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Record W7132981152

Chemical Partitioning and Multiphase Oxidation of Organic Contaminants in the Indoor Environment

2024· dissertation· W7132981152 on OpenAlexfundno aff
Yu Jie

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoAlfred P. Sloan Foundation
KeywordsContaminationPartition coefficientPartition (number theory)PollutantVapor pressureHuman health
DOInot available

Abstract

fetched live from OpenAlex

Indoor air composition is highly dynamic and strongly influenced by human activities. Airborne chemicals undergo interactions and transformations with condensed-phase surface materials and chemicals present on surfaces, thus impacting human health via multiple chemical exposure pathways. Contrasting with the outdoor environment, indoor surfaces are huge reservoirs for gas-phase contaminants due to gas-surface partitioning and high surface area-to-volume ratios, however, systematic studies on the partitioning behavior in genuine indoor spaces are lacking. Multiphase reactions of indoor oxidants and reactive environmental pollutants on surfaces have been significantly overlooked, as compared to their transformations in the gas or aqueous phase. Thus, the goal of this thesis was to further our understanding of gas-surface partitioning and multiphase oxidation occurring in genuine indoor settings, by using specific indoor materials and pollutants. Four individual projects were conducted to achieve the goal. In the first project, the partitioning behavior of volatile organic compounds (VOCs) to cotton cloth was characterized upon exposure indoors. Under rapid equilibration time (less than a day), the equilibrium partition ratio (KCA) of three homologous series of VOCs was quantified, revealing that log KCA scales linearly with carbon number, and the logarithms of vapor pressure and the octanol-air equilibrium partition ratio (log KOA) within each series. In the second project, to simulate the short-term localized use of consumer products, VOC injections to a residential test house were monitored by online mass spectrometry to characterize the surface uptake kinetics and timescale, and to estimate the effective partitioning capacity. Faster surface uptake, larger spatial gradients and longer persistence within surface reservoirs were found for VOCs with higher log KOA. The third and fourth projects addressed the gas-to-condensed phase oxidation of one class of environmental pollutants – bisphenols – upon individual exposure to environmentally-relevant levels of ozone and OH radicals. Generalized transformation mechanisms were proposed based on product identification. The rapid nature of multiphase ozonolysis was complemented by a 3-week indoor air exposure to thin BPA films and BPA-containing thermal paper. Overall, this thesis explored two critical aspects of indoor chemistry with specific focus on genuine indoor surfaces and emerging environmental pollutants, highlighting their rapid surface partitioning and chemical transformations that can occur with complex indoor surfaces and in the presence of reactive oxidants.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.284
Teacher spread0.271 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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