Department of Physics, Università degli Studi di Milano and INFN-Milan, Via Celoria 16, 20133, Milan, Italy.
Sci Total Environ. 2011 Oct 15;409(22):4788-95. doi: 10.1016/j.scitotenv.2011.07.048.
In this work Positive Matrix Factorization (PMF) was applied to 4-hour resolved PM10 data collected in Milan (Italy) during summer and winter 2006. PM10 characterisation included elements (Mg-Pb), main inorganic ions (NH(4)(+), NO(3)(-), SO(4)(2-)), levoglucosan and its isomers (mannosan and galactosan), and organic and elemental carbon (OC and EC). PMF resolved seven factors that were assigned to construction works, re-suspended dust, secondary sulphate, traffic, industry, secondary nitrate, and wood burning. Multi Linear Regression was applied to obtain the PM10 source apportionment. The 4-hour temporal resolution allowed the estimation of the factor contributions during peculiar episodes, which would have not been detected with the traditional 24-hour sampling strategy.
本研究应用正定矩阵因子分解法(PMF)对 2006 年夏季和冬季米兰(意大利)每 4 小时采集的 PM10 数据进行解析。PM10 的特征包括元素(镁-铅)、主要无机离子(铵根、硝酸盐、硫酸盐)、左旋葡聚糖及其异构体(甘露糖、半乳糖)、有机碳和元素碳(OC 和 EC)。PMF 解析出了 7 种因子,分别为建筑工程、再悬浮尘、二次硫酸盐、交通、工业、二次硝酸盐和木材燃烧。多线性回归被用来获得 PM10 的源解析。4 小时的时间分辨率允许在特殊事件期间估算因子的贡献,而这在传统的 24 小时采样策略中是无法检测到的。