基于受体模型和源成分谱的缺失组分反演算法

针对目前在线监测仪器无法监测Al、Si标识组分可能增加源解析结果不确定性的问题，基于正交矩阵因子分解法模型和实测源成分谱反演Al、Si受体数据，提出缺失组分反演-嵌套PMF算法.设置周期性和非周期性模拟实验，利用PMF模型对缺失Al、Si受体数据、含反演Al、Si受体数据和含Al、Si受体数据分别进行源解析评估，并将源解析结果与真实值进行对比分析，验证缺失组分反演-嵌套PMF算法的可靠性.研究结果表明对于周期性扰动模拟实验，缺失Al、Si对解析土壤源、机动车、燃煤源有较大的影响，反演Al、Si能优化源解析结果，降低源解析结果的不确定性.

Reverse modeling of source markers based on receptor model and source profiles

Source markers are very important for apportioning the particulate matter sources. However, some markers like aluminum and silicium could not be measured by online instruments,which might increase the uncertainty of source apportionment results. To figure out this problem, this work proposed a new inverse method to estimate Al and Si concentrations base on PMF (Positive Matrix Factorization) and measured source profiles. Several simulation experiments was designed to estimate the performance of the new method. Three input data, including data without Al and Si, data with reversed Al and Si, data with Al and Si, were setup and run separately by PMF, the calculated source profiles and contributions were compared with the corresponding true values. The results show that running model without Al and Si data increases uncertainties of results, and the new method can improve the model performance, for some cases.