Aquatic biological risk assessment of Pb and Hg in sediments after tailings reservoir accidents based on non-parametric kernel density estimation model

Mercury (Hg) and lead (Pb) are two typical heavy metal pollutants, which have strong toxic effects on aquatic ecosystems and are important indicators of environmental management. The leakage of Longxing Antimony Industry Tailings Pond in November 2015 had caused heavy metal pollution in a river section of about 346 km. High content of Hg and Pb in the tailing sands mainly end up in the river sediments that the pollutant passed through and the ecological risks of the basin caused by the high content of Hg and Pb in the tailings sands had not been comprehensively assessed scientifically. In order to solve the problem of less data on the toxic effects of pollutants in sediments on aquatic organisms, the phase equilibrium distribution method was adopted to use a large number of aquatic organisms toxicological test results and converted them into corresponding sediment toxic effect data. The non-parametric kernel density model was used to evaluate the ecological risks of Hg and Pb and compare it with three distribution models of Normal, Logistic and Weibull. The K-S test statistics, root mean square error (RMSE) and sum of square error (SSE) of Hg non-parametric kernel density model were 0.111 1, 0.025 04, and 0.000 627, respectively, which were the smallest compared to other models. The K-S test statistics of the non-parametric kernel density model of Pb was 0.125 0, which was the smallest amount, and RMSE and SSE were 0.028 42 and 0.000 807, respectively, which was a better result. The results showed that the non-parametric kernel density estimation model had good adaptability to the toxicity data of the two heavy metals and could obtain better simulation results; Pb concentration in the sediment was significantly higher than that of Hg, but the ecological risk level of Hg in the sediment was far higher than that of Pb.