Quantitative identification of nitrate pollution sources and uncertainty analysis based on dual isotope approach in an agricultural watershed
Quantitative identification of nitrate pollution sources and uncertainty analysis based on dual isotope approach in an agricultural watershed
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基于双同位素方法的农业流域硝酸盐污染源定量识别及不确定性分析
DOI：
10.1016/j.envpol.2017.06.100
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发表时间：
2017-10-01
影响因子：
8.9
通讯作者：
Lu, Jun
中科院分区：
文献类型：
--
作者：
Ji, Xiaoliang;Xie, Runting;Lu, Jun
硝酸盐氮（NO3--N）来源的定量识别是控制农业流域非点源氮污染的关键。结合水质监测，采用环境同位素（Δ D-H2O、Δ O-18-H2O、Δ N-18-NO3-和Δ O-18-NO3-）分析和马尔可夫链蒙特卡罗（MCMC）混合模型来确定来自四个潜在NO3--N源（即大气沉降（AD）、化学氮肥（NF）、土壤氮素（SN）和粪污氮素（M&S）。结果表明，该流域氮素的主要形态为NO3--N，约占总氮浓度的74%。NO3--N污染的地表水和地下水之间存在着强烈的水力相互作用。NO3--N同位素组成的变化表明，微生物硝化作用是占主导地位的地表水中的氮转化过程，而显着的反硝化作用在地下水中观察到。MCMC混合模型结果显示，M&S是河流NO3--N污染的主要贡献者（平均贡献41.8%），其次是SN（34.0%），NF（21.9%）和AD（2.3%）。最后，我们构建了一个不确定性指数，UI 90，定量描述固有的不确定性NO3--N源解析，并讨论了背后的不确定性的原因。(C)2017爱思唯尔有限公司版权所有
Quantitative identification of nitrate (NO3--N) sources is critical to the control of nonpoint source nitrogen pollution in an agricultural watershed. Combined with water quality monitoring, we adopted the environmental isotope (delta D-H2O, delta O-18-H2O, delta N-18-NO3-, and delta O-18-NO3-) analysis and the Markov Chain Monte 'Carlo (MCMC) mixing model to determine the proportions of riverine NO3--N inputs from four potential NOT-N sources, namely, atmospheric deposition (AD), chemical nitrogen fertilizer (NF), soil nitrogen (SN), and manure and sewage (M&S), in the ChangLe River watershed of eastern China. Results showed that NO3--N was the main form of nitrogen in this watershed, accounting for approximately 74% of the total nitrogen concentration. A strong hydraulic interaction existed between the surface and groundwater for NO3--N pollution. The variations of the isotopic composition in NO3--N suggested that microbial nitrification was the dominant nitrogen transformation process in surface water, whereas significant denitrification was observed in groundwater. MCMC mixing model outputs revealed that M&S was the predominant contributor to riverine NO3--N pollution (contributing 41.8% on average), followed by SN (34.0%), NF (21.9%), and AD (2.3%) sources. Finally, we constructed an uncertainty index, UI90, to quantitatively characterize the uncertainties inherent in NO3--N source apportionment and discussed the reasons behind the uncertainties. (C) 2017 Elsevier Ltd. All rights reserved.