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Record W3109593224 · doi:10.1016/j.envint.2020.106266

Addressing uncertainty in mouthing-mediated ingestion of chemicals on indoor surfaces, objects, and dust

2020· article· en· W3109593224 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.

Bibliographic record

VenueEnvironment International · 2020
Typearticle
Languageen
FieldChemical Engineering
TopicChemical Safety and Risk Management
Canadian institutionsUniversity of TorontoThe Scarborough HospitalARC Resources (Canada)
FundersAmerican Chemistry Council
KeywordsIngestionEnvironmental healthEnvironmental scienceEnvironmental chemistryToxicologyChemistryMedicineBiology

Abstract

fetched live from OpenAlex

In indoor environments, humans ingest chemicals present as surface residues and bound to settled particles (dust), through mouthing hands (hand-to-mouth transfer) and objects (object-to-mouth transfer). Here, we introduce a novel modeling approach in support of systematic investigation into the mouthing-mediated ingestion of chemicals present in indoor environments. This model explicitly considers the indoor dynamics of dust and chemicals, building on mechanistic links with physicochemical properties of chemicals, features of the indoor environment, and human activity patterns. The evaluation of this model demonstrates that it satisfactorily reproduces chemical hand loadings and exposure data reported in the literature. We then use the evaluated model to investigate the response of mouthing-mediated ingestion to chemical partitioning between the gas phase and solid phases, expressed as the octanol–air partition coefficient (KOA). Assuming a unit emission rate to the indoor environment, we find that low-volatility chemicals (with a KOA greater than 109) are more efficiently enriched in hand skin, resulting in higher mouthing-mediated ingestion than other compounds. For individuals living in a room with a typical level of dustiness, more than half of the chemical mass found in their hands comes from dust transfer, whereas more than half of the chemical mass ingested is the fraction present as residues on hands. We also use the new model to explore how the mouthing-mediated ingestion of chemicals is dependent on factors describing the indoor environment and human behavior. The model predicts that less frequent cleaning leads to higher accumulation of dust on indoor surfaces, thereby transferring more chemicals to hands and mouth in each contact. Introducing more dust into the room, but maintaining the same cleanup frequency, increases the dustiness of indoor surfaces, which promotes the transfer of relatively volatile chemicals (with a KOA lower than 109) to hands and mouth but decreases the transfer of chemicals with low volatility. More frequent hand contact with indoor surfaces increases both the hand loading and mouthing-mediated ingestion of chemicals, but the increases are more remarkable for adults than children because the higher surface contact frequency of children “saturates” hand loadings. An increase in handwashing frequency lowers the hand loading and mouthing-mediated ingestion of chemicals and this mitigating process is more prominent for relatively volatile chemicals. The new evaluated modeling approach can facilitate the prediction of mouthing-mediated ingestion for various age groups and the model predictions can be used to aid future fate and (bio)monitoring studies focusing on indoor contamination.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.238
Teacher spread0.210 · 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