Microplastic Contamination of Seafood Intended for Human Consumption: A Systematic Review and Meta-Analysis

Vol. 128, No. 12 ReviewOpen AccessMicroplastic Contamination of Seafood Intended for Human Consumption: A Systematic Review and Meta-Analysisis companion ofMicroplastics in Seafood: How Much Are People Eating? Evangelos Danopoulos, Lauren C. Jenner, Maureen Twiddy, and Jeanette M. Rotchell Evangelos Danopoulos Address correspondence to Evangelos Danopoulos, Allam Medical Building, Hull York Medical School, University of Hull, Hull, HU6 7RX UK. Email: E-mail Address: [email protected] Hull York Medical School, University of Hull, Hull, UK Search for more papers by this author , Lauren C. Jenner Hull York Medical School, University of Hull, Hull, UK Search for more papers by this author , Maureen Twiddy Hull York Medical School, University of Hull, Hull, UK Search for more papers by this author , and Jeanette M. Rotchell Department of Biological and Marine Sciences, University of Hull, Hull, UK Search for more papers by this author Published:23 December 2020CID: 126002https://doi.org/10.1289/EHP7171Cited by:1AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack CitationsCopy LTI LinkHTMLAbstractPDF ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Microplastics (MPs) have contaminated all compartments of the marine environment including biota such as seafood; ingestion from such sources is one of the two major uptake routes identified for human exposure.Objectives:The objectives were to conduct a systematic review and meta-analysis of the levels of MP contamination in seafood and to subsequently estimate the annual human uptake.Methods:MEDLINE, EMBASE, and Web of Science were searched from launch (1947, 1974, and 1900, respectively) up to October 2020 for all studies reporting MP content in seafood species. Mean, standard deviations, and ranges of MPs found were collated. Studies were appraised systematically using a bespoke risk of bias (RoB) assessment tool.Results:Fifty studies were included in the systematic review and 19 in the meta-analysis. Evidence was available on four phyla: mollusks, crustaceans, fish, and echinodermata. The majority of studies identified MP contamination in seafood and reported MP content <1 MP/g, with 26% of studies rated as having a high RoB, mainly due to analysis or reporting weaknesses. Mollusks collected off the coasts of Asia were the most heavily contaminated, coinciding with reported trends of MP contamination in the sea. According to the statistical summary, MP content was 0–10.5 MPs/g in mollusks, 0.1–8.6 MPs/g in crustaceans, 0–2.9 MPs/g in fish, and 1 MP/g in echinodermata. Maximum annual human MP uptake was estimated to be close to 55,000 MP particles. Statistical, sample, and methodological heterogeneity was high.Discussion:This is the first systematic review, to our knowledge, to assess and quantify MP contamination of seafood and human uptake from its consumption, suggesting that action must be considered in order to reduce human exposure via such consumption. Further high-quality research using standardized methods is needed to cement the scientific evidence on MP contamination and human exposures. https://doi.org/10.1289/EHP7171IntroductionMicroplastics (MPs) are broadly defined as synthetic polymeric particles <5mm in diameter (Frias and Nash 2019; GESAMP 2015, 2016), often also including nanoplastics, which are <100 nm in diameter (Lusher et al. 2017a). They can be classified into two categories according to their origin: primary (intermediate feedstock, pellets/resin, by-products), and secondary (formed through fragmentation and degradation) (Carbery et al. 2018; Karlsson et al. 2018). MPs are diverse, originating from the wide variety of plastics produced for household products, construction material, and industrial applications. Human exposure is suggested to be principally via ingestion and inhalation (Abbasi et al. 2019; Wright and Kelly 2017). MPs are ubiquitous in the environment, with marine environments especially affected owing to the amount of plastic waste they receive (Burns and Boxall 2018; Gourmelon 2015; J Li et al. 2016). The degradation of plastic waste in the sea is the major source of MP contamination (Eriksen et al. 2014). The generation of plastic waste and mismanagement of its disposal is expected to triple by 2060, reaching 155–265 million metric tons per year (Lebreton and Andrady 2019). MPs are extremely persistent particles; over time they have contaminated all compartments of marine ecosystems, including the food web and biota across different trophic levels, such as bivalves (SY Zhao et al. 2018), crustaceans (F Zhang et al. 2019), fish, and mammals (Lusher et al. 2015; Nelms et al. 2018). MPs have been found in various parts of organisms such as the gastrointestinal (GI) tract (Sun et al. 2019), liver (Collard et al. 2017a), gills (Feng et al. 2019), and flesh (Akoueson et al. 2020; Karami et al. 2017b). Commercial seafood species are either consumed whole, such as bivalves, some crustaceans, and some small fish, or just parts of them, such as larger fish and mammals. Therefore, understanding the MP contamination of specific body parts, and their consumption by humans, is key.Food safety is managed in terms of hazards and risk analysis, where hazards are classified into three categories according to their potential to cause a health effect: biological, chemical, and physical (EC 2002). The MP health effects that are currently under consideration include all three categories (Smith et al. 2018; Wright and Kelly 2017). MPs contain various chemicals with differing concentration (Hartmann et al. 2019), and their effects can come from the plastics’ primary components (polymers), the additives that are used to enhance their attributes (plasticizers), the chemical contaminants absorbed while in the environment [e.g., polycyclic aromatic hydrocarbons (Hartmann et al. 2017; Ziccardi et al. 2016) and polychlorinated biphenyls (Engler 2012), or the microorganisms colonizing their surfaces (Viršek et al. 2017)]. MPs can thus be considered either the primary hazard or a pathway for a hazard, both linked to human health. The contamination of food intended for human consumption, with this emerging risk and the possible effects on health, has raised concern in the scientific community (Barboza et al. 2018; Diepens and Koelmans 2018; Santillo et al. 2017; Waring et al. 2018) as well as among stakeholders (GESAMP 2015, 2016) and policy makers globally (EFSA Panel on Contaminants in the Food Chain 2016). There is a growing body of evidence regarding effects in aquatic animals, but health effects on humans are still unclear (Karbalaei et al. 2018; Sharma and Chatterjee 2017; Smith et al. 2018). There is a clear need to address this emerging risk and promptly implement mitigation strategies for the protection of the environment and human health.This systematic review focuses on seafood intended for human consumption. The aim is to map the existing evidence, appraise study quality using a standardized approach, identify knowledge gaps, and ultimately collate the data in order to quantify human exposures. Predicted human exposures calculated using modeling could consequently be used in a risk assessment framework to characterize the risk coming from MPs through the ingestion uptake route.MethodsThis review is based on a protocol published in PROSPERO (Danopoulos et al. 2019). The protocol was created in order to standardize the methods and protect against the inclusion of bias, according to the guidelines set by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocols (PRISMA-P) (Moher et al. 2015; Shamseer et al. 2015). In brief, only primary, peer-reviewed studies with descriptive and analytic observational study designs were eligible for inclusion. There were no publication date limits. Only studies that sampled commercially relevant seafood species were included, regardless of the species of the organism (e.g., fish, mollusks, crustaceans) or the part of the body that MPs were reported to be found in, for example, the gills, GI tract, liver, and flesh. If a study focused on the GI tract of a type of seafood, it was included only if the species of the seafood was small and it was reasonable to assume that it is usually eaten whole with the GI tract intact (e.g., anchovies, shrimps). Studies reporting on samples that were not collected as food, but are regularly consumed as such (e.g., mussels), were included. Studies must have used one of the four currently validated procedures for the identification of the chemical composition of particles: namely, Fourier-transform infrared spectroscopy (FT-IR), Raman spectroscopy (RM), pyrolysis gas chromatography/mass spectrometry, and scanning electron microscopy (SEM) plus energy-dispersive X-ray spectroscopy. All included studies must have reported the use of procedural control samples to avoid post-sampling contamination.The following online databases/sources were searched from launch date: MEDLINE (OVID interface, 1946 onward), EMBASE (OVID interface, 1974 onward), and Web of Science core collection (Web of Science, 1900 onward). The initial search was executed on 10 July 2019. The searches were repeated on 5 October 2020 to include the most recently published papers. Search terms included: microplastic, nanoplastic, plastic/, micro*, fiber*, food contamination, and seafood. The full search strategy can be found in Tables S1 and S2. Study screening was completed by two independent reviewers (E.D. and L.J. for the original searches; E.D. and M.T. for the rerun of the searches) at two levels, initially reviewing titles and abstracts. Screening results were compared and disagreements discussed. Inter-rater agreement at the first level was 90%, Cohen’s κ: 0.34, for the original searches, and 97%, Cohen’s κ: 0.65, for the rerun. This was followed by a full paper review for potentially eligible papers. A third-party arbitrator (J.M.R.) resolved the discrepancies between the two reviewers (for both searches). Inter-rater agreement at this level was 100%, Cohen’s κ: 1 for both searches. Corresponding authors were contacted when more information was required with a maximum of three emails sent. Data was extracted as sample characteristics, sampling and analysis methods, MP content in any quantified unit, composition analysis results, and procedural samples results.Synthesis of ResultsThe primary outcome was reported as MP content in terms of particles per unit mass or individual organism expressed as the mean value [and standard deviation (SD) or standard error] or the range. Effort was made to convert all the data into the same unit of measurement of particles/g (wet weight) when it was appropriate, and the necessary raw data was available. The MP contents for species of the same family in the same study were pooled using the formulae for combining groups proposed by Higgins and Green (2011, Table 7.7a) (Table S3). When needed, the conversion of the five-number summary (sample minimum and maximum, median, lower and upper quartile) to the quantities needed for this review, was made using the methods and calculator developed by Shi et al. (2020). The calculator draws on the methods developed by Luo et al. (2018) for the estimation of the mean of the sample and the methods by Wan et al. (2014) for the estimation of the SD.The results of the studies were weighted using the inverse of the variance method (Chen and Peace 2013). In order to collate and quantify the data, random-effects meta-analysis models were used (Higgins et al. 2019). Random-effects models were preferred over fixed-effects models because it was assumed that the samples did not share one common true effect size that was influenced equally by the same factors but, rather, a distribution of true effect sizes (Chen and Peace 2013; Harrer et al. 2019b; Veroniki et al. 2016). The DerSimonian-Laird t2 estimator was used for all the random-effects models (DerSimonian and Laird 1986, 2015) because this accounts for variations both within and between studies. The Higgins I2 test and the chi-squared Cochran’s Q statistic were used to assess statistical heterogeneity (Higgins and Thompson 2002; Higgins et al. 2003). The I2 test is the percentage of variability in the effect size that is not produced by sampling error. The Cochran’s Q statistic refers to the null hypothesis of homogeneity and is expressed in the chi-square and p-values (Higgins et al. 2003).The source of between-study statistical heterogeneity was investigated by examining statistical outliers and an influence analysis of studies. Statistical outliers were defined as studies where the 95% confidence interval (CI) of their effect size estimate, as calculated by the random-effects model, did not overlap with the 95% CI of the pooled effect size estimate (Harrer et al. 2019b). Statistical outliers of extremely large effects were specifically targeted to account for and avoid overestimations (where the lower bound of the 95% CI of the study was higher than that of the upper bound of the 95% CI of the pooled effect). To test the influence of individual studies, the models were rerun without these outliers, and the two pooled effect size estimates compared. To further test the influence of every study, the models were rerun excluding one study each time to assess each study’s influence on the pooled effect size (Harrer et al. 2019b). Influence diagnostics included the I2 and Q values (Baujat et al. 2002) and the contribution to the pooled effect size (Viechtbauer and Cheung 2010). The results of the influence analysis were examined numerically and graphically.Methodological and sample heterogeneity were explored using subgroup analysis employing a fixed-effects (plural) model (mixed-effects model) (Harrer et al. 2019b). R (version 3.6.0; R Development Core Team) was used for all calculations and models executing all analysis via RStudio (version 1.2.1335; RStudio), using the additional packages meta (version 4.9-7; Schwarzer 2019), metaphor (version 2.1-0; Viechtbauer 2010), dmetar (Harrer et al. 2019a), robvis (McGuinness and Kothe 2019), and ggplot2 (Wickham et al. 2016). The code is provided in the Supplemental Material, “Code for R used in the meta-analysis.” Each data set was assessed separately in order to determine its suitability for meta-analysis in terms of heterogeneity. The results of the meta-analysis are presented as the MP content (in MPs per gram) with a 95% CI and p-value. Maps were created in ArcGIS Desktop (version 10.8; Esri).Risk of Bias/Quality AssessmentA bespoke risk of bias (RoB) assessment tool was created, rating the studies across four domains: study design, sampling, analysis, and reporting with a final overall assessment (Table S4). The tool comprises a checklist with questions covering all aspects of experimental protocol development, execution, and reporting. The rating of the studies was as follows: high, low, or unclear RoB, supported by a justification for each of the entries.The construction of the RoB tool was based on up-to-date scientifically robust methodology by the Cochrane organization, which is the leading scientific body in the field of systematic reviews (Higgins et al. 2011, 2019). According to the guidance, the use of scales and scores (numerical) for the assessment was avoided. Instead, for each of the entries, a question was formulated in order to prompt a response that was used as the support for the judgment (Table S4). For each item in the tool, there were two entries: the answer, with additional notes when needed, and the rating. In the answer entry, a copy of the text from the study on which the decision was made is provided, allowing transparency on how the decision was made. The rating of the studies for each entry, domain, and overall study was as follows: high, low, or unclear RoB. RoB assessment was done both on the study and on the specific outcome level. This allowed for the direct comparison of the RoB rating of a specific domain of the study against a specific outcome. For example, when reviewing the sampling methodology, the sampling domain RoB rating is more relevant than that of the overall RoB rating. For the majority of the items in the tool, the rating of high or low was based on a yes/no answer or a numerical value. The rating unclear was assigned when the study did not report sufficient information to make a judgment or when the associated risk was unknown. In order to achieve maximum transparency, all items are discussed in the section “RoB tool additional explanation” in the Supplemental Material.Weighting of Domains and QuestionsA rating was given to each of the 21 items of the RoB tool; subsequently, a rating was given to each of the four domains on the basis of the rating of the individual items in it; and, finally, the overall rating was given according to the domains’ rating. In order to decide the weighting of the individual entries in the checklist, three experts in the field were contacted and asked to provide their top three entries/questions of the table as the most important factors to judge the studies’ RoB. All three experts concentrated on four questions: 4, 8, 13, and 15 (see “RoB tool additional explanation” in the Supplemental Material). The questions focused on two topics. First, the prevention of sample contamination and its validation by the use of procedural blank samples. Second, the use of a validated method for identifying the composition of the particles and how a spectra library would be employed to do so. This expert opinion on the importance of individual entries of the RoB tool was taken into consideration for the rating of the domain as well as the overall rating of the studies.Publication bias was explored using the Egger’s test (Egger et al. 1997) visualized in funnel plots and the precision of the effect estimate (Liberati et al. 2009). Overall assessment of the certainty of the evidence was based on the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework (Higgins et al. 2019) and the Environmental-GRADE (Bilotta et al. 2014) across five domains, categorized into four certainty ratings: high, moderate, low, and very low.ResultsStudy SelectionThe initial searches led to 2,467 publications, following the removal of duplicates. In the first level screening, 2,307 citations were excluded on the basis of their title and abstract. For the second level screening, the full text of the remaining 160 studies were evaluated, and a total of 34 studies that analyzed seafood samples met the eligibility criteria set for this review (see PRISMA flow diagram, Figure S1). The update of the searches identified 16 more studies eligible for the review, bringing the total number of included studies to 50 (Figure S1).Study CharacteristicsAll the studies included are environmental field studies employing descriptive and analytic observational study designs, sampling and analyzing four phyla: mollusks, crustaceans, fish, and ehcinodermata (Table 1). Eight studies analyzed organisms coming from more than one phylum. Twenty-three studies sampled only mollusks, 15 only fish, 3 only crustaceans, and 1 only echinodermata. Five studies sampled both mollusks and crustaceans, 2 mollusks and fish, and 1 mollusks, crustaceans, and fish. The study characteristics are presented in Table 1. Twenty-eight studies used samples from Asia, 13 from Europe, 4 from the Americas, 2 from Africa, 1 from Australia/Oceania, and 2 from more than one continent (and their coasts). The overall sample size for fresh fish was n=1,269 (n=665 anchovies, n=274 sardines, n=240 painted comber, n=20 sand lance, n=19 bogue, n=19 seabass, n=12 haddock, n=10 plaice, n=10 mackerel); for dried fish, n=120 (n=30 mackerel, n=30 croaker, n=30 mullet, n=30 anchovies); and for canned fish, n=842 (n=608 sprat, n=184 sardines, n=45 tuna, n=5 mackerel). For the rest of the seafood, the overall sample size was n=4,543 [mollusks n=3,882 (n=1,728 mussels, n=1,015 oysters, n=702 clams, n=171 sea snails, n=166 scallops, n=100 cockles), crustaceans n=451 (n=262 shrimps, n=139 crabs, and n=50 barnacles), and echinodermata n=210]. Two studies did not provide the exact sample size: Qu et al. (2018) reported n∼760 mussels and Wu et al. (2020) reported 10–20 samples for each species, and Teng et al. (2020) did not report sample sizes at all. Species for all samples are presented in Table 1. An additional phylogenetic tree is provided for the molluskan species in Figure S2 to facilitate reference to nomenclature. Sample size fluctuated between the studies. Although we are not aware of a gold standard as yet for the number of samples for such environmental studies, many studies used n≥5 per species, whereas others used n≥30. Only three studies in the review used <5 organisms per species (Abidli et al. 2019; Collard et al. 2017a; F Zhang et al. 2019).Table 1 Study characteristics for seafood studies.Table 1 has eleven columns, namely, References, Geographic location, Sample Phylum or Class, Sample Species (common name), Sampling location, Habitat, uppercase n, lowercase n, Microplastics extraction procedure references, Microplastics identification method, and Outcome.ReferencesGeographic locationSample phylum/classSample species (common name)Sampling locationHabitatNnMPs extraction procedure referencesMPs identification methodOutcomeAbidli et al. 2019TunisiaEnvironmentWildHX Li et al. 2018FT-IRMean MPs content per mass with SDBivalve mollusks42Mytilus galloprovincialis (mussel)15Ruditapes decussatus (clam)24Crassostrea gigas (oyster)3Gastropod mollusks18Hexaplex trunculus (sea snail)9Bolinus brandaris (sea snail)9Akhbarizadeh et al. 2020IranFishMarket (canned)NA50Karami et al. 2017bRMMean MPs content per mass with SDThunnus tonggol (longtail tuna)25Thunnus albacares (yellowfin tuna)20Scombermorus commerson (mackerel)5Akoueson et al. 2020MarketNAJ Li et al. 2018FT-IRMean MPs content per mass and individual with SDFish42ScotlandMelanogrammus aeglefinus (haddock)12GreeceDicentrarchus labrax (seabass)10IcelandPleuronectes platessa (plaice)10ScotlandScromber scombrus (mackerel)10Bivalve mollusks20ChileZygochlamys patagonica (scallop)10ScotlandPecten maximus (scallop)10Baechler et al. 2020USABivalve mollusksEnvironment283Developed their ownFT-IRMean MPs content per mass and individual with SDC. gigas (oyster)Farmed141Siliqua patula (razor clam)Wild142Birnstiel et al. 2019BrazilBivalve mollusksPerna perna (mussel)Environment20Van Cauwenberghe et al. 2015FT-IRMPs content range per mass with SDFarmed10Wild10Bour et al. 2018NorwayEnvironmentWildAvio et al. 2015; Dehaut et al. 2016FT-IRFrequency of MPs occurrenceCrustaceanCrangon allmanni (shrimp)20Bivalve mollusksEnnucula tenuis (mussel)12Bråte et al. 2018NorwayBivalve mollusksEnvironmentWild332Dehaut et al. 2016FT-IRMean MPs content per mass and individual with SDM. edulis (mussel)NAM. trossulus (mussel)NAM. galloprovincialis (mussel)NACho et al. 2019South KoreaBivalve mollusksMarketFarmed240Karami et al. 2017aFT-IRMean MPs content per mass and individual with SDC. gigas (oyster)60M. edulis (mussel)60Tapes philippinarum (clam)60Patinopecten yessoensis (scallop)60Collard et al. 2017aMediterranean Sea, English ChannelFishEnvironmentWild15Collard et al. 2015RMFrequency of MPs occurrenceEngraulis encrasicolus (anchovy)13Sardina pilchardus (sardine)2Collard et al. 2017bEnglish Channel, Mediterranean Sea, and Northeastern AtlanticFishEnvironmentWild40Collard et al. 2015RMMean MPs content per individualE. encrasicolus (anchovy)20S. pilchardus (sardine)20Digka et al. 2018Northern Ionian SeaEnvironmentMathalon and Hill 2014FT-IRMean MPs content per individual with SDBivalve mollusksM. galloprovincialis (mussel)Wild/farmed80FishS. pilchardus (sardine)Wild36Ding et al. 2018ChinaBivalve mollusks115Developed their ownFT-IRMean MPs content per mass and individualChlamys farreri (scallop)MarketFarmed50M. galloprovincialis (mussel)MarketFarmed50EnvironmentWild15Ding et al. 2019ChinaBivalve mollusksMarketNA40Developed their ownFT-IR and SEMMean MPs content per mass and individual with SDM. galloprovincialis (mussel)20Ruditapes philippinarum (clam)10Mactra veneriformis (clam)10Ding et al. 2020ChinaMarketNA80Ding et al. 2018, 2019FT-IRMean MPs content per mass and individual with SDBivalve mollusks120M. galloprovincialis (mussel)10Perna viridis (mussel)10R. philippinarum (clam)20C. gigas (oyster)20Sinonovacula constricta (clam)20Scapharca subcrenata (clam)20Meretrix lusoria (clam)20Gastropod mollusks20Busycon canaliculatu (sea snail)20Fang et al. 2018Bering Sea and Chukchi SeaEnvironmentWildDigestion: Dehaut et al. 2016; Phuong et al. 2018aFloatation/filtration: Li et al. 2015FT-IRMean MPs content per mass with SDBivalve mollusks57Astarte crenata (clam)28Macoma tokyoensis (clam)29Gastropod mollusks43Retifusus daphnelloides (sea snail)24Latisipho hypolispus (sea snail)19Crustaceans80Pandalus borealis (Arctic shrimp)21Chionoecetes opilio (snow crab)59Feng et al. 2019ChinaFishThryssa kammalensis (rednose anchovy)EnvironmentWild19Dehaut et al. 2019; Foekema et al. 2013; Hermsen et al. 2018; Karami et al. 2017aFT-IRMean MPs content per mass and individual with SDFeng et al. 2020ChinaEchinodermataEnvironmentWild210Foekema et al. 2013; Karami et al. 2017aFT-IRMean MPs content per mass and individualStrongylocentrotus intermedius (sea urchin)NATemnopleurus hardwickii (sea urchin)NATemnopleurus reevesii (sea urchin)NAHemicentrotus pulcherrimus (sea urchin)NAHermabessiere et al. 2019FranceBivalve mollusksEnvironmentWild200Dehaut et al. 2016RM (no fibers)Mean MPs content per mass with SDM. edulis (mussel)100C. edule (cockle)100Hossain et al. 2020BangladeshCrustaceanEnvironmentWild30Li et al. 2015; Su et al. 2016FT-IRMean MPs content per mass with SDMetapenaeus monocerous (brown shrimp)20Penaeus monodon (tiger shrimp)10Karami et al. 2017bMalaysiaFishMarket (packed dried)NA120Karami et al. 2017aRMFrequency of MPs occurrenceChelon subviridis (greenback mullet)30Johnius belangerii (Belanger’s croaker)30Rastrelliger kanagurta (Indian mackerel)30Stolephorus waitei (spotty-face anchovy)30Karami et al. 2018Product of Canada, Germany, Iran, Japan, Latvia, Malaysia, Morocco, Poland, Portugal, Russia, Scotland, Thailand, and VietnamFishMarket (canned)NA792aKarami et al. 2017aRMFrequency of MPs occurrenceCanned sardines (species unknown)184aCanned sprats (species unknown)608aLeslie et al. 2017NetherlandsEnvironmentWildVan der Horst 2011, 2013FT-IRMean MPs content per massBivalve mollusks26M. edulis (mussel)20C. gigas (oyster)6Gastropod mollusksLittorina littorea (sea snail)10CrustaceanCarcinus maenas (crab)10HX Li et al. 2018ChinaBivalve mollusksSaccostrea cucullata (oyster)EnvironmentWild330Li et al. 2015FT-IRMPs content range per mass and individualJ Li et al. 2018UKBivalve mollusksM. edulis (mussel)246J Li et al. 2016FT-IRMean MPs content per mass with SDEnvironmentWild162MarketFarmed54Wild30J Li et al. 2016ChinaBivalve mollusksM. edulis (mussel)Environment390Li et al. 2015FT-IRMean MPs content per mass and individualWild222Farmed168Li et al. 2015ChinaBivalve mollusksMarketWild/farmed144Developed their ownFT-IRMean MPs content per mass with SDScapharca subcrenata (clam)NA6Tegillarca granosa (clam)NA18Alectryonella plicatula (clam)NA18R. philippinarum (clam)NA24Sinonovacula constricta (clam)NA6M. lusoria (clam)NA18Cyclina sinensis (clam)NA30M. galloprovincialis (mussel)NA18P. yessoensis (scallop)NA6Lopes et al. 2020PortugalFishEnvironmentWild226Dehaut et al. 2016FT-IRMean MPs content per individual with SDS. pilchardus (sardine)76E. encrasicolus (anchovy)131Boops boops (bogue)19McGoran et al. 2018Thames Estuary, UKCrustaceanC. crangon (brown shrimp)EnvironmentWild116Their own method without digestionFT-IRMean MPs content per individual and frequency of occurrenceNaji et al. 2018Persian GulfEnvironmentWildLi et al. 2015FT-IR, SEMMean MPs content per massGastropod molluskAmiantis umbonella (sea snail)30Bivalve mollusks63Amiantis purpuratus (scallop)30Pinctada radiate (oyster)33Nam et al. 2019VietnamBivalve molluskP. viridis (mussel)EnvironmentWild5Phuong et al. 2018bFT-IRMean MPs content per mass and individual with SDPhuong et al. 2018aFrench Atlantic coastsBivalve mollusksEnvironmentWild/farmed180Phuong et al. 2018bFT-IRMean MPs content per mass and individual with SDM. edulis (mussel)NA120C. gigas (oyster)NA60Pozo et al. 2019ChileFishStrangomera bentincki (sardine)NANA10Lindeque and Smerdon 2003FT-IRFrequency of MPs occurrenceQu et al. 2018ChinaBivalve mollusksEnvironmentWild∼760Li et al. 2015FT-IRMPs content range per mass and individualM. edulis (mussel)∼430P. viridis (mussel)∼330Renzi et al. 2019Adriatic SeaFishEnvironmentWild160Nuelle et al. 2014; Avio et al. 2015FT-IRMean MPs content per individualS. pilchardus (sardine)80E. encrasicolus (anchovy)80Su et al. 2018Middle-lower Yangtze River Basin, ChinaBivalve molluskCorbicula fluminea (Asian clam)EnvironmentWild208Li et al. 2015; Su et al. 2016FT-IRMean MPs content per mass and individual with SDSu et al. 2019ChinaFishLateolabrax maculatus (seabass)EnvironmentWild9Jabeen et al. 2017FT-IRMean MPs content per mass and individual with SDSun et al. 2019Yellow Sea, ChinaFishEnvironmentWild380Desforges et al. 2015FT-IRMean MPs content per individualSetipinna taty (anchovy)20Anchoviella commersonii (anchovy)30Engraulis japonicus (anchovy)280Ammodytes personatus (sand lance)50Tanaka and Takada 2016Tokyo Bay, JapanFishE. japonicus (Japanese anchovy)EnvironmentWild64Foekema et al. 2013; Rochman et al. 2015FT-IRMean MPs content per individual with SDTeng et al. 2019ChinaBivalve mollusksEnvironmentFarmed306Munno et al. 2018FT-IRMean MPs content per mass and individualC. gigas (oyster)NAC. angulate (oyster)NAC. hongkongensis (oyster)NAC. sikamea (oyster)NATeng et al. 2020ChinaFishSardinella zunasi (Japanese scaled sardin