Photolysis and Biodegradation of Selected Resin Acids in River Saale Water, Germany
Bibliographic record
Abstract
The River Saale is the Elbe's major tributary flowing through the state of Thuringia, Germany and receives organics inputs from several industrial facilities including pulp and paper mills. Resin acids constitute a major class of polar organics and environmental toxins derived primarily from pulp and paper processing of softwoods. Since wastewater treatment methods at pulp and paper mills are not always capable of removing the persistent resin acids prior to effluent discharge, alternative or complementary degradation methods may be required. Here, the facile photodegradation of four resin acids--abietic, dehydroabietic, isopimaric, and pimaric--was observed with pseudo-first-order kinetics when exposed to broad band and UV254-radiation. Further experimentation in rotating annular biofilm reactors with UV-exposed and unexposed River Saale water spiked with abietic and dehydroabietic acids indicated that photolysis is an effective pretreatment method for resin acid biodegradation. The bacterial toxicity of the aqueous resin acids solutions as measured with Microtox luminescence assays decreased with exposure time. Consequently, photo- and biodegradation of the resin acids did not generate any notable amounts of toxic intermediates and/or the intermediates formed were further degraded into compounds of lower toxicity than the parents. With tandem photo- and biological treatment at pulp and paper mills, as well as in-situ degradation by solar radiation and natural biofilms within the River Saale, resin acid inputs can be reduced in both concentration and toxicity to near undetectable levels with little or no ecological significance.
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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) | 0.001 | 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".