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Raman Spectral Imaging for the detection of inhalable microplastics in ambient
particulate matter samples
Stephanie L. Wright, Joseph M. Levermore and Frank J. Kelly
MRC-PHE Centre for Environment and Health, Department of Analytical, Environmental and
Forensic Sciences, King’s College London, London SE1 9NH, United Kingdom.
Abstract
Microplastics are a ubiquitous contaminant, with preliminary evidence indicating they are a novel
component of air pollution. This presents a plausible inhalation exposure pathway, should
microplastics occur in the inhalable size range; however, this remains an analytical challenge. Here,
we develop a filter-based sampling method compatible with both air quality monitoring and Raman
Spectral Imaging (RSI) for the detection of inhalable-sized microplastics. Clean and particulate matter
(PM)-contaminated filters of a range of compositions were screened. RSI was validated using a plastic
microbead suspension (polymethyl methacrylate (5 – 27 µm), polyethylene (10 – 27 µm) and
polystyrene (4 and 10 µm). Filters were loaded with the suspension before being analysed. RSI
analysis was conducted using a univariate analysis, fitting unique plastic bands to the spectral
datasets, where high spatial intensity indicated the presence of microplastics. Inhalable microplastics
were not visibly detectable against quartz or spectroscopically-detectable against
polytetrafluoroethylene (PTFE) and alumina-based filters. Whilst microplastics were detectable
against cellulose, the PM-contaminated filters (4 and 24 h) burnt during analysis. The greatest
intensities for microplastics were observed against the silver membrane filter, and inhalable
microplastics were still detectable in a 24 h PM sample. These findings will facilitate the acquisition
of inhalable microplastic concentrations, which are necessary for understanding microplastic exposure
and, ultimately, what their potential role in PM-associated health effects might be.
Introduction
Plastic waste is a key societal challenge. An estimated 8.3 billion tonnes of this durable material have
been manufactured since mass production began in the 1950s (Geyer et al., 2017). Eighty per cent of
this has accumulated in landfill (Geyer et al., 2017) or the natural environment, where it degrades into
particles and fibres known as microplastics (<5 mm). Microplastics are a ubiquitous contaminant of
global concern, permeating marine (Zhu et al., 2018), freshwater (Hurley et al., 2018) and, potentially
terrestrial (Weithmann et al., 2018) environments.
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Microplastics are either primary or secondary in origin. Primary microplastics are purposefully
manufactured plastic microparticles. These include microbeads – many between 1 and 50 µm in size –
which are used in personal care products; approximately 4400 tonnes are used in the European Union
each year (Leslie, 2015). Secondary microplastics refer to microparticles originating from the
fragmentation of larger, degraded plastic items, including synthetic textiles (Napper and Thompson,
2016) and tires (Panko et al., 2013).
Whilst predominantly regarded as a marine issue, sources of microplastics originate on land. In
Ireland, a wastewater treatment plant releases an estimated 65 million particles daily, despite retaining
98% of microplastics in the influent (Murphy et al., 2016). This implies a substantial amount (3.25 x
109) of microplastics may be incorporated into sludge at this WWTP annually. To put this in context,
recent predictions calculated between 125 and 850 tons of microplastics per million inhabitants are
applied to agricultural soils in Europe via sewage sludge and/or processed solids (Nizzetto et al.,
2016). Between 0.2 and 0.7 million tons of tyre wear particles are predicted to be released to the
environment each year (Kole et al., 2017), and microparticles from thermoplastic road marking paints
have also been found in samples from an urban river (Horton et al., 2017). Of importance for this
study, wind turbulence could cause these particles to become airborne.
To date, three studies have measured microplastics in atmospheric fallout. Microplastics have been
measured in total atmospheric fallout in China (Cai et al., 2017) and Paris (Dris et al., 2016, Dris et
al., 2017). The majority of particles observed in both studies were fibres, and approximately 30% of
counted particles were confirmed plastic. In Paris, microplastic particle diameters were between 7 and
15 μm and almost 25% of fibers were 100–500 μm in length (Dris et al., 2016). Up to 355 and 313
particles/m2/d were reported, for Paris and China, respectively (Dris et al., 2016, Cai et al., 2017). In
Paris, abundances were substantially greater in urban than suburban areas and periods of heavy
rainfall corresponded with some of the highest concentrations observed (Dris et al., 2016). There was
no indication of the sources of microplastic emissions to the atmosphere, except for via dust (Cai et
al., 2017).
Whilst the above studies highlight the potential for microplastics to be airborne, the human health
risks due to exposure via inhalation remain unclear. Aerodynamic diameter, which is a function of
geometric size, shape and density, primarily dictates where in the human airway a particle deposits
out of the inhaled air stream due to inertial impaction, sedimentation, diffusion, interception and
electrostatic precipitation (Carvalho et al., 2011). Particles <10 µm aerodynamic diameter are
typically of interest with respect to potential health effects; particles <2.5 µm aerodynamic diameter
may reach the deep lung and potentially be up taken by lung macrophages and epithelial cells. Once
in the airway, potential impacts ranging from inflammation to chemical transfer could occur (see
review by Wright and Kelly, (2017). Microplastics in this size range have not yet been reported in
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ambient particulate matter (PM) samples and such measurements remain an analytical challenge;
however, assessing exposure concentrations is critical for the understanding their impacts on human
health.
The visual microscopic analysis of microplastics in a PM sample alone is unreliable and may lead to
inaccurate concentration estimates (Song et al., 2015), especially for particles <100 µm (Lenz et al.,
2015). Furthermore, visual assessments do not provide information on polymer type, which is
important for guiding environmental impact and/or risk assessments. Thus, a range of spectroscopic
(Käppler et al., 2016) and/or thermo-analytical (Fischer and Scholz-Böttcher, 2017) techniques can be
applied to accurately identify microplastics in samples. When composition, size, morphology and
particle abundance are of interest, optical micro-spectroscopy is used, usually Fourier-transform
infrared (FTIR) or Raman spectroscopy. These methods are also complimentary (Käppler et al., 2016)
and allow for the discrimination of plastic from natural particles. Spectra from absorption or scattering
phenomena are attained and the presence of characteristic peaks – or bands – that result from excited
vibrational states in the sample enable identification.
At present, entire PM samples, usually concentrated on a filter, are visually inspected under a
microscope for microplastics, and either all or a subset of potential microplastics are analysed. This
largely relies on applying standardised morphological criteria to decide whether a particle is plastic or
not, which becomes increasingly difficult with decreasing size, as morphological features become less
obvious (Araujo et al., 2018, Lenz et al., 2015, Song et al., 2015). Not only is this therefore time-
consuming, but also unreliable, possibly leading to an underestimation of microplastics, especially
with decreasing size (Song et al., 2015, Käppler et al., 2018). As a result, this smaller size fraction is
often neglected in the microplastics literature, although predicted to be most abundant (Enders et al.,
2015). The bias towards larger-sized particles being analysed is further likely due to the limitations of
sampling techniques, such as mesh-size, or by what can be manually transferred to an appropriate
substrate for analysis.
When assessing human exposure to microplastics via inhalation, (aerodynamic) sizes <10 µm are of
interest. Since the spatial resolution of FTIR spectroscopy is limited to 10-20 µm (Araujo et al.,
2018), Raman spectroscopy, with a spatial resolution down to 1 µm, is the focus of the current study.
This produces a spectrum representing inelastically scattered photons from a sample irradiated with a
laser, revealing energies of the vibrational modes of chemical bonds in the sample. In addition to
Raman microscopy, Raman mapping, or Raman Spectral Imaging (RSI), can provide spectral
information for each x-y coordinate of a sample with the aid of an automated motorised stage. Thus,
RSI presents an opportunity to analyse microplastics directly on filters without the need for visual
pre-sorting.
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Several studies have assessed the applicability of automated Raman spectroscopy (e.g. Frere et al.,
2016, Schymanski et al., 2018, Oßmann et al., 2017). In an online laboratory resource, Fischer et al.
(2015) directly analysed microplastics (polyethylene and polystyrene between 1 and 100 µm) in
model sand using RSI. All presented images depicted microplastics >10 µm and no minimum
measured size was reported (Fischer et al., 2015). Elert et al. (2017), whilst directly analysing
microplastics in a complex (soil) sample using RSI, reported on microplastics measuring between 150
and 200 µm. Kaeppler et al. (2016), whilst analysing microplastics including sizes <10 µm directly in
a sample, performed this on a density-separated i.e. concentrated sediment sample, undoubtedly less
complex than it’s original counterpart would be under direct analysis. Thus, no study has so far
confirmed the capacity for RSI to detect microplastics spanning the inhalable size range directly in an
unprocessed, ambient particulate matter sample.
PM samples are typically collected via low- or high-volume samplers onto inorganic or organic filters,
depending on the type of sample being taken and analysis being performed. For example, quartz fibre
filters may be used for gravimetric analysis (Charron et al., 2004), whilst mixed cellulose ester filters
may be used for metal analysis (Tremper et al., 2018). However, there is not ‘one filter for all’. With
respect to microplastics, various filter substrates (cellulose, alumina, silver and polycarbonate) have
been assessed for the applicability of RSI for microplastic detection down to 1 µm (Oßmann et al.,
2017). However, the authors favoured substrate is not commercially available and analysis was only
performed on positive controls.Given the urgent need to build a robust, baseline understanding of
whether microplastics contaminate the air in the inhalable size range, further work is needed to verify
and progress this work with view to sample airborne microplastics. However, the smaller the particles
of interest, the more challenging this is.
Here we screen different sample substrates and present an analytical approach compatible with air
monitoring instruments, which uses RSI to detect inhalable-sized microplastics directly in particulate
matter samples. This presents an opportunity to include automation and reduce observer bias, enabling
airborne microplastic analysis in respect to human exposure, with application beyond air samples.
Methods
Materials
Microplastics
Synthetic polymeric microbeads, herein referred to as microplastics, were used as reference material.
These were selected based on a combination of their global production levels, commonality in the
environment and commercial availability in defined size ranges down to the (sub)micron level.
Polymethylmecrylate (PMMA) beads (5 – 27 µm) and polyethylene (PE) beads (10 – 27 µm) were
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purchased from Cospheric LLC (CA, USA). Polyamide (PA) powder (5 – 50 µm) was purchased
from Goodfellow Cambridge Ltd (UK). Polystyrene (PS) beads (4 and 10 µm) were purchased from
Spherotech (IL, USA). Microplastics were stored as per the supplier recommendations. Stock
suspensions were sonicated (Soniprep 150 Plus, amplitude level 10) for 3-5 seconds immediately
before use.
Filters
To identify an optimum substrate composition for RSI, a range of different 47 mm filters routinely
used in air quality monitoring were tested. These included 2.2 µm pore size quartz microfibre filters
(QM-A, GE Healthcare Whatman™, UK); 2.0 µm pore size polytetrafluoroethylene (PTFE,)
membrane filters (SKC Ltd., UK), 0.8 μm pore size mixed cellulose ester membrane filters (GN-4
Metricel®, PALL, NY, USA), 0.2 µm pore size alumina-based membrane filters (GE Healthcare
Whatman™ Anodisc™, UK) and 1.2 µm pore size silver membrane filters (Sterlitech, WA, USA).
Filter Loading
To test the applicability of RSI to the identification of small microplastics on air filters, positive
controls were first prepared. Microplastics were suspended in deionised water at equi-concentrations
to a total concentration of 0.05% w/v, resulting in a heterogenous mix and sonicated as above
immediately before use. The suspension was sprayed onto filters using an atomiser. Filters were then
allowed to air dry at room temperature before Raman analysis.
Particulate Matter Samples
To validate whether RSI can detect small microplastics in an environmental sample, mock filters were
generated. PM samples were collected from an urban background site in London, UK. Samples were
collected using a Partisol 2025 Sequential Air Sampler, which draws ambient air through an inlet
(sampling total suspended particulates in this instance), and then though a 47 mm diameter filter with
a flow of 16.71 min−1. Samples were collected for 4 h on filters, the composition of which depended
on the outcome of RSI on plastic+/PM- samples. These were then spiked with microplastics as outlined
in ‘Filter Loading’. If imaging was successful, the same sample filters were deployed for a further 20
h, resulting in an accumulative 24 h sample and hence environmentally-conditioned microplastics.
Raman Spectroscopy
Raman spectroscopy was performed using an inVia™ Raman microscope (Renishaw Plc, Wotton-
under-Edge, UK) equipped with a 785 nm diode laser and 600 lines/mm diffraction grating. Spectra
were acquired in the extended (over the full range of Raman shifts for a user-defined point in a
sample), static (over a focused – or centred – shorter range, which is predefined by the user, for a
user-defined point in a sample) and StreamLine™ (RSI acquisition which collects static spectra for
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every pixel in a predefined area) scanning modes through a 20x/0.40 air objective. The system was
calibrated using an internal silicon sample with a characteristic band at 520 cm−1 before use.
Unique Band Identification (Extended and Streamline™ Scanning Mode)
As the ultimate goal was to validate the applicability of RSI, which generates a static spectrum for
every pixel in a predefined area, it was first necessary to validate the fingerprint region containing
unique peaks (bands) to curve-fit to the resulting dataset for image analysis purposes. Aliquots from
the individual microplastic stocks (PS, PMMA, PA and PE) were drop-cast onto a CaF2 substrate,
resulting in a dense, homogenous layer. Reference extended spectra were attained (in triplicate) with
19mw laser power and 10 s CCD exposure time and compared. A unique region containing the
greatest abundance of unique bands for each plastic type was identified.
Raman Spectral Imaging
Average RSI spectra of reference microplastics were also acquired in StreamLine™ scanning mode
centred at 1300 cm−1 (2 s CCD exposure time, 19mw laser power) on CaF2 substrate. To assess
whether RSI can detect small microplastics against filters of varying composition with and without
environmentally representative backgrounds, spectral images of plastic-/PM-, plastic+/PM- and
plastic+/PM+ filters were taken (Table S1). Briefly, a filter was carefully transferred to a motorised
stage and a quick visual search was performed to locate a random area with at least 1 microplastic in
the field of view. A RSI was then acquired in StreamLine™ scanning mode as above, predominantly
using 19mw laser power. This was step-wise reduced down to 4 mw for cellulose filters containing an
environmental sample, to prevent the filter from burning. RSI were exported into in-house curve-
fitting software to fit the peaks using a Gaussian function and the parameters minimum energy,
maximum energy, peak energy and Full-Width Half Maximum (FWHM).
The size of particles were assessed using Image J. Briefly, a fluorescence profile was plotted across
individual particles. A Gaussian function was then fit to the intensity profile data and the diameter
converted to µm using the parameter 2.35 of the function FWHM=2 √ 2 ln 2 c≈ 2.35482 c.
For proof of principal, three example RSIs of potential microplastics within the PM+24 h silver
membrane filters were also acquired at through a 50x air objective. Image analysis was performed as
above, fitting characteristic peaks from a range of different plastics and associated pigments
belonging to an in-house Raman spectral library. To validate their composition, extended spectra were
subsequently collected and matched in BioRad KnowItAll® Informatics System – Raman ID Expert,
where both the hit quality index and matching peak wavenumber positions were used to identify
composition.
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Results
Unique Peak Identification
A comparison of the extended spectra of three commonly produced types of plastic revealed that the
region centred on 1300 cm-1 contained numerous bands unique to each plastic type with the least
overlap. In Streamline™ scanning mode, PE has unique bands near 1064, 1133, 1296 and 1442 cm-1,
which correspond to C-C stretching (crystalline and anisotropic) for 1064 and 1133 cm-1; CH2 twisting
(crystalline, anisotropic) for 1296 cm-1; and CH2 bending (crystalline) for 1442 cm-1 (Bentley and
Hendra, 1995, Sato et al., 2002). PMMA has wider, less intense bands than PS and PE. Those with the
highest intensity within the centred region were selected; near 969 and 1453 cm-1. PS has unique
bands at approximately 1003 and 1033 cm-1. These doublets correspond to ring-mode vibrations
which are characteristic of a monosubstituted aromatic compound (v(C-C) and ß(C-H)) (Nishikida
and Coates, 2003). These features were used to discriminate between the plastics in RSI and thus to
assess whether microplastics can be detected against mock PM samples.
Raman Spectral Imaging
Overall, a total of 36 RSI’s were taken, with an average no. of 7563 spectra per RSI. The total number
of spectra per RSI ranged from 3325 to 23652. RSI areas ranged from 23510 to 166037 µm2, with an
average of 56960 µm2 (Table S1). Whilst the time to take each RSI was not recorded, it was
predominantly observed to range between 5 and 20 min, depending on the selected area.
Filter Composition
Microplastics could not be visualised under a normal light microscope on quartz fibre filters (Figure
2A). Thus, they were deemed inappropriate for further assessment as it would be difficult to validate
the success of RSI without a visual counterpart and if embedded within the quartz fibre matrix, it is
likely they would also be missed by RSI. Microplastics could not be mapped on PTFE membrane
filters (not shown), although they could be located visually (Figure 2B). Microplastics could be
clearly observed on alumina filters (Figure 2D), however, the could not be mapped using RSI (not
shown). Thus, only mixed cellulose ester and silver membrane filters were further evaluated, on
which microplastics were also visible (Figure 2F and H, respectively).
Cellulose
Figure 3 shows the intensity distributions of the Raman signals recorded for the unique features of
each model microplastic (Table 1) on plastic+/PM- cellulose filters. First, all bands were identified
across replicates, except for the PS band at 1028.1 cm-1. Substantial differences in intensity were
detected. The intensity for PS bands were generally weaker than for the other plastics. There is also
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some overlap between the PMMA band at 1449.7 cm-1 and PE’s bands in Figure 3A. It can be seen
that PS was present in all 3 replicates, whilst PMMA and PE were present in Figure 3A.
One hundred percent of plastic+/PM+4 h and plastic+/PM+24 h cellulose filters burned using 19mw and 10
mw laser power. Whilst >80% of the filters did not burn at 4 mw laser power, the laser power was too
weak to detect microplastics against a PM background.
Silver
Figure 4 shows the intensity distributions of the Raman signals recorded for the bands of each model
microplastic (Table 1) dispersed on a plastic+/PM- silver membrane filter. All signals were identified,
including the PS band at 1028.1 cm-1. This was apparent for all samples, except for PE in Figure 4A.
Differences in intensity were again detected. Unlike intensity against a cellulose substrate, the
intensity for PS’s bands were as strong as the other plasticss and the PS band at 997.9 cm-1 exhibited
the second greatest intensity. There is overlap between the PS band at 1028.1 cm-1 and PE’s bands.
Unique features for microplastics were still detectable against plastic+/PM+4 h (Figure 5) and
plastic+/PM+24 h (Figure 6) silver filters at slightly weaker intensities than plastic+/PM- silver filters
(Figure 4). All bands were identified on plastic+/PM+4 h, however, there was a weak overlap between
PMMA and PS bands in Figure 5A. Based on intensity, it is likely that the shared microplastics are
PS.
The intensity distributions for microplastic bands in plastic+/PM+24 h is complicated to interpret. By
chance, it seems that PS microplastics were the only plastic type captured in the RSI’s. Consistency
across both PS bands can be seen in Figure 6A, however this is less so for Figure 6B and C; there is
weak intensity shown for the PS band at 1028.1 cm-1 in replicate 2, contradicting the 3 microplastics
indicated by the PS band at 997.9 cm-1 (which corresponds to the light micrograph). Moreover, there
is increased signal interference, with intensity distributions mapped across all plastic bands, which are
not due to the presence of reference microplastics.
Observed Sizes
PS 4 µm were detected on plastic+/PM- cellulose and silver membrane filters (Figure 3A-C and 4A
and B, respectively). PS 10 µm were detected on plastic+/PM+4 h (Figure 5A and B) and plastic+/PM+24
h (Figure 6A-C) silver membrane filters. The PE microplastic detected on plastic+/PM+4 h silver
membrane filter was approximately 20 µm (Figure 7C). PMMA microplastics on plastic+/PM- silver
membrane filters (Figure 4) were approximately 18 µm.
Environmental Particles
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A polyethylene terephthalate (PET) fibre and copper phthalocyanine particles, a synthetic organic
pigment associated with plastics, were identified on a PM+24 h silver membrane filter (Figure 7A-C).
The PET fibre was approximately 14 µm wide and 1000 µm long. The copper phthalocyanine
particles measured approximately 5 and 6 µm in their maximum dimensions. Encouragingly, the
intensities of the environmental particles’ bands are extremely high compared to the model
microplastics.
Discussion
In the present study, we show that Raman micro-spectroscopy, particularly RSI, can be used to
identify model microplastics spanning the inhalable size range for humans. Furthermore, this is
applicable against sample substrates compatible with air sampling instruments, and for the first time
we demonstrate this in a representative environmental matrix and for environmental particles. This is
an important development for the analysis of inhalable-sized microplastics in ambient air samples and
other complex sample types.
We found that quartz, alumina and PTFE filters were not appropriate for visual assessment of
microplastics and/or not compatible with direct RSI in a sample. Filter composition should not have a
prominent Raman spectrum or fluorescent background in the range of the polymer bands of interest
(Käppler et al., 2015). On cellulose filters, PS bands showed a weaker intensity compared to those of
PMMA and PE. This could be due to PS microplastics being smaller (4 µm and 10 µm diameter) than
the PMMA (5 – 50 µm diameter) and PE (10 – 27 µm) microplastics, and hence having fewer
molecular bonds. However, as this was not observed for silver membrane filters, it could be signal
interference from the cellulose. The relative Raman intensity of a particle depends on its thickness and
inherent Raman scattering efficiency. Consequently, the signal of any strongly scattering substrates
will interfere with a weakly scattering particle (Everall, 2010). Silver membrane filters elicited the
strongest intensities from microplastics both alone and in complex samples, particularly plastic+/PM+4
h filters. It is highly likely that the silver enables surface-enhanced Raman spectroscopy (Gao et al.,
2016). However, the patterned surface of the silver membrane filters, whilst smooth, did not achieve
the greatest optical contrast between microplastics and the filter surface.
The application of particle-finding software coupled to automated Raman microscopy for the
detection of small microplastics in food contamination research has been studied. It was found that in-
house fabricated, aluminium-coated polycarbonate membrane filters were optimal, however, the
method was only evaluated on plastic+ i.e. clean samples (Oßmann et al., 2017). It is likely that
complex matrices will impact results. Unfortunately, the pore size of the filters was not reported.
Given the resolution of Raman microscopy (1 µm), smaller pore sizes will sample a higher
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concentration of unidentifiable particles which may interfere with Raman signals and, importantly,
may affect the flow rate of air quality instruments. However, based on this and the present study,
metal-based or -coated membrane filters are a promising substrate for future studies, enabling the
direct analysis of microplastics in samples.
Semi-quantitative Raman micro-spectroscopy methods for microplastic detection have previously
been reported. Frere et al. (2016) used particle-finding software to analyse microplastics down to 335
µm. This software detects particles in a field of view, which are then analysed, with each particle
being positioned automatically under the Raman laser to collect a spectrum. The benefit of such
software is that it reduces measurement time. This took approximately 40 min to analyse a 25 x 21
mm sample surface (Frere et al., 2016). Similarly, Schymanski et al. (2018) used particle-finding
software to assess microplastic contamination in bottled water. The majority of microplastics
identified were between 5 and 10 µm, but procedural times took up to 18 hours to analyse one filter
(Schymanski et al., 2018). This study presented a robust analytical method for identifying
microplastics, however, these samples were relatively clean unlike those in the present study. A
limitation of these studies is that particle-finding software collects a finite number of spectra, e.g.
5000, and thus in some instances it is necessary to extrapolate microplastic abundance to the entire
sample. With RSI, whole sample areas can be analysed. Another limitation of particle-finding
software is the potential for bias, e.g. for particles to be missed due to poor optical contrast, or for the
sample substrate to be misidentified as a particle due to it’s texture creating particle-like contrast
(Oßmann et al., 2017). On this premise, Oßmann et al. (2017) rejected cellulose, alumina and silver-
based membrane filters. However, we found that even 4 µm beads were visible optically and
spectroscopically against a silver background, in the absence of particle-finding software.
For proof-of-principal, microplastics were identified in a sample by curve-fitting unique bands to the
RSI datasets, with intensity distribution indicating the presence of a microplastic. However, overlaps
in band intensities of different plastic types were observed. For example, band intensity occurred at
the same x y coordinates for both the PMMA band at 1449.7 cm-1 and the PE bands on cellulose
plastic+/PM- filter replicate A. This ‘false positive’ is likely due to it’s proximity to the PE signal
correseponding to CH2 bending at 1413.7 cm-1. Given the higher intensity of PE’s band at 1413.7 cm-1
and the distribution match to the other PE bands, it is likely PE. On the plastic+/PM+4 h silver
membrane filter A, all intensities are equally high, so in this instance it is difficult to discriminate
which plastic type the intensity distribution is mapping, except for that band intensity is not
consistently present across PE; the peak distribution is not mirrored for the band at 1413.7 cm -1. This
suggests the beads are most likely PS. A combination of band intensity and the signal being consistent
across different bands in the same plastic type are recommended for interpretation. Preliminary
Raman characterisation of different plastics comprising the most commonly produced types indicates
that there are unique identifying bands for each polymer in the RSI centred region (Levermore et al.,
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2019 unpublished). This analysis could therefore be extended to those plastics commonly found to
contaminate the environment, as demonstrated here. Moreover, applying multivariate analysis to the
RSIs would maximise the information used to discriminate particles and will be adopted in the future
to refine the current method. I fact that PS appears to be present in greater abundance on both
cellulose and silver membrane filters suggests that either the microbead dispersion was not
homogenous, or that, given the smaller size distribution, PS is present in greater abundance in the
same aliquot volume compared to PMMA and PE. Alternatively, PMMA and PE may behave
differently in suspension, potentially sorbing to the atomiser vial’s walls.
Microplastics were less discriminate in the plastic+/PM+24 h samples. These environmentally-
conditioned microplastics may undergo Raman signal interference due to the presence of fluorescent
molecules on their surface, originating from organic matter, e.g., biofilms (Frere et al., 2016, Käppler
et al., 2015). Established microbial biofilms develop on the surface of microplastics in marine (Zettler
et al., 2013) and freshwater (McCormick et al., 2016) aquatic and sedimentary (Harrison et al., 2014)
environments, with microplastic-specific microbiomes (Oberbeckmann et al., 2017). Whilst this has
not been measured for ambient microplastics, there is potential that the microplastics in this study
were colonised whilst in the PM sample, given the abundance of microbial communities in the near-
atmosphere (Bowers et al., 2013). Future studies could consider directly treating the filter to remove
organic matter, pre-analysis. Additionally, there is potential that the observed weaker band intensities
in the plastic+/PM+24 h samples are due to the contamination of their surface by other components of
PM, such as polycyclic aromatic hydrocarbons, which have an affinity for the surface of plastic (Mai
et al., 2018, Wang and Wang, 2018). Moreover, in environments with high levels of air pollution, PM
could coat microplastics in a sample, which may lead to underestimations of microplastic abundance
due to signal interference. A preliminary assessment of sampling duration in a chosen environment is
recommended, to avoid filter over-loading. Environmental microplastics will present even more of an
analytical challenge due to their complexity, with different chemical structures, compositions,
incorporated additives, colours, sizes, shapes and aging states (Käppler et al., 2016). Poor spectrum
signal qualities can also arise from weathering (Lenz et al., 2015). Thus, future work should validate
RSI on aged microplastics.
The appearance of fluorescence intensity when it was not expected indicates signal interference from
ambient particulate matter. To minimise this, a clean-up step (Munno et al., 2018) or density
separation (Quinn et al., 2017) could be introduced. However, these become less applicable when
dealing with samples as small as PM. There is also a risk of losing microplastics, which are predicted
to be present in much lower concentrations than other particulate components of ambient PM, to the
walls of the sample vessels used at the different stages of these procedures. Additionally, the more
processing steps, the greater the risk of contamination, as highlighted by Wesch et al. (2017). Another
improvement could be to increase integration time. However, whilst benefitting from automation and
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high resolution (when compared to FTIR), a limitation of RSI is that it suffers from long acquisition
times when obtaining high quality image resolution and Raman signal (Frere et al., 2016). For
example, in the present study, RSI acquisition times ranged from approximately 5 to 20 mins,
corresponding to RSIs equivalent to 0.001 to 0.01% of the total filter area. It has been proposed that
for high throughput analysis, smaller sub-areas could be analysed (Schymanski et al., 2018). Another
possible reason for background noise to occur is laser focus. When carrying out automated RSI,
particularly of uneven samples such as PM, it is possible for the laser to accidentally focus just above
the sample, leading to weaker Raman signals of the desired sample surface, or accidental analysis of
subsurface material (Everall, 2010). Where RSI systems are available without a xyz motorised stage,
users should carefully interpret results, revisiting ambiguous particles.In the current study, the Raman
microscope stage was not z automated, but microplastics were still spectroscopically detected.
For proof-of-principal, we took three RSIs of potential microplastics in the samples. Following image
analysis, extended spectra were confirmed to be of synthetic origin; one of which was a polyethylene
terephthalate fibre, the other two being copper phthalocyanine particles. Fibres are often the most
commonly observed morphology of microplastics (Woodall et al., 2015), hence this is encouraging
for other researchers who wish to use RSI to identify microplastics in environmental samples. Copper
phthalocyanine is a synthetic organic pigment commonly used as a plastic colourant. There is very
little information on this compound, however its presence in environmental samples has been reported
previously (Horton et al., 2017). It has been suggested that the Raman signals from incorporated dyes
and pigments are likely to override the weakly Raman scattering polymers (Horton et al., 2017; Imhof
et al., 2016; Smith and Dent, 2005). As per Horton et al. (2017), it may therefore be inferred that these
particles are plastics. If so, these are the smallest airborne microplastics reported to-date and are of a
size capable of reaching the central airway. The high band intensities in the analysed RSIs is likely
due to RSI collection being conducted at a higher optical resolution; there will always be a
compromise between spectral image acquisition time, spatial coverage and signal intensity.
The validation of RSI directly onto a filter demonstrates it’s compatibility with filter-based air
monitoring instruments, such as the Partisol 2025 Sequential Air Sampler. This is beneficial as these
instruments are widely and routinely used across the globe. The filters commonly used for air
monitoring, e.g., quartz fibre, cellulose or PTFE, are not appropriate but current protocols could be
adapted for microplastics work. Moreover, this instrument has interchangeable, size-selective
sampling inlets. Hence, only particles in inhalable size fractions (PM10, PM2.5) can be sampled. Given
the nomenclature for size categories established in traditional fields of research, such as exposure
science and particle toxicology, we recommend the same size classifications (PMcoarse, PM10, PMfine,
PM2.5 and PM0.1) be applied to airborne microplastics. Not only will this allow for comparisons with
other particulate components of air pollution, it will also aid predictions of the potential health
impacts and interpretation of any toxicological data.
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In conclusion, RSI is applicable for the detection of inhalable-sized microplastics directly in a sample,
and we found this to be the case only for silver membrane filters, as cellulose PM+ filters burned and
RSIs on plastic+ PTFE and alumina-based filters were unsuccessful, likely due to substrate signal
interference. Overall, we recommend that researchers carefully consider sample substrate
composition, if small microplastics are of interest. In addition to operator Raman microscopy,
supplementary RSIs of sub-areas of the total sample is advisable to account for any observer bias and
to improve the chance of identifying small microplastics. However, ultimately, there will be a trade-
off between what can be sampled (e.g. ultrafine (<0.1 µm) PM) and what can actually be identified
using this method (1 µm limit). Additionally, given the limitation of time, it may be deemed more
appropriate to use this tool qualitatively, in conjunction with a sensitive analytical technique, such as
pyrolysis-GCMS (Fischer and Scholz-Böttcher, 2017), to provide information on concentrations. We
shall continue to refine and apply these methods for the detection of airborne microplastics in order to
address the important knowledge gap of whether inhalable microplastics directly impact human
health.
Acknowledgements
We’d like to thank Dr David Green and Dr Anja Tremper for providing access to the Partisol; Mr
Peter Pilecki for Raman technical support; and Dr Bruce Main, Dr Francis O’Shea and Dr Ben
Gridley for help in accessing the sampling site; and Dr Mads Bergholt for his valuable comments on
the manuscript. The research was part funded by the National Institute for Health Research Health
Protection Research Unit (NIHR HPRU) in Health Impacts of Environmental Hazards at King’s
College London in partnership with Public Health England (PHE), and the Medical Research Council
(MRC; MR/M501669/1). The views expressed are those of the author(s) and not necessarily those of
the NHS, the NIHR, the Department of Health, PHE or the MRC.
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Table 1. The peak parameters of identified unique bands for image analysis (B). PE = polyethylene, PMMA = polymethyl methacrylate, PS = polystyrene, Low = minimum energy, High = maximum energy, Energy = peak energy, FWHM = Full Width Half Maximum.
Plastic Band # Low High Energy FWHMPE Δ 1 1040.0 1080.0 1058.8 9.9PE Δ 2 1110.0 1150.0 1127.0 10.5PE Δ 3 1275.0 1310.0 1291.7 9.0PE Δ 4 1400.0 1425.0 1413.7 14.5PMMA * 1 930.0 1020.0 975.8 116.9PMMA * 2 1400.0 1520.0 1449.7 32.5PS × 1 980.0 1012.0 997.9 9.1PS × 2 1014.0 1046.0 1028.1 11.3
Graphical abstract
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Figure 1. The identifying bands selected from average Streamline™ spectra (centred on 1300 cm-1) of
each plastic type. PE = polyethylene, PMMA = Polymethyl methacrylate and PS = polystyrene.
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Figure 2. Micrographs of positive control filters and their respective average blank Streamline™
spectra for (A) quartz fibre filter; (B-C) PTFE membrane filters; (D-E) alumina membrane filters; (F-
G) cellulose membrane filters; and (H-I) silver membrane filters. As microplastics were not visible on
quartz microfibre filters, no further assessment (Raman spectroscopy) was conducted. Scale bar = 50
µm.
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Figure 3. Raman Spectral Images of plastic+ cellulose mixed ester membrane filters (n=3, A-C),
acquired in Streamline™ scanning mode with 2 s CCD exposure time and 19mw laser power. Fixed
binning (2x2), LUT = Thallium, scalebar on micrographs = 50 µm and dashed line = approximate RSI
area. PE = polyethylene, PMMA = Polymethyl methacrylate and PS = polystyrene.
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Figure 4. Raman Spectral Images of plastic+ silver membrane filters (n=3, A-C), acquired in
Streamline™ scanning mode with 2 s CCD exposure time and 19mw laser power. Fixed binning
(1x1), LUT = Thallium, scalebar on micrographs = 50 µm and dashed line = approximate RSI area.
PE = polyethylene, PMMA = Polymethyl methacrylate and PS = polystyrene.
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Figure 5. Raman Spectral Images of plastic+/PM+ 4 h silver membrane filters (n=3, A-C), acquired in
Streamline™ scanning mode with 2 s CCD exposure time and 19mw laser power. Fixed binning
(1x1), LUT = Thallium, scalebar on micrographs = 50 µm and dashed line = approximate RSI area.
PE = polyethylene, PMMA = Polymethyl methacrylate and PS = polystyrene.
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Figure 6. Raman Spectral Images of plastic+/PM+ 24 h silver membrane filters (n=3, A-C), acquired in
Streamline™ scanning mode with 2 s CCD exposure time and 19mw laser power. Fixed binning
(1x1), LUT = Thallium, scalebar on micrographs = 50 µm and dashed line = approximate RSI area.
PE = polyethylene, PMMA = Polymethyl methacrylate and PS = polystyrene.
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Figure 7. Raman Spectral Images of environmental microplastics in a PM+ 24 h silver membrane
filter, acquired in Streamline™ scanning mode with 2 s CCD exposure time and 19mw laser power.
Fixed binning (1x1), LUT = Thallium, scalebar on micrographs = 20 µm and dashed line =
approximate RSI area. Corresponding extended spectra used for confirming composition can be found
in Figure S1.
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Table S1.
Sample
Raman Spectral Image
No. of PixelsPixel Size
(µm)Image Size (µm)
W H Total W H W H µm2
PTFE
Blank 1 71 87 6177 2.6 2.7 184.6 234.9 43362.54
Blank 2 48 86 4128 2.7 2.7 116.1 232.2 26958.42
Blank 3 53 88 4664 2.7 2.7 143.1 237.6 34000.56
plastic+/PM- #110
893 10044 2.7 2.7 291.6 251.1 73220.76
plastic+/PM- #2 58 74 4292 2.7 2.7 156.6 199.8 31288.68
plastic+/PM- #3 98 89 8722 2.6 2.7 254.8 240.3 61228.44
plastic+/PM- #414
593 13485 2.7 2.7 391.5 351.1 137455.7
plastic+/PM- #5 97 84 8148 2.7 2.7 261.9 226.8 59398.92
Alumina
Blank 1 50 82 4100 2.7 2.7 135 221.4 29889
Blank 2 46 84 3864 2.7 2.7 124.2 226.8 28168.56
Blank 3 60 83 4980 2.7 2.7 162 224.1 36304.2
plastic+/PM- #1 91 71 6461 2.6 2.7 236.6 191.7 45356.22
plastic+/PM- #2 60 75 4500 2.7 2.7 162 202.5 32805
plastic+/PM- #3 70 98 6860 2.7 2.7 189 266.6 50387.4
plastic+/PM- #4 65 64 4160 2.7 2.7 175.5 172.8 30326.4
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Cellulose
Blank 1 52 87 4524 2.6 2.7 135.2 234.9 31758.48
Blank 2 51 76 3876 2.7 2.7 137.7 205.2 28256.04
Blank 3 43 75 3225 2.7 2.7 116.1 202.5 23510.25
plastic+/PM- #111
284 9408 2.6 2.7 291.2 226.8 66044.16
plastic+/PM- #213
192 12052 2.6 2.7 340.6 248.4 84605.04
plastic+/PM- #312
493 11532 2.7 2.7 334.8 251.1 84068.28
plastic+/PM+4 h #1 62 78 4836 2.7 2.7 167.4 210.6 35254.44
plastic+/PM+4 h #2 76 65 4940 2.7 2.7 205.2 175.5 36012.6
plastic+/PM+4 h #313
086 11180 2.7 2.7 351 232.2 81502.2
Silver
Blank 1 53 69 3657 2.7 2.7 143.1 186.3 26659.53
Blank 2 46 77 3542 2.7 2.7 124.2 207.9 25821.18
Blank 3 41 89 3649 2.7 2.7 110.7 240.3 26601.21
plastic+/PM- #112
795 12065 2.6 2.7 330.2 256.5 84696.3
plastic+/PM- #213
192 12052 2.7 2.7 353.7 248.4 87859.08
plastic+/PM- #312
693 11718 2.7 2.7 340.2 251.1 85424.22
plastic+/PM+4 h #114
6162 23652 2.6 2.7 379.6 437.4 166037
plastic+/PM+4 h 4h 91 92 8372 2.7 2.7 245.7 248.4 61031.88
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#2
plastic+/PM+4 h 4h
#3
16
7130 21710 2.7 2.7 450.9 351 158265.9
plastic+/PM+24 h 24
h #161 88 5368 2.6 2.7 176.8 237.6 42007.68
plastic+/PM+24 h 24
h #269 92 6348 2.6 2.7 179.4 248.4 44562.96
plastic+/PM+24 h 24
h #368 88 5984 2.6 2.7 176.8 237.6 42007.68
Figure S1.
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Figure S1. Extended spectra corresponding to the environmental particles (figure 7), confirming
copper phthalocyanine (A-B, hit quality index (HQI) 69.11 and 68.06 for particle 1 and 2,
respectively) and polyethylene terephthalate (C, HQI 71.63). Particle 2 was partially covered by a
larger white particle (Figure 7B) and there was a strong signal in the spectrum at band ~250 cm-1,
indicative of a carbonate. This was removed to improve HQI.
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