Screening for microplastics in agricultural soils: Applying green chemistry principles in extraction and analysis
Bibliographic record
Abstract
In recent years, microplastic (MP) pollution has garnered significant attention owing to its ability to permeate various ecosystems, including soil. These particles can infiltrate the environment, either directly or through the degradation of larger plastic items. Despite growing concerns, standardized methods for quantification are still lacking. This study aimed to screen for the presence of MPs in agricultural soils while incorporating green analytical principles in the methodology. A density separation followed by centrifugation was employed, based on the principles of the QuEChERS extraction method. This approach minimized sample quantities, reagent consumption, and waste production, ensuring efficient extraction and analysis. Recovery tests using certified soils spiked with pristine MPs, specifically polystyrene, polypropylene (PP), and ethylene-vinyl acetate for larger MPs (3-5 mm), and low-density polyethylene, polyamide 6, and tire wear particles for smaller MPs (15-300 μm), achieved recovery levels exceeding 69% for smaller MPs and over 91% for larger particles. Spectroscopic analysis revealed slight alterations in the Raman spectra of MPs after extraction. Transitioning to agricultural soil analysis has revealed challenges, including spectral interferences. Nine mesoplastics (5-20 mm) were detected, predominantly consisting of PP and polyethylene (PE), along with seven MPs, three of which were individually identified as PE-based, while the remainder were inconclusive, including one fiber. The evaluation of the method's sustainability using the Analytical Eco-Scale and Analytical Greenness Calculator Metric (AGREE), with scores of 82 out of 100 and 0.66 out of 1, respectively, demonstrated its potential as a reliable approach to MP analysis in soils. This study highlights the potential of integrating green analytical chemistry principles into MP extraction methodologies and emphasizes the value of the proposed QuEChERs-based approach for improving the sustainability and efficiency of MP monitoring in agricultural soils.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".