Biokinetic food chain modeling of waterborne selenium pulses into aquatic food chains: Implications for water quality criteria
Bibliographic record
Abstract
Abstract We evaluated the use of biokinetic models to predict selenium (Se) bioaccumulation into model food chains after short-term pulses of selenate or selenite into water. Both periphyton- and phytoplankton-based food chains were modeled, with Se trophically transferred to invertebrates and then to fish. Whole-body fish Se concentrations were predicted based on 1) the background waterborne Se concentration, 2) the magnitude of the Se pulse, and 3) the duration of the Se pulse. The models were used to evaluate whether the US Environmental Protection Agency's (USEPA's) existing acute Se criteria and their recently proposed intermittent Se criteria would be protective of a whole-body fish Se tissue-based criterion of 8.1 μg g-1 dry wt. Based on a background waterborne Se concentration of 1 μg L-1 and pulse durations of 1 d and 4 d, the Se pulse concentrations predicted to result in a whole-body fish Se concentration of 8.1 μg g-1 dry wt in the most conservative model food chains were 144 and 35 μg L-1, respectively, for selenate and 57 and 16 μg L-1, respectively, for selenite. These concentrations fall within the range of various acute Se criteria recommended by the USEPA based on direct waterborne toxicity, suggesting that these criteria may not always be protective against bioaccumulation-based toxicity that could occur after short-term pulses. Regarding the USEPA's draft intermittent Se criteria, the biokinetic modeling indicates that they may be overly protective for selenate pulses but potentially underprotective for selenite pulses. Predictions of whole-body fish Se concentrations were highly dependent on whether the food chain was periphyton- or phytoplankton-based, because the latter had much greater Se uptake rate constants. Overall, biokinetic modeling provides an approach for developing acute Se criteria that are protective against bioaccumulation-based toxicity after trophic transfer, and it is also a useful tool for evaluating averaging periods for chronic Se criteria. Integr Environ Assess Manag 2016;12:230–246. © 2015 SETAC Key Points Biokinetic modeling of Se in model aquatic food chains provides a valuable tool for evaluating whether acute water quality criteria based on direct Se toxicity may be protective of bioaccumulation-based toxicity in fish. Biokinetic Se models provide a promising tool for temporally colocating Se concentrations in multiple food chain components as part of field monitoring programs in support of Se bioaccumulation modeling, especially for time-varying and/or seasonal changes in waterborne Se concentrations. Biokinetic data for Se at the base of the aquatic chain are limited to 2 periphyton assemblages and 1 phytoplankton species; biokinetic Se data for additional components at the base of the food chain are needed to broaden our understanding of how Se biokinetics varies in different food chains. Se uptake and elimination rates are not constant over a range of Se concentrations in water or diets, and Se biokinetics under varying bioavailability conditions have not been conducted to-date—evaluating these variables should be a focus of future biokinetic Se studies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 |
| 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".