Combating air pollution significantly reduced air mercury concentrations in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT In the past decade, China has motivated proactive emission control measures that have successfully reduced emissions of many air pollutants. For atmospheric mercury, which is a globally transported neurotoxin, much less is known about the long-term changes in its concentrations and anthropogenic emissions in China. In this study, over a decade of continuous observations at four Chinese sites show that gaseous elemental mercury (GEM) concentrations continuously increased until the early 2010s, followed by significant declines at rates of 1.8%–6.1% yr−1 until 2022. The GEM decline from 2013 to 2022 (by 38.6% ± 12.7%) coincided with the decreasing concentrations of criteria air pollutants in China and were larger than those observed elsewhere in the northern hemisphere (5.7%–14.2%). The co-benefits of emission control measures contributed to the reduced anthropogenic Hg emissions and led to the GEM decline in China. We estimated that anthropogenic GEM emissions in China were reduced by 38%–50% (116–151 tons) from 2013 to 2022 using the machine-learning and relationship models.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it