An assessment of global and regional emissions of trace metals to the atmosphere from anthropogenic sources worldwide
Bibliographic record
Abstract
An accurate and complete emission inventory for atmospheric trace metals on a global scale is needed for both modeler community and policy makers to assess the current level of environmental contamination by these pollutants, major emission sources and source regions, and the contribution of the atmospheric pathway to the contamination of terrestrial and aquatic environments. Major progress has been made in assessing emissions of trace metals in various countries and even regions, e.g., Europe, since the first global emission estimate for these pollutants was made by Nriagu and Pacyna (1988). These improved national and regional emission inventories have been used in this work to assess the global trace metal emissions from anthropogenic sources in the mid-1990s. The results of this work conclude that stationary fossil fuel combustion continues to be the major source of Cr, Hg, Mn, Sb, Se, Sn, and Tl with respect to the coal combustion and the major source of Ni and V with respect to oil combustion. Combustion of leaded, low-leaded, and unleaded gasoline continues to be the major source of atmospheric Pb emissions. The third major source of trace metals is non-ferrous metal production, which is the largest source of atmospheric As, Cd, Cu, In, and Zn. The largest anthropogenic emissions of atmospheric trace metals were estimated in Asia. This can be explained by growing demands for energy in the region and increasing industrial production. As a result, the Asian emissions are not only larger than the emissions on other continents, but also show an increasing trend. Another factor contributing to high emissions in Asia is the efficiency of emission control, which is lower than in Europe and North America. Concerning the two latter continents, emissions of trace metals show a decreasing tendency over the last two decades. Key words: anthropogenic sources, atmospheric emissions, trace metals, global emission inventory
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".