Replication Data for: Optimizing Methane Emission Source Localization in Oil and Gas Facilities Using Lagrangian Stochastic Models and Gradient-Based Detection Tools

The synthetic data were generated using a Gaussian Dispersion Model (GDM) and results from a Lagrangian-back-trajectory model (TERRAFEX) under various sensor-source geometries and atmospheric conditions, simulating stationary sensor deployment scenarios in oil and gas facilities.

The real-world dataset consists of concentration reconstructions and localization results from a gas distribution site in Canada, used to validate the methodology under operational conditions. The dataset includes: • Synthetic data: Simulated CH₄ concentration time series, concentration maps, and source localization outputs created using forward Gaussian dispersion modeling. These datasets cover various source-sensor distances (0–125 m), height differences (0–5 m), and four atmospheric stability classes (A–D), resulting in 456 synthetic scenarios. • Case study data: Measurement data and localization outputs collected from a gas distribution site in Canada, including CH₄ concentration data, wind measurements, and source localization maps reconstructed using TERRAFEX and the Gradient Indicator (GI) tool. • Outputs: Includes reconstructed concentration fields with TERRAFEX for both synthetic and case study scenarios. • Scripts: All analysis and visualization scripts used in this study were written in R (version 4.2.X).