Bo Zhao , Jing Zhou , Norihide Nakada , Masaru Ihara , Yuqing Liu , Yong Jie Wong , Ryo Honda , Hiroaki Tanaka
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引用次数: 0
Abstract
During the COVID-19 outbreak, N-nitrosodimethylamine (NDMA) and N-nitrosomorpholine (NMOR) and their specific precursors (N,N-dimethylformamide [DMF] for NDMA and morpholine [MOR] for NMOR) were widely detected in sewerage systems of an urban area, in which chlorine-containing disinfectants were discharged with effluent of hospitals, etc. However, little is known about the effect of chlorine influx on formation and distribution of NDMA and NMOR in sewer systems in a sudden major public health event. We investigated the spatiotemporal patterns of NDMA, NMOR, DMF and MOR in influents of sewage treatment plants (STPs), as well as its upstream sewer sites during the COVID-19 pandemic. During the pandemic, there was a significant decrease of industry-related NMOR and DMF, however, with an increase of concentration (up to 243 ng/L) and detection frequency for NDMA in influents of the biggest STP in Kyoto Prefecture. Moreover, it was found that NDMA reached a maximum of 187 ng/L with 57 % detection frequency, while NMOR reached a maximum of 101 ng/L with 51 % detection frequency in the sewer systems connecting to all the STPs for service area during the pandemic. Especially, during the pandemic, concentration (median value) of NDMA increased from 40.9 ng/L with 42 % detection frequency in 2020 to 72.5 ng/L with 77 % detection frequency in 2021, which was coincident with the change of infected population. In addition, this research clearly exhibited the possibility that unintentional chlorination and nitrosation of precursors formed NDMA and NMOR in sewer systems influenced by COVID-19 pandemic. The NDMA formation was ranked according to increased concentration (median value) as follows: addition of ClO− (669 ng/L) > addition of NO2− (138 ng/L) > without addition (34.3 ng/L), while additional ClO− and NO2− did not significantly increase NMOR formation probably caused by low existence of NMOR precursors (e.g., MOR) in raw sewage. Therefore, it is necessary to make an urgent attention on environmental issues caused by high-dose chlorine-containing disinfectants residue, because increased byproducts induced by disinfectants in raw sewage caused higher risk during the future pandemic by unexpected pollution from insufficiently treated sewage (e.g. combined sewer overflow and primary effluent bypass discharge) to receiving water bodies.
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.