Contaminants of Emerging Concern in the Urban Aquatic Environment: \nTargeted and Non-targeted Analysis
Bibliographic record
Abstract
The urban aquatic environment is a repository of anthropogenic organic chemicals. Some of these are classified as contaminants of emerging concerns (CEC) due to their newly discovered environmental and health risks. These chemicals enter the aquatic environment through various routes, including urban runoff, wastewater discharge and snow melting. While some efforts have been invested in monitoring these substances along these routes, the coverage is limited to a small set of chemicals. This thesis aims to address environmental exposures of CEC and identifying lesser-known chemicals of environmental concerns, e.g., transformation products (TPs). Targeted and non-targeted analyses were conducted on 42 surface water and 30 snow samples in Montreal. The analyses focused on automobile-derived compounds used as industrial antioxidants and vulcanization accelerators. Samples were processed using solid-phase extraction (SPE). Liquid chromatography-mass spectrometry (Orbitrap LC-MS) was used for the sample analyses. Fifteen target compounds were quantified using internal standard calibration. Non-targeted screening of lesser-known contaminants and their TPs generated over 30,000 features in snow and 17,000 in surface water. Feature prioritization was based on peak intensity (>10000) and detection frequency (≥ 50%) while identification relied on retention time and substructural information. To further facilitate the identification of TPs, an additional oxidation experiment was conducted by exposing the parent compounds to ultra-violet (UV) radiation in a multi-lamp photochemical reactor. From the non-targeted analysis and transformation experiments, 37 analytes were identified. Our findings highlight the presence of lesser-known CEC including chemicals used as tire additives and their transformation products in the urban aquatic environment. This study also underscores the importance of using targeted and non-targeted screening approaches to identify and assess these compounds and their TPs.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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 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".