Determination of methane emissions from biogas plants, using different quantification methods

Marlies Hrad*, Marion Huber-Humer, Torsten Reinelt, Bernhard Spangl, Claudia Flandorfer, Fabrizio Innocenti, Johan Yngvesson, Anders Fredenslund, Charlotte Scheutz

*Corresponding author for this work

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Abstract

Reliable and comparable quantification methods are needed for assessing the effectiveness of the biogas production and utilisation process in mitigating methane (CH4) emissions as well as improving the database for emission inventories. The objective of this study was to compare and validate CH4 emissions quantified at two agricultural biogas plants, for up to three days, using diverse on-site (two teams) and off-site methods (three teams), including differential absorption lidar (DIAL), tracer gas dispersion (TDM) and inverse dispersion modelling (IDM). For plant 1, with a constant combined heat and power (CHP) load, the average emission factor varied from 0.3% CH4 (on-site approaches) to 1.2% CH4 (off-site approaches). On-site approaches underestimated overall emissions due to many small (unquantified) CH4 leakages. All methods observed comparable average emission factors for plant 2, ranging between 1.9 and 2.2% CH4. In this case, the majority of emissions emanated from just a few sources. However, correcting the significant influence of the varying CHP load during the measurement campaign revealed significant differences between TDM and IDM (DIAL did not participate). It was demonstrated that TDM and IDM could recover the emission rate from a known point source (controlled release of CH4 via a small diffuser) within an accuracy of 93 ± 15% (TDM) and 92 ± 17% (IDM) under favourable and similar conditions.
Original languageEnglish
Article number109179
JournalAgricultural and Forest Meteorology
Volume326
Number of pages12
ISSN0168-1923
DOIs
Publication statusPublished - 2022

Keywords

  • Anaerobic digestion
  • Emission monitoring
  • Ground-based remote sensing
  • Tracer gas dispersion
  • Inverse dispersion modelling
  • Backward Lagrangian stochastic

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