Eawag: Swiss Federal Institute of Aquatic Science and Technology, Duebendorf 8600, Switzerland.
Eawag: Swiss Federal Institute of Aquatic Science and Technology, Duebendorf 8600, Switzerland; ETH Zurich, Institute of Biopollutant Dynamics, Zurich 8092, Switzerland.
Water Res. 2021 Jul 15;200:117209. doi: 10.1016/j.watres.2021.117209. Epub 2021 May 5.
Advanced treatment is increasingly being applied to improve abatement of micropollutants in wastewater effluent and reduce their load to surface waters. In this study, non-target screening of high-resolution mass spectrometry (HRMS) data, collected at three Swiss wastewater treatment plants (WWTPs), was used to evaluate different advanced wastewater treatment setups, including (1) granular activated carbon (GAC) filtration alone, (2) pre-ozonation followed by GAC filtration, and (3) pre-ozonation followed by powdered activated carbon (PAC) dosed onto a sand filter. Samples were collected at each treatment step of the WWTP and analyzed with reverse-phase liquid chromatography coupled to HRMS. Each WWTP received a portion of industrial wastewater and a prioritization method was applied to select non-target features potentially resulting from industrial activities. Approximately 37,000 non-target features were found in the influents of the WWTPs. A number of non-target features (1207) were prioritized as likely of industrial origin and 54 were identified through database spectral matching. The fates of all detected non-target features were assessed through a novel automated trend assignment method. A trend was assigned to each non-target feature based on the normalized intensity profile for each sampling date. Results showed that 73±4% of influent non-target features and the majority of industrial features (89%) were well-removed (i.e., >80% intensity reduction) during biological treatment in all three WWTPs. Advanced treatment removed, on average, an additional 11% of influent non-target features, with no significant differences observed among the different advanced treatment settings. In contrast, when considering a subset of 66 known micropollutants, advanced treatment was necessary to adequately abate these compounds and higher abatement was observed in fresh GAC (7,000-8,000 bed volumes (BVs)) compared to older GAC (18,000-48,000 BVs) (80% vs 56% of micropollutants were well-removed, respectively). Approximately half of the features detected in the WWTP effluents were features newly formed during the various treatment steps. In ozonation, between 1108-3579 features were classified as potential non-target ozonation transformation products (OTPs). No difference could be observed for their removal in GAC filters at the BVs investigated (70% of OTPs were well-removed on average). Similar amounts (67%) was observed with PAC (7.7-13.6 mg/L) dosed onto a sand filter, demonstrating that a post-treatment with activated carbon is efficient for the removal of OTPs.
高级处理技术越来越多地被应用于改善废水处理厂废水中的微量污染物的去除,并减少其对地表水体的负荷。在这项研究中,使用高分辨率质谱(HRMS)数据的非靶向筛选,该数据在瑞士三个废水处理厂(WWTP)收集,用于评估不同的高级废水处理设置,包括(1)单独使用颗粒活性炭(GAC)过滤,(2)预臭氧化后再使用 GAC 过滤,以及(3)预臭氧化后在砂滤器中投加粉末活性炭(PAC)。在 WWTP 的每个处理步骤中收集样品,并通过反相液相色谱与 HRMS 进行分析。每个 WWTP 接收一部分工业废水,并应用优先级方法选择可能源自工业活动的非靶向特征。在 WWTP 的进水口发现了大约 37000 个非靶向特征。许多非靶向特征(1207 个)被优先确定为可能源自工业,其中 54 个通过数据库光谱匹配确定。通过一种新的自动趋势分配方法评估所有检测到的非靶向特征的命运。根据每个采样日期的归一化强度曲线,为每个非靶向特征分配一个趋势。结果表明,在所有三个 WWTP 中,生物处理过程中,73±4%的进水非靶向特征和大部分工业特征(89%)被很好地去除(即,强度降低>80%)。高级处理平均去除了另外 11%的进水非靶向特征,但不同高级处理设置之间没有观察到显著差异。相比之下,当考虑 66 种已知的微量污染物的子集时,高级处理对于充分去除这些化合物是必要的,并且在新鲜的 GAC(7000-8000 床体积(BV))中观察到更高的去除率,与旧的 GAC(18000-48000 BV)相比(分别为 80%和 56%的微量污染物被很好地去除)。在 WWTP 流出物中检测到的特征中,大约一半是在各种处理步骤中形成的新特征。在臭氧处理中,1108-3579 个特征被分类为潜在的非靶向臭氧转化产物(OTPs)。在研究的 BV 下,在 GAC 过滤器中观察到它们的去除没有差异(平均有 70%的 OTP 被很好地去除)。在 PAC(7.7-13.6 mg/L)投加到砂滤器中也观察到相似的去除率(67%),这表明在处理后使用活性炭对去除 OTPs 是有效的。