Faster and more precise isotopic water analysis of discrete samples by predicting the repetitions' asymptote instead of averaging last values.

Water stable isotope analysis using Cavity Ring-Down Spectroscopy (CRDS) has a strong between-sample memory effect. The classic approach to correct this memory effect is to inject the sample at least 6 times and ignore the first two to three injections. The average of the remaining injections is then used as measured value. This is in many cases insufficient to completely compensate the memory effect. We propose a simple approach to correct this memory effect by predicting the asymptote of consecutive repeated injections instead of averaging over them. The asymptote is predicted by fitting a y = a x + b relation to the sample repetitions and keeping b as measured value. This allows to save analysis time by doing less injections while gaining precision. We provide a Python program applying this method and describe the steps necessary to implement this method in any other programming language. We also show validation data comparing this method to the classical method of averaging over the last couple of injections. The validation suggests a gain in time of a factor two while gaining in precision at the same time. The method does not have any specific requirements for the order of analysis and can therefore also be applied to an existing set of analyzes in retrospect.•We fit a simple y = a x + b relation to the sample repetitions of Picarro L2130-i isotopic water analyzer, in order to keep the asymptote (b) as measured value instead of using the average over the last couple of measurements.•This allows a higher precision in the measured value with less repetitions of the injection saving precious time during analysis.•We provide a sample code using Python, but generally this method is easy to implement in any automated data treatment protocol.

Controls on the Isotopic Composition of Nitrite (δ15N and δ18O) during Denitrification in Freshwater Sediments.

The microbial reduction of nitrate, via nitrite into gaseous di-nitrogen (denitrification) plays a major role in nitrogen removal from aquatic ecosystems. Natural abundance stable isotope measurements can reveal insights into the dynamics of production and consumption of nitrite during denitrification. In this study, batch experiments with environmental bacterial communities were used to investigate variations of concentrations and isotope compositions of both nitrite and nitrate under anoxic conditions. To this end, denitrification experiments were carried out with nitrite or nitrate as sole electron acceptors at two substrate levels respectively. For experiments with nitrate as substrate, where the intermediate compound nitrite is both substrate and product of denitrification, calculations of the extent of isotope fractionation were conducted using a non-steady state model capable of tracing chemical and isotope kinetics during denitrification. This study showed that nitrogen isotope fractionation was lower during the use of nitrite as substrate (ε = -4.2 and -4.5‰ for both treatments) as compared to experiments where nitrite was produced as an intermediate during nitrate reduction (ε = -10 and -15‰ for both treatments). This discrepancy might be due to isotopic fractionation within the membrane of denitrifiers. Moreover, our results confirmed previously observed rapid biotic oxygen isotope exchange between nitrite and water.

Legacy of contaminant N sources to the NO3- signature in rivers: a combined isotopic (δ15N-NO3-, δ18O-NO3-, δ11B) and microbiological investigation.

Nitrate content of surface waters results from complex mixing of multiple sources, whose signatures can be modified through N reactions occurring within the different compartments of the whole catchment. Despite this complexity, the determination of nitrate origin is the first and crucial step for water resource preservation. Here, for the first time, we combined at the catchment scale stable isotopic tracers (δ15N and δ18O of nitrate and δ11B) and fecal indicators to trace nitrate sources and pathways to the stream. We tested this approach on two rivers in an agricultural region of SW France. Boron isotopic ratios evidenced inflow from anthropogenic waters, microbiological markers revealed organic contaminations from both human and animal wastes. Nitrate δ15N and δ18O traced inputs from the surface leaching during high flow events and from the subsurface drainage in base flow regime. They also showed that denitrification occurred within the soils before reaching the rivers. Furthermore, this study highlighted the determinant role of the soil compartment in nitrate formation and recycling with important spatial heterogeneity and temporal variability.

Do forests represent a long-term source of contaminated particulate matter in the Fukushima Prefecture?

The Fukushima Daiichi Nuclear Power Plant (FDNPP) accident resulted in radiocesium fallout contaminating coastal catchments of the Fukushima Prefecture. As the decontamination effort progresses, the potential downstream migration of radiocesium contaminated particulate matter from forests, which cover over 65% of the most contaminated region, requires investigation. Carbon and nitrogen elemental concentrations and stable isotope ratios are thus used to model the relative contributions of forest, cultivated and subsoil sources to deposited particulate matter in three contaminated coastal catchments. Samples were taken from the main identified sources: cultivated (n = 28), forest (n = 46), and subsoils (n = 25). Deposited particulate matter (n = 82) was sampled during four fieldwork campaigns from November 2012 to November 2014. A distribution modelling approach quantified relative source contributions with multiple combinations of element parameters (carbon only, nitrogen only, and four parameters) for two particle size fractions (<63 µm and <2 mm). Although there was significant particle size enrichment for the particulate matter parameters, these differences only resulted in a 6% (SD 3%) mean difference in relative source contributions. Further, the three different modelling approaches only resulted in a 4% (SD 3%) difference between relative source contributions. For each particulate matter sample, six models (i.e. <63 µm and <2 mm from the three modelling approaches) were used to incorporate a broader definition of potential uncertainty into model results. Forest sources were modelled to contribute 17% (SD 10%) of particulate matter indicating they present a long term potential source of radiocesium contaminated material in fallout impacted catchments. Subsoils contributed 45% (SD 26%) of particulate matter and cultivated sources contributed 38% (SD 19%). The reservoir of radiocesium in forested landscapes in the Fukushima region represents a potential long-term source of particulate contaminated matter that will require diligent management for the foreseeable future.

Variability of 13C-labeling in plant leaves.

RATIONALE: Plant tissues artificially labeled with (13)C are increasingly used in environmental studies to unravel biogeochemical and ecophysiological processes. However, the variability of (13)C-content in labeled tissues has never been carefully investigated. Hence, this study aimed at documenting the variability of (13)C-content in artificially labeled leaves. METHODS: European beech and Italian ryegrass were subjected to long-term (13)C-labeling in a controlled-environment growth chamber. The (13)C-content of the leaves obtained after several months labeling was determined by isotope ratio mass spectrometry. RESULTS: The (13)C-content of the labeled leaves exhibited inter- and intra-leaf variability much higher than those naturally occurring in unlabeled plants, which do not exceed a few per mil. This variability was correlated with labeling intensity: the isotope composition of leaves varied in ranges of ca 60‰ and 90‰ for experiments that led to average leaf (13)C-content of ca +15‰ and +450‰, respectively. CONCLUSIONS: The reported variability of isotope composition in (13)C-enriched leaves is critical, and should be taken into account in subsequent experimental investigations of environmental processes using (13)C-labeled plant tissues.