Use of a Bayesian isotope mixing model to estimate proportional contributions of multiple nitrate sources in surface water
Highlights
► The dual isotope approach (δ15N- and δ18O–NO3−) identify dominant nitrate sources in 6 surface waters. ► The SIAR model estimate proportional contributions for 5 nitrate sources. ► SIAR is a reliable approach to assess temporal and spatial variations of different NO3− sources. ► The wide range of isotope values observed in surface water and of the nitrate sources limit its applicability.
Introduction
Nitrate (NO3−) contamination in water is an environmental problem world wide, and is attributed to anthropogenic activities including intensive agriculture, use of fertilizers and animal manure, and discharge of human sewage. To evaluate and manage water quality, NO3− concentration monitoring is a widely used approach. However, NO3− concentration data alone cannot fully assess the sources and respective contributions of NO3− inputs in water, which are key factors in effective management strategies. Although the implementation of the Nitrate Directive (EC, 2002) in Europe established a detailed framework for reducing NO3− input to water, NO3− is still one of the major contaminants of water resources. Since different NO3− sources (fertilizer, manure, human sewage, soil N and atmospheric N deposition) have distinct isotope ratios of nitrogen (15N/14N) and oxygen (18O/16O), it is possible to identify these different sources using isotope fingerprints.
A dual isotope approach (δ15N- and δ18O–NO3−) can provide meaningful insight for tracing sources of NO3− in water. A comparison of 16 watersheds in the U.S. by Mayer et al. (2002) demonstrated that the isotope signature of NO3− differed between forested catchment and agricultural land. In predominantly forested watersheds, NO3− was mainly derived from soil nitrification processes, resulting in low δ15N–NO3− values (less than 5‰). Enriched δ15N values (between 5 and 8‰) were found in predominantly agricultural watersheds with manure and sewage as contributors. Manure and sewage are enriched in 15N as ammonia (NH3) volatilization causes an enrichment of 15N in the residual NH4+ that is subsequently converted into 15N-enriched NO3−. Pardo et al. (2004) successfully used δ15N- and δ18O–NO3− to identify atmospheric deposition and microbial nitrification as two main sources of NO3− in streams of forested watersheds, as δ18O signatures of atmospheric NO3− (from 25‰ to 75‰) and microbial produced soil NO3− (from 0‰ to 15‰) differ significantly (Xue et al., 2009).
Some researchers also applied δ15N- and δ18O–NO3− to quantify different NO3− source contributions via a mass-balance mixing model (Phillips and Koch, 2002). Deutsch et al. (2006) successfully used the dual isotope approach to quantify riverine NO3− sources, which derived mostly from drainage water (86%), groundwater (11%) and from atmospheric deposition (3%). Voss et al. (2006) applied the dual isotope approach to quantify NO3− contributions into 12 Baltic rivers. In this study, a mass-balance mixing model was used to quantify three major NO3− source contributions, which were sewage, atmospheric deposition and pristine soils. However, a mass-balance mixing model is often performed to find unique solutions with the assumption that there is no variability within sources. In fact, three processes can introduce uncertainty on NO3− source apportionment: (a) temporal and spatial variability in δ15N and δ18O of NO3−; (b) isotope fractionation during denitrification; and (c) too many NO3− sources (number of sources > number of isotopes + 1) contribute to the mixture (Moore and Semmens, 2008, Xue et al., 2009).
A Bayesian stable isotope mixing model (Parnell et al., 2010) has been implemented in the software package SIAR (stable isotope analysis in R (a language and environment for statistical computing)). This model uses a Bayesian framework to determine the probability distribution of the proportional contribution of each source to a mixture. Furthermore, this mixing model takes into account the uncertainties mentioned above.
The objective of this study was to demonstrate the applicability of SIAR as a reliable “fingerprint” tool for estimating multiple NO3− source contributions in complex situations.
Section snippets
Site description
Flanders is situated in the northern part of Belgium with about 50% of the total surface area occupied by agriculture (Cazaux et al., 2007). The vast amount of reactive N present in Flemish surface waters is assumed to originate from intensive manure and mineral fertilizer application in agriculture. VMM (Flemish Environment Agency) operates an extensive MAP (Manure Action Plan) monitoring network assessing the evolution of the surface water quality. Six sampling points from ditches were
Physico-chemical data for different land use types
The seasonal physico-chemical data of the six sampling points are summarized in Table 1, Table 2. The NO3−–N concentrations (Table 1) of these sampling points varied widely during the monitoring period, ranging from 0.1 to 78.7 mg N L−1. Among those samples, mean NO3−–N concentrations for G sites were highest (between 19.7 and 41.2 mg N L−1) and showed relatively high standard deviations. In contrast, the H sites had relatively low mean NO3−–N concentrations (from 1.3 to 5.0 mg N L−1) and low standard
Conclusions
Our study showed that a Bayesian mixing model using stable isotope ratios of N and O in NO3− could successfully be applied to estimate proportional contributions of NO3− sources in surface water. The SIAR output, however, revealed a great variability in contribution of five potential NO3− sources. SIAR provides a “fingerprint” of potential NO3− sources, as it does not only demonstrate dominant NO3− source contributors, but also reveals other important potential contributors, which cannot be
Acknowledgement
This study was funded by the Flemish government agency for Innovation by Science and Technology within the Project “IWT050664-Development and evaluation of a classification model to identify nitrate sources in surface water”.
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