Abstract: Lead being a toxic heavy metal that mankind is
exposed to the highest levels of this metal from environmental
pollutants. A total of 180 Male scalp hair samples were collected
from different environments in Greater Cairo (GC), i.e. industrial,
heavy traffic and rural areas (60 samples from each) having different
activities during the period of, 1/5/2010 to 1/11/2012. Hair samples
were collected during five stages. Data proved that the concentration
of lead in male industrial areas of Cairo ranged between 6.2847 to
19.0432 μg/g, with mean value of 12.3288 μg/g. On the other hand,
lead content of hair samples of residential-traffic areas ranged
between 2.8634 to 16.3311 μg/g with mean value of 9.7552 μg/g.
While lead concentration on the hair of the male residents living in
rural area ranged between 1.0499-9.0402μg/g with mean value of
4.7327 μg/g. The Pb concentration in scalp hair of Cairo residents of
residential-traffic and rural traffic areas was observed to follow the
same pattern. The pattern was that of decrease concentration of
summer and its increase in winter. Then, there was a marked increase
in Pb concentration of summer 2012, and this increase was
significant. These were obviously seen for the residential-traffic and
rural areas residents. Pb pollution in residents of industrial areas
showed the same seasonal pattern, but there was marked to decrease
in Pb concentration of summer 2012, and this decrease was
significant. Lead pollution in residents of GC was serious. It is worth
noting that the atmosphere is still contaminated by lead despite a
decade of using unleaded gasoline. Strong seasonal variation in
higher Pb concentration on winter than in summer was found. Major
contributions to the pollution with Pb could include industry
emissions, motor vehicle emissions and long transported dust from
outside Cairo. More attention should be paid to the reduction of Pb
content of the urban aerosol and to the Pb pollution health.
Abstract: Structures are a combination of various load carrying members which transfer the loads to the foundation from the superstructure safely. At the design stage, the loading of the structure is defined and appropriate material choices are made based upon their properties, mainly related to strength. The strength of materials kept on reducing with time because of many factors like environmental exposure and deformation caused by unpredictable external loads. Hence, to predict the strength of materials used in structures, various techniques are used. Among these techniques, Non-destructive techniques (NDT) are the one that can be used to predict the strength without damaging the structure. In the present study, the compressive strength of concrete has been predicted using Artificial Neural Network (ANN). The predicted strength was compared with the experimentally obtained actual compressive strength of concrete and equations were developed for different models. A good co-relation has been obtained between the predicted strength by these models and experimental values. Further, the co-relation has been developed using two NDT techniques for prediction of strength by regression analysis. It was found that the percentage error has been reduced between the predicted strength by using combined techniques in place of single techniques.