The Risk of Water Quality Deterioration with Urban Flood Control—A Case in Wuxi
Bibliographic record
Abstract
There is a demand for flood control in densely populated river network areas. Therefore, small floodgates are used for long-term and rapid water flow regulation in such contexts. However, people often disregard these floodgates’ potential interference with the natural water environment. This study focused on an urban floodgate-controlled reach and monitored the monthly data of four main pollutant indicators (TN, TP, CODMn, and NH3-N) from 2016 to 2018 at six fixed sampling points (S1–S6). The difference analysis and cluster analysis results indicated that floodgate adjustments were the dominant driving factor of water quality changes in the reach, with pollutant concentration differences observed between the floodgate opening and closing periods. The results of the Canadian Council of Ministers of the Environment Water Quality Index evaluation showed that the water quality of the floodgate-controlled reach was categorized as “marginal” or “poor”. It is particularly important to note that the concentration of nitrogen compounds exceeded the allowable limits. The results of the Mann–Kendall trend and time series analyses revealed an overall upward trend in NH3-N concentration and a localized upward trend in TP concentration and presented periodic concentration fluctuations of four pollutants (TN, TP, CODMn, and NH3-N). This study highlights that flood control management using small floodgates can pose a risk of deteriorating water quality. Therefore, it is necessary to develop scientific water quality management methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".