Sorption and degradation of petroleum hydrocarbons, polycyclic aromatic hydrocarbons, alkylphenols, bisphenol A and phthalates in landfill leachate using sand, activated carbon and peat filters
Bibliographic record
Abstract
Landfill leachates are repeatedly found contaminated with organic pollutants, such as alkylphenols (APs), phthalates and polycyclic aromatic hydrocarbons (PAHs) at levels exceeding water quality standards. It has been shown that these pollutants may be present in the colloidal and truly dissolved phase in contaminated water, making particle separation an inefficient removal method. The aim of this study was to investigate sorption and degradation of petroleum hydrocarbons (PHCs), selected APs, bisphenol A (BPA), phthalates and PAHs from landfill leachate using sand, granulated activated carbon (GAC) and peat moss filters. A pilot plant was installed at an inactive landfill with mixed industrial and household waste and samples were collected before and after each filter during two years. Leachate pre-treated in oil separator and sedimentation pond failed to meet water quality standards in most samples and little improvement was seen after the sand filter. These techniques are based on particle removal, whereas the analysed pollutants are found, to varying degrees, bound to colloids or dissolved. However, even highly hydrophobic compounds expected to be particle-bound, such as the PHCs and high-molecular weight PAHs, were poorly removed in the sand filter. The APs and BPA were completely removed by the GAC filter, while mass balance calculations indicate that 50-80% of the investigated phenols were removed in the peat filter. Results suggest possible AP degradation in peat filters. No evidence of phthalate degradation in the landfill, pond or the filters was found. The PHCs were completely removed in 50% and 35% of the measured occasions in the GAC and peat filters, respectively. The opposite trend was seen for removal of PAHs in GAC (50%) and peat (63%). Oxygenated PAHs with high toxicity were found in the leachates but not in the pond sediment. These compounds are likely formed in the pond water, which is alarming because sedimentation ponds are commonly used treatment techniques. The oxy-PAHs were effectively removed in the GAC, and especially the peat filter. It was hypothesized that dissolved compounds would adsorb equally well to the peat and GAC filters. This was not completely supported as the GAC filter was in general more efficient than peat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".