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Record W4238502061 · doi:10.1016/j.mex.2020.100884

Microplastics analysis

2020· editorial· en· W4238502061 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMethodsX · 2020
Typeeditorial
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsMicroplasticsLow-density polyethylenePolyethyleneHigh-density polyethyleneEnvironmental sciencePlastic pollutionPolypropylenePlastic bagPolyvinyl chloridePlastic wasteWaste managementPulp and paper industryMaterials scienceEnvironmental chemistryChemistryEngineeringComposite material

Abstract

fetched live from OpenAlex

Microplastics (MPs) were identified as one of the major environmental threats. The work plastic comes form the Greek term plastikos, which means that it can remain shaped in various systems. Global plastic production did hit approximately 348 million tonnes in 2017, being China the largest producer responsible of 27% of worlwide pollution. It is estimated that more than 8300 million tonnes of virgin plastic have been produced to date. MPs are directly released into the water or formed by degradation of bigger plastics. In short, annually between 4 and 12 millions of tonnes of plastics are going into the oceans and most probably in 2050 will exceed the amount of fish The rest of the plastic, between 22–43%, ends up as landfills, causing the soil lose its fertility. Most of the plastic wastes are non biodegradable and can take up to 500 years to decompose. Many consumers are not aware that plastic goods are usually made in petrochemical plants. According to the 2019 Centre for International Environmental Law Report, its production will contribute approximately to 850 million tons greenhouse emissions. Plastic is part of our daily life and worldwide we use 4 trillion plastic bags annually and 1 million plastic bottles every minute. MPs, are made from diverse molecules and correspond to diverse product types. MPs are composed of diverse suite of polymer type, being the most produced and consumed ones polypropylene (PP), low density polyethylene (LDPE), high density polyethylene (HDPE), polyvinyl chloride (PVC), polyurethane, polyethylene terephthalate (PET) and polystyrene (PS) are diverse and come from a multitude of sources, also they are in different sizes, colours, shapes and types of materials. MPs pollution is nowadays a global and ubiquitous problem being detected everywhere: marine environment, sand beaches, wastewaters, surface waters, soils, sludges, sediments, biota, food and air. MPs contain additives, i.e. phthalates and they can be as well a vector of organic contaminants and pathogens that can be ingested by organisms and introduced into the food web. Airborne fibrous MPs may enter our respiratory system with risk to the environment and humans. This Special Issue (SI) includes a comprehensive list of research papers describing sampling and analytical methods for determining MPs in a variety of samples. This SI will be of great help to researchers considering that Standard Operational Procedure (SOPs) on sampling and analysis are still missing. Luckily this last year an ISO/NP method for analysing MPs in dinking water and groundwater using vibrational spectroscopy was drafted and hopefully soon will be available. Having said that, keep in mind as well that methods used for MPs analysis require the best possible reduction of natural particles whilst preserving the integrity of the targeted synthetic polymer particles. The topics of the 14 invited papers to this Special issue are as follows:1.Sediment sampling with an aluminium core sampler instead of plastic tubes to avoid contamination,2.An effective modification of the Sediment MP Isolation (SMI) unit to avoid PVC contamination3.Extraction of MPs by density separation using ZnCl2 reuse, the use of the well-known system4.The use of QuEChERS to extract MPs form a variety of complex matrices such as sediments, soils and sludge.5.Mapping of MPs fibre mixtures of PE, PP, PA, PVC, PES and PET in sand and algae using a new microFTIR method,6.Determination of LDPE in sludge using Fenton purification and FTIR identification,7.New concept of MPs separation with air bubbles followed by FTIR microscopy for the analysis of MPs in sand samples.8.Validation of an FT-IR microscopy method for the determination of MPs in surface waters9.Leaching of phthalates form PVC using an infinite sink approach10.dentification of MPs in wastewaters using cascade filtration and pyrolisis GC–MS11.Ecological approach of MPs targeting zooplankton, fish eggs, fish larvae as part of the food web in estuarine and coastal waters and the water column12.Impact of MPs in wildlife using stomach flushing technique to quantify MPs in Crocodilians followed by microscopy and FTIR.13.Plant uptake by MPs using confocal laser scanning microscope14.Simplified protocol of passive deposition of atmospheric particles followed by Nile Red and micro-Raman to identify natural and synthetic microfibers and MPs in indoor and outdoor air. Overall this SI provides useful snapshots of current progress on the analysis of MPs. In short, this SI will serve scientists working in interdisciplinary fields, like chemistry, biology, environmental, marine and soil sciences and beyond. It will help as well to bridge approaches between researchers, the public as well as policy. In this context I would like to add few recommendations developed in Canada already few years ago to mitigate plastic pollution: (i) law and waste management strategies, (ii) education, outreach and awareness, (iii) source identification and (iv) increasing monitoring and further research. This Methods X SI should be in the radar of all of you, specially for those already experts in the field as well as newcomers and students who want to learn more about sampling and analysis of MPs in the total environment. Finally as Guest Editor I would like to thank all authors for their excellent contributions, all reviewers for the time and expertise advice and to the Methods X technical support team as well for their valuable help preparing and editing this SI.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.111
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.268
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it