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1980~2015年长江流域净人为氮输入与河流氮输出动态特征
摘要点击 1915  全文点击 687  投稿时间:2021-04-24  修订日期:2021-05-18
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中文关键词  长江流域  人为氮输入  河流氮输出  源解析  模型
英文关键词  Yangtze River basin  net anthropogenic nitrogen inputs  riverine nitrogen export  source identification  model
作者单位E-mail
姚梦雅 浙江大学环境与资源学院, 杭州 310058 21814152@zju.edu.cn 
胡敏鹏 浙江大学环境与资源学院, 杭州 310058  
陈丁江 浙江大学环境与资源学院, 杭州 310058 chendj@zju.edu.cn 
中文摘要
      为推进长江氮污染的精准防治,分析了1980~2015年的净人为氮输入量(net anthropogenic nitrogen inputs,NANI)和大通站河流可溶性无机氮(DIN)输出通量的年际动态变化特征,构建了定量描述两者之间动态响应关系的多元回归模型,识别了河流DIN来源组成.结果表明,1980~2015年期间,长江流域NANI(以N计)从1980年的4166 kg·(km2·a)-1持续增加至2015年的8571 kg·(km2·a)-1,人口密度和畜禽养殖密度增加是NANI快速增加的主要原因.化肥氮和净食物/饲料氮输入是NANI的主要来源,69%的NANI进入农林地(NANIN),31%的NANI进入人居地(NANIP).长江DIN输出通量(以N计)由1980年的455 kg·(km2·a)-1持续增加到了2015年的1811 kg·(km2·a)-1.长江DIN输出通量的时间变化不仅与NANI及其组分密切相关,而且受到遗留氮库和建坝库容的影响.基于NANIN、NANIP和建库容量的多元非线性回归模型能解释长江DIN输出通量92%的时间变异性.该模型估算结果显示当年NANI、遗留氮库和自然背景源对长江DIN输出通量的36 a平均贡献率分别为58%、36%和6%.因此,加强NANI和遗留氮协同管理是有效控制长江氮污染的关键.
英文摘要
      To advance accurate controls of riverine nitrogen pollution in the Yangtze River basin (YRB), historical trends of net anthropogenic nitrogen inputs (NANI) and riverine dissolved inorganic nitrogen (DIN) export at Datong station and their dynamic relationship were addressed to develop a multiple regression model for predicting riverine DIN export and its source. Results showed that NANI in the YRB increased continuously from 4166 kg·(km2·a)-1 in 1980 to 8571 kg·(km2·a)-1 in 2015. Increasing population density and livestock density are major drivers for the rapid increase of NANI. Chemical fertilizer and net food/feed input were the main sources of NANI, with 69% of NANI entering into agricultural/forest lands (NANIN) and 31% of NANI entering into residential lands (NANIP). Riverine DIN export increased continuously from 455 kg·(km2·a)-1 in 1980 to 1811 kg·(km2·a)-1 in 2015. Riverine DIN export was not only closely related to NANI, NANIN, and NANIP, but was also influenced by watershed legacy nitrogen sources and dam storage capacity. A multiple non-linear regression model incorporating NANIN, NANIP, and dam storage capacity could explain 92% of temporal variability in riverine DIN flux. This multiple regression model estimated that the current year's NANI, legacy nitrogen source, and natural background source contribute to 58%, 36%, and 6% of annual riverine DIN flux on a 36-year average, respectively. Therefore, enhancing collaborative management of NANI and legacy nitrogen is necessary to efficiently control nitrogen pollution in the Yangtze River.

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