Geostatistical estimates of groundwater nitrate-nitrogen concentrations with spatial auxiliary information on DRASTIC-LU-based aquifer contamination vulnerability.

Groundwater nitrate-nitrogen contamination typically involves several natural and anthropogenic factors, including those related to hydrology, hydrogeology, topography, and land use (LU). DRASTIC-LU-based aquifer contamination vulnerability could be used to characterize the pollution potentials of groundwater nitrate-nitrogen and to determine groundwater protection zones. This study used regression kriging (RK) with environmental auxiliary information on DRASTIC-LU-based aquifer contamination vulnerability to investigate groundwater nitrate-nitrogen pollution in the Pingtung Plain of Taiwan. First, the relationship between groundwater nitrate-nitrogen pollution and assessments of aquifer contamination vulnerability was determined using stepwise multivariate linear regression (MLR). Subsequently, the residuals between the nitrate-nitrogen observations and MLR predictions were estimated by kriging techniques. Finally, the groundwater nitrate-nitrogen distributions were spatially analyzed using RK, ordinary kriging (OK), and MLR. The findings indicated that the land used for orchards and the medium- and coarse-sand fractions of vadose zones were associated with groundwater nitrate-nitrogen concentrations. The fertilizer used for orchards was identified as the primary source of groundwater nitrate-nitrogen pollution. The RK estimates could be used to analyze the characteristics of the pollution source for land used for orchards and exhibited high spatial variability and accuracy after residual correction. Moreover, RK had an excellent estimate ability for extreme data compared to MLR and OK. Correctly determining groundwater nitrate-nitrogen distributions using RK was useful for administering environmental resources and preventing public health hazards.
(© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)