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评估和预测从氧化铜面片释放的铜量

Zengqing Bai¹, Chenchen Sun¹, Jinyan Liu¹

¹School of Engineering and Technology, China University of Geosciences, Beijing, China.

Frontiers in public health

|October 13, 2025

概括

从一次性面部件中释放的铜在恶劣条件下增加. 一个机器学习模型准确地预测了工人暴露水平,有助于职业健康监测.

科学领域:

职业健康职业健康职业健康
材料科学材料科学材料科学
环境科学环境科学

背景情况:

一次性面具是工人呼吸道保护的关键个人防护设备.
含有铜 (Cu) 的面部部件可能会释放Cu,造成潜在的健康风险.
评估暴露对于工人安全和健康监测至关重要.

研究的目的:

在模拟老化条件下,从一次性面部件中量化铜释放.
开发和验证用于预测工人铜暴露水平的机器学习模型.
建立与接触铜有关的职业健康监测的科学基础.

主要方法:

对36组面部部件进行了衰老实验,模拟了高温,辐射和工作速度.
铜释放量被测量以评估工人暴露水平.
机器学习模型 (SVM,BPNN,RF) 使用数据增强和粒子群优化进行了开发和优化,以预测暴露.

主要成果:

模拟老化导致铜释放量从7.25μg到23.65μg不等,在恶劣条件下增加.
暴露水平被分为III级 (27组) 和II级 (9组).
优化的支持矢量机 (SVM) 模型实现了高预测精度 (R2=0.9045,RMSE=0.0762,MAE=0.0525),相对误差大多低于5%.

结论:

开发的方法有效地评估了工人对铜的暴露水平.
这项研究为加强职业健康监测提供了科学基础.
这些发现强调了监测保护设备释放的铜的重要性.

关键词:

一次性使用面部部件暴露水平暴露水平暴露水平机器学习是机器学习.释放金额释放金额释放金额支持矢量机器的支持矢量机器.

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