Proactive Screening Approach for Detecting Groundwater Contaminants along Urban Streams at the Reach-Scale
Bibliographic record
Abstract
Here we outline and demonstrate a screening approach for the detection of groundwater contaminants along urban streams within unconsolidated beds. It involves the rapid acquisition of groundwater samples along urban stream reaches at a spacing of about 10 m and from depths of about 25-75 cm below the streambed, with analyses for a suite of potential contaminants. This screening approach may serve two functions: a) providing information for assessing and mitigating the toxicity and eutrophication risks to aquatic ecosystems posed by groundwater contaminants and b) detecting and identifying groundwater contamination in urban settings more rapidly and inexpensively compared to land-based well installations. The screening approach was tested at three urban streams, each affected by a known chlorinated-solvent plume. All three known groundwater plumes were detected and roughly delineated. Multiple, previously unknown, areas or types of groundwater contamination were also identified at each stream. The newly identified contaminants and plumes included petroleum hydrocarbons (BTEX, naphthalene, MTBE), 1,4-dioxane, nitrate and phosphate, road salt, and various metals (including arsenic, cadmium, chromium, copper, lead) at elevated concentrations compared to background values and relevant Canadian water quality guidelines. These findings suggest that this screening approach may be a useful tool for both ecologists performing ecological assessments and stream restorations and for hydrogeologists undertaking groundwater protection activities. Given the numerous contaminants detected, it may be appropriate to apply this technique proactively to better determine the pervasiveness of urban groundwater contaminants, especially along urban streams.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".