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Record W2177711138 · doi:10.5194/acp-16-2417-2016

Mercury transformation and speciation in flue gases from anthropogenic emission sources: a critical review

2016· review· en· W2177711138 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAtmospheric chemistry and physics · 2016
Typereview
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Key Research and Development Program of ChinaMajor State Basic Research Development Program of ChinaNational Natural Science Foundation of China
KeywordsFlue gasMercury (programming language)Fly ashChemistryElectrostatic precipitatorEnvironmental chemistryCoalIncinerationWaste managementNOxFerrousCoal combustion productsSmeltingParticulatesCombustion

Abstract

fetched live from OpenAlex

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 Hg0 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 Hg0 to gaseous divalent mercury (Hg2+), with a portion of Hg2+ adsorbed onto fly ash to form particulate-bound mercury (Hgp). Halogen is the primary oxidizer for Hg0 in flue gases, and active components (e.g., TiO2, Fe2O3, 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 Hgp, and wet flue gas desulfurization system (WFGD) captures 60–95 % of Hg2+. In non-ferrous metal smelters, most Hg0 is converted to Hg2+ 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, Hgp accounts for less than 5 % in flue gases. In China, mercury emission has a higher Hg0 fraction (66–82 % of total mercury) in flue gases from coal combustion, in contrast to a greater Hg2+ fraction (29–90 %) from non-ferrous metal smelting, cement and iron and/or steel production. The higher Hg2+ 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.024
GPT teacher head0.304
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it