A Preliminary Study on the Occurrence of Pharmaceutically Active Compounds in the River Basins and Their Removal in Two Conventional Drinking Water Treatment Plants in Chongqing, China
Bibliographic record
Abstract
The occurrence of 21 selected pharmaceutically active compounds (PhACs) in river water was examined; meanwhile the presence and removal of these PhACs at different water treatment steps in two conventional drinking water treatment plants (DWTPs) were evaluated. Monitoring results showed that of all 21 target PhACs, twelve PhACs were ubiquitously detected in water samples from the Yangtze river and its two tributaries (Jialing River and Huaxi stream), whereas alodipine was not found in any river water sample. The concentrations of PhACs detected in Huaxi stream were statistically significantly higher than those found in the Jialing River and the Yangzi River because of the direct discharges of uncontrolled domestic sewage and poultry wastewater and the abuse of PhACs in the rural areas served by the Huaxi stream. An environmental risk assessment of PhACs based on the risk quotient value approach indicated that sulfamethoxazole, ofloxacin and dehydrated erythromycin and clofibric acid had a medium risk, and the mixture of the studied PhACs showed high risk for the aquatic ecosystems. Fourteen out of 21 target PhACs were found in the raw water of the two studied DWTPs with azithromycin having the highest concentration (about 20 ng/L). Following the conventional water treatment processes, the studied PhACs were found to be removed by an average of 54 and 48% in DWTP A and DWTP B, respectively. Among the target PhACs, six and three PhACs were detected in finished water of DWTP B and DWTP A, respectively. This is the first study in southwestern China reporting the presence of PhACs in surface water and drinking water.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".