XRF analysis of PM2.5-bound heavy metals at three sites in Hanoi
Bibliographic record
Abstract
Exposure to PM2.5-bound heavy metals has attracted significant attention in Southeast Asia in recent decades.Focusing on Hanoi, the capital of Vietnam, this study aimed to compare PM2.5 concentrations and semi-quantitative concentrations of heavy metals at representative urban, traffic, and industrial sites.Daytime and nighttime samples were collected independently to present high and low emission periods.A total of 30 samples were collected from 17 th to 26 th December 2025.The results showed that daily PM2.5 concentrations were 257 ± 57, 194 ± 4, and 119 ± 22 µg/m 3 at industrial, traffic, and urban sites, respectively, which were several times higher than the national ambient air quality standard.Notably, nighttime PM2.5 levels were lower than those in the daytime at traffic and industrial sites, while the opposite trend was observed at the urban site.Elemental analysis was performed using an Energy-Dispersive X-ray Fluorescence (EDXRF) with an advanced configuration X-ray Extended Polarization Optical System (XEPOS).Among target heavy metals (Cr, Mn, Fe, Cu, As, Se, Pb), Fe showed the highest levels at all sampling sites, while Se exhibited the lowest.The traffic site recorded the highest levels of heavy metals.This study raises the need for further investigation into the mechanisms underlying spatial and diurnal differences of heavy metals to better understand their atmospheric behavior and potential health impacts.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".