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Fate of microplastic particles in agricultural soil systems: Transport and accumulation processes in contrasting environments

2020· article· en· W3085477386 on OpenAlexaffabout
Rachel Hurley, Jill Crossman, Theresa Schell, Andreu Rico, Martyn N. Futter, Marco Vighi, Luca Nizzetto

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMicroplasticsEnvironmental scienceContaminationSoil waterSurface runoffSewage sludgeAgricultureSewageEnvironmental engineeringHydrology (agriculture)Environmental chemistryEcologySoil scienceBiologyChemistryGeology

Abstract

fetched live from OpenAlex

There is a paucity of data regarding the sources and fate of microplastics in agricultural settings. This is despite indication that these environments may receive significant contributions of microplastics from a range of inputs. Several studies have documented the enrichment of sewage sludge by microplastic particles as a result of wastewater treatment processes. In many countries, sludge is applied to agricultural soils as a soil conditioner. Based on the extent of application and microplastic loads in sludge material, it is expected that sludge application to land represents a considerable release pathway for microplastic particles to the environment. The fate of these particles across spatial and temporal scales is, however, unknown. This includes the potential for the propagation of contamination to connected aquatic systems and beyond. The Water JPI-funded IMPASSE project addresses significant gaps in our understanding of microplastic contamination in agricultural systems. As part of this project, two case study locations in contrasting environments were selected for study: the semi-arid Henares catchment in central Spain and the humid continental Beaver and Orillia catchments in the Lake Simcoe watershed in Ontario, Canada. Agricultural fields subjected to different sludge application treatments (timing and origin of material) were assessed for microplastic contamination through repeat soil core sampling. This was coupled with runoff experiments using modified Pinson collectors to track the mobilisation of sewage sludge-derived particles from soils. Laboratory analysis was performed according to Hurley et al. (2018). Thorough characterisation of all microplastics particles down to a lower size limit of 50 µm was achieved, including particle size, morphology, polymer type, and estimated mass. Microplastic loads in soils increased following sludge application. The dynamics of contamination from soil core analyses show complex spatio-temporal patterns of accumulation and vertical and lateral transport of particles. Through the use of experimental runoff plots, the mobilisation of microplastic particles from agricultural soils has been documented for the first time. Preferential accumulation and transport of different particle morphologies – e.g. fibres vs fragments – was also observed. These findings form the basis of innovative modelling work in the case study catchments to predict dynamics of agricultural microplastic contamination and subsequent transfer to aquatic environments.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.222

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.204
Teacher spread0.185 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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