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The communality for each element expresses the proportion of the total variability of that element that 
is contained in the factors. The first factor is dominated by zinc and lead, which accounts for 45.4% of 
total variance, while the second is Co, Mn, Ni, Fe factor, which explained 25.8%, and the third is Co, 
Mn, Ni factor, which explained 14.8% of the total information. The remaining 14% of the variance are 
explained by factors, which make small contribution. The importance of factor scores, which contain 
informations about more than one element with respect to the extracted factors, should be taken in 
consideration within multi-element geochemical exploration. The following observations can be made 
about the factor scores for Gabal El Rusas area. Regarding factor 1, which is a zinc and lead factor, 
thirty six samples have scores below or equal to the mean value, while five samples have scores above 
1.0 and less than 2.0. Moreover only three samples exceed 2.0. Factor 2 which is a Co-Mn-Ni-Fe, 
where fifty and nine samples have scored below or equal to the mean value, while two samples are 
above 1.0 and less than 2.0 and three samples exceed 2.0. Factor 3 is a Co-Mn-Ni factor, in which the 
distribution of factor scores is nearly centered at zero and is almost bell-shaped with two extreme 
values, which represent two samples, one having the highest and the other the lowest iron contents. 
Thirty-seven samples have scores below or equal to the mean value, while seven samples have scores 
above 1.0 and less than 2.0, and only three samples exceed 2.0. Both factors 2 and 3 are probably a 
result of adsorption and co-precipitation of Co and Ni in manganese iron hydroxides. The areal 
distribution of the three factor score values indicates that the samples with high factor 1 scores 
correlate with the known Pb-Zn occurrence in the area. Actually, all anomalous samples on the two Pb 
and Zn maps of single variables are shown in a single map. The samples with higher absolute scores on 
factor 2 and 3 may represent false anomalies and the higher values appear in their lead and zinc 
contents probably result from co-precipitation and scavenging of these elements within the iron and 
manganese hydroxides, which characterize these two factors. 
5 CONCLUSIONS 
The applicability of both cluster and factor analyses as an aid in the interpretation of reconnaissance 
stream sediment data from Gabal El Rusas area has shown. Clustering of data and its representation by 
a dendrogram provides relatively a simple and direct way to classify the samples into anomalous and 
background groups. The use of R-mode factor analysis made it possible to reduce the seven initial 
variables (Pb, Zn, Cu, Ni, Co, Mn and Fe) into three: Factor 1, a Pb-Zn factor; Factor 2, a Co-Mn-Ni- 
Fe factor; and Factor 3, a Co-Mn-Ni factor. Factor 1 is largely explained by the ore-related elements 
and reflects readily all the anomalies in the stream sediments related to lead-zinc occurrences. Factors 2 
and 3 probably explained in terms of geological and surface processes caused by the effects of co- 
precipitation of trace elements in manganese and iron hydroxides. Also, factor analysis helps in 
compressing of the anomalous samples indicated by two single variable maps into a single map. 
REFERNCES 
Garrett R. and Nichol I., 1969. Factor analysis in the interpretation of regional geochemical stream 
sediment data. Q.Colo.Sch.Mine, 64, pp. 245-264. 
Morsy M., 1981a. Selection of size fractions. Bull. Fac. Sc., Alex.Univ., 21(3), pp. 5-14. 
Morsy M., 1981b. Selective extraction techniques in geochemical exploration. Bull. Fac. Sc., Alex. 
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Morsy M. and Hassan F, 1982. Geochemical study on soil and stream sediments. Bull. Fac. Sc., Alex. 
Univ., 31p. 
Morsy M., 1993. An example of application of factor analysis on geochemical stream sediment survey. 
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Morsy M., 1997. Tin-rare metal mineralization near Mersa Alam. Proc.30" Int'l Geol.Congr., 
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International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 
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