An effect factor approach for quantifying the impact of plastic additives on aquatic biota in life cycle assessment

Abstract

Purpose
Plastic pervades now almost every aspect of our daily lives, but this prosperity has led to an increasing amount of plastic debris, which is now widespread in the oceans and represents a serious threat to biota. However, there is a general lack of consideration regarding marine plastic impacts in life cycle assessment (LCA). This paper presents a preliminary approach to facilitate the characterization of chemical impacts related to marine plastic within the LCA framework.

Methods
A literature review was carried out first to summarize the current state of research on the impact assessment of marine plastic. In recent years, efforts have been made to develop LCA-compliant indicators and models that address the impact of marine littering, entanglement, and ingestion. The toxicity of plastic additives to marine biota is currently a less understood impact pathway and also the focus of this study. Relevant ecotoxicity data were collected from scientific literature for a subsequent additive-specific effect factor (EF) development, which was conducted based on the USEtox approach. Extrapolation factors used for the data conversion were also extracted from reliable sources.

Results and discussion
EFs were calculated for six commonly used additives to quantify their toxicity impacts on aquatic species. Triclosan shows an extremely high level of toxicity, while bisphenol A and bisphenol F are considered less toxic according to the results. Apart from additive-specific EFs, a generic EF was also generated, along with the species sensitivity distribution (SSD) illustrating the gathered data used to calculate this EF. Further ecotoxicity data are expected to expand the coverage of additives and species for deriving more robust EFs. In addition, a better understanding of the interactive effect between polymers and additives needs to be developed.

Conclusions
This preliminary work provides a first step towards including the impact of plastic-associated chemicals in LCA. Although the toxicity of different additives to aquatic biota may vary significantly, it is recommended to consider additives within the impact assessment of marine plastic. The generic EF can be used, together with a future EF for adsorbed environmental pollutants, to fill a gap in the characterization of plastic-related impacts in LCA.

Full text

https://doi.org/10.1007/s11367-022-02046-9
LCIA OFIMPACTS ONHUMAN HEALTH ANDECOSYSTEMS
An effect factor approach forquantifying theimpact ofplastic
additives onaquatic biota inlife cycle assessment
YiboTang1· RoseNangahMankaa1 · MarziaTraverso1
Received: 22 October 2021 / Accepted: 23 March 2022
© The Author(s) 2022
Abstract
Purpose Plastic pervades now almost every aspect of our daily lives, but this prosperity has led to an increasing amount of
plastic debris, which is now widespread in the oceans and represents a serious threat to biota. However, there is a general
lack of consideration regarding marine plastic impacts in life cycle assessment (LCA). This paper presents a preliminary
approach to facilitate the characterization of chemical impacts related to marine plastic within the LCA framework.
Methods A literature review was carried out first to summarize the current state of research on the impact assessment of
marine plastic. In recent years, efforts have been made to develop LCA-compliant indicators and models that address the
impact of marine littering, entanglement, and ingestion. The toxicity of plastic additives to marine biota is currently a less
understood impact pathway and also the focus of this study. Relevant ecotoxicity data were collected from scientific litera-
ture for a subsequent additive-specific effect factor (EF) development, which was conducted based on the USEtox approach.
Extrapolation factors used for the data conversion were also extracted from reliable sources.
Results and discussion EFs were calculated for six commonly used additives to quantify their toxicity impacts on aquatic
species. Triclosan shows an extremely high level of toxicity, while bisphenol A and bisphenol F are considered less toxic
according to the results. Apart from additive-specific EFs, a generic EF was also generated, along with the species sensitivity
distribution (SSD) illustrating the gathered data used to calculate this EF. Further ecotoxicity data are expected to expand
the coverage of additives and species for deriving more robust EFs. In addition, a better understanding of the interactive
effect between polymers and additives needs to be developed.
Conclusions This preliminary work provides a first step towards including the impact of plastic-associated chemicals in
LCA. Although the toxicity of different additives to aquatic biota may vary significantly, it is recommended to consider
additives within the impact assessment of marine plastic. The generic EF can be used, together with a future EF for adsorbed
environmental pollutants, to fill a gap in the characterization of plastic-related impacts in LCA.
Keywords Characterization factor· Ecotoxicity· Industrial ecology· LCA· Marine plastic· Plastic additives
1 Introduction
Plastic, with a wide variety of forms and application fields,
has become an essential component of our daily lives. The
global plastics industry has been growing dramatically since
the beginning of massive plastic production in the 1950s
(Geyer etal. 2017). Meanwhile, poor waste management and
inappropriate human behavior have resulted in the ubiquity
and profusion of plastic debris (Barnes etal. 2009). Misman-
aged plastic waste can eventually end up in the marine envi-
ronment through multiple pathways, including atmospheric
and river transport (Lebreton etal. 2017), beach littering
(Bravo etal. 2009), and sea-based activities such as aqua-
culture and fishing (Bugoni etal. 2001). Plastic debris has
been observed on the world’s most remote islands and within
every marine habitat (STAP 2011; do Sul and Costa 2014).
It is estimated that 4.8–12.7 million metric tons of plastics
enter the ocean per annum, and this figure will continue
increasing sharply if there are no improvements in waste
management (Jambeck etal. 2015). As the growing amount
Communicated by Michael Z. Hauschild
* Rose Nangah Mankaa
rose.mankaa@inab.rwth-aachen.de
1 Institute ofSustainability inCivil Engineering,
RWTH Aachen University, Mies-van-der-Rohe-Str. 1,
52074Aachen, Germany
/ Published online: 21 April 2022
The International Journal of Life Cycle Assessment (2022) 27:564–572
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

of plastic litter is causing serious pollution in terrestrial and
aquatic ecosystems, there is universal agreement that this
issue needs to be addressed urgently (Koelmans etal. 2014).
Litter around the globe has been reported to affect over
3800 terrestrial, freshwater, and marine species (https://
litte rbase. awi. de/ inter action_ detail; Accessed: 20 January
2022). Specifically, the potential risks of plastic litter to
human health and the environment have drawn increasing
public attention in recent years (Koelmans etal. 2017). To
tackle these concerns, various regional and international
instruments and initiatives have been established, such as
the International Coastal Cleanup and the Plastic Leak Pro-
ject (Ocean Conservancy 2020; Quantis 2020). In the ocean
environment, the impact of plastic debris can be divided
into physical impacts (e.g., entanglement and ingestion)
(Gall and Thompson 2015), chemical impacts (caused by
the build-up or release of toxic substances) (Amec Foster
Wheeler 2017), and other impacts such as dispersal via raft-
ing and transport of alien species (SCBD 2016).
As regards the chemical impacts, plastic-associated
chemicals pose potential hazards to marine organisms and
can eventually affect human health through the food chain.
Plastic contains a great diversity of functional additives
incorporated during manufacture, such as antioxidants, sta-
bilizers, and plasticizers (Hermabessiere etal. 2017). Plastic
can also adsorb persistent organic pollutants from the envi-
ronment (SCBD 2016). Wiesinger etal. (2021) established
a comprehensive database of chemical substances used in
plastic production based on an extensive review of indus-
trial, scientific, and regulatory sources. The database covers
over 10,000 substances, of which more than 2400 were iden-
tified as substances of potential concern. Recent studies have
indicated that plastic additives may have significant toxicity
impacts on aquatic species (Beiras etal. 2020; Capolupo
etal. 2020).
Life cycle assessment (LCA) is a sustainability assess-
ment methodology commonly used by decision-makers for
quantifying the environmental impact of human activities
(Woods etal. 2018). Life cycle impact assessment (LCIA),
the third phase of LCA, links inventory data with specific
impact categories and indicators with the aim of under-
standing the significance of the environmental impact
throughout a product’s life cycle (ISO 14040 2006). As
stated in the Medellin Declaration, the impact of marine
plastic is not adequately addressed in the LCA methodol-
ogy (Sonnemann and Valdivia 2017). Hence, there is an
urgent need for LCA-compliant impact assessment models
and characterization factors. The LCA community also rec-
ommends the development of species sensitivity distribu-
tion (SSD)-based models and metrics that can be used in
LCIA (Woods etal. 2019). In the next sections, we first
summarize the existing research on the impact assessment
of marine plastic within LCA. Afterwards, we introduce the
data collection and the EF calculation in detail. Finally, we
discuss how the results of this work can make a contribution
in practical contexts and put forward recommendations for
further methodological development.
2 State oftheart
A number of recent LCA studies have considered the poten-
tial amount of plastic littered into the sea. In a comparative
LCA of carrier bags conducted in Spain, Civancik-Uslu etal.
(2019) introduced a novel indicator to evaluate the impact of
discarded waste in marine waters. Similarly, Stefanini etal.
(2020) evaluated the impact of empty bottle littering in the
Mediterranean Sea within an LCA study on pasteurized milk
bottles. Regarding the impact of marine plastic on biota,
Woods etal. (2019) proposed a preliminary EF approach for
quantifying the entanglement of marine species in macro-
plastic debris. Starting from this approach, McHardy (2019)
made significant improvements by developing region- and
taxon-specific SSD models to better link marine plastic
quantities to species entanglement rates. Saling etal. (2020)
developed a midpoint characterization model for assessing
the impact of microplastic ingestion by organisms.
Founded in 2019, the scientific working group MarILCA
(Marine Impacts in LCA) is actively supporting the devel-
opment of methodologies to incorporate marine impacts
in LCA, with a focus on marine plastic litter (Boulay etal.
2021). In the modeling framework proposed by the group
(Woods etal. 2021), the ecotoxicity of plastic-associated
chemicals will be assessed separately from the impacts
caused by the presence of polymers. In line with this frame-
work, Lavoie etal. (2021) developed EFs regarding the
physical impact (resulting from the intake by organisms) of
micro- and nanoplastics (MNPs) in aquatic environments.
Data were acquired from experiments based on virgin poly-
mers (i.e., without consideration of additives). EFs were
derived for a common scenario (the ALL EF using all data
points), a best possible scenario (the BEST EF using almost
only chronic EC50s), and seven other subgroups according
to particle size, particle shape, and polymer type. Lavoie
etal. (2021) also highlighted the need for future ecotoxic
EFs that account for the impact of additives and the impact
of toxic substances adsorbed onto plastic debris.
One suitable option to address this research need is pre-
sented in USEtox, a scientific consensus model for charac-
terizing human- and ecotoxicological impacts of chemical
emissions (Rosenbaum etal. 2008). USEtox can be applied
in the context of LCA, and its main purpose is to compare
alternatives instead of calculating absolute risks (Fantke
etal. 2017). At midpoint level, a USEtox characterization
factor provides an estimate of the potentially affected frac-
tion of species (PAF) integrated over time and volume per
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unit mass of a chemical released. The model results can be
further extended to determine endpoint effects expressed as
the potentially disappeared fraction of species (PDF), apply-
ing a translation factor of 0.5 (Jolliet etal. 2003). The SSD-
midpoint is HC50EC50 and it denotes the concentration at
which 50% of species are exposed above their EC50 values
(Fantke etal. 2017). In the current USEtox database (USE-
tox version 2.12, https:// usetox. org/), ecotoxicity EFs are
available for some additives (e.g., bisphenol A and dibutyl
phthalate). However, as USEtox EFs are derived using effect
data for freshwater species only, the toxicity impact of these
additives on marine biota is not considered.
The methodological review shows that some LCAs have
addressed marine littering impacts and attempts have been
made to quantify macroplastic entanglement and microplas-
tic ingestion within LCIA. However, the toxicity of plastic-
related chemicals to biota is not yet considered in any impact
assessment methods associated with marine plastic. Inspired
by Lavoie etal. (2021), this study focuses on the ecotoxicity
of plastic additives and aims to reflect the environmental
significance of these substances. Results of recent aquatic
ecotoxicity tests were collected for a subsequent additive-
specific EF development.
3 Methods
3.1 Data acquisition
A literature search was conducted to collect relevant data
on the toxicity effect of plastic additives on aquatic species.
Information was acquired from the search engines Science-
Direct, Google Scholar, and Web of Science. Relevant pub-
lications were identified from peer-reviewed journals using
groups of keywords combining “plastic additives,” “plas-
tic chemicals” with “ecotoxicity,” “toxicity,” “impact,” or
“effect.” Moreover, articles mentioned in these publications
were further analyzed if they provided useful test results.
Data were collected applying the following selection criteria:
• Ecotoxicity tests conducted in recent years (2016–2021)
• Ecotoxicity tests with detailed information on experimen-
tal conditions
• Ecotoxicity tests with quantitative results such as EC50s
and NOECs
• Ecotoxicity tests focusing on one specific plastic additive
Recent ecotoxicological studies were considered if they
provide detailed information on the experimental context
and quantitative outcomes for the EF calculation. However,
many existing studies assessed the combined effect of addi-
tives and polymers or focused on leachates containing vari-
ous chemicals. These studies were not considered because
their results are not suitable for this additive-specific EF
development. Moreover, when a study contained informa-
tion on multiple additives, species, or test endpoints, each
combination was considered as a separate input value. All
extracted data points were compiled into a separate Excel
file, and a further data quality assessment was carried out
during the EF calculation. Consequently, some effect data
were eliminated, and the EFs for certain additives were
considered as unsatisfactory outcomes (see “Sect.4.2” for
details).
3.2 Data analysis andprocessing
The collected data points were classified according to the
additive, species, test endpoint, and exposure duration. The
exposure type of each input value was identified based on the
USEtox approach, and the identification was distinguished
between vertebrates, invertebrates, and algae (Table1).
As the main goal of this study is to assess the ecotoxicity
of additives in line with the approach applied for virgin MNP
particles in Lavoie etal. (2021), the USEtox approach was
adopted as the basis for the EF development. The underly-
ing data for “USEtox recommended” characterization factors
must cover at least three trophic levels, normally represented
by algae, crustaceans, and fish (Fantke etal. 2017). Accord-
ing to this requirement, sufficient ecotoxicity data are avail-
able for eight additives in the database (Table2). The CAS
Registry Number and major function of each additive, as
well as information on compatible polymers sourced from
Wiesinger etal. (2021), are also provided in the table for
easier orientation.
In the USEtox approach, an acute-to-chronic ratio is
used to extrapolate chronic values from acute data. Simi-
larly, two types of extrapolation factors were applied in this
study, converting acute EC50s to chronic EC50s, chronic
NOECs to chronic EC50s, respectively (Table3). These
were extracted from a recent study, which provided a set
of robust extrapolation factors for different species groups
(Aurisano etal. 2019). The extrapolation factors for fish
were applied for amphibians, and sub-chronic values were
considered chronic. After the extrapolation of data, all EC50
values were converted into a standard unit of mg/L. For
reported values expressed in molar concentration (μM), the
Table 1 Criteria for the classification of ecotoxicological tests as acute,
sub-chronic, or chronic (Fantke etal. 2017)
Group Acute Sub-chronic Chronic
Vertebrates < 7days ≥ 7days; < 32days ≥ 32days
Invertebrates < 7days ≥ 7days; < 21days ≥ 21days
Algae < 3days - ≥ 3days
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molecular weight of the corresponding additives was used
for the conversion.
3.3 Effect factor calculation
The USEtox approach was adopted for the EF calculation as
follows (Eqs.1 and 2):
with
EF Ecotoxicological effect factors for aquatic eco-
systems [PAF·m3·kg−1]
HC50EC50 Geometric mean of chronic EC50s [kg·m−3]
EC50,i Concentration at which 50% of the test organ-
isms of species i are affected [mg·L−1]
When a species is represented by more than one data
point, the EC50,i value for that species equals the geomet-
ric mean of all available EC50s. Apart from the additive-
specific EFs, a generic EF was derived based on all data
points without exclusion of any value. The idea behind this
EF is to gain an insight into the environmental significance
(1)
EF
=
0.5
HC50
EC50
(2)
log
10HC50EC 50 =1
n×
∑
n
i=1log10
(EC
50,i
1000
)
of plastic additives. In addition, an SSD was generated for
this EF to provide information on the species and phyla rep-
resented in the compiled data. Regarding additive-specific
EFs, SSDs were generated solely for bisphenol A and dibu-
tyl phthalate due to the limited number of species (no more
than five) represented for other additives. The “ssdtools”
package in R, proposed by Thorley and Schwarz (2018),
was applied to plot the SSDs based on a log-normal dis-
tribution. In addition, uncertainty is given with the 95%
confidence interval (CI) of each EF, and the 95% CI calcu-
lation was done on HC50EC50 values using the R package
“DescTools”.
4 Results anddiscussion
4.1 Compiled data
Data for the EF development were obtained from 23 peer-
reviewed journal articles. Our database covers 27 additives
in total and contains detailed information on the experi-
mental condition of each data point (see TableS1 in Online
Resource). In summary, a total of 95 data points were
extracted from ecotoxicity tests on 21 aquatic species (14
freshwater and 7 marine) (Fig.1).
4.2 Effect factors
The SSD for the generic EF is presented here for quick visu-
alization of the gathered data (Fig.2). The generic EF is
represented by 21 species from 9 phyla. For those species
represented by several input values, the converted EC50s
may have a wide range. The dots representing each species
in Fig.2 refer to the corresponding EC50,i values.
Additive-specific EFs were initially calculated for eight
additives, but 4-nonylphenol and benzophenone-1were
excluded because of poor data quality from a statistical per-
spective. There is a huge disparity in the EC50,i values for
4-nonylphenol (see TableS4 in Online Resource), and the
EF for benzophenone-1has an excessively wide 95% CI
range (see TableS5 in Online Resource). Of all additives
Table 2 Additives investigated Additive CAS number Major function Compatible polymers
4-Nonylphenol 104-40-5 Stabilizer PET, PP, PUR, PVC
Benzophenone-1 131-56-6 Stabilizer PA, LDPE, HDPE, PET, PP, PS, PVC
Bisphenol A 80-05-7 Plasticizer LDPE, PA, PC, PUR, PVC
Bisphenol AF 1478-61-1 Plasticizer PA, PE, PC
Bisphenol F 620-92-8 Plasticizer Epoxy resin, PC
Dibutyl phthalate 84-74-2 Plasticizer LDPE, HDPE, PA, PET, PP, PS, PUR, PVC
Nonylphenol 25154-52-3 Antioxidant PA, PUR, PVC
Triclosan 3380-34-5 Biocide PE, PA, PC, PET, PP, PS, PUR, PVC
Table 3 Extrapolation factors and 95% confidence interval (CI) ranges
per species group (Aurisano etal. 2019)
Extrapolation between
endpoints Species group Extrapolation
factor (95% CI)
To EC50chronic from EC50acute Fish 1.71 (1.13–2.58)
Invertebrates 3.14 (2.20–4.48)
Algae and bacteria 1.14 (0.57–2.29)
To EC50chronic from
NOECchronic
Fish 0.31 (0.20–0.47)
Invertebrates 0.36 (0.30–0.44)
Algae and bacteria 0.18 (0.16–0.20)
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considered (Table4), triclosan has the highest EF with over
3000 PAF·m3·kg−1. This result is in line with the study of
Beiras etal. (2020), which defines triclosan as a “very toxic”
functional additive. In contrast, EFs for bisphenol A and bis-
phenol F are under 100 PAF·m3·kg−1, suggesting comparably
lower levels of ecotoxicity effect on biota. Such ranking can
eventually help the plastics industry in the choice of addi-
tives that are least harmful or innocuous to aquatic life. For
instance, it would be better to reduce the use of triclosan in
the plastics industry by finding less toxic alternatives.
Fig. 1 Summary of acquired
ecotoxicity data from scientific
literature. Data used to create
these charts can be found in
Online Resource (TableS2) 61
23
11 Descriptor
EC50 (64.2%)
LC50 (24.2%)
NOEC (11.6%) 66
29 Exposure
Acute (69.5%)
Chronic (30.5%)
11
18
36
6
5
14
5
Category
Alkylphenols (11.6%)
Benzophenones (18.9%)
Bisphenols (37.9%)
Chlorinated phenols (6.3%)
Citrate esters (5.3%)
Phthalates (14.7%)
Rest (5.3%)
Fig. 2 Species sensitivity
distribution (SSD) for the
generic EF. Each dot represents
an EC50,i for a single species.
SSDs for bisphenol A and
dibutyl phthalate can be found
in Online Resource (Fig.S1).
Underlying data used to gener-
ate the SSDs can be found in
Online Resource (TableS3)
A. clausi
A. salina
A. tonsa
C. pyrenoidosa
Cyclotella sp.
D. magna
D. rerio
D. subspicatus
H. australis
K. brevis
M. macrocopa
P. hypophthalmus
P. lividus
P. lucida
P. vivipara
R. subcapitata
S. caliendrum
S. obliquus
T. japonicus
V.fischeri
X. laevis
HC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/LHC50 = 1.54 mg/L
0%
20%
40%
60%
80%
100%
0.001 0.01 0.1 110 100 1,000
Additive concentration (mg/L)
Potentially affected fraction of species
Phylum
Arthropoda
Chlorophyta
Chordata
Cnidaria
Echinoder
mata
Mollusca
Myzozoa
Ochrophyta
Proteobacteria
568 The International Journal of Life Cycle Assessment (2022) 27:564–572
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As mentioned above, Lavoie etal. (2021) derived the
BEST EF (72.90 PAF·m3·kg−1) and the ALL EF (82.28
PAF·m3·kg−1) to quantify the toxicity impact of MNPs on
aquatic biota from a holistic viewpoint. Likewise, a generic
EF was generated in this study (about 320 PAF·m3·kg−1)
(see TableS6 in Online Resource). Direct comparison of
EFs derived in different studies is not appropriate due to the
differences in research focus, data sources, and extrapola-
tion factors used. However, since plastic additives were not
considered in Lavoie etal. (2021), the generic EF developed
in this preliminary work suggests that additives can be more
toxic than pure polymers and thus deserve serious considera-
tion within the impact assessment of marine MNPs.
4.3 Limitations oftheapproach
This study is a first attempt to quantify the toxicity impact
of plastic additives in the marine environment, for which
a systematic assessment is currently missing in the LCIA
toolbox. However, the proposed EF approach is still at an
early stage, and there are several factors that have to be con-
sidered alongside the results. Firstly, due to a general lack of
effect data, ecotoxicity data for freshwater species were also
included to calculate the EFs. Secondly, as shown in Fig.1,
the majority of the compiled data are acute values which
need to be extrapolated, and the calculation of 95% CIs did
not consider the proportion of extrapolated values. Finally,
as the compiled data come from diverse sources, uncertainty
can arise from differences in toxicity test methods, exposure
concentrations, etc.
To close the gap in the underlying data of this EF devel-
opment, future ecotoxicological studies are needed to
provide data for more marine species as well as for other
commonly used plastic additives. Chronic toxicity data are
particularly in demand, so that the calculated EFs can rely
less on extrapolated values. More consistent, standardized
chronic data could also enable the dots representing each
species to fit better in the SSDs generated for the generic EF
and for individual additives.
Regarding the calculation of the generic EF, an important
limitation lies in the fact that the mass of additives released
into the marine compartment was not included, which is
essential for a better understanding of the environmental sig-
nificance of these pollutants. This aspect was not considered
herein owing to a lack of relevant information, especially
from industrial data sources. Further research might explore
how to assign weight factors to different additives by inves-
tigating their amount and leaching behavior. It is also worth
noting that the additive-specific EFs, calculated based on the
USEtox approach, can be used for comparative purposes but
not for describing absolute toxicity risks.
4.4 Suggestions fortheway forward
The outcome of this study contributes to the impact assess-
ment of marine MNPs. The impact of virgin polymers has
been addressed by Lavoie etal. (2021), and this study goes
one step further by including the impact of plastic additives.
Future studies should attempt to quantify the impact of marine
MNPs as a vector for environmental contaminants as well as
for invasive species. To complete the impact characterization
of MNPs, we also need fate factors and exposure factors for
polymers and associated chemicals. In this respect, Saling
etal. (2020) proposed a fate model focusing on the fragmen-
tation and degradation process of microplastics in the marine
environment. The USEtox model contains fate and eco-expo-
sure factors for five of the eight additives investigated here
(4-nonylphenol, bisphenol A, dibutyl phthalate, nonylphenol,
and triclosan), quantifying their dispersion in various environ-
mental compartments (air, soil, freshwater, and marine) and
their dissolved fraction in freshwater respectively. At present,
some researchers from MarILCA are working on the develop-
ment of fate and exposure factors for MNPs.
One major challenge identified for further research is the
interactive effect between polymers and additives. This phe-
nomenon has been proven by recent studies. Li etal. (2020)
observed antagonistic toxicity effects between polystyrene
microplastics and dibutyl phthalate on the marine copepod
T. japonicus for both acute and chronic reproduction tests.
Conversely, polyethylene fragments and benzophenone-3
exhibited synergistic effects on both lethal and sublethal
toxicity to the freshwater crustacean D. magna (Na etal.
2021). As regards the pollutants adsorbed from the environ-
ment, Bellas and Gil (2020) demonstrated that the presence
Table 4 Calculated effect
factors (EFs) with 95% CI
ranges and associated HC50s
Additive HC50EC50 [kg·m−3]EF [PAF·m3·kg−1] 95% CI ranges
Bisphenol A 8.82E-03 56.71 (22.17–145.06)
Bisphenol AF 1.48E-03 338.05 (73.49–1555.04)
Bisphenol F 1.53E-02 32.78 (7.36–145.97)
Dibutyl phthalate 4.36E-03 114.70 (27.95–470.70)
Nonylphenol 3.51E-03 142.49 (13.94–1456.74)
Triclosan 1.57E-04 3188.80 (687.02–14,800.75)
The generic EF 1.54E-03 324.61 (100.83–1045.05)
569The International Journal of Life Cycle Assessment (2022) 27:564–572
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

of polyethylene microplastics increased the toxicity of chlor-
pyrifos to the marine copepod A. tonsa. Similarly, the pres-
ence of polystyrene particles led to increasing toxicity of
triphenyltin chloride to the freshwater algae C. pyrenoidosa
(Yi etal. 2019). The mechanisms of such interactions are
differing and still less understood. Comprehensive analy-
sis and cautious interpretation are essential in defining the
combined ecotoxicity of polymers and associated chemicals,
since there are often discrepancies between calculated and
measured values when it comes to the ecotoxicity of mix-
tures (Gade etal. 2012).
The harmonization of different assessment methods
is of vital importance to ensure their comparability and
soundness. Toxicity effects of MNPs on biota are affected
by various parameters such as particle size, particle shape,
and polymer type (Miloloža etal. 2021). Regarding this
issue, Koelmans etal. (2020) made a great start by devel-
oping rescaling methods to improve the alignment of
approaches adopted in microplastic research, correcting
for differences in particle size range, concentration unit,
and threshold effect data used in SSDs. To avoid potential
double-counting of marine plastic impacts in LCIA, it is
necessary to have a meaningful combination of methods
that focus on different size classes (e.g., macro, micro, and
nano), particle shapes (e.g., pellets, beads, and films), and
impact pathways (e.g., entanglement, ingestion, and ecotox-
icity). Further discussion is needed about whether such an
all-embracing impact assessment is necessary and sensible.
In other words, consensus building processes among model
developers, LCA practitioners and other stakeholders can
be foreseen. Although decisions may vary among differ-
ent products or processes, achieving an optimum balance
between complexity and simplicity would be beneficial.
5 Conclusions
Marine plastic is now a global issue of great concern, yet
its environmental impacts are not adequately addressed in
LCA. In recent years, attempts have been made to quantify
several specific impacts such as marine littering, entangle-
ment in macroplastics, and ingestion of microplastics. How-
ever, there is still a lack of quantification methods regard-
ing the toxicity of plastic-related chemicals to marine biota.
To fill this gap, the proposed EF approach brings together
recent ecotoxicity data to develop EFs for plastic additives.
Based on the USEtox methodology, EFs were derived for six
commonly used additives. While triclosan shows extremely
high toxicity to aquatic species, bisphenol A and bisphenol
F are considered less toxic. The generic EF shows that the
effect of additives is likely to be significant in the impact
assessment of marine MNPs and should thus be included.
This EF can be used together with the BEST EF proposed
by Lavoie etal. (2021) and a future ecotoxicity EF consid-
ering plastic particles as a vector for other adsorbed con-
taminants. Further ecotoxicity data are expected to expand
the coverage of plastic additives and marine species for this
EF approach. The interactive effect between polymers and
additives remains to be further explored. Additionally, col-
laborative research efforts are required for a comprehensive
impact characterization of marine plastic and its integration
into the LCA framework.
Supplementary information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s11367- 022- 02046-9.
Funding Open Access funding enabled and organized by Projekt
DEAL.
Data availability The datasets generated and analyzed during the cur-
rent study are available in the Supplementary Information.
Declarations
Competing interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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