First International Conference on MOLDAVIAN RISKS – FROM ...

<strong>First</strong> <strong>International</strong> <strong>Conference</strong> on MOLDAVIAN RISKS - FROM GLOBAL TO LOCAL SCALE
16-19 May 2012, Bacau, Romania
USING MULTIVARIATE STATISTICS AND GIS ON
ASSESSING HEAVY METAL POLLUTION OF SOIL IN 22
CITIES OF MOLDAVIA
Ema Faciu 1 , Iulia Lazar 2 , Irina Ifrim 2 , Gabriel Lazar 1
1 “Vasile Alecsandri” University of Bacau, Department of Mechanical and Environmental
Engineering
2 “Vasile Alecsandri” University of Bacau, Department of Chemical and Food Engineering
Corresponding author: Gabriel Lazar, glazar@ub.ro
Abstract: The knowledge of the regional variability for potential harmful elements in soil
is of critical importance to assess the risks for human health. A series of data on
concentration of heavy metals (Pb, Cd, Cr, Cu, Zn, Ni and Mn) in soil, were collected
between years 2006 and 2011 from 22 cities belonging to the 8 counties of Moldavia.
Geostatistical analysis and multivariate statistical analysis differently revealed correlations
among the studied metals, complementing each other. Multivariate statistical analysis
(Correlation Analysis CA and Principal Component Analysis PCA) and spatial distribution
showed distinctly different associations among the studied metals in different cases. There
have been identified links with different intensities (low, medium and strong) between the
heavy metal concentrations specific to each county in the analysis. High intensity links
were identified between Pb and Cd in Galati county (R = 0.771, p = 0.000) and Pb and Zn
in Neamt County (R = 0.538, p = 0.000). Our results indicated that the heavy metals, Cd,
Ni, Pb were correlated with population density (e.g. Cd for Galati, (R = 0.448, p = 0.000),
Ni for Neamt, (R = 0.552, p = 0.000), Pb for Vrancea, (R=-0.596, p = 0.000)). Further
spatial analysis identified the specific factors and the main sources of pollution in the
analyzed area. The aim of Principal Component Analysis is to identify the number of
independent variables of the dependent ones that best explain their variation. Orthogonal
rotation determined the factorial structure. At the first PC1 factor the largest share was
attributed to Mn (0.901), followed by Ni (0.809) and Cr (0.770). The establishment of
more accurate correlations between the different heavy metals associated with anthrop
activities or specific local parameters (e.g. population density) and possible multiple
regression laws that govern them can predict the dynamics of their concentration in time
and the possible effect on human health.
Keywords: multivariate statistics, GIS, soil, heavy metals.
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