Investigating drivers of microplastic pollution in urban settings
Bibliographic record
Abstract
As one of the emerging contaminants and the major by-products of plastic materials, microplastics (MPs) have recently been stated as being remarkable contaminants of different environmental matrices including soils, sediments, groundwater, and surface water. Stormwater and flowing surface water are important carriers of MPs to downstream surface water bodies such as ponds and lakes, yet, little work has been done to develop models for predicting MP loads in these systems. One common approach in contaminant load modeling is to couple a hydrological model with relationships relating the contaminant concentration or load to explanatory variables such as water discharge, typically the most important variable controlling concentrations and loads, and variables representing other drivers of contaminant loading such as land use and climate variables. In this work, our goal is therefore to assemble a database of MP load and/or concentration and discharge measurements in different flowing surface water systems as well as potential explanatory variables such as catchment land use and climate conditions to examine the dependencies of MP loading on these explanatory variables. We searched the Scopus and Web of Science databases and found 64 articles focusing on quantifying MP loads or concentrations in different surface water systems and extracted or calculated the relevant data for the database. The main focus of this work is urban settings, or their shear impact on microplastic production in larger areas of mixed land cover types. Despite inconsistencies in the definition of MPs as well as in sampling, extraction, and analytical methods, the results indicated a significant relationship between impervious land cover and MP loading within urban catchments (polynomial R2 = 0.75), where each hectare of imperviousness corresponds up to 7% of increase in MP concentration. MP loads were, unsurprisingly, highly positively correlated with flow (R2 of up to 0.86), which is the basis for the relationship between MP concentration and climatic factors. We also found that there is a high positive correlation between total suspended solid (TSS) concentrations and MP concentrations, and therefore also between their respective loads, which has been reported by others before and indicates that TSS loads can be used to estimate MP loads in the absence of sufficient data. The relative importance of discharge, land use and climate variables as drivers of MP loading has not yet been investigated, and our assembled database will enable the prediction of MP loads in stormwater, streams and rivers at the watershed scale using the explanatory relationships derived from our analysis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".