Search

Fluoride (F¯) concentration over and above the permissible limits (1.5 mg/l) in drinking water leads to human health hazards, such as dental and skeletal fluorosis affecting millions of people in many parts of India. Preliminary investigations indicate that severe health disorders have been identified in parts of the Kachnarwa region, which is in the upper Panda river basin, Sonbhadra District, Uttar Pradesh, due to excess intake of fluoride through drinking water. The lithological units of the study area mainly consist of granite and gneissic complex rocks of the Chota-Nagpur Plateau. In order to understand the probable source of fluoride and its concentration, 17 groundwater samples mostly from granite and phyllite regions were collected for fluoride estimation during May 2006. The concentration of fluoride in the groundwater of the study area varies from 0.483 to 6.7 mg/l. Among the 17 samples analysed, 8 exceeded the maximum permissible limits of fluoride in the study area. The source of fluoride in the groundwater of the study area is mainly from geological occurrence (i.e. fluoridebearing minerals, viz. apatite and biotitic mica). Microscopic analysis of the rock samples showed 5-10% apatite and 20-25% biotite, but other fluoride-bearing minerals like fluorite and hornblende were characteristically absent. X-ray diffraction studies showed that apatite mineral peak profile of the rock samples corroborated with the fluorapatite of the standard set by the JCPDS. The worst fluoride-affected villages are Rohiniyadamar, Madhuri, Neruiyadamar, Gobardaha and Kunrwa. Most people in these villages suffer from dental and skeletal fluorosis such as mottling of teeth, deformation of ligaments, bending of spinal column and ageing problem. An urgent need is to educate the people on the causes of fluorosis, encouraging rainwater harvesting and providing fluoride-free drinking water in the study area. [ABSTRACT FROM AUTHOR]

In order to establish season-wise relationship between total dissolved solids (TDS), electrical conductance (EC) and silica (SiO2) in upper Gunjaneru River basin, Kadapa district, Andhra Pradesh, a study has been made by collecting 51 and 46 groundwater samples during post-monsoon and pre-monsoon season, respectively. TDS, EC and silica are some of the groundwater quality parameters measured in a laboratory generally. EC and silica measurements are made quickly and are less costly than TDS measurement. The results indicate that 99.7% and 99.8% of the variability in TDS could be ascribed to the variable EC and SiO2 concentrations and the rest by some other unaccounted variables in the post-monsoon and pre-monsoon season, respectively. Inclusion of SiO2 concentration in the regression models produces, for individual samples, a lower per cent difference between measured and estimated TDS for both seasons. However, on the average the inclusion of SiO2 in regression models reduced the mean difference 0.09% for the post-monsoon season and 0.11% for the pre-monsoon. From regression analysis it is found that the multiple regression model did not improve the predictability of TDS values over the linear regression model for both post-monsoon and pre-monsoon seasons. TDS can be estimated from either EC or EC and SiO2 by these regression models within the acceptable limit of error. This is due to the low content of SiO2 in the groundwater samples in shale aquifers of the study area. [ABSTRACT FROM AUTHOR]

Correlations and multiple linear regressions were used to develop models relating well water chemical quality parameters to a set of independent chemical variables in post- and pre-monsoon seasons in the upper Gunjanaeru River basin, Cuddapah district, Andhra Pradesh, South India. The correlation between the specific electrical conductance (SEC) and other parameters except potassium (K+) is significantly positive, whereas Ca2+ + Mg2+/Na+ + K+ is significantly negative for both post- and pre-monsoon seasons. In predicting SEC for both post- and pre-monsoon, the independent variables, viz. HCO3-, SO42- and Cl- in the model had a significant effect (from 't' test for partial regression coefficient at the 5% level of probability). The multiple R² values 0.982 and 0.997 indicate that 98.2 and 99.7% of variability in the observed SEC could be ascribed to the combined effect of Na+, Cl-, Ca2+ + Mg2+, HCO3-, and SO42- for post- and pre-monsoon seasons, respectively. Out of the 98.2% variability in SEC due to the combined effect of Na+, Ca2+ + Mg2+, HCO3-, SO42- and Cl-; 44.8% is due to HCO3-, 44.4% is due to Cl-, 9% is due to SO42- and 2% each is due to Na+ and Ca2+ + Mg2+ for post-monsoon season. Whereas in the pre-monsoon season, 99.7% variability in SEC is due to the combined effect of Na+, Ca2+ + Mg2+, HCO3-, SO42- and Cl-; 38.8% is due to Cl-, 36.9% is due to HCO3-, 12% is due to Na+, 7% is due to SO42- and 3% is due to Ca2+ + Mg2+. This shows that HCO3-, Cl-, SO42- and Cl-, HCO3-, Na+, SO42- are the most significant independent variables in predicting SEC for post- and pre-monsoon seasons respectively. [ABSTRACT FROM AUTHOR]