Determination of the spatial correlation characteristics for selected groundwater pollutants using the geographically weighted regression model: A case study in Weinan, Northwest China

Abstract

Abstract Groundwater pollution is a serious issue in arid and semi-arid regions. In this study, the ordinary least squares (OLS) regression and geographically weighted regression (GWR) model were used to assess the relationship between hydrochemical parameters (NO3-N, NO2-N, NH4-N, and F-) and explanatory variables related to anthropogenic and natural factors, including elevation, slope, population density, groundwater electrical conductivity, groundwater pH, and land use in the Weinan region of China. The results showed that NO3-N, NH4-N, and F- at 24, 4, and 54% of the samples exceeded the standard limits, respectively. Crop fields, grassland, and forest are the most common land use types in the study area, accounting for 62.84, 16.77, and 8.76%, respectively. The effects of explanatory variables on groundwater quality show strong spatial variation. Both positive and negative correlations were observed between groundwater nitrogen (NO2-N, NO3-N) and orchard, and between F- and crop field. The water area has significant impacts on NH4-N in Pucheng, Fuping and Linwei districts. The GWR model also suggested significant effects of water and orchard areas on groundwater NO2-N concentration in western Fuping County and eastern Dali County, which was neglected by the OLS model. The research shows advantages of the GWR model in capturing local variation.