Yue Wei, Stölzel Matthias, Cyrys Josef, Pitz Mike, Heinrich Joachim, Kreyling Wolfgang G, Wichmann H-Erich, Peters Annette, Wang Sheng, Hopke Philip K
Institute of Epidemiology, GSF-National Research Center for Environment and Health, Neuherberg, Germany.
Sci Total Environ. 2008 Jul 15;398(1-3):133-44. doi: 10.1016/j.scitotenv.2008.02.049. Epub 2008 Apr 22.
Particle size distribution data collected between September 1997 and August 2001 in Erfurt, Germany were used to investigate the sources of ambient particulate matter by positive matrix factorization (PMF). A total of 29,313 hourly averaged particle size distribution measurements covering the size range of 0.01 to 3.0 microm were included in the analysis. The particle number concentrations (cm(-3)) for the 9 channels in the ultrafine range, and mass concentrations (ng m(-3)) for the 41 size bins in the accumulation mode and particle up to 3 microm in aerodynamic diameter were used in the PMF. The analysis was performed separately for each season. Additional analyses were performed including calculations of the correlations of factor contributions with gaseous pollutants (O(3), NO, NO(2), CO and SO(2)) and particle composition data (sulfate, organic carbon and elemental carbon), estimating the contributions of each factor to the total number and mass concentration, identifying the directional locations of the sources using the conditional probability function, and examining the diurnal patterns of factor scores. These results were used to assist in the interpretation of the factors. Five factors representing particles from airborne soil, ultrafine particles from local traffic, secondary aerosols from local fuel combustion, particles from remote traffic sources, and secondary aerosols from multiple sources were identified in all seasons.
利用1997年9月至2001年8月在德国爱尔福特收集的粒径分布数据,通过正定矩阵因子分解法(PMF)研究环境颗粒物的来源。分析中纳入了总共29313个每小时平均粒径分布测量值,粒径范围为0.01至3.0微米。PMF分析使用了超细范围内9个通道的粒子数浓度(cm⁻³),以及积聚模式下41个粒径区间和空气动力学直径达3微米的粒子的质量浓度(ng m⁻³)。对每个季节分别进行分析。还进行了其他分析,包括计算因子贡献与气态污染物(O₃、NO、NO₂、CO和SO₂)以及颗粒物成分数据(硫酸盐、有机碳和元素碳)之间的相关性,估算每个因子对总数浓度和质量浓度的贡献,使用条件概率函数确定源的方向位置,以及检查因子得分的日变化模式。这些结果用于辅助解释各因子。在所有季节中都识别出了五个因子,分别代表来自空气中土壤的颗粒物、来自本地交通的超细颗粒物、来自本地燃料燃烧的二次气溶胶、来自远程交通源的颗粒物以及来自多个源的二次气溶胶。