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Record W3081101829 · doi:10.1039/d0em00194e

Passive air sampling for semi-volatile organic chemicals

2020· article· en· W3081101829 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Science Processes & Impacts · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsThe Scarborough HospitalUniversity of TorontoToronto Public Health
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEnvironmental scienceEnvironmental chemistryPassive samplingSampling (signal processing)Organic chemicalsWaste managementChemistryEngineeringTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

During passive air sampling, the amount of a chemical taken up in a sorbent from the air without the help of a pump is quantified and converted into an air concentration. In an equilibrium sampler, this conversion requires a thermodynamic parameter, the equilibrium sorption coefficient between gas-phase and sorbent. In a kinetic sampler, a time-averaged air concentration is obtained using a sampling rate, which is a kinetic parameter. Design requirements for kinetic and equilibrium sampling conflict with each other. The volatility of semi-volatile organic compounds (SVOCs) varies over five orders of magnitude, which implies that passive air samplers are inevitably kinetic samplers for less volatile SVOCs and equilibrium samplers for more volatile SVOCs. Therefore, most currently used passive sampler designs for SVOCs are a compromise that requires the consideration of both a thermodynamic and a kinetic parameter. Their quantitative interpretation depends on assumptions that are rarely fulfilled, and on input parameters, that are often only known with high uncertainty. Kinetic passive air sampling for SVOCs is also challenging because their typically very low atmospheric concentrations necessitate relatively high sampling rates that can only be achieved without the use of diffusive barriers. This in turn renders sampling rates dependent on wind conditions and therefore highly variable. Despite the overall high uncertainty arising from these challenges, passive air samplers for SVOCs have valuable roles to play in recording (i) spatial concentration variability at scales ranging from a few centimeters to tens of thousands of kilometers, (ii) long-term trends, (iii) air contamination in remote and inaccessible locations and (iv) indoor inhalation exposure. Going forward, thermal desorption of sorbents may lower the detection limits for some SVOCs to an extent that the use of diffusive barriers in the kinetic sampling of SVOCs becomes feasible, which is a prerequisite to decreasing the uncertainty of sampling rates. If the thermally stable sorbent additionally has a high sorptive capacity, it may be possible to design true kinetic samplers for most SVOCs. In the meantime, the passive air sampling community would benefit from being more transparent by rigorously quantifying and explicitly reporting uncertainty.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.017
GPT teacher head0.250
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it