| 富营养化湖区CH4排放特征及其影响因素 |
| 摘要点击 2509 全文点击 791 投稿时间：2018-03-20 修订日期：2018-05-15 |
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| 中文关键词 富营养湖泊 扩散通量 时间变化 空间变化 影响因子 |
| 英文关键词 eutrophic lake diffusion flux temporal variation spatial variation influencing factors |
| 作者 | 单位 | E-mail | | 商东耀 | 南京信息工程大学, 气象灾害预报预警与评估协同创新中心, 江苏省农业气象重点实验室, 南京 210044 | 1978769569@qq.com | | 肖启涛 | 中国科学院南京地理与湖泊研究所, 中国科学院流域地理学重点实验室, 南京 210008 | qtxiao@niglas.ac.cn | | 胡正华 | 南京信息工程大学, 气象灾害预报预警与评估协同创新中心, 江苏省农业气象重点实验室, 南京 210044 | zhhu@nuist.edu.cn | | 谢燕红 | 南京信息工程大学大气环境中心, 南京 210044 | | | 黄文晶 | 南京信息工程大学大气环境中心, 南京 210044 | | | 张弥 | 南京信息工程大学大气环境中心, 南京 210044 | |
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| 中文摘要 |
| 为明确富营养湖区CH4排放特征及其影响因素，对太湖梅梁湾湖区和湖心区进行为期1a的观测，分析影响富营养化湖泊CH4扩散通量时空格局的环境要素.结果表明，太湖不同湖区均表现为大气CH4的源，但富营养化梅梁湾湖区的CH4扩散排放量[年均值：0.140 mmol·（m2·d）-1）]要明显高于中营养化湖心区的排放量[年均值：0.024 mmol·（m2·d）-1]，并且在富营养化湖区中，湖岸区的CH4排放量最高.CH4通量表现出显著的季节变化：夏季排放量最高，冬季排放量最低，并且季节间的差异可达一个数量级大小.太湖CH4通量的空间变化与水体DOC浓度显著正相关（R2=0.62，P<0.01），富营养化湖区中较高DOC浓度导致其出现高CH4排放量.太湖CH4扩散通量的时间变化受风速和水温等气象因素的驱动，部分水质因子对此有间接影响作用.鉴于湖泊CH4扩散通量强烈的时空变化以及环境因素巨大的影响，湖泊CH4排放量准确的估算依赖于较大空间和较长时间的观测. |
| 英文摘要 |
| In order to identify methane (CH4) diffusion emissions characteristics and their impact factors in an eutrophic lake, CH4 flux across the lake-air interface was observed in Meiliang Bay and the central zone of Lake Taihu over one year. The relationships between CH4 flux and environmental factors and water quality indices were analyzed. The results indicated that the annual mean CH4 diffusion flux in the eutrophic zone was significantly higher than that in the central zone, which were 0.140 mmol·(m2·d)-1 and 0.024 mmol·(m2·d)-1, respectively. Additionally, the highest CH4 flux appeared in the eutrophic littoral zone. The CH4 flux varied seasonally, which was consistent with water temperature that peaked in summer. Furthermore, the difference in CH4 flux between seasons was an order of magnitude. The temporal variation in CH4 flux was mostly driven by wind speed and water temperature. The spatial correlation between CH4 flux and dissolved organic carbon concentration was highly significant (R2=0.62, P<0.01). Observing temporal and spatial patterns of CH4 flux was necessary to accurately estimate whole-lake CH4 emissions due to large variability across time and space. |
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