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西维因与胡椒基丁醚对蚤状溞协同毒性作用 被引量:1

Joint toxicity of carbaryl and piperonyl butoxide to Daphnia pulex
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摘要 目的探讨农药混合物联合毒性的评定方法。方法选择蚤状溞作为受试生物,测定西维因和胡椒基丁醚对蚤状溞的单一毒性及其联合毒性,基于单一化合物的浓度-反应曲线,引入交互作用系数,运用浓度相加(CA)与独立作用(IA)模型对二元混合物的联合毒性进行预测,并将模型预测结果与混合物毒性实验数据进行比较分析。结果西维因、胡椒基丁醚及2者混合物对蚤状溞的急性毒性数据均可通过Weibull方程拟合,R2分别为0.890 05、0.928 34、0.944 41,由拟合方程得到的半数效应浓度(EC50)分别为0.071 4、5.714 7、2.968 0μmol/L;西维因与胡椒基丁醚对蚤状溞急性毒性呈协同作用;西维因与胡椒基丁醚混合物毒性引入交互作用系数K后,IA模型预测曲线与实验数据拟合曲线更接近。结论农药混合物毒性可以通过模型预测,化合物的交互作用可通过K函数修饰。 Objective To explore the toxicity and its assessment methods of pesticide mixtures. Methods The Daphnia pulex were exposed to carbaryl ,piperonyl butoxide as well as the mixtures of the two pesticides and the activity inhibition rates of Daphnia pulex were detected. Then based on the concentration-response curves of individual compound and interaction coefficient, two models including concentration addition (CA) and independent action ( IA ) were employed to predict the joint toxicity of the mixtures. The concentration-response curves predicted by CA and IA were com- pared with that of based on the experimental data. Results The data of acute toxicity experiment of carbaryl, piperonyl butoxide and the mixture could be fitted by Weibull equation, and the correlation coefficients ( R2 ) were 0. 89005, 0. 92834 ,and 0. 944 41. The values of median effective concentration (ECs0) obtained by the equations were 0. 0714, 5. 7147, and 2. 968 μmol/L. A synergistic interaction between carbaryl and piperonyl butoxide was observed. After introducing K-functions, the fitted curve predicted by IA models was more approximate to the fitting curve based on the experimental data in the assessment of joint toxicity of the two pesticides. Conclusion Toxic effects of pesticide mixture can be predicted by models, and the interactions of the pesticide mixture can be modified by K-function.
出处 《中国公共卫生》 CAS CSCD 北大核心 2016年第5期621-624,共4页 Chinese Journal of Public Health
基金 中央高校基本科研业务费专项资金 江苏省普通高校研究生科研创新计划资助项目(SJLX_0109)
关键词 联合毒性 浓度相加(CA) 独立作用(IA) 交互作用 预测模型 joint toxicity concentration addition independent action interaction predictive model
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