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Characterization of advanced wastewater treatment with ozone and activated carbon using LC-HRMS based non-target screening with automated trend assignment

机译:基于LC-HRMS的非目标筛选与自动化趋势分配的臭氧和活性炭进行高级废水处理的特征

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摘要

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 37,0 0 0 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 (7,0 0 0 & ndash;8,0 0 0 bed volumes (BVs)) compared to older GAC (18,0 0 0 & ndash;48,0 0 0 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 & ndash;13.6 mg/L) dosed onto a sand filter, demonstrating that a post-treatment with activated carbon is efficient for the removal of OTPs. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
机译:越来越多地应用先进的治疗,以改善废水流出物中微拷贝的分解,并将其负荷降低到表面水域。在该研究中,在三次瑞士废水处理厂(WWTPS)中收集的高分辨率质谱(HRMS)数据的非目标筛选用于评估不同的先进废水处理设置,包括(1)粒状活性炭(GAC)单独过滤,(2)预臭氧化合物,然后进行GAC过滤,(3)预臭氧处理,然后用粉末状活性炭(PAC),其给沙滤网上。在WWTP的每个处理步骤中收集样品,并用偶联与HRMS的反相液相色谱法分析。每个WWTP接收到一部分产业废水,并应用优先级方法,以选择潜在的工业活动导致的非目标特征。在WWTP的影响中发现了大约37,0 0 0个非目标特征。许多非目标特征(1207)优先于工业原产地的可能性,通过数据库光谱匹配来确定54。通过新型自动化趋势分配方法评估所有检测到的非目标特征的命名。基于每个采样日期的标准化强度分布,对每个非目标功能分配了趋势。结果表明,在所有三种WWTPS中,73 +/- 4%的影响非目标特征和大多数工业特征(89%)在生物处理过程中被彻底除去(即,强度降低80%)。先进的治疗,平均除外,额外的11%的流入的非目标特征,在不同的先进治疗环境中观察到没有显着差异。相反,当考虑66个已知的微渗透剂的子集时,需要在新鲜GAC(7,0 0 0&Ndash; 8,0 0 0床(BVS))中观察到这些化合物的先进治疗。比较较旧的GAC(18,0 0 0&Ndash; 48,0 0 0 bvs)(80%vs 56%的微孔子分别被良好除去)。在WWTP流出物中检测到的大约一半的特征是在各种处理步骤期间新形成的特征。在臭氧化中,1108-3579之间的特征被归类为潜在的非目标臭氧化转化产品(OTPS)。没有差异可以观察到它们在GAC过滤器中除去BVS调查的(70%的OTPS平均均匀除去)。用PAC(7.7&Ndash; 13.6mg / L)观察到类似的量(67%),其在砂滤器上进行,表明用活性炭的后处理是有效的,用于去除OTPS。 (c)2021作者。由elsevier有限公司出版。这是CC的开放式访问文章,由许可证(http://creativecommons.org/licenses/by/4.0/)

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