Heavy Metal-Induced Assembly of DNA Network Biosensor from Double-Loop Hairpin Probes for Ultrasensitive Detection of UO22+ in Water and Soil Samples.

The uranyl ion (UO22+) is the most stable form of uranium, which exhibits high toxicity and bioavailability posing a severe risk to human health. The construction of ultrasensitive, reliable, and robust sensing techniques for UO22+ detection in water and soil samples remains a challenge. Herein, a DNA network biosensor was fabricated for UO22+ detection using DNAzyme as the heavy metal recognition element and double-loop hairpin probes as DNA assembly materials. UO22+-activated specific cleavage of the DNAzyme will liberate the triggered DNA fragment, which can be utilized to launch a double-loop hairpin probe assembly among Hab, Hbc, and Hca. Through multiple cyclic cross-hybridization reactions, hexagonal DNA duplex nanostructures (n[Hab•Hbc•Hca]) were formed. This DNA network sensing system generates a high fluorescence response for UO22+ monitoring. The biosensor is ultrasensitive, with a detection limit of 2 pM. This sensing system also displays an excellent selectivity and robustness, enabling the DNA network biosensor to work even in complex water and soil samples with excellent accuracy and reliability. With the advantages of enzyme-free operation, outstanding specificity, and high sensitivity, our proposed DNA network biosensor provides a reliable, simple, and robust method for trace levels of UO22+ detection in environmental samples.

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.

Modeling spatial trends and exchange fluxes of contaminants in agricultural soil under pollution prevention measures.

Modeling the long-term trends of contaminants in topsoil under controlled measures is critical for sustainable agricultural environmental management. Traditional mass balance equations cannot predict spatial variation and exchange flux of regional soil contaminants for it lacks a method of assigning input-output parameters to each simulated cell. To overcome this limitation, we allocate the estimated source contribution flux to the spatial grid cell in the regional chemical mass balance by integrated positive matrix factorization (P-RCMB) with historical trends quantification. Focusing on Cd and As, which are elements with elevated risks of food intake and volatilization/infiltration, the model is applied to 30 ha of agricultural land near the enterprise. Predictions indicate an additional 13.5% of the soil is contaminated, and approximately 2.57 ha may accrue after 100 years at the site, with an uncertainty range of 0.98-5.3 ha. Clean water irrigation (CWI) reduces contamination expansion by approximately 42%, including approximately 4813 g ha-1 yr-1 net As infiltration, playing a dominant role in preventing the formation of severely contaminated soil. Stop straw return, green fertilizers use, and reduced atmospheric deposition control the exchange flux of Cd (114.9 g ha-1 yr-1) in moderate/slight contamination areas. For the different contaminants' cumulative trends in dryland and paddy fields, achieving a net cumulative flux close to zero in marginally contaminated areas presents a viable approach to optimize current emission standards. if trade-off straw removal and additional fertilizer inputs, a straw return rate of approximately 40% in Cd-contaminated soil will yield overall benefits. This model contributes valuable insights and tools for policymaking in contaminated land sustainable utilization and emission standard optimization.

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.

Study on the response analysis of LID hydrological process to rainfall pattern based on framework for dynamic simulation of urban floods.

An in-depth analysis of the urban flood disaster level in response to different rainfall characteristics and Low Impact Development (LID) measures is of significant importance for addressing unfavorable management conditions and implementing effective flood control measures. This study proposes a dynamic urban flood simulation framework based on the Storm Water Management Model (SWMM) and Geographic Information System (GIS) spatial analysis, incorporating an active inundation seed search algorithm. The framework is calibrated and validated using nine historical urban flood events. Subsequently, the impact of rainfall patterns on urban inundation under LID measures is analyzed based on the dynamic urban flood simulation framework. The results show that the urban flood simulation framework exhibits good applicability, with Nash-Sutcliffe Efficiency (NSE) values of 0.825 and 0.763 during the calibration and validation periods, respectively. The extent of inundation shows little variation for rainfall events with a return period greater than 20 years, and the location of flooding is minimally affected by rainfall patterns. LID measures have a decreasing effect on urban inundation control as the return period of rainfall increases, and there are variations in hydrological responses to different rainfall patterns under the same return period. For single-peak rainfall events with the same return period, the control rates of inundation volume, flow, and infiltration decrease as the rainfall peak coefficient increases, indicating a weakening effect of LID measures on flood control with increasing rainfall peak coefficient. Under the same return period conditions, LID measures exhibit the best runoff control effect for uniform rainfall, while their effectiveness is lower for double-peak rainfall events and single-peak rainfall events with an r = 0.75 coefficient. The findings of this study provide a theoretical basis for urban flood warning and management of Low Impact Development measures.

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.

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.

Lake Sediment Archive Reveals a Distinct Response to Anthropogenic Pb and Zn Deposition with Historical Periods: Pb-Zn Isotope Evidence.

Anthropogenic activities release large quantities of heavy metals into the atmosphere. In China, the input of these heavy metals through local and trans-boundary atmospheric deposition is poorly understood. To assess this issue, herein, we use Pb and Zn isotopes to constrain the sources of Pb and Zn in a 210Pb-dated sediment core collected from the enclosed lake in South China. We observed a progressive shift toward higher 208Pb/206Pb and Pb fluxes (0.79-4.02 µg·cm-2·a-1) from 1850 to 1950 and a consistent decrease in δ66ZnIRMM (as low as -0.097 ± 0.030‰) coupled with an increase in Pb (1.74-3.36 µg·cm-2·a-1) and Zn (8.07-10.44 µg·cm-2·a-1) fluxes after 1980. These distinguished isotopic signals and flux variations reveal the presence of trans-boundary Pb since 1900, with the addition of local industrial Pb and Zn pollution after 1980. Up to 72.3% of Pb deposited at our site can be attributed to long-distance transportation from previously industrialized countries, resulting in a noteworthy legacy of Pb in China since 1900. Despite the phasing out of leaded gasoline, Chinese gasoline still contributes an average of 20.9%. The contribution of China's mining and smelting activities to Pb has increased steadily since 1980 and remained stable at an average of 25.1% since 2000.

Comparison and application of SOFM, fuzzy c-means and k-means clustering algorithms for natural soil environment regionalization in China.

Soil attributes and their environmental drivers exhibit different patterns in different geographical directions, along with distinct regional characteristics, which may have important effects on substance migration and transformation such as organic matter and soil elements or the environmental impacts of pollutants. Therefore, regional soil characteristics should be considered in the process of regionalization for environmental management. However, no comprehensive evaluation or systematic classification of the natural soil environment has been established for China. Here, we established an index system for natural soil environmental regionalization (NSER) by combining literature data obtained based on bibliometrics with the analytic hierarchy process (AHP). Based on the index system, we collected spatial distribution data for 14 indexes at the national scale. In addition, three clustering algorithms-self-organizing feature mapping (SOFM), fuzzy c-means (FCM) and k-means (KM)-were used to classify and define the natural soil environment. We imported four cluster validity indexes (CVI) to evaluate different models: Davies-Bouldin index (DB), Silhouette index (Sil) and Calinski-Harabasz index (CH) for FCM and KM, clustering quality index (CQI) for SOFM. Analysis and comparison of the results showed that when the number of clusters was 13, the FCM clustering algorithm achieved the optimal clustering results (DB = 1.16, Sil = 0.78, CH = 6.77 × 106), allowing the natural soil environment of China to be divided into 12 regions with distinct characteristics. Our study provides a set of comprehensive scientific research methods for regionalization research based on spatial data, it has important reference value for improving soil environmental management based on local conditions in China.

BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region.

In recent years, urban flood disasters caused by sudden heavy rains have become increasingly severe, posing a serious threat to urban public infrastructure and the life and property safety of residents. Rapid simulation and prediction of urban rain-flood events can provide timely decision-making reference for urban flood control and disaster reduction. The complex and arduous calibration process of urban rain-flood models has been identified as a major obstacle affecting the efficiency and accuracy of simulation and prediction. This study proposes a multi-scale urban rain-flood model rapid construction method framework, BK-SWMM, focusing on urban rain-flood model parameters and based on the basic architecture of Storm Water Management Model (SWMM). The framework comprises two main components: 1) constructing a SWMM uncertainty parameter sample crowdsourcing dataset and coupling Bayesian Information Criterion (BIC) and K-means clustering machine learning algorithm to discover clustering patterns of SWMM model uncertainty parameters in urban functional areas; 2) coupling BIC and K-means with SWMM model to form BK-SWMM flood simulation framework. The applicability of the proposed framework is validated by modeling three different spatial scales in the study regions based on observed rainfall-runoff data. The research findings indicate that the distribution pattern of uncertainty parameters, such as depression storage, surface Manning coefficient, infiltration rate, and attenuation coefficient. The distribution patterns of these seven parameters in urban functional zones indicate that the values are highest in the Industrial and Commercial Areas (ICA), followed by Residential Areas (RA), and lowest in Public Areas (PA). All three spatial scales' REQ, NSEQ, and RD2 indices were superior to the SWMM and less than 10%, greater than 0.80, and greater than 0.85, respectively. However, when the study area's geographical scale expands, the simulation's accuracy will decline. Further research is required on the scale dependency of urban storm flood models.

Arsenic release from microbial reduction of scorodite in the presence of electron shuttle in flooded soil.

Scorodite (FeAsO4·H2O) is a common arsenic-bearing (As-bearing) iron mineral in near-surface environments that could immobilize or store As in a bound state. In flooded soils, microbe induced Fe(III) or As(V) reduction can increase the mobility and bioavailability of As. Additionally, humic substances can act as electron shuttles to promote this process. The dynamics of As release and diversity of putative As(V)-reducing bacteria during scorodite reduction have yet to be investigated in detail in flooded soils. Here, the microbial reductive dissolution of scorodite was conducted in an flooded soil in the presence of anthraquinone-2,6-disulfonate (AQDS). Anaeromyxobacter, Dechloromonas, Geothrix, Geobacter, Ideonella, and Zoogloea were found to be the dominant indigenous bacteria during Fe(III) and As(V) reduction. AQDS increased the relative abundance of dominant species, but did not change the diversity and microbial community of the systems with scorodite. Among these bacteria, Geobacter exhibited the greatest increase and was the dominant Fe(III)- and As(V)-reducing bacteria during the incubation with AQDS and scorodite. AQDS promoted both Fe(III) and As(V) reduction, and over 80% of released As(V) was microbially transformed to As(III). The increases in the abundance of arrA gene and putative arrA sequences of Geobacter were higher with AQDS than without AQDS. As a result, the addition of AQDS promoted microbial Fe(III) and As(V) release and reduction from As-bearing iron minerals into the environment. These results contribute to exploration of the transformation of As from As-bearing iron minerals under anaerobic conditions, thus providing insights into the bioremediation of As-contaminated soil.

Tetracycline-Induced Release and Oxidation of As(III) Coupled with Concomitant Ferrihydrite Transformation.

Cocontamination with tetracycline (TC) and arsenic (As) is very common in paddy fields. However, the process and underlying mechanism of arsenite (As(III)) transformation on iron mineral surfaces in the presence of antibiotic contaminants remain unclear. In this study, the release and oxidation of As(III) on ferrihydrite (Fh) surfaces and Fh transformation in the presence of TC under both aerobic and anaerobic conditions were investigated. Our results indicated that the TC-induced reductive dissolution of Fh (Fe(II) release) and TC competitive adsorption significantly promote the release of As, especially under anaerobic conditions. The release of As was increased with increasing TC concentration, whereas it decreased with increasing pH. Interestingly, under both aerobic and anaerobic conditions, the addition of TC enhanced the oxidation of As(III) by Fh and induced the partial transformation of Fh to lepidocrocite. Under aerobic conditions, the adsorbed Fe(II) activated the production of reactive oxygen species (·OH and 1O2) from dissolved O2, with Fe(IV) being responsible for As(III) oxidation. Under anaerobic conditions, the abundant oxygen vacancies of Fh affected the oxidation of As(III) during Fh recrystallization. Thus, this study provided new insights into the role of TC on the migration and transformation of As coupled with Fe in soils.

Adsorption of cadmium on clay-organic associations in different pH solutions: The effect of amphoteric organic matter.

Clay minerals are important soil components and usually coexist with organic matter, forming mineral-organic associations (MOAs), which control the speciation, mobility, and bioavailability of heavy metals. However, the adsorption mechanism of cadmium (Cd) by MOAs is still unclear, especially for the associations of amphotericorganic matter and clay minerals. In this study, 12-aminododecanoic acid (ALA) and montmorillonite (Mt) were chosen to prepare MOAs via intercalation (Mt-ALA composite) and physical mixing (Mt-ALA mixture). Batch experiments were conducted to investigate the adsorption mechanism of Cd(II) by MOAs under different pH values and initial Cd(II) concentrations. The results showed that the Cd(II) adsorption capacities followed as Mt > Mt-ALA mixture > Mt-ALA composite under acidic conditions, Mt-ALA mixture > Mt > Mt-ALA composite under neutral conditions, and Mt-ALA mixture > Mt-ALA composite > Mt under alkaline conditions, suggesting the adsorption behaviors of Cd(II) by MOAs were primarily constrained by the speciation of ALA and solution pH. Under acidic conditions, cationic HALA+ could intercalate into the interlayer of Mt and occupy the adsorption sites, reducing the adsorption capacity of Cd(II). As pH increased to neutral, HALA+ decreased and changed to a zwitterionic state, which caused ALA to release out from the interlayer of Mt-ALA composite or not easily enter into Mt-ALA mixture and promoted Cd(II) adsorption. Under alkaline conditions, the increase of anion ALA- would cause ALA to be mainly adsorbed on the surface of Mt and chelate with Cd(II), enhancing the adsorption of Cd(II). Further analysis by Fourier transform infrared and X-ray photoelectron spectroscopy indicated that the carboxyl and amino groups of ALA both participated in the adsorption of Cd(II). These findings could extend the knowledge on the mobility and fate of Cd in clay-based soils and be used as a basis for understanding the biogeochemical behavior of Cd in the environment.

Photooxidation of Fe(II) to schwertmannite promotes As(III) oxidation and immobilization on pyrite under acidic conditions.

Pollution of arsenic (As) in acid mine drainage (AMD) is a universal environmental problem. The weathering of pyrite (FeS2) and other sulfide minerals leads to the generation of AMD and accelerates the leaching of As from sulfide minerals. Pyrite can undergo adsorption and redox reactions with As, affecting the existing form and biotoxicity of As. However, the interaction process between As and pyrite in AMD under sunlight radiation remains unclear. Here, we found that the oxidation and immobilization of arsenite (As(III)) on pyrite can be obviously promoted by the reactive oxygen species (ROS) in sunlit AMD, particularly by OH. The reactions between hole-electron pairs and water/oxygen adsorbed on excited pyrite resulted in the production of H2O2, OH and O2-, and OH was also generated through the photo-Fenton reaction of Fe2+/FeOH2+. Weakly crystalline schwertmannite formed from the oxidation of Fe2+ ions by OH contributed much to the adsorption and immobilization of As. In the mixed system of pyrite (0.75 g L-1), Fe2+ (56.08 mg L-1) and As(III) (1.0 mg L-1) at initial pH 3.0, the decrease ratio of dissolved total As concentration was 1.6% under dark conditions, while it significantly increased to 69.0% under sunlight radiation. The existence of oxygen or increase in initial pH from 2.0 to 4.0 accelerated As(III) oxidation and immobilization due to the oxidation of more Fe2+ and production of more ROS. The present work shows that sunlight significantly affects the transformation and migration of As in AMD, and provides new insights into the environmental behaviors of As.

FeNX(C)-Coated Microscale Zero-Valent Iron for Fast and Stable Trichloroethylene Dechlorination in both Acidic and Basic pH Conditions.

FeNX in Fe single-atom catalysts can be the active site for adsorption and activation of reactants. In addition, FeNX species have been shown to facilitate electron transfer between Fe and the carbon supports used in newly developed metal-air batteries. We hypothesized that the combination of FeNX species with granular zero-valent iron (ZVI) might result in catalyzed reductive decontamination of groundwater contaminants such as trichloroethylene (TCE). Here, such materials synthesized by ball milling microscale ZVI with melamine and the resulting N species were mainly in the form of pyridinic, pyrrolic, and graphitic N. This new material (abbreviated as N-C-mZVIbm) dechlorinated TCE at higher rates than bare mZVIbm (about 3.5-fold) due to facilitated electron transfer through (or around) the surface layer of iron oxides by the newly formed Fe-NX(C). N-C-mZVIbm gave higher kTCE (0.4-1.14 day-1) than mZVIbm (0-0.4 day-1) over a wide range of pH values (4-11). Unlike most ZVI systems, kTCE for N-C-mZVIbm increased with increasing pH values. This is because the oxide layer that passivates Fe0 at a high pH is disrupted by Fe-NX(C) formed on N-C-mZVIbm, thereby allowing TCE dechlorination and HER under basic conditions. Serial respike experiments gave no evidence of decreased performance of N-C-mZVIbm, showing that the advantages of this material might remain under field applications.

Coincorporation of N and S into Zero-Valent Iron to Enhance TCE Dechlorination: Kinetics, Electron Efficiency, and Dechlorination Capacity.

Sulfidated zero-valent iron (S-ZVI) enhances the degradation of chlorinated hydrocarbon (CHC) in contaminated groundwater. Despite numerous studies of S-ZVI, a versatile strategy to improve its dechlorination kinetics, electron efficiency (εe), and dechlorination capacity is still needed. Here, we used heteroatom incorporation of N(C) and S by ball-milling of microscale ZVI with melamine and sulfur via nitridation and sulfidation to synthesize S-N(C)-mZVIbm particles that contain reactive Fe-NX(C) and FeS species. Sulfidation and nitridation synergistically increased the trichloroethene (TCE) dechlorination rate, with reaction constants kSA of 2.98 × 10-2 L·h-1·m-2 by S-N(C)-mZVIbm, compared to 1.77 × 10-3 and 8.15 × 10-5 L·h-1·m-2 by S-mZVIbm and N(C)-mZVIbm, respectively. Data show that sulfidation suppressed the reductive dissociation of N(C) from S-N(C)-mZVIbm, which stabilized the reactive Fe-NX(C) and reserved electrons for TCE dechlorination. In addition to lowering H2 production, S-N(C)-mZVIbm dechlorinated TCE to less reduced products (e.g., acetylene), contributing to the material's higher εe and dechlorination capacity. This synergistic effect on TCE degradation can be extended to other recalcitrant CHCs (e.g., chloroform) in both deionized and groundwater. This multiheteroatom incorporation approach to optimize ZVI for groundwater remediation provides a basis for further advances in reactive material synthesis.

Microaerophilic Oxidation of Fe(II) Coupled with Simultaneous Carbon Fixation and As(III) Oxidation and Sequestration in Karstic Paddy Soil.

Microaerophilic Fe(II)-oxidizing bacteria are often chemolithoautotrophs, and the Fe(III) (oxyhydr)oxides they form could immobilize arsenic (As). If such microbes are active in karstic paddy soils, their activity would help increase soil organic carbon and mitigate As contamination. We therefore used gel-stabilized gradient systems to cultivate microaerophilic Fe(II)-oxidizing bacteria from karstic paddy soil to investigate their capacity for Fe(II) oxidation, carbon fixation, and As sequestration. Stable isotope probing demonstrated the assimilation of inorganic carbon at a maximum rate of 8.02 mmol C m-2 d-1. Sequencing revealed that Bradyrhizobium, Cupriavidus, Hyphomicrobium, Kaistobacter, Mesorhizobium, Rhizobium, unclassified Phycisphaerales, and unclassified Opitutaceas were fixing carbon. Fe(II) oxidation produced Fe(III) (oxyhydr)oxides, which can absorb and/or coprecipitate As. Adding As(III) decreased the diversity of functional bacteria involved in carbon fixation, the relative abundance of predicted carbon fixation genes, and the amount of carbon fixed. Although the rate of Fe(II) oxidation was also lower in the presence of As(III), over 90% of the As(III) was sequestered after oxidation. The potential for microbially mediated As(III) oxidation was revealed by the presence of arsenite oxidase gene (aioA), denoting the potential of the Fe(II)-oxidizing and autotrophic microbial community to also oxidize As(III). Thisstudy demonstrates that carbon fixation coupled to Fe(II) oxidation can increase the carbon content in soils by microaerophilic Fe(II)-oxidizing bacteria, as well as accelerate As(III) oxidation and sequester it in association with Fe(III) (oxyhydr)oxides.

Changes in the microbial community during microbial microaerophilic Fe(II) oxidation at circumneutral pH enriched from paddy soil.

Fe(II)-oxidizing bacteria (FeOB) are important catalysts for iron cycling in iron-rich marine, groundwater, and freshwater environments. However, few studies have reported the distribution and diversity of these bacteria in flooded paddy soils. This study investigates the microbial structure and diversity of microaerophilic Fe(II)-oxidizing bacteria (mFeOB) and their possible role in Fe(II) oxidation in iron-rich paddy soils. Using enrichment experiments that employed serial transfers, the changes in microaerophilic microbial community were examined via 16S rRNA gene high-throughput sequencing. During enrichments, the Fe(II) oxidation rate decreased as transfers increased, and the maximum rate of Fe(II) oxidation was observed in the first transfer (0.197 mM day-1). Results from X-ray diffraction of minerals and scanning electron microscopy of the cell-mineral aggregates revealed that cell surfaces in all transfers were partly covered with amorphous iron oxide formed by FeOB. After four transfers, the phyla of Proteobacteria had a dominant presence that reached up to 95%. Compared with the original soil, the relative abundances of Cupriavidus, Massilia, Pseudomonas, Ralstonia, Sphingomonas, and Variovorax increased in FeS gradient tubes and became dominant genera after transfers. Cupriavidus, Pseudomonas, and Ralstonia have been identified as FeOB previously. Furthermore, the structure of the microbial community tended to be stable as transfers increased, indicating that other bacterial species might perform important roles in Fe(II) oxidation. These results suggest the potential involvement of mFeOB and these other microorganisms in the Fe(II)-oxidizing process of soils. It will be helpful for future studies to consider their role in related biogeochemical processes, such as transformation of organic matters and heavy metals.

Versatile Sensing Platform for Cd2+ Detection in Rice Samples and Its Applications in Logic Gate Computation.

A versatile sensing platform was designed for Cd2+ detection utilizing Mg2+-dependent DNAzyme as the biocatalyst and toehold-mediated strand replacement as the reaction mechanism. The Cd2+-aptamer interaction brings the split subunits of the Mg2+-dependent DNAzyme into close-enough proximity, which generates an active DNAzyme that can catalyze the cleavage reaction toward the hairpin substrate strand (H1). The trigger DNA fragment in H1 can open another hairpin probe (H2) to activate the cyclic signal amplification process. The generated numerous G-quadruplex DNAzyme structures will produce a high fluorescence response after incubation with the fluorescence dye N-methyl mesoporphyrin IX (NMM). This detection platform is ultrasensitive and the detection limit (LOD) is 2.5 pM (S/N = 3). The sensing system is robust and can work effectively even in a complex sample matrix, enabling the quantitative analysis of Cd2+ content in rice samples with good reliability. Showing the unique features of simple operation, label-free and enzyme-free format, high sensitivity and selectivity, and universal signal amplification mode, our proposed sensing protocol holds great promise for becoming a competitive alternative for the routine monitoring of Cd2+ pollution. Importantly, this flexible and versatile sensing platform was used to construct some exquisite logic gates, including AND, OR, INHIBIT, IMPLICATION, NOR, and NAND.

Species richness promotes ecosystem carbon storage: evidence from biodiversity-ecosystem functioning experiments.

Plant diversity has a strong impact on a plethora of ecosystem functions and services, especially ecosystem carbon (C) storage. However, the potential context-dependency of biodiversity effects across ecosystem types, environmental conditions and carbon pools remains largely unknown. In this study, we performed a meta-analysis by collecting data from 95 biodiversity-ecosystem functioning (BEF) studies across 60 sites to explore the effects of plant diversity on different C pools, including aboveground and belowground plant biomass, soil microbial biomass C and soil C content across different ecosystem types. The results showed that ecosystem C storage was significantly enhanced by plant diversity, with stronger effects on aboveground biomass than on soil C content. Moreover, the response magnitudes of ecosystem C storage increased with the level of species richness and experimental duration across all ecosystems. The effects of plant diversity were more pronounced in grasslands than in forests. Furthermore, the effects of plant diversity on belowground plant biomass increased with aridity index in grasslands and forests, suggesting that climate change might modulate biodiversity effects, which are stronger under wetter conditions but weaker under more arid conditions. Taken together, these results provide novel insights into the important role of plant diversity in ecosystem C storage across critical C pools, ecosystem types and environmental contexts.