Estimating Water Quality Guidelines for Environmental Contaminants Using Multimodal Species Sensitivity Distributions: A Case Study with Atrazine

The species sensitivity distribution (SSD) is a statistical approach that is used to estimate either the concentration of a chemical that is hazardous to no more than x% of all species (the HCx) or the proportion of species potentially affected by a given concentration of a chemical. Despite a significant body of published research and critical reviews over the past 20 yr aimed at improving the methodology, the fundamentals remain unchanged. Although there have been some recent suggestions for improvements to SSD methods in the literature, in general, few of these suggestions have been formally adopted. Furthermore, critics of the approach can rightly point to the fact that differences in technical implementation can lead to marked differences in results, thereby undermining confidence in SSD approaches. Despite the limitations, SSDs remain a practical tool and, until a demonstrably better inferential framework is available, developments and enhancements to conventional SSD practice will and should continue. We therefore believe the time has come for the scientific community to decide how it wants SSD methods to evolve. The present study summarizes the current status of, and elaborates on several recent developments for, SSD methods, specifically, model averaging, multimodality, and software development. We also consider future directions with respect to the use of SSDs, with the ultimate aim of helping to facilitate greater international collaboration and, potentially, greater harmonization of SSD methods. Environ Toxicol Chem 2021;40:293–308. © 2020 SETAC

Section: Statistical Mixture Modelling (Smm)mentioning

confidence: 99%

Recent Developments in Species Sensitivity Distribution Modeling

Fox

Dam

Fisher

et al. 2021

“…There are numerically large differences at the level of the median value (HC50 EC50 ), but expectedly lower numerical consequences in the tails between the nonsplit and split SSD approaches (e.g., Zajdlik et al 2009). Furthermore, a lower percentile will be more representative of actually occurring pressures from chemicals present in the environment.…”

Section: Damage Metricsmentioning

confidence: 99%

“…The choice of a lower percentile than the median will also reduce the discrepancy with contemporary approaches in chemical risk assessment that ask for the use of several SSD models in the case of chemicals with a specific mode of action (most pesticides). There are numerically large differences at the level of the median value (HC50 EC50 ), but expectedly lower numerical consequences in the tails between the nonsplit and split SSD approaches (e.g., Zajdlik et al 2009).…”

Section: Damage Metricsmentioning

confidence: 99%

Toward harmonizing ecotoxicity characterization in life cycle impact assessment

Fantke

Aurisano

Bare

et al. 2018

Ecosystem quality is an important area of protection in life cycle impact assessment (LCIA). Chemical pollution has adverse impacts on ecosystems on a global scale. To improve methods for assessing ecosystem impacts, the Life Cycle Initiative hosted by the United Nations Environment Programme established a task force to evaluate the state-of-the-science in modeling chemical exposure of organisms and the resulting ecotoxicological effects for use in LCIA. The outcome of the task force work will be global guidance and harmonization by recommending changes to the existing practice of exposure and effect modeling in ecotoxicity characterization. These changes will reflect the current science and ensure the stability of recommended practice. Recommendations must work within the needs of LCIA in terms of 1) operating on information from any inventory reporting chemical emissions with limited spatiotemporal information, 2) applying best estimates rather than conservative assumptions to ensure unbiased comparison with results for other impact categories, and 3) yielding results that are additive across substances and life cycle stages and that will allow a quantitative expression of damage to the exposed ecosystem. We describe the current framework and discuss research questions identified in a roadmap. Primary research questions relate to the approach toward ecotoxicological effect assessment, the need to clarify the method's scope and interpretation of its results, the need to consider additional environmental compartments and impact pathways, and the relevance of effect metrics other than the currently applied geometric mean of toxicity effect data across species. Because they often dominate ecotoxicity results in LCIA, we give metals a special focus, including consideration of their possible essentiality and changes in environmental bioavailability. We conclude with a summary of key questions along with preliminary recommendations to address them as well as open questions that require additional research efforts. Environ Toxicol Chem 2018;37:2955-2971. © 2018 SETAC.

“…The first step is to choose a distribution that seems appropriate to describe the data. Possible options include a Weibull distribution to emphasize the tails of the distribution; a triangular distribution with a finite‐size support when no species are more sensitive/resilient than a certain threshold value; or a multimodal distribution for an assemblage of several taxons, for example. Log‐normal and log‐logistic distributions are the customary choices , although an extensive list of distributions have been applied to SSD.…”

Section: Review Of Methods To Fit An Ssdmentioning

confidence: 99%

MOSAIC_SSD: A new web tool for species sensitivity distribution to include censored data by maximum likelihood

King

Veber

Charles

et al. 2014

Censored data are seldom taken into account in species sensitivity distribution (SSD) analysis. However, they are found in virtually every dataset and sometimes represent the better part of the data. Stringent recommendations on data quality often entail discarding a lot of these meaningful data, resulting in datasets of reduced size which lack representativeness of any realistic community. However, it is reasonably simple to include censored data in SSD by using an extension of the standard maximum likelihood method. The authors detail this approach based on the use of the R-package fitdistrplus, dedicated to the fit of parametric probability distributions. The authors present the new Web tool MOSAIC_SSD, that can fit an SSD on datasets containing any type of data, censored or not. The MOSAIC_SSD Web tool predicts any hazardous concentration and provides bootstrap confidence intervals on the predictions. Finally, the authors illustrate the added value of including censored data in SSD, taking examples from published data.