Methane emissions from a waste treatment site: Numerical analysis of aircraft-based data

Abstract Methane (CH4) is an important greenhouse gas. Emissions from landfills and waste treatment sites are regarded as the third largest anthropogenic source in the world. Precise estimates of CH4 emissions depend on a well-designed sampling procedure and detailed analyses. This study estimates CH4 emissions from a waste treatment site by analyzing the data from a flight, which was carried out in Eastern Ontario, Canada, on May 6, 2011. The CH4 dispersion plume at about 140 m above ground level downwind of the source is analyzed with data from 14 flight tracks. The observation period of about 72 min is characterized by unstable boundary layer conditions, strong wind, and shifting wind direction. To replicate the boundary layer turbulence and the CH4 dispersion during the measurement period, a combined large-eddy simulation (LES) and Lagrangian stochastic (LS) particle dispersion modeling method is used. A preliminary simulation run with mean meteorological variables during the experiment's duration is carried out. The results reproduced the shifts in wind direction well, in addition to the dispersion plume, but in a smaller magnitude in comparison to the measured data. Therefore, two sets of LES runs are carried out to represent the boundary layer turbulence before and after the wind shift. Another deficiency in the LES runs is that the simulated turbulent velocity variances are systematically smaller than the observed counterparts. In response to this discrepancy, additional terms are incorporated into the LS dispersion model so as to represent the turbulence dispersion process better. With these treatments, the LES-LS modeling results replicated the dispersion plume both in lateral width and in concentration distribution shape. Then, by using the simulated relationship between source and concentrations/vertical fluxes in space, the CH4 emission rate of the site is estimated as 2393 (kg CH4 hr−1) / 2088 (kg CH4 hr−1) respectively.