Discussion of Environmental Quality Standards (EQS) and their development for the monitoring of impacts from the use of pesticides and drugs at marine aquaculture sites
Bibliographic record
Abstract
Environmental Quality Standards (EQS) are numerical thresholds selected to protect ecosystems by limiting the release of a chemical to levels that will not result in irreparable harm or toxicity to sensitive aquatic species. They are an integral part of the design of an effective monitoring program and can have time (dispersion in water for bath pesticides) and spatial notions (allowable zone of deposition for in-feed drugs) embedded in their application process. EQS values exist for water and/or sediment compartments based on the targeted compounds n- Octanol/Water Partition Coefficient (KoW). Water EQS can be divided into two main types: one related to maximum acute chemical (MAC-EQS) exposure and one to chronic exposure (AA EQS). For sediment EQS there is no short-term versus long-term EQS considering the exposure route (i.e., organisms would be constantly exposed while living in the sediment). In this document, we tested a process for EQS value inference for a few in-feed drugs (emamectin benzoate (EMB), ivermectin, teflubenzuron and lufenuron) and pesticides (azamethiphos and hydrogen peroxide) used in Canadian finfish aquaculture operations by relying on relevant and accessible toxicological data. These EQS values are proposed to illustrate the method used for threshold determination and are related to active ingredients. The selection of the final thresholds will have to be guided by the determination of clear management goals to be defined by policy makers. The following points summarize the main approach and recommendations of this working paper: There are two different approaches for EQS setting based on the quality and quantity of available toxicity data: the species sensitivity distributions (SSD) applied when a minimum of ten similar ecotoxicity end-points on a minimum of eight taxonomic groups are available, and the deterministic approach applied in situations where these data requirements are not met (European technical guidance document (CCME, 2007; TGD, 2018). The toxicity data of the in-feed drugs (EMB, ivermectin, teflubenzuron and lufenuron) do not meet the requirements for the completion of SSD (other than lufenuron as per previously completed SSD). Therefore, only a deterministic approach can be used to derive EQS as per the accessible data. This approach was also used for azamethiphos and hydrogen peroxide. However, a recommendation to test the feasibility of a SSD in the future is made for deriving MAC-EQS values with special considerations for dispersion timelines. This step will also have to be guided by inter-departmental regulatory considerations for pesticide usage. Quality assessment of available toxicity studies, both critical and supporting data as determined by regulatory bodies and/or international expert groups, was compiled. In addition, quality assessment of recent studies was completed using the published Criteria for Reporting and Evaluating ecotoxicity (CRED) and supporting guidance (Moermond et al., 2015; TGD, 2018) but will require additional considerations by groups of experts. Adjustments to assessment factors were guided by information on specific mode of action for chemicals such as azamethiphos, ivermectin, teflubenzuron, and lufenuron and whether data on identified sensitive target organisms were available. In addition, the selection of time-relevant toxicological data was also applied in the case of EQS values suggested for azamethiphos based on previous work on dispersion patterns. The EQS values presented in this document illustrate the process employed for their derivation and provide an overview of the toxicity data readily available. Enhanced access to confidential data provided to regulators for marketing authorisation will have to be facilitated to ensure the appropriate derivation of environmental standards. Ultimately, defining clear management goals by policy makers and additional expert discussions will guide the selection of the final EQS thresholds and their regulatory usage.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".