Particle size distribution of total suspended sediments in urban stormwater runoff: Effect of land uses, precipitation conditions, and seasonal variations
Bibliographic record
Abstract
Understanding particle size distribution (PSD) of total suspended sediments in urban runoff is essential for pollutant fate and designing effective stormwater treatment measures. However, the PSDs from different land uses under different weather conditions have yet to be sufficiently studied. This research conducted a six-year water sampling program in 15 study sites to analyze the PSD of total suspended sediments in runoff. The results revealed that the median particle size decreased in the order: paved residential, commercial, gravel lane residential, mixed land use, industrial, and roads. Fine particles less than 125 μm are the dominant particles (over 75%) of total suspended sediments in runoff in Calgary, Alberta, Canada. Roads have the largest percentage of particles finer than 32 μm (49%). Gravel lane residential areas have finer particle sizes than paved residential areas. The results of PSD were compared with previous literature to provide more comprehensive information about PSD from different land uses. The impact of rainfall event types can vary depending on land use types. A long antecedent dry period tends to result in the accumulation of fine particles on urban surfaces. High rainfall intensity and long duration can wash off more coarse particles. The PSD in spring exhibits the finest particles, while fall has the largest percentage of coarse particles. Snowmelt particles are finer for the same land use than that during rainfall events because the rainfall-runoff flows are usually larger than the snowmelt flows. • A six-year water sampling program was conducted to analyze particle size distribution. • Particle size distributions in runoff from six urban land uses were analyzed. • Fine particles are the dominant particles of suspended sediments in runoff. • Effects of rainfall types on particle size distributions vary with land use types. • Snowmelt particles are finer for the same land use than that of rainfall events.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".