## 3 Case Study
### Dataset and Computation
#### 3.2.2 Hydrogeological Parameter Dataset  
To obtain the vulnerability indexes, eight hydrogeological setting and land use datasets were prepared (see Supplementary Figures S5-S12). [PERSON] et al. [24] indicated the use of geostatistics (kriging) as the best alternative for DRASTIC to assess the vulnerabilities based on the amount and type of available data. In this work, the kriging was used to obtain the depth to the water table and aquifer thickness. The depth to the water table can be calculated as the difference in the elevation of the ground surface and water table. The altitude of the study area is based on the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) [52], which has a spatial resolution of 1 arc second or 30 m and is highly accurate [53; 54]. The water table was interpolated from the water table of 103 shallow wells in the study area (Supplementary Figure S1). For the interpolation, the universal kriging (UK) with a second-order polynomial (on x and y coordinates of the wells) trend surface was used, considering the influence of anisotropy. The ME and the KRMSE are 0.086 and 1.003, respectively. Due to the lack of data close to the boundary of the area, the kriging variance increases in that area and the depth to the water table data near the boundary were replaced with data derived from the \"Atlas of Groundwater Sustainable Utilization in the North China Plain\" [55]. The water depth varies from 0 to >50 m.  
The net recharge data were derived from the work of [PERSON] et al. [56]. The values vary from 0 to 300 mm/a. The rating division slightly differed from that of the original scheme. According to the findings of [PERSON] et al. [11], the rating scores for net recharge exhibited a nearly linear trend. Consequently, the range from 150 to 254 mm was segmented into two sub-ranges and assigned scores of 7 and 8, respectively. The rating scores are listed in Table 1.  
[PERSON]'s common DRASTIC model uses the aquifer media type to assess vulnerability, which is a qualitative parameter. Based on the work of [PERSON] [32] and [PERSON] and [PERSON] [34], aquifer thickness was used instead of aquifer media in this paper. The data were obtained by subtracting the water table from the bottom elevation of the shallow aquifer. The bottom elevation was derived from UK interpolation of data from 277 boreholes distributed in the study area (Supplementary Figure S2). The UK model shows a second-order trend on the x and y coordinates of the boreholes but is not anisotropic. The ME and the KRMSE are \(-\)0.006 and 0.983, respectively. The thickness of the aquifer varies from 0 to >200 m.  
The soil, landform, and hydraulic conductivity parameters were all obtained from the \"Atlas of Groundwater Sustainable Utilization in the North China Plain\" [55]. Soil media affect the transport of water and pollutants from the surface to the aquifer. Although physical processes, chemical reactions, and biodegradation take place in the soil, the rating scores appear to be based on the ability of the contaminants to migrate to the aquifer. Infiltration coefficients were used instead of the soil media type. The infiltration coefficients vary from 0.2 to 0.65. The coefficients were linearly scaled to the range of 1 to 10, which represents the rating scores for soil media (Table 1). The vadose zone of the study area exhibits one lithology and is mainly composed of clay, silt, and sand, which generally exhibit a range from coarser textures in the piedmont region and in paleochannels to finer textures in plains [56]. The lithological changes in the vadose zone can therefore be characterized by the landform. The study area can be divided into five types of geomorphic units: proluvial fans, alluvial-proluvial fans, alluvial and flood plains, lakes and depressions and river belts (Supplementary Figure S10). The rating scores of the different geomorphic units can be determined by calculating the average nitrate concentration in the samples of different geomorphological units (Table 2).