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Estimation of herbicide species sensitivity distribution using single‐species algal toxicity data and information on the mode of action
Author(s) -
Nagai Takashi,
Taya Kiyoshi
Publication year - 2015
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.2828
Subject(s) - sensitivity (control systems) , algae , logarithm , ec50 , toxicity , statistics , mode of action , environmental science , range (aeronautics) , biological system , biology , toxicology , environmental chemistry , ecology , mathematics , chemistry , mathematical analysis , biochemistry , materials science , organic chemistry , electronic engineering , engineering , in vitro , composite material
Although species sensitivity distribution (SSD) is a key concept for quantitative ecological risk assessment, its application is limited owing to a lack of sufficient data for the analysis, especially on the toxicity of herbicides for primary producers. The authors developed a method of herbicide SSD estimation using single‐species toxicity data and information on the herbicide mode of action. The authors' method was based on 2 assumptions: the slopes of the SSD of the same MOA herbicides are the same and the relative sensitivities of standard algae in the SSD of the same MOA herbicides are the same. The 2 parameters of log‐normal SSD, mean sensitivity, and variation in sensitivity, for 92 herbicides were determined to establish the estimation model. Mean sensitivities were linearly correlated with logarithmic 50% effect concentrations (EC50) for standard algae. The average of variations in sensitivity significantly differed among MOA, and variations in sensitivity were constant independently of EC50 values for standard algae for the same MOA herbicides. These results were all consistent with the assumptions of the SSD estimation method. The outcome was validated by comparing the estimated SSDs using the proposed method with the generated SSDs using toxicity data which were independent of method development. These SSDs were consistent, and considering MOA information improved the accuracy of estimating SSD markedly. Environ Toxicol Chem 2015;34:677–684. © 2014 SETAC