibrate the PTR-ToF-MS and the HPLC-UV are described in the experimental
section. Two different commercial methylglyoxal solutions were used to
generate both the gas-phase standard for the PTR-ToF-MS and the liquid
standard for the HPLC-UV. While we cannot rule out an issue with the MGLY
solutions, it seems unlikely that the disagreement between the two
techniques is only due to unreliable MGLY solutions since the good agreement
observed on some days (21–22, 27, 31 July and 1 August) contrasts with the
poor agreement observed on other days (17 July, 2–5 August) when MGLY peaks
during the daytime (200–300 ppt).
As previously mentioned for the PTR-ToF-MS calibration, varying the
concentration of MGLY in the range 0.6–11 ppbv with the LCU led to a change
in RH. Calculating the calibration factor at each concentration, i.e., at
different RHs, from the ratio of the measured normalized signal to the MGLY
concentration and plotting it as a function of the m/z 37–m/z 19 ratio
(Fig. S2) does not indicate a significant water dependence of the PTR-ToF-MS
response. The humidity dependence of the DNPH–HPLC–UV method has been
recently investigated for some ketone compounds, including acetone and MEK
(S. S. H. Ho et al., 2014). It was shown that the collection efficiency is
inversely related to relative humidity, with up to 35 %–80 % of the
ketones being lost for RH values higher than 50 % at 22 ∘C.
While MGLY exhibits a ketone function, it also exhibits an aldehyde function
and it is not clear whether this compound will behave as simple ketones. The
color coding shown in Fig. 4 indicates that, when higher RH values are
observed (60 %–100 %), lower MGLY concentrations and larger relative
differences between the two techniques are also observed. Figure 5 displays a
scatter plot of the difference between the PTR-ToF-MS and DNPH–HPLC–UV
measurements and relative humidity, showing a weak linear correlation with a
negative slope. This trend with humidity seems to support that the collection
efficiency of MGLY on DNPH cartridges decreases with RH. It is interesting to
note that a collection efficiency lower than 100 %, even at low RH
values, may explain lower concentrations measured by the DNPH–HPLC–UV method,
on average, for the overall campaign.
Scatter plots of the difference between the two techniques and
MEK + butanal (a) or relative humidity (RH, b).
A positive or negative bias in the PTR-ToF-MS measurements could be due to an
inadequate peak-fitting procedure to separate the signals detected at m/z 73.029 (MGLY) and m/z 73.065 (MEK + butanal) (see Sect. 2.2). To check
whether the peak-fitting procedure can lead to a bias in the measurements,
Fig. 5 also presents a scatter plot of the
difference between the PTR-ToF-MS and DNPH–HPLC–UV measurements and the
MEK + butanal concentration measured by PTR-ToF-MS. This scatter plot
indicates a very weak correlation (R2 of 0.05), suggesting
that the fitting procedure was able to deconvolute the signals from MGLY and
MEK + butanal. A weaker correlation is even found when the difference is
plotted as a function of butanal, which was measured by DNPH–HPLC–UV (see
Supplement Sect. S3).
An ionic water cluster, (H2O)3 H3O+, can also be
detected at m/z 73.050. However, as mentioned previously, the signal from
this cluster is recorded during blank measurements and subtracted from
ambient measurements. As a consequence, the detection of
(H2O)3 H3O+ at m/z 73 should not impact MGLY
measurements reported in this study. Since the abundance of
(H2O)3 H3O+ is highly dependent on the ambient water
concentration, relative humidity was used as a proxy to investigate whether
the cluster signal is efficiently recorded in the blank signal, which was
performed hourly to ensure that RH does not change significantly between two
blank measurements. A good correlation (R2=0.45±0.21, from daily analyses) observed between the blank signal at m/z 73 and
the m/z 37–m/z 19 ratio (proxy for humidity content), indicates that the
(H2O)3 H3O+ water cluster signal is indeed recorded
during blank measurements.
Scatter plots of the difference between PTR-ToF-MS and DNPH–HPLC–UV
measurements with O3, acetaldehyde and nopinone were generated (see
Supplement Sect. S3) to check whether the possible secondary formation of isobaric
OVOCs (malondialdehyde, acrylic acid) in the atmosphere, from the oxidation
of ambient VOCs or in the sampling line from reaction of O3 with
unsaturated compounds adsorbed on surfaces, could lead to a positive bias in
the PTR-ToF-MS measurements. The very weak correlations (R2 of
0.02, 0.01 and <0.01 for O3, acetaldehyde and nopinone,
respectively) observed in Fig. S3 rule out this possibility.
Similar correlation plots were made for m/z 137 (monoterpenes), 139
(nopinone), 151 (pinonaldehyde) and 155 (unidentified oxidation product of
monoterpenes) measured by PTR-ToF-MS (see Supplement Sect. S4) to track whether
differences observed between the techniques could be explained by
interferences from the fragmentation of larger compounds observed at
significant concentrations during the CharMEx field campaign. Poor
correlations were found (R2<0.06) suggesting that
MGLY measurements were free of interferences from the fragmentation of
compounds measured at these four masses. Nevertheless, we cannot rule out
interferences from the fragmentation of other higher m/z compounds.
A closer look at the blank signals measured at m/z 73 shows that this signal
correlates with the total m/z 73 signal on some days (21–22 July, 26–27 July,
1–3 August), with R2 factors ranging from 0.36 to 0.56. Lower correlations
are observed on other days (R2<0.20). Interestingly, a scatter
plot between the coefficients of determination for the above mentioned
correlations and the daily averaged relative humidity exhibits an
anticorrelation (negative slope, R2=0.58) (see Supplement Sect. S5). This
type of correlation has also been observed by de Gouw et al. (2003), who
explained this behavior by the sticky nature of MGLY, which could cause a
memory effect in the sampling lines. Different sampling line lengths and
characteristics (heated at 50 ∘C for PTR-ToF-MS and not heated for
DNPH cartridges, presence of a stainless steel particle filter and a KI
ozone scrubber for DNPH cartridges) could lead to different artifacts
related to adsorption or heterogeneous reaction on line surfaces for the two
techniques. It is worth noting that performing blank measurements every hour
and the use of a high flow rate and heated sampling line likely reduces this
artifact for the PTR-ToF-MS, while blank measurements for DNPH cartridges
only take into account passive contamination of the cartridges, without considering any
artifacts from lines. It is interesting to note that, while the
difference between the two techniques is not correlated to the PTR-ToF-MS
measurements (see Supplement Sect. S3), a fair correlation (R2=0.35) is observed with the cartridge measurements, which may suggest a
bias on the cartridge measurements.
While reasonable agreement is observed between the two techniques, a close
look at the correlation between the two measurement sets indicates that the
DNPH–HPLC–UV methods measured lower concentrations than the PTR-ToF-MS
technique by 18 % on average. The above discussion highlights several
potential reasons for this disagreement: (i) calibration standards of MGLY
are difficult to generate for both techniques and require further work to
straighten out this aspect, (ii) the impact of artifacts from sampling lines
needs to be further investigated to evaluate their significance, (iii) the
collection efficiency of MGLY in DNPH cartridges needs to be investigated
under ambient sampling conditions to assess whether MGLY is completely
collected and whether there is a humidity dependence.
Finally, we cannot exclude that differences observed between PTR-ToF-MS and
DNPH–HPLC–UV measurements of MGLY are partly due to differences in sampling
sequences (3 h continuous sampling for DNPH–HPLC–UV, 3 h sampling minus 3
times 10 min of blank measurements for PTR-ToF-MS). However, the impact
of differences in timescale for the two techniques should lead to random
scatter when the measurements are compared and not to a systematic
difference as observed in this study.
MGLY loss rate
Loss rates of MGLY are presented in Fig. 6. They
were calculated as described in Sect. 2.4 using PTR-ToF-MS measurements
since the DNPH–HPLC–UV measurements may suffer from inlet effects and an
overestimated collection efficiency and since PTR-ToF-MS measurements have
higher temporal resolution. The total loss rate peaks during the daytime around
14:00 LT at values ranging from 100 to 350 pptv h-1. The
calculated loss rate is almost equally divided into photolysis and oxidation
by OH, accounting for 53 % and 47 %, respectively, of the average
diurnal loss from 10:00 to 19:00 LT.
Time series of MGLY loss rates (pptv h-1) from photolysis and
reaction with OH.
A thorough investigation of the MGLY budget would require calculating the
total MGLY production rate from the oxidation of ambient VOCs for comparison
to the total loss rate presented above. However, as mentioned in the
introduction, MGLY is produced during the oxidation of many VOCs (isoprene,
monoterpenes, acetone, aromatics, etc.) at average yields which are
strongly dependent on ambient radical concentrations and NOx as recently
reported for isoprene (Jenkin et al., 2015). It is also worth noting that
calculating MGLY production rates based on ambient concentrations of
precursors and average yield values would only be robust for
first-generation oxidation products since no intermediate species is taken
into account. Taking into account that MGLY is also a second- and higher-generation oxidation product in most degradation mechanisms would lead to a
delayed formation. Indeed, MGLY production can take hours in NO-rich
environments and even days in low-NOx environments such as in this study
(Fu et al., 2008). As a consequence, it would be hazardous to try to
calculate local MGLY production rates from the measured VOC precursors.
When a gaseous species exhibits a lifetime lower than a few seconds or minutes,
such as radical species or highly photolabile compounds, this species should
reach a photostationary state and chemical production and loss rates should
balance each other out since transport processes such as advection and vertical
dilution are too slow to significantly impact the local concentration of
these short-lived species. MGLY exhibits a lifetime of approximately 1–2 h during the daytime, which may be short enough for the photostationary state to
hold. In this case, production rates of MGLY should mimic the loss rate
displayed in Fig. 6. However, Washenfelder et al. (2011) showed a
breakdown of the photostationary state when applied to glyoxal, a dicarbonyl
compound exhibiting a lifetime of the same order of magnitude as MGLY, and
as a consequence the calculated loss rate reported in this study only
provides a rough estimation of the local production rate.
Conclusions and discussion
To the best of our knowledge, this study presents the first ambient
measurements of methylglyoxal by PTR-ToF-MS. This work aims to describe a
simple and proper procedure with which to perform reliable measurements, relying on (i) the data processing proposed by de Gouw and Warneke (2007) to account for
the impact of ambient humidity on the PTR-ToF-MS sensitivity, (ii) automatized blank measurements performed every hour to suppress potential
memory effects and interferences from water clusters, and (iii) a Gaussian
peak-fitting analysis to deconvolute the methylglyoxal signal from other
compounds exhibiting a similar mass unit but different exact masses (i.e.,
butanone and butanal).
The ChArMEx SOP2 field campaign was conducted in an environment
characterized by high biogenic emissions, including isoprene, at the extreme
north of Corsica. This campaign therefore provides a good
opportunity to study methylglyoxal measurements, since this compound is
mainly formed via isoprene oxidation. Furthermore, concomitant measurements
of methylglyoxal by PTR-ToF-MS and DNPH–HPLC–UV allowed an intercomparison of
these two techniques to test their reliability.
Time series of methylglyoxal measured by both PTR-ToF-MS and DNPH–HPLC–UV
revealed concentration levels ranging from 28 to 365 pptv with a clear diurnal
cycle due to the secondary nature of this compound. The visual comparison of
the measured time series shows a reasonable agreement, with the DNPH–HPLC–UV
methods measuring concentrations lower by 18 % on average compared to the
PTR-ToF-MS technique. A linear regression analysis performed between the two
measurement sets indicates a fair correlation with a determination
coefficient (R2) of 0.48, a slope significantly different than unity
(0.58±0.10, 1σ) and a nonzero intercept (88.3±15.9 pptv, 1σ). Interferences from (H2O)3 H3O+,
butanone and butanal can be excluded for the PTR-ToF-MS measurements,
validating the procedure used for data acquisition and analysis.
Methylglyoxal formation into sampling lines due to heterogeneous reactions
of O3 with adsorbed organic compounds is also not likely. Potential
remaining uncorrected artifacts from lines on some days for both techniques
could be partly responsible for measurements disagreements and this aspect
needs to be further investigated to evaluate its significance. In addition,
this work questions the collection efficiency of MGLY in DNPH cartridges and recommends an investigation under ambient sampling conditions to assess
whether all the MGLY is collected and whether a humidity dependence exists.
Comparisons of PTR-ToF-MS with other existing techniques in the field and/or
in atmospheric simulation chambers would be of interest to identify
potential artifacts causing the disagreement observed in this study.
Nevertheless, PTR-ToF-MS seems promising for methylglyoxal measurements.
The methylglyoxal loss rate was studied at Corsica Cape, revealing that the
contributions of direct photolysis and OH oxidation were almost similar.