Sofowote Uwayemi M, Allan Laurie M, McCarry Brian E
Department of Chemistry and Chemical Biology, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada.
J Environ Monit. 2010 Feb;12(2):425-33. doi: 10.1039/b909663a. Epub 2009 Nov 4.
Two factor analysis (FA)-based receptor modeling methods were applied to a polycyclic aromatic hydrocarbon (PAH) dataset from extracts of 75 PM(10) air particulate samples collected concurrently at 4 sampling sites proximate to the urban-industrial area in Hamilton, Ontario, Canada. The total PAH concentrations of 48 target compounds ranged from 0.23 to 172 ng m(-3). Principal component analysis (PCA) and positive matrix factorization (PMF) analysis were followed by multilinear regression analyses to identify and quantify PAH source contributions, together with spatial and temporal trends. The correlations between predicted and observed total PAH levels were excellent in both models (R(2) > 0.98). The PCA afforded large negative contributions in a number of samples, so further analysis was abandoned. The PMF analysis showed 3 factors which were identified as gasoline emissions, diesel emissions and coke oven emissions. Contributions of gasoline emissions and diesel emissions factors were surprisingly similar at all 4 sites indicative of a background of vehicle emissions across the city. The PMF coke oven emission factor showed the greatest variability in total loadings, consistent with the large PAH emissions from the steel industries and the large influence of wind direction on PAH concentrations. The highest coke oven contributions were observed at sites closest to the industrial area on days when these sites were downwind of the industries. The PMF coke oven impact factor showed good correlations with two commonly used PAH diagnostic ratios when the ratios were combined into a single ratio. This integrated approach allowed us to categorize >90% of the samples based on the wind direction of the impacting source.
基于双因子分析（FA）的受体建模方法应用于来自加拿大安大略省汉密尔顿市城市工业区附近4个采样点同时采集的75个PM(10)空气颗粒物样本提取物中的多环芳烃（PAH）数据集。48种目标化合物的总PAH浓度范围为0.23至172 ng m(-3)。在主成分分析（PCA）和正定矩阵因子分解（PMF）分析之后进行多线性回归分析，以识别和量化PAH的来源贡献以及空间和时间趋势。两种模型中预测的和观测的总PAH水平之间的相关性都非常好（R(2) > 0.98）。PCA在许多样本中产生了很大的负贡献，因此放弃了进一步分析。PMF分析显示有3个因子，分别被确定为汽油排放、柴油排放和焦炉排放。汽油排放和柴油排放因子的贡献在所有4个站点都惊人地相似，表明整个城市存在车辆排放背景。PMF焦炉排放因子在总负荷方面显示出最大的变异性，这与钢铁行业的大量PAH排放以及风向对PAH浓度的巨大影响一致。在最靠近工业区的站点，当这些站点处于工业下风时，观察到焦炉贡献最高。当将两个常用的PAH诊断比值合并为一个单一比值时，PMF焦炉影响因子与该比值显示出良好的相关性。这种综合方法使我们能够根据影响源的风向对90%以上的样本进行分类。
