Effects of COVID-19 Control Measures on the Concentration and Composition of PM2.5-Bound Polycyclic Aromatic Hydrocarbons in Shanghai
Bibliographic record
Abstract
In order to explore the effects of COVID-19 control measures on the concentration and composition of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs), and to better understand the sources of PM2.5-bound PAHs, PM2.5, samples were collected at two sites in urban and suburban areas of Shanghai before the lockdown, during the lockdown, after the lockdown in 2020, and during the same periods in 2019. The mass concentrations of 21 individual PAHs were determined via GC-MS analysis. While the COVID-19 control measures significantly reduced the absolute concentration of PM2.5-bound PAHs, they had no significant effect on their relative abundances, indicating that the significantly reduced traffic emission may not originally be the major source of PAHs in Shanghai. The differences in the composition of PM2.5-bound PAHs at three different lockdown-related periods may be caused by the gas-particle distribution of semi-volatile PAHs. The similarity in the composition of PM2.5-bound PAHs in different functional areas and different periods brings more uncertainties to the identification of PAH sources using the diagnostic ratios. During the lockdown period, the toxic equivalent concentration of PM2.5-bound PAHs in Shanghai was estimated to decrease by about 1/4, which still exhibits substantial carcinogenic risk upon exposure via inhalation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".