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A total of 176 (water and sediment) samples from 22 stations belonging to four different (urban, semi-urban, rural, and holy places) human habitations of Tamil Nadu beaches were collected and analyzed for physiochemical and microbial parameters during 2008-2009. Bacterial counts were two- to tenfold higher in sediments than in water due to strong bacterial aggregations by dynamic flocculation and rich organic content. The elevated bacterial communities during the monsoon explain rainfalls and several other wastes from inlands. Coliform counts drastically increased at holy and urban places due to pilgrimage and other ritual activities. Higher values of the pollution index (PI) ratio (>1) reveals, human fecal pollutions affect the water quality. The averaged PI ratio shows a substantial higher microbial contamination in holy places than in urban areas and the order of decreasing PI ratios observed were: holy places > urban areas > semi-urban areas > rural areas. Correlation and factor analysis proves microbial communities were not related to physicochemical parameters. Principal component analysis indicates 55.32 % of the total variance resulted from human/animal fecal matters and sewage contaminants whereas 19.95 % were related to organic contents and waste materials from the rivers. More than 80 % of the samples showed a higher fecal coliform and Streptococci by crossing the World Health Organization's permissible limits.

Domestic sewage discharge and live stocks are the main sources of pathogenic contaminations in rivers. The river Tamiraparani in southern India is affected by such nonpoint source pollution throughout the year. We collected a total of 264 samples (water and sediment) from 22 locations in 2-month intervals during a period of 1 year. Bacteriological analysis such as total viable counts (TVC), total coliform (TC), total Streptococcus (TS), Vibrio like organisms (VLO) and five pathogens as well as 12 geochemical parameters (pH, EC, TDS, Cl, HCO 3 , Ca, Mg, Na, K, PO 4 , other nutrients and total hardness) were studied. Principal Component Analysis (PCA) and correlation analysis proved that microbial communities were separated with geochemical parameters in order to gain their efficacy. Factor analysis confirmed separate loading rates of microbial (32.3 %) and geochemical (32.7 %) parameters representing 'allochthonousgeochemical' and 'fecal mattersmicrobial' interactions, respectively. We used geographical information systems (GIS) for mapping the occurrence of indicator organisms from non-point sources throughout the river basin.

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Descriptions of significant associations found from a logistic regression analysis typically are based on adjusted odds ratios. Unfortunately, odds ratios provide no information about the prevalence of response. In this paper, we justify and recommend using standardized risks, i.e., standardized probabilities, which do provide information about prevalence, in addition to adjusted odds ratios, for pairwise comparisons of the levels of a significant factor. We illustrate the advantages of generally reporting standardized risk estimates, in the context of assessing the effect of blood lead levels during the preschool years on occurrence of academic problems in kindergarten. Results are more meaningfully interpreted when accompanied by standardized risk estimates.