Impact of polystyrene nanoplastics on primary sludge fermentation under acidic and alkaline conditions: Significance of antibiotic resistance genes
Bibliographic record
Abstract
As a part of industrial or commercial discharge, the influx of nanoplastics (NPs) to the wastewater treatment plants is inevitable. Consequently, it has become a must to understand the effects of these NPs on different unit processes. This study aimed to investigate the impact of three different concentrations of polystyrene nanoplastics (PsNPs) on the fermentation of primary sludge (PrS), implemented in batch anaerobic bioreactors, at pH 5 and 10, considering the pH-dependent nature of the fermentation process. The results showed that PsNPs stimulated hydrogen gas production at a lower dose (50 μg/L), while a significant gas suppression was denoted at higher concentrations (150 μg/L, 250 μg/L). In both acidic and alkaline conditions, propionic and acetic acid predominated, respectively, followed by n-butyric acid. Under both acidic and alkaline conditions, exposure to PsNPs boosted the propagation of various antibiotic resistance genes (ARGs), including tetracycline, macrolide, β-lactam and sulfonamide resistance genes, and integrons. Notably, under alkaline condition, the abundance of sul2 gene in the 250 μg PsNPs/L batch exhibited a 2.4-fold decrease compared to the control batch. The response of the microbial community to PsNPs exposure exhibited variations at different pH values. Bacteroidetes prevailed at both pH conditions, with their relative abundance increasing after PsNPs exposure, indicating a positive impact of PsNPs on PrS solubilization. Adverse impacts, however, were detected in Firmicutes, Chloroflexi and Actinobacteria. The observed variations in the survival rates of various microbes stipulate that they do not have the same tolerance levels under different pH conditions.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".