Separation of Dissolved Organic Matter (DOM) and Per- and Polyfluoro-Alkyl Substances (PFAS) from Landfill Leachate Using Modified Coal Fly-Ash (CFA)
Bibliographic record
Abstract
Per and Poly-fluoroalkyl Substances (PFAS) has been a major subject of research in environmental sector ever since it was found in the environment and blood serums at toxic levels. As landfills are the final disposal method for majority of the waste, PFAS concentration in landfill leachate have been found in the range of few µg/L to mg/L. Only few conventional treatments such as Activated Carbon, Reverse Osmosis, and Ion-Exchange has been proven effective in removing PFAS. However, these treatment methods are proving to be very expensive and generate secondary contamination that needs to be disposed-off or treated. Since the phase out of C8-PFAS compounds, more short chain PFAS compounds are detected in landfill leachate. Hence, an effective treatment strategy is needed to keep up with the rising concentration levels and variety of PFAS compounds. The purpose of this study was to develop a sustainable and cost-effective process using modified Coal Fly-Ash (CFA) that can treat both short chain and long chain PFAS compounds. Previous studies have shown application of CFA in removal of dye and metals from different types of wastewaters. In previous studies CFA was modified to enhance its surface properties, that can improve the adsorption of organic and anionic contaminants. In this study, thermo-chemical modification was used on CFA to remove organic matter and PFAS compounds. Preliminary results showed that, CFA can remove more than 90% UV absorbance, more than 80% TOC and approximately 40% of total PFAS compounds. The maximum adsorption capacity for total PFAS was found to be 84 ng PFAS per g CFA, out of which 70 ng was for short chain PFAS and 14 ng for long chain PFAS compounds. An effective removal of organic matter and PFAS compounds, show a promising application of CFA in leachate treatment. However, further research is needed to analyze the adsorption dynamics, kinetics, post-treatment disposal method, and any possible contamination when mixing CFA with landfill leachate.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".