中国北方典型岩溶河流硝酸盐来源解析及H2SO4和HNO3对流域岩石风化碳汇的影响
DOI:
作者:
作者单位:

1.阜阳师范大学历史文化与旅游学院;2.中国海洋大学环境科学与工程学院;3.中国科学院地球化学研究所环境地球化学国家重点实验室;4.中国地质大学 武汉

作者简介:

通讯作者:

中图分类号:

基金项目:

国家自然科学基金面上项目(41877192);阜阳师范大学校级自然科学重点项目(2024FSKJ13ZD);阜阳师范大学博士科研启动基金(2023KYQD0017;2024KYQD0029);阜阳师范大学校级自然科学一般项目(2022FSKJ0300)


Identification of nitrate sources and effects of H2SO4 and HNO3 on carbonate weathering carbon sinks in typical karst rivers in northern China
Author:
Affiliation:

1.College of History,Culture and Tourism,Fuyang Normal University;2.College of Environmental Science and Engineering, Ocean University of China;3.School of Geophysics and Geomatics,China University of Geosciences Wuhan

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 文章评论
    摘要:

    定量评估中国北方岩溶河流碳酸盐岩风化和人为酸引起的碳源汇变化,对于我国实现“双碳”目标具有重要的现实意义. 基于2019年旱季中国北方典型岩溶河流—玉符河地表水的野外监测数据,通过水文地球化学和多同位素指标 (δ15NNO3、δ15NNH4、δ18ONO3和δ13CDIC)分析,结合贝叶斯同位素混合模型 (Simmr) 和水化学平衡法,对NO3-来源进行定量解析,并定量估算了碳酸 (H2CO3)、硝酸 (HNO3)和硫酸 (H2SO4)对碳酸盐岩风化和CO2碳汇通量的影响。研究结果表明:① 水化学、δ15NNO3、δ15NNH4、δ18ONO3和Simmr模型揭示玉符河流域河水NO3-主要来源于土壤氮 (34.6~39.3%) 和铵态氮肥 (27.7~39.9%),其次为硝态氮肥 (9.5~16.7%),污水粪肥和大气降水贡献比例较低,分别为7.1~12%和4.4~7.5%; ② 离子比值和δ13CDIC揭示人为活动产生的HNO3和H2SO4显著地参与流域碳酸盐岩风化. ③ 水化学平衡法估算H2CO3溶解碳酸盐所消耗的CO2量为锦阳川 (JYC) >玉符河 (YFR) >锦绣川 (JXC)>二仙河 (EXR),HNO3和H2SO4溶解碳酸盐岩所释放的CO2的量分别为EXR> JXC>JYC>YFR和EXR>JXC>YFR>JYC; ④ 流域CO2净消耗量由高到低分别为JYC (+0.26 mmol/L)、YFR (+0.01 mmol/L)、JXC (-0.81 mmol/L)和EXR (-2.13 mmol/L); JYC和YFR表现为岩溶碳汇效应,而JXC和EXR则主要表现为碳源效应。故集约化农业施用还原性氮肥和硫肥形成的人为酸参与碳酸盐岩风化是导致中国北方岩溶区河流碳汇效应减弱的重要因素。本研究可为中国北方或温带岩溶区河流碳循环模型的构建和流域碳汇的精准评估提供重要的科学依据。

    Abstract:

    Quantitative identification of carbon source and sink changes caused by carbonate weathering and anthropogenic acid (HNO3 and H2SO4) in karst river in northern China is of great practical significance for promoting “carbon peaking and carbon neutrality” in China. In this study, the surface water of Yufu River, a typical karst river in northern China, was conducted in the dry season of 2019. The hydrogeochemistry and isotopic tracer methods (δ15NNO3, δ15NNH4, δ18ONO3 and δ13CDIC) combined with Bayesian isotope mixing model (Simmr) and hydrochemical balance method are employed. The sources of river nitrate are quantitatively identified, and the impact of H2CO3, HNO3 and H2SO4 on carbonate weathering and CO2 carbon sink are quantitatively estimated. The results show that: the combined Simmr model of the hydrochemistry, hydrochemistry, δ15NNO3, δ15NNH4, δ18ONO3 reveal that the main sources of riverine nitrate in Yufuhe River watershed are soil nitrogen (34.6~39.3%) and ammonium fertilizer (27.7~39.9%), followed by nitrate fertilizer (9.5~16.7%). The contribution proportions of sewage manure (7.1-12%) and atmospheric precipitation (4.4-7.5%) are relatively low. The ion ratios and δ13CDIC reveal that HNO3 and H2SO4 produced by anthropogenic activities are significantly involved in the carbonate weathering. The estimation of CO2 consumption by hydrochemical equilibrium method is JYC > YFR > JXC > EXR; The amount of CO2 released by sulfuric acid and nitric acid on carbonate weathering is EXR > JXC > JYC > YFR and EXR > JXC > YFR > JYC, respectively. The net CO2 consumption in the Yufu river watershed is JYC (+0.26 mmol/L), YFR (+0.01 mmol/L), JXC (-0.81 mmol/L) and EXR (-2.13 mmol/L), respectively. JYC and YFR show karst carbon sink effect, while JXC and EXR mainly show carbon source effect. Thus, anthropogenic acids formed by intensive agricultural application of reducing nitrogen fertilizer and sulfur fertilizer are significantly involved in carbonate weathering, which is an essential factor resulting in the weakening of river carbon sink effect in karst areas in northern China. This study provides a crucial scientific basis for the construction of river carbon cycle models and the accurate assessment of basin carbon sinks in northern China or temperate karst areas.

    参考文献
    相似文献
    引证文献
引用本文

张结,袁周伟,张林,袁甲,邵明玉,石亮星,吕杰杰,靳孟贵.中国北方典型岩溶河流硝酸盐来源解析及H2SO4和HNO3对流域岩石风化碳汇的影响.生态学报,,(). http://dx. doi. org/[doi]

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数: