Using dual isotopes and a Bayesian isotope mixing model to evaluate sources of nitrate of Tai Lake, China
Using dual isotopes and a Bayesian isotope mixing model to evaluate sources of nitrate of Tai Lake, China
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DOI：
10.1007/s11356-018-3242-1
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发表时间：
2018-09
影响因子：
5.8
通讯作者：
Shasha Liu;Fengchang Wu;Weiying Feng;Wenjing Guo;Fanhao Song;Hao Wang;Ying Wang;Zhongqi He
中科院分区：
文献类型：
--
作者：
Shasha Liu;Fengchang Wu;Weiying Feng;Wenjing Guo;Fanhao Song;Hao Wang;Ying Wang;Zhongqi He
淡水湖硝酸盐(NO3-)来源的识别和量化为富营养化管理和改善湖泊水质提供了有用的信息。采用双δ15N-和δ18O-NO3-同位素和贝叶斯同位素混合模型，对太湖水体中NO3-的来源进行了识别，并估算了它们对太湖NO3-浓度的贡献。太湖水体中δ15N-NO3-的值在3.8~10.1‰之间，δ18 O的值在2.2~12.0‰之间。这些结果表明，NO3-主要来源于农业和工业来源。R中的稳定同位素分析被称为SIAR模型，用来估计4个潜在的NO3-源(农业、工业废水、生活污水和雨水)的比例贡献。SIAR的输出结果显示，农业径流为湖泊提供了最大比例的NO3-(50.8%)，其次是工业废水(33.9%)、雨水(8.4%)和生活污水(6.8%)。各主要来源对太湖各亚区的贡献差异显著(p<；0.05)。在北部地区，工业源(35.4%)贡献了最大比例的NO-3-，其次是农业径流(27.4%)、生活污水(21.3%)和雨水(15.9%)。而南部地区，农业贡献的NO3-比例(38.6%)略高于工业贡献的比例(30.8%)，这与附近入水支流的结果相似。因此，为了通过解决富营养化问题来改善水质，减少浮游植物的初级生产，应该减少来自非点源农业源和工业点源的NO-3-。
Identification and quantification of sources of nitrate (NO 3–) in freshwater lakes provide useful information for management of eutrophication and improving water quality in lakes. Dual δ 15 N-and δ 18 O-NO 3–isotopes and a Bayesian isotope mixing model were applied to identify sources of NO 3–and estimate their proportional contributions to concentrations of NO 3–in Tai Lake, China. In waters of Tai Lake, values for δ 15 N-NO 3–ranged from 3.8 to 10.1‰, while values of δ 18 O ranged from 2.2 to 12.0‰. These results indicated that NO 3–was derived primarily from agricultural and industrial sources. Stable isotope analysis in R called SIAR model was used to estimate proportional contributions from four potential NO 3–sources (agricultural, industrial effluents, domestic sewage, and rainwater). SIAR output revealed that agricultural runoff provided the greatest proportion (50.8%) of NO 3–to the lake, followed by industrial effluents (33.9%), rainwater (8.4%), and domestic sewage (6.8%). Contributions of those primary sources of NO 3–to sub-regions of Tai Lake varied significantly (p< 0.05). For the northern region of the lake, industrial source (35.4%) contributed the greatest proportion of NO 3–, followed by agricultural runoff (27.4%), domestic sewage (21.3%), and rainwater (15.9%). Whereas for the southern region, the proportion of NO 3–contributed from agriculture (38.6%) was slightly greater than that contributed by industry (30.8%), which was similar to results for nearby inflow tributaries. Thus, to improve water quality by addressing eutrophication and reduce primary production of phytoplankton, NO 3–from both nonpoint agricultural sources and industrial point sources should be mitigated.