A machine learning model to estimate BLM-based PNECs for CuDevelopment of a machine learning model to estimate the biotic ligand model-based predicted no-effect concentrations for copper in freshwater.

The copper biotic ligand model (BLM) has been used for ecological risk assessment by taking into account the bioavailability of copper in freshwater. The copper 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. In this study, 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 of the major cations and alkalinity. Furthermore, deep neural network (DNN) models have been used to predict the nonlinear relationships between the PNEC (outcome variable) and the required input variables (explanatory variables). The predictive capacity of DNN models was compared with the results of other existing PNEC estimation tools using a look-up table, multiple linear and multivariate polynomial regression methods. Three DNN models, using different input variables, provided better predictions of the copper PNECs compared with the existing tools for four test datasets, i.e., Korean, the United States, Swedish, and Belgian freshwaters. Consequently, it is expected that copper BLM-based risk assessment can be applied to various monitoring datasets, and that the most applicable model among the three different types of DNN models could be selected according to data availability for a given monitoring database.