Simulating fate and transport of non‐steroidal anti‐inflammatory drugs using Root Zone Water Quality Model 2
Bibliographic record
Abstract
Abstract The fate and transport of non‐steroidal anti‐inflammatory drugs (NSAIDs) in soil are determined by various processes, and the complexity of the system lends itself to the use of computer simulation models to help understand it. This study demonstrated the first attempt to use empirical data from lab incubation and field studies to parameterize and test a process‐based agricultural systems model, Root Zone Water Quality Model 2 (RZWQM2), to simulate the fate and transport of naproxen (NPX), ibuprofen (IBF), and ketoprofen (KTF) in field‐based lysimeters amended with alkaline‐treated biosolids (ATBs). The model calibrated for the soil–water balance module and contaminant transport module was used to predict water seepage through the soil profile in 2017 and 2018 within a 15% error of the field measured data, with model performance statistics such as Nash—Sutcliffe model efficiency (NSE) and R 2 all greater than 0.70. The overall predicted percent recovery of initial spiked NSAIDs in both soil and water samples, after further calibration of the contaminant transport module, was within the same order of magnitude as the measured data. The model underestimated the percent recovery of initial spiked NSAIDs at the 30‐ to 55‐cm soil depth for all treatments on day 3. The calibrated soil subsurface aerobic half‐lives of NPX and IBF were found to be considerably lower than their laboratory‐measured half‐lives obtained from the incubation study. The overall performance of RZWQM2 in simulating the soil hydrology and behavior of NSAIDs in soil profiles receiving various rates of ATB amendments was satisfactory.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".