Groundwater vulnerability evaluation to nitrate pollution on a regional scale using GIS

Kumar Chandra Sekhar Navulur, Purdue University

Abstract

A new technique was developed for estimating groundwater vulnerability to nitrate contamination from non-point sources (NPS) on a regional scale. The technique follows a numerical ranking approach and considers the hydrogeologic settings of the region along with a GIS (Geographic Information System) data layer showing spatial distributions of nitrate leached, for predicting pollution potential of a region to groundwater contamination. The new technique was applied to evaluate vulnerability of groundwater systems in Indiana using a GIS environment at a 1:250,000 scale. The new technique classified 57% of the state within highly and very highly vulnerable areas. A Bayesian weights of evidence model was used for statistically validating the choice of factors considered in the new model for regional scale assessment of groundwater quality. Spatial statistics were computed to eliminate some of the detections due to point sources of pollution in a water quality database. The results from the new technique were compared with the water quality database to determine the accuracy of the model predictions. Ninety two (92) percent of the nitrate detections fell in highly and very highly vulnerable areas as predicted by the new model. Vulnerability of Indiana aquifer systems to NPS of pollution was also evaluated using DRASTIC and SEEPAGE analyses. DRASTIC classified 25% of the state within highly and very highly vulnerable areas, while SEEPAGE classified 28% of the state within highly and very highly vulnerable areas. A comparison of the model predictions with the water quality database showed that 80% of the nitrate detections were within the highly and very highly vulnerability areas as predicted by the DRASTIC model and 64% of the nitrate detections were within highly and very highly vulnerability areas as predicted by the SEEPAGE model. The new model performed better than both the DRASTIC and SEEPAGE models in predicting areas vulnerable to nitrate pollution. Overall, the new technique shows significant potential to improve regional groundwater vulnerability estimates.

Degree

Ph.D.

Advisors

Engel, Purdue University.

Subject Area

Agricultural engineering|Environmental science

Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server
.

Share

COinS