Development of Nontargeted Screening Algorithms for Indoor Contaminants
Bibliographic record
Abstract
Humans spend most of their lives indoors, but there are thousands of chemical contaminants in indoor environments and the majority of these remain undetected. The focus of this thesis was to develop methods for nontargeted screening and prioritization of previously unreported indoor chemical contaminants using high-resolution mass spectrometry. To achieve this, a one – stop suspect screening algorithm was developed incorporating retention time prediction, isotopic peak calculation and in silico MS2 prediction. The algorithm was benchmarked for halogenated compounds and was further applied to screen for previously unrecognized indoor contaminants. Among 39 chlorinated compounds detected, 18 previously unrecognized azo dyes were detected as the biggest class of indoor chlorinated compounds (Chapter 2). To further characterize the indoor sources of these chlorinated compounds, various consumer products were collected from Toronto homes and screened for toxic halogenated contaminants with an emphasis on chlorinated paraffins. Chlorinated paraffin compounds were detected in 84 of 96 products analyzed, including electronic devices and plastic children’s toys, despite these compounds being prohibited for manufacture or import in Canada as of 2013 (Chapter 3). In addition to direct emission sources, the indirect sources of indoor contaminants formed through indoor reactions were investigated. To achieve this, an R package (‘indoortransformer’) was developed to predict indoor transformation products of organophosphorus compounds (OPCs). By further expanding the R package to incorporate in silico MS2 fragmentation prediction, 40 OPCs were detected in 23 house dust samples among which 24 OPCs were predicted to be formed through indoor reactions (Chapter 4). To systematically investigate the emission of contaminants from indoors, wastewater samples were collected over a 1.5 year period during the COVID-19 pandemic and screened against the ToxCast and Tox21 databases. Among 1037 ToxCast compounds, 43 were detected with high frequency plus 8 additional dyes and OPCs. Several plasticizers and disinfectants also showed an increase in concentration during periods of public lockdown (Chapter 5). This thesis provides a systematic exploration of the occurrence, source, reactions and temporal trends of many indoor contaminants. Future studies are warranted to expand the nontargeted screening methods developed in the current study to additional indoor compound classes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".