Field Sampling and Determination of Formaldehyde in Indoor Air with Solid-Phase Microextraction and On-Fiber Derivatization
Bibliographic record
Abstract
A new sampling and analysis method for formaldehyde in indoor air was tested in several indoor air surveys. The method was based on the use of solid-phase microextraction (SPME) poly(dimethylsiloxane)/divinylbenzene,65-microm fiber and gas chromatography. Indoor air surveys included grab and time-weighted average (TWA) sampling and were completed at six locations using (a) the SPME method employing on-fiber formaldehyde derivatization with o-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine hydrochloride and (b)the conventional National Institute for Occupational Safety and Health (NIOSH) 2451 method. Sampling time for SPME fiber ranged from 10 min for grab sampling to 8 h for TWA sampling. Sampling locations included a residential house, a rental apartment, an office building, and industrial workplaces. The air concentrations measured by SPME ranged from 10 to 380 ppbv and correlated well with those estimated by the NIOSH method. Results also indicated thatin some cases the formaldehyde concentrations measured in residential air could be much higher than those allowed in occupational settings. The SPME method proved to be accurate, fast, sensitive, and cost-efficient in field sampling applications. This research should be of interest to research, industrial, and regulatory agencies as well as to the general public concerned with indoor air quality.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".