Validation of a four‐day <i>Ceriodaphnia</i> toxicity test and statistical considerations in data analysis

Masters, Julie A.; Lewis, Michael A.; Davidson, Daniel; Bruce, Robert D.

doi:10.1002/etc.5620100106

Cited by 27 publications

References 20 publications

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Toxicity Identification Evaluation

Markle

2004

Water Encyclopedia

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Identification of what is “toxic” in aquatic toxicity tests is most often determined quantitatively through the regulatory process as permit limits or objectives and no definitive standards have been defined or are universally used. Although no universal definitions exist, several measures of toxicity are more commonly employed and seem to have achieved greater acceptance. These include the use of the “no observed effect concentration” (NOEC) or “no observed effect level” (NOEL). The NOEC and NOEL use hypothesis testing to identify statistically significant differences relative to a nontoxic control. The NOEC or NOEL is defined as the highest concentration not significantly different from the control. Another commonly used analysis involves various point estimation techniques. These quantitative analyses use dose–response data to estimate “safe” or “nontoxic” concentrations or effects using metrics such as the LC 50 (lethal concentration causing a 50% effect) or EC/IC 25 (effective or inhibition concentration causing a 25% effect). Due to the large amount of toxicity testing required in the TIE process, reductions in testing replication and exposure volume and duration are often made to save on sample and staffing resources.

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Toxicity Identification Evaluation

Markle

2004

Water Encyclopedia

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Demonstrating the Reliability of bio‐met for Determining Compliance with Environmental Quality Standards for Metals in Europe

Peters

Nys²,

Merrington

et al. 2020

Enviro Toxic and Chemistry

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The importance of considering the bioavailability of metals in understanding and assessing their toxicity in freshwaters has been recognized for many years. Currently, biotic ligand models (BLMs) are being applied for the derivation and implementation of environmental quality standards (EQS) for metals under the Water Framework Directive in Europe. bio-met is a simplified tool that was developed for implementing bioavailability-based EQS for metals in European freshwaters. We demonstrate the reliability of the relationship between the full BLM predictions and the thresholds (hazardous concentration affecting 5% of species [HC5] values) predicted by bio-met in 3 stages, for the metals copper, nickel, and zinc. First, ecotoxicity data for specific species from laboratory tests in natural waters are compared with predictions by the individual species BLMs included in the full BLMs. Second, the site-specific HC5 values predicted by bio-met for the natural waters used for ecotoxicity testing are compared with those provided by the full BLMs. The reliability of both relationships is demonstrated for all 3 metals, with more than 80% of individual species BLM predictions being within a factor of 3 of the experimental results, and 99% of bio-met local HC5 predictions being within a factor of 2 of the full BLM result. Third, using a larger set of European natural waters in addition demonstrates the reliability of bio-met over a broad range of water chemistry conditions. bio-met is therefore an appropriate tool for performing compliance assessments against EQS values in Europe, due to the demonstrated consistency with the toxicity test data.

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Statistical treatment of data from microbial toxicity tests

Nyholm¹,

Sørensen²,

Kusk³

et al. 1992

Enviro Toxic and Chemistry

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The response measured in microbial toxicity tests is typically a continuous variable, and the associated error is an experimental error related to the physical measurement of the response and to handling differences between experimental units (e.g., test flasks). The traditional statistical theory for treating quantal (or categorical) data from tests with animals is based on the concept of a tolerance distribution for individual test subjects that is not appropriate for microbial tests. Data from microbial tests should be dealt with by conventional curve‐fitting techniques without any confusion with tolerance distributions. Using an empirical mathematical function that describes a sigmoid curve, concentration–response curves can be fitted by nonlinear regression directly onto the data or by weighted linear regression after a linear transformation of the data. Calculation of confidence intervals around the estimated EC figures (calculated effective concentrations that cause a certain percentage of inhibition; for example, EC10 for 10% inhibition) is less complicated with weighted linear regression on transformed data than it is with nonlinear regression. The paper provides a general discussion of the problem of analyzing data from microbial toxicity tests and gives some examples of feasible methods.

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Validation of a four‐day Ceriodaphnia toxicity test and statistical considerations in data analysis

Cited by 27 publications

References 20 publications

Toxicity Identification Evaluation

Toxicity Identification Evaluation

Demonstrating the Reliability of bio‐met for Determining Compliance with Environmental Quality Standards for Metals in Europe

Statistical treatment of data from microbial toxicity tests

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