Fate Modeling of Xenobiotic Organic Compounds (XOCs) in Wastewater Treatment Plants
Bibliographic record
Abstract
Xenobiotic Organic Compounds (XOCs) are present in wastewater and wastewater-impacted environmental systems. Pharmaceuticals and personal care products are a broad and varied category of chemicals that are included among these compounds. Although, these compounds have been detected at low levels in surface water, concerns that these compounds may have an impact on human health and aquatic life, have led to increased interest in how XOCs are removed during wastewater treatment. Recognizing specific mechanisms in recent literature and simulating those mechanisms responsible for the removal of XOCs is the main objective of this study. Conventional models, such as the popular activated sludge models (ASM1, ASM2, etc), do not sufficiently address the removal processes; therefore, a fate model is created to provide a means of predicting and simulating removal mechanisms along with experimental analyses. \nGPS-X is a multi-purpose modeling tool for the simulation of municipal and industrial wastewater treatment plants. This software package includes conventional models as built-in libraries, which can be used as bases on which new models can be created. In this thesis, the removal mechanisms of XOCs are recognized and investigated; a new library for GPS-X is also created to include XOCs. \nAs a first step the uncalibrated fate model, which includes all mechanisms of interest with their process rates and state variables, is developed using in GPS-X software. A modified ASM1 (Mantis model) is used as a basis for developing the fate model. Since only a group of mechanisms is responsible for the removal of each compound the mechanisms are categorized in three different case studies as the next step. Thus, one submodel is associated with each case study. The model developer toolbar in GPS-X software is used to develop the model for these case studies. The first case study involves the removal of antibiotics, such as Sulfamethoxazole. The removal mechanisms used in this case are biodegradation, sorption, and parent compound formation, with co-metabolism and competitive inhibition effects being inserted into the structure of the model. Secondly, the removal of nonylphenol ethoxylates (NPEOs) occurs through abiotic oxidative cleavage, hydrolysis, and biodegradation. The third case study includes removal mechanisms of biodegradation and sorption for neutral and ionized compounds. \nIn the calibration process, model parameters are tuned such that the model can best simulate the experimental data using optimization methods. A common error criterion is Sum of Squared Errors (SSE) between the simulated results and the measured data. By minimizing SSE, optimal values of parameters of interest can be estimated. In each case study different data sets were used for the validation process. \nTo validate the calibrated model, simulated results are compared against experimental data in each case study. The experimental data set used in the validation process is different from that used for calibrating the model, which means the validation process data set was obtained from the different literature. By looking at the validation results, it is concluded that the proposed model successfully simulates removal of XOCs. \nSince the operating parameters of wastewater treatment plants, such as Solids Retention Time (SRT) and Hydraulic Retention Time (HRT) are crucial for the fate of XOC’s, a sensitivity analysis is carried out to investigate the effect of those parameters. Moreover, the pH effect is studied because it relates to the ionized XOCs. Sensitivity analysis results show that the fate model is more sensitive to model parameters i.e. biodegradation rate constant (kb) than the operational parameters, i.e. SRT and HRT. Furthermore, the responses showed sensitivity to pH, whereby acidic conditions provide a better environment for removing neutral forms and alkaline conditions were suitable for removing ionized forms, according to the ionized compound fate model.
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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 | 0.000 |
| Research integrity | 0.000 | 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 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".