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建筑湿地作为管理和多醇基物质 (PFAS) 的自然解决方案:证据,机制和建模

Pinelopi Savvidou¹, Gabriela Dotro¹, Pablo Campo¹

¹School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK43 0AL, United Kingdom.

The Science of the total environment

|May 18, 2024

概括

建筑湿地在去除一种持续性环境污染物的和多基物质 (PFAS) 方面表现有前途. 人工智能模型有效地预测PFAS的去除,强调需要进一步研究现实世界废水处理应用.

科学领域:

环境科学环境科学
环境工程环境工程
生态毒理学生态毒理学

背景情况:

和多醇基物质 (PFAS) 是持久的,有毒的微污染物,需要有效的去除策略.
建筑湿地 (CWs) 是建立的基于自然的水和废水处理解决方案,提供环境和社会效益.
现有的关于CW去除PFAS的能力,潜在机制和命运建模的知识仍然不完整.

研究的目的:

系统地审查和评估建筑湿地 (CWs) 在去除基和多基基物质 (PFAS) 的有效性.
调查负责在CW中去除PFAS的主要机制.
评估和比较数据驱动模型 (多重回归,人工神经网络) 和机械模型对CW中PFAS命运的预测性能.

主要方法:

在CW中对PFAS去除进行了系统的文献审查,并纳入未发表的实验数据.
在不同的CW配置中分析了PFAS清除效率的中位数:自由水面 (FWS),水平流 (HF) 和垂直流 (VF).
采用多重回归和人工神经网络 (ANN) 来进行PFAS清除的预测建模,并将其性能与吸附异热模型进行比较.

主要成果:

在各种CW类型和影响性特征中,观察到PFAS去除效率的中位数为64% (FWS),46% (HF) 和0% (VF).
PFAS对基质和植物根/树根球的吸附被确定为一个重要的去除机制.
与机械模型相比,数据驱动模型,特别是ANN,对PFAS去除的预测准确度更高,特别是当详细的化学性质数据有限时.

结论:

建筑湿地是一个可行的以自然为基础的解决方案,用于减轻水中遗留PFAS污染物的影响.
人工智能提供了一种有效的方法来建模像PFAS这样的新兴污染物的去除,即使化学数据不完整.
进一步长期监测真正的废水处理的全尺度CW对于稳健的模型开发和验证至关重要.

关键词:

新出现的污染物污染物运气模型的命运模型永远的化学品永远的化学品机器学习是机器学习.微污染物微污染物处理湿地处理湿地

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