有毒气体泄漏是危化品工业潜在的重大安全隐患，对人员生命、财产安全及生态环境构成严重威胁。为了在事故发生后快速获取泄漏源项信息，为应急决策和人员疏散提供科学依据，该文提出了一种结合Monte Carlo预约束、差分进化（differential evolution, DE）全局搜索与L-BFGS-B局部优化的毒气泄漏源项反演方法（MDO方法），并对数据扩充步骤进行了讨论。基于Gauss烟羽模型的数值模拟及补充的气象参数、监测噪声和监测点数量的影响分析结果表明：在基准工况下，该方法能够稳定反演泄漏源位置与强度；稳定度失配和风向偏差会显著放大定位误差，监测噪声增大和监测点数量减少会削弱反演精度，而数据扩充对精度改善并不具有普适性。该方法可为稀疏监测条件下的毒气泄漏源项反演提供一种稳定求解框架，并为更复杂场景下的模型扩展提供参考。

Objective: Toxic gas leakage accidents in hazardous chemical industries can rapidly threaten human life safety, property, and the surrounding environment. The emergency response following such accidents depends on timely knowledge of the source term, including the source location and source strength. However, direct access to the leakage point is often difficult because the accident scene may involve high toxicity, poor visibility, and rapidly changing dispersion conditions. Against this backdrop, source-term inversion based on sparse monitoring data provides an important computational approach for supporting emergency decision-making. This study aims to develop a stable source-term inversion framework for toxic gas leakage accidents under sparse monitoring conditions and to evaluate how meteorological parameters, monitoring noise, monitoring-point density, and data augmentation affect the inversion results. Methods: A Monte Carlo–differential evolution (DE) optimization method, referred to as the MDO method, was constructed by combining Monte Carlo preconstraints, DE global search, and local optimization using the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm with bound constraints (L-BFGS-B). The Gaussian plume model was used as the forward dispersion model to calculate toxic gas mass concentrations at monitoring points. First, Monte Carlo sampling was used to generate candidate source-term parameters within predefined search ranges and to identify a feasible search region according to the mismatch between simulated and monitored concentrations. This step reduced the search space for subsequent optimization. Second, DE was used to globally search for the leakage source coordinates, thereby reducing the dependence of inversion results on a single initial guess. Third, L-BFGS-B was used to locally refine the source strength under bound constraints. To examine the role of sparse monitoring data, interpolation-based data augmentation was treated as an optional preprocessing step rather than a mandatory component. The method was tested using four controlled source-term scenarios with six monitoring points. Additional numerical simulations were conducted to examine atmospheric stability class, wind direction deviation, wind speed perturbation, monitoring noise level, and the number of monitoring points. Baseline comparisons with full-domain DE, Monte Carlo-only estimation, and local multi-start optimization were also performed. Results: Under the baseline-controlled conditions, the proposed MDO framework completed the joint inversion of source location and source strength within a short computation time. Meteorological sensitivity analysis showed that atmospheric stability mismatch strongly affected the location result. When the stability class used in inversion was consistent with the forward simulation condition, namely class C, the mean location error was 0.174 1 m. When the stability class was B, D, or E, the mean location errors increased to 9.564 0, 12.315 4, and 11.423 4 m, respectively. Wind direction deviation also increased the location error. When the deviation changed from 0° to ±10° and ±15°, the error generally increased, indicating that wind direction accuracy is important for source-term inversion. In contrast, small wind speed perturbations around the baseline wind speed had a weaker influence on location error in the present setting. Robustness analysis showed that when the monitoring noise level increased from 0% to 15%, the mean location error increased from 2.125 9 to 2.417 0 m, and the relative error of source strength increased from 8.451 7% to 10.917 5%. The degradation was gradual. Increasing the number of monitoring points improved the inversion accuracy: when the number of monitoring points increased from 4 to 10, the mean location error decreased from 3.018 8 to 1.163 3 m. The data augmentation comparison showed that interpolation did not always improve inversion accuracy. In the default scenario, the mean location error obtained using the original monitoring points was 3.620 9 m, whereas the error after interpolation-based augmentation was 6.596 3 m. The baseline comparison further showed that the MDO method produced results of the same order of magnitude as those of the comparison methods; however, it did not show a distinct advantage over all alternatives across every metric. Conclusions: The MDO method provides a feasible multi-stage framework for source-term inversion of toxic gas leakage accidents under controlled numerical conditions and sparse monitoring data. The results indicate that atmospheric stability and wind direction accuracy are key meteorological factors affecting inversion precision, whereas higher monitoring-point density helps improve source localization. Data augmentation should be used cautiously because interpolated points are not independent measurements and may introduce additional uncertainty under unfavorable monitoring-point distributions or interpolation errors. The present study is still based on the Gaussian plume model and controlled numerical scenarios. Complex terrain, building interference, unsteady wind fields, and real accident monitoring errors were not fully considered. Future work should incorporate higher-fidelity dispersion models and field monitoring data to further evaluate the engineering applicability of the proposed method.