Determination of Residual Carbamate, Organophosphate, and Phenyl Urea Pesticides in Drinking and Surface Water by High-Performance Liquid Chromatography/Tandem Mass Spectrometry
Bibliographic record
Abstract
Methods using SPE followed by HPLC/MS/MS analysis were developed and validated for the determination of 39 pesticides in different aquatic environmental matrixes. The target pesticides included 12 carbamates, 15 organophosphates, and 12 phenyl ureas, out of which 16 are regulated in North America. Method detection limits were in the low ng/L range using the U.S. Environmental Protection Agency's protocol and multiple reaction monitoring (MRM) data acquisition, meeting the regulatory needs in the United States, Canada, and European Union. Isotope-labeled compounds were used as injection internal standards, as well as method surrogates to improve the data quality. QC/QA data (e.g., method recovery and within-run and between-run method precision) derived from multiyear monitoring activities were used to demonstrate method ruggedness. The same QC/QA data also showed that the method exerted no obvious matrix effect on the target analytes. Parameters that affect method performance, such as preservatives, pH values, sample storage time, and sample extract storage time, were also studied in detail. Accredited by the Canadian Association for Laboratory Accreditation and licensed by the Ontario government for drinking water analysis, these methods have been applied to the analysis of drinking water, ground water, and surface water samples collected in the province of Ontario, Canada, to ensure the pristine nature of Ontario's aquatic environment. Using the scheduled MRM (sMRM) data acquisition algorithm, it was demonstrated that sMRM improved the S/N of extracted ion chromatograms by at least two- to six-fold and, therefore, enhanced the short- and long-term instrument precision, demonstrated the ability to offer high throughput multiresidue analysis, and allowed the use of two MRM transitions for each compound to achieve higher confidence for compound identification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".