Occurrence and multiple-level ecological risk assessment of pharmaceuticals and personal care products (PPCPs) in two lakes of China
Bibliographic record
Abstract
Abstract Background: Management of pharmaceuticals and personal care products (PPCPs) in the environment has become a social problem. In the present study, concentrations of 140 PPCPs at 20 sites in Baiyangdian Lake and Tai Lake from 2016 to 2017 were analyzed by Ultra Performance Liquid Chromatography Mass Spectrometer (HPLC-MS). Risk quotients (RQ) were calculated for each detected chemical at all sites and a prioritization index (PI) based on maximum RQ was calculated. To assess the risk of chemicals that identified high priority (PI>1), a more accurate method of joint probability curves (JPCs) was applied. Results: A total of 41 PPCPs were quantitatively detected in the two lakes, with maximum concentrations ranging from 0.04 to 889 ng/L. Among these, seven PPCPs were identified as high or moderate risk pollutants for at least one site, 3 in Tai Lake and 5 in Baiyangdian Lake. Carbamazepine posed ecological risk at all 20 sites, to which more attention should be paid in the future. Based on results of the JPCs, sulfamethoxazole, caffeine, diethyltoluamide, and carbamazepine were categorized as high or intermediate risk. Conclusion: Occurrences and distributions of PPCPs were different in the two lakes. Multiple-level risk assessment from simple to more complex was appropriate in chemical risk management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".