Performance of pilot-scale constructed wetland treatment systems for flue gas desulfurization waters
Bibliographic record
Abstract
Abstract Federal laws regarding ambient air quality are currently requiring industries to reduce emissions of sulfur and nitrous oxides. Coal-fired power plants have therefore begun implementing flue gas desulfurization (FGD) scrubbers that use a highly oxygenated water stream (calcium-carbonate-saturated water) to transform sulfur gases into soluble anion species (sulfite and sulfate). The chemical compositions of FGD waters are dependent on the FGD scrubber design, coal types burned, chemical additives, and scrubbing solution source. The FGD waters contain potentially toxic elements including arsenic, cadmium, chemical oxygen demand (COD), copper, mercury, selenium, chloride, sulfates, and zinc. Therefore, these waters must be treated before discharge into a receiving system because of constituents that can elicit toxicity. The specific objectives of this research were to (1) characterize FGD waters in terms of chemical composition and constituents of concern, (2) design constructed wetland treatment systems (CWTSs) for the remediation of constituents of concern in FGD waters, and (3) measure the performance of CWTSs for formulated and actual FGD waters based on discharge criteria established by the United States Environmental Protection Agency and regulated by National Pollutant Discharge Elimination System permits. The FGD waters are characteristically high in total dissolved solids (calcium, chloride, magnesium, and sulfate), are semineutral in pH, contain high concentrations of total suspended solids, and contain several potentially toxic constituents. Constituents of concern were identified as cadmium, COD, chloride, copper, mercury, selenium, and zinc. Pilot-scale CWTSs were designed based on biogeochemical data, and each system contained an equalization basin and two reducing and oxidizing wetland reactors in series. Three FGD waters were introduced in the pilot-scale CWTS, and the performance was assessed by measuring targeted constituents of concern (mercury and selenium) and the toxicity of pre- and posttreatment waters. Results from these studies indicate that mercury and selenium concentrations in FGD waters can be decreased using CWTSs, and, with an appropriate comanagement of low-ionic strength water for chloride concentrations, toxicity of posttreatment samples is decreased to acceptable discharge limits.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".