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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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) |
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Binary mixture Conservatism Model-dependent Mixture risk assessment Mixture toxicology Environmental pollution TD172-193.5 Environmental sciences GE1-350 |
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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 |
_version_ |
1718443949333413888 |