Water Quality Management Based on Division of Dry and Wet Seasons in Pearl River Delta, China
Bibliographic record
Abstract
Abstract In the Pearl River Delta (PRD), river water quality deteriorates continually due to the population increase and ongoing industrialization and urbanization. In this study, a water quality management paradigm based on the seasonal variation is proposed. For better exploring the seasonal change of water quality, wavelet analysis was used to analyze the division of dry and wet seasons in the PRD during 1952–2009. Then water quality seasonal variation in 2008 and relevant impact factors were analyzed by multivariate statistic methods as a case to make some management measures. The results show that there are some differences of dry and wet seasons division among different years. Wet season mainly appear from April to September, which occupy the largest proportion among the 58 years (about 70%) and then followed by the wet season from May to October (about 13.8% of the total years). As to the water quality of 2008, significant differences exist between dry and wet seasons for 17 water quality parameters except TP, ${\rm NO}_{3}^{- } $ , Fe2+, and Zn2+. Levels of parameters pH, EC, CODMn, BOD5, ${\rm NH}_{4}^{ + } $ , ${\rm SO}_{4}^{2- } $ , and Cl− in dry season are much higher than those in wet season. In dry season the variations of river water quality are mainly influenced by domestic sewage, industrial effluents, and salt water intrusion. While in wet season, except the aforementioned pollution sources, drainages from cultivated land and livestock farm are also the main factors influencing water pollution. Thus, water quality management measures are proposed in dry and wet seasons, respectively. The results obtained from this study would further facilitate water quality protection and water resources management in the PRD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 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".