Atmospheric Mercury Concentrations and Isotopic Compositions Impacted by Typical Anthropogenic Mercury Emissions Sources
Bibliographic record
Abstract
Coal-fired power plants (CFPPs) and cement plants (CPs) are important anthropogenic mercury (Hg) emission sources. Mercury speciation profiles in flue gas are different among these sources, leading to significant variations in local atmospheric Hg deposition. To quantify the impacts of Hg emissions from CFPPs and CPs on local-scale atmospheric Hg deposition, this study determined concentrations and isotopes of ambient gaseous elemental mercury (GEM), particulate-bound mercury (PBM), and precipitation total Hg (THg) at multiple locations with different distances away from a CFPP and a CP. Higher concentrations of GEM and precipitation THg in the CFPP area in summer were caused by higher Hg emission from the CFPP, resulting from higher electricity demand. Higher concentrations of GEM, PBM, and precipitation THg in the CP area in winter compared to those in summer were related to the higher output of cement. Atmospheric Hg concentration peaked near the CFPP and CP and decreased with distance from the plants. Elevated GEM concentration in the CFPP area was due to flue gas Hg 0 emissions, and high PBM and precipitation Hg concentrations in the CP area were attributed to divalent Hg emissions. It was estimated that Hg emissions from the CFPP contributed 58.3 ± 20.9 and 52.3 ± 25.9% to local GEM and PBM, respectively, and those from the CP contributed 47.0 ± 16.7 and 60.0 ± 25.9% to local GEM and PBM, respectively. This study demonstrates that speciated Hg from anthropogenic emissions posed distinct impacts on the local atmospheric Hg cycle, indicating that Hg speciation profiles from these sources should be considered for evaluating the effectiveness of emission reduction policies. This study also highlights the Hg isotope as a useful tool for monitoring environmental Hg emissions.
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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 | 0.000 | 0.000 |
| 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".