Quantification of methane fluxes from industrial sites using a combination of a tracer release method and a Gaussian model

Tuesday, 15 December 2015: 14:10
3001 (Moscone West)
Sebastien Ars1, Gregoire Broquet1, Camille Yver-Kwok1, Lin Wu1, Philippe Bousquet1 and Yelva Roustan2, (1)LSCE Laboratoire des Sciences du Climat et de l'Environnement, Gif-Sur-Yvette Cedex, France, (2)CEREA Centre d'Enseignement et de Recherche en Environnement Atmosphérique, Marne la Vallée, France
Greenhouse gas (GHG) concentrations keep on increasing in the atmosphere since industrial revolution. Methane (CH4) is the second most important anthropogenic GHG after carbon dioxide (CO2). Its sources and sinks are nowadays well identified however their relative contributions remain uncertain. The industries and the waste treatment emit an important part of the anthropogenic methane that is difficult to quantify because the sources are fugitive and discontinuous. A better estimation of methane emissions could help industries to adapt their mitigation’s politic and encourage them to install methane recovery systems in order to reduce their emissions while saving money.

Different methods exist to quantify methane emissions. Among them is the tracer release method consisting in releasing a tracer gas near the methane source at a well-known rate and measuring both their concentrations in the emission plume. The methane rate is calculated using the ratio of methane and tracer concentrations and the emission rate of the tracer.

A good estimation of the methane emissions requires a good differentiation between the methane actually emitted by the site and the methane from the background concentration level, but also a good knowledge of the sources distribution over the site. For this purpose, a Gaussian plume model is used in addition to the tracer release method to assess the emission rates calculated. In a first step, the data obtained for the tracer during a field campaign are used to tune the model. Different model’s parameterizations have been tested to find the best representation of the atmospheric dispersion conditions. Once these parameters are set, methane emissions are estimated thanks to the methane concentrations measured and a Bayesian inversion. This enables to adjust the position and the emission rate of the different methane sources of the site and remove the methane background concentration.