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Record W2792637210 · doi:10.4209/aaqr.2017.11.0461

Health Risk of Ambient PM10-bound PAHs at Bus Stops in Spring and Autumn in Tianjin, China

2018· article· en· W2792637210 on OpenAlexafffund
Taosheng Jin, Miao Han, Kun Han, Xuemei Fu, Limin Xu, Xiaohong Xu

Bibliographic record

VenueAerosol and Air Quality Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Windsor
FundersNational Key Research and Development Program of ChinaChina Scholarship CouncilNational Natural Science Foundation of ChinaUniversity of Windsor
KeywordsEnvironmental scienceHealth riskSpring (device)Winter seasonEnvironmental chemistryEnvironmental engineeringToxicologyEnvironmental healthChemistryMedicineEngineering

Abstract

fetched live from OpenAlex

A study was conducted to measure ambient concentrations of PM10 and PM10-bound PAHs to estimate health risks due to exposure to PAHs during wait times at bus stops in September 2012 and March 2014. Samples were collected by personal exposure monitors at Balitai and Haiguangsi bus stops in Tianjin, China. The equivalent concentration of benzo[a]pyrene (BaPeq) was used to estimate the health risks of PAHs in PM10 inhaled by passengers waiting at these bus stops. The results showed the average PM10 level was higher in autumn (non-heating season) compared to spring (heating season) (307 ± 67 µg m–3 vs. 226 ± 100 µg m–3). When averaged over the two bus stops, concentrations of total PAHs in PM10 were much higher during spring compared to autumn (417 vs. 193 ng m–3), while BaPeq was slightly lower during spring (29.7 vs. 32.8 ng m–3). The incremental lifetime cancer risks (ILCR) at the two bus stops, Balitai and Haiguangsi, in spring were 9.0 × 10–8 and 4.5 × 10–8, and in autumn were 1.1 × 10–7 and 7.7 × 10–8, respectively. All these risk values were lower than the acceptable risk range of 10–6–10–4 approved by US Environmental Protection Agency.

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 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)0.012
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.102
GPT teacher head0.423
Teacher spread0.321 · 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 teacher head, 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

Citations15
Published2018
Admission routes2
Has abstractyes

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