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The low-resolution characteristic of Gravity Recovery and Climate Experiment (GRACE) satellite data greatly limits their application in many fields at regional or local scales. Aiming to overcome this limitation, the partial least squares regression (PLSR) model is firstly utilized to assess the importance of some independent variables that are commonly employed in GRACE downscaling research. Three kinds of downscaling models are chosen to improve the resolution of GRACE-based water storage estimates from 1 to 0.25°, namely: multivariable linear regression, random forest (RF), and NoahV2.1. Results indicate that terrestrial water storage anomalies are more closely related to four independent variables in the Haihe River Basin, China: these variables are evapotranspiration, land surface temperature, air temperature, and soil moisture. With respect to the spatial distribution, the downscaled results based on the NoahV2.1 and RF models can effectively capture the subgrid heterogeneity while preserving the water storage characteristics at the original scale. By verifying the downscaled results with measured groundwater levels, it can be observed that the correlation coefficient between the RF-based downscaled groundwater storage anomalies (GWSA) and in-situ measurements is increased by 20.55% (Beijing), 9.13% (Tianjin), and 10.48% (Hebei) relative to the downscaled results based on the NoahV2.1 model. The cross wavelet transform illustrates that the meteorological factors have a strong influence on the GWSA series in the Haihe River Basin with an approximately 12-month signal during 2003–2016. This study can provide high-resolution GWSA datasets for water resources management and also provide a reference for the selection of dominant independent variables.

The sponge city is a new concept of stormwater management for ecological city construction, which aims to restore water-cycle processes and reduce runoff. Cities in coastal districts are suffering from serious instability due to high population density, urbanization, and land-use changes. However, previous research contains few evaluations of balancing urban ecological indicators of sponge city performance, including geographical, environmental, economic, and social factors, and their effect on resilience at a macro level to develop low-impact development schemes. In this study, we developed an integrated framework using factor analysis, geographical statistics, multi-objective analysis, and remote sensing methods to extract the factors influencing sponge city resilience and to establish spatial pattern schemes. The results indicated that the urbanization degree and plant adaptability had the greatest impact on sponge city performance, with weights of 45 and 27%, respectively. Sponge city spatial pattern schemes performed the best in the combination scenario of 14.8–46.8% green roofs (by area ratio) supported by grooves and rain barrels +10% herbaceous basins divided into units by ecological tree pools +10% permeable pavements and sidewalks. This scenario balanced facilities and cost to optimize the spatial pattern, which improved sponge city adaptability and urban ecological conditions.

It is of significance to elucidate the groundwater quality and hydrogeochemical processes for sustainable utilization of groundwater resources in water shortage regions. A total of 256 groundwater samples were collected in typical watersheds in Zhangjiakou, northern China. The hydrochemical parameters, conventional ions, and trace elements were measured, and δD and δ 18 O data were collected to delineate the groundwater quality and hydrogeochemical processes. The results showed that 32.91% of the groundwater could be directly used for drinking water sources in the Bashang Plateau, north of the study area. The F − and NO 3 − -N were the main parameters above the standard threshold for drinking water. In contrast, the groundwater quality in the Baxia River Basins, south of the study area, was of a better scenario. Nonetheless, high concentrations of F − , total hardness, and SO 4 2− were still observed. Most samples in the Bashang Plateau had relatively higher salinity than the Baxia River Basins. Both surface water and groundwater in the study area originated from local meteoric water with considerable hydraulic connections. The high-fluoride groundwater was primarily formed by dissolution of fluoride-rich minerals under conditions of high pH and Na + , low Ca 2+ , and rich in HCO 3 − . The dissolution of carbonate and silicate minerals accompanied by strong cation exchange and weak evaporation was the dominant water-rock interaction affecting the hydrochemical composition of groundwater, and anthropogenic NO 3 − input had an extra influence on hydrochemical process. This study provides a scientific guideline for the protection and allocation of local groundwater resources.

As an important drinking water source for Beijing, the capital of China, the water inflow of Miyun Reservoir has been decreasing year by year, which has affected the urban water supply security. To understand the variation trend of the inflow and analyze the main factors influencing the runoff change, this research focused on the watershed of Miyun Reservoir as the target. Based on the runoff data from 1984 to 2020 at the outlet of the basin, as well as the precipitation, potential evaporation intensity, NDVI (normalized difference vegetation index), population, and GDP (Gross Domestic Product) data, combined with correlation analysis methods, empirical statistical methods, the SCRCQ (Slope Change Ratio of Cumulative Quantity) method, and the GIS, the interannual variation characteristics of various elements in the basin were analyzed, the correlation between runoff and other factors was studied, and the influencing degrees of precipitation, water surface evaporation intensity, human activities, and other factors on the runoff change in the basin were quantitatively separated. The research results showed that the runoff exhibited a distinct decreasing trend, and there were two mutation points in the basin runoff from 1984 to 2020, which were 1995 and 2014, respectively. The runoff change was divided into three stages: 1984–1995 (upward trend in T1), 1995–2014 (downward trend in T2), and 2014–2020 (stable trend in T3). Runoff was significantly correlated with four indicators: the summer leaf area index of the Chaohe River and Baihe River, the regional GDP and population, among which the correlation of the summer leaf area index was the largest. Compared with the period T1, the contribution rates of climate change to the runoff reduction in T2 and T3 were 6.38% and 5.73%, and the contribution rates of human activities to the runoff reduction were 93.62% and 94.27%, respectively. Therefore, the change in annual runoff in the Miyun Reservoir watershed is mainly affected by human activities, and the contribution of climate change to the runoff attenuation is weak. This study is significant in the maintenance and enhancement of runoff in typical watershed.

The northeastern part of the Ordos Basin is the main recharge area of regional groundwater, the groundwater resources are relatively scarce. The main water supply source in the area is shallow groundwater, and there are many industrial and mining enterprises in the district. The potential groundwater pollution risk is high, and the the shallow groundwater vulnerability evaluation in the region is of great significance for groundwater resources protection.The weight of each indicator of the traditional DRASTIC model is fixed and does not change with the regional conditions, which may cause deviations in the evaluation results. This time, based on the DRASTIC model, the entropy weight coefficient method is introduced to determine the index weight, and the DRASTIC entropy weight model is established to obtain a more scientific and close to the actual conditions of the study area, and provide an important reference and basis for the protection of regional groundwater resources.

The ecological environment of Northeastern Ordos Basin which located in the Northwestern China belongs to arid and semi-arid area. The hazards of water and soil loss here are serious caused by gully river erosion. Since Soil erosion is an important factor leading to the destruction of regional ecology and has a profound impact on the long-term and healthy development of the economy and society, it is necessary to do research on the sensitivity analysis about soil erosion. In this study, the AHP (Analytic Hierarchy Process) method was used to determine the index weight of the factors for soil erosion, and made it more consistent with the whole regional condition. The results demonstrated that the soil medium, slope and vegetation coverage had the highest influence on soil erosion under the same climatic and meteorological conditions in the study area. This research evaluated the sensitivity factors and provided a significant guidance for soil erosion control.

Over the past decade, heavy metal (HMs) contamination in soil environments has become severe worldwide. However, their resulting ecological and health risks remained elusive across a variety of soil ecosystems due to the complicated distributions and sources. This study investigated the HMs (Cr, As, Cu, Pb, Zn, Ni, Cd, and Hg) in areas with multi-mineral resources and intensive agricultural activities to study their distribution and source apportionment using a positive matrix factorization (PMF) model coupled with self-organizing map (SOM). The potential ecological and health risks were assessed in terms of distinct sources of HMs. The results disclosed that the spatial distribution of HM contaminations in the topsoil was region-dependent, primarily located in areas with high population intensity. The geo‑accumulation index (Igeo) and enrichment factor (EF) values collectively displayed that the topsoils were severely contaminated by Hg, Cu, and Pb, particularly in residential farmland areas. The comprehensive analysis combined with PMF and SOM identified both geogenic and anthropogenic sources of HMs including natural, agricultural, mining, and mixed sources (caused by multi-anthropogenic factors), accounting for 24.9%, 22.6%, 45.9%, and 6.6% contribution rates, respectively. The potential ecological risk was predominantly due to the enrichment of Hg, followed by Cd. The non-carcinogenic risks were mostly below the acceptable risk level, while the potential carcinogenic health risks caused by As and Cr should be paid prime attention to, particularly for children. In addition to the 40% geogenic sources, agricultural activities contributed to 30% of the non-carcinogenic risk, whereas mining activities contributed to nearly half of the carcinogenic health risks.

Miyun Reservoir plays a vital role as a source of drinking water for Beijing, however it grapples with nitrogen contamination issues that have been poorly understood in terms of their distribution, source, and associated health risks. This study addresses this knowledge gap by employing data on nitrate nitrogen (NO 3 − –N), chloride (Cl − ), dual isotopic compositions of NO 3 − (δ 15 N–NO 3 − and δ 18 O–NO 3 − ) data in water ecosystems, systematically exploring the distribution, source and health risk of nitrogen contaminants in Miyun reservoir watersheds. The results showed that over the past 30 years, surface water runoff has exhibited a notable decrease and periodic fluctuations due to the combined influence of climate and anthropogenic activities, while the total nitrogen (TN) concentration in aquatic ecosystems presented an annual fluctuating upward trend. The TN concentration in the wet season was predominantly elevated because a large amount of nitrogen contaminants migrated into water ecosystems through heavy rainfall or river erosion. The concentration of NO 3 − –N, the main contaminant of the water ecosystems, showed distinct variations across different watersheds, followed as rivers over the Miyun reservoir. Moreover, NO 3 − –N levels gradually increased from upstream to downstream in different basins. NO 3 − –N in surface water was mainly derived from the mixture of agricultural ammonia fertilizer and sewage and manure, with a minority of samples potentially undergoing denitrification. Comparatively, the main sources of NO 3 − –N in groundwater were soil N and sewage and manure, while the denitrification process was inactive. The carcinogenic risks caused by NO 3 − –N in groundwater were deemed either nonexistent or minimal, while the focus should predominantly be on potential non-carcinogenic risks, particularly for infants and children. Therefore, it is crucial to perform proactive measures aimed at safeguarding water ecosystems, guided by an understanding of the distribution, sources, and associated risks of nitrogen contamination.

In semi-arid inland basins, groundwater resources are already scarce, and the presence of high fluoride (F − ) concentrations in groundwater poses potential health risks to residents, further compromising the safety of drinking water. Seventy groundwater samples were collected from the ChahanNur Basin to investigate the factors controlling high F − concentrations. Hydrogeochemical analysis combined with machine learning techniques was applied to identify the key processes driving fluoride enrichment in the region. The results revealed that high F − groundwater is primarily concentrated in the western-central part of the basin, where the predominant chemical types are Cl-Na and Cl-Ca. Regression analysis of water chemistry indicated that F − enrichment was influenced by hydrogeochemical processes, including the dissolution and precipitation of fluoride-bearing minerals, competitive adsorption, cation exchange, and evaporation. The CatBoost model, utilizing these four key factors as predictor variables, was applied to predict groundwater F − concentrations. The model’s performance was evaluated by calculating the root mean square error (RMSE) for both the train and test sets, which yielded values of 0.18 and 0.22, respectively. The relative contributions of mineral dissolution and precipitation, evaporation, competitive adsorption, and cation exchange to groundwater F − enrichment were 66.2%, 18.3%, 11.2%, and 4.3%, respectively. Furthermore, the risk assessment identified regions where different population groups are particularly affected by high F − concentrations in groundwater. These findings provide a scientific basis for the effective management of high F − groundwater in inland basins, ensuring the safety of water supplies for local communities.

Bioretention systems are an important measure in sandy city construction to remove pollutants. This study collected all the operating data samples of bioretention ponds currently in operation in China and used the Mantel test and Redundancy Analysis (RDA) to optimize the main factors that affect the changes in pollutant-removal efficiency. Genetic Algorithms (GA) were used to optimize the Back Propagation (BP) neural network model, and a predictive index system was established to predict the efficiency of removing typical pollutants (ammonia nitrogen and nitrite nitrogen) by bioretention ponds. The operating efficiency of bioretention ponds was evaluated and predicted based on the GA-BP neural network model. The results indicated that the highest positive correlation with the operating efficiency of the bioretention system was X3 (rainfall duration), with a correlation coeffi-cient of 0.79, while the highest negative correlation was with X7 (percentage of strong percolating soil) and X12 (the proportion of mineral fillers), with correlation coefficients of −0.89. The overall regression coefficients of the GA-BP model for predicting the efficiency of removing typical pollu-tants (ammonia nitrogen and nitrite nitrogen) were 0.9397 and 0.9303, respectively, indicating high accuracy and representativeness. The overall regression index of the model is 0.9298, and the overall predicted removal rates of typical pollutants in the blank area are 78.72%, 70.31%, and 86.92%, re-spectively. The model can provide a basis and reference for the construction and planning of bio-retention ponds in areas lacking them.