A high-frequency greenhouse gas flux analysis tool: Insights from automated non-steady-state transparent soil chambers

Abstract

Non-steady-state chambers are widely employed for quantifying soil emissions of CO2, CH4, and N2O. Automated non-steady-state (a-NSS) soil chambers, when coupled with online gas analysers, offer the ability to capture high-frequency measurements of greenhouse gas (GHG) fluxes. While these sampling systems provide valuable insights into GHG emissions, they present post-measurement challenges, including the management of extensive datasets, intricate flux calculations, and considerations for temporal upscaling. In this study, a computationally efficient algorithm was developed to compute instantaneous fluxes and estimate diel flux patterns using continuous, high-resolution data obtained from an a-NSS sampling system. Applied to a 38-day dataset, the algorithm captured concurrent field measurements of CO2, CH4, and N2O fluxes. The automated sampling system enables the acquisition of high-frequency data, allowing the detection of episodic gas flux events. By using shape-constrained additive models, a median percentage deviation (bias) of −1.031 and −4.340% was achieved for CO2 and N2O fluxes, respectively. Simpson's rule allowed for efficient upscale from instantaneous to diel flux values. As a result, the proposed algorithm can rapidly and simultaneously calculate CO2, CH4, and N2O fluxes, providing both instantaneous and diel values directly from raw, high-temporal-resolution data. These advancements significantly contribute to the field of GHG flux measurement, enhancing both the efficiency and accuracy of calculations for a-NSS soil chambers and deepening our understanding of GHG emissions and their temporal dynamics.