基于分子结构预测废水处理中的微污染物去除:基准数据和模型
José Andrés Cordero Solano1, Jasmin Hafner1,2, Michael S McLachlan3
1Department of Environmental Chemistry, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf 8600, Switzerland.
Environmental science & technology
|October 8, 2025
概括
新模型预测废水处理厂 (WWTP) 仅使用化学结构去除微污染物,改进环境风险评估和化学设计.
科学领域:
- 环境化学环境化学
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 准确预测微污染物的环境命运对于化学安全评估和设计更安全的化学品至关重要.
- 污水处理厂 (WWTP) 是防止微污染物释放的关键,突破表明持久性.
- 当前的模型通常需要不可用的降解速率常数,从而限制了它们的适用性.
研究的目的:
- 开发用于传统废水处理过程中微污染物去除的预测模型.
- 用化学结构作为输入,绕过对实验性降解数据的需求.
- 为监管和工业应用提供可靠的*in silico*工具.
主要方法:
- 使用机器学习算法开发预测模型,特别是随机森林.
- 使用基于基结构的指纹 (例如,MACCS) 来自化学结构.
- 经过训练和验证的模型使用1000多种化学品的现场监测数据.
主要成果:
- 基于MACCS指纹和随机森林的模型显示了WWTP移除的高预测精度.
- 确定了影响去除的关键子结构与已知的生物转化途径保持一致.
- 开发的模型表现优于监管框架中使用的现有基于过程的模型.
结论:
- 基于结构的新型模型为预测微污染物WWTP突破提供了可靠和可访问的方法.
- 这些模型增强了替代品评估,安全设计倡议和风险评估的*in silico*工具包.
- 公共可用的数据,代码和PEPPER模型促进了环境命运建模的未来进展.
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