GIST Scholar
College of Engineering
Department of Environment and Energy Engineering
1. Journal Articles
Estimating the combined toxicity by two-step prediction model on the complicated chemical mixtures from wastewater treatment plant effluents
- Type
- Article
- Citation
- Environmental Toxicology and Chemistry, v.25, no.8, pp.2107 - 2113
- Issued Date
- 2006-08
- Abstract
- The toxicities of chemical mixtures containing 10 compounds, detected in wastewater treatment plant (WWTP) effluents, were investigated using Daphnia magna in a two-step prediction (TSP) model. The 10 chemicals determined by gas chromatography/mass spectrometry in WWTP effluents included three groups: Three acetylcholinesterase inhibitors, six narcosis inhibitors, and one seedling root inhibitor. In the first step, a concentration addition (CA) model was used to predict the mixture toxicities for the three component groups with similar modes of action; in the second step, an independent action (IA) model was used for the newly developed concentration-response curves from the three CA predictions. The CA predictions did not show a statistically significant difference from the observed results with respect to the three groups of chemicals, whereas the IA model did not conform to the experimental results. Therefore, the concentration-response curves obtained from the mixture toxicity tests in each group was considered as a single curve and applied in the next step of the mixture toxicity prediction. However, the observed toxicity of the 10-chemical mixture showed large differences from the results of the IA and CA model predictions, whereas the TSP model predicted the toxicity well and with statistical significance (p = 0.0501, n = 17). This suggests that the TSP model would provide a valid prediction for a randomly selected chemical mixture having various modes of action if the concentration-response function for an individual component is obtained.
- Publisher
- Society of Environmental Toxicology and Chemistry
- ISSN
- 0730-7268
- Appears in Collections:
- Department of Environment and Energy Engineering > 1. Journal Articles
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