热带海洋学报

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钦州湾水体硝酸盐分布特征及其主要来源解析

娄志凤1, 2, 3, 李雨辰1, 2, 3, 殷雪华1, 2, 3, 孙溶1, 2, 田崇国1, 2, 4*
  

  1. 1. 中国科学院烟台海岸带研究所, 中国科学院海岸带环境过程与生态修复重点实验室, 山东 烟台 264003;

    2. 山东省海岸带环境过程重点实验室, 山东 烟台 264003;

    3. 中国科学院大学, 北京 100049;

    4. 陆海统筹生态治理与系统调控重点实验室, 山东省生态环境规划研究院, 山东 济南 250101



  • 收稿日期:2025-09-10 修回日期:2025-11-17 接受日期:2025-11-21
  • 通讯作者: 田崇国
  • 基金资助:
    国家自然科学基金项目(42177089); 中国科学院仪器设备功能开发技术创新项目(E32P030301)

The distribution characteristics and main sources of nitrate in the waters of Qinzhou Bay

LOU Zhifeng1, 2, 3, LI Yuchen1, 2, 3, YIN Xuehua1, 2, 3, SUN Rong1, 2, TIAN Chongguo1, 2 ,4*    

  1. 1. Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, Shandong, China;

    2. Shandong Key Laboratory of Coastal Environmental Processes, Yantai 264003, Shandong, China;

    3. University of Chinese Academy of Sciences, Beijing 100049, China;

    4. Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation, Shandong Academy for Environmental Planning, Jinan 250101, Shandong, China



  • Received:2025-09-10 Revised:2025-11-17 Accepted:2025-11-21
  • Supported by:
    National Natural Science Foundation of China(42177089); the Instrument and Equipment Function Development Technology Innovation Project of the Chinese Academy of Sciences(E32P030301)

摘要: 钦州湾作为发展对外贸易与旅游业的“黄金海岸”,近年来随着经济与农业的快速发展,其水体富营养化问题日益凸显。为系统解析该区域硝酸盐钦州湾作为发展对外贸易与旅游业的“黄金海岸”,近年来随着经济与农业的快速发展,其水体富营养化问题日益凸显。为系统解析该区域硝酸盐(NO₃⁻)污染的特征与来源,本研究于2023年9月、10月和12月在钦州湾开展实地监测,在此基础上分析了硝酸盐的时空分布规律,并应用MixSIAR模型对其来源进行定性与定量解析。研究结果表明,钦州湾NO₃⁻在空间分布上总体呈现自北向南、自内湾向湾口递减的趋势,高浓度区主要集中在茅尾海钦江等河流入海口附近。在时间变化上,钦州湾12月硝酸盐浓度(454.15±356.11)显著高于9月(144.22±81.51)和10月(80.13±61.68),这与季节性生物活动及陆源输入量变化有关。进一步分析表明,钦州湾水体中的硝酸盐转化以硝化作用为主,反硝化作用不明显。在来源方面,定量解析显示,粪肥和污水是钦州湾硝酸盐最主要的来源,贡献率达67%;其次为土壤氮,贡献率为31%;而大气沉降和化肥的贡献率较低,均仅为1%。该结果说明陆源输入尤其是生活污水和农业粪肥排放是造成钦州湾硝酸盐污染的主要因素。污染的特征与来源,本研究于2023年9月、10月和12月在钦州湾开展实地监测,在此基础上分析了硝酸盐的时空分布规律,并应用MixSIAR模型对其来源进行定性与定量解析。研究结果表明,钦州湾NO₃⁻在空间分布上总体呈现自北向南、自内湾向湾口递减的趋势,高浓度区主要集中在茅尾海钦江等河流入海口附近。在时间变化上,钦州湾12月硝酸盐浓度(454.15±356.11)显著高于9月(144.22±81.51)和10月(80.13±61.68),这与季节性生物活动及陆源输入量变化有关。进一步分析表明,钦州湾水体中的硝酸盐转化以硝化作用为主,反硝化作用不明显。在来源方面,定量解析显示,粪肥和污水是钦州湾硝酸盐最主要的来源,贡献率达67%;其次为土壤氮,贡献率为31%;而大气沉降和化肥的贡献率较低,均仅为1%。该结果说明陆源输入尤其是生活污水和农业粪肥排放是造成钦州湾硝酸盐污染的主要因素。

关键词: 硝酸盐, MixSIAR模型, 污染源识别

Abstract: As a "Golden Coast" for foreign trade and tourism development, Qinzhou Bay has experienced increasingly prominent eutrophication issues in recent years alongside rapid economic and agricultural growth. To systematically investigate the characteristics and sources of nitrate (NO₃⁻) pollution in this region, this study conducted field monitoring in Qinzhou Bay in September, October, and December 2023. Based on the collected data, the spatiotemporal distribution patterns of nitrate were analyzed, and the MixSIAR model was applied to qualitatively and quantitatively identify its sources. The results revealed that the spatial distribution of NO₃⁻ generally exhibited a decreasing trend from north to south and from the inner bay to the bay mouth, with high-concentration areas primarily located near the estuaries of rivers such as the Qinjiang River in Maowei Sea. Temporally, the nitrate concentration in December(454.15±356.11) was significantly higher than that in September(144.22±81.51) and October(80.13±61.68), which may be attributed to seasonal biological activities and variations in terrestrial input. Further analysis indicated that nitrification was the dominant process of nitrate transformation in Qinzhou Bay, with no significant denitrification detected. Source apportionment results demonstrated that manure and sewage were the major sources of nitrate, accounting for 67% of the total contribution, followed by soil nitrogen (31%). In contrast, atmospheric deposition and chemical fertilizers made minor contributions, each constituting only 1%. These findings underscore that terrestrial inputs, particularly domestic wastewater and agricultural manure emissions, are the primary drivers of nitrate pollution in Qinzhou Bay.

Key words: Nitrate, MixSIAR model, pollution source identification