Microplastics in Mediterranean Sea: A protocol to robustly assess contamination characteristics

Kedzierski, Mikaël; Villain, Jonathan; Falcou-Préfol, Mathilde; Kerros, Marie Emmanuelle; Henry, Maryvonne; Pedrotti, Maria Luiza; Bruzaud, Stéphane

doi:10.1371/journal.pone.0212088

Cited by 49 publications

(30 citation statements)

References 45 publications

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“…A total of 13,374 particles, larger than 315 µm from 42 mantas trawls, were then transferred to 96well microplates and named with a unique identifier at the Institut de Recherche Dupuy de Lôme (IRDL, Lorient, France) (Kedzierski et al, 2019). To perform the tests of the machine learning method, 3,090 particles were randomly selected.…”

Section: Laboratory Preparations and Subsamplingmentioning

confidence: 99%

“…To perform the tests of the machine learning method, 3,090 particles were randomly selected. For samples M14 (767 particles) and M23 (207 particles), the particles were entirely analyzed during a previous study (Falcou-Préfol et al, 2018;Kedzierski et al, 2019). Therefore, a total of 4,064 spectra, performed on the samples of Tara Expedition, were used in the study.…”

Section: Laboratory Preparations and Subsamplingmentioning

confidence: 99%

“…The learning database consists of 969 spectra, with 64.4% of them came from the M14 and M23 sites already analysed during a previous study (Falcou-Préfol et al, 2018;Kedzierski et al, 2019) (Fig. 2.B).…”

Section: Machine Learning Databasementioning

confidence: 99%

“…application of the statistical method previously developed (Falcou-Préfol et al, 2018;Kedzierski et al, 2019) to these data will enable to refine the obtained results and determine the percentages of the various polymers in the North-West Mediterranean basin, as well as manta by manta. Fig.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Among the parameters studied on these particles, the chemical nature of microplastics is increasingly determined (Gewert et al, 2017;Imhof et al, 2017;Qiu et al, 2016;Wang et al, 2017). As the number of particles to be analyzed can range from several hundred to several thousand (Falcou-Préfol et al, 2018;Pedrotti et al, 2018Pedrotti et al, , 2016, it is necessary to develop methods for efficient microplastics analysis (Kedzierski et al, 2019). When determining the chemical nature of samples, methods based on thermal degradation as pyrolysis-gas chromatography in M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 3 preserved over the long term in library.…”

Section: Introductionmentioning

confidence: 99%

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A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea

et al. 2019

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The development of methods to automatically determine the chemical nature of microplastics by FTIR-ATR spectra is an important challenge. A machine learning method, named k-nearest neighbors classification, has been applied on spectra of microplastics collected during Tara Expedition in the Mediterranean Sea (2014). To realize these tests, a learning database composed of 969 microplastic spectra has been created. Results show that the machine learning process is very efficient to identify spectra of classical polymers such as poly(ethylene), but also that the learning database must be enhanced with less common microplastic spectra. Finally, this method has been applied on more than 4000 spectra of unidentified microplastics. The verification protocol showed less than 10% difference in the results between the proposed automated method and a human expertise, 75% of which can be very easily corrected. Highlights ► A machine learning algorithm was developed to determine the chemical nature of microplastics. ► This method allows a fast and reliable automated identification even when several thousand of FTIR spectra have to be studied.► This method is the first part of a software dedicated to the study of microplastics: POSEIDON.

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Section: Laboratory Preparations and Subsamplingmentioning

confidence: 99%

Section: Laboratory Preparations and Subsamplingmentioning

confidence: 99%

Section: Machine Learning Databasementioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea

et al. 2019

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Anthropogenic particle concentrations and fluxes in an urban river are temporally variable and impacted by storm events

Berg,

Dila,

Schaul

et al. 2024

Water Environment Research

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Anthropogenic particles (AP), which include microplastics and other synthetic, semisynthetic, and anthropogenically modified materials, are pollutants of concern in aquatic ecosystems worldwide. Rivers are important conduits and retention sites for AP, and time series data on the movement of these particles in lotic ecosystems are needed to assess the role of rivers in the global AP cycle. Much research assessing AP pollution extrapolates stream loads based on single time point measurements, but lotic ecosystems are highly variable over time (e.g., seasonality and storm events). The accuracy of models describing AP dynamics in rivers is constrained by the limited studies that examine how frequent changes in discharge drive particle retention and transport. This study addressed this knowledge gap by using automated, high‐resolution sampling to track AP concentrations and fluxes during multiple storm events in an urban river (Milwaukee River) and comparing these measurements to commonly monitored water quality metrics. AP concentrations and fluxes varied significantly across four storm events, highlighting the temporal variability of AP dynamics. When data from the sampling periods were pooled, there were increases in particle concentration and flux during the early phases of the storms, suggesting that floods may flush AP into the river and/or resuspend particles from the benthic zone. AP flux was closely linked to river discharge, suggesting large loads of AP are delivered downstream during storms. Unexpectedly, AP concentrations were not correlated with other simultaneously measured water quality metrics, including total suspended solids, fecal coliforms, chloride, nitrate, and sulfate, indicating that these metrics cannot be used to estimate AP. These data will contribute to more accurate models of particle dynamics in rivers and global plastic export to oceans.Practitioner Points Anthropogenic particle (AP) concentrations and fluxes in an urban river varied across four storm events. AP concentrations and fluxes were the highest during the early phases of the storms. Storms increased AP transport downstream compared with baseflow. AP concentrations did not correlate with other water quality metrics during storms.

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Micro and Nano-Plastics in the Environment: Research Priorities for the Near Future

Vighi

Bayo

Fernández-Piñas

et al. 2021

Reviews of Environmental Contamination and Toxicology Volume 257

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Microplastics in Mediterranean Sea: A protocol to robustly assess contamination characteristics

Cited by 49 publications

References 45 publications

A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea

A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea

Anthropogenic particle concentrations and fluxes in an urban river are temporally variable and impacted by storm events

Micro and Nano-Plastics in the Environment: Research Priorities for the Near Future

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