Development of a Machine Learning Model to Estimate the Biotic Ligand Model-Based Predicted No-Effect Concentrations for Copper in Freshwater.

The copper (Cu) biotic ligand model (BLM) has been used for ecological risk assessment by taking into account the bioavailability of Cu in freshwater. The Cu BLM requires data for many water chemistry variables, such as pH, major cations, and dissolved organic carbon, which can be difficult to obtain from water quality monitoring programs. To develop an optimized predicted no-effect concentration (PNEC) estimation model based on an available monitoring dataset, we proposed an initial model that considers all BLM variables, a second model that requires variables excluding alkalinity, and a third model using electrical conductivity as a surrogate for the major cations and alkalinity.