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通过生物配体模型分析铜和镉组合对浮萍的毒性。

Toxicity of copper and cadmium in combinations to Duckweed analyzed by the biotic ligand model.

作者信息

Hatano Ayumi, Shoji Ryo

机构信息

Department of Chemical Science and Engineering, Tokyo National College of Technology, 1220-2 Kunugida, Hachioji, Tokyo 193-0997, Japan.

出版信息

Environ Toxicol. 2008 Jun;23(3):372-8. doi: 10.1002/tox.20348.

Abstract

The biotic ligand model (BLM) of acute toxicity to aquatic organisms is based on the concept that metals binding onto biotic ligand may cause toxic effect on the organism. The BLM can take into incorporation between metal speciation and the protective effects of competing cations account. The demonstrated BLM can provide a good estimation of the amount of single metal effect under various conditions such as pH, coexistence of other non toxic cations. However, toxic metals are often found as mixture in nature. This study estimated combined toxicity of Cu and Cd examined by growth inhibition of Duckweed (Lemna paucicostata) by using single toxicity data as toxic unit (TU) derived by three types of model, BLM and two conventional models, free ion activity model (FIAM), and total metal concentration model. According to our results, single toxicity data derived by the BLM can estimate combined toxicity described as a function of TU. Particularly under the high level of heavy metals stress, BLM clearly predicted toxicity of heavy metals compared with other two models. According to numeric correlation (R(2), root mean square error), the order is BLM (R=0.83, RMSE=13.5)> total metal concentration model (R=0.41, RMSE=24.9)> FIAM (R=0.36, RMSE=26.1).

摘要

水生生物急性毒性的生物配体模型(BLM)基于这样的概念,即金属与生物配体结合可能对生物体产生毒性作用。BLM可以考虑金属形态与竞争性阳离子保护作用之间的相互关系。已证实的BLM能够在各种条件下,如pH值、其他无毒阳离子共存的情况下,对单一金属的效应量提供良好的估计。然而,有毒金属在自然界中往往以混合物形式存在。本研究通过使用由三种模型(BLM以及两种传统模型,即自由离子活性模型(FIAM)和总金属浓度模型)得出的单一毒性数据作为毒性单位(TU),以浮萍(少根紫萍)生长抑制来检验铜和镉的联合毒性。根据我们的结果,由BLM得出的单一毒性数据能够估计以TU函数表示的联合毒性。特别是在重金属高胁迫水平下,与其他两种模型相比,BLM能更清晰地预测重金属毒性。根据数值相关性(R²,均方根误差),顺序为BLM(R = 0.83,RMSE = 13.5)>总金属浓度模型(R = 0.41,RMSE = 24.9)> FIAM(R = 0.36,RMSE = 26.1)。

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