General Introduction to Species Sensitivity Distributions

Posthuma, Leo; Traas, Theo P.; Suter, Glenn W.

doi:10.1201/9781420032314.sec1

Cited by 194 publications

(314 citation statements)

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“…SSDs provide a relatively simple and understandable means of converting the large body of species-level ecotoxicity data into a single community metric (i.e., the hazardous concentrations 5%, HC5), which corresponds to a concentration at which 5% of species would be expected to be adversely affected after exposure to a chemical (Posthuma et al 2001;Wheeler et al, 2002). However, SSD methodologies tend to only deal with interspecies differences and they make a number of assumptions.…”

Section: Deriving Wqgs For Ecosystem Protectionmentioning

confidence: 99%

Water quality guidelines for chemicals: learning lessons to deliver meaningful environmental metrics

Merrington

Grist

et al. 2013

Environ Sci Pollut Res

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Many jurisdictions around the globe have well developed regulatory frameworks for the derivation and implementation of water quality guidelines (WQGs) or their equivalent (e.g. environmental quality standards, criteria, objectives or limits). However, a great many more still do not have such frameworks and are looking to introduce practical methods to manage chemical exposures in aquatic ecosystems. There is a potential opportunity for learning and sharing of data and information between experts from different jurisdictions in order to deliver efficient and effective methods to manage potential aquatic risks, including the considerable reduction in the need for aquatic toxicity testing and the rapid identification of common challenges. This paper reports the outputs of an international workshop with representatives from 14 countries held in Hong Kong in December 2011. The aim of the workshop and this paper was to identify 'good practice' in the development of WQGs to deliver to a range environmental management goals. However, it is important to broaden this consideration to cover often overlooked facets of implementable WQGs, such as demonstrable field-validation (i.e., does the WQG protect what it is supposed to?), fit for purpose of monitoring frameworks (often an on-going cost) and finally how are these monitoring data used to support management decisions in a manner that is transparent and understandable to stakeholders. It is clear that regulators and the regulated community have numerous pressures and constraints on their resources. Therefore the final section of this paper addresses potential areas of collaboration and harmonisation. Such approaches could deliver a consistent foundation from which to assess potential chemical aquatic risks, including, for example, the adoption of bioavailability-based approaches for metals, whilst reducing administrative and technical burdens in jurisdictions.

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Section: Deriving Wqgs For Ecosystem Protectionmentioning

confidence: 99%

Water quality guidelines for chemicals: learning lessons to deliver meaningful environmental metrics

Merrington

Grist

et al. 2013

Environ Sci Pollut Res

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“…It therefore reduces the risk of protecting substantially less than 95% of the species, as could occur with an HC5 determined from insufficient data. Indeed, the confidence interval is greater and the HC5-95% value consequently lower when the quantity of available data decreases and/or the variability of these data increases (19,35,36). However, in some cases, not enough NOEC data are available to set up a valid SSD.…”

Section: Conceptmentioning

confidence: 99%

“…The assumption that this constant ACR exists, especially this value of 10, has been often discussed and challenged (see ref 16) for review) as the ACR value can greatly vary depending on mode of action (61), or if the mode of action is different in acute and chronic endpoints (60). For algal toxicity however, which is relevant in the context of this study, a constant ACR is more likely because EC50 and NOEC data are typically both derived from the same growth inhibition test.Comparison between SSD-EC50 and SSD-NOEC for some compounds showed that the assumption of a constant ACR is reasonable for atrazine and malathion, but not for chlorpyriphos (19), if all the available data are included in the calculation, i.e., not only primary producers. In the present study, an ACR of 8 was found for atrazine between SSD-EC50 and SSD-NOEC which is in agreement with the results by Posthuma et al (19).…”

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confidence: 99%

Including Mixtures in the Determination of Water Quality Criteria for Herbicides in Surface Water

Chèvre

Loepfe

Singer

et al. 2005

Environ. Sci. Technol.

119

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Monitoring programs throughout America and Europe have demonstrated the common occurrence of herbicides in surface water. Nevertheless, mixtures are rarely taken into account in water quality regulation. Taking mixtures into account is only feasible if the water quality criteria (WQC) of the single compounds are derived by a common and consistent methodology, which overcomes differences in data quality without settling on the lowest common denominator but making best use of all available data. In this paper, we present a method of defining a risk quotient for mixtures of herbicides with a similar mode of action (RQ m ). Consistent and comparable WQC are defined for single herbicides as a basis for the calculation of the RQ m . Derived from the concentration addition model, the RQ m can be expressed as the sum of the ratios of the measured environmental concentration and the WQC for each herbicide. The RQ m should be less than one to ensure an acceptable risk to aquatic life. This approach has the advantage of being easy to calculate and communicate, and is proposed as a replacement for the current limit of 0.1 µg/L for herbicides in Switzerland. We illustrate the proposed approach on the example of five commonly applied herbicides (atrazine, simazine, terbuthylazine, isoproturon, and diuron). Their risk profile, i.e., the RQ m as a function of time for one exemplary river, clearly shows that the single compounds rarely exceeded their individual WQC. However, the contribution of peaks of different seasonally applied herbicides, whose application periods partially overlap, together with the continuously emitted herbicides from nonagricultural use, results in the exceedance of the RQ m threshold value of one upon several occasions. IntroductionPesticides, including herbicides, differ from most industrial organic compounds in being introduced into the environment with the explicit intention of exerting effects on one or more target organisms. Unfortunately, they do not exert their toxic action only where they are applied, but can, through persistence and transport, reach other compartments of the ecosystem. Monitoring programs throughout North America and Europe have demonstrated the widespread presence of pesticides in various freshwater bodies (1-6). Over the past decade, as public concern has focused on the possible impacts of pesticides on the environment, several European and North American countries (7)(8)(9)(10)(11)(12)(13)(14) have defined specific water quality criteria (WQC) for each pesticide in surface waters. Within the EU, these WQC are often equivalent to the predicted no-effect concentration (PNEC), which aims to ensure the overall protection of aquatic life (9,12,14). This parameter is usually estimated by finding the lowest reliable aquatic effect concentration and applying a safety factor to account for various uncertainties, such as interspecies differences in sensitivity, acute-to-chronic ratios, and laboratory-to-field extrapolations (for review see refs 15-17). The drawback of this approach is...

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“…The SSD method was first proposed by Kooijman (1987) and later improved by subsequent studies (Aldenberg and Slob, 1993;Newman et al, 2000;Posthuma et al, 2002;Wagner and Lokke, 1991). It is generally applied to circumstances in which there are at least 10 available toxicity data (Jin et al, 2009;Balk et al, 1995;Lei et al, 2009).…”

Section: Species Sensitivity Distribution Methodsmentioning

confidence: 99%

Predicted no-effect concentrations for mercury species and ecological risk assessment for mercury pollution in aquatic environment

Wei

Tan

et al. 2015

Journal of Environmental Sciences

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Mercury (Hg) exists in different chemical forms presenting varied toxic potentials. It is necessary to explore an ecological risk assessment method for different mercury species in aquatic environment. The predicted no-effect concentrations (PNECs) for Hg(II) and methyl mercury (MeHg) in the aqueous phase, calculated using the species sensitivity distribution method and the assessment factor method, were 0.39 and 6.5 × 10 −3 μg/L, respectively. The partition theory of Hg between sediment and aqueous phases was considered, along with PNECs for the aqueous phase to conduct an ecological risk assessment for Hg in the sediment phase. Two case studies, one in China and one in the Western Black Sea, were conducted using these PNECs. The toxicity of mercury is heavily dependent on their forms, and their potential ecological risk should be respectively evaluated on the basis of mercury species.

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General Introduction to Species Sensitivity Distributions

Cited by 194 publications

References 0 publications

Water quality guidelines for chemicals: learning lessons to deliver meaningful environmental metrics

Water quality guidelines for chemicals: learning lessons to deliver meaningful environmental metrics

Including Mixtures in the Determination of Water Quality Criteria for Herbicides in Surface Water

Predicted no-effect concentrations for mercury species and ecological risk assessment for mercury pollution in aquatic environment

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