Formation mechanisms of carcinogenic N-nitrosamines from dissolved organic matter derived from nitrogen-containing microplastics during chloramine disinfection

IF 12.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2025-08-01 Epub Date: 2025-04-21 DOI:10.1016/j.watres.2025.123696
Run Zhou , Kecheng Zhu , Zhuo Gao , Xuemin Feng , Qian Hu , Lingyan Zhu
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Abstract

The high occurrence of microplastics (MPs) in water treatment facilities may complicate the source-control of disinfection by-products. Herein, we reported that the carcinogenic N-nitrosamines, such as N-nitrosodimethylamine (NDMA) and N-nitrosodiethylamine (NDEA), were generated during monochloramine disinfection of water in which nitrogen-containing microplastics (N-MPs, such as polyamide and polyacrylonitrile) were present. The precursors of NDMA and NDEA were mainly derived from the dissolved organic matter released from N-MPs (N-MP-DOM), which were characteristic of a significantly higher proportion of polar and non-cationic fractions, favouring the N-nitrosamine formation. The results of excitation-emission-matrix spectra and orbitrap-mass spectrometry indicated that the polar components were mainly CHON and highly hydrogen-saturated molecules (H/C ≥ 1.5) (such as protein-like substrates), which are potential precursors of N-nitrosamines. Further mass difference network analysis revealed that the reactions of amine and nitro/nitroso groups in the precursors made predominant contribution to the generation of N-nitrosamines. Two potent NDMA precursors bearing a (CH3)2N–R structure were identified based on the diagnostic fragments (e.g., 45.0578 Da and m/z 58.0651) and in silico fragmentation tool (MetFrag 2.2) in MS2 spectra. Our findings provide valuable insights into understanding the potential risks of N-MPs due to monochloramine disinfection in water treatment systems.

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含氮微塑料中溶解有机物在氯胺消毒过程中致癌n-亚硝胺的形成机制
水处理设施中微塑料(MPs)的高发生率可能使消毒副产物的源头控制复杂化。在此,我们报道了在含氮微塑料(N-MPs,如聚酰胺和聚丙烯腈)存在的水的单氯胺消毒过程中产生致癌的n-亚硝胺,如n-亚硝基二甲胺(NDMA)和n-亚硝基二乙胺(NDEA)。NDMA和NDEA的前体主要来源于N-MPs (N-MP-DOM)释放的溶解有机质,其极性和非阳离子组分的比例显著高于n-亚硝胺的形成。激发-发射-基质光谱和轨道-质谱分析结果表明,其极性组分主要为CHON和高氢饱和分子(H/C≥1.5)(如蛋白样底物),是n-亚硝胺的潜在前体。进一步的质量差网络分析表明,前驱体中胺和硝基/亚硝基的反应对n -亚硝胺的生成起主导作用。基于诊断片段(45.0578 Da和m/z 58.0651)和MS2光谱中的硅破碎工具(MetFrag2.2),鉴定了两种具有(CH3)2N-R结构的强效NDMA前体。我们的研究结果为理解水处理系统中单氯胺消毒引起的N-MPs的潜在风险提供了有价值的见解。
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公司名称
产品信息
麦克林
KH?PO?
麦克林
Na?HPO?
麦克林
Ammonium chloride
麦克林
Sodium hypochlorite
麦克林
PAN
麦克林
PA
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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