In this case study, we consider only single-source emissions because our goal is to maximize detection efficiency. Thus, achieving successful detection of single-source emissions should inherently guarantee success in detecting multiple-source emissions. For demonstration purposes, we fix the emission duration for each emission event to be one hour and assume all five equipment have equal likelihood to be an emission source. Additionally, we prescribe three possible emission rates, [1, 5, 10] kg/h to each of the potential emission sources with equal probability. This constructs a simple uniform distribution, i.e., P (x 0,i , q j ) = 1/15, i = 1, 2, . . . , 5, j = 1, 2, 3. It's important to mention that the fixed duration value, emission rates and probabilities we used in this experiment are only for demonstration purpose. In practice, a realistic distribution should be derived based on actual site-specific data and practitioners' insights, and an example of this guidance is provided in the case study at the end of this paper. For wind data, we approximate the true wind distribution f w by using one-hour non-overlapping segments from the historical wind data throughout the entire experiment period to reflect the actual meteorological conditions on the site within the experiment period. In practice, a full year of wind data is desired to thoroughly represent the actual meteorological conditions, including seasonal patterns, which is implemented in the case study for a real oil and gas site. Finally, this yields a total number of 38,685 emission scenarios which comprehensively represent the actual emission conditions on the site during the experiment period.