Principal Components Analysis and spatial analysis integration for enhanced assessment of pollution emission sources

3rd International Conference on Energy Engineering and Environmental Protection

能源学;生态环境科学

Grazia Bonelli, Maria^1^2 ; Manni, Andrea^3

Dept. ICMA, University of Rome la Sapienza, Via Eudossiana 18, Rome

00198, Italy^1

CINIGeo, Corso Vittorio Emanuele II 244, Rome

00186, Italy^2

Chemical Research 2000 Srl, Via Santa Margherita di Belice 16, Rome

00133, Italy^3

关键词: Agricultural fields;    Environmental analysis;    Kriging algorithm;    Pollution emissions;    Principal Components;    Principal components analysis;    Spatial assessment;    Statistical techniques;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/227/6/062013/pdf
DOI  :  10.1088/1755-1315/227/6/062013

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Principal Component Analysis (PCA) is a statistical technique to reduce the dimension of the original dataset. This method is often used in environmental analysis to detect the pollution sources in ambient air and in soil. However, PCA results can be difficult to read, especially when some variables seem to correlate to any principal component. In this case, it should be helpful to combine PCA with another method to better explain the uncertainty. In this study, the use of PCA coupled to Kriging algorithm has been investigated to better identify the emission sources responsible for soil pollution in an agricultural field near an industrial site in the surroundings of the city of Brescia, in Northern Italy, where organic and inorganic micropollutants concentrations in soils are beyond the legal limits. Three clusters of variables have determined as many as the emission sources in the investigated area. PCs condensed the information from most heavy metals and organic compounds. It has been demonstrated that the spatial assessment can be useful to find emission sources for those elements where it has not been possible by PCA analysis only.