Assessing contributions of natural surface and anthropogenic emissions to atmospheric mercury in a fast-developing region of eastern China from 2015 to 2018
Bibliographic record
Abstract
Mercury (Hg) is a global toxic pollutant that can be released into the atmosphere through anthropogenic and natural sources. The uncertainties in the estimated emission amounts are much larger from natural than anthropogenic sources. A method was developed in the present study to quantify the contributions of natural surface mercury emissions to ambient gaseous elemental mercury (GEM) concentrations through application of positive matrix factorization (PMF) analysis with temperature and NH 3 as indicators of GEM emissions from natural surfaces. GEM concentrations were continuously monitored at a 2-hourly resolution at a regional background site in the Yangtze River Delta in eastern China during 2015–2018. Annual average GEM concentrations were in the range of 2.03–3.01 ng m −3 , with a strong decreasing trend at a rate of -0.32±0.07 ng m −3 yr −1 from 2015 to 2018, which was mostly caused by reduced anthropogenic emissions since 2013. The estimated contributions from natural surface emissions of mercury to the ambient GEM concentrations were in the range of 1.00–1.13 ng m −3 on annual average, with insignificant interannual changes, but the relative contribution increased significantly from 41 % in 2015 to 57 % in 2018, gradually surpassing those from anthropogenic sources.
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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 | 0.000 | 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".