This study aims to investigate hydrogeochemical characteristics and groundwater quality in the Hebei Plain and to discuss factors controlling the groundwater quality. A total of 54 groundwater samples were collected and analyzed for 31 hydrogeochemical parameters, and a fuzzy synthetic evaluation (FSE) method was used for assessing groundwater quality. Results show groundwater total hardness, total dissolved solids (TDS), and major ions excluding K⁺ in phreatic aquifers higher than that in confined aquifers. From the Piedmont plain to the littoral plain, phreatic aquifers towards the reducing environment, and the enhancement of water–rock interaction, ion exchange process, and evaporation probably resulted in the increase in groundwater TDS, major ions (excluding HCO₃⁻ and SO₄²⁻), B, and Mn concentrations. Moreover, phreatic groundwater chemistry was mainly controlled by rock weathering changing into evaporite dissolution and seawater intrusion from the Piedmont plain to the littoral plain, according to the Gibbs diagram. The proportion of drinkable groundwater in confined aquifers was 1.6 times that in phreatic aquifers. In phreatic aquifers, the proportion of drinkable groundwater in the Piedmont plain was as high as 68%, but none of the drinkable groundwater occurred in the central and littoral plains. Groundwater quality in phreatic aquifers was mainly controlled by five factors, including the water–rock interaction, the marine geogenic sources, the agricultural pollution, the acidification, and the reductive environment. By contrast, groundwater quality in confined aquifers was mainly controlled by three factors, including the water–rock interaction and redox processes, agricultural pollution, and the input of external water. Therefore, in the Hebei Plain, groundwater in confined aquifers is more suitable for drinking purposes than in phreatic aquifers. Additionally, phreatic groundwater in the Piedmont plain should be protected.

Elevated hardness concentrations in groundwater have become a noteworthy concern in recent decades because long-term drinking of groundwater with high levels of hardness is an important factor resulting in chronic kidney diseases. In this study, the distribution and origins of groundwater total hardness (TH) in various sub-plains and different land-use areas of the Hebei Plain (HBP) were investigated. A total of 445 groundwater samples in the HBP were collected once in 2021, and twelve chemical parameters, including TH in groundwater, were analyzed. Results showed that TH-rich (>450 mg/L) shallow groundwater in both the central and littoral plains was more than twice that in the Piedmont plain. Similarly, TH-rich deep groundwater accounted for about 18% in the central plain but was negligible in the Piedmont plain. In the Piedmont plain, TH-rich shallow groundwater in urban areas was twice or more than in other land use types. By contrast, both TH-rich shallow and deep groundwaters in agricultural areas in the central plain were higher than those in rural areas. This was opposite to TH-rich shallow groundwater in the littoral plain. In the Piedmont plain, TH-rich shallow groundwater was mainly attributed to water-rock interaction, groundwater over-extraction, and the infiltration of domestic sewage and animal waste. In the central plain, both TH-rich shallow and deep groundwaters likely ascribed to the evaporite dissolution and seawater intrusion. By contrast, the leaching of agricultural fertilizers resulting in the dissolution of Ca-rich and Mg-rich minerals in the vadose zone was mainly responsible for the occurrence of TH-rich shallow groundwater in the littoral plain. Therefore, in order to limit elevated hardness concentrations in groundwater in the HBP, limiting shallow groundwater extraction and strengthening the supervision of the domestic sewage and animal waste in the Piedmont plain are recommended. Besides, restricting the use of nitrogenous fertilizers in the littoral plain is also recommended.

Gray water footprint (GWF) is an effective method to evaluate the degree of water pollution and water quality. It is the amount of freshwater needed to dilute water pollutants to meet ambient water quality standards. Accounting and analyzing the GWF will be significant for promoting an improved water environment and sustainable water ecology in Ningxia Autonomous District. We accounted for the GWF of all cities in Ningxia from 2012 to 2020 and evaluated its spatial-temporal variations by the GWF accounting method proposed by Hoekstra. Then, the Logarithmic Mean Divisia Index (LMDI) method was applied to investigate the contributions of four driving factors: the population scale effect, economic development effect, technological effect, and industrial structure effect. And then, the changes in the GWF in the Ningxia region were analyzed. The results showed that the GWF in the Ningxia region changed from 79.21 × 10⁸ to 29.09 × 10⁸ m³/yr during 2012–2020, making a significant decreasing trend. Among all cities, Wuzhong City contributes the most in terms of the GWF. More specifically, economic development and technology structure are the positive and negative drivers of the GWF, respectively. The water pollution levels in Ningxia (0.49–1.3) indicated that the waste assimilation capacity has fallen short of taking up the pollutant load, which had an unfavorable impact on the groundwater according to actual water quality data. NO₃-N and NH₃-N are detected in the groundwater throughout the Ningxia region, with the highest NH₃-N content in the groundwater in Yinchuan, which almost exceeded the groundwater quality standard of category III. Above all, this study reflected the current water pollution situation better by combining the GWF with actual water quality data in Ningxia. The finding of this study is valuable for addressing water quality threats and developing sustainable development.

Water resources are essential for healthy economic growth. Based on data on water consumption and industrial structure, this paper analyzes the evolution trends of the regional water consumption structure and industrial structure in Jinan City, Shandong Province, with the theory of the niche and ecological entropy, which may provide guidance for water resource utilization and social economy development in Jinan City. By establishing a coupling coordination model of the niche and ecological entropy, the dynamic niche evolution of the water consumption structure and industrial structure in Jinan City is analyzed. The results show the following: (1) The niche of agricultural water consumption accounts for the largest portion of the niche of water consumption, with an overall decreasing trend year by year. On the contrary, those of industrial and domestic water consumption have shown slow and fluctuating upward trends, respectively. Similar characteristics and trends are also reflected in the ecological entropy of water consumption. (2) In terms of industrial structure, the niche of the secondary industry accounts for the largest proportion among the three main industries, and only the niche of the tertiary industry is fluctuating upward. (3) Over the years, the overall coupling and coordination degree between the water consumption structure and industrial structure in Jinan was classified as a low degree of imbalance–weak coupling coordination. Among them, the coupling and coordination degree of the tertiary industry is the highest, that of the primary industry is the lowest, and that of the secondary industry is increasing year by year. Thus, it is necessary for Jinan to develop efficient water-saving agriculture and high-tech industry, maintain the development momentum of the tertiary industry, and continue to improve its coupling and coordination status.

The effective recovery of water level is a crucial measure of the success of comprehensive groundwater over-exploitation management actions in North China. However, traditional evaluation method do not directly capture the relationship between mining and other equilibrium elements. This study presents an innovative evaluation method to assess the water level recovery resulting from mining reduction based on the relationship between variation in exploitation and recharge. Firstly, the recharge variability of source and sink terms for both the base year and evaluation year is calculated and the coefficient of recharge variation β is introduced, which is then used to calculate the effective mining reduction and solve the water level recovery value caused by the effective mining reduction, and finally the water level recovery contribution by mining reduction is calculated by combining with the actual volume of mining reduction in the evaluation area. This research focuses on Baoding and Shijiazhuang Plain area, which share similar hydrogeological conditions but vary in groundwater exploitation and utilization. As the effect of groundwater level recovery with mining reduction was evaluated in these two areas as case study. In 2018, the results showed an effective water level recovery of 0.17 m and 0.13 m in the shallow groundwater of Shijiazhuang and Baoding Plain areas, respectively. The contributions of recovery from mining reduction were 76% and 57.98% for these two areas, respectively. It was notable that the water level recovery was most prominent in the foothill plain regions. From the evaluation results, it is evident that water level recovery depends not only on the intensity of groundwater mining reduction, but also on its effectiveness. The value of water level recovery alone cannot accurately indicate the intensity of mining reduction, as recharge variation significantly influences water level changes. Therefore, in practice, it is crucial to comprehensively assess the impact of mining reduction on water level recovery by combining the coefficient of recharge variation with the contribution of water level recovery from mining reduction. This integrated approach provide a more reasonable and scientifically supported basis, offering essential data support for groundwater management and conservation. To improve the accuracy and reliability of evaluation results, future work will focus on the standardizing and normalizing raw data processing.