Evaluation of Data Processing Strategies for Methane Isotopic Signatures Determined During Near-Source Measurements

Abstract

Mobile, near-source measurements are broadly used for determining δ¹³CH₄ of individual methane (CH₄) emissions sources. To answer the need for robust and comparable measurement methods, we aim to define the best practices to determine isotopic signatures of CH₄ sources from atmospheric measurements, considering instrument accuracy and precision. Using the Keeling and Miller-Tans methods, we verify the impact of linear fitting methods, averaging approaches, and for the Miller-Tans method, different background composition. Measurement techniques include Isotope Ratio Mass Spectrometry (IRMS) and Cavity Ring Down Spectroscopy (CRDS). The use of the active AirCore system for sampling, coupled to CRDS for measurement, is examined. Due to their higher precision and accuracy, the chosen data processing strategy does not significantly influence IRMS results. Comparatively lower-precision CRDS measurements are more sensitive to methodological choices. Fitting methods with forced symmetry like Major Axis or Bivariate Correlated Errors and Intrinsic Scatter (BCES) with orthogonal sub-method introduce significant bias in the determined δ¹³CH₄ signatures using measurements from the lower-precision CRDS. The most reliable results are obtained for non-averaged data using fitting methods, which include uncertainties of x- and y-axis values, like York fitting or BCES (Y|X) sub-method, where x is treated as an independent variable. The Ordinary Least Squares method provides sufficiently robust results and can be used to determine δ¹³CH₄ in near-source conditions. The present recommendations are aimed at laboratories measuring δ¹³CH₄ source signatures to encourage consistency in the required methods for data analysis.