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Effect of pH on Interaction Between Dissolved Organic Matter and Copper: Based on Spectral Features |
HU Bin1, 2, WANG Pei-fang1, 2*, ZHANG Nan-nan3, SHI Yue4, BAO Tian-li1, 2, JIN Qiu-tong1, 2 |
1. Key Laboratory of Integrated Regulation and Resource Development of Shallow Lakes of Ministry of Education (Hohai University), Nanjing 210024, China
2. College of Environment, Hohai University, Nanjing 210024, China
3. Jiangsu Academy of Environmental Industry and Technology Corp., Nanjing 210019, China
4. Jiangsu Suli Environmental Technology Company Limited, Nanjing 210019, China
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Abstract Changes in environmental conditions could alter the composition structure and chemical properties of dissolved organic matter (DOM), then affect its biogeochemical cycling processes. In this study, synchronous fluorescence spectroscopy, three-dimensional fluorescence spectrum, and Fourier transform infrared spectroscopy (FTIR) combined with two-dimensional correlation spectroscopy were applied to evaluate the effect of pH on DOM characteristics and its interaction with Cu2+. (1) Our results suggested that the fluorescence intensity of different DOM components increased remarkably along with the pH value increasing from 5 to 10. Humic-like components showed the most significant changes, and fulvic-like components responded fastest to pH changes. It was caused by pH changes induced exposure of some functional groups, such as carbonyl, phenolic, and carboxyl. (2) Two-dimensional correlation analysis of fluorescence spectra revealed that changes in pH could significantly affect the Cu2+-binding capacity of different DOM components but could not affect the binding sequence to Cu2+ with DOM. Three fluorescent components were identified by parallel factor analysis of the three-dimensional fluorescence spectrum. The nonlinear fitting of quenching curve for fluorescent components quantitatively verified our results. (3) FTIR results showed that, under hinger pH conditions, DOM has more binding sites and stronger binding affinities with Cu2+. The structural change of DOM upon Cu2+ addition under pH 5 and 10 conditions followed the order ofpolysaccharide C—O>phenols>aldehyde and ketone C═O>aromatic C—H and polysaccharide C—O>amide II C—N>phenols>aliphatic C—H>aldehyde and ketone C═O>carboxyl C—OH>aromatic C—H>carboxyl C═O, respectively. Furthermore, two-dimensional hyperspectral correlation analysis of fluorescence spectra and FTIR indicated that humic-like fractions of DOM participated in the Cu2+binding after the phenolic and aryl groups.
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Received: 2022-03-09
Accepted: 2022-06-10
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Corresponding Authors:
WANG Pei-fang
E-mail: pfwang2005@hhu.edu.cn
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[1] Cuss C W, Shi Y X, McConnel S M, et al. Journal of Geophysical Research-Biogeosciences, 2014, 119(9): 1850.
[2] Timko S A, Gonsior M, Cooper W J. Water Research, 2015, 85: 266.
[3] FENG Wei-ying, WANG Sheng-rui, ZHANG Sheng, et al(冯伟莹,王圣瑞,张 生,等). Environmental Chemistry(环境化学), 2014, 33(2): 229.
[4] Murphy K R, Timko S A, Gonsior M, et al. Environmental Science & Technology, 2018, 52(19): 11243.
[5] LIU Le, CAI Min, CHEN Fei-zhou, et al(刘 乐,蔡 敏,陈非洲,等). Journal of Ecology and Rural Environment(生态与农村环境学报), 2018, 34(10): 917.
[6] Mosley L M, Liss P S. Marine and Freshwater Research, 2020, 71(3): 300.
[7] YANG Yi, YANG Xia-xia, MA Xin-pei, et al(杨 毅,杨霞霞,马新培,等). Environmental Chemistry(环境化学), 2015, 34(10): 1804.
[8] Hu B, Wang P F, Wang C, et al. Chemosphere, 2017, 188: 424.
[9] LI Lu-lu, JIANG Tao, LU Song, et al(李璐璐,江 韬,卢 松,等). Environmental Science(环境科学), 2014, 35(9): 3408.
[10] Duan P F, Wei M J, Yao L G, et al. Science of the Total Environment, 2022, 823: 153617.
[11] Hu B, Wang P F, Qian J, et al. Journal of Great Lakes Research, 2017, 43: 1165.
[12] Chen W, Habibul N, Liu X Y, et al. Environmental Science & Technology, 2015, 49(4): 2052.
[13] Luo H W, Cheng Q Q, Fan Q F, et al. Science of the Total Environment, 2022, 819: 152047.
[14] Li W, Zhang F, Ye Q, et al. Chemosphere, 2017, 172: 496.
[15] Zhou Y Q, Zhang Y L, Shi K, et al. Journal of Great Lakes Research, 2015, 41(2): 597.
[16] Noda I. Chinese Chemical Letters, 2015, 26(2): 167.
[17] Hu B, Wang P F, Wang C, et al. Chemosphere, 2019, 219: 45.
[18] ZHOU Shi-lei, CHEN Zhao-ying, ZHANG Tian-na, et al(周石磊,陈召莹,张甜娜,等). Environmental Science(环境科学), 2021, 42(8): 3731.
[19] Ren H, Yao X, Ma F, et al. Environmental Science and Pollution Research International, 2021, 28(41): 58730.
[20] DU Ying-xun, DAI Jia-ru, ZHANG Qiao-ying, et al(杜瑛珣,戴家如,张巧颖,等). Enivronmental Science(环境科学), 2022, 43(8): 4108.
[21] Zhou Y, Zhou J, Jeppesen E, et al. Science of the Total Environment, 2016, 543: 405.
[22] Xu H C, Zhou L, Guan D X, et al. Science of the Total Environment, 2019, 665: 828.
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