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Fluorescence excitation-emission matrix as a novel indicator of assimilable organic carbon in wastewater: Implication from a coal chemical wastewater study

废水 流出物 化学 总有机碳 溶解有机碳 环境化学 超滤(肾) 碳纤维 荧光 制浆造纸工业 环境科学 色谱法 环境工程 有机化学 材料科学 工程类 物理 复合数 复合材料 量子力学
作者
Shu Wang,Yufang Li,Kang Xiao,Xia Huang
出处
期刊:Science of The Total Environment [Elsevier]
卷期号:804: 150144-150144 被引量:15
标识
DOI:10.1016/j.scitotenv.2021.150144
摘要

Assimilable organic carbon (AOC) is recognized as an important parameter to evaluate the biostability of water. Studies have been carried out to investigate the easier and faster AOC detection methods in recent years. In our study, the relationship between AOC and excitation-emission matrix (EEM) was investigated through analysis of wastewater from a coal chemical industrial corporation, including biochemical effluent, ultrafiltration effluent, and reverse osmosis concentrate. Considering the influence of water sample properties on AOC distribution, these water samples were fractionated according to their hydrophilicity and acid/base properties. Neutrals and hydrophobic acids were major components of total organic carbon and AOC concentration of these fractions was measured. EEM spectra of water samples were divided into five regions according to fluorescence peaks. Distribution of fluorescence region integration (FRI) of water samples was also calculated, as well as other fluorescence parameters. Statistical analysis showed that the concentration of AOC presented high positive correlation with the FRI in region H2, with R2 = 0.696. Monte Carlo simulation also proved that the proportion of significant R2 (p < 0.05) was high at 89.1%, suggesting that the model was reliable at least at the qualitative level. In that case, FRI in Region H2 could be an indication for AOC concentration in water samples. Our findings focus on fundamental insights into establishing relationship between spectroscopy method and AOC in wastewater and provide an easier way of accessing AOC in coal chemical industrial wastewater. Further investigation could be oriented to the dynamic analysis of AOC transformation and tracing.
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