Cd/Pb behavior during combustion in a coal-fired power plant and their spatiotemporal impacts on soils: New insights from Cd/Pb isotopes.

Coal power plants annually generate quantities of byproducts that release environmentally hazardous heavy metals like Cd and Pb. Understanding the behavior and spatiotemporal impacts on soils of these releases is crucial for pollution control. This study investigated the concentrations and isotope ratios of Cd/Pb in combustion byproducts, depositions and soils collected from a coal-fired power plant or its surrounding area. The pulverized fuel ash (PFA) and desulfurized gypsum (DG) exhibited heavier Cd isotopes with Δ114Cd values of 0.304‰ and 0.269‰, respectively, while bottom ash (BA) showed lighter Cd isotopes (Δ114CdBA-coal = -0.078‰), compared to feed coal. We proposed a two-stage condensation process that governs the distribution of Cd/Pb, including accumulation on PFA and DG within electrostatic precipitators and desulfurization unit, as well as condensation onto fine particles upon release from the stack. Emissions from combustion and large-scale transport make a significant contribution to deposition, while the dispersion of Cd/Pb in deposition is primarily influenced by the prevailing wind patterns. However, the distribution of Cd/Pb in soils not only exhibit predominant wind control but is also potentially influenced by the resuspension of long-term storage byproducts. The power plant significantly contributes to soil in the NW-N-NE directions, even at a considerable distance (66%-79%), demonstrating its pervasive impact on remote regions along these orientations. Additionally, based on the vertical behavior in the profile, we have identified that Cd tends to migrate downward through leaching, while variations in Pb respond to the historical progression of dust removal.

Cd mobilization in mining-impacted soils with different bedrock lithology: Insights from stable Cd isotopes.

The environmental risk of Cd in soils strongly depends on the mobilization of Cd in soils. However, limited knowledge exists on the redistribution of exogenic Cd inputs in soils, especially across diverse lithological regions. Herein, we aimed to investigate the fate of Cd in soils from two mining areas with contrasting lithologies (siliceous and calcareous) using stable Cd isotopes. The isotope tracing results confirm that mining activities are the main Cd source in both areas. The positive correlation between δ114/110Cd values and goethite/dolomite content indicates the release of heavy Cd isotopes during the dissolution of exogenetic minerals. Additionally, high contents of exchangeable Cd (11 % to 36 %) and Fe oxide-bound Cd (29 % to 42 %) drive plant pumps to transport heavy Cd isotopes from the deeper to upper horizons of the soils from the siliceous area. In the calcareous area, the total organic carbon content is positively correlated with the Cd concentration and δ114/110Cd value, suggesting potential complexation of Cd with organic matter due to the stabilizing effect of carbonate minerals on soil organic matter. This study highlights the different redistributions of exogenous Cd in soils from diverse lithological regions, emphasizing the need to consider regional lithology when developing soil quality standards for Cd.

Study on long short-term memory based on vector direction of flood process for flood forecasting.

Accurate flood forecasting is crucial for flood prevention and mitigation, safeguarding the lives and properties of residents, as well as the rational use of water resources. The study proposes a model of long and short-term memory (LSTM) combined with the vector direction (VD) of the flood process. The Jingle and Lushi basins were selected as the research objects, and the model was trained and validated using 50 and 49 measured flood rainfall-runoff data in a 7:3 division ratio, respectively. The results indicate that the VD-LSTM model has more advantages than the LSTM model, with increased NSE, and reduced RMSE and bias to varying degrees. The flow simulation results of VD-LSTM better match the observed flow hydrographs, improving the underestimation of peak flows and the lag issue of the model. Under the same task and dataset, with the same hyperparameter settings, VD-LSTM can more quickly reduce the loss function value and achieve a better fit compared to LSTM. The proposed VD-LSTM model couples the vectorization process of flood runoff with the LSTM neural network, which contributes to the model better exploring the change characteristics of rising and receding water in flood runoff processes, reducing the training gradient error of input-output data for the LSTM model, and more effectively simulating flood process.

First Insight into the Mobilization and Sequestration of Arsenic in a Karstic Soil during Redox Changes.

Karst terrains provide drinking water for about 25% of the people on our planet, particularly in the southwest of China. Pollutants such as arsenic (As) in the soil can infiltrate groundwater through sinkholes and bedrock fractures in karst terrains. Despite this, the underlying mechanisms responsible for As release from karst soils under redox changes remain largely unexplored. Here, we used multiple synchrotron-based spectroscopic analyses to explore As mobilization and sequestration in As-polluted karstic soil under biogeochemical conditions that mimic field-validated redox conditions. We observed that As in the soil exists primarily as As(V), which is mainly associated with Fe(oxyhydr)oxides. The concentration of the dissolved As was high (294 µM) and As(III) was dominant (∼95%) at low Eh (≤-100 mV), indicating the high risk of As leaching under reducing conditions. This As mobilization was attributed to the fact that the dissolution of ferrihydrite and calcite promoted the release and reduction of associated As(V). The concentration of the dissolved As was low (17.0 µM) and As(V) was dominant (∼68%) at high Eh (≥+100 mV), which might be due to the oxidation and/or sequestration of As(III) by the recrystallized ferric phase. Our results showed that the combined effects of the reductive release of As(V) from both ferric and nonferric phases, along with the recrystallization of the ferric phase, govern the redox-induced mobilization and potential leaching of As in soils within karst environments.

Prediction of water level at Huayuankou station based on rating curve.

The construction of large reservoirs has modified the process of water and sediment transport downstream, resulting in changes in the morphology of the river cross-section. Changes in water and sand transport and cross-sectional morphology are reflected in the rating curve at the cross-section. This study analyzed the variations in the rating curve at the Huayuankou (HYK) section and their influencing factors, and conducted water level predictions based on this relationship. The findings revealed that while the annual mean water level has shown a declining tendency over the past 20 years, the annual mean discharge has shown a constant pattern. The rating curve at this stretch narrowed from a rope-loop type curve in its natural condition to a more stable single curve as a result of the construction of the dam upstream of the HYK section. The effect of pre-flood section morphology and the water-sediment process on the scattering degree of the rating curve is inverse; increasing roughness and hydraulic radius decreases scattering degree, while increasing sand content and sand transport rate increases scattering degree. Using the measured data from 2020 as an example, the feasibility of predicting cross-sectional water levels using the rating curve was verified. The prediction results were accurate when the flow was between 1000 and 2800 m3/s; However, when the flow was between 2800 and 4000 m3/s, the forecast results were typically slightly lower than the measured values. Overall, the method demonstrates good predictive accuracy. Insight from the method can be used to predict water levels to better inform decision making about water resources management, and flood emergency response in the lower Yellow River.

Photochemical Origins of Iron Flocculation in Acid Mine Drainage.

Acid mine drainage (AMD) raises a global environmental concern impacting the iron cycle. Although the formation of Fe(III) minerals in AMD-impacted waters has previously been reported to be regulated by biological processes, the role of abiotic processes remains largely unknown. This study first reported that a photochemical reaction coupled with O2 significantly accelerated the formation of Fe(III) flocculates (i.e., schwertmannite) in the AMD, as evidenced by the comparison of samples from contaminated sites across different natural conditions at latitudes 24-29° N. Combined with experimental and modeling results, it is further discovered that the intramolecular oxidation of photogenerated Fe(II) with a five-coordinative pyramidal configuration (i.e., [(H2O)5Fe]2+) by O2 was the key in enhancing the photooxidation of Fe(II) in the simulated AMD. The in situ attenuated total reflectance-Fourier transform infrared spectrometry (ATR-FTIR), UV-vis spectroscopy, solvent substitution, and quantum yield analyses indicated that, acting as a precursor for flocculation, [(H2O)5Fe]2+ likely originated from both the dissolved and colloidal forms of Fe(III) through homogeneous and surface ligand-to-metal charge transfers. Density functional theory calculations and X-ray absorption spectroscopy results further suggested that the specific oxidation pathways of Fe(II) produced the highly reactive iron species and triggered the hydrolysis and formation of transient dihydroxo dimers. The proposed new pathways of Fe cycle are crucial in controlling the mobility of heavy metal anions in acidic waters and enhance the understanding of complicated iron biochemistry that is related to the fate of contaminants and nutrients.

Highly oxidized intraplate basalts and deep carbon storage.

Deep carbon cycle is crucial for mantle dynamics and maintaining Earth's habitability. Recycled carbonates are a strong oxidant in mantle carbon-iron redox reactions, leading to the formation of highly oxidized mantle domains and deep carbon storage. Here we report high Fe3+/∑Fe values in Cenozoic intraplate basalts from eastern China, which are correlated with geochemical and isotopic compositions that point to a common role of carbonated melt with recycled carbonate signatures. We propose that the source of these highly oxidized basalts has been oxidized by carbonated melts derived from the stagnant subducted slab in the mantle transition zone. Diamonds formed during the carbon-iron redox reaction were separated from the melt due to density differences. This would leave a large amount of carbon (about four times of preindustrial atmospheric carbon budget) stored in the deep mantle and isolated from global carbon cycle. As such, the amounts of subducted slabs stagnated at mantle transition zone can be an important factor regulating the climate.

Sulfidation-reoxidation enhances heavy metal immobilization by vivianite.

Iron minerals in nature are pivotal hosts for heavy metals, significantly influencing their geochemical cycling and eventual fate. It is generally accepted that, vivianite, a prevalent iron phosphate mineral in aquatic and terrestrial environments, exhibits a limited capacity for adsorbing cationic heavy metals. However, our study unveils a remarkable phenomenon that the synergistic interaction between sulfide (S2-) and vivianite triggers an unexpected sulfidation-reoxidation process, enhancing the immobilization of heavy metals such as cadmium (Cd), copper (Cu), and zinc (Zn). For instance, the combination of vivianite and S2- boosted the removal of Cd2+ from the aqueous phase under anaerobic conditions, and ensured the retention of Cd stabilized in the solid phase when shifted to aerobic conditions. It is intriguing to note that no discrete FeS formation was detected in the sulfidation phase, and the primary crystal structure of vivianite largely retained its integrity throughout the whole process. Detailed molecular-level investigations indicate that sulfidation predominantly targets the Fe(II) sites at the corners of the PO4 tetrahedron in vivianite. With the transition to aerobic conditions, the exothermic oxidation of CdS and the S sites in vivianite initiates, rendering it thermodynamically favorable for Cd to form multidentate coordination structures, predominantly through the Cd-O-P and Cd-O-Fe bonds. This mechanism elucidates how Cd is incorporated into the vivianite structure, highlighting a novel pathway for heavy metal immobilization via the sulfidation-reoxidation dynamics in iron phosphate minerals.

A kinetics-coupled multi-surface complexation model deciphering arsenic adsorption and mobility across soil types.

The diversity of soil adsorbents for arsenic (As) and the often-overlooked influence of manganese (Mn) on As(III) oxidation impose challenges in predicting As adsorption in soils. This study uses Mössbauer spectroscopy, X-ray diffraction of oriented clay, and batch experiments to develop a kinetic coupled multi-surface complexation model that characterizes As adsorbents in natural soils and quantifies their contributions to As adsorption. The model integrates dynamic adsorption behaviors and Mn-oxide interactions with unified thermodynamic and kinetic parameters. The results indicate that As adsorption is governed by five primary adsorbents: poorly crystalline Fe oxides, well crystalline Fe oxides, Fe-rich clay, Fe-depletion clay, and organic carbon (OC). Fe oxides dominate As adsorption at low As concentrations. However, at higher As concentrations, soils from carbonate strata, with higher content of Fe-rich clay, exhibit stronger As adsorption capabilities than soils from Quaternary sediment strata. The enrichment in Fe-rich clay can enhance the resistance of adsorbed As to reduction processes affecting Fe oxides. Additionally, extensive redox cycles in paddy fields increase OC levels, enhancing their As adsorption compared to upland fields. This model framework provides novel insights into the intricate dynamics of As within soils and a versatile tool for predicting As adsorption across diverse soils.

Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting.

Accurate multi-step ahead flood forecasting is crucial for flood prevention and mitigation efforts as well as optimizing water resource management. In this study, we propose a Runoff Process Vectorization (RPV) method and integrate it with three Deep Learning (DL) models, namely Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Transformer, to develop a series of RPV-DL flood forecasting models, namely RPV-LSTM, RPV-TCN, and RPV-Transformer models. The models are evaluated using observed flood runoff data from nine typical basins in the middle Yellow River region. The key findings are as follows: Under the same lead time conditions, the RPV-DL models outperform the DL models in terms of Nash-Sutcliffe efficiency coefficient, root mean square error, and relative error for peak flows in the nine typical basins of the middle Yellow River region. Based on the comprehensive evaluation results of the train and test periods, the RPV-DL model outperforms the DL model by an average of 2.82%-22.21% in terms of NSE across nine basins, with RMSE and RE reductions of 10.86-28.81% and 36.14%-51.35%, respectively. The vectorization method significantly improves the accuracy of DL flood forecasting, and the RPV-DL models exhibit better predictive performance, particularly when the lead time is 4h-6h. When the lead time is 4-6h, the percentage improvement in NSE is 9.77%, 15.07%, and 17.94%. The RPV-TCN model shows superior performance in overcoming forecast errors among the nine basins. The research findings provide scientific evidence for flood prevention and mitigation efforts in river basins.

Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting.

One of the important non-engineering measures for flood forecasting and disaster reduction in watersheds is the application of machine learning flood prediction models, with Long Short-Term Memory (LSTM) being one of the most representative time series prediction models. However, the LSTM model has issues of underestimating peak flows and poor robustness in flood forecasting applications. Therefore, based on a thorough analysis of complex underlying surface attributes, this study proposes a framework for distinguishing runoff models and integrates a Grid-based Runoff Generation Model (GRGM). Simultaneously considering the time series characteristics of runoff processes, including rising, peak, and recession, a runoff process vectorization (RPV) method is proposed. In this study, a hybrid deep learning flood forecasting framework, GRGM-RPV-LSTM, is constructed by coupling the GRGM, RPV, and LSTM neural network models. Taking the Jialu River in the Zhongmu station control basin as an example, the model is validated using 18 instances of measured floods and compared with the LSTM and GRGM-LSTM models. The study shows that the GRGM model has a relative error and average coefficient of determination for simulating runoff of 8.41% and 0.976, respectively, indicating that considering the spatial distribution of runoff patterns leads to more accurate runoff calculations. Under the same lead time conditions, the GRGM-RPV-LSTM hybrid forecasting model has a Nash efficiency coefficient greater than 0.9, demonstrating better simulation performance compared to the GRGM-LSTM and LSTM models. As the lead time increases, the GRGM-RPV-LSTM model provides more accurate peak flow predictions and exhibits better robustness. The research findings can provide scientific basis for coordinated management of flood control and disaster reduction in watersheds.

From lake to fisheries: Interactive effect of climate and landuse changes hit on lake fish catch?

Global warming and unpredictable nature possess a negative impact on fisheries and the daily activities of other habitats. GIS and remote sensing approach is an effective tool to determine the morphological characteristics of the lake. The present study addresses the interactive effect of climate and landuse changes hit on fish catch in lake fisheries. We used a combination of the landscape disturbance index, vulnerability index, and loss index to construct a complete ecological risk assessment framework based on the landscape structure of regional ecosystems. The results indicate an increase from around 45%-76% in the percentage of land susceptible to moderate to ecological severe risk in the landscape from 2004 to 2023. Since 1950, temperature changes have increased by 0.4%, precipitation has decreased by 6%, and water levels have decreased by 4.2%, based on the results. The results indicate that landuse, water temperature, precipitation, and water depth significantly impact the aquaculture system. The findings strongly suggest integrating possible consequences of environmental change on fish yield for governance modeling techniques to minimize their effects.

Changes in sediment load in the Lower Yellow River and its driving factors from 1919 to 2021.

Rivers are not only an essential component of the development of civilization and the carbon cycle worldwide, but also a main contributor to natural disasters, especially the Lower Yellow River (LYR). With the functional degradation of the water-sediment regulation scheme (WSRS), LYR has reached a new stage. Thus, the changes in the sediment load in the Suspended River and its driving factors have significant practical applications. In this study, the sediment load in the LYR was analyzed from 1919 to 2021 based on improved sediment identity factor decomposition, wavelet analysis, and a double cumulative curve. The results showed that the changes in discharge and sediment exhibited poor synchronicity at different timescales. The sediment load decreased significantly, with evident periodicity of 9-10 years (years denoted as 'a') since 1950, and 69-a, 32-a, and 9-a since 1919. The changes in the sediment load can be divided into four phases: 1919-1959, 1960-1979, 1980-1998, and 1999-2021. Artificial levees can effectively constrain water flow and enhance sediment transport when the levee spacing is less than 6 km. To restrain deposition of the LYR, large dams control the incoming sediment coefficient so as to not exceed 0.009 kg∙s m-6. However, the WSRS reached its limit in 2010, and the wandering reach showed a deteriorating trend. Human activities control the changes in the sediment load. The reduction in the sediment load was mainly attributed to decreases in effective water yield capacity (53 %-75 %) before 1999 and sediment concentrations (46 %-65 %) after 1999. These results can provide a reference for further management of the suspended river.

Phase transformation of schwertmannite in paddy soil under different water management regimes and its impact on the migration of arsenic in soil.

Schwertmannite (Sch) holds a great promise as an iron material for remediating Arsenic (As)-contaminated paddy soils, due to its extremely high immobilization capacities for both arsenate [As(V)] and arsenite [As(III)]. However, there is still limited knowledge on the mineral phase transformation of this metastable iron-oxyhydroxysulfate mineral in paddy soils, particularly under different water management regimes including aerobic, intermittent flooding, and continuous flooding, and how its phase transformation impacts the migration of As in paddy soils. In this study, a membrane coated with schwertmannite was first developed to directly reflect the phase transformation of bulk schwertmannite applied to paddy soils. A soil incubation experiment was then conducted to investigate the mineral phase transformation of schwertmannite in paddy soils under different water management regimes and its impact on the migration of As in paddy soil. Our findings revealed that schwertmannite can persist in the paddy soil for 90 days in the aerobic group, whereas in the continuous flooding and intermittent flooding groups, schwertmannite transformed into goethite, with the degree or rate of mineral phase transformation being 5% Sch >1% Sch > control. These results indicated that water management practices and the amount of schwertmannite applied were the primary factors determining the occurrence and degree of mineral transformation of schwertmannite in paddy soil. Moreover, despite undergoing phase transformation, schwertmannite still significantly reduced the porewater As (As(III) and As(V)), and facilitated the transfer of non-specifically adsorbed As (F1) and specifically adsorbed As (F2) to amorphous iron oxide-bound As (F3), effectively reducing the bioavailability of soil As. These findings contribute to a better understanding of the mineralogical transformation of schwertmannite in paddy soils and the impact of mineral phase transformation on the retention of As in soil, which carry important implications for the application of schwertmannite in remediating As-contaminated paddy soils.

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning.

Baseflow is a crucial water source in the inland river basins of high-cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high-cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting. The proposed method was successfully implemented using a 63-year time series (1958-2020) of flow data from the Tai Lan River (TLR) basin in the high-cold mountainous region, along with 21 years of ERA5-land meteorological data and MODIS data (2000-2020). The results indicate that: (1) GWO-DFM can rapidly identify the optimal filtering parameters. It employs the arithmetic average of three methods, namely Chapman, Chapman-Maxwell and Eckhardt filter, as the best baseflow separation approach for the TLR basin. Additionally, the baseflow significantly increases after the second mutation of the baseflow rate. (2) Baseflow sources are mainly influenced by precipitation infiltration, glacier frozen soil layers, and seasonal ponding. (3) Solar radiation, temperature, precipitation, and NDVI are the primary factors influencing baseflow changes, with Nash-Sutcliffe efficiency coefficients exceeding 0.78 in both the LSTM model training and prediction periods. (4) Changes in baseflow are most influenced by solar radiation, temperature, and NDVI. This study systematically analyzes the changes in baseflow and response mechanisms in high-cold mountainous region, contributing to the management of water resources in mountainous basins under changing environmental conditions.

Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation.

Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. This study introduces a hybrid model, the RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), and the Transformer architecture. Applied to the typical Jingle watershed in the middle reaches of the Yellow River, this model utilises rainfall and runoff data from basin sites to simulate flood processes, and its outcomes are compared against those from RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models. It was evaluated against RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models using the Nash-Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias percentage as metrics. At a 1-h lead time during calibration and validation, the RS-LSTM-Transformer model achieved NSE, RMSE, MAE, and Bias values of 0.970, 14.001m3/s, 5.304m3/s, 0.501% and 0.953, 14.124m3/s, 6.365m3/s, 0.523%, respectively. These results demonstrate the model's superior simulation capabilities and robustness, providing more accurate peak flow forecasts as the lead time increases. The study highlights the RS-LSTM-Transformer model's potential in flood forecasting and the advantages of integrating various data-driven approaches for innovative modelling.

Comparison of multiple preservation and digestion methods for determination of tungsten concentrations in environmental media using ICP-MS.

Tungsten is an emerging environmental pollutant. However, a proved robust method for preserving and determining the concentrations of tungsten in environmental media is still lacking. This study examined and compared the suitability of classic methods and previously reported tungsten-oriented methods on preserving dissolved tungsten and recovering tungsten from soil/sediment matrix. Tungsten concentrations in the water samples and digestates were then determined by inductively coupled plasma mass spectrometry. Our data showed that the tungsten-oriented HF and alkaline preservatives indeed successfully maintained the stability of dissolved tungsten. Even when preserved using HNO3 or HCl, dissolved tungsten concentrations did not notably change in most of our water samples over the course of ∼4 months. Using glass containers for storing water samples also did not produce much difference from using high-density polyethylene containers. Our data further suggested that the addition of HF in digestion was important for tungsten solubilization from soil/sediment matrix. The digestion methods with HNO3/HCl/HF and HNO3/HF/NH4OH/EDTA both yielded quantitative recoveries of tungsten from certified reference materials and known synthetic samples, while the other tested methods had limited recoveries. The methods validated by this study could be used to accurately determine tungsten concentrations in environmental media and thereby to assess the fate and potential risks of tungsten.

Antimony and arsenic migration in a heterogeneous subsurface at an abandoned antimony smelter under rainfall.

While antimony (Sb) and arsenic (As) co-contamination in subsurface soil systems due to the legacy of Sb smelting wastes has been documented, the role of inherent heterogeneity on pollutant migration is largely overlooked. Herein this study investigated Sb and As migration in a slag impacted, vertically stratified subsurface at an abandoned Sb smelter. A 2-dimensional flume was assembled as a lab-scale analogue of the site and subject to rainfall and stop-rain events. Reactive transport modeling was then performed by matching the experimental observations to verify the key factors and processes controlling pollutant migration. Results showed that rainfall caused Sb and As release from the shallow slag layer and promoted their downward movement. Nevertheless, the less permeable deeper layers limited physical flow and transport, which led to Sb and As accumulation at the interface. The re-adsorption of Sb and As onto iron oxides in the deeper, more acidic layers further retarded their migration. Because of the large difference between Sb and As concentrations, Sb re-adsorption was much less effective, which led to higher mobility. Our findings overall highlight the necessity of understanding the degree and impacts of physicochemical heterogeneity for risk exposure assessment and remediation of abandoned Sb smelting sites.

Mechanism of zinc stress on magnesium deficiency in rice plants (Oryza sativa L.): Insights from magnesium isotopes.

Magnesium (Mg) and zinc (Zn) are essential nutrients for plants. Mg deficiency often occurs in rice plants grown in Zn-polluted soil. However, the mechanism for this correlation is unclear. Here, we performed culture experiments on rice plants (Oryza sativa L.) and used Mg isotopes to investigate mechanisms of Zn stress on plant Mg deficiency. Our results show that excess Zn can significantly reduce the uptake of Mg in rice tissues. The root displays positive Δ26Mgplant-nutrient values (δ26Mgplant-δ26Mgnutrient; 1.90 ‰ to 2.06 ‰), which suggests that Mg enters the root cells mainly via Mg-specific transporters rather than non-selective diffusion. The decreased Δ26Mgplant-nutrient values with increasing Zn supply can be explained by the competition between Zn and Mg, both of which combine with same transporters in roots. In contrast, the shoots (stem and leaf) display much lower δ26Mg values than roots, which suggests that the transport of Mg from roots to aerial biomass is mainly via free Mg ions, during which Zn cannot competitively inhibit the movement of Mg. Our study suggests that the Mg deficiency in rice plants can be caused by high Zn-levels in soils and highlights the necessity of soil Zn-remediation in solving Mg deficiency problems in rice plants.

Structural Fe(II)-induced generation of reactive oxygen species on magnetite surface for aqueous As(III) oxidation during oxygen activation.

Magnetite is a reductive Fe(II)-bearing mineral, and its reduction property is considered important for degradation of contaminants in groundwater and anaerobic subsurface environments. However, the redox condition of subsurface environments frequently changes from anaerobic to aerobic owing to natural and anthropogenic disturbances, generating reactive oxygen species (ROS) from the interaction between Fe(II)-bearing minerals and O2. Despite this, the mechanism of ROS generation induced by magnetite under aerobic conditions is poorly understood, which may play a crucial role in As(III) oxidation. Herein, we found that magnetite could activate O2 and induce the oxidative transformation of As(III) under aerobic conditions. As(III) oxidation was attributed to the ROS generated via structural Fe(II) within the magnetite octahedra oxygenation. The electron paramagnetic resonance and quenching tests confirmed that O2•-, H2O2, and •OH were produced by magnetite. Moreover, density function theory calculations combined with experiments demonstrated that O2•- was initially formed via single electron transfer from the structural Fe(II) to the adsorbed O2; O2•- was then converted to •OH and H2O2 via a series of free radical reactions. Among them, O2•-and H2O2 were the primary ROS responsible for As(III) oxidation, accounting for approximately 52 % and 19 % of As(III) oxidation. Notably, As(III) oxidation mainly occurred on the magnetite surface, and As was immobilized further within the magnetite structure. This study provides solid evidence regarding the role of magnetite in determining the fate and transformation of As in redox-fluctuating subsurface environments.