This work presents a hyperspectral reflectance dataset of macroplastic
samples acquired using
Analytical Spectral Devices (ASD) FieldSpec 4. Samples analyzed consisted of
pristine, artificially
weathered, and biofouled plastic items and plastic debris samples collected
in the docks of the Port of
Antwerp and in the river Scheldt near Temse Bridge (Belgium). The
hyperspectral signal of each sample
was measured in controlled dry conditions in an optical calibration facility
at the Vlaamse Instelling voor Technologisch Onderzoek (VITO; Flemish Institute for
Technological Research) and, for a subset of plastics, under wet and
submerged conditions in a silo tank
at Flanders Hydraulics. The wet and submerged hyperspectral signals were
measured in a mesocosm
setting that mimicked environmentally relevant concentrations of freshwater
microalgae and
suspended sediment. The ASD was equipped with an 8∘ field of view
at the calibration facility, and a 1∘
field of view was used in the mesocosm setting. The dataset obtained
complies with the FAIR (Findable, Accessible, Interoperable, Reusable) principles and is available in the open-access repository
Marine Data Archive (10.14284/530, Leone et al., 2021).
Introduction
The spectral reflectance measurements, collected in the framework of the
Plastic Flux for Innovation and
Business Opportunities in Flanders (PLUXIN) project, contribute to the
current knowledge on the
detection of plastics using remote sensing techniques.
In recent years, focus has been placed on the detection of plastic litter
using remote sensing techniques
such as optical sensors on satellites, aircraft, and drones (Garaba and
Dierssen, 2018; Martínez-Vicente et al., 2019). With the increasing demand for these technologies, it
is crucial to generate
knowledge on the diagnostic spectral properties of not only pristine but
also weathered and biofouled
plastics (Moshtaghi et al., 2021) that are representative of the variety of
environmental plastics.
Currently, the spectral reflectance of dry plastics is known and is already
applied in the field of material
recycling (Moroni et al., 2015), but it is restricted to items assessed for
dry measurements. To be able to
identify plastic litter in aquatic environments such as rivers, harbors, and
oceans, we require the
acquisition of the spectral features when plastics are either wet or
submerged (Moshtaghi et al., 2021),
as water absorbs the light in both the near (NIR) and shortwave infrared
(SWIR). In addition, other water
constituents such as sediment or algae could further impact the reflected
signal of plastic items
(Moshtaghi et al., 2021).
To date, only a limited number of high-quality datasets consisting of
hyperspectral measurements of wet
and submerged plastic litter have been published in open-access
repositories (e.g., Garaba and
Dierssen, 2018, 2020; Knaeps et al., 2021). As plastics
in our environment are so
diverse in polymer type, color, transparency, thickness, state (pristine,
biofouled, weathered, wrinkled),
and wetness (dry, wet, submerged), it is critical to generate, within the
scientific community,
substantiated datasets which represent plastics in many different facets.
The collected spectra can
serve as references or endmember spectra in future remote sensing plastic
detection techniques and
help to understand the complexity of plastic detection through spectral
analysis. It is recognized that not
all possible scenarios can be measured in an experimental way, therefore the
dataset can further be
used to compare with and complement numerical simulations. The dataset
described in the current paper
aims to complement the existing datasets by adding new information about
the hyperspectral
reflectance of pristine plastic items, harvested plastic litter, and
artificially weathered and biofouled plastic
samples. In addition, the dataset reports the optical features of plastics
acquired in various water turbidity
conditions obtained by adding sediment or algae to the water at selected
concentrations.
The dry spectral reflectance of plastic specimens, consisting of different
polymers, was measured using
an Analytical Spectral Device (ASD) FieldSpec 4. For a selection of these
samples, the spectral
reflectance was also collected in wet and submerged settings.
The presented data can be used to generate insights into the spectral
properties of plastic litter and how
these features change when plastics are exposed to natural agents or
different depths. The dataset was
compiled following the FAIR (Findable, Accessible, Interoperable, Reusable;
https://www.go-fair.org/fair-principles/, last access: 24 April 2022; GO FAIR, last update 2022) principles, and
it is publicly available at https://doi.org/10.14284/530 (Leone et al.,
2021).
Data collection
Data collection consisted of measuring the spectral reflectance of different
plastic specimens using the
ASD. The measurements were conducted in two different settings: at an
optical calibration facility located
at the Vlaamse Instelling voor Technologisch Onderzoek (VITO; Flemish Institute for Technological Research; Mol, Belgium), and at a
silo tank at the Flanders
Hydraulics Research facility (Antwerp, Belgium). The spectral reflectance of
each plastic specimen was
collected at least 5 times (“pseudo-replicates”) within 1 min. These
pseudo-replicates are
measurements taken by slightly changing the position of the plastic sample.
Therefore, very
homogeneous plastics will have less variation in the pseudo-replicates'
reflectance.
Plastic specimens
We aimed to collect the spectral reflectance of plastic items as
representative as possible of commonly
found plastics in the environment (Li et al., 2016) and to provide
additional information on their
reflectance when exposed to natural agents such as sunlight (UV radiation)
or biofilm growth. To do so,
we have selected and analyzed a total of 10 polymer types. The selected
polymers were polyethylene
(PE), polypropylene (PP), polystyrene (PS), two types of polyethylene
terephthalate (PET) –
crystalline and amorphous – fluorocarbon, thermoplastic elastomer, polyvinyl
chloride (PVC), nylon 6
(PA6), and paraffin (Table 1; S1). In addition, we further
artificially weathered and biofouled
a selection of six plastic polymer types. The polymer discrimination was based
on the available information
from the supplier or marked on the plastic itself (e.g., plastic bottles or
bags with an identifiable polymer
tag). In addition, for a set of samples, we confirmed the polymer type using
a micro-Fourier transform
infrared spectroscopy (micro-FTIR, PerkinElmer, FTIR spectrometer
Frontier). The percentage of matching
scores with a library was recorded, and it is reported in the dataset. Table 1 provides an overview of
measured plastic types within this study and compared them to existing
datasets. This dataset adds
additional information on plastics spectral reflectance of similar polymers
to what is already available in the
literature, allowing comparison but also novel conditions and treatments
(Table 1).
Comparison of polymers and conditions analyzed in this
dataset and in the literature. (D: dry; W: wet;
S: submerged; PE: polyethylene; PP: polypropylene; PS: polystyrene; PET: polyethylene terephthalate;
PVC: polyvinyl chloride; PA6: polyamide 6; PA 6.6: polyamide 66; LD-PE: low-density polyethylene; FEB: fluorinated
ethylene propylene Teflon; ABS: terpolymer lustran 752; and PMMA: polymethyl methacrylate.)
Polymer typeThis dataset Knaeps et al. (2021) Garaba and Dierssen (2018) Garaba and Dierssen (2020) DWSDWSDWSDWSPristine sample PE×××PP××××××××PS×××××PET amorphous×××PET crystalline×××PET unspecified××××PVC×××Thermoplastic elastomer×PA6 or PA 6.6×××Paraffin×LD-PE××××Polyester×FEB××ABS××Merlon××PMMA××Non-specified polymers××××Environmentally weathered and biofouled samples PE×××PP×××PS×PET unspecified×××Fluorocarbon×Non-specified polymers×××××Artificially biofouled samples PE×××PP×××PS×××PET amorphous×××PET crystalline×××PVC×Artificially weathered samples PE×××PP×××PS×××PET amorphous×××PET crystalline×××PVC×Pristine plastic specimens
To enhance the physio-chemical properties of an end plastic product, plastic
manufacturers often
incorporate additive substances into the pristine polymer (Garaba et al.,
2021). Additives included in
plastics, such as colored plastics, may be influencing the spectra (Garaba
et al., 2021). Thus, to avoid
confounding measurements, sheets of 6 × 6 cm were obtained from a commercial
supplier (Carat,
Germany, https://www.carat-lab.com, last access: 24 April 2022) as pure pristine plastic polymers
without any additional additive
for the following polymers: PP, PE, PS, PET amorphous, and PET crystalline. In
addition, no color or stains
were added to the commercial plastics, and as such, these selected samples
were transparent or white.
The other pristine plastic samples were either purchased from a local shop
(Ostend, Belgium) or
obtained from other institutes (Marine Remote Sensing Group, University of
the Aegean in Plastic Litter Projects, 2021–2022, The Ocean Cleanup).
Weathered plastics
To mimic the effect of solar radiation on plastic items in the environment,
pristine plastics without
additives were exposed to UV radiation in an Atlas SunTest CPS+ weathering
chamber, simulating 1
solar year in central Europe (Gewert et al., 2018) (Table 2). Prior to the
treatment, plastic specimens
were cut, using a hot knife, into 2 × 10 cm sheets in order to fit them into
closed quartz cuvettes. Although
UV exposure in a laboratory cannot perfectly mimic environmental conditions,
to reproduce them as
accurately as possible, we have tested different treatments for weathering
conditions: i.e., dry UV
exposure, seawater UV exposure (35 PSU), and dark controls (dry and wet),
and each treatment consisted
of a set of three independent replicates for each polymer type (Table 4).
Parameters of the UV chamber used in artificial
weathering experiments.
UV irradiationBlack standard temperatureChamber temperatureTest durationsW m-2(BST) ∘C(CHT) ∘Ch605030–35917
Aquarium setup for biofilm growth on pristine plastic
specimens. The picture was taken by Giulia Leone.
Biofouled plastics
To test the effects of biofilm attached to the surface of plastic items on
their spectral reflectance, we
induced biofilm growth on the two surfaces of pristine samples. Since
surface roughness can affect
biofilm growth and its survival (Rodriguez et al., 2012), one of
the two surfaces of the
commercial plastic specimens was manually treated for approximately 10 s with sandpaper
(grain size 80) to create a rougher surface compared to the smooth and
untouched one. An aquarium
was filled with unfiltered seawater collected from the port of Ostend
(Belgium), which was renewed every
2 weeks, kept at 20 ∘C, and aerated with an air pump. Plastic
specimens were suspended in the
aquarium with paper clippers and rope (Fig. 1) to allow the biofilm to form
on all of the surfaces.
Field plastic items
Plastic specimens were collected from the Port of Antwerp and in the Scheldt
River, near Temse Bridge
(Belgium), and are naturally exposed to weathering and biofouling and are,
compared to pristine polymers,
non-homogeneous. The plastic coming from the river Scheldt was collected by
a plastic collector
installed by the Belgian company Dredging, Environmental and Marine
Engineering NV (DEME)
Environmental Contractors (DEC) on behalf of Vlaamse Waterweg (the Flemish
authority responsible for
waterways in Flanders, Belgium).
Spectral measurements of plastic items
The spectral reflectance measurements of plastic specimens were performed
using an Analytical Spectral Device (ASD) Field-Spec4 (Table 3). The reflectance was automatically derived and normalized
to a 99 % Labsphere Spectralon® Lambertian panel. In the
silo tank, the Spectralon panel was positioned
on the holder of the adjustable arm at the same distance from the ASD field
of view as the dry plastic
specimen.
Hyperspectral-radiometer specifications used in the two
locations of the study: calibration facility and water silo tank.
ASD: Analytical Spectral Device; VNIR: visible-near infrared; and SWIR:
shortwave infrared.
ASD specifications inASD specifications inthe calibration facilitythe tankSpectral range (nm)350–2500350–2500Spectral resolution (nm)VNIR: ca. 3 nmVNIR: ca. 3 nmSWIR: 10–12 nmSWIR: 10–12 nmScans per measurements3010Replicate measurements55Foreoptic field of view8∘1∘Experimental setups
Following the procedure described by Knaeps et al. (2021), we measured the
spectral reflectance of the
different plastic samples at two different facilities: (1) measurements done
in dry conditions were
performed at the optical calibration laboratory of the Flemish Institute for
Technological Research (VITO),
Belgium; and (2) the series of wet and submerged measurements were acquired in a
mesocosm setting, with
experiments being performed in a conical shaped silo tank at the Flanders
Hydraulics Research facility
in Antwerp, Belgium. In both laboratory and silo tank setups (Fig. 2), we
have attached a laser pen to
the ASD pistol grip to ensure that, at all times, the fiber optics of the
ASD were pointing at the plastic samples.
Experimental setup; (a) laboratory setup and (b) silo tank
setup. Pictures were taken by Giulia Leone.
Spectra of field sample and figure of the object from which the spectra were obtained (a). Spectrum (b) shows the
pseudo-replicates of the low homogeneous field samples. Spectrum (c) is the mean of all the pseudo-replicates. No splice
correction was applied. The picture was taken by Giulia Leone.
Overview of the polymer and treatment performed during the
study (SSC: suspended sediment concentration).
TreatmentPolymer testedOrigin/supplierState of samplesCondition of the water in the tank– Pristine – Artificially dryweathered – Artificially seawaterweathered – Artificially biofouledon pristine surface – Artificially biofouled on rough surface – Field– PS – PE – PP – PET amorphous – PET crystalline – PVC – Extruded polystyrene – Thermoplastic elastomer – Fluorocarbon – Paraffin – PA6– Carat – The Ocean Cleanup – Shop – Port of Antwerp – Scheldt – VITO – Marine RemoteSensing Group– Dry – Wet – Submerged– Clear water, no turbidity – Algae: 1. Nominal conc. 3000 cells mL-1 2. Nominal conc. 1500 cells mL-1 – Sediment: 1. SSC 4 mg L-1 2. SSC 16 mg L-1Laboratory setup
All the plastic specimens were measured in the optical calibration facility
(VITO, Belgium). This laboratory
consisted of a dark room, where a desk equipped with two halogen tungsten
lamps and a holder for the
ASD field of view allowed us to collect the measurements.
Silo tank setup
The water silo tank (diameter: 2 m, depth: 3 m), at Flanders Hydraulics
Research (Belgium), was
equipped with a controlled mixer with a double pitch blade impeller that
permitted the mixing of the water
to obtain suspensions of sediment or algae. No information about the rpm (revolutions per minute) was
available on the metal
impeller at the water tank. We attached a tailor-made aluminum frame to the
water tank for the mounting
of the spectroradiometer detector and light, together with a plexiglass
sample holder to lower the
plastic specimens in the tank and measure their spectral reflectance at
different water depths. The
plexiglass was cut to fit the plastic samples which were held by black
plastic paper clips. In the current
study, we acquired data using a single lamp attached to the frame, with an
angle of 40∘. To
reduce undesired stray light, a dark environment was created with a black
plastic cover around the
experimental setup, and in addition, black plastic bags were held against
the tank by means of
wooden rings with weights and waterproof tape. In the tank, samples attached
to the plexiglass sample
holder were first measured at dry conditions just above the water level and
then carefully lowered in
the water at fixed depths. The water level above the sample was measured
with a ruler and the chosen
depths were: 1, 2, 4, and 8 cm. After the submersion, the plastic
was measured again above
the water level as a wetted sample. The same steps were performed in clear
and turbid water.
Water with added sediment
Natural sediment was collected using a manual Van Veen grab by the Flanders
Marine Institute (VLIZ)
during a sampling campaign as part of the PLUXIN project in Nieuwpoort
(Belgium). The sediment was
transported to the laboratory (VLIZ), placed into a metal container, and
dried in the oven for 4 d at 60 ∘C, and was afterward manually crushed using a mortar and stored
in the dark until further use. To add
the crushed sediment into the tank, before any measurements, the tank metal
impeller was activated for
1.5 min to allow the sediment to stay in suspension in the
water. This action was repeated
approximately every 30 min to maintain the sediment in suspension. The
actual concentration of
sediment added to the tank was measured by Flanders Hydraulics. The
suspended sediment
concentration (SSC) was determined gravimetrically, after filtration of the
sample through a filter with a
pore size of 0.45 µm and drying at a temperature of 105 ∘C. The results showed a low concentration of
sediment of 4 mg L-1 and a higher concentration of 16 mg L-1.
Water with added microalgae
To evaluate how the readings of spectral reflectance of plastic specimens
can be affected by turbidity
in the water due to microalgae suspension, measurements were performed using
two concentrations of
freshwater Pseudokirchneriella subcapitata. The concentrations were selected to mimic the natural
conditions of spring and late summer seasonal concentrations of green algae
for a western European
river (Ibelings et al., 1998). The nominal concentrations of microalgae were
1500 and 3000 cells mL-1. The microalgae P. subcapitata was cultured at the Research unit “Health” at
VITO, and the
stock solution of 8.18 × 106 algae mL-1 was kept for 11 d in a cold
room and the dark before being used
in the experimental setup. After pouring 450 mL of stock solution, before
any measurements, the metal
impeller was activated for 1.5 min to allow the algae to
suspend in the water. This
action was repeated approximately every 30 min to maintain the algae in
suspension. To obtain a
higher concentration of algae that would mimic a nominal concentration of
3000 cells mL-1, another 450 mL
of stock solution was then added into the tank.
Data description
In this study, we created a dataset with the spectral reflectance of 10
plastic polymers that had undergone two
treatments (i.e., artificial weathering and artificial biofouling), in
addition to field-collected samples and
pristine plastics (Table 4). All data included in the presented dataset were
curated before submission
and consisted of the raw reflectance data of the samples. A metadata
section, included in the dataset, explains the data more in detail. For
instance, information on the polymer
type, micro-FTIR results with the corresponding library matching score,
the origin of the sample, date, and time
of collection. The sample code was created following the naming convention
of Knaeps et al. (2021). To
ensure that end users can correctly interpret each section of the metadata,
a README file is available
together with the dataset. From the presented dataset, it is possible to
visually show the spectral
reflectance of different plastic polymers and compare the different
conditions experimentally tested (Figs. S1, S2). For instance, it is possible to derive the effect of
weathering and biofouling on the spectral
reflectance of a polymer when compared with the same pristine one.
We suggest that users of this dataset perform splice correction on the data
using Python or any suitable
software. This processing step will lead to the obtention of spectra that do
not present radiometric steps
at the joints of the detectors. In addition, pseudo-replicates of each
plastic sample were taken, and
therefore, we advise that users calculate the mean of these measurements to
obtain a single spectrum
for each observed item (Fig. 3).
Data availability
The data are available in the open access repository Marine Data Archive at 10.14284/530
(Leone et al., 2021).
Conclusions
The use of remote sensing technologies can be used in the detection,
observation, and monitoring of
marine plastic pollution. However, due to a lack of knowledge of the optical
features of environmental
plastics, small steps can be made in designing algorithms to appropriately
detect plastic pollution.
The presented hyperspectral dataset is a step forward in the knowledge of
the optical features of plastic
litter when exposed to natural agents such as UV radiations or the growth of
biofilm. In addition, from the
data presented it is possible to investigate the effects that biofouling and
weathering have on different
polymers. Lastly, the conditions in which a plastic polymer is (i.e., dry,
wet, or submerged with different
turbidity) are also described and assessed in the presented dataset.
Therefore, we anticipate that this
dataset will contribute to the definition of optical spectral bands and
assist in the development of
algorithms for the observation, monitoring, and discrimination of plastics
in a (semi-) operational
environment.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-15-745-2023-supplement.
Author contributions
GL: conceptualization, methodology, writing – (original draft and review and editing), visualization, funding acquisition, data curation, and investigation. AIC: conceptualization,
methodology, writing (review and editing), and supervision. LDK: conceptualization and
writing (review and editing). MB: investigation, methodology, and
writing (review and editing). EK: conceptualization, methodology, writing (review and editing),
supervision, and funding acquisition.
GE: conceptualization, methodology, funding acquisition,
supervision, and writing (review and
editing).
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We would like to thank Flanders Hydraulics for the use of the silo tank and
for the sediment concentration
measurements, and Hilda Witters and Guy Geukens of the research Unit Health at
VITO (Belgium) for
culturing the freshwater algae. Special thanks to the Marine Remote Sensing
Group, University of the
Aegean (Greece), and The Ocean Cleanup (the Netherlands) for sharing plastic
specimens with us.
Financial support
This research has been supported by the cSBO Plastic Flux for Innovation and Business Opportunities in Flanders
(PLUXIN, HBC.2019.2904)
project, financed by Flanders Innovation & Entrepreneurship (VLAIO) and
supported by The Blue
Cluster, a Flemish members organization for sustainable entrepreneurship in
blue growth (Belgium).
Since November 2021, Giulia Leone is supported by the Research Foundation of
Flanders (FWO), as a
PhD grant strategic basic research, application number 1S13522N (Belgium).
Review statement
This paper was edited by David Carlson and reviewed by Chuanmin Hu and one anonymous referee.
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