Nitrate transformation and source tracking of rivers draining into the Bohai Sea using a multi-tracer approach combined with an optimized Bayesian stable isotope mixing model

doi:10.1016/j.jhazmat.2023.132901

	Nitrate transformation and source tracking of rivers draining into the Bohai Sea using a multi-tracer approach combined with an optimized Bayesian stable isotope mixing model
	Ren, Xinwei 1; Yue, Fu-Jun 1,2,3; Tang, Jianhui4 ; Li, Cai 5; Li, Si-Liang 1,2,3,6
发表期刊	JOURNAL OF HAZARDOUS MATERIALS
ISSN	0304-3894
	2024-02-05
卷号	463 页码:11
关键词	Dual stable isotope Nitrate appointment MixSIAR Parameter optimization Nitrogen load
DOI	10.1016/j.jhazmat.2023.132901
通讯作者	Yue, Fu-Jun([email protected]) ; Li, Si-Liang([email protected])
英文摘要	Excessive levels of NO3 -can result in multiple eco-environmental issues due to potential toxicity, especially in coastal areas. Accurate source tracing is crucial for effective pollutant control and policy development. Bayesian models have been widely employed to trace NO3 -sources, while limited studies have utilized optimized Bayesian models for NO3 - tracing in the coastal rivers. The Bohai Rim is highly susceptible to ecological disturbances, particularly N pollution, and has emerged as a critical area. Therefore, identification the N fate and understanding their sources contribution is urgent for pollution mitigation efforts. In addition, understanding the influenced key driven factors to source dynamic in the past ten years is also implication to environmental management. In this study, water samples were collected from 36 major river estuaries that drain into the Bohai Sea of North China. The main transformation processes were analyzed and quantified the sources of NO3 -using a Bayesian stable isotope mixing model (MixSIAR) with isotopic approach (815N-NO3- and 818O-NO3-). The overall isotopic composition of 815N-NO3- and 818O-NO3- in estuary waters ranged from -0.8-19.3%o (9.3 +/- 4.6%o) and from -7.1-10.5%o (5.0 +/- 4.3%o), respectively. The main sources of nitrate in most river estuaries were manure & sewage, and chemical fertilizer, while weak denitrification and mixed processes were observed in Bohai Rim
资助机构	National Natural Science Foundation of China ; National Science & Technology Fundamental Resources Investigation Program of China ; Haihe Laboratory of Sustainable Chemical Transformations
收录类别	SCI
语种	英语
关键词[WOS]	CHANGJIANG RIVER ; LAIZHOU BAY ; NITROGEN ; IDENTIFICATION ; POLLUTION ; TRENDS ; CHINA
研究领域[WOS]	Engineering ; Environmental Sciences & Ecology
WOS记录号	WOS:001110179500001
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/33211
专题	中国科学院海岸带环境过程与生态修复重点实验室中国科学院海岸带环境过程与生态修复重点实验室_海岸带环境过程实验室
通讯作者	Yue, Fu-Jun; Li, Si-Liang
作者单位	1.Tianjin Univ, Sch Earth Syst Sci, Inst Surface Earth Syst Sci, Tianjin 300072, Peoples R China 2.Tianjin Univ, Tianjin Bohai Rim Coastal Earth Crit Zone Natl Obs, Tianjin 300072, Peoples R China 3.Tianjin Univ, Tianjin Key Lab Earth Crit Zone Sci & Sustainable, Tianjin 300072, Peoples R China 4.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China 5.Huaiyin Normal Univ, Sch Urban & Environm Sci, Huaian 223300, Peoples R China 6.Haihe Lab Sustainable Chem Transformat, Tianjin 300192, Peoples R China
推荐引用方式 GB/T 7714	Ren, Xinwei,Yue, Fu-Jun,Tang, Jianhui,et al. Nitrate transformation and source tracking of rivers draining into the Bohai Sea using a multi-tracer approach combined with an optimized Bayesian stable isotope mixing model[J]. JOURNAL OF HAZARDOUS MATERIALS,2024,463:11.
APA	Ren, Xinwei,Yue, Fu-Jun,Tang, Jianhui,Li, Cai,&Li, Si-Liang.(2024).Nitrate transformation and source tracking of rivers draining into the Bohai Sea using a multi-tracer approach combined with an optimized Bayesian stable isotope mixing model.JOURNAL OF HAZARDOUS MATERIALS,463,11.
MLA	Ren, Xinwei,et al."Nitrate transformation and source tracking of rivers draining into the Bohai Sea using a multi-tracer approach combined with an optimized Bayesian stable isotope mixing model".JOURNAL OF HAZARDOUS MATERIALS 463(2024):11.