期刊
ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
卷 215, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ecoenv.2021.112155
关键词
Characteristic parameter k.ECx; The mode and strength of joint action; IA; rMDR(x); Exponential function
资金
- National Natural Science Foundation of China [51708447]
This study evaluates the joint toxicity of mixtures using the parameter k.ECx, finding that it can reflect the shape of concentration response curves and can be fitted by an exponential function to describe the relative differences and ratios. It suggests that k.ECx is a valuable tool for assessing the joint toxicity of binary mixtures, providing important data for planning experiments and reducing the number of experiments.
The evaluation of joint toxicity of mixtures is an important topic in toxicology. Previous studies have found that the parameter k.ECx of concentration response curves (CRCs) can be used to assess the applicability of concentration addition model (CA). This study further assesses the predictability of k.ECx on the joint toxicity evaluation. The toxicities of the twelve environmental pollutants, as well as those of binary mixtures with an equivalent-effect concentration ratio, to Vibrio fischeri were determined by using the microplate toxicity analysis. The toxicity evaluation of mixtures was conducted by CA and independent action model (IA). The relationship between the joint toxicity (measured by the relative model deviation ratio (rMDR)) and the k.EC(x )was studied. The results shows that the k.ECx could reflect the shape of CRCs in the whole concentration range. According to the IA and CA, 65% of the mixtures produce strong antagonistic or synergistic effect due to the significant difference of k.ECx. The percentage of the relative difference of k.ECx, of components and the rMDR(x) can be fitted by an exponential function. Different types of interactions could be described using this function. It is suggested that the joint toxicity of binary mixtures can be assessed with the parameter k.ECx, which can quickly get very important data when planning experiments, but also reduce the number of experiments.
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