Laboratory of Applied Physical Chemistry - ISOFYS, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
Environ Pollut. 2012 Feb;161:43-9. doi: 10.1016/j.envpol.2011.09.033. Epub 2011 Oct 25.
To identify different NO(3)(-) sources in surface water and to estimate their proportional contribution to the nitrate mixture in surface water, a dual isotope and a Bayesian isotope mixing model have been applied for six different surface waters affected by agriculture, greenhouses in an agricultural area, and households. Annual mean δ(15)N-NO(3)(-) were between 8.0 and 19.4‰, while annual mean δ(18)O-NO(3)(-) were given by 4.5-30.7‰. SIAR was used to estimate the proportional contribution of five potential NO(3)(-) sources (NO(3)(-) in precipitation, NO(3)(-) fertilizer, NH(4)(+) in fertilizer and rain, soil N, and manure and sewage). SIAR showed that "manure and sewage" contributed highest, "soil N", "NO(3)(-) fertilizer" and "NH(4)(+) in fertilizer and rain" contributed middle, and "NO(3)(-) in precipitation" contributed least. The SIAR output can be considered as a "fingerprint" for the NO(3)(-) source contributions. However, the wide range of isotope values observed in surface water and of the NO(3)(-) sources limit its applicability.
为了识别地表水的不同硝酸盐(NO3-)来源,并估计它们对地表水硝酸盐混合物的比例贡献,应用了双重同位素和贝叶斯同位素混合模型来研究受农业影响的六种不同地表水、农业区的温室和家庭。年平均 δ15N-NO3-值在 8.0 到 19.4‰之间,而年平均 δ18O-NO3-值在 4.5 到 30.7‰之间。SIAR 用于估计五个潜在硝酸盐(NO3-)来源(降水 NO3-、肥料 NO3-、肥料和雨 NH4+-N、土壤 N、粪肥和污水)的比例贡献。SIAR 表明,“粪肥和污水”的贡献最高,“土壤 N”、“肥料 NO3-”和“肥料和雨 NH4+-N”的贡献居中,而“降水 NO3-”的贡献最低。SIAR 的输出结果可以被认为是硝酸盐来源贡献的“指纹”。然而,观测到的地表水和硝酸盐来源的同位素值范围很广,限制了其适用性。