Wastewater alkalinity enhancement for carbon emission reduction and marine CO<sub>2</sub> removal
Bibliographic record
Abstract
Abstract Wastewater alkalinity enhancement is a promising approach for ocean alkalinity enhancement due to its potential to deliver strong bases with minimum secondary precipitation and its potential use of the global network of wastewater treatment plants (WWTPs). WWTPs are also significant sources of CO2 due to organic matter oxidation, and integrating alkalinity addition into treatment processes may both reduce in-plant CO2 emissions and increase downstream CO2 uptake. This study presents a modeling framework that combines a modern activated sludge model-based WWTP simulator with an integrated hydrodynamic-biogeochemical-carbonate chemistry model of coastal oceans. We evaluate the effects of adding alkalinity either upstream (UpAdd) of the biological treatment stage or downstream at the discharge location (DnAdd) on WWTP carbon emission reduction and marine CO2 removal. The carbon emission from WWTPs decreases with increasing alkalinity dosage in UpAdd and can be eliminated at a dosage level that is feasible to implement. However, carbon uptake in the surrounding oceanic water is much reduced due to elevated dissolved inorganic carbon in the discharge water. DnAdd does not affect CO2 emissions from WWTPs but enhances carbon uptake in the ocean, with the net oceanic uptake of atmospheric CO2 increasing with increasing dosage level. Across all tested dosage levels, total CO2 removal, including emission reduction at the WWTPs and enhanced carbon uptake in the ocean, is 30% greater in UpAdd than in DnAdd. WWTP treatment tanks have much higher pCO2 than in the ocean, and aeration of process tanks enhances the gas transfer. The upstream alkalinity addition leads to sharp declines in pCO2 in the treatment tanks and large reductions in carbon emission from the WWTPs. These results have implications for developing strategies to reduce global carbon emission and enhance oceanic carbon burial using WWTPs as a delivery mechanism.
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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".