Studying the quality and health risks of groundwater is of great significance for sustainable water resources utilization, especially in arid and semi-arid areas around the world. The current study is carried out to evaluate the quality and potential health risks of groundwater in the Tongchuan area on the Loess Plateau, northwest China. Water quality index (WQI) and hydrochemical correlation analysis were implemented to understand the status of groundwater quality. Daily average exposure dosages through the oral and dermal contact exposure pathways were taken into consideration to calculate the health risks to the human body. Additionally, graphical approaches such as Piper diagram, Durov diagram and GIS mapping were used to help better understand the results of this study. The WQI approach showed that 77.1% of the samples were of excellent quality. The most significant parameters affecting water quality were NO₃⁻, F⁻, and Cr⁶⁺. The health risk assessment results showed that 27.1% and 54.2% of the samples lead to non-carcinogenic risks through oral intake for adults and children, respectively. In contrast, 12.5% of the groundwater samples would result in carcinogenic risks to the residents. This study showed that the WQI method needs to be supplemented by a health risk evaluation to obtain comprehensive results for groundwater quality protection and management in the Tongchuan area.

The expansion and exploitation of mining resources are essential for social and economic growth. Remote sensing provides vital tools for surface-mining monitoring operations as well as for reclamation efforts in the central Salt Range of the Indus River Basin, Pakistan. This research demonstrates the applicability of remote sensing techniques to the coal mining monitoring scheme to allow for effective and efficient monitoring and to offset the adverse consequences of coal mining activities. Landsat 8 OLI images from June 2019 and 2020, and a Landsat 7 ETM+ image from June 2002, were used for this study. A three-phase methodology including Normalized Difference Vegetation Index (NDVI) analysis, land cover mapping, and change detection approaches was adopted. Image classification based on Tasseled Cap Transformation and the brightness temperature At-satellite using the K-means algorithm was implemented in a GIS program to identify seven land cover classes within the study area. The results show some level of surface disturbance to the landscape due to the coal mining reclamation activities that had taken place over the 18-year time period. From 2019 to 2020, about 3.622 km² of coal mines or barren land were converted into bare agricultural land. Over the years, it was also observed that reclamation areas exhibited higher values of NDVI than coal mining areas. The mean NDVI for coal mining areas was 0.252 km², and for areas of reclamation, it was 0.292 km² in 2020, while in 2019, the value for coal mining sites was 0.133 km², and 0.163 km² for reclamation sites. This trend suggests that coal-mining operations can be monitored using satellite data, and the progress of reclamation efforts can be assessed using satellite NDVI data from the target locations. This study is beneficial to agencies responsible for monitoring land cover changes in a coal mine because it provides a cost-effective, efficient, and robust scientific tool for making mine site allocation decisions and for monitoring the progress of reclamation efforts.