将非目标分析与机器学习建模集成在一起,以优先考虑地表水中有气味的挥发性有机化合物
Yuanxi Huang1, Lingjun Bu1, Shumin Zhu1
1Hunan Engineering Research Center of Water Security Technology and Application, Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Hunan University, Changsha 410082, China.
Journal of hazardous materials
|April 23, 2024
概括
一种新方法使用非目标选和机器学习来识别水中的主要气味引起的挥发性有机化合物 (VOC). 这种方法可以准确预测气味值,有助于优先考虑水质管理工作.
科学领域:
- 环境化学环境化学
- 分析化学 分析化学
- 评估水质水质的评估.
背景情况:
- 评估来自挥发性有机化合物 (VOC) 的水气味风险是具有挑战性的,因为大量的气味和难以测量感官属性.
- 水的气味问题给全球的水处理厂带来了重大问题,通常是由各种VOCs引起的.
研究的目的:
- 建立一种新的,可访问的方法来识别和优先考虑源水中的关键气味物质.
- 将非目标查与机器学习相结合,以准确评估气味风险.
主要方法:
- 使用二维气体染色学和飞行时间质谱法进行非目标查,以识别有气味的VOC.
- 机器学习模型开发,用于预测已识别的VOC的气味值.
- 开发一种基于气味值的优先级方法来排名气味剂.
主要成果:
- 在地表水样本中发现了29种有气味的VOC,主要是来自生物来源的和.
- 一个机器学习模型在预测气味值方面取得了79%的准确性.
- 鉴定出二甲基玻利和非门是研究地点水气味的主要原因.
结论:
- 开发的方法可以准确和快速地确定源水中的关键气味.
- 这种方法有助于制定有效的气味控制策略,并提高整体水质管理.
- 这项研究为全球持续存在的水臭挑战提供了切实可行的解决方案.
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