Riverine nitrate source apportionment using dual stable isotopes in a drinking water source watershed of southeast China.

Abstract

It is crucial to quantitatively track riverine nitrate (NO) sources and transformations in drinking water source watersheds for preventing current and future NO pollution, and ensuring a safe drinking water supply. This study identified the significant contributors to riverine NO in Zhaoshandu reservoir watershed of Zhejiang province, southeast China. To achieve this goal, we used hydrochemistry parameters and stable isotopes of NO (δN-NO and δO-NO) accompanied with a Markov Chain Monte Carlo mixing model to estimate the proportional contributions of riverine NO inputs from atmospheric deposition (AD), chemical nitrogen fertilizer (NF), soil nitrogen (SN), and manure and sewage (M&S). Results indicated that the main form of riverine nitrogen in this region was NO, constituting ~60% of the total nitrogen mass on average (total organic nitrogen ~37% & ammonium ~3%). Variations in the isotopic signatures of NO demonstrated that microbial nitrification of NF, SN and M&S was the primary nitrogen transformation process within the Zhaoshandu reservoir watershed, whereas denitrification was minimal. A classical dual isotope bi-plot incorporating chloride concentrations suggested NF, SN and M&S were the major contributors of NO to the river. Riverine NO source apportionment results were further refined using the Markov Chain Monte Carlo mixing model, which revealed that AD, NF, SN and M&S contributed 7.6 ± 4.1%, 22.5 ± 12.8%, 27.4 ± 14.5% and 42.5 ± 11.3% of riverine NO at the watershed outlet, respectively. Finally, uncertainties associated with NO source apportionment were quantitatively characterized as: SN > NF > M&S > AD. This work provides a comprehensive approach to distinguish riverine NO sources in drinking water source watersheds, which helps guide implementation of management strategies to effectively control NO contamination and protect drinking water quality. SUMMARY OF THE MAIN FINDING FROM THIS WORKS (CAPSULE): We utilized NO stable isotope analysis and a Markov Chain Monte Carlo mixing model to quantify riverine nitrate pollution sources in a drinking water source watershed in Zhejiang province, southeast China. Markov Chain Monte Carlo mixing model output showed that NF, SN and M&S were the dominant sources of riverine NO during the sampling period in Zhaoshandu watershed. Uncertainty analysis characterized the variation strength associated with contributions of individual nitrate sources and indicated the greatest uncertainty for SN, followed by NF, M&S and AD.