A methodology for the statistical and spatial assessment of soil contamination around mining districts

Citation
A. Korre et S. Durucan, A methodology for the statistical and spatial assessment of soil contamination around mining districts, T I MIN M-A, 108, 1999, pp. A181-A191
Citations number
33
Categorie Soggetti
Geological Petroleum & Minig Engineering
Journal title
TRANSACTIONS OF THE INSTITUTION OF MINING AND METALLURGY SECTION A-MINING INDUSTRY
ISSN journal
03717844 → ACNP
Volume
108
Year of publication
1999
Pages
A181 - A191
Database
ISI
SICI code
0371-7844(199909/12)108:<A181:AMFTSA>2.0.ZU;2-C
Abstract
Assessment of the nature and level of soil contamination and quantification of its sources present a serious challenge to environmental engineers and scientists. In the past, statistical and geostatistical analysis techniques have been used successfully to assess the volume and spatial distribution of soil contamination. These techniques require, however, vast amounts of d ata and investment in sampling and analysis in order to distinguish natural background levels from human-induced contamination. Furthermore, the inter pretation has been limited to a qualitative assessment of the pollution sou rces and cannot be applied to sites with a complex or poorly recorded histo ry. A methodology is proposed that combines simple statistical analysis tools w ith multivariate techniques (principal component analysis, factor analysis and canonical correlation), geostatistics and geographical information syst ems to incorporate the quantitative, qualitative and spatial information in the study and distinguish between coexisting pollution sources. The method ology is illustrated through its application to soil-contamination data fro m the Lavrio mining area in the southeast of the Attiki peninsula, Greece. The famous Lavrio mines exploited silver-bearing orebodies over a period of more than 25 centuries. As a result of high natural levels and human activ ity, the soils around the city of Lavrio display high heavy-metal loads. Th e methodology is used to distinguish, quantitatively and spatially, the dif ferent sources of soil contamination.