Analysis of wastewater treatment plant data identifies the drivers of PFAS enrichment in foams

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2024-09-16 DOI:10.1016/j.watres.2024.122397
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Abstract

The concept of incorporating foam fractionation in aerated bioreactors at wastewater treatment plants (WWTPs) for the removal of per- and polyfluoroalkyl substances (PFAS) has recently been proposed. The extent of PFAS enrichment in aerated bioreactors’ foams, as indicated by enrichment factors (EFs), has been observed to vary widely. Laboratory evidence has shown that factors affecting PFAS enrichment in foams include conductivity, surfactant concentrations and initial PFAS concentrations. However, real wastewaters are complex heterogenous matrices with physical, chemical and biological characteristics potentially contributing to the phenomenon of PFAS partitioning into foams. In this study, we characterised mixed liquor suspensions, including conductivity, filament content, aqueous PFAS concentrations, surface tension and total suspended solids concentrations (TSS) as well as foams, including bubble size and half-life. We used statistical tools – linear mixed-effects model – to establish relationships between PFAS enrichment in aerated bioreactor foams and the examined characteristics. We found that some of the examined characteristics, specifically filament content, surface tension and TSS concentrations measured in mixed liquor suspension and foam half-life, are negatively and significantly associated with the enrichment of longer chain PFAS (with perfluorinated carbon number ≥ 6). Of these, filament content is the important determinant of PFAS enrichment, potentially leading to an increase in, for example, perfluorooctanoic acid (PFOA) EF from 3 to 100 between typical filamentous and non-filamentous suspended biomass. However, enrichment of shorter chain PFAS (with perfluorinated carbon number ≤ 5) is negligible and is not affected by the characteristics that were measured. The findings of our study may serve as valuable information for the implementation of foam fractionation at WWTPs by elucidating the drivers that contribute to the enrichment of longer chain PFAS, under conditions typically found at WWTPs.

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污水处理厂数据分析确定了泡沫中 PFAS 富集的驱动因素
最近,有人提出了在污水处理厂(WWTPs)的充气生物反应器中加入泡沫分馏以去除全氟和多氟烷基物质(PFAS)的概念。据观察,充气生物反应器泡沫中 PFAS 的富集程度(以富集因子 (EF) 表示)差异很大。实验室证据表明,影响泡沫中 PFAS 富集的因素包括电导率、表面活性剂浓度和初始 PFAS 浓度。然而,实际废水是一种复杂的异质基质,其物理、化学和生物特性可能会导致 PFAS 在泡沫中的分区现象。在本研究中,我们对混合液悬浮液进行了表征,包括电导率、丝状物含量、水性 PFAS 浓度、表面张力和总悬浮固体浓度 (TSS),并对泡沫进行了表征,包括气泡大小和半衰期。我们使用统计工具--线性混合效应模型--来确定充气生物反应器泡沫中富集的 PFAS 与所研究特征之间的关系。我们发现,所研究的一些特征,特别是长丝含量、混合液悬浮液中测量的表面张力和 TSS 浓度以及泡沫半衰期,与长链 PFAS(全氟碳数≥ 6)的富集呈显著负相关。其中,丝状物含量是全氟辛烷磺酸富集的重要决定因素,有可能导致典型丝状物与非丝状物悬浮生物量之间的全氟辛酸(PFOA)EF 从 3 增加到 100。不过,较短链全氟辛酸(全氟碳数≤ 5)的富集可以忽略不计,而且不受测量特征的影响。通过阐明在污水处理厂的典型条件下导致长链 PFAS 富集的驱动因素,我们的研究结果可作为在污水处理厂实施泡沫分馏的宝贵信息。
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来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
期刊介绍: Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.
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