基于PMF模型的大气颗粒物多点位来源解析研究

随着环境监测数据空间分辨率的提高，越来越多研究人员选择将大气颗粒物多点位数据合并进行解析.本文通过模拟试验的方法，共设定了三大类八小类情景评估了不同条件下将多点位受体（大气颗粒物）进行解析的结果，同时结合合肥市2014年PM_2.5数据进一步验证多点位数据合并解析的适用性.结果表明：各点位间源贡献时间趋势完全一致时，多点位合并解析并不会使PMF模型的结果变好.各点位间源贡献时间趋势差异明显时，多点位合并解析更易解析出结果.各点位间源贡献时间趋势部分相同时，多点位合并解析的结果整体趋于变好，但是对某些源类的解析可能更差.

Evaluating the performance of PMF model for Atmospheric PM source apportionment in multi-site

With the improvement of spatial resolution for environmental monitoring, spatial information which normally displayed as multi-site datasets is available and could be used for atmospheric particular matter source apportionment. PMF was performed on the combined simulated multi-site datasets that including eight scenarios (three major types). Meanwhile, ambient data of PM_2.5 in Hefei was used to evaluate the performance of PMF model for source apportionment of multi-sites data. When the time series of source contributions were fully consistent with each other, it turned out that the PMF results of combined multi-site datasets were not better than these of an individual site. And the combined multi-site datasets could yield a reliable PMF result with different time series of source contributions. What's more, when the time series of source contributions were partly consistent, the PMF results generally became better, but some specific sources may have high uncertainties for some specific sources.