The rapid and accurate prediction of the release source and concentration of pollutants remains a crucial issue in emergency rescue. A suitable gas diffusion model, an appropriate distribution of monitoring points, and an inverse calculation method are required to solve this problem. Optimization modeling using monitoring data is proposed in this paper. The objective function is established using the sum of the squared errors between the observed and calculated concentrations. The Gaussian plume model was improved with ground reflection coefficient and modified H_e and compared with AFTOX as the monitoring data to increase the accuracy of the inverse calculation of the source strength. The stochastic inertia weight particle swarm optimization algorithm is utilized to meet the needs of emergency rescue operations for the more accurate prediction of the leakage point. The results show that it is necessary to establish a gas diffusion model for each location to ensure the accuracy of the source strength estimate.

快速准确地预测污染物的释放源和浓度仍然是应急救援中的关键问题。解决这个问题需要合适的气体扩散模型、合适的监测点分布和逆计算方法。本文提出了使用监测数据的优化建模。目标函数是使用观察到的和计算出的浓度之间的误差平方和来建立的。高斯羽模型用地面反射系数改良，变形ħ _Ë并与AFTOX作为监测数据进行对比，提高了源强反演计算的精度。利用随机惯性权重粒子群优化算法，满足应急救援行动对泄漏点更准确预测的需求。结果表明，需要针对每个位置建立气体扩散模型，以确保源强度估计的准确性。