Linking in vitro effects and detected organic micropollutants in surface water using mixture-toxicity modeling - Archive ouverte HAL Access content directly
Journal Articles Environmental Science and Technology Year : 2015

Linking in vitro effects and detected organic micropollutants in surface water using mixture-toxicity modeling

(1) , (2) , (3) , (2) , , (4) , (5) , (4) , (3) , (5) , (3) , (4) , (2) , (4) , (1, 3)
1
2
3
4
5
Michael S. Denison
  • Function : Author

Abstract

Surface water can contain countless organic micropollutants, and targeted chemical analysis alone may only detect a small fraction of the chemicals present. Consequently, bioanalytical tools can be applied complementary to chemical analysis to detect the effects of complex chemical mixtures. In this study, bioassays indicative of activation of the aryl hydrocarbon receptor (AhR), activation of the pregnane X receptor (PXR), activation of the estrogen receptor (ER), adaptive stress responses to oxidative stress (Nrf2), genotoxicity (p53) and inflammation (NF-kappa B) and the fish embryo toxicity test were applied along with chemical analysis to water extracts from the Danube River. Mixture-toxicity modeling was applied to determine the contribution of detected chemicals to the biological effect. Effect concentrations for between 0 to 13 detected chemicals could be found in the literature for the different bioassays. Detected chemicals explained less than 0.2% of the biological effect in the PXR activation, adaptive stress response, and fish embryo toxicity assays, while five chemicals explained up to 80% of ER activation, and three chemicals explained up to 71% of AhR activation. This study highlights the importance of fingerprinting the effects of detected chemicals.

Dates and versions

ineris-01854100 , version 1 (06-08-2018)

Identifiers

Cite

Peta A. Neale, Selim Ait-Aissa, Werner Brack, Nicolas Creusot, Michael S. Denison, et al.. Linking in vitro effects and detected organic micropollutants in surface water using mixture-toxicity modeling. Environmental Science and Technology, 2015, 49 (24), pp.14614-14624. ⟨10.1021/acs.est.5b04083⟩. ⟨ineris-01854100⟩

Collections

INERIS
38 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More