Online analysis of isotopic signatures of nitrate, nitrite and ammonium in aqueous samples by isotope ratio mass spectrometry

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Nitrate, nitrite and ammonium are key chemical species of the N cycle in soil and aquatic ecosystems. Their prominent role in plant nutrition, microbial metabolism and ecosystem functioning is long known and extensively studied. Nitrite is an interface for the main N transformation processes and there is growing evidence that nitrite, although rarely observed in soils and waters and sometimes neglected in N cycle studies, is highly relevant for the control of many N pathways. Natural abundance levels of 15N in nitrate, nitrite and ammonium in soil solution and water bodies are important to elucidate the origin and transformation processes of these N species. To date, only off-line methods are available to analyze nitrate and/or ammonium from aqueous solutions at natural abundance levels. An on-line method using quadrupole mass spectrometry (QMS) has been developed for the selective analysis of 15N abundances of labelled ammonium, nitrite or nitrate. However, this method is not precise enough for isotope ratio analysis at natural abundance levels due to the limited accuracy of the mass spectrometer. As a result, until now the means to elucidate the interaction of N turnover processes and N pools is hampered by the lack of methods to analyze natural abundance of N substrates efficiently enough to allow the analysis of large enough sample numbers to achieve an adequate temporal and spatial resolution of data to improve the understanding of the N cycle. The aim of the project is to develop an on-line method for analyzing nitrate, nitrite and ammonium N isotope ratios at natural abundance by isotope ratio mass spectrometry from aqueous solutions. The method will be based on the chemical conversions used for the QMS approach. A gas permeable membrane will be used to interface the analytes from liquid phase into the isotope ratio mass spectrometer. The setup will allow automated sample preparation and analysis and thus increase sample throughput considerably as compared to off-line methods. This ultimately will pave the way for substantial progress in N cycle research.