Mixture predicted no-effect concentrations derived by independent action model vs concentration addition model based on different species sensitivity distribution models

In the hazard assessment of mixtures, the mixture predicted no-effect concentration (mPNEC) is always derived by the concentration addition (CA) model (mPNECCA) to assess the risk of mixtures combined with exposure assessment. However, the independent action (IA) model, which is also widely used as...

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Autores principales: Ze-Jun Wang, Shu-Shen Liu, Peng Huang, Ya-Qian Xu
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Publicado: Elsevier 2021
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spelling oai:doaj.org-article:e2e6fe657afc4e7ba40f1b9ab50eaa3b2021-11-06T04:15:54ZMixture predicted no-effect concentrations derived by independent action model vs concentration addition model based on different species sensitivity distribution models0147-651310.1016/j.ecoenv.2021.112898https://doaj.org/article/e2e6fe657afc4e7ba40f1b9ab50eaa3b2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S0147651321010101https://doaj.org/toc/0147-6513In the hazard assessment of mixtures, the mixture predicted no-effect concentration (mPNEC) is always derived by the concentration addition (CA) model (mPNECCA) to assess the risk of mixtures combined with exposure assessment. However, the independent action (IA) model, which is also widely used as the CA model in the prediction and evaluation of mixture toxicity, is always used to calculate the population fraction showing a predefined effect, not mPNEC, and this limits the application of IA model in the mixture risk assessment. In this study, we explored the process of mPNEC derived by the IA method (mPNECIA) based on the species sensitivity distribution (SSD) and compared mPNECIA with mPNECCA. Taking two common pesticides, dimethoate (DIM) and dichlorvos (DIC), exposed in the actual water environment as an example, their SSD models were constructed separately using nine distribution functions after toxicity data screening and quality testing. For both DIC and DIM, all different nine models had passed the Kolmogorov-Smirnov test. Then, the PNECs of two pesticides were derived based on SSD models. Finally, mPNECIA with different concentration ratios was derived and compared to mPNECCA based on 81 combinations of nine SSD models. Most mPNEC values derived by IA model were more conservative than those by CA. It is worth noting that the mPNECIA is more conservative than mPNECCA for the commonly used log-logit distribution (function 7), log-normal distribution (8), and log-Weibull distribution (9). This study provides a new direction for the application of IA in the risk assessment and enriches the framework of mixture risk assessment.Ze-Jun WangShu-Shen LiuPeng HuangYa-Qian XuElsevierarticleBinary mixtureConservatismModel-dependentMixture risk assessmentMixture toxicologyEnvironmental pollutionTD172-193.5Environmental sciencesGE1-350ENEcotoxicology and Environmental Safety, Vol 227, Iss , Pp 112898- (2021)
institution DOAJ
collection DOAJ
language EN
topic Binary mixture
Conservatism
Model-dependent
Mixture risk assessment
Mixture toxicology
Environmental pollution
TD172-193.5
Environmental sciences
GE1-350
spellingShingle Binary mixture
Conservatism
Model-dependent
Mixture risk assessment
Mixture toxicology
Environmental pollution
TD172-193.5
Environmental sciences
GE1-350
Ze-Jun Wang
Shu-Shen Liu
Peng Huang
Ya-Qian Xu
Mixture predicted no-effect concentrations derived by independent action model vs concentration addition model based on different species sensitivity distribution models
description In the hazard assessment of mixtures, the mixture predicted no-effect concentration (mPNEC) is always derived by the concentration addition (CA) model (mPNECCA) to assess the risk of mixtures combined with exposure assessment. However, the independent action (IA) model, which is also widely used as the CA model in the prediction and evaluation of mixture toxicity, is always used to calculate the population fraction showing a predefined effect, not mPNEC, and this limits the application of IA model in the mixture risk assessment. In this study, we explored the process of mPNEC derived by the IA method (mPNECIA) based on the species sensitivity distribution (SSD) and compared mPNECIA with mPNECCA. Taking two common pesticides, dimethoate (DIM) and dichlorvos (DIC), exposed in the actual water environment as an example, their SSD models were constructed separately using nine distribution functions after toxicity data screening and quality testing. For both DIC and DIM, all different nine models had passed the Kolmogorov-Smirnov test. Then, the PNECs of two pesticides were derived based on SSD models. Finally, mPNECIA with different concentration ratios was derived and compared to mPNECCA based on 81 combinations of nine SSD models. Most mPNEC values derived by IA model were more conservative than those by CA. It is worth noting that the mPNECIA is more conservative than mPNECCA for the commonly used log-logit distribution (function 7), log-normal distribution (8), and log-Weibull distribution (9). This study provides a new direction for the application of IA in the risk assessment and enriches the framework of mixture risk assessment.
format article
author Ze-Jun Wang
Shu-Shen Liu
Peng Huang
Ya-Qian Xu
author_facet Ze-Jun Wang
Shu-Shen Liu
Peng Huang
Ya-Qian Xu
author_sort Ze-Jun Wang
title Mixture predicted no-effect concentrations derived by independent action model vs concentration addition model based on different species sensitivity distribution models
title_short Mixture predicted no-effect concentrations derived by independent action model vs concentration addition model based on different species sensitivity distribution models
title_full Mixture predicted no-effect concentrations derived by independent action model vs concentration addition model based on different species sensitivity distribution models
title_fullStr Mixture predicted no-effect concentrations derived by independent action model vs concentration addition model based on different species sensitivity distribution models
title_full_unstemmed Mixture predicted no-effect concentrations derived by independent action model vs concentration addition model based on different species sensitivity distribution models
title_sort mixture predicted no-effect concentrations derived by independent action model vs concentration addition model based on different species sensitivity distribution models
publisher Elsevier
publishDate 2021
url https://doaj.org/article/e2e6fe657afc4e7ba40f1b9ab50eaa3b
work_keys_str_mv AT zejunwang mixturepredictednoeffectconcentrationsderivedbyindependentactionmodelvsconcentrationadditionmodelbasedondifferentspeciessensitivitydistributionmodels
AT shushenliu mixturepredictednoeffectconcentrationsderivedbyindependentactionmodelvsconcentrationadditionmodelbasedondifferentspeciessensitivitydistributionmodels
AT penghuang mixturepredictednoeffectconcentrationsderivedbyindependentactionmodelvsconcentrationadditionmodelbasedondifferentspeciessensitivitydistributionmodels
AT yaqianxu mixturepredictednoeffectconcentrationsderivedbyindependentactionmodelvsconcentrationadditionmodelbasedondifferentspeciessensitivitydistributionmodels
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