# Pre-detection of microplastics using active thermography

Mikaël Kedzierski, Edouard Geslain, Maria Luiza Pedrotti, Jean-François

Ghiglione, Stéphane Bruzaud

► To cite this version:

Mikaël Kedzierski, Edouard Geslain, Maria Luiza Pedrotti, Jean-François Ghiglione, Stéphane Bruzaud. Pre-detection of microplastics using active thermography. Chemosphere, 2021, 262, pp.127648. 10.1016/j.chemosphere.2020.127648. hal-03954710

HAL Id: hal-03954710

https://hal.science/hal-03954710v1

Submitted on 2 Aug 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 **PRE-DETECTION OF MICROPLASTICS USING ACTIVE THERMOGRAPHY

2 Mikaël Kedzierski*.,†, Edouard Geslain†, Maria Luiza Pedrotti§, Jean-François Ghiglione×,

3 Stéphane Bruzaudª

4 †Université Bretagne Sud, UMR CNRS 6027, IRDL, F-56100 Lorient, France

5 §Sorbonne Universités, UMR CNRS 7093, LOV, F-06230 Villefranche sur mer, France

6 ×Sorbonne Universités, UMR CNRS 7621, LOMIC, F-66650 Banyuls-sur-Mer, France

7 *Corresponding author:

8 E-mail: mikael.kedzierski@univ-ubs.fr

9 **HIGHLIGHTS**

10 - Active thermography and FTIR can measure material emissivity in the same
11 wavelengths.

12 - Emissivity is related to the chemical nature of a material.

13 - Active thermography was used to test the pre-detection of microplastics in sand
14 particles.

15 - Only a few tens of seconds are necessary to detect microplastic particles.

16

17 **ABSTRACT**

18 Spectrometric analysis is one of the most widely used approaches to characterize the chemical
19 nature of microplastics. Despite recent developments, this key step remains time consuming.

20 The aim of this paper is to propose a new method for the pre-detection of microplastics based
21 on mid-infrared imaging. Plastic particles were mixed with sand particles and placed on a
22 glass filter. Infrared observation with a thermal camera shows a stronger thermal contrast
measured between the filter and the plastics than between the filter and the sand, which
reveals the plastic particles in a few tens of seconds. An image processing tool is then used to
amplify this contrast. Furthermore, this pre-detection method makes it possible to propose
hypotheses on the most probable chemical nature of the particles identified. Consequently,
pre-detection using active thermography constitutes a promising way of significantly
accelerating microplastic study.

## Keywords

Microplastics, active thermography, FTIR, chemometrics, pollution detection.

31

## 1. INTRODUCTION

The interest shown by scientists in environmental contamination by microplastics (<5 mm) has led to a rapid increase in studies on this subject (GESAMP, 2019). The chemical nature of the microplastics is now considered an important parameter for their accurate identification (Käppler et al., 2016; Lenz et al., 2015; Löder and Gerdts, 2015; Shim et al., 2017). Several studies have shown that the false positive rate of identification is very high when identification is carried out by microscopy without verification of the chemical nature of the particles (Lenz et al., 2015; Löder and Gerdts, 2015). Recently, a new publication highlighted the complementarity of pyrolysis gas chromatography-mass spectrometry (py-GCMS) and Fourier transform infrared spectroscopy (FTIR) in the identification of the chemical nature of microplastics (Käppler et al., 2018). When determining the chemical nature of samples, a method based on thermal degradation, such as pyrolysis-gas chromatography in combination with mass spectrometry (py-GCMS) is fast and efficient (Dümichen et al., 2017, 2015; Gimeno et al., 2015; Zarfl, 2019). However, this method results in the destruction of the sample being analysed, resulting in the loss of information such as size distribution, particle
physical characteristics, or the plastisphere (Amaral-Zettler et al., 2015; Dussud et al., 2018; Primpke et al., 2018; Zettler et al., 2013). Therefore, this loss is considered as an important limitation in the study of microplastics (Primpke et al., 2018; Rocha-Santos and Duarte, 2015). In addition, in the case of large sampling campaigns, the samples, taken at a given location and time, are unique and must be kept in a long-term library for reference or potential re-analysis (Kedzierski et al., 2019a, 2019b). Other methods, based on Raman (Frère et al., 2016; Imhof et al., 2017, 2012; Lenz et al., 2015; Zhao et al., 2017) or Fourier Transform Infrared (FTIR) spectroscopy (Ioakeimidis et al., 2016; Löder and Gerdts, 2015; Primpke et al., 2018, 2017), are also very effective in determining the chemical nature of microplastics.

However, if the number of particles to be analysed is important, these methods require a significant time investment. Hence, in recent years, new methodologies have contributed to reducing this working time. Thus, statistical methods of random sampling have been used to reduce the number of particles to be analysed significantly while enabling control on the margin of error of the results (Kedzierski et al., 2019b). Second, using micro-FTIR and Focal Plane Array-Based Reflectance Micro-FTIR now makes it possible to automate the analysis of a surface (often a circular filter) to search for microplastics (Löder and Gerdts, 2015; Mintenig et al., 2017; Primpke et al., 2017; Qiu et al., 2016). Finally, new tools for automated analysis of FTIR spectra have been developed in recent years to complement these approaches. From 2017 to 2019, an automated method based on the determination of the most relevant bands was tested (Renner et al., 2019, 2017). In 2018, an automated determination method based on hierarchical cluster analysis was published (Primpke et al., 2018), and in 2019, a new methodology based on a machine learning method, named k-nearest neighbours (knn) classification, was applied to spectra of microplastics collected during the Tara Expedition in the Mediterranean Sea (Kedzierski et al., 2019a). All of these methods have shown a high degree of spectrum determination.
Nevertheless, the analysis of the collected samples takes too long because it often takes
several hours for an FTIR to scan the entire surface to be analysed (Tagg et al., 2015). In
addition, the amount of data to be analysed per sample is often very large, in the order of
several gigabytes. Hence, there is a great need for new approaches to facilitate this step, for
example by enabling the pre-detection of microplastic particle potentials. In this paper, we
propose to study the potential of thermal imaging in the pre-detection of microplastics. Active
thermography is an imaging method for non-destructive testing. It is usually used to detect
defects in opaque materials without damaging them. The purpose of the method is to examine
the thermal response on the sample following the thermal solicitation imposed. In this study,
thermography was used to detect plastic particles on a fiberglass filter. The first goal was to
test the detectability of plastic microparticles larger than 300 µm in sand samples. Then, using
FTIR spectra of microplastics, the goal was to determine the theoretical detectability of
different kinds of microplastics in infrared imaging. The strengths and limitations of this new
approach are discussed.

## 2. MATERIALS AND METHODS

### 2.1. *Materials*

#### 2.1.1. *Particles tested*

The particles tested were made from sheets of different polymers: poly(ethylene) (PE),
poly(propylene) (PP), poly(styrene) (PS), poly(vinyl chloride) (PVC), poly(amide) (PA),
poly(methyl metacrylate) (PMMA), poly(ethylene terephthalates) (PET). The plastic sheets
were manually abraded to generate irregularly shaped microparticles. The sand used for these
experiments came from the beach of the Kernevel harbour (GPS coordinates: 47.7173,
-3.3666; France) located in the Bay of Lorient; this type of sand is mostly composed of
particles of quartz (Qz), shells (Ca), and fragments of rocks such as shale (Sh) and granite
(Gr) (Kedzierski et al., 2018, 2016). Green algae (*Ulva lactuca*) were harvested at Kernevel harbour and oven-dried at 60°C (Memmert oven). These algae were then crushed by hand until they formed submillimeter-sized particles. In order to constitute the test samples, the plastic particles and sand were then sieved separately with different mesh sieves: 63, 125, 250, 315, 500, 1000 µm.

## 2.1.2. *Active thermography camera*

The heating plate was composed of four Peltier cells (Huilcapi et al., 2017) of 30 x 30 mm² covered by a thin copper foil to facilitate temperature homogenisation. The Peltier cells were fixed on a steel block to hold them in place and the mass of the block helped to dampen the temperature change on the opposite side of the peltier cells. The ATTEN APS3005s 30V 5A power supply current intensity was set on 5 A and the voltage applied depends on the temperature (4.6 V at room temperature). A switch manually controlled the activation of power. The temperature increased from room temperature (20 °C) to 60 °C in about 60 s (0.7 °C/s). The temperature was then maintained at 60°C for a period of time until the sample temperature was homogenised. The homogenisation time was variable depending on the nature and size of the particle being tested. A K-type thermocouple of 125 µm was fixed between heat plate and filter. A thermocouple reader gave the temperature to the operator. Peltier cells are selected to apply heating steps.

Filters were placed on the heating plate and wetted so that the filter was in contact over its entire surface (Fig. 1). The FLIR X6580sc camera performed its observations between 6667 cm⁻¹ (1.5 µm) and 1961 cm⁻¹ (5.1 µm) by means of an indium antimonide (InSb) detector and a 50 mm lens. The camera was calibrated for a temperature range comprised between 0 °C and 300 °C. The sensor of the camera was a 640 x 512 pitches InSb. The pitch size was about 160 µm and the frame rate 20 fps.
<figure><img src="image_2.png" /></figure>

120

121

Fig. 1. Presentation of the investigational device. A 50mm macroscopic lens was used in this experiment.

122

123

### 2.1.3. Microplastics from the Mediterranean Sea

124

125

126

127

128

129

130

131

Microplastic samples were collected from Mediterranean Sea waters between May and November 2014. Sampling was conducted using a 4.4 m-long manta net (mesh size: 333 µm; net opening: 16 x 60 cm) from 120 sites that were selected based on ocean colour satellite images supplied by ACRI-ST and on the predictions of the Mercator circulation model. Geographical coordinates and dates of the sampling are available at Pangea Data Publisher (https://www.pangaea.de/). For each of these sampling sites, the manta net was towed on the sea surface for about 60 minutes behind the boat at an average speed of 2.5 knots. Consequently, about 507 m³ of seawater were filtered for each site.

132

133

134

135

Under a dissecting microscope, preserved organic material was removed from the microplastics. Particles were then counted and measured using the ZooScan image analysis method at the Laboratoire d'Océanographie de Villefranche-sur-Mer (LOV, Villefranche-sur-Mer, France) (Pedrotti et al., 2016). A total of 13,374 particles, larger than 315 µm from 42
136

manta trawls, were then transferred to 96-well microplates and named with a unique identifier

137

at the Institut de Recherche Dupuy de Lôme (IRDL, Lorient, France), and 4,064 particles

138

were randomly selected and analysed with Fourier-transform infrared spectroscopy (see 2.2.2)

139

(Kedzierski et al., 2019b).

140

## 2.2. Methods

141

### 2.2.1. Infrared imaging protocol and image analysis

142

In order to test the thermal properties of the different particles, model samples were

143

manufactured (Table 1). To this end, the particles were deposited on glass microfiber filters

144

(pore size: 0.8 µm, diameter 55 mm, Fisherbrand MF300) wetted with distilled water.

145

Particles on filter (microplastics, sand, or algae fragments) have different thermal and

146

radiative properties (eg. Density, specific heat capacity, thermal conductivity, emissivity).

147

These differences affect the temperature evolution in the particles in response to the thermal

148

solicitation. The heat flux measured by the camera depends on the surface temperature and

149

emissivity. The emissivity depends on the material, its surface condition, inclination,

150

thickness, and temperature. As emissivity is different for each pixel on the map, it is difficult

151

to measure absolute temperature. The temperature values indicated by the camera are

152

calculated for a blackbody emissivity (ε=1). The data are processed in apparent temperature.

153

Thermal images were then processed with the Fiji software (8 bits, binarization) to test the

154

possibilities of improving the display of the microplastics. In order to present the gains

155

brought by the proposed methodology, the photography in visible imaging (camera: Olympus

156

Tough TG-5), infrared imaging, and infrared imaging after treatment with Fiji is shown in the

157

result section.

158

Table 1: Description of the particles used.
<table><thead><tr><td>Sample n°</td><td>Particles</td><td>Particle size range</td></tr></thead><tbody><tr><td>1</td><td>Sand, PE</td><td>Sand (63 µm – 10 mm)<br/>PE (500 µm – 2 mm)</td></tr><tr><td>2</td><td>Fine sand, PE</td><td>Fine sand (125 µm – 250 µm)<br/>PE (500 µm – 1 mm)</td></tr><tr><td>3</td><td>PE, PP, PS, PVC, PA, PMMA, PET, sand (quartz, shale, shell), algae</td><td>500 µm – 10 mm</td></tr></tbody></table>

### 2.2.2. Fourier-transform infrared spectroscopy (FTIR)

The spectra of the microplastics collected in the Mediterranean Sea and of the tested plastics were acquired using an Attenuated Total Reflection Fourier Transform Infrared spectrometer (ATR-FTIR Vertex70v, Bruker). Each particle was placed onto the germanium diamond cell (ATR Golden Gate). All spectra were recorded in absorbance mode in the 4,000-600 cm⁻¹ region with 4 cm⁻¹ resolution and 16 scans. After each analysis by ATR-FTIR, the sample holder was cleaned with ethanol. The sample chamber was also cleaned with a vacuum cleaner after 60 analyses. In the particular case of microplastics collected in the Mediterranean Sea, 4,064 particles were analysed (Kedzierski et al., 2019a, 2019b).

### 2.2.3. FTIR spectra determination

FTIR spectra of microplastics collected in the Mediterranean Sea were already analysed during previous studies using POSEIDON (Plastic pOllutionS ExtractIon, DetectiOn and aNalysis) software, which is an under development (Beta version) free and open source software (Kedzierski et al., 2019a, 2019b). This software, developed with R i386 3.1.2 software (The R Core Team, 2019), is designed to integrate functionalities developed on R software in a single tool in order to facilitate the study of pollution by plastics.
176 Synthetically, the software first performed two pre-processing steps (Kedzierski et al., 2019a).
177 First, the baseline of the spectra was corrected using the 4S Peak Filling baseline estimation
178 procedure of the function "baseline()" (library "baseline") and then the spectra were
179 normalized (Kedzierski et al., 2019a; Liland, 2015). The machine learning process was
180 performed using k-nearest neighbour classification (Ripley, 1996; Venables et al., 2002) of
181 the "knn()" function (library "class"). This technique is based on the calculation of the
182 distances between objects (Wehrens, 2011). The learning database comprises 969 spectra of
183 microplastics and other particles (natural organic materials) collected during the Tara 2014
184 scientific campaign (Falcou-Préfol et al., 2018; Kedzierski et al., 2019b). Finally, the 4,064
185 microplastics collected and analysed by this method included 17 chemical categories such as
186 PE, PP, or PS (Supplementary material, Fig. 1).

187 2.2.4. Calculation of theoretical temperatures of microplastics for infrared camera
188 observation

189 The relation between transmittance (τ), reflectance (ρ), absorption (α), and emissivity (ε) can
190 be noted as (Okada et al., 2016):

191 $$ \epsilon(\lambda) = \alpha(\lambda) = 1 - \rho(\lambda) - \tau(\lambda) \quad (\text{Eq. 1}) $$

192 Knowledge of the transmittance value determined by infrared spectrometry makes it possible
193 to calculate the theoretical emissivity values that are measured by the thermal camera.

194 Spectra of the different polymers were acquired in transmission by infrared spectrometry for
195 the spectral ranges of observation of the thermal camera. The area under the FTIR curve was
196 then calculated on R software ("lintegrate" function, library "tis"). The ratio between the
197 transmittance of the reference polymers (τparticle) and that of the glass filter (τfilter) was then
198 calculated, and the parameters (a and b) of the equation line ΔT were calculated as follows:
199

$$ \Delta T = a \frac{\tau_{particle}}{\tau_{filter}} - b \quad (\text{Eq. 2}) $$

200 with ΔT the theoretical temperature difference between the polymer and the glass filter that is 201 measured by the thermal camera. Finally, based on Equation 2 and the transmittance values 202 ($\tau_{particle}$) of the microplastics collected in the Mediterranean Sea, the ΔT of each of these 4,064 203 microplastics was calculated.

204

# 3. RESULTS

205

## 3.1. Detection of potential microplastics in coarse sand

206 The mixture of coarse sand, gravel, and poly(ethylene) was observed in infrared during a 240- 207 second heating/cooling cycle (Fig. 2.a and 2.b). The distribution of sand and gravel on the 208 filter was heterogeneous, with particles mainly concentrated in the centre of the filter (Fig. 2.b). Granite (Gr) and quartz (Qz) gravel particles were visible, and in some areas, several 209 layers of sand particles could overlap. The PE microparticles were white, slightly lighter than 210 the filter, thus enabling observation of their distribution into three main groups of a few 211 microparticles. 212

213 The heating phase lasted 175 seconds and the cooling phase 65 seconds (Fig. 2.a). The 214 estimated temperature of the filter increased from 25°C to 57°C in its right part and to 67°C in 215 its left part during the first phase. It then decreased to 36°C. The estimated temperature of the 216 granite particles increased from 25°C to 65°C during the heating phase. Thus, the estimated 217 temperature during this phase was lower than that estimated for the filter. During the cooling 218 phase, the estimated temperature of the granite particles decreased from 65°C to 40°C. During 219 this phase, it remained a few degrees higher than the estimated filter temperature. Finally, the 220 estimated temperature of the PE particles increased from 26 to 61°C during the heating phase, 221 then decreased from 61 to 35°C during the cooling phase. The estimated temperature was 222 therefore always a few degrees lower than the filter temperature.
Two photos were extracted from the IR video for a recorded temperature of the filter at 60°C (Fig. 2.c) and 45°C (Fig. 2.d). The perceived temperature of the filter was more homogeneous during the cooling phase, and plastic particles appeared more clearly (T=45°C; Fig. 2.d). In both cases, darker areas were clearly visible on the filter. Nevertheless, it was only during the cooling phase that these grey areas corresponded precisely to the location of the microplastics.

The first optical observation showed the presence of several PE particles between 500µm and 2mm (Fig. 2.e). Isolated particles of sand, varied in colour from black to light brown, were also visible in the photo. The infrared photograph showed darker areas, corresponding to plastics that had lower emissivity than the filter (Fig. 2.f). By computer processing, darker and lighter areas were isolated (Fig. 2.g). These dark areas corresponded to microplastics observed in Figure 2.e.

The second optical observation showed the presence of several PE particles around a granite particle (Fig. 2.h). The infrared photograph showed a strong contrast between organic and inorganic particles (Fig. 2.i). By computer processing, darker and lighter areas were easily isolated, revealing the presence of microplastics among the sand particles (Fig. 2.j)

238
<figure><img src="image_3.png" /><figcaption>Fig. 2. Detection of MP-PE dispersed in coarse sand on the surface of a glass fibre filter. a) Temperature profile recorded on the filter surface by the thermal camera for two filter areas (F1 and F2), a granite particle (Gr) and a polyethylene (PE) particle. The times in which photographs c,d,f,i were taken are indicated on the curve by arrows. b) Photography of the sample in the visible wavelength range. The arrows indicate the position of a few particles of quartz (Qz), granite (Gr), coal (C) and polyethylene (PE). Two magnifications of the photography have been made and their positions are indicated by the two black frames (e and h). c) Infrared photography of the sample during the heating phase. The entire surface of the filter was not homogeneous in terms of temperature: the left side of the filter was hotter than the right side. d) Infrared photography of the sample during the cooling phase. The entire surface of the filter was homogeneous. The MP-PE appeared dark in infrared imaging, while the sand particles were less distinct or lighter than the filter. e) Magnification of the filter area "e", observation in the visible spectrum. f) Magnification of the filter area "e" during the heating phase, observation in infrared. g) Magnification of filter area "e" during the cooling phase, infrared observation. h) Magnification of filter area "h", observation in the visible spectrum. i) Magnification of filter area "h" during the heating phase, infrared observation. j) Magnification of filter area "h" during the cooling phase, infrared observation.</figcaption></figure>
257

258

## 3.2. Detection of potential microplastics in fine sand

259

260

261

262

263

264

265

A mixture of fine sand and poly(ethylene) was observed in infrared during a 260-second heating/cooling cycle (Fig. 3.a and 3.b). The distribution of sand on the filter was heterogeneous (Fig. 3.b). Areas of high concentration, where several layers of sand particles were superimposed, were visible. Areas of lower concentration, where sand particles were well individualized, were also visible. The PE microparticles were white, slightly lighter than the filter, thus enabling observation of their distribution into three main groups of a few microplastic particles.

266

267

268

269

270

The heating phase lasted 200 seconds and the cooling phase 60 seconds (Fig. 3.a). The temperature of the filter increased from 27°C to 63°C during the first phase, then decreased from 63°C to 44°C. The estimated temperature of the sand was relatively close to that of the filter. The estimated temperature of the polyethylene was lower than that of the filter. On average, the estimated temperature difference was about 10°C.

271

272

Two photos were extracted from the IR video for a recorded temperature of the filter at 56°C (Fig. 3.c) and 43°C (Fig. 3.d). In both cases, darker areas were clearly visible on the filter. These corresponded precisely to the location of the microplastics. The particles of sand were not visible on the infrared images. The perceived temperature of the filter-sand assembly was more homogeneous during the cooling phase, and plastic particles appeared clearly (T=43°C; Fig. 3.d).

273

274

275

276

277

278

279

280

Optical observation showed the presence of several PE particles between 500µm and 2mm (Fig. 3.e). Isolated particles of sand, varied in colour from black to light brown, were also visible in the photo. The infrared photograph showed darker areas, corresponding to plastics that had lower emissivity than the filter (Fig. 3.f). Sand particles did not appear on the infrared
photograph. By computer processing, darker and lighter areas were isolated (Fig. 3.g). These white areas corresponded to microplastics observed in Figure 3.e. On the IR photograph, the general geometry of the particles was rather well preserved. However, details below about 500µm were lost. For example, an ovoid PE particle (length of the major axis in the order of 500µm) was visible in the central area, and after image processing, it appeared as a circular white area.

<figure><img src="image_4.png" /><figcaption>Fig. 3. Detection of MP-PE dispersed in fine sand on the surface of a glass fibre filter. a) Temperature profile recorded on the filter surface by the thermal camera of a filter area (F), a sand particle (Sd) and a polyethylene (PE) particle. The times in which photographs c,d,f were taken are indicated on the curve by arrows. b) Photography of the sample in the visible wavelength range. The arrows indicate the position of a few particles of sand (Sd)and</figcaption></figure>
polyethylene (PE). A magnification of photography had been made and its position is indicated by the black frame (e). c) Infrared photography of the sample during the heating phase. d) Infrared photography of the sample during the cooling phase. The MP-PE appeared dark in infrared imaging, while the sand particles were not visible. e) Magnification of the filter area "e", observation in the visible spectrum. f) Magnification of the filter area "e" during the cooling phase, infrared observation. g) Area "e" after computer processing: MP-PE appeared very clearly in infrared imaging whereas sand particles were not distinguished from the filter.

### 3.3. Observation of reference microplastics

Plastic particles were mainly arranged in aggregates (Fig. 4.a). Three algae fragments and different inorganic particles (quartz, shell fragments, and shales) were set on the upper part of the filter.

The heating/cooling process lasted 120 seconds, of which about 60 seconds involved heating and 60 seconds cooling (Fig. 4.d). During the heating phase, the estimated average filter temperature increased from 24°C to 59°C. During the cooling phase, this temperature decreased from 59°C to 34°C.

Differences in emissivity were observed between the filter and the particles. During the heating phase, after about 30 seconds, three groups were distinguished (Fig. 4.d). Quartz particles ($SiO_2$), shell fragments ($CaCO_3$), PS, PP, PA, PA, and PE had lower emissivity than the filter, resulting in a temperature difference with the filter of 2 to 5°C. Next, PVC, algae, and PMMA formed a second group whose estimated temperature difference was about 0.5 to 1°C below that of the filter. Finally, PET was between 1 and 2°C warmer than the filter temperature. It is possible that the temperature of the filter in the vicinity of the sample was underestimated. The central area of the filter had a large temperature gradient between the
warmer filter centre and the colder periphery. In the case of PET, the reference point for the filter temperature was most likely chosen too far out in the periphery. During the cooling phase, the SiO₂ temperature becomes very similar to that of the filter. The temperature of other materials remains lower than that of the filter within a limit of 2°C.

After 40 seconds of cooling, the thermal image taken by the camera was analysed (Fig. 4.b).
This thermal image showed that particles were more or less easily discernible depending on their chemical nature. Thus, aggregates and MP-PS, MP-PET, MP-PE, and MP-PP were very clearly visible. MP-PVC, MP-PA, algae, aggregates, and debris were sometimes more difficult to detect. The sand particles and polyamide particles located in the centre of the filter were not discernible. In the case of PA particles, this was due to the higher temperature at the centre of the filter compared to its periphery. This difficulty in homogeneously heating the filter led to a greater increase in the temperature of the particles in the centre of the filter.
Thus, during the cooling phase, these particles appeared less contrasted than if they had been on the periphery of the filter.

After computer processing of the image, the areas of interest appeared clearly (Fig. 4.c). This was particularly true for particles of PS, PE, PVC, PET, and PP. Sand particles, especially if they were SiO₂-based (e.g. quartz), were not detectable. The algae signal also disappeared more or less strongly. When several algal thicknesses were superimposed, the signal could be confused with that of plastics.
<figure><img src="image_5.png" /></figure>

Fig. 4. Observation of particles of different chemical nature by active thermography. (a) Observation of the sample in the visible spectral range. Particles of poly(ethylene) (PE), poly(propylene) (PP), poly(styrene) (PS), poly(vinyl chloride) (PVC), poly(amide) (PA), poly(ethylene terephthalates) (PET), but also grains of quartz (Qz), shale (Sh), calcite (Ca) and algae fragments are dispersed on the filter surface. b) Infrared photography of the sample during the cooling phase (T=110s). c) Infrared photography of the sample after computer processing. d) Evolution of the measured temperature difference between the particles and the filter as a function of time.

### 3.4. Prediction of the ΔT of microplastics aged in the Mediterranean Sea

The average ΔT was calculated for each of the particles over the period between 40 and 50s after the start of cooling (Fig. 4.d). These values, placed in a graph according to the transmittance ratios, enabled establishing the existence of a linear relationship between these two parameters (Fig. 5.a). Thus, PE, PA, and PP particles had a lower average temperature and transmittance than the filter. In contrast, silica (quartz) had an average temperature and transmittance similar to that of the filter. Calcite had an average ΔT and a transmittance close to PVC, PMMA, and PET. There is therefore a risk of confusing the signal of a shell with that of plastic if these particles are of similar thickness. The case of algae was an exception, because the average ΔT was close to the filter whereas the transmittance ratio was much
lower. The value of the average ΔT of the algae being very biased by the thickness of the algae, it was decided to exclude this value from the calculation of the linear equation and the associated R².

<figure><img src="image_6.png" /><figcaption>Fig. 5. Prediction of the ΔT of microplastics aged in the Mediterranean Sea. a) Evolution of ΔT measured by active thermography for particles of different chemical natures, as a function of the absorbance ratio measured for these particles and the filter by infrared spectrometry. b) ΔT calculated using the equation determined (5.a) after the FTIR spectra of the microplastics collected in the Mediterranean Sea. The calculated values are valid only under these experimental conditions. c) Density diagram showing the evolution of the proportions of the different polymers as a function of ΔT. Since different polymers have the same ΔT, it does not seem possible to determine the chemical nature of a microplastic using this information alone.</figcaption></figure>

Using this equation, the theoretical ΔT was calculated from the transmittance of the FTIR spectra obtained during the Tara campaign. The analysis of the results obtained (Fig. 5.b
and5.c) showed that the average ΔT of these microplastics was in a range between 0.1 and 3°C. Thus, the ΔT of the MP-PE was 1.4±0.3°C, namely values very similar to those observed for the unaged MP-PE used in the experiment. For more heavily fouled MP-PE, the average ΔT was 1.5±0.4°C when two outliers were excluded (values between 23 and 24°C). This average ΔT was very similar to the reference PE values (ΔT=1.4±0.2) obtained in the study and to the values obtained on the less fouled PM. In the case of MP-PP, the average ΔT was 0.9±0.3°C, again very close to the value of unaged PP (ΔT=1.0±0.2). The mixture of PE and PP, MP-EPRs had an intermediate ΔT (ΔT=1.3±0.2°C). The temperature values observed for MP-PS (ΔT=0.5±0.3°C) were also close to the values of the non-aging test sample (ΔT=0.8±0.1°C). The same was true for MP-PMMA (ΔT=0.7±0.2°C), with its test reference (ΔT=0.5±0.1°C). However, only three MP-PMMA spectra were available in the database at the time of the study. For MP-PA, a difference was observed between the calculated mean ΔT (1.7±0.7°C) and that obtained by measuring non-aging particles (0.9±0.1°C). The particles called "cellulose like", whose spectra were similar to those of the algae used as a reference, had an average temperature of 0.5±0.5°C. Finally, cellulose acetate had a very high average temperature: 18.5±1.0°C.

From these predictions of T and for the proposed protocol, the chemical nature of a particle cannot be accurately determined. Nevertheless, the range of possibilities can be restricted. With a ΔT ≈ 0°C, the particle will probably be a particle of quartz (Fig.5.c). If the ΔT of a particle is equal to 0.1, it is probably algae. For ΔT=0.2, the particle can be an alga, a shell fragment, or possibly polystyrene. Between 0.2 and about 4°C, the particle is highly likely to be a microplastic. It is not possible to determine clearly the precise chemical nature of the microplastic due to the superposition of the ΔT of the different polymers. However, if ΔT ∈ [0.2-1.0] °C, it is more likely to be PP, PS, or PMMA. In contrast, if ΔT ∈ [1.0-2.0] °C, it is
probably PE, PA, EPR, or PEVA. Finally, if $\Delta T \in [2.0-4.0]$, it is more likely to be PA. Above this figure, the particles could be made of cellulose acetate aged in the marine environment.

## 4. DISCUSSION

The thickness of the algae was about 10 micrometres compared to a few hundred micrometres for plastics. This implies that the estimated temperature of the algae was very close to the filter and was very underestimated compared to what it would have been if the algae had had a thickness close to that of the plastic particles.

### 4.1. Strengths of the approach

Infrared imaging technologies based on indium antimonide (InSb) detectors constitute a profitable avenue for the detection of microplastics. This technology has two main advantages for the analysis of microplastics.

First, this method enables the pre-detection of microplastics in a short period of time, since only a few tens of seconds are needed to image an entire filter. In the case where only a fraction of the surface of the filter is really worth investigating, as the main part may be empty or covered with sand for example, using this type of imaging tools would make it possible to better preselect the areas of interest and to directly analyse the particles with temperature anomalies compatible with microplastics. Spectrometry tools such as micro-FTIR or FPA-FTIR are already used to scan entire filters in an automated way. However, even with a technology as powerful as an FPA-FTIR, it takes several hours to scan an entire filter.

The second advantage is that this method enables the observation of plastics in a wavelength range partly overlapping the wavelength range conventionally used in FTIR spectrometry (Fig. 6). This observation range, between 2000 and 4000 cm⁻¹, corresponds essentially, in the case of plastics, to the absorption bands of -CH₂, -CH₃ (between 2800 and 3000 cm⁻¹), -NH (3300 cm⁻¹) and -OH (broad band between 3200 and 3400 cm⁻¹). It is the of plastics which,
418

leading to the appearance of carboxylic bonds, results in turn in the appearance and
development of the latter.

419

420

Algae are constituted, among other things, of polysaccharides that are responsible for a broad
-OH band absorbing infrared radiation between 3200 and 3400 cm⁻¹. The Si-O bonds of the
silicates (quartz of the sand particles, filter fibres), absorb infrared radiation between 1000 and
1100 cm⁻¹, thus outside the spectral range observed by the thermal camera. Hence, this
spectral range offers the possibility of obtaining comparable absorption results using two
different methods. Thus, FTIR spectra enable linking the chemistry of the material and the
results obtained in infrared imaging. For example, the high thermal contrast observed for the
PE samples (chemical formula: -[C₂H₄]ₙ) can be explained by the very intense absorption
bands corresponding to the -CH₂ bonds. In contrast, PET (chemical formula: -[O-(CH₂)₂-O-
CO-*p*Ph-CO]ₙ) has bands of low intensity in this spectral range (ethyl and phenyl ring bonds),
which results in low absorption in this spectral range and therefore less thermal contrast with
the filter.

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

If it is possible to explain the behaviour of materials observed in thermal imaging cameras on
the basis of their chemical nature, it is also possible to question the possibilities offered by
this type of technology to determine the chemical nature of the observed particles. This
requires a calibration step using plastic materials for which the FTIR spectra were made.
Plastics such as PET, PS, and PE facilitate establishing the correlation line between the
absorption values obtained using these two methods. In this way, it is possible to predict
approximately the most probable chemical types. For example, since microplastics found in
the natural environment are mainly composed of PE, PP, and PS, the most likely chemical
types can be predicted (Schwarz et al., 2019). As these have different temperature anomalies,
it is possible to propose a first interpretation of the chemical nature of the most probable
442

particles after image analysis. This approach is less efficient than FTIR or Raman analysis,
but it is still worth mentioning.

443

<figure><img src="image_7.png" /></figure>

444

Fig. 6. FTIR spectra of different polymers and an alga in the spectra range observed by the camera. The area under the curve of the FTIR spectra can be compared to the temperature anomaly measured by the thermal camera.

445

446

447

448

## 4.2. Limitations of the approach

449

450

451

452

453

454

The difference in visibility between the particles is also influenced by the diffusivity of the particles. This factor is a function of the heat capacity, thermal conductivity and density of the material. This parameter is important during the heating phase and influences the time required for the entire sample to homogenize in temperature during the heating phase. If this parameter seems to have less influence during the cooling phase, it is nevertheless necessary to specify its influence on the ΔT.

455

456

457

458

459

The minimum particle size that can be observed using this experimental device is about 300µm. Therefore, only the largest microplastics (5 mm - 300 µm) can be detected in this way at the moment. The finest fraction, inferior to 500 µm in dimension, and which seems to have the most toxic impact on living organisms (Crawford and Quinn, 2017; Triebskorn et al., 2019; Van Cauwenberghe et al., 2015), cannot for the moment be detected using this
experimental device. An effort must therefore be made to propose a modified device enabling
the observation of smaller microplastics.

While the detection of microplastics in beach sand is very efficient, the presence of algae and
more broadly of organic matter may increase the number of false positives. The spectra of
algae show a wide band centred around 3300 cm⁻¹. Absorption is therefore significant and
leads to a strong thermal contrast. However, the opposite was observed during the
experiments, due to the very low thickness (~20µm) of the algae used (Ulva sp.). At constant
exchange surface, the lower the thickness of the material, the lower the thermal contrast
measured with the filter will be. In the specific case of the algae used, this property was rather
positive since the algae differ little from the filter. Furthermore, calculations using
microplastics show that materials with a chemical signature close to algae ("cellulose-like")
generally have a lower thermal contrast than plastics aged in the marine environment. Thus,
initial experimental and theoretical results are encouraging and suggest that discrimination
between algal fragments and plastics is possible in many cases.

Samples of microplastics collected in the marine environment have an -OH band that
develops during polymer ageing. Therefore, an aged polymer should show greater absorption,
and therefore be more contrasting, than the unaged polymers used in the experiment. Thus,
the calculations carried out on the FTIR spectra of microplastics collected in the
Mediterranean Sea show an increase in thermal contrast for some polymers (e.g. PA). For
others, however, few differences are observed (e.g. PS, PE), and further analyses are
necessary to better understand the behaviour of aged and non-aged plastic materials observed
in infrared cameras.

4.3. *Next steps*
483

This new approach is giving very encouraging results and needs to be developed along several lines of research in order to be fully operational.

484

485

First, the difficulty of the size limit of the particles that can be detected by this device can be partly overcome by using a camera lens enabling the magnification of the sample. Thus, we plan to test a new lens enabling higher magnification, but with a narrower range of wavelengths captured (3-5 µm; 3333-2000 cm⁻¹) than the one used in this study (1.5-5.1 µm; 6667-1960 cm⁻¹). This change should make it possible to lower the observation threshold for particles down to a size range of the order of 100 µm. This change will also have a potential impact on the quality detection of plastics, this impact will have to be specified.

486

487

488

489

490

491

492

Next, it will be necessary to improve the computer processing of the thermal images obtained, carried out under FIJI in this study, but which could be programmed and automated under R language. The goal will be to integrate this step into the POSEIDON software (Kedzierski et al., 2019a). This step would also enable the extraction of other data (e.g. coordinates of the particles on the filter, dimensions, shape). It would then be possible to develop a database containing many images of microplastics observed in infrared imaging. This database could be used to support the development and testing of a *machine learning* tool to improve the pre-detection rate of microplastics.

493

494

495

496

497

498

499

500

Finally, once this pre-detection stage is fully operational, it will be necessary to couple this stage with those already in place. This will make it possible to carry out a random draw on the coordinates of the microplastic potentials present on the filter using the statistical method (Kedzierski et al., 2019b). Once these coordinates are available, a micro-FTIR or an FPA-FTIR with a mobile platform would be used to make the spectrometric measurements on the selected particles automatically. Coupling the pre-detection method with the statistical method would thus save a great deal of time in the context of a batch of particles to be analysed.

501

502

503

504

505

506
507 5. CONCLUSION

508 In a context where the chemical analysis of microplastics has to be ever faster, the pre-
509 detection of these before a spectrometric analysis is a relevant field of research. In this study,
510 we have shown the value of active infrared imaging-based technologies. The observation of
511 plastic samples in the mid-infrared range enables the detection of thermal anomalies due in
512 particular to the variable capacity of the materials to absorb infrared radiation. Some of the
513 limitations identified in this study are of a purely technical nature and seem to be easily
514 overcome. The case of algae deserves to be investigated in more detail in order to clarify their
515 capacity to hamper pre-detection. Finally, coupling pre-detection with the random sampling
516 methodology would make it possible to envisage an even easier analysis of pollution by
517 microplastics.

518 Acknowledgments

519 We thank the commitment of the following institutions, persons and sponsors: CNRS, UPMC,
520 LOV, Genoscope/CEA, the Tara Expeditions Foundation and its founders: Agnes b.®, Etienne
521 Bourgois, Romain Troublé, the Veolia Environment Foundation, Lorient Agglomeration,
522 Serge Ferrari, the Foundation Prince Albert II de Monaco, IDEC, the “Tara” schooner and
523 teams. We thank MERCATOR-CORIOLIS and ACRI-ST for providing daily satellite data
524 during the expedition. We are also grateful to the French Ministry of Foreign Affairs for
525 supporting the expedition and to the countries that graciously granted sampling permission.
526 This article is contribution number 5 of Tara Mediterranean and 1 of Tara to the investigation
527 of the sources of plastic pollution.

528
529 REFERENCES

530 Amaral-Zettler, L.A., Zettler, E.R., Slikas, B., Boyd, G.D., Melvin, D.W., Morrall, C.E.,
531 Proskurowski, G., Mincer, T.J., 2015. The biogeography of the Plastisphere: implications
532 for policy. Front. Ecol. Environ. 13, 541–546. https://doi.org/10.1890/150017

533 Crawford, C.B., Quinn, B., 2017. 7 - The biological impacts and effects of contaminated
534 microplastics, in: Microplastic Pollutants. Elsevier, pp. 159–178.
535 https://doi.org/http://dx.doi.org/10.1016/B978-0-12-809406-8.00007-4

536 Dümichen, E., Barthel, A.K., Braun, U., Bannick, C.G., Brand, K., Jekel, M., Senz, R., 2015. Analysis of polyethylene microplastics in environmental samples, using a thermal decomposition method. Water Res. 85, 451–457.
537
538
539 https://doi.org/10.1016/j.watres.2015.09.002

540 Dümichen, E., Eisentraut, P., Bannick, C.G., Barthel, A.K., Senz, R., Braun, U., 2017. Fast identification of microplastics in complex environmental samples by a thermal degradation method. Chemosphere 174, 572–584.
541
542
543 https://doi.org/10.1016/j.chemosphere.2017.02.010

544 Dussud, C., Meistertzheim, A.L., Conan, P., Pujo-Pay, M., George, M., Fabre, P., Coudane, J., Higgs, P., Elineau, A., Pedrotti, M.L., Gorsky, G., Ghiglione, J.F., 2018. Evidence of niche partitioning among bacteria living on plastics, organic particles and surrounding seawaters. Environ. Pollut. 236, 807–816.
545
546
547
548 https://doi.org/https://doi.org/10.1016/j.envpol.2017.12.027

549 Falcou-Préfol, M., Kedzierski, M., Villain, J., Kerros, M.E., Elineau, A., Pedrotti, M.L., Bruzaud, S., 2018. Statistical Methodology for Identifying Microplastic Samples Collected During TARA Mediterranean Campaign. Springer International Publishing, Cham, pp. 31–35. https://doi.org/10.1007/978-3-319-71279-6_5
550
551
552
553 Frère, L., Paul-Pont, I., Moreau, J., Soudant, P., Lambert, C., Huvet, A., Rinnert, E., 2016. A semi-automated Raman micro-spectroscopy method for morphological and chemical characterizations of microplastic litter. Mar. Pollut. Bull. 113, 461–468.
554
555
556 https://doi.org/10.1016/j.marpolbul.2016.10.051

557 GESAMP, 2019. Guidelines for the monitoring and assessment of plastic litter in the ocean,
558 GESAMP Rep. ed. GESAMP.

559 Gimeno, P., Spinau, C., Lassu, N., Maggio, A.F., Brenier, C., Lempereur, L., 2015. Identification and quantification of bisphenol A and bisphenol B in polyvinylchloride and polycarbonate medical devices by gas chromatography with mass spectrometry. J Sep Sci 38, 3727–3734. https://doi.org/10.1002/jssc.201500552

563 Imhof, H.K., Schmid, J., Niessner, R., Ivleva, N.P., Laforsch, C., 2012. A novel, highly efficient method for the separation and quantification of plastic particles in sediments of aquatic environments. Limnol. Oceanogr. Methods 10, 524–537.

566 Imhof, H.K., Sigl, R., Brauer, E., Feyl, S., Giesemann, P., Klink, S., Leupolz, K., Loder, M.G., Loschel, L.A., Missun, J., Muszynski, S., Ramsperger, A.F., Schrank, I., Speck, S., Steibl, S., Trotter, B., Winter, I., Laforsch, C., 2017. Spatial and temporal variation of macro-, meso- and microplastic abundance on a remote coral island of the Maldives, Indian Ocean. Mar. Pollut. Bull. 116, 340–347.
567
568
569
570
571 https://doi.org/10.1016/j.marpolbul.2017.01.010

572 Ioakeimidis, C., Fotopoulou, K.N., Karapanagioti, H.K., Geraga, M., Zeri, C., Papathanassiou, E., Galgani, F., Papatheodorou, G., 2016. The degradation potential of PET bottles in the marine environment: An ATR-FTIR based approach. Sci. Rep. 6, 1–8.
573
574
575 https://doi.org/10.1038/srep23501

576 Käppler, A., Fischer, D., Oberbeckmann, S., Schernewski, G., Labrenz, M., Eichhorn, K.-J.,
577 Voit, B., 2016. Analysis of environmental microplastics by vibrational
578 microspectroscopy: FTIR, Raman or both? Anal. Bioanal. Chem. 408, 8377-8391.
579 https://doi.org/10.1007/s00216-016-9956-3

580 Käppler, A., Fischer, M., Scholz-Böttcher, B.M., Oberbeckmann, S., Labrenz, M., Fischer,
581 D., Eichhorn, K.-J., Voit, B., 2018. Comparison of μ-ATR-FTIR spectroscopy and py-
582 GCMS as identification tools for microplastic particles and fibers isolated from river
583 sediments. Anal. Bioanal. Chem. 410, 5313-5327. https://doi.org/10.1007/s00216-018-
584 1185-5

585 Kedzierski, M., D'Almeida, M., Magueresse, A., Le Grand, A., Duval, H., César, G., Sire, O.,
586 Bruzaud, S., Le Tilly, V., 2018. Threat of plastic ageing in marine environment.
587 Adsorption/desorption of micropollutants. Mar. Pollut. Bull. 127, 684-694.
588 https://doi.org/10.1016/j.marpolbul.2017.12.059

589 Kedzierski, M., Falcou-Préfol, M., Kerros, M.E., Henry, M., Pedrotti, M.L., Bruzaud, S.,
590 2019a. A machine learning algorithm for high throughput identification of FTIR spectra:
591 Application on microplastics collected in the Mediterranean Sea. Chemosphere 234,
592 242-251. https://doi.org/10.1016/j.chemosphere.2019.05.113

593 Kedzierski, M., Le Tilly, V., Bourseau, P., Bellegou, H., Cesar, G., Sire, O., Bruzaud, S.,
594 2016. Microplastics elutriation from sandy sediments: A granulometric approach. Mar.
595 Pollut. Bull. 107, 315-323. https://doi.org/10.1016/j.marpolbul.2016.03.041

596 Kedzierski, M., Villain, J., Falcou-Préfol, M., Kerros, M.E., Henry, M., Pedrotti, M.L.,
597 Bruzaud, S., 2019b. Microplastics in Mediterranean Sea: A protocol to robustly assess
598 contamination characteristics. PLoS One 14, e0212088.
599 https://doi.org/10.1371/journal.pone.0212088

600 Lenz, R., Enders, K., Stedmon, C.A., Mackenzie, D.M., Nielsen, T.G., 2015. A critical
assessment of visual identification of marine microplastic using Raman spectroscopy for
analysis improvement. Mar. Pollut. Bull. 100, 82-91.
https://doi.org/10.1016/j.marpolbul.2015.09.026

Liland, K.H., 2015. 4S Peak Filling – baseline estimation by iterative mean suppression.
MethodsX 2, 135–140. https://doi.org/10.1016/j.mex.2015.02.009

Löder, M.G.J., Gerdts, G., 2015. Methodology Used for the Detection and Identification of
Microplastics—A Critical Appraisal, in: Bergmann, M., Gutow, L., Klages, M. (Eds.),
Marine Anthropogenic Litter. Springer International Publishing, Cham, pp. 201–227.
https://doi.org/10.1007/978-3-319-16510-3_8

Mintenig, S.M., Int-Veen, I., Löder, M.G.J., Primpke, S., Gerdts, G., 2017. Identification of
microplastic in effluents of waste water treatment plants using focal plane array-based
micro-Fourier-transform infrared imaging. Water Res. 108, 365–372.
https://doi.org/https://doi.org/10.1016/j.watres.2016.11.015

Okada, T., Ishige, R., Ando, S., 2016. Analysis of Thermal Radiation Properties of Polyimide
and Polymeric Materials Based on ATR-IR spectroscopy. J. Photopolym. Sci. Technol.
29, 251–254. https://doi.org/10.2494/photopolymer.29.251

Pedrotti, M.L., Petit, S., Elineau, A., Bruzaud, S., Crebassa, J.C., Dumontet, B., Marti, E.,
Gorsky, G., Cozar, A., 2016. Changes in the Floating Plastic Pollution of the
Mediterranean Sea in Relation to the Distance to Land. PLoS One 11, e0161581.
https://doi.org/10.1371/journal.pone.0161581

Primpke, S., Lorenz, C., Rascher-Friesenhausen, R., Gerdts, G., 2017. An automated
approach for microplastics analysis using focal plane array (FPA) FTIR microscopy and
image analysis. Anal. Methods 9, 1499–1511. https://doi.org/10.1039/c6ay02476a
Primpke, S., Wirth, M., Lorenz, C., Gerdts, G., 2018. Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy. *Anal. Bioanal. Chem.* 410, 5131–5141. https://doi.org/10.1007/s00216-018-1156-x

Qiu, Q., Tan, Z., Wang, J., Peng, J., Li, M., Zhan, Z., 2016. Extraction, enumeration and identification methods for monitoring microplastics in the environment. *Estuar. Coast. Shelf Sci.* 176, 102–109. https://doi.org/10.1016/j.ecss.2016.04.012

Renner, G., Nellessen, A., Schwiers, A., Wenzel, M., Schmidt, T.C., Schram, J., 2019. Data preprocessing & evaluation used in the microplastics identification process: A critical review & practical guide. *TrAC - Trends Anal. Chem.* 111, 229–238. https://doi.org/10.1016/j.trac.2018.12.004

Renner, G., Schmidt, T.C., Schram, J., 2017. A New Chemometric Approach for Automatic Identification of Microplastics from Environmental Compartments Based on FT-IR Spectroscopy. *Anal. Chem.* 89, 12045–12053. https://doi.org/10.1021/acs.analchem.7b02472

Ripley, B.D., 1996. Pattern recognition and neural networks. Cambridge University Press, Cambridge ; New York.

Rocha-Santos, T., Duarte, A.C., 2015. A critical overview of the analytical approaches to the occurrence, the fate and the behavior of microplastics in the environment. *TrAC Trends Anal. Chem.* 65, 47–53. https://doi.org/10.1016/j.trac.2014.10.011

Schwarz, A., Ligthart, T., Boukris, E., Van Harmelen, T., 2019. Sources, transport, and accumulation of different types of plastic litter in aquatic environments: A review study. ☆, *Marine Pollution Bulletin*. https://doi.org/10.1016/j.marpolbul.2019.04.029
Shim, W.J., Hong, S.H., Eo, S.E., 2017. Identification methods in microplastic analysis: a review. *Anal. Methods* 9, 1384–1391. https://doi.org/10.1039/C6AY02558G

Tagg, A.S., Sapp, M., Harrison, J.P., Ojeda, J.J., 2015. Identification and Quantification of Microplastics in Wastewater Using Focal Plane Array-Based Reflectance Micro-FT-IR Imaging. *Anal. Chem.* 87, 6032–6040. https://doi.org/10.1021/acs.analchem.5b00495

The R Core Team, 2019. R: A Language and Environment for Statistical Computing.

Triebskorn, R., Braunbeck, T., Grummt, T., Hanslik, L., Huppertsberg, S., Jekel, M., Knepper, T.P., Krais, S., Müller, Y.K., Pittroff, M., Ruhl, A.S., Schmieg, H., Schür, C., Strobel, C., Wagner, M., Zumbülte, N., Köhler, H.R., 2019. Relevance of nano- and microplastics for freshwater ecosystems: A critical review. *TrAC - Trends Anal. Chem.* 110, 375–392. https://doi.org/10.1016/j.trac.2018.11.023

Van Cauwenberghe, L., Devriese, L., Galgani, F., Robbens, J., Janssen, C.R., 2015. Microplastics in sediments: A review of techniques, occurrence and effects. *Mar. Environ. Res.* 111, 5–17. https://doi.org/10.1016/j.marenvres.2015.06.007

Venables, W.N., Ripley, B.D., Venables, W.N., 2002. Modern applied statistics with S, 4th ed., Statistics and computing. Springer, New York.

Zarfl, C., 2019. Promising techniques and open challenges for microplastic identification and quantification in environmental matrices. *Anal. Bioanal. Chem.* 411, 3743–3756. https://doi.org/10.1007/s00216-019-01763-9

Zettler, E.R., Mincer, T.J., Amaral-Zettler, L.A., 2013. Life in the “plastisphere”: Microbial communities on plastic marine debris. *Environ. Sci. Technol.* 47, 7137–7146. https://doi.org/10.1021/es401288x

Zhao, S., Danley, M., Ward, J.E., Li, D., Mincer, T.J., 2017. An approach for extraction,
670 characterization and quantitation of microplastic in natural marine snow using Raman

671 microscopy. Anal. Methods 9, 1470–1478. https://doi.org/10.1039/c6ay02302a

672