We propose a methodology to estimate single and multiple emission sources of atmospheric contaminants. It combines hybrid metaheuristic/gradient-descent optimization techniques and Tikhonov-type regularization. The dispersion problem is solved by the Galerkin/Least-squares finite element formulation, which allows more realistic modeling. The accuracy of the proposed inversion model is tested under different contexts with experimental data. To identify single and multiple emissions, we use experimental field data. We consider different configurations for both the Tikhonov-type functional and optimization techniques. Several single and composite data misfit functions are tested. We also use a discrepancy-based choice rule for the regularization parameter. The resulting inversion tool is highly versatile and presents accurate results under different contexts with a competitive computational cost.

我们提出了一种方法来估算大气污染物的单个和多个排放源。它结合了混合元启发式/梯度下降优化技术和Tikhonov型正则化。色散问题通过Galerkin /最小二乘有限元公式解决，从而可以进行更逼真的建模。所提出的反演模型的准确性在不同背景下用实验数据进行了测试。为了识别一次和多次排放，我们使用实验现场数据。我们为Tikhonov型功能和优化技术考虑了不同的配置。测试了多个单一数据和复合数据失配功能。我们还将正则化参数使用基于差异的选择规则。