| 引用本文: | 张柳青,杨艳,李元鹏,石玉,周蕾,周永强,张运林.高邮湖、南四湖和东平湖有色可溶性有机物来源组成特征.湖泊科学,2020,32(2):428-439. DOI:10.18307/2020.0205 |
| ZHANG Liuqing,YANG Yan,LI Yuanpeng,SHI Yu,ZHOU Lei,ZHOU Yongqiang,ZHANG Yunlin.Sources and optical dynamics of chromophoric dissolved organic matter in Lake Gaoyou, Nansi, and Dongping. J. Lake Sci.2020,32(2):428-439. DOI:10.18307/2020.0205 |
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| 高邮湖、南四湖和东平湖有色可溶性有机物来源组成特征 |
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张柳青1,2, 杨艳2, 李元鹏1, 石玉1, 周蕾1,3, 周永强1,3, 张运林1,3
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1.中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008;2.西华师范大学环境科学与工程学院, 南充 637000;3.中国科学院大学, 北京 100049
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| 摘要: |
| 高邮湖、南四湖和东平湖作为南水北调东线枢纽湖泊，其水质状况对保障调水安全起到关键性作用.本文运用三维荧光光谱-平行因子分析法（EEMs-PARAFAC）分析了3个湖泊在不同水文情景下有色可溶性有机物（CDOM）吸收、荧光光谱特征以及荧光组分与主要水质参数的相关性，以探究3个湖泊CDOM来源组成特征.结果表明，平行因子分析法解析CDOM三维荧光图谱，得到陆源类腐殖质C1、类色氨酸C2和类酪氨酸C3.不同水文情景对高邮湖CDOM来源与结构组成影响较明显，丰水期其类腐殖质荧光强度显著大于枯水期（t-test，P<0.01），并且与a（254）呈正相关（R2=0.85，P<0.01），表明类腐殖质是CDOM主要部分，该荧光组分贡献率可达50%[FmaxC1/（FmaxC1+FmaxC2+FmaxC3）×100%]，高邮湖受到入湖河流来水的影响较大，丰水期入湖口附近荧光强度明显高于其他水域.东平湖和南四湖CDOM来源组成特征相似，丰水期东平湖和南四湖组分C2和C3显著低于枯水期（t-test，P<0.01），两湖泊枯水期工农业等人为污染源影响较大.相关性分析表明高邮湖中类腐殖质荧光特征在一定程度是能反演DOC浓度，并且类腐殖质的输入会增加湖泊总磷、总氮和叶绿素a浓度.而东平湖和南四湖CDOM荧光特征与主要水质参数的相关性较差，这与高邮湖水体存在较大差异. |
| 关键词: 南水北调 高邮湖 南四湖 东平湖 有色可溶性有机物 平行因子分析 |
| DOI：10.18307/2020.0205 |
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| 基金项目:国家自然科学基金项目（41807362， 41621002）、江苏省自然科学基金项目（BK20181104）、中国科学院前沿科学重点研究计划项目（QYZDB-SSW-DQC016）和中国科学院南京地理与湖泊研究所启动基金项目（NIGLAS2017QD08）联合资助. |
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| Sources and optical dynamics of chromophoric dissolved organic matter in Lake Gaoyou, Nansi, and Dongping |
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ZHANG Liuqing1,2, YANG Yan2, LI Yuanpeng1, SHI Yu1, ZHOU Lei1,3, ZHOU Yongqiang1,3, ZHANG Yunlin1,3
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1.State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, P. R. China;2.College of Environment Science and Engineering, China West Normal University, Nanchong 637000, P. R. China;3.University of Chinese Academy of Sciences, Beijing 100049, P. R. China
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| Abstract: |
| Lake Gaoyou, Nansi, and Dongping are key connecting points on the east line of the South-to-North Water Diversion Project and play vital roles in the safety of water transfer. In this study, three-dimensional fluorescence spectroscopy coupled with parallel factor analysis (EEMs-PARAFAC) was used to analyze the optical characteristics of CDOM and the relationships between fluorescence components and water quality parameters in the three lakes in different hydrological seasons, and to unravel the sources and optical composition characteristics of CDOM. Three fluorescent components were obtained using parallel factor analysis (PARAFAC), including a terrestrial humic-like C1, a tryptophan-like C2 and a tyrosine-like C3. Upstream inflowing discharge have notable effects on the source and optical composition of CDOM in Lake Gaoyou with fluorescence intensity (Fmax) of C1 in the flood season is significantly higher than in the dry season (t-test, P<0.01), and C1 is positively correlated with a(254) (R2=0.85, P<0.01), indicating that terrestrial humic-like substances contributed primarily to the DOM pool in the lake. The contribution rate of C1 can reach 50%[FmaxC1/(FmaxC1 + FmaxC2 + FmaxC3)×100%]. Lake Gaoyou is greatly affected by the inflow water. Lake Dongping and Lake Nansi share similar source and optical composition of CDOM. The Fmax mean of C2 and C3 in Lake Dongping and Lake Nansi were significantly lower in the flood season than in the dry season (t-test, P<0.01), indicating the two lakes have a higher autochthonous CDOM contribution during the dry season. Humic-like C1 in Lake Gaoyou can be used to trace the dynamics of dissolved organic carbon concentration, and the accumulation of humic-like substances may increase the concentration of total phosphorus, totoal nitrogen, and chlorophyll-a in the lake. However, the correlations between C1 and the main water quality parameters including humic-like C1 were poor in Lake Dongping and Lake Nansi, showing different CDOM optical characteristics from that of Lake Gaoyou. |
| Key words: The South-to-North Water Diversion Project Lake Gaoyou Lake Nansi Lake Dongping chromophoric dissolved organic matter (CDOM) parallel factor analysis (PARAFAC) |
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