AUTHOR=Bai Zengqing , Sun Chenchen , Liu Jinyan , Liu Zenghui 

TITLE=Assessment and prediction of copper release amount from copper oxide facepieces

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1664838

DOI=10.3389/fpubh.2025.1664838

ISSN=2296-2565

ABSTRACT=BackgroundDisposable facepieces, as important personal protective equipment, provide respiratory protection for workers. However, Cu containing facepieces may cause Cu release, posing a potential danger to human health.MethodsIn this study, aging experiments were conducted on 36 groups of facepieces, simulating the use of facepieces under high temperature, radiation environment and work rate to assess the exposure levels of workers to Cu amount. Meanwhile, a machine learning model was developed based on the Cu release amount to predict the exposure level.ResultsThe research found that after simulating the aging of facepieces, the Cu release ranged from 7.25µg to 23.65µg, and the release trend showed an increasing trend under the simulated harsh conditions. The exposure levels in different scenarios were evaluated based on the release amount. Among them, 27 groups were evaluated as level III and 9 groups were evaluated as level II. Furthermore, the prediction results of Support Vector Machine (SVM), Backpropagation Neural Network (BPNN), and Random Forest (RF), test and training sets were evaluated using coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). Among them, the SVM algorithm performed the best, further improving its predictive ability by using data augmentation methods and Particle Swarm Optimization (R2 of 0.9045, RMSE of 0.0762, and MAE of 0.0525). The relative errors between the predicted values and the true values of all samples were mostly less than 5%.ConclusionThe research method in this study can effectively assess the Cu exposure level of workers and provide a scientific basis for occupational health monitoring.