Applying EEM-PARAFAC combined with moving-window 2DCOS and structural equation modeling to characterize binding properties of Cu (II) with DOM from different sources in an urbanized river

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2022-12-01 DOI:10.1016/j.watres.2022.119317
Dongping Liu , Hongjie Gao , Huibin Yu , Yonghui Song
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引用次数: 11

Abstract

Dissolved organic matter (DOM) in aquatic environment distinctly affects the behavior and fate of heavy metals via complexation, while the interfacial mechanisms and processes are still lacking in detail. Here, Cu (II) binding characteristics of DOM originated from hilly (NDOM), rural (RDOM) and urban (UDOM) regions in an urbanized river was explored by fluorescence excitation-emission matrix spectroscopy (EEM) combined with principal component coefficients, parallel factor analyses (PARAFAC), moving-window two-dimensional correlation spectroscopy (MW2DCOS) and structural equation modeling (SEM). Eight components were extracted from the titrants through EEM-PARAFAC, i.e., phenol-like substance (C1), tyrosine-like substance (C2), visible tryptophan-like substance (C3), ultraviolet tryptophan-like substance (C4), recent biological production (C5), wastewater-derived organic matter (C6), microbial humic-like substance (C7) and fulvic-like substance (C8). Interestingly, NDOM only contained C1, C3, C5 and C8, while nearly all components were found in RDOM (except for C2) and UDOM (except for C4). The f value of C1 (1.239) in NDOM was much higher than those in RDOM (0.134) and UDOM (0.115), so was of C8. It indicated that phenol-like and fulvic-like derived from autochthonous sources exhibited great binding ratios in the complexation with Cu (II). Moreover, C3 and C5 from UDOM exhibited higher f values (0.591 and 1.983) than those from NDOM and RDOM, suggesting that Cu (II) has a great binding capacity on protein-like from domestic and industrial wastewater. The MW2DCOS revealed that phenol-like and protein-like in NDOM and RDOM were essential for the binding of 160 μmol L−1 Cu (II), whereas fulvic-like in NDOM and UDOM could react significantly with 10 μmol L−1 Cu (II). Based on SEM, Cu (II) concentration had a negative direct effect on the fluorescence intensity of C7 or C8, whereas it showed an indirect positive effect on C7 or C8 through influencing C5, so was C6. It suggested that Cu (II) showed an indirect positive effect on the C8. This study might present a further comprehend of the environmental behaviors of Cu (II) in rivers.

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应用EEM-PARAFAC结合移动窗口2DCOS和结构方程模型表征城市化河流中Cu (II)与不同来源DOM的结合特性
水体环境中溶解有机物(DOM)通过络合作用明显影响重金属的行为和归宿,但其界面机制和过程尚不清楚。本文采用荧光激发-发射矩阵光谱(EEM)结合主成分系数、平行因子分析(PARAFAC)、移动窗口二维相关光谱(MW2DCOS)和结构方程建模(SEM),研究了城市化河流中来自丘陵(NDOM)、农村(RDOM)和城市(UDOM)地区的DOM的Cu (II)结合特征。通过EEM-PARAFAC从滴定剂中提取了8种成分,即类酚物质(C1)、类酪氨酸物质(C2)、类可见光色氨酸物质(C3)、类紫外色氨酸物质(C4)、近期生物产物(C5)、废水衍生有机物(C6)、微生物腐殖质样物质(C7)和类黄腐酸物质(C8)。有趣的是,NDOM只包含C1、C3、C5和C8,而几乎所有的成分都存在于RDOM(除了C2)和UDOM(除了C4)中。NDOM中C1(1.239)的f值远高于RDOM(0.134)和UDOM (0.115), C8的f值也远高于RDOM。结果表明,来自天然来源的类酚类和类黄腐酸类化合物与Cu (II)的络合具有较大的结合率,且UDOM中的C3和C5的f值(0.591和1.983)高于NDOM和RDOM,表明Cu (II)对生活和工业废水中的类蛋白具有较大的结合能力。MW2DCOS结果表明,160 μmol L−1 Cu (II)的结合需要NDOM和RDOM中的类酚和类蛋白,而10 μmol L−1 Cu (II)的结合需要NDOM和RDOM中的类黄嘌呤(fulvici)。扫描电镜显示,Cu (II)浓度对C7或C8的荧光强度有直接的负影响,而通过影响C5对C7或C8有间接的正影响,C6也是如此。说明Cu (II)对C8有间接的正向作用。本研究为进一步了解Cu (II)在河流中的环境行为提供了依据。
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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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