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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. 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 and multiple linear and multivariate polynomial regression methods. Three DNN models, using different input variables, provided better predictions of the Cu PNECs compared with the existing tools for the following four test datasets: Korean, United States, Swedish, and Belgian freshwaters. Consequently, it is expected that Cu 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. Environ Toxicol Chem 2023;42:2271–2283. © 2023 SETAC [ABSTRACT FROM AUTHOR]

For the first nationwide representative survey on the environmental health of children and adolescents in Korea, we designed the Korean Environmental Health Survey in Children and Adolescents (KorEHS-C) as a two-phase survey and planned a sampling strategy that would represent the whole population of Korean children and adolescents, based on the school unit for the 6-19 years age group and the household unit for the 5 years or less age group. A pilot study for 351 children and adolescents aged 6 to 19 years in elementary, middle, and high school of two cities was performed to validate several measurement methods and tools, as well as to test their feasibility, and to elaborate the protocols used throughout the survey process. Selected exposure biomarkers, i.e., lead, mercury, cadmium in blood, and bisphenol A, metabolites of diethylhexyl phthalate and di-n-butyl phthalate and cotinine in urine were analyzed. We found that the levels of blood mercury (Median: 1.7 ug/L) and cadmium (Median: 0.30 ug/L) were much higher than those of subjects in Germany and the US, while metabolites of phthalates and bisphenol A showed similar levels and tendencies by age; the highest levels of phthalate metabolites and bisphenol A occurred in the youngest group of children. Specific investigations to elucidate the exposure pathways of major environmental exposure need to be conducted, and the KorEHS-C should cover as many potential environmental hazards as possible., (Copyright © 2013 Elsevier GmbH. All rights reserved.)