Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang Provincial Key Laboratory of Agricultural and Environment, Zhejiang University, Hangzhou 310058, China.
Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang Provincial Key Laboratory of Agricultural and Environment, Zhejiang University, Hangzhou 310058, China.
Sci Total Environ. 2018 Oct 15;639:1175-1187. doi: 10.1016/j.scitotenv.2018.05.239. Epub 2018 May 26.
Identifying and eliminating pollutant sources of water bodies is critical for drinking water safety. In this research, river water, reservoir water and groundwater samples (n = 259) were collected from November 2015 to January 2017. Spatial Analysis was made of the isotopic compositions of potential nitrate sources (i.e., manure, sewage, chemical nitrogen fertilizer, soil organic nitrogen and rainfall) so as to obtain the site source isotopic signatures. Different sources pools and fractionation factors were loaded to a Bayesian isotope mixing model to ensure posterior estimates with less uncertainty. Results showed that the total nitrogen (TN) concentrations in Hexi Reservoir watershed were higher than the Environmental Quality Standards for Surface Water of China (GB 3838-2002), and NO-N was the dominant form of TN (accounting for 68.63% on average). There are significant spatio-temporal variations in the isotope data (δN-NO and δO-NO) and the dominant nitrate sources, which were related to the land use types. Loading the site source isotopic signatures to the Bayesian isotope mixing model effectively improved the accuracy and precision of nitrate source apportionment. Chemical nitrogen fertilizer (NF) was the foremost largest contributor of NO-N (38.82%), especially for Hexi North Stream (34.19%) and Yangmei Stream (44.39%), while atmospheric deposition (AD) contributed the least to NO-N (0.47%) of river water in the watershed; soil organic nitrogen (NS) contributed more to NO-N in the dry season than in the wet season; and manure and sewage (M&S) contributed approximately 30.22% in the whole study period, 53.60% in September 2016 and 41.33% in Hexi South Stream. This research suggests that combination of Spatial Analysis and the Bayesian isotope mixing model with the measured isotopic signatures of potential nitrate sources accurately apportion the nitrate source contributions.
识别和消除水体的污染源对于饮用水安全至关重要。本研究于 2015 年 11 月至 2017 年 1 月采集了河水、水库水和地下水样本（n=259）。对潜在硝酸盐源（即粪肥、污水、化学氮肥、土壤有机氮和降雨）的同位素组成进行了空间分析，以获得地点源同位素特征。将不同的源池和分馏因子加载到贝叶斯同位素混合模型中，以确保具有较小不确定性的后验估计。结果表明，河西水库流域的总氮（TN）浓度高于《地表水环境质量标准》（GB 3838-2002），且硝酸盐氮是 TN 的主要形式（平均占 68.63%）。同位素数据（δN-NO 和 δO-NO）和主要硝酸盐源具有显著的时空变化，这与土地利用类型有关。将地点源同位素特征加载到贝叶斯同位素混合模型中，有效地提高了硝酸盐源分配的准确性和精度。化学氮肥（NF）是 NO-N 的首要最大贡献者（38.82%），特别是对河西北溪（34.19%）和杨梅溪（44.39%），而大气沉降（AD）对流域河水的 NO-N 贡献最小（0.47%）；土壤有机氮（NS）在旱季对 NO-N 的贡献大于雨季；粪肥和污水（M&S）在整个研究期间的贡献约为 30.22%，2016 年 9 月为 53.60%，河西南溪为 41.33%。本研究表明，空间分析与贝叶斯同位素混合模型相结合，并结合潜在硝酸盐源的实测同位素特征，可准确分配硝酸盐源的贡献。
