| 2018~2022年海河流域总氮浓度时空变化特征及驱动因素分析 |
| 摘要点击 1824 全文点击 221 投稿时间：2023-12-25 修订日期：2024-04-03 |
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| 中文关键词 京津冀 海河流域 总氮 时空变化特征 降雨 |
| 英文关键词 Beijing-Tianjin-Hebei Haihe River Basin total nitrogen spatiotemporal variation characteristics precipitation |
| 作者 | 单位 | E-mail | | 熊伟光 | 中国环境科学研究院流域水环境污染综合治理研究中心, 北京 100012
中国环境科学研究院河口与海岸带环境重点实验室, 北京 100012 | xiongweiguang22@mails.ucas.ac.cn | | 彭嘉玉 | 中国环境科学研究院流域水环境污染综合治理研究中心, 北京 100012
中国环境科学研究院河口与海岸带环境重点实验室, 北京 100012 | | | 李晓光 | 中国环境科学研究院流域水环境污染综合治理研究中心, 北京 100012
中国环境科学研究院河口与海岸带环境重点实验室, 北京 100012 | | | 郭朝臣 | 中国环境科学研究院流域水环境污染综合治理研究中心, 北京 100012
中国环境科学研究院河口与海岸带环境重点实验室, 北京 100012 | | | 杨坤 | 中国海洋大学环境科学与工程学院, 青岛 266100 | | | 王文辉 | 中国海洋大学环境科学与工程学院, 青岛 266100 | | | 吕旭波 | 中国环境科学研究院流域水环境污染综合治理研究中心, 北京 100012
中国环境科学研究院河口与海岸带环境重点实验室, 北京 100012 | | | 雷坤 | 中国环境科学研究院流域水环境污染综合治理研究中心, 北京 100012
中国环境科学研究院河口与海岸带环境重点实验室, 北京 100012 | leikun2023@163.com |
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| 中文摘要 |
| 京津冀地区是环渤海区域经济、资源与环境矛盾最为尖锐的地区,入海河流携带丰富总氮输入渤海湾,是引起海湾富营养化的主要陆源输入.将京津冀海河流域划分成112个（2018~2019年）和187个（2020~2022年）控制单元,结合欧式距离分析法和K-均值聚类分析法,系统分析2018~2022年京津冀地区海河流域地表水总氮浓度时空变化特征.结果表明,区域总氮年均浓度表现为先降低（2018~2020年）再缓慢升高（2021~2022年）的趋势,其中,2021年6月至2022年6月总氮浓度上升显著；年内总氮浓度则呈现“U”型分布特征,丰水期浓度较低,枯水期浓度较高.相较于枯水期,丰水期高浓度区间的控制单元数减少,低浓度控制单元数增加.相较于平水期（3~5月）,平水期（10~12月）控制单元的总氮浓度更高.浓度梯度超过10 mg·L-1和0~1 mg·L-1的控制单元具有明显的空间迁移特征,超过10 mg·L-1控制单元质心中部向东北方向转移,0~1 mg·L-1的控制单元质心由中部向中南部转移.识别出控制单元总氮浓度变化表现出3种模式,1种U型和1种W型模式表现出汛期总氮浓度升高的特征.流域总氮浓度时空分布特征受土壤蓄积氮量、降雨和气温等因素的共同作用,2021年后总氮浓度显著上升,则是受到当年夏季极端降雨事件的短期冲刷,以及暴雨后地下水抬升影响造成. |
| 英文摘要 |
| The Beijing-Tianjin-Hebei （Jing-Jin-Ji） Region is home to the most acute economic, resource, and environmental conflicts in the Bohai Sea region, and the rivers entering the sea carry abundant total nitrogen （TN） input into the Bohai Bay, which is the main land-based input causing eutrophication of the bay. The Haihe River Basin in the Jing-Jin-Ji Region was divided into 112 （2018-2019） and 187 （2020-2022） control units, and the spatial and temporal variations in TN concentration in the surface water of the Haihe River Basin in the Jing-Jin-Ji Region were systematically analyzed from 2018 to 2022 by combining the Euclidean distance analysis method and the K-means clustering analysis method. The results showed that the annual average concentration of TN in the region showed a trend of decreasing （2018-2020） and then increasing （2021-2022）, in which the concentration of TN increased significantly from June 2021 to June 2022. The concentration of TN throughout the year showed a “U”-shaped distribution, with a lower concentration in the abundant water period and a higher concentration in the dry water period. Compared with that in the dry water period, the number of control units in the high-concentration interval decreased, and the number of control units in the low-concentration interval increased in the abundant water period. TN concentrations were higher in control units during the flat-water period （October to December） compared to those in the flat-water period （March to May）. Control units with concentration gradients exceeding 10 mg·L-1 and 0-1 mg·L-1 were characterized by significant spatial migration, with the center of mass of control units exceeding 10 mg·L-1 shifting to the northeast, and the center of mass of control units with 0-1 mg·L-1 shifting from the center to the south-central part. Three patterns of TN concentration changes in the control unit were identified, and one “U”-shaped pattern and one “W”-shaped pattern characterized the increase in TN concentration in the flood season. The spatial and temporal distribution of TN concentration in the watershed was characterized by the combined effects of soil nitrogen storage, rainfall, and temperature. The significant increase in TN concentration after 2021 was caused by the short-term flushing of the extreme rainfall event in the summer of that year, and by the effect of groundwater uplift after heavy rainfall. |
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