< back to main site

Publications

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

Hrad, M; Huber-Humer, M; Reinelt, T; Spangl, B; Flandorfer, C; Innocenti, F; Yngvesson, J; Fredenslund, A; Scheutz, C (2022) Determination of methane emissions from biogas plants, using different quantification methods. Agricultural and Forest Meteorology, 326. 109179

[img]
Preview
Text
eid9769.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview

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.

Item Type: Article
Keywords: Anaerobic digestion; Emission monitoring; Ground-based remote sensing; Tracer gas dispersion; Inverse dispersion modelling; Backward Lagrangian stochastic
Subjects: Environmental Measurement > Atmospheric Science, Emission and Security
Divisions: Atmospheric Environmental Sciences
Identification number/DOI: 10.1016/j.agrformet.2022.109179
Last Modified: 03 Jul 2023 13:40
URI: http://eprintspublications.npl.co.uk/id/eprint/9769

Actions (login required)

View Item View Item