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Environ Sci Pollut Res Int. 2018 Nov;25(32):32631-32639. doi: 10.1007/s11356-018-3242-1. Epub 2018 Sep 21.

Using dual isotopes and a Bayesian isotope mixing model to evaluate sources of nitrate of Tai Lake, China.

Author information

1
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
2
USDA-ARS Southern Regional Research Center, 1100 Robert E Lee Blvd, New Orleans, LA, 70124, USA.
3
Department of Veterinary Biomedical Sciences and Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan, S7N 5B3, Canada.
4
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China. tzwork@hotmail.com.

Abstract

Identification and quantification of sources of nitrate (NO3-) in freshwater lakes provide useful information for management of eutrophication and improving water quality in lakes. Dual δ15N- and δ18O-NO3- isotopes and a Bayesian isotope mixing model were applied to identify sources of NO3- and estimate their proportional contributions to concentrations of NO3- in Tai Lake, China. In waters of Tai Lake, values for δ15N-NO3- ranged from 3.8 to 10.1‰, while values of δ18O ranged from 2.2 to 12.0‰. These results indicated that NO3- 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 NO3- sources (agricultural, industrial effluents, domestic sewage, and rainwater). SIAR output revealed that agricultural runoff provided the greatest proportion (50.8%) of NO3- to the lake, followed by industrial effluents (33.9%), rainwater (8.4%), and domestic sewage (6.8%). Contributions of those primary sources of NO3- 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 NO3-, followed by agricultural runoff (27.4%), domestic sewage (21.3%), and rainwater (15.9%). Whereas for the southern region, the proportion of NO3- 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, NO3- from both nonpoint agricultural sources and industrial point sources should be mitigated. Graphical abstract ᅟ.

KEYWORDS:

Bayesian isotope mixing model; Dual isotopes; Eutrophication; Nitrate; Sources; Tai Lake

PMID:
30242656
DOI:
10.1007/s11356-018-3242-1
[Indexed for MEDLINE]

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