Assessment of the backward Lagrangian Stochastic dispersion technique for continuous measurements of CH4 emissions

Continuous measurements of methane (CH4) emissions from agricultural facilities over a period of several days are needed to assess the mitigation effectiveness of management practices. In this study, we examined the feasibility of using an inverse dispersion technique (backward Lagrangian Stochastic model, bLS) for obtaining 15-min averages of CH4 emissions over a period of 5 days in the Ottawa area. Using two open-path lasers and pan-tilt scanning units, a ground level source was enclosed to measure the CH4 emissions (Q) with any wind direction. After application of the recommended data quality criteria screening for low friction velocity and extreme atmospheric stability, the average recovery (ratio of estimates QbLS to Q) was 1.09 with a standard deviation of 0.45. We observed a tendency in the recovery results toward underestimation during unstable stratification and overestimation during stable stratification. Using a test on developed turbulence instead of a fixed friction velocity threshold improved the accuracy of the emission estimates slightly. An additional data quality criterion based on the standard deviation output of the bLS model led to a significant improvement with a recovery of 1.00 and a standard deviation of 0.30. A strategy for averaging the resulting incomplete dataset is discussed. An assessment of the applicability of this approach to farm-size facilities led to the conclusion that this technique is suitable to determine emissions from realistic CH4 sources, such as dairy cattle barns, continuously in order to characterize both seasonal and diurnal characteristics of CH4 emissions.