Inputs of disinfection by-products to the marine environment from various industrial activities: Comparison to natural production
Bibliographic record
Abstract
Oxidative treatment of seawater in coastal and shipboard installations is applied to control biofouling and/or minimize the input of noxious or invasive species into the marine environment. This treatment allows a safe and efficient operation of industrial installations and helps to protect human health from infectious diseases and to maintain the biodiversity in the marine environment. On the downside, the application of chemical oxidants generates undesired organic compounds, so-called disinfection by-products (DBPs), which are discharged into the marine environment. This article provides an overview on sources and quantities of DBP inputs, which could serve as basis for hazard analysis for the marine environment, human health and the atmosphere. During oxidation of marine water, mainly brominated DBPs are generated with bromoform (CHBr3) being the major DBP. CHBr3 has been used as an indicator to compare inputs from different sources. Total global annual volumes of treated seawater inputs resulting from cooling processes of coastal power stations, from desalination plants and from ballast water treatment in ships are estimated to be 470–800 × 109 m3, 46 × 109 m3 and 3.5 × 109 m3, respectively. Overall, the total estimated anthropogenic bromoform production and discharge adds up to 13.5–21.8 × 106 kg/a (kg per year) with contributions of 11.8–20.1 × 106 kg/a from cooling water treatment, 0.89 × 106 kg/a from desalination and 0.86 × 106 kg/a from ballast water treatment. This equals approximately 2–6% of the natural bromoform emissions from marine water, which is estimated to be 385–870 × 106 kg/a.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".