Quantifying nitrate pollution sources of the drinking water source area using a Bayesian isotope mixing model in the northeastern suburbs of Beijing, China

ABSTRACT

Nitrate pollution has become an environmental problem of global concern. One effective way for controlling the nitrate pollution of water is to identify the pollution source and reduce the input of nitrate. This study traces and quantifies the sources of nitrate contamination to groundwater and surface water in the northeastern suburbs of Beijing, where an emergency groundwater source zone is located. Nitrogen and oxygen stable isotope analysis, geospatial analysis techniques, principal component analysis, correlation analysis, and a Bayesian isotope mixing model were used to achieve our goals. The results show that the main sources of nitrate pollution in groundwater were manure and sewage (M&S) (42.6 %) > soil nitrogen (SN) (26.6 %) > ${\mathrm{NH}}_4^{+}$ in fertilizer and rain (NHF&R) (24.5 %) > ${\mathrm{NO}}_3^{-}$ fertilizer (NOF) (5.0 %) > ${\mathrm{NO}}_3^{-}$ in atmospheric deposition (NAD) (1.3 %), and main sources of nitrate in surface water were M&S (28.8 %) > SN (20.4 %) > NAD (19.8%) > NOF (16.5%) > NHF&R (14.5 %). Due to the high permeability of the aquifer in the study area, there was a strong hydraulic connection between groundwater and surface water. The discharge of treated wastewater (reclaimed water) into the mostly dried river channel in the study area might aggravate nitrate pollution in the groundwater.

KEYWORDS:

• Beijing
• dual isotope
• groundwater
• isotope hydrology
• nitrate
• nitrogen-15
• oxygen-18
• quantifying sources
• SIAR
• surface water

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors wish to express their deep gratitude to the Natural Science Foundation of Beijing Municipality [grant number Z170004], the National Natural Science Foundation of China [grant number 41672227], and the National Key R&D Program of China [grant number 2018YFC1800905] for support rendered in the course of this research.