A Computational Tool for High-Frequency GHG Flux Analysis: Instantaneous and Diel Estimates from Automated Non-Steady-State Soil Chambers

Non-steady-state chambers are commonly used to measure soil and manure emissions of CO₂, CH₄, and N₂O. When paired with online gas analyzers, automated non-steady-state (a-NSS) chambers enable high-frequency monitoring of greenhouse gas (GHG) fluxes. Despite their advantages in capturing detailed emission patterns, these systems pose challenges in handling large datasets, performing complex flux calculations, and scaling results over time. This study introduces a computationally efficient algorithm designed to process continuous, high-resolution data from a-NSS chambers, providing instantaneous flux calculations and diel emission estimates. The algorithm was validated using field dataset, capturing simultaneous flux measurements for CO₂, CH₄, and N₂O. High-frequency data collection allowed for the identification of episodic flux events. By employing shape-constrained additive models, the algorithm achieved median percentage deviations (bias) of -1.03% for CO₂ and -4.34 % for N₂O. Temporal upscaling from instantaneous to diel fluxes was performed using Simpson’s rule, ensuring accurate integration over time. This tool offers a rapid, reliable method for real-time flux computation, significantly improving GHG flux measurement accuracy and enhancing insights into the temporal variability of soil emissions.