Improvement on species sensitivity distribution methods for deriving site-specific water quality criteria

Wang, Yeyao; Zhang, Lingsong; Meng, Fansheng; Zhou, Yuexi; Jin, Xiaowei; Giesy, John P.; Liu, Fang

doi:10.1007/s11356-014-3783-x

Cited by 14 publications

(7 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. Indeed, numerous studies have attempted to address many of the limitations, including issues of sample size, species representativeness and selection, test endpoints, ecological relevance, phylogenetic relatedness, and routes of exposure (e.g., de Zwart and Posthuma 2005; Dyer et al 2006; Fox 2010; Wang et al 2015; Warne et al 2018; Belanger and Carr 2019; Carr and Belanger 2019; Moore et al 2019; Schwarz and Tillmanns 2019). Although certain improvements to formal SSD methods have recently been adopted (i.e., methods typically approved and recommended for use by national, provincial, and state regulatory bodies; see: Warne et al 2018; British Columbia Ministry of Environment and Climate Change Strategy 2019), in general, few of the outcomes of SSD studies from the past 20 yr have been formally adopted.…”

Section: Introductionmentioning

confidence: 99%

Recent Developments in Species Sensitivity Distribution Modeling

Fox

Dam

Fisher

et al. 2021

Enviro Toxic and Chemistry

View full text Add to dashboard Cite

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

show abstract

Section: Introductionmentioning

confidence: 99%

Recent Developments in Species Sensitivity Distribution Modeling

Fox

Dam

Fisher

et al. 2021

Enviro Toxic and Chemistry

View full text Add to dashboard Cite

show abstract

“…Yet, the biological community does not necessarily follow 1 single distribution (Xu et al 2015). Nonparametrical SSD approaches have been developed to tackle this challenge (Newman et al 2000;Wang et al 2015;Monti et al 2018). One of these approaches is the probabilistic species sensitivity distribution (PSSD) (Gottschalk and Nowack 2013).…”

Section: Introductionmentioning

confidence: 99%

Systematic Consideration of Parameter Uncertainty and Variability in Probabilistic Species Sensitivity Distributions

Wigger

Kawecki

Nowack

et al. 2019

Integr Envir Assess & Manag

View full text Add to dashboard Cite

The calculation of a species sensitivity distribution (SSD) is a commonly accepted approach to derive the predicted no‐effect concentration (PNEC) of a substance in the context of environmental risk assessment. The SSD approach usually is data demanding and incorporates a large number of ecotoxicological values from different experimental studies. The probabilistic SSD (PSSD) approach is able to fully consider the variability between different exposure conditions and material types, which is of great importance when constructing an SSD for any chemical, especially for nanomaterials. The aim of our work was to further develop the PSSD approach by implementing methods to better consider the uncertainty and variability of the input data. We incorporated probabilistic elements to consider the uncertainty associated with uncertainty factors by using probability distributions instead of single values. The new PSSD method (named “PSSD+”) computes 10 000 PSSDs based on a Monte Carlo routine. For each PSSD calculated, the hazardous concentration for 5% of species (HC5) was extracted to provide a PNEC distribution based on all data available and their associated uncertainty. The PSSD+ approach also includes the option to consider a species weighting according to a typically constituted biome. We applied this PSSD+ approach to a previously published data set on C nanotubes and Ag nanoparticles. The evaluation of the uncertainty factor distributions and species weighting have shown that the proposed PSSD method is robust with respect to the calculation of the PNEC value. Furthermore, we demonstrated that the PSSD+ can handle both small and more comprehensive data sets because the PNEC distributions are a close representation of the data available. Finally, the sensitivity testing toward data set variations showed that the maximum variation of the mean PNEC was of a factor of about 2, so that the method is relatively insensitive to missing data points as long as the most sensitive species is included. Integr Environ Assess Manag 2020;16:211–222. © 2019 SETAC

show abstract

“…Firstly, log-normal distribution test was conducted on the exposure data of some PAEs in Baiyangdian Lake and typical rivers in China and the chronic toxicity data of aquatic organisms based on the endpoint of reproductive toxicity test. After that, the cumulative function of chronic toxicity data and the anti-cumulative function of PAEs exposure data were plotted to obtain the JPCs of PAEs [ 38 , 39 ].…”

Section: Methodsmentioning

confidence: 99%

Risk Assessment of Phthalate Esters in Baiyangdian Lake and Typical Rivers in China

Hou

Li³

et al. 2023

Toxics

View full text Add to dashboard Cite

Phthalate esters (PAEs) are frequently tracked in water environments worldwide. As a typical class of endocrine disruptor chemicals (EDCs), PAEs posed adverse effects on aquatic organisms at low concentration. Thus, they have attracted wide attention in recent years. In the present study, the concentrations of seven typical PAEs from 30 sampling sites in Baiyangdian Lake were measured, and the environmental exposure data of PAEs were gathered in typical rivers in China. Then, based on the aquatic life criteria (ALCs) derived from the reproductive toxicity data of aquatic organisms, two risk assessment methods, including hazard quotient (HQ) and probabilistic ecological risk assessment (PERA), were adopted to evaluate the ecological risks of PAEs in water. The sediment quality criteria (SQCs) of DEHP, DBP, BBP, DIBP and DEP were deduced based on the equilibrium partitioning method. Combined with the gathered environmental exposure data of seven PAEs in sediments from typical rivers in China, the ecological risk assessments of five PAEs in sediment were conducted only by the HQ method. The results of ecological risk assessment showed that in terms of water, DBP and DIBP posed low risk, while the risk of DEHP in Baiyangdian Lake cannot be ignored and should receive attention. In typical rivers in China, BBP and DEP posed no risk, while DIBP and DBP posed potential risk. Meanwhile, DEHP posed a high ecological risk. As far as sediment is concerned, DBP posed a high risk in some typical rivers in China, and the other rivers had medium risk. DEHP posed a high risk only in a few rivers and low to medium risk in others. This study provides an important reference for the protection of aquatic organisms and the risk management of PAEs in China.

show abstract

Improvement on species sensitivity distribution methods for deriving site-specific water quality criteria

Cited by 14 publications

References 25 publications

Recent Developments in Species Sensitivity Distribution Modeling

Recent Developments in Species Sensitivity Distribution Modeling

Systematic Consideration of Parameter Uncertainty and Variability in Probabilistic Species Sensitivity Distributions

Risk Assessment of Phthalate Esters in Baiyangdian Lake and Typical Rivers in China

Contact Info

Product

Resources

About