Assessment of Groundwater Nitrate Pollution Potential in Central Valley Aquifer Using Geodetector-Based Frequency Ratio (GFR) and Optimized-DRASTIC Methods

Abstract

Groundwater nitrate contamination in the Central Valley (CV) aquifer of California is widespread throughout the valley because of excess nitrogen fertilizer leaching down into the aquifer. The percolation of nitrate depends on several hydrogeological conditions of the valley. Groundwater contamination vulnerability mapping uses hydrogeologic conditions to predict vulnerable areas. This paper presents a new Geodetector-based Frequency Ratio (GFR) method and an optimized-DRASTIC method to generate nitrate vulnerability index values for the CV. The optimized-DRASTIC method combined the individual weights and rating values for Depth to water, Recharge rate, Aquifer media, Soil media, Topography, Impact of vadose zone, and Hydraulic conductivity. The GFR method incorporated the Frequency-Ratio (FR) method to derive rating values and the Geodetector method to derive relative Power of Determinant (PD) values as weights to generate nitrate susceptibility index map. The optimized-DRASTIC method generated very-high to high index values in the eastern part of the CV. The GFR method showed very-high index values in most part of the San Joaquin and Tulare basin. The quantitatively derived rating values and weights in the GFR method improved the vulnerability index and showed better consistency with the observed nitrate contamination pattern than optimized-DRASTIC index, suggesting that GFR is a better method for groundwater contamination vulnerability mapping in the CV aquifer.

Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods

“…Lower elevations have more opportunities than high elevations to allow infiltration of nitrate into an aquifer, as flat topography encourages water accumulation and allows more time to infiltrate (Sahoo et al, 2016;Shrestha and Luo, 2018). Also, agricultural lands are more often at lower elevations.…”

Spatiotemporal characteristics and driving forces of terrorist attacks in Belt and Road regions

“…GeoDetector offers more advantages than general statistics because it is more difficult for two variables to be consistent in two-dimensional spatial distribution than in a one-dimensional curve of two variables. GeoDetector has been used in a wide range of fields from natural environment to social development phenomenon, including environmental sciences [ 20 – 22 ], soil science [ 23 – 25 ], groundwater nitrate contamination [ 26 , 27 ], landslide [ 28 , 29 ], PM2.5 [ 30 , 31 ], remote sensing [ 32 ], land use [ 33 – 35 ], urban expansion [ 36 ], and public health [ 37 – 40 ]. Its research area can be large to the national scale [ 41 – 43 ] and small to a township scale [ 44 , 45 ].…”

Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models

“…Among the prediction techniques for groundwater quality modeling, the susceptibility mapping methodologies have been well received within the hydrology research community as well as environmental management and policymakers [13][14][15][16][17][18]. The interactive and graphical nature of susceptibility models is highly efficient in communicating insight into the state of problems (Arabameri et al, 2019).…”