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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]

Background: Ultrasound is not widely used to evaluate optimal supraglottic airway positioning even though it could potentially be used to identify and correct problem areas. Aims: We evaluated a new ultrasound scoring method to identify the position of the supraglottic airway and detect the location of air leaks during ventilation in pediatric patients. Methods: Using a prospective observational study design, we enrolled 90 pediatric patients of ASA physical status I–III scheduled for elective surgery under general anesthesia. After anesthesia induction, patients were assigned to a noncorrection group or a correction group after their first ultrasound evaluation. Noncorrection group comprised patients with tolerable I‐Gel positioning based on ultrasound evaluation and no problems with clinical parameters, while the correction group comprised patients with I‐Gel mispositioning based on ultrasound. Results: After the first ultrasound evaluation, 61 patients did not need I‐Gel correction (noncorrection group), while 29 patients needed I‐Gel correction (correction group) and underwent a second ultrasound evaluation. Airway sealing pressure and total ultrasound score showed a negative correlation (r = −.845, p <.001). The area under the receiver operating curve for total ultrasound score was 0.97 (95% confidence interval, 0.94–0.99; p <.001). In the correction group, ultrasound score and ventilation parameters improved after correction based on ultrasound evaluation. Conclusions: Ultrasound scores were negatively correlated with airway sealing pressure in pediatric patients. Ultrasound evaluation is useful for detecting misplacement of the I‐Gel and can be a useful tool for correction. [ABSTRACT FROM AUTHOR]