Analysis of urban composite non-point source pollution characteristics and its contribution to river DOM based on EEMs and FT-ICR MS

IF 11.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2024-09-06 DOI:10.1016/j.watres.2024.122406

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Abstract

Urban composite non-point source (UCNPS) has an increasing degree of influence on the urban receiving waters. However, there remains a dearth of precise techniques to characterize and evaluate the contribution of UCNPS. Therefore, this study developed a source analytical methodology system based fluorescence excitation-emission matrices spectroscopy (EEMs) and Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS).Specifically, it utilized parallel factor analysis (PARAFAC), two-dimensional correlation spectroscopy (2D-COS), end-member mixing analysis (EMMA), and non-metric multidimensional scaling (NMDS) to analysis UCNPS pollution characteristics and quantify its contributions to river DOM. The results of its application in typical hilly and plain urban within the Yangtze River Basin, China revealed that road and roof runoff exhibited high aromaticity and humic-like content, and the characteristics of pipe sediment was similar with domestic sewage. The component of Rivers had sequences of changes under rainfall perturbations. But terrestrial humic-like represented the initial input in all cases, and it can provide some indication of UCNPS input. The results of EMMA showed that the contribution of road runoff, roof runoff, pipeline sediment and domestic sewage to river DOM was 9.0 %-36.0 %, 2.6 %-19.1 %, 2.3 %-28.8 % and 5.9 %-25.9 %, respectively, and the specific contribution was mainly affected by rainfall level, regional terrain and drainage system. The methodology system of this study can provide technical support for the traceability and precise control of UCNPS pollution.

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基于 EEMs 和 FT-ICR MS 的城市综合非点源污染特征及其对河流 DOM 的贡献分析

城市复合非点源（UCNPS）对城市受纳水体的影响越来越大。然而，目前仍然缺乏精确的技术来表征和评估 UCNPS 的贡献。因此，本研究开发了基于荧光激发-发射矩阵光谱法（EEMs）和傅立叶变换离子回旋共振质谱法（FT-ICR MS）的污染源分析方法系统。具体而言，该系统利用并行因子分析（PARAFAC）、二维相关光谱法（2D-COS）、末端成员混合分析（EMMA）和非计量多维尺度（NMDS）来分析 UCNPS 污染特征并量化其对河流 DOM 的贡献。该方法在中国长江流域典型丘陵和平原城市中的应用结果表明，道路和屋顶径流具有较高的芳烃和腐殖质含量，管道沉积物的特征与生活污水相似。在降雨扰动下，河流成分有序列变化。但陆生类腐殖质在所有情况下都代表了初始输入，它可以在一定程度上说明 UCNPS 的输入。EMMA 的结果表明，道路径流、屋顶径流、管道沉积物和生活污水对河流 DOM 的贡献率分别为 9.0%-36.0%、2.6%-19.1%、2.3%-28.8% 和 5.9%-25.9%，其具体贡献率主要受降雨量、区域地形和排水系统的影响。本研究的方法体系可为 UCNPS 污染的可追溯性和精确控制提供技术支持。

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来源期刊

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.