Assessing the land use type and environment factors affecting groundwater nitrogen in an arid oasis in northwestern China

Response of soil carbon and nitrogen stocks to irrigation - A global meta-analysis

Irrigation has profound influences on carbon (C) and nitrogen (N) stocks in agricultural soil. However, the global-scale irrigation effects on C and N pools in farmland soils, as well as the C: N ratio (C/N), remain unclear. This study integrates existing studies on C and N in irrigated farmland worldwide and investigates the responses of soil C and N concentrations, stocks, and the C/N to irrigation by meta-analysis. The results suggest that irrigation has a significantly positive impact on soil organic carbon (SOC) and total nitrogen (TN) stocks overall, with the stocks increase by 10.9 % and 7.4 %, respectively, and a 3.1 % increase in the C/N, but has no significant impact in soil microbial biomass carbon (MBC). The positive feedback of SOC (6.0 %) and TN (6.6 %) stocks in topsoil is more pronounced in response to irrigation than that in subsoil. The impact of irrigation on SOC stocks is greater in semi-arid regions and under flood irrigation. Furthermore, SOC stocks increase more in sandy and loamy soils compared to clay soil, while TN exhibits larger increases in clay soil. The results also indicate that the response of C/N to irrigation is more pronounced under the condition of deep soil, sandy soil, and semi-arid regions. The influence of irrigation on SOC stocks and the C/N increases with the duration of irrigation, while the impact on TN stocks tends to weaken. Our study deepens the understanding of the mechanisms behind irrigation's effects on soil C and N stocks and therefore provides theoretical insights for the management of soil fertility in irrigated agriculture.

Seasonal dynamics of dissolved inorganic nitrogen in groundwater: Tracing environmental controls and land use impact

High levels of dissolved inorganic nitrogen (DIN) in groundwater pose challenges for regions like northern Anhui Province, China, where groundwater is a crucial domestic resource. This study utilized modern geostatistics to explore the spatial and temporal dynamics of DIN in groundwater. Significant seasonal influences on DIN concentrations were identified: ammonium peaks during wet season driven by agricultural activities, while nitrate peaks during the dry season primarily influenced by municipal inputs. This study established a Bayesian Maximum Entropy - Random Forest (BME-RF) model based on Land Use/Land Cover data to infer the spatio-temporal performance of DIN, achieving an interpretation rate above 90 %. It also highlighted the role of hydrogeological conditions and aquifer types in the evolution of DIN. By employing a DIN environmental interaction model, it further analyzed the eco-hydrological drivers and seasonal trends affecting DIN variability, enhancing the understanding of groundwater nitrogen dynamics and their link to environmental factors with low consumption. SYNOPSIS: This study reveals seasonal shifts in groundwater DIN, links them to human activity, and uses the BME model to guide targeted nitrogen fluctuation.

Simulating arsenic discharge flux at a relic smelting site in Guangxi Zhuang Autonomous Region, China

Anthropogenic groundwater arsenic (As) pollution is common in many aquifers in Southwest China. It is concerned that long-term random disposal of As smelting slag could induce the transport of high-As groundwater into previously uncontaminated aquifers. Here, we used HELP-MODFLOW-MT3DMS model simulations to integrate the percolation, groundwater flow, and solute transport processes at an aquifer at site scale, constrained by weather, hydrogeology, and monitoring data. Our simulations provide a new method framework of the simulated percolation by HELP model and have induced As spatiotemporal distribution in the aquifer. According to the HELP model simulation results, percolation volume accounts for 24% of rainfall over 18 years. This work determined that the As discharge trend was fitted by double-constants kinetics based on the leaching experiment. And this work calculates total mass distribution of As in the aquifer over 18 years. We have found that the sustained As pollution relies on the rainfall that acts as the primary contributor of elevated As concentrations. Model simulation results suggest that 51.70% of the total As mass (1.96 × 104 kg) was fixed in low permeability solid media. The total As mass discharged into groundwater reached 9.3 × 103 kg, accounting for 24.68%. The accumulative outflow mass of arsenic was 8.0 × 103 kg, accounting for 21.62%.

Comparative study of the performance of controlled release materials containing mesoporous MnOx in catalytic persulfate activation for the remediation of tetracycline contaminated groundwater

Controlled release materials (CRMs) are an emerging oxidant delivery technique for in-situ chemical oxidation (ISCO) that solve the problems of contaminant rebound, backflow and wake during groundwater remediation. CRMs were fabricated using ordered mesoporous manganese oxide (O-MnOx) and sodium persulfate (Na₂S₂O₈) as active components, for the removal of antibiotic pollutants from groundwater. In both static and dynamic groundwater environments, persulfate can first be activated by O-MnOx within CRMs to form sulfate radicals and hydroxyl radicals, with these radicals subsequently dissolving out from the CRMs and degrading tetracycline (TC). Due to their excellent persulfate activation performance and good stability, the constructed CRMs could effectively degrade TC in both static and dynamic simulated groundwater systems over a long period (>21 days). The TC removal rate reached >80 %. Changing the added content of O-MnOx and persulfate could effectively regulate the performance of the CRMs during TC degradation in groundwater. The process and products of TC degradation in the dynamic groundwater system were the same as in the static groundwater system. Due to the strong oxidizing properties of sulfate radicals and hydroxyl radicals, TC molecules were completely mineralized within the groundwater systems, resulting in only trace levels of degradation products being detectable, with low- or non-toxicity. Therefore, the CRMs constructed in this study exhibited good potential for practical application in the remediation of organic pollutants from both static and dynamic groundwater environments.

Modelling approaches for linking the residual concentrations of antibiotics in soil with antibiotic properties and land-use types in the largest urban agglomerations in China: A review

Persistently high concentrations of antibiotics have been reported in soils worldwide due to the intensive use of veterinary antibiotics, and continuous adsorption and transport of various antibiotics in soils occur, posing a significant threat to the environment and human health. This study systematically reviews the spatial distribution and ecological risk of four commonly detected antibiotic residues in soil in China, including sulphonamides (SAs), fluoroquinolones (FQs), tetracyclines (TCs) and macrolides (MLs), using various models, such as redundancy analysis (RDA), principal coordinate analysis (PCoA) and structural equation modelling (SEM). Antibiotic residual concentration data were obtained from relevant repositories and the literature. The results suggest a high level of antibiotic pollution and ecological risk in the largest urban agglomerations (LUAs), including Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Guangdong-Hong Kong-Macao Greater Bay Area (GBA), with a 100% detection rate. SAs, FQs, TCs and MLs were the dominant antibiotic residues in soils, mainly attributed to manure fertilization and wastewater reuse in agriculture. These antibiotic concentrations ranged from 10^-3 to 10³ μg kg^-1, and their ecological risk varied significantly across different regions of China, with SAs posing the most serious ecological risk to the soil environment (p < 0.05). These models established a significant association (p < 0.05) between the physicochemical properties of antibiotics and land-use type (LUT) with antibiotic residues in soil. The structure of the antibiotic exerted the greatest influence on antibiotic residues, followed by the LUT, while regional differences had the weakest effect.

Imputation of Ammonium Nitrogen Concentration in Groundwater Based on a Machine Learning Method

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.

Assessment of the hydrochemistry, water quality, and human health risk of groundwater in the northwest of Nansi Lake Catchment, north China

In this study, the formation mechanism and water quality of groundwater in the northwest of Nansi Lake Catchment (NNLC) were analyzed through mathematical statistics, hydrochemical analysis and entropy weighted water quality index (EWQI), and the human health risk of nitrate was also evaluated. To this end, 89 wells in the NNLC were sampled, and the groundwater samples were divided into three groups (I, II, and III) according to cluster analysis results and spatial distribution. The main results are as follows: Topographically, Groups I, II, and III correspond to the alluvial plains, apron plain, and low hills and its front margin, respectively. According to the Piper diagram, the hydrochemical types of Groups I and II groundwater are Na-SO₄·Cl and Ca·Mg-HCO₃, respectively, and that of Group III is more concentrated, mostly corresponding to the Ca-HCO₃ type. Hydrochemical analysis indicated that the development of groundwater hydrochemistry is mainly attributable to water-rock interactions, with the primary process being the dissolution of minerals such as calcite, dolomite, gypsum, and albite. Evaporation exhibited an increasing trend from the northeast to the southwest. Groups I and III presented obvious effects of human activities, with Group I showing sulfate pollution and Group III mainly showing nitrate pollution. Analysis of the characteristics and causes of the groundwater hydrochemistry revealed the proposed approach has excellent performance for classification in areas with complex hydrogeological conditions. The results of EWQI showed that the overall water quality was good, following the order Group III > Group II > Group I. The overall human health risk of nitrate in groundwater was low, but the risk was slightly higher for children than for adults. Therefore, the effects of nitrate contamination should be considered when exploiting hilly and peri-urban groundwater for drinking water.