Overview of receptor-based source apportionment studies for speciated atmospheric mercury
Bibliographic record
Abstract
Abstract. Receptor-based source apportionment studies of speciated atmospheric mercury are not only concerned with source contributions, but also the influence of transport, transformation, and deposition processes on speciated atmospheric mercury concentrations at receptor locations. Previous studies applied multivariate receptor models including Principal Components Analysis and Positive Matrix Factorization, and back trajectory receptor models including Potential Source Contribution Function, Gridded Frequency Distributions, and Concentration-back trajectory models. Anthropogenic combustion sources, crustal/soil dust, and chemical and physical processes, such as gaseous elemental mercury (GEM) oxidation reactions, boundary layer mixing, and GEM flux from surfaces, were inferred from the multivariate studies, which were predominantly conducted at receptor sites in Canada and the US. Back trajectory receptor models revealed potential impacts of large industrial areas such as the Ohio River Valley in the US and throughout China, metal smelters, mercury evasion from the ocean and Great Lakes, and free troposphere transport on receptor measurements. Input data and model parameters specific to atmospheric mercury receptor models are summarized and model strengths and weaknesses are also discussed. One area of improvement that applies to all receptor models is the greater focus on evaluating the accuracy of receptor models at identifying potential speciated atmospheric mercury sources, source locations, and chemical and physical processes in the atmosphere.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".