Assessing Methane Emission Patterns and Sensitivities at High-Emission Point Sources in China via Gaussian Plume Modeling

Accurate quantification of methane (CH4) emissions from individual point sources is essential for understanding localized greenhouse gas dynamics and supporting mitigation strategies. This study employs satellite-based point-source emission rate data from the Carbon Mapper initiative, combined with ERA5 meteorological reanalysis, to simulate near-surface CH4 dispersion using a Gaussian plume model coupled with Monte Carlo simulations. This approach captures local dispersion characteristics around each emission source. Simulations driven by these emission inputs reveal a highly skewed, heavy-tailed concentration distribution (consistent with log-normal characteristics), where the 95th percentile (1292.1 ppm) significantly exceeds the mean (475.9 ppm), indicating the dominant influence of a small number of super-emitters. Sectoral analysis shows that coal mining contributes the most high-emission sites, while the solid waste and oil & gas sectors present higher per-source intensities, averaging 1931.1 ppm and 1647.6 ppm, respectively. Spatially, emissions are concentrated in North and Northwest China, particularly Shanxi Province, which hosts 62 high-emission sites with an average maximum of 1583.9 ppm. Sensitivity analysis reveals that emission rate perturbations produce nearly linear responses in concentration, whereas wind speed variations induce an inverse and asymmetric nonlinear response, with sensitivity amplified under low wind speed conditions (a ±30% change in wind speed results in more than ±25% variation in concentration). Under stable atmospheric conditions (Class E), concentrations are approximately 1.3 times higher than those under weakly unstable conditions (Class C). Monte Carlo simulations further indicate that output uncertainty peaks within 150–300 m downwind of emission sources. These results provide a quantitative basis for improving uncertainty characterization in satellite-based methane inversion and for prioritizing risk-based monitoring strategies.