Advanced Treatment of Landfill Leachate Induced Dissolved Organic Nitrogen (DON) and Its Influence on the Estuarine Algal Community
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Landfill leachate is a major source of refractory dissolved organic nitrogen (rDON), which can exacerbate eutrophication and harmful algal blooms in downstream aquatic ecosystems. This study evaluates the effectiveness of two advanced physicochemical treatments─Fenton oxidation and granular activated carbon (GAC) adsorption─for rDON removal from biologically treated landfill leachate blended with sewage, and their impacts on the estuarine algal (phytoplankton) community with in situ algal bioassays. Fenton oxidation achieved 52%–60% rDON removal by converting rDON into ammonium nitrogen (NH 4 + -N), enhancing its biodegradability and suitability for subsequent biological treatments. In contrast, GAC adsorption achieved higher removal efficiencies (86%–92%) by physically adsorbing nitrogenous species, including rDON and NH 4 + -N, without altering their chemical structure. We deployed in situ algal bioassays to analyze the impacts of advanced wastewater treatment processes on the algal growth dynamics. Bioassays revealed distinct effects on algal growth: Fenton treatment temporarily increased algal biomass due to elevated NH 4 + -N levels, while GAC treatment mitigated nutrient availability, inhibiting algal proliferation. While GAC was more effective overall, its regeneration requirements and associated costs pose applicability challenges. Fenton treatment is best suited as a pretreatment step to enhance rDON biodegradability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".