Health Risk Assessment of PM2.5-bound Components in Beijing, China during 2013–2015
Bibliographic record
Abstract
Risk assessment methods of the US Environment Protection Agency (U.S. EPA) were employed to estimate lifetime cancer risk in Beijing using the following fine particulate matter (PM2.5) components: six elements and 16 U.S. EPA priority polycyclic aromatic hydrocarbons (PAHs), and lifetime non-cancer hazard quotients (HQ) using 11 elements, based on data collected in Beijing during 2013–2015. The three-year average PM2.5 mass concentration was 82 µg m–3. Both lifetime cancer risk (1.9E-4) from exposure to ambient PM2.5-bound elements and PAHs and non-cancer HQ (18) from exposure to ambient PM2.5-bound elements in Beijing were much higher than the corresponding U.S. EPA acceptable levels. Cancer risks by source were, in descending order, road dust (7.3E-5), fossil fuel combustion (4.4E-5), vehicle exhaust (3.8E-5), soil dust (8.4E-6), metal processing (8.2E-6), secondary sulphur (8.0E-6), and biomass burning (6.4E-6). Cancer risks by PM2.5 component were, in descending order, As (1.1E-4), Cr(VI) (3.4E-5), total PAHs (1.5E-5), Pb (1.1E-5), Co (8.4E-6), Ni (3.9E-6), and Cd (3.9E-6). HQ by PM2.5-bound elements were, in descending order, Cl (14), As (1.8), Pb (0.94), P (0.81), Cd (0.22), Mn (0.22), Ni (0.18), Ba (0.1), Cr(VI) (0.03), Co (0.01), and Se (0.002). Fossil fuel combustion and vehicle exhaust were the top two sources, accounting for 77% of total HQ. HQ by target organ were, in descending order, respiratory (15), reproductive (1.8), nervous (1.2), renal (0.22), fetus (0.1), and alimentary system (0.002). The seasonal variations in PM2.5 mass concentrations, risks, as well as source and element contributions were largely due to increased coal combustion in winter.
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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 | 0.006 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".