Mercury transformation and speciation in flue gases from anthropogenic emission sources: a critical review
Bibliographic record
Abstract
Mercury transformation mechanisms and speciation profiles are reviewed for mercury formed in and released from flue gases of coal-fired boilers, non-ferrous metal smelters, cement plants, iron and steel plants, waste incinerators, biomass burning and so on. Mercury in coal, ores, and other raw materials is released to flue gases in the form of Hg 0 during combustion or smelting in boilers, kilns or furnaces. Decreasing temperature from over 800 °C to below 300 °C in flue gases leaving boilers, kilns or furnaces promotes homogeneous and heterogeneous oxidation of Hg 0 to gaseous divalent mercury (Hg 2+ ), with a portion of Hg 2+ adsorbed onto fly ash to form particulate-bound mercury (Hg p ). Halogen is the primary oxidizer for Hg 0 in flue gases, and active components (e.g., TiO 2 , Fe 2 O 3 , etc.) on fly ash promote heterogeneous oxidation and adsorption processes. In addition to mercury removal, mercury transformation also occurs when passing through air pollution control devices (APCDs), affecting the mercury speciation in flue gases. In coal-fired power plants, selective catalytic reduction (SCR) system promotes mercury oxidation by 34–85 %, electrostatic precipitator (ESP) and fabric filter (FF) remove over 99 % of Hg p , and wet flue gas desulfurization system (WFGD) captures 60–95 % of Hg 2+ . In non-ferrous metal smelters, most Hg 0 is converted to Hg 2+ and removed in acid plants (APs). For cement clinker production, mercury cycling and operational conditions promote heterogeneous mercury oxidation and adsorption. The mercury speciation profiles in flue gases emitted to the atmosphere are determined by transformation mechanisms and mercury removal efficiencies by various APCDs. For all the sectors reviewed in this study, Hg p accounts for less than 5 % in flue gases. In China, mercury emission has a higher Hg 0 fraction (66–82 % of total mercury) in flue gases from coal combustion, in contrast to a greater Hg 2+ fraction (29–90 %) from non-ferrous metal smelting, cement and iron and/or steel production. The higher Hg 2+ fractions shown here than previous estimates may imply stronger local environmental impacts than previously thought, caused by mercury emissions in East Asia. Future research should focus on determining mercury speciation in flue gases from iron and steel plants, waste incineration and biomass burning, and on elucidating the mechanisms of mercury oxidation and adsorption in flue gases.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".