Simultaneous Filtration and Solid-Phase Extraction Combined with Large-Volume Injection in GC/MS for Ultra-Trace Analysis of Polar Pesticides in Surface Water
Bibliographic record
Abstract
A method combining simultaneous filtration and solid-phase extraction (SPE) with large-volume injection (LVI) in gas chromatography/mass spectrometry (GC/MS) was developed to determine 13 polar pesticides in surface water. The selected pesticides - 4 organophosphorus, 7 organonitrogens and 2 triazine degradation products - were extracted from 0.5-L samples of filtered and raw water using cartridges filled with a silica-bonded material (1 g of ISOLUTE triazine, C-18) and a depth filter. No obstruction was observed during the extraction of raw water drawn from the St. Lawrence River (concentration of suspended particulate matter (SPM) ranging from 2 to 58 mg L−1). Overall percent recoveries were satisfactory for all the target pesticides (>60%) except desisopropyl-atrazine (more polar), which varied from 29 to 46% according to sample pH. The coefficient of variation was below 10% for the majority of the target pesticides and detection limits ranged from 0.1 to 0.8 ng L−1. Applied to real samples drawn from the St. Lawrence River, this method allowed for the detection of atrazine, cyanazine, desethyl-atrazine (DEA), desisopropyl-atrazine (DIA), metolachlor and simazine, at concentrations of 6 to 91 ng L−1. Using atrazine and metolachlor as examples, the correlation between filtered and raw water samples was more significant for the former (r = 0.87) than for the latter (r = 0.67). Temporal variations in atrazine and metolachlor in filtered water drawn from the St. Lawrence River, for example, were similar whether using the established method, based on liquid-liquid large-volume extraction (LVE) combined with GC/NPD analysis, or the one proposed herein. The latter method, however, systematically found atrazine concentrations 62% higher than those obtained by the older one, applied to the same field samples. Thus, the switch to the new analytical method will require the application of a correction factor to the atrazine concentration time series acquired with the previously used method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".