Seasonal Source Identification And Source-Specific Health Risk Assessment of Pollutants On Road Dust In Tianjin
Bibliographic record
Abstract
Abstract Human exposure to metals on road dust might have potential health risks through touching, ingesting, and inhaling. There were limited studies to link seasonal emission sources to health risks from metals on road dust. In this study, metals on road dust from different functional areas were seasonally monitored. The pollutant sources in study city varied slightly with the seasons, but the major pollutant source in the particular study site were significantly affected by the seasons. By combining the source apportionment model (PMF), line sources model and health risk models (HI: Hazard index and ILCR: Incremental Lifetime Carcinogenic risk), industrial and construction activity was identified as the crucial source of both the pollutants on road dust (29% - 47%), and the HI for adults (27% - 45%) and children (41% - 50%) in different seasons. The traffic non-exhaust emission dominated in the carcinogenic risks for children in spring (45%) and summer (36%). Factors such as seasons, particle size, metal bioavailability, human exposure time, and exposure area were all taken into consideration to avoid overestimating or underestimating health risks. The carcinogenic risks for children (1.6 E-06) and adults (2.8 E-06) exposed to Cr both exceed the minimum threshold (10-6). Measured metals mainly posed hazard to human health through ingestion route. Pb and Mn, Fe and Mn were the main harmful elements that induced non-carcinogenic risks for adults and children, respectively. Effectively identifying the source-specific health risks in different seasons will help in the formulation of adaptive strategies to diminish the potential risks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".