Ecological Risk Assessment of DDT Accumulation in Aquatic Organisms of Taihu Lake, China

ABSTRACT

DDT (Dichlorophenyltrichloroethane) is a toxic, ubiquitous, and persistent bioaccumulative pollutant in the global environment. Although its use as an insecticide has been banned in China since 1983, residual DDT levels in the Taihu Lake are evident. Aimed at the protection of fish resources, three species of high-value fish in the Taihu Lake (Protosalanx hylocranius (Abbott), Salangichthys tangkahkeii (Wu), and Coilia nasus Temminck et Schlegel) were selected and an ecological risk assessment was used to estimate the DDT threats to fish populations. Based on a food web model, a bioaccumulation model was used to estimate the DDT concentration in zooplankton, benthos, and fish and the Monte Carlo (MC) simulation method was used to analyze the uncertainty in the bioaccumulation process. Then the chronic toxicity dose-response relationship of DDT on the Protosalanx hyalocranius (Abbott) population, estimated by ICE and ACE software, was used to calculate the mortality rates in different fish stages with the DDT concentrations. Last, the demographic modeling (Leslie matrix) was used to assess the ecological risk of DDT damage on the Protosalanx hyalocranius (Abbott) population. The results show that the estimated DDT concentrations in benthos and zooplankton were 0.14–16.56 μ g/kg, 2.15–99.30 μ g/kg, respectively. The fish DDT concentration results are a series of increasing curves and the maximal values reach 266, 101, and 211 μ g/kg for Protosalanx hylocranius (Abbott), Salangichthys tangkahkeii (Wu), and Coilia nasus Temminck et Schlegel, respectively. In the process of ecological risk quantization, a 4.06–7.25% reduction in the biomass of the Protosalanx hyalocranius (Abbott) population was estimated in 10,000 MC imulations and 4.21%, 4.89%, and 5.69% reductions were estimated in 75%, 50%, and 25% probability, respectively.

Key Words:

• bioaccumulation model
• DDT
• Taihu Lake
• Monte Carlo simulation
• demographic model

ACKNOWLEDGMENTS

The thoughtful comments of two anonymous reviewers were instrumental in improving the clarity of this article. The authors thank the useful discourse with Zhang Heng, Duan Lijie, and Zhao Benliang.