In actual water environment, the toxic effects of metals are affected by multiple environmental factors. It is the premise and basis for setting "in situ" water quality criteria (WQC) and performing risk assessments by using the quantitative characterization of metal morphology and bioavailability. The Biotic Ligand Model (BLM) is limited in water quality management due to difference of species and opacity of the modeling process. Based on existing biological ligand theory and chronic toxicological data for native species in China, the present study aims to establish an approach to predict WQC within the framework of the BLM model and acquire higher prediction accuracy through simplification and optimization of water environmental parameters. Taking copper as an example, the chronic toxicity endpoints of 4 phyla and 22 species in China, by Multiple Linear Regression (MLR) analysis, were positively correlated with the logarithm of Dissolved Organic Carbon (DOC), Hardness (H), and pH（r2
=23.43. Compared with the BLM model, the prediction accuracy of our MLR prediction model was increased by 20% (RFx, 2.0), and the rating scores of residual analyses were 0.739 (MLR) and 0.466 (BLM). Through gradient assignment of environmental parameters, predictive values for 22 aquatic organisms were calculated, indicating that the Sigmoidal-Weibull function had the best fitting by the Species Sensitivity distribution (SSDs) (R2
< 0.001). These findings not only provide the possibility to derive the site-specific WQC of copper in Chinese river basins, but also introduce scientific basis and technical support for the control of aquatic ecological risks of other metals and enhance water quality management distinctively and precisely.