基于主成分回归的整合模型预测重金属混合物毒性
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摘要
为解决CA和IA模型预测结果共线性的问题,基于主成分回归改进已有整合加和模型ICIM,建立新的混合物整合模型(PCR-IAM),并预测加和、协同和拮抗相互作用的重金属混合物联合毒性.以混合物实验浓度为因变量,浓度加和与独立作用预测混合物效应浓度的主成分回归为自变量,建立了PCR-IAM模型.以4个二元混合物体系(Ni-Fe、Ni-Pb、Ni-Cd和Ni-Cr)共20条混合物射线的联合毒性(共240个样本点)验证PCR-IAM模型的预测能力.结果表明,所有二元混合物的PCR-IAM模型的决定系数(R~2)和留一法(LOO)交叉验证相关系数(Q~2)值均大于0.95,表明PCR-IAM模型能够准确预测20条加和效应、协同和拮抗作用混合物的联合毒性.因此,经验数学模型PCR-IAM模型可以准确预测加和效应、协同和拮抗作用混合物毒性,为构建更合理的整合模型及环境混合污染物的风险评估提供可靠的技术手段.
In order to solve the problem of the prediction collinearity from CA and IA models,a new model with principal component regression(PCR-IAM)was developed.The PCR-IAM model is able to predict the joint toxicities of heavy metal mixtures with additive,synergetic and antagonistic effects.The PCR-IAM model was developed by using the experimental mixture concentration as dependent variable,and the principal component regression of concentration addition and independent action predictions as independent variable.Four binary mixture systems(Ni-Fe,Ni-Pb,Ni-Cd,and Ni-Cr)representing 20 mixture rays from 240 sampling points was used to verify the predictive power of the PCR-IAM model.The results showed that the coefficient of determination(R2)and leave-one-out cross-validation correlation coefficient(Q2)were greater than 0.95,which proved that the PCR-IAM model can accurately predict the mixture toxicities of 20mixture rays that presented additive,synergistic,and antagonistic effects.Therefore,the PCR-IAM model can precisely predict additive,synergistic,and antagonistic mixture toxicity,which provides a reliable method for risk assessment of environmental mixtures.
In order to solve the problem of the prediction collinearity from CA and IA models,a new model with principal component regression(PCR-IAM)was developed.The PCR-IAM model is able to predict the joint toxicities of heavy metal mixtures with additive,synergetic and antagonistic effects.The PCR-IAM model was developed by using the experimental mixture concentration as dependent variable,and the principal component regression of concentration addition and independent action predictions as independent variable.Four binary mixture systems(Ni-Fe,Ni-Pb,Ni-Cd,and Ni-Cr)representing 20 mixture rays from 240 sampling points was used to verify the predictive power of the PCR-IAM model.The results showed that the coefficient of determination(R2)and leave-one-out cross-validation correlation coefficient(Q2)were greater than 0.95,which proved that the PCR-IAM model can accurately predict the mixture toxicities of 20mixture rays that presented additive,synergistic,and antagonistic effects.Therefore,the PCR-IAM model can precisely predict additive,synergistic,and antagonistic mixture toxicity,which provides a reliable method for risk assessment of environmental mixtures.
引文
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[3]Liu S,Liu L,Chen F,et al.Application of the concentration addition model in the assessment of chemical mixture toxicity[J].Acta Chimica Sinica,2013,71(10):1335.
[4]Nagai T.Predicting herbicide mixture effects on multiple algal species using mixture toxicity models[J].Environmental Toxicology&Chemistry,2017.
[5]Qin L T,Liu S S,Zhang J,et al.A novel model integrated concentration addition with independent action for the prediction of toxicity of multi-component mixture[J].Toxicology,2011,280(3):164-172.
[6]Sanches A,Vieira B H,Reghini M V,et al.Single and mixture toxicity of abamectin and difenoconazole to adult zebrafish(Danio rerio)[J].Chemosphere,2017,188(11):582-587.
[7]Wang S,Zhuang W,Chen M,et al.Co-exposure of freshwater microalgae to tetrabromobisphenol A and sulfadiazine:Oxidative stress biomarker responses and joint toxicity prediction[J].Bulletin of Environmental Contamination&Toxicology,2017:1-7.
[8]葛会林,刘树深,苏冰霞.通用浓度加和模型预测有机磷与三嗪农药对绿藻的联合毒性[J].中国环境科学,2014,34(9):2413-2419.
[9]杨蓉,李娜,饶凯锋,等.环境混合物的联合毒性研究方法[J].生态毒理学报,2016,11(1):1-13.
[10]Loewe S,Muischnek H.Effect of combinations:Mathematical basis of problem[J].1926.
[11]Bliss C.I.The toxicity of poisons applied jointly[J].Annals of Applied Biology,1939,26(3):585–615.1939.
[12]Regenmortel T V,De K S.Mixtures of Cu,Ni and Zn Act Mostly Non-Interactively on Pseudokirchneriella subcapitata Growth in Natural Waters[J].Environmental Toxicology&Chemistry,2017,37(2).
[13]刘树深,刘玲,陈浮.浓度加和模型在化学混合物毒性评估中的应用[J].化学学报,2013,71(10):1335-1340.
[14]王成林,张瑾,刘树深,等.3种离子液体与甲霜灵二元混合物的联合毒性[J].中国环境科学,2012,32(11):2090-2094.
[15]Mulaisho M,Wang X Z,Buontempo F V,et al.Prediction of Noninteractive Mixture Toxicity of Organic Compounds Based on a Fuzzy Set Method[J].J Chem Inf Comput Sci,2004,44(5):1763-1773.
[16]覃礼堂,刘树深,莫凌云.改进的整合加和模型INFCIM及其应用于混合物毒性预测[J].中国环境科学,2014,34(7):1890-1896.
[17]Kim J.Development of a partial least squares-based integrated addition model for predicting mixture toxicity[J].Human&Ecological Risk Assessment An International Journal,2014,20(1):174-200.
[18]Qin L T,Wu J,Mo L Y,et al.Linear regression model for predicting interactive mixture toxicity of pesticide and ionic liquid[J].Environmental Science&Pollution Research,2015,22(16):1-10.
[19]Carusso S,Juárez A B,Moretton J,et al.Effects of three veterinary antibiotics and their binary mixtures on two green alga species[J].Chemosphere,2018,194:821-827.
[20]王猛超,刘树深,陈浮.拓展浓度加和模型预测三种三嗪类除草剂混合物的时间依赖毒性[J].化学学报,2014,72(1):56-60.
[21]秦萌.六种常见重金属对三种绿藻的毒性研究.[D].桂林:桂林理工大学,2016.
[22]Moa L,Zhenga M,Meng Q,et al.Quantitative characterization of the toxicities of Cd-Ni and Cd-Cr binary mixtures using combination index method[J].Bio Med Research International,2016,(2016-12-1),2016,2016(4):1-6.