Liquid chromatography/tandem mass spectrometry analysis of neonicotinoids in environmental water
Bibliographic record
Abstract
RATIONALE: Neonicotinoids (NNIs) are the fastest expanding group of pesticides in the world over the last two decades; however, they may be a significant contributing factor to bee mortality. The widespread use of NNIs makes it critical to monitor their residuals in the environment. Published methods for NNI analysis are mainly focused on agricultural and food products, and many of them only measured a portion of the commercially available NNIs. METHODS: Utilizing a biphenyl stationary-phase column, a sensitive liquid chromatography/tandem mass spectrometry (LC/MS/MS) method was developed to determine eight NNIs, including acetamiprid, clothianidin, dinotefuran, flonicamid, imidacloprid, nitempyram, thiacloprid and thiamethoxam in environmental water. Two multiple reaction monitoring (MRM) transitions were monitored for each compound to ascertain true positive identification. Isotope-labelled NNIs, d3-acetamiprid, d3-clothianidin, d4-imidacloprid and d3-thiamethoxam, were used to compensate for extraction efficiency, matrix effects and instrument variability while monitoring real-time method performance. Target compounds in aqueous samples were analyzed by direct aqueous injection (DAI) or after solid-phase extraction (SPE). RESULTS: The method detection limits (MDLs) of NNIs in drinking water, surface water and groundwater were in the ranges of 50 to 190 and 2 to 7 ng/L for DAI and SPE procedures, respectively, and target compound recoveries ranged from 78 to 110%. The stability of target compounds in water samples and SPE extracts was also investigated for the first time to ensure accurate results. No obvious degradation was observed for target compounds within four weeks in either water samples or SPE extracts. CONCLUSIONS: The method developed for neonicotinoid pesticide analysis is very sensitive and efficient. It provides good flexibility to meet various environmental monitoring needs and is employed for an extensive study to determine the distribution of NNIs in Ontario's water.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".