Enhancing micro-pollutants removal from hospital wastewater by biological design
Enhancing micro-pollutants removal from hospital wastewater by biological design
批准号:
1991506
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --
中文摘要
在不断增长的需求威胁到水安全的时候,生物活性污染物在自然水域中的释放是一个严重的问题。虽然许多这些外来化合物已经认识到了生态毒理效应,但人们也越来越关注它们对人类的毒性。天然水中微污染物的日益普遍,为它们在饮用水中的存在提供了可能性。传统的饮用水处理方法旨在去除病原体,降低浊度,控制气味和味道,所采用的工程工艺在一定程度上减少了微污染物的负荷。然而,完全清除废水涉及高化学品和能源需求的技术,因此,在目前的知识状态下,其潜在的好处不值得环境支出。来自医院的废水可能含有高浓度的药物及其降解产物,但在到达污水之前没有经过任何进一步的废水处理。这是令人担忧的,因为一些微污染物不能通过传统的水处理方法去除,并可能影响我们消费的饮用水。与NHS Highland合作,有人提议调查医院废水的生物技术预处理的使用。处理生物技术通过物理和生物过程的混合来去除病原体、营养物质和过渡金属。例如,氨氧化细菌(AOB)和氨氧化古细菌(AOA)通过其氨单加氧酶(AMO)酶进行氨的转化。重要的是,AMO酶具有广泛的底物特异性,还可以氧化生态代谢广泛的污染物。这一特性为氨氧化生物处理群落的优化提供了可能性,从而有效地去除营养物质和微污染物。该项目的目的是确定关键的硝化微生物群落和条件,以设计生物滤池的库仑,以便在去除氨的同时进行污染物的非定向生物转化。最初的工作将是在各种环境中创造微型世界,包括沿海和河流沉积物、硝化活性污泥和实验室规模的生物过滤器,在硝化条件下添加氨和与环境相关的微污染物负荷。利用新的分析方法、定量聚合酶链式反应和靶向基因高通量测序,将表征硝化菌及其对医院废水中存在的微污染物的共代谢反应。根据这些结果,将设计出一个具有氧化多种微污染物能力的硝化群落。通过一系列新的微观世界,进一步研究了该种子群落对高浓度污染物混合物的去除速率及其与氨浓度和其他化学参数的关系。最后,将建立定量结构-活性关系模型,以预测未测试污染物的降解,并进行多元回归分析,以建议失败的补救方法(氨水剂量、pH变化...)。
英文摘要
The release in natural waters of biologically active contaminants is a serious concern at a time when ever-increasing demand threatens water security. While many of these xenobiotic compounds have recognized ecotoxicological effects, there is a growing concern on their human toxicity too. Increasing ubiquity of micro-pollutants in natural waters opens the possibility of their presence in drinking water. Conventional drinking water treatments aim to remove pathogens, reduce turbidity and control odour and taste and the employed engineering processes reduce somewhat the load of micro-pollutants. Complete removal, however, involves high chemicals and energy demand technologies and therefore, in the current state of knowledge, theirpotential benefits are not worth the environmental expenditure.Waste water from hospitals is likely to have high concentrations of pharmaceutical substances and their degradation products but does not undergo any further waste water treatment before reaching the sewage. This is of concern as some micro-pollutants are not removed by the traditional water treatment methods, and may affect drinking water we consume. Working alongside NHS Highlands, it is proposed to investigate the use of a biotechnological pre-treatment for hospital wastewater.Treatment biotechnologies remove pathogens, nutrients and transition metals through a mixture ofphysical and biological processes. For instance, ammonia oxidizing bacteria (AOB) and ammoniaoxidizing archea (AOA) carry out transformation of ammonia via their ammonia monooxygenase(AMO) enzyme. Importantly, the AMO enzyme has broad substrate specificities and canalso oxidiseco-metabolically a wide range of contaminants. This property opens the possibility that the ammonia oxidising community of biological treatments can be optimisedto efficiently remove both nutrients and micro-pollutants. The aim of this project is to identify key nitrifying microbial communities and conditions to design an inncoulum for biological filters for untargeted biotransformation of contaminants alongside ammonia removal. Initial work will be to create microcosms from various environments including coastal and river sediments, nitrifying activated sludge and lab-scale biological filters under nitrifying conditions spiked with ammonia and environmentally relevant loads of micro-pollutants. Using novel analytical approaches, quantitative polymerase chain reaction and targeted gene high-throughput sequencing, the nitrifiers and their co-metabolic response to micropollutants present in hospital wastewater will be characterised. Based on these results, a nitrifying community with the capacity to also oxidise a broad range of micro-pollutants will be engineered. Through a new series of microcosms, further investigation into the removal rates of mixtures of contaminants at high concentrations by this seed community and their relationship to ammonia concentrations and other chemical parameters. Finally, quantitative structure-activity relationship modelling will be establish to predict degradation of untested pollutants and multiple regression analysis to recommend remedial approaches to failure (dosing of ammonia, change in pH...).
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