利用绝对主成分得分（APCS）受体模型对气态大气污染物进行源解析。
Source apportionment of gaseous atmospheric pollutants by means of an absolute principal component scores (APCS) receptor model.
作者信息
Bruno P, Caselli M, de Gennaro G, Traini A
机构信息
Department of Chemistry, University of Bari, Italy.
出版信息
Fresenius J Anal Chem. 2001 Dec;371(8):1119-23. doi: 10.1007/s002160101084.
A multivariate statistical method has been applied to apportion the atmospheric pollutant concentrations measured by automatic gas analyzers placed on a mobile laboratory for air quality monitoring in Taranto (Italy). In particular, Principal Component Analysis (PCA) followed by Absolute Principal Component Scores (APCS) technique was performed to identify the number of emission sources and their contribution to measured concentrations of CO, NOx, benzene toluene m+p-Xylene (BTX). This procedure singled out two different sources that explain about 85% of collected data variance.
一种多元统计方法已被应用于对放置在意大利塔兰托空气质量监测移动实验室中的自动气体分析仪所测得的大气污染物浓度进行 apportion。具体而言，进行了主成分分析（PCA），随后采用绝对主成分得分（APCS）技术来确定排放源的数量及其对一氧化碳（CO）、氮氧化物（NOx）、苯、甲苯、间二甲苯和对二甲苯（BTX）测量浓度的贡献。该程序识别出两个不同的源，它们解释了约 85%的收集数据方差。
Zotero 插件