SSDs revisited: part II—practical considerations in the development and use of application factors applied to species sensitivity distributions

Application factors are routinely applied in the extrapolation of laboratory aquatic toxicity data to ensure protection from exposure to chemicals in the natural environment. The magnitude of the application factor is both a scientific and a policy decision, but in any case, it should be rooted in scientific knowledge so as to not be arbitrary. Information‐rich chemicals are often subjected to species sensitivity distribution (SSD) analysis to transparently describe certain aspects of assessment uncertainty and are normally subjected to much smaller application factors than screening information data sets. We describe a new set of tools useful to assess the quality of SSDs. Twenty‐two data sets and 19 chemicals representing agrochemicals, biocides, surfactants, metals, and common wastewater contaminants were compiled to demonstrate how the tools can be used. “Add‐one‐in” and “leave‐one‐out” simulations were used to investigate SSD robustness and develop quantitative evidence for the use of application factors. Theoretical new toxicity data were identified for add‐one‐in simulations based on the expected probabilities necessary to lower the hazardous concentration to 5% of a species (HC5) by a factor of 2, 3, 5, or 10. Simulations demonstrate the basis for application factors in the range of 1 to 5 for well‐studied chemicals with high‐quality SSDs. Leave‐one‐out simulations identify the fact that the most influential values in the SSD come from the extremes of the sensitive and tolerant toxicity values. Mesocosm and field data consistently demonstrate that HC5s are conservative, further justifying the use of small application factors for high‐quality SSDs. Environ Toxicol Chem 2019;38:1526–1541. © 2019 SETAC

• Publication year

  2019

• Venue

  Environmental Toxicology and Chemistry

• Publication date

  2019-06-24

• Fields of study

  Medicine, Chemistry, Environmental Science, Computer Science

• Identifiers
  DOI 10.1002/etc.4444PMID 30994956
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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