Monitoring of Environmental Contaminants in Mixed-Use Watersheds Combining Targeted and Nontargeted Analysis with Passive Sampling
Bibliographic record
Abstract
Abstract Understanding the environmental fate, transport, and occurrence of pesticides and pharmaceuticals in aquatic environments is of utmost concern to regulators. Traditionally, monitoring of environmental contaminants in surface water has consisted of liquid chromatography–tandem mass spectrometry analyses for a set of targeted compounds in discrete samples. These targeted approaches are limited by the fact that they only provide information on compounds within a target list present at the time and location of sampling. To address these limitations, there has been considerable interest in suspect screening and nontargeted analysis (NTA), which allow for the detection of all ionizable compounds in the sample with the added benefit of data archiving for retrospective mining. Even though NTA can detect a large number of contaminants, discrete samples only provide a snapshot perspective of the chemical disposition of an aquatic environment at the time of sampling, potentially missing episodic events. We evaluated two types of passive chemical samplers for nontargeted analysis in mixed-use watersheds. Nontargeted data were processed using MS-DIAL to screen against our in-house library and public databases of more than 1300 compounds. The data showed that polar organic chemicals integrative samplers (POCIS) were able to capture the largest number of analytes with better reproducibility than organic compound–diffusive gradients in thin film (o-DGT), resulting from the greater amount of binding sorbent. We also showed that NTA combined with passive sampling gives a more representative picture of the contaminants present at a given site and enhances the ability to identify the nature of point and nonpoint pollution sources and ecotoxicological impacts. Environ Toxicol Chem 2022;41:1131–1143. © 2021 Her Majesty the Queen in Right of Canada Environmental Toxicology and Chemistry © 2021 SETAC. Reproduced with the permission of the Minister of Agriculture and Agri-Food Canada. Abstract Environmental surface water was collected by discrete sampling and two types of passive chemical samplers; organic compound–diffusive gradients in thin film (o–DGT) and polar organic chemicals integrative samplers (POCIS). Samples were analyzed by targeted and nontargeted liquid chromatography–tandem mass spectrometry (LC–MS/MS) for determination of pesticide and pharmaceutical contaminants in surface water. QQQ = triple quadrupole mass spectrometer; NTA = nontargeted 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".