Application of an AQUATOX model for direct toxic effects and indirect ecological effects assessment of Polycyclic aromatic hydrocarbons (PAHs) in a plateau eutrophication lake, China

To evaluate the direct toxic effects and indirect ecological effects of polycyclic aromatic hydrocarbons (PAHs) in a plateau eutrophication lake ecosystem, we built AQUATOX model that focus on the interaction among phytoplankton, zooplankton, macrobenthos, and fish populations. The AQUATOX model was first implemented using 12 pelagic-benthic populations came from monitoring data obtained in 20 sampling sites at Dianchi Lake between April 2015 and January 2016. At the meantime, PAHs concentration was simultaneously investigated in surface water. According to the characteristics of trophic state, all of sampling sites can be subdivided into four areas: (1) Area I: algae-dense area; (2) Area II: aquatic organism protection area; (3) Area III: littoral zone damaged area; (4) and Area IV: ecosystem recovery area. The result of sensitivity analysis showed that most of populations were very sensitive to optimal temperature and respiration rate. Through calibration and validation analysis, the value of rB is range from 0.142 to 0.331, and the value of F is range from 0.116 to 0.300, which indicated that the model can describe the seasonal variation of 12 dominant populations in Dianchi Lake. Risk assessment showed that Rotifer and Chironomidae populations showed more sensitive for PAHs pollution. For the Rotifer and Copepoda, the loss of biomass caused by PAHs pollution was showed the highest value in Area II, followed by Area III, Area I, and Area IV. For the temporal variation, Rotifer and Copepoda were showed the highest difference values in winter. In additional, the risk can change according to the temporal variation, for Dianchi Lake, more attention should be pay in winter for the pollutants management. The model was effective in estimating the indirect ecological effects of competition, prey-predatory, biomass dilution or other effects passed through the foodweb. Therefore, our study showed that the importance of ecosystem level studies to predict the ecological risk of pollutants.