Optimization of a Multiresidual Method for the Determination of Waterborne Emerging Organic Pollutants Using Solid-Phase Extraction and Liquid Chromatography/Tandem Mass Spectrometry and Isotope Dilution Mass Spectrometry
Bibliographic record
Abstract
A high-throughput, liquid chromatography/tandem mass spectrometry (LC/MS-MS) method has been developed for the determination of 51 emerging organic pollutants (EOPs) in environmental waters. The method was validated for the analysis of 38 pharmaceutically active, 10 endocrine disrupting, and three perfluoroalkylated compounds. Method performance parameters, including sample preservatives, pH values used in the solid-phase extraction (SPE), sample storage, sample extract storage time, and matrix effects were discussed in detail for different aquatic matrices, including drinking water, wastewater, and surface water. Isotope-labeled compounds were used as injection internal standards (IIS) or isotope dilution quantitation standards (IDQS) to improve the data quality, investigate the behavior of matrix effects during SPE sample preparation and LC/MS-MS analysis, and to validate isotope dilution mass spectrometric (IDMS) determination of selected compounds. Method detection limits were determined to be in the low ng/L range forthe compounds evaluated. By application of this method to the analysis of effluents and samples downstream of a wastewater treatment plant, more than 35 target EOPs were quantified. We demonstrated method ruggedness by quality control and quality assurance (QC/QA) data, showed that matrix effects were dependent on modes of electrospray ionization, and could not be removed via SPE or cleanup procedures, exerting the same effect to target compounds in both raw and extracted samples. Both 13C- and 2H-labeled IDQS could be added to samples before sample extraction, and their recoveries used to correct matrix effects in LC/MS-MS EOP analyses.
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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".