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Record W3004158120 · doi:10.5194/acp-21-4103-2021

Simulations of anthropogenic bromoform indicate high emissions at the coast of East Asia

2021· article· en· W3004158120 on OpenAlexaff
Josefine Maas, Susann Tegtmeier, Yue Jia, Birgit Quack, Jonathan V. Durgadoo, Arne Biastoch

Bibliographic record

VenueAtmospheric chemistry and physics · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Saskatchewan
FundersGEOMAR Helmholtz-Zentrum für Ozeanforschung KielChristian-Albrechts-Universität zu KielDeutsche Forschungsgemeinschaft
KeywordsBromoformEnvironmental scienceAtmosphere (unit)Atmospheric sciencesClimatologyMeteorologyGeologyChemistryGeography

Abstract

fetched live from OpenAlex

Bromoform is the major by-product from chlorination of cooling water in coastal power plants. The number of power plants in East and Southeast Asian economies has increased rapidly, exceeding mean global growth. Bottom-up estimates of bromoform emissions based on few measurements appear to under-represent the industrial sources of bromoform from East Asia. Using oceanic Lagrangian analyses, we assess the amount of bromoform produced from power plant cooling-water treatment in East and Southeast Asia. The spread of bromoform is simulated as passive particles that are advected using the three-dimensional velocity fields over the years 2005/2006 from the high-resolution NEMO-ORCA0083 ocean general circulation model. Simulations are run for three scenarios with varying initial bromoform concentrations based on the range of bromoform measurements in cooling-water discharge. Comparing the modelled anthropogenic bromoform to in situ observations in the surface ocean and atmosphere, the two lower scenarios show the best agreement, suggesting initial bromoform concentrations in cooling water to be around 20–60 µg L −1 . Based on these two scenarios, the model produces elevated bromoform in coastal waters of East Asia with average concentrations of 23 and 68 pmol L −1 and maximum values in the Yellow Sea, Sea of Japan and East China Sea. The industrially produced bromoform is quickly emitted into the atmosphere with average air–sea flux of 3.1 and 9.1 nmolm-2h-1, respectively. Atmospheric abundances of anthropogenic bromoform are derived from simulations with the Lagrangian particle dispersion model FLEXPART based on ERA-Interim wind fields in 2016. In the marine boundary layer of East Asia, the FLEXPART simulations show mean anthropogenic bromoform mixing ratios of 0.4–1.3 ppt, which are 2–6 times larger compared to the climatological bromoform estimate. During boreal winter, the simulations show that some part of the anthropogenic bromoform is transported by the northeasterly winter monsoon towards the tropical regions, whereas during boreal summer anthropogenic bromoform is confined to the Northern Hemisphere subtropics. Convective events in the tropics entrain an additional 0.04–0.05 ppt of anthropogenic bromoform into the stratosphere, averaged over tropical Southeast Asia. In our simulations, only about 10 % of anthropogenic bromoform is outgassed from power plants located in the tropics south of 20 ∘ N, so that only a small fraction of the anthropogenic bromoform reaches the stratosphere. We conclude that bromoform from cooling-water treatment in East Asia is a significant source of atmospheric bromine and might be responsible for annual emissions of 100–300 Mmol of Br in this region. These anthropogenic bromoform sources from industrial water treatment might be a missing factor in global flux estimates of organic bromine. While the current emissions of industrial bromoform provide a significant contribution to regional tropospheric budgets, they provide only a minor contribution to the stratospheric bromine budget of 0.24–0.30 ppt of Br.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2021
Admission routes1
Has abstractyes

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