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基于贝叶斯模型定量识别中国亚热带三种主要森林类型地表径流中的硝酸盐来源。

Quantitative identification of nitrate sources in the surface runoff of three dominant forest types in subtropical China based on Bayesian model.

机构信息

Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou 311400, China.

Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou 311400, China.

出版信息

Sci Total Environ. 2020 Feb 10;703:135074. doi: 10.1016/j.scitotenv.2019.135074. Epub 2019 Nov 1.

Abstract

Nitrate pollution is a global environmental issue. Forests play an important role in altering hydrological processes and purifying water pollutants in rainfall and runoff. The quantitative identification of nitrate concentration and sources in surface runoff is of great significance for watershed management and water environment improvement. In this study, water quality of surface runoff was monitored in three typical forest types in subtropical eastern China: Phyllostachys pubescens, Cunninghamia lanceolate, and Cyclobalanopsis glauca. Combined with hydrochemical analysis, we adopted the dual isotope approach (δN-NO and δO-NO) and Bayesian model (SIAR) to identify nitrate sources in forests that are subject to low anthropogenic disturbance. Results showed that the temporal variability of NO-N concentrations was similar for all forest types, with higher values in periods of low rainfall and lower values in heavy rainfall periods. The NO-N concentration in runoff was much higher in C. glauca forests relative to P. pubescens and C. lanceolata. Both the Cl concentrations and NO/Cl molar ratio suggested the fertilizer inputs was the dominant source of nitrate in surface runoff. In agreement, δN-NO and δO-NO values inferred atmospheric deposition and chemical fertilizers to be the main sources of nitrate in all forest types. The Bayesian model outputs demonstrated that atmospheric deposition was the main source in the runoff in P. pubescens and C. lanceolate forests, contributing 28.83% and 35.08% to the total nitrate, respectively. In contrast, chemical fertilizers were identified as the main source in C. glauca forests, with NH fertilizers and NO fertilizers accounting for 27.07% and 24.83%, respectively. Both chemical and isotopic analysis indicated that nitrification had little contribution to nitrate concentrations in runoff. Our results suggest that, even in forests with low anthropogenic disturbance, the application of fertilizer in surrounding agricultural regions should be effectively managed to minimize watershed nitrogen contamination.

摘要

硝酸盐污染是一个全球性的环境问题。森林在改变水文过程和净化降雨和径流水体中的水污染物方面发挥着重要作用。定量识别地表径流水体中的硝酸盐浓度及其来源,对于流域管理和改善水环境保护具有重要意义。本研究对中国亚热带东部三种典型森林类型(毛竹林、杉木林和木荷林)的地表径流水质进行了监测。结合水化学分析,采用双同位素方法(δN-NO 和 δO-NO)和贝叶斯模型(SIAR),对受人为干扰程度较低的森林中的硝酸盐来源进行了识别。结果表明,所有森林类型的 NO-N 浓度的时间变化相似,在降雨量较低的时期浓度较高,在降雨量较大的时期浓度较低。与毛竹林和杉木林相比,木荷林的地表径流水体中 NO-N 浓度更高。Cl 浓度和 NO/Cl 摩尔比均表明,肥料输入是地表径流中硝酸盐的主要来源。同样,δN-NO 和 δO-NO 值推断大气沉降和化肥是所有森林类型硝酸盐的主要来源。贝叶斯模型的输出结果表明,大气沉降是毛竹林和杉木林地表径流的主要来源,分别贡献了总硝酸盐的 28.83%和 35.08%。相比之下,化肥被认为是木荷林的主要来源,其中 NH 肥料和 NO 肥料分别占 27.07%和 24.83%。化学和同位素分析均表明,硝化作用对地表径流水体中硝酸盐浓度的贡献较小。研究结果表明,即使在人为干扰程度较低的森林中,也应有效管理周边农业区的化肥施用,以最大程度地减少流域氮素污染。

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