Mine Area Farmland Heavy Metal Pollution Assessment Based on Synthetic Principal Component Analysis Model

摘要

Referring to GB15618—1995 about heavy metal pollution, and using statistical analysis SPSS, the major pollutants of mine area farmland heavy metal pollution were identified by variable clustering analysis. Assessment and classification were done to the mine area farmland heavy metal pollution situation by synthetic principal components analysis (PCA). The study result implied that variable clustering analysis is efficient to identify the principal components of mine area farmland heavy metal pollution. Sort and clustering were done to the synthetic principal components scores of soil sample, which is given by synthetic principal components analysis. In this paper, data structure of soil heavy metal contaminations, relationships and pollution level of different soil samples were discovered. The results of mine area farmland heavy metal pollution quality assessed and classified with synthetic component scores reflect the influence of both the major and compound heavy metal pollutants. Identification and assessment results of mine area farmland heavy metal pollution can provide reference and guide to propose control measures of mine area farmland heavy metal pollution and focus on the key treatment region.