Indicator and probability kriging methods for delineating Cu, Fe, and Mn contamination in groundwater of Najafgarh Block, Delhi, India.
Two non-parametric kriging methods such as indicator kriging and probability kriging were compared and used to estimate the probability of concentrations of Cu, Fe, and Mn higher than a threshold value in groundwater. In indicator kriging, experimental semivariogram values were fitted well in spherical model for Fe and Mn. Exponential model was found to be best for all the metals in probability kriging and for Cu in indicator kriging. The probability maps of all the metals exhibited an increasing risk of pollution over the entire study area. Probability kriging estimator incorporates the information about order relations which the indicator kriging does not, has improved the accuracy of estimating the probability of metal concentrations in groundwater being higher than a threshold value. Evaluation of these two spatial interpolation methods through mean error (ME), mean square error (MSE), kriged reduced mean error (KRME), and kriged reduced mean square error (KRMSE) showed 3.52% better performance of probability kriging over indicator kriging. The combined result of these two kriging method indicated that on an average 26.34%, 65.36%, and 99.55% area for Cu, Fe, and Mn, respectively, are coming under the risk zone with probability of exceedance from a cutoff value is 0.6 or more. The groundwater quality map pictorially represents groundwater zones as "desirable" or "undesirable" for drinking. Thus the geostatistical approach is very much helpful for the planners and decision makers to devise policy guidelines for efficient management of the groundwater resources so as to enhance groundwater recharge and minimize the pollution level.
Adhikary
Partha Pratim
PP
Indian Agricultural Research Institute, Pusa, New Delhi, 110 012, India. pp_a@rediffmail.com
Dash
Ch Jyotiprava
ChJ
Bej
Renukabala
R
Chandrasekharan
H
H
D003300:Copper / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D007194:India; D007501:Iron / Q000032:analysis*; D008345:Manganese / Q000032:analysis*; D014874:Water Pollutants, Chemical / Q000032:analysis*
D014874:Water Pollutants, Chemical; D008345:Manganese; D003300:Copper; D007501:Iron