Passive sampling for semi-volatile organic chemicals in the atmosphere: theory, calibration, and application
Bibliographic record
Abstract
The need to monitor and control the emissions of semi-volatile organic chemicals (SVOCs) to the atmosphere has contributed to the widespread use of passive air samplers (PASs). Combining theoretical calculations, model simulations, and field and laboratory studies, the research described in this thesis endeavored to address important knowledge gaps that still hamper the confident use of PASs. Specifically, it aimed to assess uncertainties and offer guidance on PAS methodologies, enhance understanding of the performance of PAS, and delineate a practical field application of a PAS. An evaluation suggested that sampling with the polyurethane foam-based PAS (PUF-PAS) incurs uncertainties above 50%, mostly because of the need to know the sorbent capacity of a sampler operating outside of the linear uptake regime. Graphical tools were created using theoretical calculations and model simulations to provide guidance on the planning and interpretation of campaigns relying on the PUF-PAS. Uncertainties are lower for PAS with high uptake capacity that are more likely to remain in the linear uptake regime, such as the styrene-divinylbenzene co-polymeric resin-based PAS (XAD-PAS). A “gold standard” calibration study was, therefore, conducted for the XAD-PAS, which broadened the knowledge of this sampler’s uptake kinetics in their dependence on meteorological conditions and chemical properties and of the limits of linear uptake of more volatile SVOCs. Sampling rates were obtained for 120 SVOCs. The performance of models was evaluated, and sorption and degradation rate constants were estimated by combining model simulations with experimental results. Furthermore, the broad utility of XAD-PAS in the atmospheric monitoring of organophosphate esters (OPEs) was investigated through the implementation of two expansive networks in Southern Canada. When combined with precipitation and active air sampling, the sources, partitioning and exchange between phases, spatial patterns, the influence of temperature and population, and the transport of OPEs in the atmosphere were revealed.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".