A regional numerical environmental multimedia modeling approach to assess spatial Eco-Environmental exposure risk of perfluorooctane sulfonate (PFOS) in the Pearl river basin
Bibliographic record
Abstract
This paper presents a novel numerical environmental multimedia modeling system (RNEMM) for assessing the environmental fate of emerging organic contaminants and their relative health risk at a regional scale. The RNEMM is developed based on an integrated numerical algorithm that comprises four sub-models: a river network simulation module, a gaseous phase simulation module, a mass balance based simulation module for soil compartment, and a food web analysis module. This RNEMM has been applied to simulate the spatial distribution of PFOS and assess the consequent health risks for a central water basin region of the Pearl River in China. The study region includes the urban areas of Guangzhou, Foshan, and Dongguan Cities with emission sources of PFOS, which was detected in local water, sediments, and air environment. The spatial concentration distributions of PFOS in water, sediment, air, soil, and various fish species are examined based on RNEMM and compared with the measured data. With a focus on water environment, it shows that the simulated results essentially agree well with measured concentrations. Comparing the simulated results and the measured data collected in 2013, the relative errors are mostly less than 40 % in the surface water and sediment zones for this regional scale field study. Whereas the relative error in the atmosphere zone is less than 5%. In addition, the health risk assessment for children and adults is conducted based on the RNEMM approach. The hazard quotient (HQ) values for the 95th percentile in most subareas of the study region are higher than 0.1, showing a low-risk level for the study period. The results indicate that the RNEMM is a useful modeling tool to manage the environmental and health risks associated with emerging contaminants on regional air, water, soil, and ecosystem at an adequate spatial-temporal resolution.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".