Water quality of the Gomti River, a major tributary of the Ganga River was monitored at eight different sites selected in relatively low, moderate and high pollution regions, regularly over a period of 5 years (1994 1998) for 24 parameters.
Cases were selected for analysis if their ZIP code centroids fell within these 8 air pollution regions.
These remaining missing values were imputed using mean-adjusted data from one of the other nearby air pollution regions.
The comparative results from both sites declared that monitoring stations (SA3, SA4), (SA1, SA2), (SA5, SA6) for FSR and (SB2, SB4), (SB5), and (SB1, SB3) for XXR have relatively low, medium, and high pollution regions, respectively.
The ratio of G-to-T k-ras transversion mutation (codon 12) relative to wild-type was significantly higher in tumors from the high-pollution region (0.90) than tumors from the non-pollution site (0.28) (P = 0.03).
RESULTS: Rates of K-ras mutation in codon 12 and of p53 mutation in exons 5-8 were higher in tumors of patients from the high-pollution region as compared with the low-pollution regions (61.5 versus 34.2%, respectively, for K-ras, P = 0.01; 25.9 versus 11.6%, respectively, for p53, P = 0.08).
Relative to tumors with wild-type, the ratio of p53 mutations in exons 5, 7 or 8 to wild-type in tumors from the high-pollution region was significantly higher than the ratio from the non-pollution site (0.28 versus 0.03, P = 0.01).
METHODS: We studied the molecular pathology of 54 pancreatic adenocarcinomas from Egyptian patients residing in a heavily polluted region of the eastern Nile River delta and compared the findings with 45 tumors from patients residing in low-pollution regions.
CONCLUSIONS: Our study reveals that there are differences in the frequencies and types of K-ras and p53 mutations found in pancreatic adenocarcinomas of patients in high-pollution and low-pollution regions in Egypt and suggests that environmental factors may explain these differences.
Regional variation of AOT can be observed, showing the atmospheric variability for clear sky conditions by: large scale variation of aerosol turbidity, regional pollution, urban regions, effects of contrails and cases of aerosol-cloud interaction.
