Quantifying precision and accuracy of measurements of dissolved inorganic carbon stable isotopic composition using continuous-flow isotope-ratio mass spectrometry

Abstract

RATIONALEWe describe an analytical procedure that allows sample collection and measurement of carbon isotopic composition (δ13CV-PDB value) and dissolved inorganic carbon concentration, [DIC], in aqueous samples without further manipulation post field collection. By comparing outputs from two different mass spectrometers, we quantify with the statistical rigour uncertainty associated with the estimation of an unknown measurement. This is rarely undertaken, but it is needed to understand the significance of field data and to interpret quality assurance exercises.METHODSImmediate acidification of field samples during collection in evacuated, pre-acidified vials removed the need for toxic chemicals to inhibit continued bacterial activity that might compromise isotopic and concentration measurements. Aqueous standards mimicked the sample matrix and avoided headspace fractionation corrections. Samples were analysed using continuous-flow isotope-ratio mass spectrometry, but for low DIC concentration the mass spectrometer response could be non-linear. This had to be corrected for.RESULTSMass spectrometer non-linearity exists. Rather than estimating precision as the repeat analysis of an internal standard, we have adopted inverse linear calibrations to quantify the precision and 95% confidence intervals (CI) of the δ13CDIC values. The response for [DIC] estimation was always linear. For 0.05–0.5 mM DIC internal standards, however, changes in mass spectrometer linearity resulted in estimations of the precision in the δ13CVPDB value of an unknown ranging from ± 0.44‰ to ± 1.33‰ (mean values) and a mean 95% CI half-width of ±1.1–3.1‰.CONCLUSIONSMass spectrometer non-linearity should be considered in estimating uncertainty in measurement. Similarly, statistically robust estimates of precision and accuracy should also be adopted. Such estimations do not inhibit research advances: our consideration of small-scale spatial variability at two points on a small order river system demonstrates field data ranges larger than the precision and uncertainties. However, without such statistical quantification, exercises such as inter-lab calibrations are less meaningful.

An international intercomparison of stable carbon isotope composition measurements of dissolved inorganic carbon in seawater

“…In most oceanographic and hydrogeological studies, δ 13 C-DIC of water samples is measured by isotope-ratio mass spectrometry (IRMS) coupled with various front-end peripherals (e.g., Salata et al 2000;Torres et al 2005;Assayag et al 2006;Waldron et al 2014). In recent years, laser-based optical spectroscopy such as Isotope Ratio Infrared Spectrometer (IRIS) and cavity ring-down spectroscopy (CRDS) have also been used for detection (e.g., Bass et al 2012;Call et al 2017).…”

Hydraulics are a first‐order control on CO2 efflux from fluvial systems

“…At all sites, within 15 min of the flux measurement, water samples were collected for determining dissolved inorganic carbon concentration ([DIC]) using a headspace method [ Waldron et al ., ] and analysis on a Thermo‐Fisher‐Scientific Gas Bench/Delta V Plus. Partial pressure of CO 2 ( p CO 2 ) was calculated from the [DIC] using pH and temperature [ Rebsdorf et al ., ; Stumm and Morgan , ].…”

“…Where pH was measured using a Troll 9000 or 9500 (DW, NC, MT, LT, and TP), uncertainty in pH is ±0.1 pH unit according to the manufacturers specifications and 1.3% for RK indicated by the percent residuals (Figure S3). Uncertainty in [DIC] is 0.027 nM [ Waldron et al ., ].…”

Challenges in modeling detailed and complex environmental data sets: a case study modeling the excess partial pressure of fluvial $$\hbox {CO}_2$$ CO 2

“…The sampling spanned a wide range of flow conditions. DIC (mmol L −1 C) is quantified by direct measurement using a headspace analysis approach (Waldron et al 2014), to internal precision better than ±0.03 mmol L −1 . This was regressed onto discharge, which is measured semi-continuously, to generate a relationship from which DIC is predicted, thus creating a continuous DIC profile (Waldron et al 2007).…”

“…Troll 9000EXP data loggers (In-Situ, Inc.) were used to generate 15 min frequency time series of temperature, pH and atmospheric pressure from October 2003 to September 2006. These parameters allowed the excess partial pressure of carbon dioxide (EpCO 2 ) to be indirectly calculated from the continuous DIC profile (Waldron et al 2014). Estimates of the capacity for CO 2 efflux, are described as the "Excess partial pressure of CO 2 ", EpCO 2 , a ratio of over-saturation [for more details, see Neal (1998) and Dawson et al (2009)].…”