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Micro- and Nanoplastics in Alpine Snow: A New Method for
Chemical Identification and (Semi)Quantification in the Nanogram
Range
Duš
an Materić
,*Anne Kasper-Giebl, Daniela Kau, Marnick Anten, Marion Greilinger, Elke Ludewig,
Erik van Sebille, Thomas Röckmann, and Rupert Holzinger
Cite This: https://dx.doi.org/10.1021/acs.est.9b07540
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sıSupporting Information
ABSTRACT: We present a new method for chemical characterization of micro- and nanoplastics
based on thermal desorption−proton transfer reaction−mass spectrometry. The detection limit for
polystyrene (PS) obtained is <1 ng of the compound present in a sample, which results in 100 times
better sensitivity than those of previously reported by other methods. This allows us to use small
volumes of samples (1 mL) and to carry out experiments without a preconcentration step. Unique
features in the high-resolution mass spectrum of different plastic polymers make this approach suitable
for fingerprinting, even when the samples contain mixtures of other organic compounds. Accordingly,
we got a positive fingerprint of PS when just 10 ng of the polymer was present within the dissolved
organic matter of snow. Multiple types of microplastics (polyethylene terephthalate (PET), polyvinyl
chloride, and polypropylene carbonate), were identified in a snowpit from the Austrian Alps; however,
only PET was detected in the nanometer range for both snowpit and surface snow samples. This is in
accordance with other publications showing that the dominant form of airborne microplastics is PET
fibers. The presence of nanoplastics in high-altitude snow indicates airborne transport of plastic
pollution with environmental and health consequences yet to be understood.
■INTRODUCTION
Micro- and nanoplastics are well recognized as environmental
pollutants. They are present in most environmental habitats
and can potentially affect organisms in ways that are not
completely understood.
1
Much of the scientific research
studies on plastic polymers in the environment has focused
on microplastics in the marine environment (particles, <5
mm),
2,3
and only recently, nanoplastics (<1 μm) have been
successfully analyzed.
4
Various methods are used in microplastic research; however,
when a polymer fragment gets to the nanoscale, conventionally
used optical and chemical methods are not suitable anymore
(sensitivity issues and physical and optical limits). The lack of
suitable analytical tools results in a perceived knowledge gap in
our understanding of the nanoplastics.
5,6
Here, we present a highly sensitive method for detection,
speciation, and quantification of micro- and nanoplastics based
on thermal desorption−proton transfer reaction−mass spec-
trometry (TD-PTR-MS). The detection technique, PTR-MS,
is widely used in the analysis of various complex organic
mixtures in the environment including real-time monitoring of
volatile organic compounds, semivolatiles, and organic aerosols
in air and, recently, dissolved organic matter (DOM) in
environmental waters and ice.
7−11
A particular strength of the TD-PTR-MS method is the
combination of high sensitivity and high-mass resolution.
9,10
While the latter allows chemical identification of the
compounds to the level of chemical formula, high sensitivity
provides quantitative information of the low-concentrated
organics. In addition, required sample sizes are small, which
makes this method suitable for high-throughput analysis.
In this work, for the first time, we applied the TD-PTR-MS
method to the analysis of common micro- and nanoplastics
polymers. We developed a fingerprint algorithm for polymer
identification when present in a complex organic matrix, such
as dissolved organic matter in snow, and successfully identified
micro- and nanoplastic polymers in snow samples collected in
the Alps at 3 km above sea level.
■MATERIALS AND METHODS
Standards’Preparation. A small amount of different
plastic polymers was grinded with a fine metal saw, and pieces
of <0.3 mm were used for further analysis. We used
polyethylene terephthalate (PET), high-density polyethylene
(HDPE), low-density polyethylene (LDPE), linear low-density
polyethylene (LLDPE), polypropylene (PP), polypropylene
carbonate (PPC), polyvinyl chloride (PVC), and polystyrene
(PS). The polymers were provided by the TCR Plastics,
Received: December 11, 2019
Revised: January 8, 2020
Accepted: January 17, 2020
Published: January 17, 2020
Articlepubs.acs.org/est
© XXXX American Chemical Society A
https://dx.doi.org/10.1021/acs.est.9b07540
Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Netherlands, except PET, PP, and PS, which were taken from
the clean end products (plastic bottle, laboratory tubes, and
laboratory cold storage material, respectively). One grain of the
polymer was loaded into clean 10 mL vials, and for each type,
we prepared triplicate samples and used empty vials as
procedural blanks.
For the sensitivity test, we used PS standard spheres of 1 μm
in diameter (PS-ST-1.0, Microparticles GmbH, Berlin,
Germany), prepared series of solutions in water, and loaded
the experimental vials to contain 1, 5, 10, 20, and 50 ng of PS.
The number concentration of the PS solution was 1819
particles per 1 ng. For this experiment, we used 10 mL glass
vials with a 5 mm quartz filter to provide a surface for PS
particles to attach, all prebaked at 250 °C overnight.
Snow Sample Preparation. The samples were collected
from the glaciers next to the Sonnblick Observatory, Austria, at
about 3100 m altitude, distant to any anthropogenic activity
and within the Austrian National Park Hohe Tauern. A
complete snow profile was collected on April 2017,
representing the complete snow accumulation for the previous
winter season starting in October, reaching an absolute depth
of 3.9 m. Sampling was performed in 20 cm sections, which
were taken with a stainless steel cylinder. For this project, we
have analyzed three samples at nominal section depths of 2.6,
2.8, and 3.0 m, as this part of the profile was preserved as
compact cores with 5.6 cm diameter, suitable for excluding
possible contamination at the surfaces that can occur during
sampling or storage. The outer 1 cm of the surface was shaved
offwith a clean ceramic knife, a longitudinal subsample was
taken for the measurement (not filtered), and the rest of the
core was gently melted in a microwave and mixed with a clean
glass stick. The longitudinal subsamples were melted at room
temperature resulting in 20−30 mL of each sample. The
samples were well hand-mixed (homogenized), but not
vortexed, to avoid possible fragmentation of microplastics.
From the rest of the melted core (approximately 0.5 L), 10 mL
was subsampled and filtered through a 0.2 μm pore size PTFE
filter to separate microplastics from nanoplastics. Process
blanks were prepared by exposing a similar amount of Milli-Q
water to the same containers and surfaces for the same amount
of time including the laboratory materials such as syringes,
pipette tips, and filters.
Surface snow samples were taken in the periods 2017-03-20
to 2017-04-01 close to the Sonnblick Observatory, and
analyses were performed as previously described.
10
Mass
spectra of the surface snow samples were already analyzed and
interpreted with respect to organic aerosol deposition
dynamics.
12
For the work presented here the original mass
spectra were re-evaluated addressing the presence of nano-
plastics.
TD-PTR-MS. Low-pressure evaporation/sublimation was
performed as described in our previous work.
9
We used the
TD-PTR-MS protocol and parameters as stated earlier
(ramping from 35 to 350 °C at 40 degrees/min, E/N 120
Td).
10,11
For the data processing, we used the custom-made
software package PTRwid.
13
The PTR-MS signal was
integrated over 10 min starting when the TD temperature
reached 50 °C. This way, the thermal degradation of each
polymer (which produces different degradation products at
different temperatures) was integrated in the analysis. No
compound has been observed in carry-over tests, suggesting
that thermal desorption was complete regardless of the type
and size of the polymers present. The thermograms of all the
plastic types and all the ions are included in the Supporting
Information.
The mass spectra of each measurement were corrected for a
blank signal, and masses below the 3σdetection limit were not
considered for analysis. For PCA and fingerprinting analysis,
we only used ions with m/z> 100.
Fingerprinting Algorithms. We developed four finger-
printing algorithms to score the similarity between mass
spectra of a standard material (further referred as library mass
spectra) and a sample. The algorithms 1 and 2 (ALG1 and
ALG2) select the highest nion signals (e.g., 20) above m/z
100 and normalize the signal to the highest peak. The
algorithms 3 and 4 (ALG3 and ALG4) normalize the signal to
the sum of all peaks.
ALG2 and ALG4 calculate the absolute difference
(msDIFF) from the library mass spectra as
∑
=| −
|
=
msDIFF mz mz
i
n
1
Si Li (1)
where mzsis the normalized concentration of the sample for
ion i and mzLis the normalized concentration in the library
mass spectrum for ion i. The ratio of msDIFF in eq 1to the
maximal theoretical difference (i.e., when the library and the
sample mass spectrum share no common ion) returns a value
between 0 and 1 (i.e., 0 is identical and 1 is 100% different, see
the script in the Supporting Information for more details).
In ALG1 and ALG3, the difference for each ion is weighted
by the normalized ion intensity (eq 2) and normalized to the
maximal possible difference, again resulting in a value between
0 and 1.
∑
=|−|×
=
msDIFF ( mz mz mz )
i
n
1
Si Li Li
(2)
where mzsis the normalized concentration of the sample for
ion i and mzLis the normalized concentration in the library
mass spectrum for ion i. This way, in ALG2 and ALG4, the
library ions with higher concentrations are proportionally
valued higher in the calculation of the total difference. For
example, an absolute difference (msDIFFi) of 1 for the library
ion with the relative concentration 100% (the highest ion)
would be weighted with a factor 1; however, the weighting
factor for the library ion with the relative concentration 50%
would be 0.5.
Following calculating the total differences (msDIFF), the
algorithms calculate the mass spectra similarity (match score)
as
=−
M
1msDIF
F
(3)
The fingerprinting evaluation (e.g., false positive and false
negative) can be found in the Supporting Information Figure
S1 and Table S1.
Fingerprinting the Samples. We performed the finger-
printing for all the samples and all the blanks (available in the
Supporting Information) using a script developed for this
purpose (also available in the Supporting Information).
Considering all the challenges associated with the complex
organic matrix in snow (Table S1), for the fingerprinting, we
used the 30 most abundant library ions with m/z> 100.
Smaller-molecular-weight ions were excluded in the finger-
printing as they may contain higher levels of ions coming from
the organic matrix and not the polymers.
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B
The principle of the quantification protocol for PTR-MS and
TD-PTR-MS was explained earlier.
9,11,14−16
In short, due to
the extensive knowledge of physics behind proton transfer
reaction in the instrument reaction chamber (drift tube), the
compound concentrations [C] can be calculated from ion
signals as follows:
[
]= × [· ]
[]
×
+
+
·
+
+
C
kt
mz
mz
1MH
HO
(/)
(/)
3
HO
MH
3
(4)
where kis the reaction rate coefficient, tis the residence time
of the primary ions in the drift tube, [M·H+] and [H3O+] are
the ion counts representing the protonated analyte and
primary ions, respectively, and (m/z)H3O+and (m/z)M·H+
represent the mass-of-charge of protonated water and the
analyte M,respectively.
Quantification of the polymers in this work was based on the
30 ions that matched the library mass spectra. The
quantification calculation from eq 4was improved in this
work via preservation of the ratio of the ion signal from the
plastics library so that possible contamination from natural
DOM was excluded. For example, if compounds from natural
DOM produce one or more ions with the exact mass as the
nanoplastic polymer, to prevent overestimation, such ion
signals are reduced according to the expected values calculated
from the library ions of that particular polymer (for more, see
the scripts in the Supporting Information).
Using the 30 ions from the library and the known
concentration of PS, we obtained a recovery of 15% (Figure
S2), corresponding to an underestimation factor of 6.7 for
polystyrene. This recovery level is between previously reported
values for DOM (12%) and tests with semivolatile standards
(e.g., levoglucosan and glutaric acid, 62% and 18%,
Figure 1. Example of ions obtained by the analysis of the pure polymer, which can be used to identify and distinguish the different types of plastics.
HDPE, high-density polyethylene; LDPE, low-density polyethylene; LLDPE, linear low-density polyethylene; PET, polyethylene terephthalate; PP,
polypropylene; PPC, polypropylene carbonate; PS, polystyrene; and PVC, polyvinyl chloride. The signal is normalized to the total concentration of
all the ions. The arrows are added to highlight the differences between plastic types.
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Environ. Sci. Technol. XXXX, XXX, XXX−XXX
C
respectively).
11,17
This is likely due to thermal degradation and
PTR efficiency resulting in not detectable fragments/molecules
(neutral fragments, CO, CO2, etc.). Future improvement in
instrument coating, ionization optimization, and utilizing
different carrier gasses may improve the recovery rate.
However, this does not affect the quantification as (1) ion
yields are stable and (2) an appropriate correction factor can
be used, (e.g., for PS). The correction factors and recovery
rates for polymers other than PS are expected to be of a similar
magnitude (i.e., between DOM and other semivolatiles);
however, no nanoplastic standards are yet available to test this.
In this work, the correction factors are not applied. Thus, the
reported concentrations of the polymers found in the snow/ice
samples reported here are semiquantitative values, not
corrected for TD/PTR recovery and losses during sample
treatment (e.g., filtration), and as such they represent low
thresholds for the concentrations.
Quality Control. Contamination with microplastics and
nanoplastics from the sampling equipment and laboratory
materials or during the preparation for the analysis can pose a
severe threat to the quality of the results, so adequate
procedures should be adopted depending on the nature of the
experiment.
For our snowpit samples, to exclude possible contamination
from the equipment during the sampling, we shaved the
outside (∼1 cm) of the sample cores before sampling for our
analysis. To assess possible contamination during sample
storing and measurement, we performed procedural blank tests
in which we exposed Milli-Q water to all laboratory materials
we used.
For the surface snow samples, field blanks were taken at the
sampling site by exposing Milli-Q water to the impurities of the
equipment in the sampling process in order to quantify
possible sampling contamination. These blanks were also
processed in the same way as the samples to evaluate other
sources of contamination during the analysis.
Although samples and blanks were stored in HDPE bags
(snow core samples) or PP containers (surface snow samples),
no PE or PP fingerprint has been found in any of samples or
blanks. Moreover, no positive fingerprint of any type of plastics
was found in the blanks (see the Supporting Information),
which excludes significant contamination during sampling,
storage, and analysis.
■RESULTS AND DISCUSSION
Thermal desorption followed by PTR-MS analysis of plastic
polymers results in rich mass spectra containing more than 300
ions of different masses. Different polymers of plastics express
various unique features (pointed by arrows), which can be
used for the identification (Figure 1). For example, the ion m/z
105.065 (Figure 1, top right), corresponding to C8H9+
(protonated styrene), is a thermal decomposition product of
polystyrene that can be used for its quantification, with a little
or no interference by the ions from the other polymers. The
polyethylene varieties (HDPE, LDPE, and LLDPE) show
clearly elevated levels at m/z101.024 (C4H4O3H+), PET at m/
z123.044 (C7H6O2H+), and PVC at m/z129.059
(C6H8O3H+).
Using calibration standards, the unique ions present in the
mass spectrum of a certain polymer can be used for micro- and
nanoplastic quantification. In a calibration experiment with
polystyrene, we obtained a sensitivity of 23 ppt gas-phase
concentration of an ion with m/z105.069 per ng solid sample
material (over the 10 min TD time) (see Figure 2). The
calculated detection limit (3σof the blanks) for ion m/z
105.069 is 7.8 ppt (0.34 ng); thus, we can successfully quantify
subnanogram concentrations of pure polystyrene in a sample.
This sensitivity is ∼100 times higher than that of previously
reported methods
5,6,18
and allows the analysis of various
environmental samples (e.g., samples of snow, rain, and
drinking water) without a preconcentration step, with sample
volumes as low as 1 mL. From Figure 1, we can also notice that
some polymers have an insignificantly different expression of
single-ion signals (e.g., HDPE, LDPE, and LLDPE). In the
case of ion m/z109.099 (C8H13+) of PP and PPC (Figure 1),
the single-ion approach does not allow analytical separation.
The single-ion identification presented in Figure 1 is
illustrative but does not fully exploit the richness of the mass
spectra. In addition, identification with single ions is likely
challenging when the plastic polymers are present in a mixture
with other organics (e.g., DOM). Figure 3 shows that most
types of plastics can be clearly distinguished by principal
component analysis (PCA), which is a multivariate approach
where more ions are considered to improve the separation.
PCA of the PTR-MS data for all the polymers used in this
work illustrates clustering for all types of plastics except PE of
different densities (LDPE, LLDPE, and HDPE) and PP/PPC.
In the following, we exploit these differences in our sensitive
method for plastic fingerprinting when different types of
plastics are present within a natural organic matrix (see the
Materials and Methods section and Supporting Information).
It is challenging to successfully fingerprint the plastic
polymers when they are present together with other organics.
In complex organic mixtures, such as DOM in snow, ions of
the same m/zas those from the plastic polymers can also be
present in significant abundance, which could mask the typical
micro/nanoplastic signal. On the other hand, the strict
detection limit applied in the data processing could result in
detection of only highly abundant polymer ions; whereas, low-
abundant ions close to the detection limit get filtered out. A
suitable method, algorithm, and analysis strategy should
successfully deal with these challenges reducing the possibility
of both false-positive and false-negative results. In the
Supporting Information, we showed that the selection of the
Figure 2. Sensitivity and linearity test of polystyrene ion m/z105.069.
Error bars represent standard deviation (n=4;R2= 0.9998). The
linear fit is forced through the origin.
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D
30 most concentrated ions with m/z> 100 allows successful
fingerprinting within the complex organic mixture of snow
samples when a cutoffscore of >60% is used. Other algorithms
facilitating a more “guided”approach (e.g., considering just a
preselected list of ions) could be valuable for the fingerprinting,
especially after the removal of the organic matrix, but they are
not addressed in this pilot study.
As the first scientific application, we used the new technique
for detection and quantification of different types of plastic
polymers in high-altitude surface snow and snowpit samples.
Re-analysis of the surface snow PTR-MS mass spectra obtained
in our previous work
10
revealed positive fingerprints for PET
for all the samples except the sample collected on 2017-03-26,
which had a match score of 57%, thus just below the set
threshold (60%). Successful fingerprinting (the level of the
match score) depends on the concentration of the polymers in
the sample. For instance, although the match score was below
the threshold for the sample 2017-03-26, we calculated the
concentration of PET to be 27 ng/mL (Table 1), which is
elevated compared to those of the previous and later sampling
periods (∼40% higher). The lower match score may be due to
the presence of other organics from the matrix with the same
m/zas the PET ion products, decreasing the match score
below the threshold. Thus, there is a trade-offbetween a good
match score and potential overestimation in the quantification.
In other words, if the presence of the other organic ions
masking the polymer signal is substantially high, this can result
in a negative match to prevent a severe overestimation of the
concentration by the method. Therefore, in the case of the
presence of such complex organic mixtures in the sample (e.g.,
high DOM loads), it is advisable to remove the matrix before
the analysis of the nanoplastics.
We further investigated what would be the required
minimum concentration of a polymer in a complex organic
matrix of snow for a positive fingerprint. Using the polystyrene
as an example and the in silico addition of its mass spectrum to
the mass spectra of natural samples analyzed here (75 to 661
ng/mL of DOM retrieved by PTR-MS), we obtained positive
fingerprints when ∼10 ng of PS was present in samples. This
value could be different for different polymers and types of
organic matrixes present thus should be evaluated for each
experiment. We expect that this value would be much lower if a
polymer preconcentration or digestion of the organic matrix is
used and higher if more organics are present in the sample
(e.g., high concentration of natural DOM in the samples).
The snowpit taken on April 2017 from a nearby glacier field
has been sampled and, in this work, we analyzed cores of the
pit section 13−15 (2.6−3.0 m in depth) because this was the
best-preserved section, suitable for analysis of inner parts of the
core. No precise dating was performed, but most likely, this
part of the pit reflects snow samples accumulated during early
winter. Chemical analysis of the melted cores showed the
presence of PET, PPC, and PVC; however, after the 0.2 μm
filtration only PET nanoplastics were found (Table 1).
For both the melted and filtered snow from the snowpit
samples and for the surface snow, PET shows the highest
concentrations among the different types of plastics, even in
the fresh precipitation. In the fresh snow sample of 20th of
March 2017, the fingerprint score for PET nanoplastics was
81.4 (Table 1), indicating high contribution of this polymer
relative to other DOM in the fresh precipitation. These
nanoparticles are most likely a product of microplastic
Figure 3. PCA of different plastic polymers considering ions of >100
m/z. HDPE, high-density polyethylene; LDPE, low-density poly-
ethylene; LLDPE, linear low-density polyethylene; PET, polyethylene
terephthalate; PP, polypropylene; PPC, polypropylene carbonate; PS,
polystyrene; and PVC, polyvinyl chloride.
Table 1. Concentrations of the Different Types of Micro/Nanoplastics Observed in the Surface Snow and Snowpit Samples
a
melted snow filtered snow (0.2 μm)
sample match score quantity [ng/mL] match score quantity [ng/mL]
snowpit 2017, depth of 2.6 m PET 61.5 7.0
PPC 64.4 16.5
snowpit 2017, depth of 2.8 m PET 65.8 ±3.1 22.9 ±13.9 PET 65.6 ±7.7 18.5 ±1.5
snowpit 2017, depth of 3.0 m PET 78.2 ±14.2 5.6 ±0.9
PPC 60.9 ±2.4 10.8 ±4.1
PVC 62.6 ±5.4 6.9 ±0.2
surface snow 2017-03-20 n/a n/a PET 81.4 ±7.1 4.6 ±0.9
surface snow 2017-03-23 n/a n/a PET 60.4 ±9.7 18.5 ±2.2
surface snow 2017-03-26 n/a n/a PET? 57.1 ±1.5 23.6 ±3.0
surface snow 2017-03-29 n/a n/a PET 70.0 ±1.7 12.1 ±2.3
surface snow 2017-04-01 n/a n/a PET 62.7 ±2.5 13.8 ±2.3
a
Not corrected with an underestimation factor of ∼6.7 (see Materials and Methods). Errors represent standard deviation over the triplicate of all
filtered samples and duplicates for melted snow samples, except the pit sample 2.6 M (single run). All the match scores can be found in the
Supporting Information.
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E
degradation of PET, which is one of the most common plastics
used mostly for synthetic fibers in the clothes industry and
plastic bottles for beverages. Our finding of the predominant
presence of PET compared to the other polymers is consistent
with previous work on urban air microplastic pollution
research, where it was discovered that urban microplastics
consist mostly of fibers.
19,20
Recently, the presence of microplastics in remote areas of
the Pyrenees has been reported, with higher abundance of
sheets and fragments compared to fibers.
21
The chemical
compounds were identified as PS and PE by a spectroscopy
technique. It has also been reported that nonfiber polymer
deposition correlated with the amount of precipitation during
the sampling period.
19
The disagreement between the results
obtained in the urban areas
19,20
(comparable with our findings
in the high-altitude Alps) and the remote Pyrenees
21
raises the
question of the sampling strategy that needs to be developed
for atmospheric measurements of micro- and nanoplastics.
Simple deposition funnels with a bottle at the bottom to collect
dry and wet deposition might be inadequate and selective to
heavier particles such as sheets and fragments while light fibers
might escape the funnel, especially during nonprecipitation
periods. However, the lack of PS in our samples might be due
to the chemical changes that PS nanoparticles underwent via
weathering and UV oxidation, as described previously.
22
This
would result in a shift in mass spectra and change in the
fingerprint, thus more research is needed to address this issue.
Concentrations of the micro/nanoplastics observed in the
surface snow and snowpit samples are shown in Table 1. These
concentrations were not corrected with an underestimation
factor of 6.7 measured for polystyrene (see Materials and
Methods) and thus represent lower limits for the actual
concentrations. The filtered snowpit samples had a positive
fingerprint of PET only for the sample core of 2.8 m in depth.
The PET loads of unfiltered samples for the other two cores
were as low as 8.9 and 8.3 ng/mL. The low abundance may
explain why these compounds could not be detected with a
positive fingerprint after the filtration. The minimal presence of
nanoplastics in the snowpit samples compared to the surface
snow samples can be likely attributed to the nanoplastic
deposition processes. The surface samples show that in the
fresh surface snow sample (2017-03-20), the concentration of
nanoplastics was low and it increased due to dry deposition in
the later precipitation-free period (Table 1). Apparently, the
big precipitation events, which provide most of the snow to the
pit, contain only small loads of nanoplastics. During
precipitation-free periods, higher levels of nanoplastics are
delivered to the snow surface by dry deposition. This
potentially results in thin plastic-rich layers within the pit,
which were diluted in our samples, which integrate a plastic
content of 20 cm of the snow core. On the other hand, higher
depositions of most of the inorganic and organic compounds
are reported at the same site during spring compared to the
winter snow, which might also be similar for the nano-
plastics.
23−25
We suggest that higher-resolution sampling (e.g.,
1 cm sections) should be used for measurements of plastics in
snow and ice cores.
Total organic loads in the snow surface samples coming
from different semivolatile organic compounds have been
previously published (ranging from 75 to 661 ng/mL for the
period).
10
Our new evaluation shows that concentrations of
PET (between 5.4 and 27.4 ng/mL) for the same surface snow
samples may account for several percent (3−6% in this
example) of the total organics retrieved by the same method.
These numbers may be affected by the instrument recovery
rate. However, since TD-PTR-MS is most sensitive to the
semivolatile DOM and the recovery rate 12% of the total
DOM
11
is similar to the 15% found here for PS (Figure 2), our
first results indicate potentially concerning levels of global
atmospheric nanoplastic pollutions that are transported even to
remote alpine regions. More research studies are needed to
address this issue.
It is clear that the novel field of nanoplastic research in
environmental samples has its challenges, especially when the
polymers are present at low concentrations such as in air,
snow, and natural and drinking water. The sensitivity of the
TD-PTR-MS method presented here offers a novel contribu-
tion to the field, closing the methodological gap of chemical
characterization and quantification at a nanogram scale.
However, many unknowns still exist as a consequence of
non-existent nanoplastic standards of different types and sizes.
The loss during filtration, nanoplastic weathering effect,
oxidation changes, and degradation protocols therefore still
need to be systematically described. Further development is
also needed to combine the accuracy of this sensitive chemical
characterization technique with a technique that would add the
size and shape information of the particles, allowing more
detailed assessment of the potential harm of nanoplastics in the
environment.
■ASSOCIATED CONTENT
*
sıSupporting Information
The Supporting Information is available free of charge at
https://pubs.acs.org/doi/10.1021/acs.est.9b07540.
Supplementary information containing more details on
the method and associated supplementary figures and
tables (PDF)
Mass spectra of PS standard different concentrations,
mass spectra library of all the plastics types, snow core
mass spectra, and match scores of the blanks and all the
samples against all the polymer types (XLSX)
Thermograms for each plastic type and all the ions
analyzed (ZIP)
Scripts and library mass spectra that were used for
fingerprinting and the fingerprint method evaluation
(ZIP)
■AUTHOR INFORMATION
Corresponding Author
Duš
an Materić
−Institute for Marine and Atmospheric
Research, Faculty of Science, Utrecht University, 3584 CC
Utrecht, The Netherlands; orcid.org/0000-0002-6454-
3456; Email: dusan.materic@gmail.com
Authors
Anne Kasper-Giebl −Institute of Chemical Technologies and
Analytics, Vienna University of Technology, Wien 1060, Austria
Daniela Kau −Institute of Chemical Technologies and Analytics,
Vienna University of Technology, Wien 1060, Austria
Marnick Anten −Institute for Marine and Atmospheric
Research, Faculty of Science, Utrecht University, 3584 CC
Utrecht, The Netherlands
Marion Greilinger −Institute of Chemical Technologies and
Analytics, Vienna University of Technology, Wien 1060,
Environmental Science & Technology pubs.acs.org/est Article
https://dx.doi.org/10.1021/acs.est.9b07540
Environ. Sci. Technol. XXXX, XXX, XXX−XXX
F
Austria
Elke Ludewig −Zentralanstalt für Meteorologie und
Geodynamik (ZAMG), Vienna 1190, Austria
Erik van Sebille −Institute for Marine and Atmospheric
Research, Faculty of Science, Utrecht University, 3584 CC
Utrecht, The Netherlands
Thomas Röckmann −Institute for Marine and Atmospheric
Research, Faculty of Science, Utrecht University, 3584 CC
Utrecht, The Netherlands
Rupert Holzinger −Institute for Marine and Atmospheric
Research, Faculty of Science, Utrecht University, 3584 CC
Utrecht, The Netherlands
Complete contact information is available at:
https://pubs.acs.org/10.1021/acs.est.9b07540
Notes
The authors declare no competing financial interest.
■ACKNOWLEDGMENTS
D.M. acknowledges the support of The Netherlands Earth
System Science Centre (NESSC) research network. E.v.S. was
supported through funding from the European Research
Council (ERC) under the European Union’s Horizon 2020
Research and Innovation Programme (grant agreement no.
715386). Snowpit sampling was performed within the ongoing
monitoring program supported by the BMNT.
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