Assessing analyte recovery values and reporting standards for monitoring exposure to airborne semi-volatile organic compounds
Bibliographic record
Abstract
Analyte recovery is a critical quality assurance and quality control (QA/QC) metric widely used to quantify bias when using sampling methods and measurement technologies. However, no study has systematically evaluated how well studies adhere to recommended recovery guidelines and reporting standards for measuring airborne semi-volatile organic compounds (SVOCs). This systematic review and meta-analysis evaluated 87 studies deploying passive and active air samplers to measure SVOC concentrations in air. We compared recoveries in the assessed studies to the US EPA and European Union's recommended threshold of 70-120% mean recovery and ≤20% relative standard deviation (RSD). Overall, 39% of recoveries were outside either the recommendation for mean recovery or RSD regardless of compound class and sorbent type. This deviation may be reasonable for qualitative studies but is concerning for quantitative assessment of airborne SVOCs. In assessed calibration studies, differences in recovery between passive and active air samplers did not explain uptake rate variability. We also found wide variation in how recoveries are reported and treated in the literature. Our findings highlight that poor recoveries are prevalent in studies assessing airborne exposure to SVOCs. Reporting and treatment of recoveries is also inconsistent across studies. We recommend future studies to report individual compound recoveries, their treatment, and to recovery correct. We also recommend studies to investigate sample preparation methods to identify steps that are most critical to poor recoveries. Our findings and recommendations presented in this work will help improve quantitative assessment of airborne chemical exposures and standardize recovery reporting across labs.
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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 | 0.231 | 0.410 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".