Comparison of Spot and Time Weighted Averaging (TWA) Sampling with SPME-GC/MS Methods for Trihalomethane (THM) Analysis
Bibliographic record
Abstract
Water samples were collected and analyzed for conductivity, pH, temperature and trihalomethanes (THMs) during the fall of 2014 at two monitored municipal drinking water source ponds. Both spot (or grab) and time weighted average (TWA) sampling methods were assessed over the same two day sampling time period. For spot sampling, replicate samples were taken at each site and analyzed within 12 h of sampling by both Headspace (HS)- and direct (DI)- solid phase microextraction (SPME) sampling/extraction methods followed by Gas Chromatography/Mass Spectrometry (GC/MS). For TWA, a two day passive on-site TWA sampling was carried out at the same sampling points in the ponds. All SPME sampling methods undertaken used a 65-µm PDMS/DVB SPME fiber, which was found optimal for THM sampling. Sampling conditions were optimized in the laboratory using calibration standards of chloroform, bromoform, bromodichloromethane, dibromochloromethane, 1,2-dibromoethane and 1,2-dichloroethane, prepared in aqueous solutions from analytical grade samples. Calibration curves for all methods with R2 values ranging from 0.985–0.998 (N = 5) over the quantitation linear range of 3–800 ppb were achieved. The different sampling methods were compared for quantification of the water samples, and results showed that DI- and TWA- sampling methods gave better data and analytical metrics. Addition of 10% wt./vol. of (NH4)2SO4 salt to the sampling vial was found to aid extraction of THMs by increasing GC peaks areas by about 10%, which resulted in lower detection limits for all techniques studied. However, for on-site TWA analysis of THMs in natural waters, the calibration standard(s) ionic strength conditions, must be carefully matched to natural water conditions to properly quantitate THM concentrations. The data obtained from the TWA method may better reflect actual natural water conditions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".