Occurrence, Prediction and Photocatalytic Oxidation of Substituted Polycyclic Aromatic Compounds in Industrial Wastewater.
Bibliographic record
Abstract
Polycyclic aromatic compounds (PACs) are ubiquitous organic pollutants occurring in industrial wastewater, particularly from steel mills and petrochemical facilities. Recent efforts have improved the monitoring and removal of PACs, but substituted PACs, such as alkylated (APACs) and nitrogen-sulfur-oxygen heterocyclic PACs (HPACs), are now being reported as a greater concern. These substituted PACs not only exhibit higher toxicity but are also resistant to conventional biological and tertiary treatment processes currently employed in the wastewater treatment plants. Additionally, some studies suggest that existing treatment methods may even promote the substitution of parent PACs, especially in coking wastewater. This data is needed to examine the adequacy of existing guidelines and regulations. This study examines the occurrence of 61 PACs, including 16 parent PACs, 31 APACs, and 14 HPACs, in wastewater and sludge samples from two steel mills and one petrochemical plant in Southern Ontario. Detectable quantities of substituted PACs were found in the industrial effluents discharged to the receiving water bodies. A mass balance model applied to the influent and effluent wastewater samples indicated potential transformation of parent PACs to heterocyclic derivatives in one out of three industrial facilities during treatment, however variability in characterization methods prevented their precise quantification. Biological treatment step in the petrochemical refinery was found to be led by biodegradation while biosorption dominated the removal of substituted PACs in the wastewater treatment plant of steel mill. Machine learning models were developed using standard wastewater quality indicators like COD, DOC, TSS, and NH₃-T. Support vector machine regression (SVR) accurately predicted HPAC concentrations in effluent (R² = 0.83). Model analysis revealed that SVR model weighed the input variables contributing to the substitution of PACs highly, and thus predicted the occurrence of more toxic substituted PACs with better accuracy. Such a model may be applied for the real-time monitoring of PACs in coking wastewater. Solar photocatalysis using immobilized Ag/AgBr/TiO2 was found to effectively treat PAC-contaminated wastewater. A novel spectro-kinetic model using electron paramagnetic resonance (EPR) spectroscopy was proposed, which takes into account the temporal rise and decay of hydroxyl (•OH) and phenyl radicals (•CR), generated from electron (e −) and hole (h+) charge transfer on the catalyst surface. Oxidation pathways for direct h+ and •OH attack on substituted PACs were modeled to predict the concentration of a spiked compound in coking wastewater. This mechanistic kinetic model could be useful in photocatalytic process modelling, and ultimately in designing and optimizing wastewater treatment systems targeting substituted PACs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".