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树状图、热图和主成分分析——统计方法在 PM10 中微量元素源解析中的实际应用。

Dendrograms, heat maps and principal component analysis - the practical use of statistical methods for source apportionment of trace elements in PM10.

机构信息

Department of Air Protection, Silesian University of Technology, Gliwice, Poland.

Institute of Mathematics, Silesian University of Technology, Gliwice, Poland.

出版信息

J Environ Sci Health A Tox Hazard Subst Environ Eng. 2023;58(3):163-170. doi: 10.1080/10934529.2019.1670026. Epub 2019 Sep 27.

Abstract

Dendrogram (DE), heat map (HM) and principal component analysis (PCA) methods were used in order to identify possible emission sources of As, Cd, Co, Cr, Hg, Mn, Ni, Pb, Sb and Se in ambient PM10 collected in the surroundings of working power plants. Each statistical tool resulted in slightly different clusters. The best approximation of possible emission sources was received by the use of statistical analysis of trace-element concentrations combined with characterization of the sampling sites. In the study, PCA was indicated as the most useful statistical tool for source apportionment of trace elements in PM10. Major sources identified by PCA included: (1) coal combustion, (2) soil and road-dust resuspension, (3) the use of pesticides and (4) waste incineration.

摘要

为了识别工作火力发电厂周围环境中 PM10 中砷、镉、钴、铬、汞、锰、镍、铅、锑和硒的可能排放源,使用了聚类分析(DE)、热图(HM)和主成分分析(PCA)方法。每个统计工具都产生了略有不同的聚类。通过结合采样点特征描述对痕量元素浓度进行统计分析,获得了可能排放源的最佳近似值。在该研究中,PCA 被指出是 PM10 中痕量元素源解析的最有用的统计工具。PCA 确定的主要来源包括:(1)煤炭燃烧,(2)土壤和道路尘埃再悬浮,(3)农药使用和(4)废物焚烧。

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