Li Xing, Xu Y Jun, Ni Maofei, Wang Chunlin, Li Siyue
Institute of Changjiang Water Environment and Ecological Security, School of Environmental Ecology and Biological Engineering, Key Laboratory for Green Chemical Process of Ministry of Education, Hubei Key Laboratory of Novel Reactor and Green Chemical Technology, Wuhan Institute of Technology, Wuhan, 430205, China.
School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA, 70803, USA; Coastal Studies Institute, Louisiana State University, Baton Rouge, LA, 70803, USA.
Environ Res. 2023 Apr 1;222:115380. doi: 10.1016/j.envres.2023.115380. Epub 2023 Jan 27.
A mixed land use/land cover (LULC) catchment increases the complexity of sources and transformations of nitrate in rivers. Spatial paucity of sampling particularly low-resolution sampling in tributaries can result in a bias for identifying nitrate sources and transformations. In this study, high spatial resolution sampling campaigns covering mainstream and tributaries in combination with hydro-chemical parameters and dual isotopes of nitrate were performed to reveal spatio-temporal variations of nitrate sources and transformations in a river draining a mixed LULC catchment. This study suggested that point sources dominated the nitrate in the summer and winter, while non-point sources dominated the nitrate in the spring and autumn. A positive correlation was observed between proportions from sewage and land use index (LUI). However, negative correlations between soil nitrogen/nitrogen fertilizer and LUI were observed. With an increase of urban areas, the increased contribution from domestic sewage resulted in an increase of NO concentrations in rivers. Both urban and agricultural inputs should be considered in nitrate pollution management in a mixed LULC catchment. We concluded that the seasonal variations of nitrate sources were mainly affected by flow velocity conditions and agricultural activities, while spatial variations were mainly affected by LULC. In addition, we found a novel underestimation of dominated sources from Bayesian model because of mixing effect of isotope values from the tributaries to mainstream, however, high spatial resolution sampling can make up for this shortcoming. δN and δO values of nitrate indicated that nitrate originated from nitrification in soils. The nitrate concentrations and correlation between δN and 1/[NO] suggested little contribution of nitrate removal by denitrification. Thus, the nitrate reduction in the Yuehe River basin needs to be strengthened. The study provides new implications for estimation of nitrate sources and transformations and basis for nitrate reduction in the river with mixed LULC catchment.
混合土地利用/土地覆盖（LULC）集水区增加了河流中硝酸盐来源和转化的复杂性。采样的空间稀缺性，特别是支流中的低分辨率采样，可能导致在识别硝酸盐来源和转化方面出现偏差。在本研究中，开展了覆盖干流和支流的高空间分辨率采样活动，并结合水化学参数和硝酸盐的双同位素，以揭示流经混合LULC集水区的河流中硝酸盐来源和转化的时空变化。本研究表明，点源在夏季和冬季主导硝酸盐，而非点源在春季和秋季主导硝酸盐。污水比例与土地利用指数（LUI）之间存在正相关。然而，土壤氮/氮肥与LUI之间存在负相关。随着城市面积的增加，生活污水贡献的增加导致河流中NO浓度升高。在混合LULC集水区的硝酸盐污染管理中，应同时考虑城市和农业输入。我们得出结论，硝酸盐来源的季节变化主要受流速条件和农业活动影响，而空间变化主要受LULC影响。此外，我们发现由于支流到干流的同位素值混合效应，贝叶斯模型对主导源的估计存在新的低估，然而，高空间分辨率采样可以弥补这一缺点。硝酸盐的δN和δO值表明硝酸盐源自土壤中的硝化作用。硝酸盐浓度以及δN与1/[NO]之间的相关性表明反硝化作用对硝酸盐去除的贡献很小。因此，需要加强月河流域的硝酸盐还原。该研究为混合LULC集水区河流中硝酸盐来源和转化的估计提供了新的启示，并为硝酸盐还原提供了依据。
