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Multivariate canonical correlation analysis has been carried out taking physical variables (mean, standard deviation, skewness, kurtosis) as predictors and chemical variables (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) as responses from the soils and sediments of Thane Creek and Ulhas River areas in Bombay, India. Soil samples provide the first canonical correlation to the presence of more clay fractions, which controls the concentration of trace elements such as Co, Fe, and Ni. In sediment samples, the first canonical correlation explains the role of mean particle size in controlling the concentration of Pb and Ni. The second correlation shows the role of clay minerals in controlling the concentration of the trace elements such as Fe and Zn. The plot of transformed scores of first canonical correlation for soil illustrate the high correlation between sets of variables as all points are grouped closely within an ellipsoidal field. The plot of transformed scores of first canonical correlation illustrate that there is a clear distinction between the type of sediments collected from Thane creek and the Ulhas river region. 相似文献
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Application of statistical methods to study seasonal variation in the mine contaminants in soil and groundwater of Goa, India 总被引：1，自引：0，他引：1
Soil and groundwater samples were collected during two seasons, premonsoon (April 1990) and postmonsoon (December 1990),
and analyzed for major elements (Na, Mg, Al, Si, P, K, Ca, Ti, Mn and Fe), trace elements (Ni, Pb, Co, Cr and Zn) and water
parameters (pH, conductivity, acidity, alkalinity, hardness, Cl and SO4). All the data were subjected to linear discriminant analysis and partial correlation analysis in order to understand the
seasonal variation in the data. It was observed from the Mahalanobis generalized distance that in both soil and groundwater
samples there was a large difference in the concentration level of premonsoon and postmonsoon data. Linear discriminant functions
were calculated to distinguish between premonsoon and postmonsoon samples. From the partial correlation coefficient analysis
of soil samples, dominance of chemical weathering and precipitation of atmospheric fallout during monsoon were inferred. In
the case of the water samples, high conductivity and high hardness in the postmonsoon samples as well as atmospheric fallout
of Pb and Ni during the premonsoon period was suggested from partial correlation of water samples.
Received: 19 September 1995 · Accepted: 12 December 1995 相似文献
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Source and distribution of metals in urban soil of Bombay,India, using multivariate statistical techniques 总被引：7，自引：0，他引：7
Simplification of a complex system of geochemical variables obtained from the soils of an industrialized area of Bombay is attempted by means of R-mode factor analysis. Prior to factor analysis, discriminant analysis was carried out taking rock and soil chemical data to establish the anthropogenic contribution of metals in soil. Trace elements (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) are expressed in terms of three rotated factors. The factors mostly indicate anthropogenic sources of metals such as atmospheric fallout, emission from different industrial chimneys, crushing operations in quarries, and sewage sludges. Major elements (Na, Mg, Al, Si, P, K, Ca, Ti, Mn, and Fe) are also expressed in terms of three rotated factors indicating natural processes such as chemical weathering, presence of clay minerals, and contribution from sewage sludges and municipal refuse. Summary statistics (mean, standard deviation, skewness, and kurtosis) for the particle size distribution were interpreted as moderate dominance of fine particles. Mineralogical studies revealed the presence of montmorillonite, kaolinite, and illite types of clay minerals. Thus the present study provides information about the metal content entering into the soil and their level, sources, and distribution in the area. 相似文献
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Venkataraman G. Madhavan B. Babu Ratha D. S. Antony Joju P. Goyal R. S. Banglani S. Roy S. Sinha 《Natural Resources Research》2000,9(1):27-42
This study involves the integration of information interpreted from data sets such as LandsatTM, Airborne magnetic, geochemical, geological, and ground-based data of Rajpura—Dariba,Rajasthan, India through GIS with the help of (1) Bayesian statistics based on the weights ofevidence method and (2) a fuzzy logic algorithm to derive spatial models to target potentialbase-metal mineralized areas for future exploration. Of the 24 layers considered, five layers(graphite mica schist (GMS), calc-silicate marble (CALC), NE-SW lineament 0–2000 mcorridor (L4-NESW), Cu 200–250 ppm, and Pb 200–250 ppm) have been identified from theBayesian approach on the basis of contrast. Thus, unique conditions were formed based onthe presence and absence of these five map patterns, which are converted to estimate posteriorprobabilities. The final map, based on the same data used to determine the relationships, showsfour classes of potential zones of sulfide mineralization on the basis of posterior probability.In the fuzzy set approach, membership functions of the layers such as CALC, GMS, NE-SWlineament corridor maps, Pb, and Cu geochemical maps have been integrated to obtain thefinal potential map showing four classes of favorability index. 相似文献
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