Explore the vast range of titles available.

Groundwater is subjected to contamination threats from human activities, such as agriculture, especially long-term farming in the Sanjiang Plain, China. Identifying the sources and distribution of pollution is essential for its reasonable prevention and control. In this study, we analysed the chemical characteristics of 389 samples at 60 shallow groundwater monitoring points from 2011 to 2015 in the Sanjiang Plain using traditional hydrochemical methods, water quality assessment, Pearson’s correlation, and principal component analysis (PCA). Although groundwater type in this area was predominantly HCO3-Ca·Mg, three forms of nitrogen pollution (ammonia, nitrate, and nitrite) were all detected in this area. The interaction of natural geochemical and anthropogenic factors during hydrochemical formation is confirmed by the high coefficients of variation and Gibbs plots of the main ions in the water. The overall shallow groundwater situation was described as good, with more than 40% and 90% of groundwater samples suitable for drinking and irrigation according to the quality assessment, respectively. The proportion of poor water quality in the wet season was higher than that in the dry season. NO3-N and NH3-N were identified as the major anthropogenic pollutants in the study area. Results from Pearson’s correlation and principal component analysis shows two main pollutants fall into two chemical controlling factors together with natural chemical parameters, which implies that the migration and transformation of pollutants may have affected the overall hydrochemical characteristics of the regional groundwater. Therefore, findings from this paper can provide insight into the chemical evolution of groundwater in response to long-term agricultural activities and can help contribute to better management of groundwater resources and agricultural sustainable development.

Spring snowmelt has a significant impact on the hydrological cycle in seasonally frozen soil areas. However, scholars hold differing, and even opposing, views on the role of snowmelt during the thawing period in groundwater recharge. To explore the potential recharge effects of spring snowmelt on groundwater in seasonal frozen soil areas, this study investigated the vadose zone dynamics controlled by soil freeze–thaw processes and snowmelt infiltration in the Northeast of China for 194 days from 31 October 2020 to 12 May 2021. Responses of groundwater level and soil moisture to snowmelt infiltration show that most snowmelt was infiltrated under the site despite the ground being frozen. During the unstable thawing period, surface snow had already melted, and preferential flow in frozen soil enabled the recharge groundwater by snowmelt (rainfall), resulting in a significant rise in groundwater levels within a short time. The calculated and simulated snowmelt (rainfall) infiltration coefficient revealed that during the spring snowmelt period, the recharge capacity of snowmelt or rainfall to groundwater at the site is 3.2 times during the stable thawing period and 4.5 times during the non-freezing period.

Sponge City is an integrated urban stormwater management approach and practice to tackle waterlogging, flooding, water scarcity, and their related problems. Despite many positive effects of Sponge City on flood control that have been investigated and revealed, the effect on aquifer recharge is still less known. Considering maximizing the function of natural elements such as surface water bodies and subsurface storage space, to minimize the use of a gray drainage system, a Sponge City design was proposed to substitute the planning development scheme in the study area. The stormwater management model of SWMM (storm water management model) and the groundwater flow model of MODFlow (Modular Three-dimensional Finite-difference Groundwater Flow Model) were adopted to evaluate the flood-control effect and aquifer-recharge effect, respectively. Compared with the traditional planning scenario, the peak runoff is approximately 92% less than that under the traditional planning scenario under the condition of a 5-year return period. Due to the increase in impervious areas of urban construction, the total aquifer recharge from precipitation and surface water bodies was decreased both in the present planning scenario and the Sponge City design scenario. However, the Sponge City design has a positive impact on maintaining groundwater level stabilization and even raises the groundwater level in some specific areas where stormwater seepage infrastructure is located.

Ammonium is one of the main inorganic pollutants in groundwater, mainly due to agricultural, industrial and domestic pollution. Excessive ammonium can cause human health risks and environmental consequences. Its temporal and spatial distribution is affected by factors such as meteorology, hydrology, hydrogeology and land use type. Thus, a groundwater ammonium analysis based on limited sampling points produces large uncertainties. In this study, organic matter content, groundwater depth, clay thickness, total nitrogen content (TN), cation exchange capacity (CEC), pH and land-use type were selected as potential contributing factors to establish a machine learning model for fitting the ammonium concentration. The Shapley Additive exPlanations (SHAP) method, which explains the machine learning model, was applied to identify the more significant influencing factors. Finally, the machine learning model established according to the more significant influencing factors was used to impute point data in the study area. From the results, the soil organic matter feature was found to have a substantial impact on the concentration of ammonium in the model, followed by soil pH, clay thickness and groundwater depth. The ammonium concentration generally decreased from northwest to southeast. The highest values were concentrated in the northwest and northeast. The lowest values were concentrated in the southeast, southwest and parts of the east and north. The spatial interpolation based on the machine learning imputation model established according to the influencing factors provides a reliable groundwater quality assessment and was not limited by the number and the geographical location of samplings.

Background Bloodstream infections (BSIs) are a major cause of morbidity and mortality in Intensive Care Units (ICUs). Although blood cultures remain the diagnostic gold standard, their long turnaround time may hinder early risk warning. Early risk stratification using routinely available laboratory data may facilitate prompt clinical intervention. This study aimed to develop and validate machine learning (ML) models to predict the likelihood of BSIs in ICU patients based on laboratory tests obtained within the first 24 h of admission. Methods This was a retrospective, two-center cohort study using data from the Sixth Affiliated Hospital of Sun Yat-sen University (SAH-SYSU) and the Medical Information Mart for Intensive Care IV (MIMIC, v3.0) database. Adult ICU patients (≥ 18 years) with available first-day laboratory results and blood culture data were included. Multiple ML algorithms, including random forest, XGBoost, GBM, LightGBM, and SVM, were trained and validated using tenfold cross-validation. Model performance was assessed via area under the receiver operating characteristic curve (AUROC), calibration curves, Brier score, and decision curve analysis (DCA). Feature selection was conducted using the Boruta algorithm to develop simplified models. External validation was performed across cohorts using shared features. Results A total of 754 patients from SAH-SYSU (BSI prevalence: 27.7%) and 3,136 patients from MIMIC (BSI prevalence: 14.0%) were included. Tree-based models outperformed linear classifiers. In internal validation, XGBoost achieved the best performance (AUROC = 0.87 in MIMIC, 0.83 in SYSU). Simplified models using Boruta-selected features retained similar predictive performance (p > 0.05). Cross-cohort validation yielded AUROCs of 0.61 (MIMIC → SYSU) and 0.64 (SYSU → MIMIC). A compact four-feature model demonstrated moderate performance (AUROC up to 0.65), supporting feasibility in resource-limited settings. Conclusions ML-based models using only routine laboratory tests from the first ICU day can effectively identify patients at increased risk of BSIs. These models offer a rapid, interpretable, and generalizable tool for early clinical decision-making. Future studies should prospectively validate the model and explore its integration into electronic health records to support real-time risk stratification.

As one of the largest agricultural areas, the Sanjiang Plain of Northeast China has faced serious inorganic nitrogen pollution of groundwater, but the sources and the formation mechanism of pollution in the regional shallow groundwater remain unclear, which constrains the progress of pollution control and agricultural development planning. An investigation on potential nitrogen sources, groundwater inorganic nitrogen compounds (NH4+, NO3−, NO2−), and topsoil total nitrogen concentration (TN) was conducted in a typical paddy irrigation area of Sanjiang Plain. Multivariate statistical analysis combined with geospatial-based assessment was applied to identify the sources, determine the governing influencing factors, and analyze the formation process of inorganic nitrogen compounds in shallow groundwater. The results show that the land use type, oxidation-reduction potential (Eh), groundwater depth, NO2− concentration, and electrical conductivity (EC) are highly correlated with the NO3− pollution in groundwater, while DO and Eh affected the distribution of NH4+ most; the high concentrations of NO3− in sampling wells are most likely to be found in the residential land and are distributed mainly in densely populated areas, whereas the NH4+ compounds are most likely to accumulate in the paddy field or the lands surrounded by paddy field and reach the highest level in the northwest of the area, where the fields were cultivated intensively with higher fertilization rates and highest values of topsoil TN. From the results, it can be concluded that that the NO3− compounds in groundwater originated from manure and domestic waste and accumulated in the oxidizing environment, while the NH4+ compounds were derived from N fertilization and remained steady in the reducing environment. NO2− compounds in groundwater were the immediate products of nitrification as a result of microorganism activities.

The use of stormwater for managed aquifer recharge (MAR) has become one of the most important ways to deal with water shortages and the corresponding environmental geological problems, especially in the north of China. The Fe (III) clogging of porous media is a common and significant problem that influences the effect of the infiltration rate. This paper focuses on the migration characteristics and clogging mechanisms of iron hydroxides in sand columns. The results indicate that the permeability of porous media significantly decreased at the inlet of the fine sand column and inside the coarse sand column. We demonstrated that, when the Fe (III) concentration was higher, a smaller infiltration medium size was produced more rapidly, and there was more significant clogging. More than 80% of the injected Fe (III) remained in the sand column, and more than 50% was retained within 1 cm of the column inlet. The mass retention increased with the decrease in the size of the infiltration medium particles and with the increase in the injected Fe (III) concentration. The main material that caused Fe (III) clogging was iron hydroxide colloids, which were in the form of a granular or flocculent membrane coating the quartz sand. The mechanisms of clogging and retention were blocking filtration and deep bed filtration, adsorption, and deposition, which were strongly affected by the coagulation of Fe (III) colloidal particles.

In data-sparse areas, due to the lack of hydrogeological data, numerical groundwater models have some uncertainties. In this paper, a nested model and a multi-index calibration method are used to improve the reliability of a numerical groundwater model in a data-sparse region, the Nalinggele River catchment in the Qaidam Basin. Referencing this key study area, a regional three-dimensional groundwater flow model is developed in a relatively complete hydrogeological unit. A complex set of calibration indices, including groundwater fitting errors, dynamic groundwater trends, spring discharges, overflow zone location, and groundwater budget status, are proposed to calibrate the regional numerical groundwater model in the Nalinggele alluvial–proluvial fan. Constrained by regional groundwater modeling results, a local-scale groundwater model is developed, and the hydrogeological parameters are investigated to improve modeling accuracy and reliability in this data-sparse region.

To avoid the water quality deterioration that are caused by artificial recharge (AR) of groundwater, potable drinking water has been used as one of the source water for AR to control the side effects caused by the over-exploitation of groundwater. Chemical clogging problems can still be caused by certain chemical components, especially Fe and Al, and a lower concentration of these elements can cause a notable decrease in hydraulic conductivity at the top layer of the infiltration medium. Some components in AR source water can be obstructed by the clogging layer, leading to a change in water quality. The accumulation of total suspended solids (TSS) at the clogging layer can cause physical clogging and worsen the degree of chemical clogging. Although clogging and the related change in water quality were the dominant issues that affect the infiltration rate and health risks during the AR process, the dissolution of the aquifer matrix should also be taken into account. This dissolution contributed to not only the hydraulic conductivity of the infiltration medium but also the potential change in water quality during the aquifer recharge, storage and recovery processes.

The Second Songhua River is the biggest river system in Jilin Province, China. In recent years, the rapid economic development in this area has increased the prominence of water resources and water-related environmental problems; these include surface water pollution and the overexploitation of groundwater resources. Bank infiltration on the floodplains of the Second Songhua River is an important process of groundwater-surface water exchange under exploitation conditions. Understanding this process can help in the development of water resource management plans and strategies for the region. In this research, a multi-criteria evaluation index system was developed with which to evaluate the suitability of bank filtration along the Second Songhua River. The system was comprised of main suitability indexes for water quantity, water quality, the interaction intensity between surface water and groundwater, and the exploitation condition of groundwater resources. The index system was integrated into GIS (Geographic Information System) to complete the evaluation of the various indicators. According to the weighted sum of each index, the suitability of river bank filtration (RBF) in the study area was divided into five grades. Although the evaluation index system and evaluation method are applicable only to the Second Songhua River basin, the underlying principle and techniques it embodies can be applied elsewhere. For future generalization of the evaluation index system, the specific evaluation index and its scoring criteria should be modified appropriately based on local conditions.