Source identification of nitrate by means of isotopic tracers in the Baltic Sea catchments
Source identification of nitrate by means of isotopic tracers in the Baltic Sea catchments
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DOI：
10.5194/bg-3-663-2006
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发表时间：
2006-12
期刊：
影响因子：
4.9
通讯作者：
M. Voss;B. Deutsch;R. Elmgren;C. Humborg;P. Kuuppo;M. Pastuszak;C. Rolff;U. Schulte
中科院分区：
文献类型：
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作者：
M. Voss;B. Deutsch;R. Elmgren;C. Humborg;P. Kuuppo;M. Pastuszak;C. Rolff;U. Schulte
抽象的。流入河流的硝酸盐主要受流域内土地利用的控制。我们比较了波罗的海12条河流中硝酸盐同位素特征所携带的信息，这些信息与其流域的植被覆盖、土地利用和农业用地施肥有关。我们发现硝酸盐的同位素值δ 15 N为-2 ‰至14‰，δ 18 O为8 ‰至25‰。河流硝酸盐同位素签名的年度变化详细介绍了一个北欧，凯米约基，和两个南部河流，维斯瓦河和奥得河。在北欧河流的集水区，相对原始的植被不仅表现出较低的δ 15 N值和较高的δ 18 O-NO3 −，而且比排放人口稠密地区的河流的年变化率更低。在所有河流中都发现了季节性信号。我们使用负荷加权硝酸盐同位素数据和数据从三个主要的N源（农田/污水，大气沉降和径流的原始土壤）从理论上估计这些来源的硝酸盐的份额。同位素混合模型（IMM-1）的结果与来自全球土地覆盖（GLC）数据库的农业用地估计值相当吻合，偏差在-16%到+26%之间。与排放模型（EM）的比较显示，密集使用的集水区相对较好的协议（-18%至+18%的偏差）。原始集水区的IMM-1和GLC计算之间的一致性相当不令人满意（-36%至+50%偏差）。测试模型的优点和局限性进行了讨论。
Abstract. Nitrate input to a river is largely controlled by land use in its catchment. We compared the information carried by the isotopic signatures of nitrate in 12 Baltic rivers, in relation to the vegetation cover, land use, and fertilization of agricultural land of their catchments. We found isotope values in nitrate ranging from −2 to 14‰ for δ15N and 8 to 25‰ for δ18O. The annual variability of riverine nitrate isotope signatures is presented in detail for one Nordic, the Kemijoki, and two southern rivers, the Vistula and Oder. Nordic rivers with relatively pristine vegetation in their catchments show not only low δ15N values and high δ18O-NO3− but also lower annual variability than rivers draining densely populated land. Seasonal signals were found in all the rivers. We used load weighted nitrate isotope data and data from the three major N sources (farmland/sewage, atmospheric deposition and from runoff of pristine soils) to theoretically estimate the shares of nitrate from these sources. The results of an isotope mixing model (IMM-1) agree reasonably well with the same estimates for agricultural land derived from a Global Land Cover (GLC) data base, with a deviation varying from −16% to +26%. The comparison with an emission model (EM) reveals relatively good agreements for intensively used catchments (−18 to +18% deviation). Rather unsatisfactory agreement was found between the IMM-1 and GLC calculations for pristine catchments (−36 to +50% deviation). Advantages and limitations of the tested model are discussed.