Occurrence, variations, and risk assessment of neonicotinoid insecticides in Harbin section of the Songhua River, northeast China
Bibliographic record
Abstract
Neonicotinoid insecticides (NNIs) have been intensively used and exploited, resulting in their presence and accumulation in multiple environmental media. We herein investigated the current levels of eight major NNIs in the Harbin section of the Songhua River in northeast China, providing the first systematic report on NNIs in this region. At least four NNIs in water and three in sediment were detected, with total concentrations ranging from 30.8 to 135 ng L-1 and from 0.61 to 14.7 ng g-1 dw, respectively. Larger spatial variations in surface water NNIs concentrations were observed in tributary than mainstream (p < 0.05) due to the intensive human activities (e.g., horticulture, urban landscaping, and household pet flea control) and the discharge of wastewater from many treatment plants. There was a significant positive correlation (p < 0.05) between the concentrations of residual imidacloprid (IMI), clothianidin (CLO), and Σ4NNIs in the sediment and total organic carbon (TOC). Due to its high solubility and low octanol-water partition coefficient (Kow), the sediment-water exchange behavior shows that NNIs in sediments can re-enter into the water body. Human exposure risk was assessed using the relative potency factor (RPF), which showed that infants have the highest exposure risk (estimated daily intake (ΣIMIeq EDI): 31.9 ng kg-1 bw·d-1). The concentration thresholds of NNIs for aquatic organisms in the Harbin section of the Songhua River were determined using the species sensitivity distribution (SSD) approach, resulting in a value of 355 ng L-1 for acute hazardous concentration for 5% of species (HC5) and 165 ng L-1 for chronic HC5. Aquatic organisms at low trophic levels were more vulnerable to potential harm from NNIs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".