Wind Data Wind data was collected over a year at one-minute intervals from two anemometers co-located with the existing sensors. To create a single time series for our framework, we averaged the wind speed and direction (x and y component wise) when both anemometers had data, using the available value when one was NA. Instances with both NA (0.684% of the data) remained NA. We then randomly selected 1,000 nonconsecutive hours of gap-free data, with the sampled distribution closely matching the full year's data (see Figure ). Emission Scenarios Operator guidance indicated that the 10 site compressors were the primary sources of fugitive emissions, and overflight data had suggested that emissions of 5 kg/hr were typical. We simulated emissions using these values across the 1,000 hourly wind data samples for each compressor, generating 1,000 scenarios per compressor and 10,000 total for all sources combined, allowing us to find the overall optimal placement. Simulation, Detection, and Optimization All simulations used the Gaussian puff model with parameters listed in Section S2 of the SI. For each simulation, we recorded the candidate locations along the 2 m resolution site fenceline where the simulated methane concentration exceeded 1 ppm for at least 1 minute in accordance with a conservative estimate of sensor sensitivity provided by the sensor vendor. We first determined optimal sensor placements for each compressor individually (see Figure in the SI) then combined the results to create a single detection matrix D as described in Step 4. The PORSS algorithm (see Step 5) was then used to find the optimal sensor locations. Lastly, we verified our solution's stability by comparing it against 10 million random samples of 4 sensor locations along the fenceline, imposing a 30 m minimum distance between sensors. The PORSS algorithm found the optimal solution in 210 seconds, about 30 times faster than evaluating the random samples which took 6,177 seconds, with nearly identical results but a slightly higher coverage ratio for the PORSS algorithm (see Figure in SI).