Process-based models for nitrogen dynamics in subsurface flow constructed wetlands: A state-of-the-art review
Bibliographic record
Abstract
In the last two decades, several process-based models had been developed to describe the transport and fate of contaminants in subsurface flow constructed wetlands (SSF CWs) under a variety of conditions. These models have become valuable tools to better design, control, and optimize the SSF CW system. Moreover, they can aid investigations on the effects of variables of interest on the system. While excellent review papers on these models have been published, the ability of the existing process-based models to simulate the nitrogen transformations in the SSF CWs have not been highlighted. Consequently, a critical review of the simulation of nitrogen transformation processes is desirable. This paper presents an overview of the available models that are capable of modelling the nitrogen dynamics in the SSF CWs. The existing process-based models can be mainly categorized as process-dedicated models and Gujer matrix models. A process-dedicated model is a feasible tool for design purposes. Meanwhile, the Gujer matrix model delivers valuable insights into scientific studies. In this paper, the existing models are summarized and critically discussed with regards to their capability and practicality in simulating the nitrogen dynamics in SSF CWs. Organic nitrogen, ammonium, and nitrate are the common nitrogen compounds considered in the process-based models of SSF CWs. Meanwhile, nitrite was frequently considered together with nitrate, owing to its low concentration in the effluent of SSF CWs. By comparison, ammonification, nitrification, denitrification, plant uptake, and ammonium adsorption are the typical nitrogen transformation and degradation processes found in the existing models, whereas ammonia volatilization processes are rarely found in the literature. Oxygen and organic matter are the major limiting reactants of nitrogen transformation and degradation processes in the modelling, and other factors such as microbial population, temperature, and pH are also considered. The recommendations and future research directions on the influence of microorganism distribution, resting period, oxygen release, ammonium adsorption, and plants are outlined.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".