Quantitative optical gas imaging (QOGI) system can rapidly quantify leaks detected by optical gas imaging (OGI) cameras across the oil and gas supply chain. A comprehensive evaluation of the QOGI system\'s quantification capability is needed for the successful adoption of the technology. This study conducted single-blind experiments to examine the quantification performance of the FLIR QL320 QOGI system under near-field conditions at a pseudo-realistic, outdoor, controlled testing facility that mimics upstream and midstream natural gas operations. The study completed 357 individual measurements across 26 controlled releases and 71 camera positions for release rates between 0.1 kg Ch4/h and 2.9 kg Ch4/h of compressed natural gas (which accounts for more than 90% of typical component-level leaks in several production facilities). The majority (75%) of measurements were within a quantification factor of 3 (quantification error of -67% to 200%) with individual errors between -90% and 831%, which reduced to -79% to +297% when the mean of estimates of the same controlled release from multiple camera positions was considered. Performance improved with increasing release rate, using clear sky as plume background, and at wind speeds ≤1 mph relative to other measurement conditions.

This study investigates temporal variability on landfill methane (CH4) emissions from an old abandoned Danish landfill, caused by the rate of changes in barometric pressure. Two different emission quantification techniques, namely the dynamic tracer dispersion method (TDM) and the eddy covariance method (EC), were applied simultaneously and their results compared. The results showed a large spatial and temporal CH4 emission variation ranging from 0 to 100 kg h-1 and 0 to 12 μmol m-2 s-1, respectively. Landfill CH4 emissions dynamics were influenced by two environmental factors: the rate of change in barometric pressure (a strong negative correlation) and wind speed (a weak positive correlation). The relationship between CH4 emissions and the rate of change in barometric pressure was more complicated than a linear one, thereby making it difficult to estimate accurately annual CH4 emissions from a landfill based on discrete measurements. Furthermore, the results did not show any clear relationship between CH4 emissions and ambient temperature. Large seasonal variations were identified by the two methods, whereas no diurnal variability was observed throughout the investigated period. CH4 fluxes measured with the EC method were strongly correlated with emissions from the TDM method, even though no direct relationship could be established, due to the different sampling ranges of the two methods and the spatial heterogeneity of CH4 emissions.