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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Abstract 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.

Key words： joint toxicity principal component regression additive effects interaction heavy metals

Received: 18 November 2018

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	Articles by authors
	DENG Yang
	QIN Li-Tang
	ZENG Hong-Hu
	QIN Meng
	MO Ling-Yun
	LIANG Yan-Peng
	SONG Xiao-Hong

Cite this article:

DENG Yang,QIN Li-Tang,ZENG Hong-Hu等. Prediction of toxicity of heavy metal mixture by integrated model based on principal component regression[J]. CHINA ENVIRONMENTAL SCIENCECE, 2018, 38(5): 1970-1978.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2018/V38/I5/1970

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