HQ-SGMN:一种定性结构导向的分子网络方法,用于对废水处理过程的综合评估
Qian Zhang1, Ninghui Song2, Yuxin Qiao2
1Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, PR China; College of Environment, Hohai University, Nanjing 210098, PR China.
Journal of hazardous materials
|December 3, 2025
概括
一种新方法,高可信度定性结构引导分子网络 (HQ-SGMN),识别了废水处理厂中的有机污染物的转化. 这种方法有助于了解污染物去除效率和优化处理过程.
科学领域:
- 环境化学环境化学
- 分析化学 分析化学
- 环境科学 环境科学
背景情况:
- 有效的环境管理需要了解废水处理厂 (WWTP) 中的有机污染物 (OC) 转化.
- 高分辨率质谱 (HRMS) 与非目标查 (NTS) 允许高通量污染物检测,但在大规模数据集分析方面存在困难.
- 从大型NTS数据集中提取污染物转化途径是一个重大的分析挑战.
研究的目的:
- 开发和验证一种新的分析方法,用于确定WWTP中的有机污染物转化关系.
- 为了使转化途径的识别,而不需要参考标准或预定义的反应规则.
- 为了评估有机化合物去除和转化在全面的WWTP中的效率.
主要方法:
- 结构引导分子网络 (SGMN) 与非向查 (NTS) 的整合,以创建一个高可信度的定性SGMN (HQ-SGMN) 方法.
- 分析有机化合物的静态分布和去除效率在一个全尺寸的WWTP.
- 应用化学转化规则 (CTS) 和工艺环境标准,从广泛的化合物对数据中确定潜在的转化关系.
主要成果:
- 从超过一百万个移除生成化合物对中确定了44个潜在的转换关系.
- 确定A2/O过程是有机化合物转化的主要单元,涉及氧化还原,胺水解和连锁反应.
- 在HQ-SGMN框架内,一个新的标准证明了强大的概括性和可扩展性,用于识别转换关系.
结论:
- 综合的HQ-SGMN-NTS方法有效地挖掘大型NTS数据集以获得污染物转化见解.
- 这种方法有助于更深入地了解WWTP中的污染物转化机制.
- 该方法支持优化废水处理过程,以改善环境管理.
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