资源环境科技发展态势分析平台

We demonstrate with field data the benefit of using high-time-resolution chemical speciation data in achieving more robust source apportionment of fine particulate matter (PM2.5) using positive matrix factorization (PMF). Hourly composition data were collected over a month in Shanghai, including four inorganic ions, 13 elements, organic, and elemental carbon. PMF analysis of the hourly data set (PMF1h) resolves eight factors: secondary nitrate/sulfate, vehicular/industrial emissions, coal combustion, secondary sulfate, tire wear, Cr and Ni point source, residual oil combustion, and dust, with the first three being the major ones and each contributing to >20% of PM2.5 mass. To characterize the benefit gained from time resolution, we carried out separate PMF analyses of 4- and 6-hr averaged data of the same data set (PMF6h and PMF4h). PMF6h and PMF4h produce an eight-factor solution sharing similar factors to those by PMF1h but show less stability and more mixing in source profiles. Profile mixing was especially noticeable for tire wear, coal combustion, and Cr and Ni point source in PMF6h, as the 6-hr averaging significantly decreased between-sample variability and increased rotational ambiguity. While the three sets of PMF solutions were similar in contributions for factors with major species as source markers (e.g., secondary nitrate/sulfate), larger variations existed for factors with trace species as markers due to mixing of major species in the profiles and higher rotational uncertainties in PMF4h and PMF6h. Our results indicate that hourly time series of elements and major components could achieve more robust source apportionment through better capturing of diurnal-scale dynamics in source activities.