信用案例:迈向二级生物废水处理中固体分配和病毒去除的机制模型

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL
Phillip Wang , Tyler Hill , Christina Morrison , Andrew Black , Katherine Crank , Bonnie Mull , Jacimaria Batista , Daniel Gerrity
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引用次数: 0

摘要

饮用水重复使用的管理框架通常包括针对病毒和原生动物的严格日志减少值(LRV)目标。为了可靠地保护公众健康,同时也考虑到高级水处理的可持续性,准确和快速地评估病原体去除情况,并避免在饮用水回用处理系列中低估任何单元过程,这一点至关重要。本研究系统地评估了二级生物废水处理的固体保留时间(SRTs),从2天到20天不等,并使用培养和分子方法表征了12种病毒的衰减,包括粪便指标、替代噬菌体和肠道病原体。本研究还提出了一个基于固体分配/吸附框架的物理去除机制模型,该框架包含混合液悬浮物(MLSS)浓度。黄瓜绿斑驳花叶病毒(CGMMV)的平均样本特异性分配系数(Kd)为2.1 log10 mL/g, phiX174为3.8 log10 mL/g。Kd值与lrv密切相关,尽管所得的吸附模型(样品特异性、线性和Freundlich)不能完全解释在所有条件下病毒去除的巨大变异性。所有病毒的lrv均为1-3,但驱动调控决定的第5百分位lrv通常为<;0.5。本研究提出了几种使用定量微生物风险评估(QMRA)以及观察到的二级处理性能或二级出水浓度来授予≥1隐含lrv的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The case for credit: toward a mechanistic model of solids partitioning and virus removal for secondary biological wastewater treatment

The case for credit: toward a mechanistic model of solids partitioning and virus removal for secondary biological wastewater treatment
Regulatory frameworks for potable reuse often include stringent log reduction value (LRV) targets for viruses and protozoa. To reliably protect public health while also considering the sustainability of advanced water treatment, it is critically important to accurately and rapidly assess pathogen removal and avoid under-crediting of any unit process in a potable reuse treatment train. This study systematically evaluates secondary biological wastewater treatment across solids retention times (SRTs) ranging from 2 to 20 days and uses culture and molecular methods to characterize attenuation of 12 viruses, including fecal indicators, surrogate bacteriophages, and enteric pathogens. This study also proposes a mechanistic model for physical removal based on a solids partitioning/adsorption framework incorporating mixed liquor suspended solids (MLSS) concentrations. Mean sample-specific partitioning coefficients (Kd) ranged from 2.1 log10 mL/g for cucumber green mottle mosaic virus (CGMMV) to 3.8 log10 mL/g for phiX174. Kd values were strongly correlated with LRVs, although the resulting adsorption models (sample-specific, linear, and Freundlich) could not fully explain the large variability in virus removal across all conditions. LRVs of 1-3 were observed for all viruses, but the 5th percentile LRVs that drive regulatory determinations were often <0.5. This study proposes several approaches for awarding implicit LRVs of ≥1 using quantitative microbial risk assessment (QMRA) coupled with observed secondary treatment performance or secondary effluent concentrations.
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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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