Analysis strategy of contamination source using chemical fingerprint information based on GC-HRMS: A case study of landfill leachate

IF 12.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2025-04-01 Epub Date: 2024-12-29 DOI:10.1016/j.watres.2024.123067
Qian Zhang , Ninghui Song , Hang Xu
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Abstract

With the increasing prevalence of emerging contaminants (ECs) in the environment, gaining a deeper understanding of the chemical information pertaining to the contamination source is a crucial step toward effective prevention and control of these ECs. This study presents a novel strategy for analyzing the chemical information of contamination sources using gas chromatography-high resolution mass spectrometry (GC-HRMS) and demonstrates it on landfill leachate, a common and representative environmental contamination source. Initially, a non-targeted screening approach using HRMS was used to characterize a total of 5344 organic compounds with identification confidence levels 1 and 2 in 14 landfill leachate samples. Leveraging this as a base data set, the similarity analysis was first performed, and the classification fingerprints exhibited a pronounced level of similarity. Second, 169 characteristic marker contaminants with important and significant differences were identified in the 3 groups of landfill leachate with different solid waste compositions (mostly kitchen waste, mostly plastic & daily chemical product waste, and proportion average) by difference analysis. Finally, 101 hazardous chemicals (HCs) were screened in the data set. The results demonstrated that a class of contamination source exhibited certain common characteristics, while different groups of samples had their own distinct contamination signatures. This work offers a unique perspective on the interpretation of chemical information from contamination sources, aiming to provide a valuable reference for environmental pollution management.

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基于GC-HRMS的化学指纹信息污染源分析策略——以垃圾渗滤液为例
随着环境中新兴污染物(ECs)的日益普遍,深入了解与污染源有关的化学信息是有效预防和控制这些污染物的关键一步。本文提出了一种利用气相色谱-高分辨率质谱(GC-HRMS)分析污染源化学信息的新策略,并在垃圾填埋场渗滤液这一常见且具有代表性的环境污染源上进行了验证。最初,使用HRMS的非靶向筛选方法对14个垃圾渗滤液样本中的5344种有机化合物进行了鉴定置信水平为1和2的表征。利用此作为基础数据集,首先进行相似性分析,分类指纹显示出明显的相似性。其次,在3组不同固体废物组成(以厨余垃圾为主,以塑料为主)的垃圾渗滤液中,鉴定出169种具有重要和显著差异的特征标记污染物;日用化工产品废弃物,与比例平均值)进行差异分析。最后,在数据集中筛选了101种有害化学物质。结果表明,一类污染源具有一定的共同特征,而不同组的样品具有不同的污染特征。本研究为污染源化学信息的解释提供了一个独特的视角,旨在为环境污染管理提供有价值的参考。
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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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