Methane (CH₄) is a critical greenhouse gas with significant implications for the energy and environmental sectors. It plays a pivotal role in advancing global energy transitions. Despite its shorter atmospheric lifetime compared to carbon dioxide (CO₂), CH₄’s per-molecule radiative forcing is substantially higher. Anthropogenic CH₄ emissions contribute significantly to global climate change, and making their reduction is a key strategy to mitigate global warming. The coal, oil, and gas industries account for most anthropogenic CH₄ emissions. Quantifying CH₄ leakage rates and pinpointing leakage sources are vital steps in achieving measurable CH₄ emission reductions. However, the development of cost-effective and efficient CH₄ monitoring methods remains a challenge. While vehicle-mounted and airborne observations offer mobility, they lack the continuity required for long-term plant monitoring. Fixed-point remote sensing systems provide a promising alternative. In this paper, we propose a composite observation model leveraging laser-based TDLAS sensors for quantitative monitoring of CH₄ leakage sources and rates. By utilizing miniaturized and universally adaptable observation equipment, the model can further provide solid theoretical and methodological support for global CH₄ leakage emission monitoring.

We utilize an active laser TDLAS sensor, a miniaturized tachymeter, and a visible-light camera to create an elevation-based model for leakage monitoring in industrial plants. The laser instrument measures the integral CH₄ volume fraction along its path, while a scanning head enables broad-area observations. Using a visible-light camera and rangefinder, an observation field-of-view model is constructed. The laser scans the field to capture CH₄ volume fraction points, and coordinates are calculated based on the scanner’s angles and tachymeter data. This yields a comprehensive data matrix of CH₄ volume fraction and location. Environmental parameters like temperature and pressure, obtained from meteorological stations, are factored into the volume fraction calculations. An improved Gaussian plume diffusion model that incorporates wind direction is utilized to align with the camera’s observation field of view, simulating data point acquisition across the entire observation model. A dedicated algorithm for quantifying CH₄ leakage rates and locating leakage sources is developed, with its performance evaluated using theoretical data generated by the observation model. Key error sources, including sampling concentration errors, deviations of wind speed and wind direction, coordinate inaccuracies of sampling points, and data point errors, are thoroughly analyzed. We integrate multiple algorithms, compare their adaptability to various error sources, and examine the overall performance of the theoretical observation model and the algorithms.

Simulation results indicate that under the IPPF algorithm, a 30% sampling volume fraction error results in a leakage rate deviation of about 3 mg/s, with upper and lower quartile deviations of about 10 mg/s. For a preset leakage rate of 500 mg/s, the relative deviation is about 2%. Wind direction errors of 60° can cause a maximum leakage rate deviation of 100 mg/s, while coordinate deviations of 2.5 m result in a 40 mg/s leakage rate error. Increasing sampling points improves leakage rate accuracy (Fig. 6). Wind direction and observation point coordinates significantly influence leakage source localization, with X-coordinates being more sensitive than Y-coordinates. For low wind speeds (<0.5 m/s), the error in leakage source localization is negligible (Fig. 7). Under different atmospheric stability conditions, quantification performs best under condition A, where greater lateral dispersion enhances sampling distribution (Figs. 8?10). Among algorithms, IPPF and GA+IPPF yield similar results for leakage rates (Fig. 11), while GA+PSO demonstrates improved robustness against wind direction bias, coordinate errors, and sample point density. However, GA+PSO underperforms the other two algorithms in scenarios involving wind speed errors.

To address CH₄ leakage source localization and rate quantification in industrial plants, we propose a multi-device fusion model combining TDLAS sensors, a miniaturized tachymeter, and a visible-light camera. Simulation results show that under a 30% sampling volume fraction error, the IPPF algorithm achieves a leakage rate deviation of about 3 mg/s, with a relative error of about 2% for a theoretical rate of 500 mg/s. Wind speed and wind direction significantly affect leakage rate quantification, with deviations of 5 mg/s observed for wind speed errors of 0.5 m/s. Atmospheric stability conditions further influence quantification accuracy, with condition A providing optimal results. The GA+PSO algorithm effectively addresses uncertainties arising from wind direction bias, coordinate errors, and sampling density, while IPPF and GA+IPPF demonstrate reliability under severe concentration and wind speed errors. Our study offers a robust theoretical and methodological foundation for continuous large-scale CH₄ leakage monitoring in industrial settings.