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Updated: Jan 14, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
Simulation of polycyclic aromatic compounds in the Athabasca River Basin: integrated models and insights
Qianyang Wang1, Maricor J Arlos2, Jinqiang Wang3
1Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada; Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, T6G 2E3, Canada.
Abstract:
The environmental risks associated with polycyclic aromatic compounds (PACs) stemming from oil sands mining have become a concern in the Athabasca River basin (ARB), Alberta, Canada. Their complex environmental behavior complicates mechanistic, basin-scale modeling, making it difficult to assess the relevance of their sources, fate/transport, and potential impacts on exposed organisms. To address these challenges, a Python-based Soil Water Assessment Tool-Load Calculator (SWAT-LC) was developed and coupled with SWAT and Water Quality Analysis Simulation Program 8 (WASP8) models to describe PACs' behavior in the ARB, including transport via surface runoff, soil lateral flow, groundwater baseflow, direct flux, and outcrop/sediment erosion. Chrysene, naphthalene, C4-phenanthrenes/anthracenes, and C4-dibenzothiophenes were selected to demonstrate the applicability of our modeling approach for simulating PACs of petrogenic and pyrogenic origins, diverse physico-chemical properties, and varying environmental relevance. The simulation results indicated that including the description of flow-driven natural outcrop erosion processes significantly enhanced model performance, while the temperature-dependent mechanism showed potential for improving erosion process characterization. Overall, the model performed well for chrysene, C4-phenanthrenes/anthracenes, and C4-dibenzothiophenes (NSE = 0.19∼0.75, d = 0.66∼0.95, PBIAS = -23∼47 % at middle and downstream stations), but its performance was weaker for naphthalene (NSE = -2.16∼-0.40, d = 0.35∼0.53, PBIAS = 17∼51 % at all reference stations). Nonetheless, by integrating a comprehensive set of mechanistic processes, this model is now well-suited for scenario testing, especially for representative PACs that have major environmental and health relevance.
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