Microplastics in Stormwater Runoff: Comparing Baseflow to Rainfall Events
Bibliographic record
Abstract
Due to their widespread use, plastics have become a significant pollutant in the environment. Among these are microplastics, those that are under 5mm in size. These particles pose many potential hazards to aquatic ecosystems, such as bioaccumulation and leeching of chemical compounds into soil and freshwater. Previous studies have addressed microplastic contamination in urban river systems, atmospheric fallout, and wastewater treatment plants, but very few studies have investigated microplastics in urban stormwater runoff. Stormwater runoff washes over impervious surfaces (such as roadways) and often drains into rivers and other freshwater bodies, and acts as a source of microplastics that can then contaminate freshwater systems. In this research project, the concentration of microplastics in stormwater runoff under both baseflow and rain event conditions were analyzed to determine the difference in microplastic concentration between the two scenarios. Samples were obtained from a stormwater collection basin in Calgary, Alberta, at eight different times between May and September. Microplastics were extracted through a combination of filtration, digestion with hydrogen peroxide, and size fractionation, resulting in five size classes. Following the extraction, the samples were analyzed using visual microscopy and Raman spectroscopy to obtain the quantity and chemical composition of the microplastics, respectively. Understanding the concentration of microplastics in stormwater runoff allows for proper risk assessment of microplastics in freshwater systems, as well as providing insight into the sources of microplastics in other freshwater systems. Discipline: Physical Sciences Faculty Mentor: Dr. Matthew Ross
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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; both teacher heads agree on what is shown here.
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".