Modeling Exposure to Trihalomethanes: A Multidisciplinary Approach Integrating Environmental Occurrence and Toxicokinetic Profile
Bibliographic record
Abstract
TAA2-O-07 Although THMs are the most abundant and studied DBPs, precising risks relative to these chlorination byproducts remains a major challenge, in particular because of the difficulty to assess the population exposure. The spatial and temporal variations of THM environmental concentrations, as well as interindividual differences (multiple “behaviors” during water use activities, specific susceptibilities) make exposure assessment particularly hard to achieve. Taking into account both environmental and biologic variations into the same methodologic frame is needed to define THM exposure more suitably. Such a frame has been developed to facilitate the estimation of THM exposure through a friendly use computer modeling. First, environmental data collection allows to generating statistical predictive models for the occurence of THMs in drinking water distribution systems and reaching greater precision in estimating external DBP levels. Then, these external concentrations are used as inputs of a physiologically based pharmacokinetics model (PBPK) to predict THM internal concentrations (eg, blood/tissues). An epidemiologic study taking place in the great region of Quebec City to document intrauterine growth retardation risks relative to THMs exposure is an opportunity to apply this methodology for pregnant women, but also to define in which extent such model could be generalized for other populations in other locations. Besides, by studying specifically chloroform (CHCl3), the pertinence and performance of simulating various water use activities (eg, swimming) has been evaluated with the PBPK model, reviewing data extracted from the current litterature. Among all activities, bathing and showering result in the highest and only truly relevant THM internal levels. In addition, simulations showed that typical exposure by swimming for 1 hour (CHCL3 AIR, 125–230 μg/m3; CHCL3 WATER, 7–25 μg/L) can result in absorbed doses (2.3–5.0 μg/kg) at least equivalent to the one corresponding to a typical exposure scenario in a typical household (2.3 μg/kg), which includes ingestion of 5 glasses of water (CHCL3 WATER: 50 μg/L), a 10-minute shower (CHCL3 AIR, 530 μg/m3), and a 24-hour inhalation exposure at a constant household air concentration (CHCL3 AIR, 3 μg/m3). Thus, taking particular care to define inhalation conditions and also documenting impact of activities such as swimming in an indoor pool appears of primary concern to assess correctly exposure to THMs. Supported by IRSC Canada and RRSE Quebec.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".