Delta regions worldwide face escalating coastal flood risks driven by the compound effects of sealevel rise (SLR) and vertical land motion (VLM). Existing studies often analyze these hazards separately and rely heavily on simplified static inundation models, limiting the accuracy of flood impact assessments and neglecting dynamic socioeconomic factors. This study develops an integrated framework combining high-resolution VLM monitoring (SBAS-InSAR), dynamic hydrodynamic modeling (LISFLOOD-FP), and socioeconomic projections (Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, SSP5-8.5) for comprehensive flood impact evaluation in three globally significant deltas: the Ganges-Brahmaputra-Meghna (GBM), Mississippi, and Yangtze. Results highlight severe and spatially variable subsidence rates-most notably in the GBM Delta (-8.98 mm/year), followed by the Mississippi (-2.93 mm/year) and Yangtze (-1.60 mm/year)- with human activities likely playing an important role in driving surface deformation. Projected flood scenarios (2050 and 2080) indicate significant increases in inundation extents and exposed populations and economic assets, particularly under combined SLR + VLM scenarios. The Yangtze Delta shows the highest economic exposure (up to approximately 1 trillion USD), whereas the GBM Delta exhibits the greatest demographic vulnerability, potentially affecting approximately 20 million individuals. The relative contributions analysis emphasizes an increasing dominance of SLR over time, especially under high-emission scenarios. These findings underscore the critical importance of tailored, region-specific adaptation strategies including resilient infrastructure, nature-based solutions, and adaptive spatial planning.

Salt-marsh Fairy circles (FC) are enigmatic, quasi-circular structures linked to interacting biogeophysical processes, yet they remain difficult to detect and quantify at scale from conventional RGB imagery. Limited labeled data, transient and variable FC appearance, and severe class-imbalance make single-model machine learning (ML) unreliable for quantitative monitoring. We propose a framework for automatic FC recognition and enumeration on 3-band imagery. A zero-shot foundation model (SAM) segments images into instance-level blocks. Novel distribution-pattern and geometric features, class-equalized losses, weighted resampling, and augmentation are applied within deep-learning (U-Net, Attention-U-Net, Swin-Unet) and ensemble-learning (Random Forest, XGBoost) models. The key innovation is an imbalance-aware Bayesian method that fuses pixel-wise probabilities across models; a counting algorithm then tallies FC instances. We evaluate eight pan-sharpened scenes covering four sites along China’s coast. No individual ML model or standard Bayesian fusion is fully satisfactory. The imbalance-aware Bayesian method improves over the best single model: tight scheme: κ rises from 0.69 to 0.76, F1-score from 70.9% to 75.8% (Class 1) and from 63.5% to 68.2% (Class 2), and AUC from 84.8% to 93.1% and from 78.5% to 84.8%; loose scheme: κ increases from 0.74 to 0.79, AUC from 85.1% to 90.3%, F1-score from 74.3% to 78.6%. The counting algorithm achieves RMSE 1.62 and MAPE 0.33% over 1,135 instances, outperforming DBSCAN. A 22-month case study on Chongming Island captures marsh expansion and dieback dynamics through shifts between FC classes. Our framework delivers reliable FC recognition and enumeration on a small dataset with severe class-imbalance, generalizing across salt-marsh types.

ICESat-2 provides near-global, high-precision elevation observations for intertidal mapping and time-series analysis. However, photon heterogeneity across ground, vegetation, water surfaces, and noise, together with tidal dynamics and dense vegetation, limits the effectiveness of conventional filtering and classification methods. To address this, we propose Lsr-RF, a multi-level adaptive framework that integrates the local sparsity rate with a random forest classifier. The method fuses ICESat-2 multi-scale geometric features with Sentinel-1 polarization and Sentinel-2 spectral features, applying RF-based feature selection and classification to distinguish noise, bare tidal-flat, vegetation, and ground-under-vegetation photons. Evaluated across strong/weak-beam and day/night conditions, Lsr-RF was compared with denoising-oriented baselines (ATL08, DBSCAN, OPTICS) and XGBoost. Lsr-RF improved overall accuracy by 0.52-28.06 percentage points and the Kappa coefficient by 0.02-0.61, and achieved a multi-class classification overall accuracy of 0.99 with a Kappa coefficient of 0.99. These results demonstrate its potential for accurate photon-level classification and broader intertidal wetland mapping applications.

High-resolution topographic mapping of intertidal wetlands is essential for geomorphic analysis, yet existing remote-sensing methods often struggle with vegetation interference, dependence on dense time-series data, and limited representation of fine geomorphic features. We propose a canopy-height-constrained stratified cooperative inversion framework for the entire intertidal wetland, integrating single-phase sub-meter optical imagery (Jilin-1), spaceborne photon-counting LiDAR (ICESat-2), and machine learning. To accurately construct DEM and canopy height model (CHM) training samples in salt-marsh environments, we developed an ATL03 photon-classification workflow combining histogram-based control-point extraction and morphological refinement to generate these samples directly from ICESat-2 ATL03 photons. The retrieved CHM was then introduced as a structural constraint in the DEM retrieval model to support canopy-terrain signal decoupling in vegetated salt-marsh areas. A case study on Chongming Island, Shanghai, China, demonstrated that the DEM retrieval achieved high accuracy on the test set (R² = 0.94, RMSE = 0.28 m) and maintained consistent performance against independent UAV-LiDAR validation data (R² = 0.53-0.77, RMSE = 0.34-0.53 m). The retrieved 0.5 m DEM reproduced regional elevation gradients, tidal-creek networks, and micro-topographic variations across bare flats and vegetated marshes. SHAP analysis showed that elevation retrieval over bare mudflats relied mainly on spectral predictors, whereas vegetated areas exhibited a complementary spectral-texture-CHM structure, with CHM consistently ranking as a mid-to-high predictor (4th-7th). This further supports the role of CHM as an effective structural constraint. By using only single-phase imagery and ATL03-derived DEM/CHM samples, the framework enables intertidal topographic retrieval that includes vegetated areas. It therefore provides an efficient and low-cost pathway for high-accuracy intertidal topographic monitoring under complex environmental conditions and limited image availability.

River deltas are critical socio-economic and ecological regions but face heightened flood risks due to climate change and urbanization. Taking the Ganges-Brahmaputra-Meghna (GBM) River Delta, the Mississippi River Delta, and the Yangtze River Delta as case studies, this research aims to reveal the characteristics and formation mechanisms of human adaptation to flood risks across different deltaic regions. Through integrating hydrodynamic modeling, spatiotemporal analysis, and multi-source datasets, this study systematically investigates flood adaptation characteristics across three major deltas based on a newly developed comprehensive framework of Human-Flood Distance (HFD) and resilience. The results show that: spatially, while these three deltas exhibit varying degrees of inundation extent, each faces unique flood vulnerability challenges; temporally, the GBM River Delta exhibits stabilized population growth and HFD recovery after initial contraction, the Mississippi River Delta shows significant fluctuations in both population and HFD, while the Yangtze River Delta demonstrates continuous population growth with steady HFD increase; in terms of adaptation mechanisms, resilience assessment indicates that the Mississippi River Delta demonstrates the highest resilience, primarily driven by recovery capacity, the Yangtze River Delta shows limited but structurally supported resilience, while the GBM River Delta exhibits negative indices due to multiple constraints. These findings emphasize the importance of developing context-specific flood risk management strategies and provide feasible flood prevention solutions for policymakers, particularly in formulating adaptation strategies that comprehensively consider both flood safety and sustainable development.

Coastal inshore areas, recognized as invaluable yet vulnerable, are experiencing shifts between various states due to gradual environmental changes and artificial disturbance. These transitions, however, are often imperceptible with large-scale mapping or through on regional in situ surveying when using traditional techniques. Advanced 2D and 3D technologies, particularly high-resolution remote sensing (HRRS) and LiDAR, offer novel perspectives that unveil fine details and precise vertical 3D structure of coastal ingredients. These technologies enable early, rapid, and accurate identification of significant transient or persistent patterns. Additionally, machine learning (ML), encompassing parametrized algorithms, ensemble learning (EL), and deep learning (DL), provides a unique advantage for automated observation. This work aims to advance the observation of key fine components in coastal inshore areas by designing automated methods and frameworks. It considers both natural and human-made sources as targets. with the focus of Poaceae and marine debris. First, an automated 3D recognition of stalks and leaves for Poaceae in coastal mudflats. Poaceae species (Giant reed and reed) in coastal mudflats hold ecological importance and serve as indicators. However, obtaining their phenotypic parameters like stalks and leaves is challenging. Our new automated, parametrized algorithm recognizes stalks and leaves of individual Poaceae plants in coastal wetlands using terrestrial LiDAR point clouds, leveraging radiometric and geometric features. Second, a new framework for comprehensive surveying of coastal Fairy Circles (FCs). FCs, predominantly formed by Poaceae, are self-organized patterns linked to recovery processes and salt-marsh resilience. Our new framework aims for automated surveying of coastal FCs, utilizing ML methods (which includes state-of-the-art foundation model, EL, and DL methods) on 2D and 3D data (satellite-borne and airborne). It is grounded in clear principles of FCs' definition and dynamics, potentially revolutionizing our understanding of coastal FCs behavior. Third, an automated method for 2D and 3D recognition of marine debris across complex scenarios. Marine debris in coastal environments poses significant ecological and environmental issues and has garnered widespread concern. Our new method detects and extracts marine debris from terrestrial LiDAR point clouds or UAV HRRS imagery, combining calibrated radiometric data with geometric features. Fourth, we have developed a series of mathematical models for instrumentation and data processing to achieve these goals. We proposed generalized rigorous model to mathematically correct the density variation in terrestrial LiDAR point clouds, the novel distribution pattern features, and a model to eliminate the specular effect on UAV LiDAR point cloud intensity.

Piezoelectric ceramics such as PbZr1-xTixO3 (PZT) show enhanced electro-mechanical properties at morphotropic phase boundary (MPB) separating two ferroelectric polar phases in the compositional phase diagram. Designing MPB in Pb-free perovskite oxide is challenging due to the lack of suitable polar tetragonal oxide with high Curie temperature. In this study, we explored the BiFeO3 - Bi0.5K0.5TiO3 - BaTiO3 ternary phase diagram and searched for Bi-rich perovskite oxides as candidates for piezoceramics near MPB. The phase diagram offers a Bi-rich polar tetragonal (T[001]) phase [0.75BaTiO3-0.25(K0.5Bi0.5)TiO3] and a well-known rhombohedral (R[111]) phase BiFeO3. A solid solution is observed in the entire range between the T[001] phase and the R[111] phase. Structural investigation through powder diffraction and Raman spectroscopy studies suggests the existence of a complex region (MPB), well separated by the rhombohedral and tetragonal phases. The composition (Bi0.670K0.050Ba0.280) (Fe0.62Ti0.38)O3 at the MPB shows a large signal piezoelectric coefficient of d & lowast;33 = 41.5 pm/V at room temperature.

Iron (Fe)-bearing particle species (e.g., metallic Fe, magnetite, and hematite) play a critical role in determining toxicity, yet few studies compare aboveground and underground metro stations. This study employed multiple approaches, including magnetic measurements, scanning electron microscopy (SEM), and geochemical methods, to analyze the abundance and species of Fe-bearing particles in these environments. Results reveal that total Fe in underground platforms (23.2 ± 4.5 wt%) is approximately three times higher than in aboveground platforms (7.7 ± 0.9 wt%). The proportion of magnetite-derived Fe (Femag) is significantly greater in underground stations (47 ± 4 %) compared to aboveground stations (22 ± 2 %), while silicate mineral-derived Fe (Fer) is more abundant in aboveground platforms (8.8 ± 0.9 %) than underground (4.2 ± 1.6 %). Multiple lines of evidence, including magnetic properties, SEM imaging, and Fe species analysis, confirm two primary sources of Fe-bearing particles in platform dust: urban topsoil/street dust and wheel/rail abrasion. Urban topsoil/street dust likely influenced aboveground stations, whereas underground stations were mainly impacted by wheel/rail abrasion. These findings offer key insights for toxicological research, emphasizing particle composition variability in metro environments.

High-precision elevation mapping is essential for ecological restoration, marine disaster assessment, and morphodynamic simulation in intertidal zones. Current methodologies are often impeded by an over-reliance on extensive in situ measurements and are typically applicable only to regions devoid of vegetation. In this study, we first propose a novel method for spatially continuous elevation mapping of large-scale muddy intertidal zones within highly turbid estuaries, utilizing features at pixel, neighborhood, and temporal scales from satellite multispectral images. This method utilizes a random forest (RF) to model the relationships between elevations from Ice, Cloud, and Elevation Satellite 2 (ICESat-2) and band, texture, and index features from Sentinel-2, without relying on any supplementary in situ measurements. The innovation and strength of the proposed method lie in the simultaneous incorporation of two temporal features: vegetation occurrence frequency and water inundation frequency. These two features effectively utilize the variations observed in different regions and land covers within the Sentinel-2 image series caused by the unique tide periodic fluctuation phenomenon and elevation trend law in intertidal zones, thereby rendering the method applicable to elevation prediction across the entire spatial range of intertidal zones, rather than being limited to nonvegetated regions. A case study conducted on the muddy intertidal zones of the islands in the Yangtze River Estuary from 2019 to 2023 reveals that the average root mean square errors are 0.33 and 0.69 m for high-mid and mid-low intertidal zones, respectively. The proposed method demonstrates superior performance in terms of vertical accuracy, spatiotemporal resolution, and spatial continuity in comparison to the state-of-the-art waterline detection and inundation frequency methods.

Quantitative estimation and spatial mapping of aboveground biomass (AGB) for salt marsh vegetation are crucial for modeling biogeochemical cycles and assessing wetland carbon stocks. Remote sensing offers a noninvasive method for monitoring vegetation traits over large areas. However, a single technique often cannot simultaneously capture both spectral information and the vertical structure of vegetation, greatly hindering its capabilities in classifying and estimating AGB of salt marsh vegetation. This work introduces a machine learning and heterogeneous data-based approach for AGB estimation by integrating passive multispectral two-dimensional imagery and active light detection and ranging (LiDAR) three-dimensional point clouds acquired from a drone platform. Vegetation indices along with texture features are extracted from multispectral imagery, whereas intensity values and height attributes are derived from LiDAR data. Four machine learning methods, namely, extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and light gradient boosting machine (LightGBM), are employed to classify vegetation and estimate AGB through the strategic utilization of a carefully derived multispectral-LiDAR feature set. A comprehensive case study of a coastal salt marsh in Chongming Island, China, reveals that (1) No noticeable discrepancies are found for different machine learning models in AGB estimation, with XGBoost achieving the highest accuracy (R2 = 0.9207, MAE = 0.2835 kg/m2, RMSE = 0.3229 kg/m2); (2) Feature types and sensors considerably affect AGB estimation accuracy, with height and texture features having greater influence than intensity features and vegetation indices, and LiDAR outperforms multispectral data; and (3) AGB varies remarkably among species and environments, with Spartina alterniflora being more sensitive to changes in soil moisture and nutrients, while Phragmites australis maintains higher AGB even under unfavorable conditions. The proposed approach offers an alternative and effective strategy for AGB estimation and shows strong potential in quantitatively characterizing the ecological processes of salt marsh vegetation.

The Yangtze River Delta has experienced intricate sedimentary and environmental changes throughout the Holocene, driven by the interplay of fluvial and marine forcings. This study presents quartz optically stimulated luminescence (OSL) ages and luminescence sensitivity data from a Holocene sediment core MQ, analyzed across four grain-size fractions, ranging from silt to sand. The results reveal substantial variability in OSL ages and sensitivity among grain sizes, with the medium-grain (45–63 μm) fraction yielding the most consistent and reliable results. In contrast, finer and coarser grains tend to overestimate ages due to incomplete bleaching, with the accurate dating of coarser grains requiring more aliquots or single-grain measurements. The variability in luminescence sensitivity reflects changes in sediment provenance and depositional conditions between estuarine and deltaic environments. OSL ages indicate that the sedimentary evolution of the Yangtze River Delta progressed through distinct phases: rapid accumulation during the early Holocene (10–7 ka) driven by rising sea level and valley infilling; reduced sedimentation during the middle Holocene (7–3 ka) related to a dry climate in the catchment; and accelerated deposition in the late Holocene (3 ka–present) associated with enhanced fluvial input linked to intensified human activities. This study highlights the importance of selecting suitable appropriate grain sizes and carefully comparing different fractions in OSL analysis to reconstruct deltaic chronologies accurately. The finding that the medium-grain fraction yields more reliable OSL ages than finer and coarser fractions should be tested in similar settings elsewhere. The results provide valuable insights for future research on complex depositional environments and contribute to a better understanding of long-term environmental changes.

Storm waves and rising sea levels pose significant threats to low-lying coastal areas, particularly sandy beaches, which are especially vulnerable. The research on the long-time-scale changes in sandy coasts, especially the identification of tipping points in the shoreline-retreat rate, is limited. Vung Tau beach, characterized by its low terrain and rapid tourism-driven economic growth, was selected as a typical study area to quantify the shoreline retreat throughout the 21st century under various sea-level rise (SLR) scenarios, and to identify the existence of tipping points by investigating the projected annual change in shoreline retreat (m/yr). This study employs the Probabilistic Coastline Recession (PCR) model, a physics-based tool specifically designed for long-term coastline change assessments. The results indicate that shoreline retreat accelerates over time, particularly after a tipping point is reached around 2050 in the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Under the SSP5-8.5 scenario, the median retreat distance is projected to increase from 19 m in 2050 to 89 m by 2100, nearly a fourfold rise. In comparison, the retreat distances are smaller under the SSP1-2.6 and SSP2-4.5 scenarios, but the same accelerating trend is observed beyond 2050. These findings highlight the growing risks associated with sea-level rise, especially the rapid increase in exceedance probabilities for retreat distances by the end of the century. By 2100, the probability of losing the entire beach at Vung Tau is projected to be 22% under SSP5-8.5. The approach of identifying tipping points based on the PCR model presented here can be applied to other sandy coastal regions, providing critical references for timely planning and the implementation of adaptation measures.

In the northern Jiangsu coastal zone of China, the buried tidal sand body (BTSB) is suggested to share a similar origin with the offshore radial sand ridge system in the southwestern Yellow Sea. However, its chronological framework remains inadequately understood. This study conducted optically stimulated luminescence (OSL) dating of both silt- and sand-sized quartz on core LDC from the southwestern end of the BTSB. Together with data from the previously studied core XYK closer to the current coastline, this study aims to clarify the chronology of the BTSB and refine its evolution history. The results indicate that in both cores, sand-sized quartz provides more reliable age estimates than silt-sized quartz for the sandy sediment layers. Additionally, the discrepancy between ages derived from the single-grain central age model and the minimum age model is smaller within the top 11 m of the core, which was deposited over the last 0.9 ka. This period corresponds well with the southern migration of the Yellow River and its sediment discharge into the Yellow Sea from 1128 to 1855 CE. It suggests that distinct sediment sources from the Yellow and Yangtze Rivers may account for the observed differences in OSL characteristics. The OSL ages reveal significant temporal variations in sedimentation rates during the Holocene, with the most rapid deposition occurring between 1.2–0.4 ka and 10–8 ka in core LDC, and between 2–1 ka in core XYK. Together with dating results from the central part of the BTSB, it reveals complex spatiotemporal variations in sediment accumulation and emphasizes the need for detailed sediment sampling and dating to fully elucidate the evolutionary history of the coastal plain.

Accurate mapping of terrain elevations at a large scale and fine resolution can characterize the detailed surface height and geomorphic changes and is very critical for the studies of the internal motions and external forces of the earth. The emergence of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) offers unprecedented possibilities for global elevation mapping with high vertical accuracy using three-dimensional photon points. However, the ICESat-2 photon points are still sparse in terms of spatial/horizontal resolution, making it unable to satisfy the high-resolution demand of terrain elevation mapping and digital elevation model production. A few previous studies have attempted to estimate elevations/topography in regions with single landscape and landcover (e.g., forest, shallow water, and polar regions) by combining ICESat-2 data with other passive satellite remotely sensed data. However, the potential and capability of ICESat-2 for mapping elevations for spatially continuous large regions with multiple complicated land cover types remains unknown. In this study, a spatially continuous large-scale terrain elevation estimation method is developed under multiple land covers based on the random forest model and the freely accessed satellite data of ICESat-2, Sentinel-1, and Sentinel-2. The core principle is to construct a random forest model that can characterize the complicated relationships of the ICESat-2 ATL03 terrain elevations and their corresponding land cover related polarization characteristics and spectral variables from Sentinel-1 and Sentinel-2, respectively. Integrating the superiorities of the data of these three different satellites enables the proposed method to extrapolate the terrain elevations with decimeter-level vertical accuracy and 10 m spatial/horizontal resolution simultaneously without any prior in situ data or manually set parameters. The proposed method is tested using the elevations from 2021 to 2022 at the third largest island (Chongming Island, Shanghai) in China. The estimated terrain elevations are locally validated with the airborne LiDAR-derived elevations. Moreover, they are compared with the ICESat-2 ATL08 height_terrain_bestfit data and Global Ecosystem Dynamics Investigation L2A elev_lowestmode data from the global perspectives. The predicted elevations exhibit a high correlation with the measured elevations from the two airborne LiDAR validation regions with root mean square errors (RMSE) of 0.34 and 0.59 m. The averaged RMSEs of the predicted elevations at different land covers are 1.26 and 1.18 m when compared with those derived from ATL08 and GEDI L2A, respectively. No remarkable abnormal predicted elevations are observed. This finding suggests the satisfactory robustness performance of the proposed method under different land covers and a relatively good consistency between the predicted elevations and the actual terrain of the entire island. As far as we know, the present work is the first to map elevations at 10 m resolution based only on the newly available satellite active and passive remotely sensed data without any ground truth surveys, manual intervention, and prior knowledge. Different with existing studies for terrain elevation mapping only at single landcovers, the proposed method demonstrates the capability and effectiveness of ICESat-2 for any landforms and landcovers and shows great potential for high-accuracy and high-resolution time-series terrain elevation estimation and updating at regional/national/global scales.

Sulfur isotopic signatures of pyrite (delta S-34(py)) sensitively react to local environmental change. Here we elucidate mechanism of delta S-34(py) variation in the Holocene sedimentary strata of the Yangtze River Delta, through the analyses of multiple geochemical and physical indicators including total organic carbon, total nitrogen, stable isotopes of organic carbon, different iron solid phases, Sr/Ba ratios, and changes in sedimentation rates. Two heavy delta S-34(py) (similar to 30 parts per thousand) layers that formed under contrasting redox conditions are preserved in the early Holocene transgressive tidal flat and late Holocene regressive mouth bar facies, respectively. Two hypotheses are proposed to explain these heavy delta S-34(py) layers: 1) in the transgression process, the heavy delta S-34(py) layers formed under stronger reducing conditions due to methane leakage; 2) in the regression process, the heavy delta S-34(py) layers formed under relatively weaker reducing conditions due to rapid deposition and low salinity. This study emphasizes the importance of physicochemical condition characterization in sediments, including redox conditions, in distinguishing the mechanisms that act to produce heavy delta S-34(py) signals.

为了明确鄂尔多斯盆地中东部中侏罗统延安组沉积时期的古环境特征,利用泥岩样品进行微量元素和稀土元素等分析测试,对测试结果进行统计分析,结合古植物和孢粉组合等特征,对盆地中东部延安期的古环境进行恢复重建.结果表明:锶、硼、相当硼、镓、锂、镍、Sr/Ba、B/Ga、Rb/K2 O和Th/U的数值说明延安期水体主体为淡水-半咸水环境;Fe2+/Fe3+、Ni/Co、V/Cr、V/(V +Ni)、U/Th、δU和Cu/Zn的数值反映延安期主要存在弱还原弱氧化-氧化的沉积条件;通过对比延安组和直罗组Sr/Cu、Mg/Ca、Rb/Sr、FeO/MnO、Al2 O3/MgO和Sr/Ba的数值,结合古植物和孢粉组合特征,认为从早侏罗世富县期到中侏罗世延安期,再到中侏罗世直罗期,鄂尔多斯盆地中东部经历了炎热半干旱半湿润—温暖湿润—炎热半干旱半湿润的气候变化过程.研究成果对于恢复中侏罗世延安期盆地沉积格局、重建古地理及成藏研究具有重要意义.

The Yaojiang Valley (YJV) on the southern Hangzhou Bay Plain, eastern China, was home to Neolithic cultures that date back to 8 ka. Reconstructions of ancient human cultures on the coastal plain in response to sea-level and hydroclimate changes have been well documented. However, centennial-resolution geological records are still lacking. Here, we present a combined approach of environmental (mineral) magnetism, geochemistry and diffuse reflectance spectroscopy (DRS) on a sediment core (YJ1503) in YJV to decipher the centennial hydroclimate oscillations of the coastal plain since early Holocene. Our results show that ferrimagnetic greigite and paramagnetic pyrite bearing layers correspond to periods of reducing condition that were caused by a higher water table while the presence of antiferromagnetic hematite/goethite corresponds to drier periods favorable for soil formation. Centennial scale fluctuations of magnetic mineralogy around the 8.2 ka cooling event and 7.5-6.6 cal ka BP reflect frequent and unstable environments, with limited Neolithic occupation. A strong drought event occurred around 6.0 cal ka BP, which was preceded by an extensive marine inundation event in the study area. This inundation event interrupted rice farming. Our study indicates that magnetic mineralogy can be used to reconstruct high-resolution hydrological processes in coastal plain and floodplain environments. These results are important to understand hydroclimate variation and Neolithic civilization response in environmentally sensitive areas.

Noncentrosymmetric (NCS) structures are of particular interest owing to their symmetry-dependent physical properties, e.g., pyroelectricity, ferroelectricity, piezoelectricity, and nonlinear optical (NLO) behavior. Among them, chiral materials exhibit polarization rotation and host topological properties. Borates often contribute to NCS and chiral structures via their triangular [BO3] and tetrahedral [BO4] units and their numerous superstructure motifs. However, no chiral compound with the linear [BO2] unit has been reported to date. Herein, an NCS and chiral mixed-alkali-metal borate, NaRb6(B4O5(OH)4)3(BO2), with a linear BO2- unit in the structure was synthesized and characterized. The structure features a combination of three types of basic building units (BBUs), [BO2], [BO3], and [BO4] with sp-, sp2-, and sp3-hybridization of boron atoms, respectively. It crystallizes in the trigonal space group R32 (No. 155), one of the 65 Sohncke space groups. Two enantiomers of NaRb6(B4O5(OH)4)3(BO2) were found, and their crystallographic relationships are discussed. These results not only expand the small family of NCS structures with the rare linear BO2- unit but also prompt recognition to the fact that NLO materials have generally overlooked the existence of two enantiomers in achiral Sohncke space groups.

Holocene climate change and human activities can lead to sediment composition variations. In this study, two drilled cores (VN and GA) from the Red River Delta, Vietnam, were subjected to magnetic measurement, as well as particle size, diffuse reflectance spectroscopy (DRS), and geochemical analyses, to infer Holocene sediment provenance changes. Both cores VN and GA cover estuarine, shallow marine, delta front slope, delta front platform, and delta plain facies in ascending order, with the former covering the whole Holocene while the latter covering the late Holocene. In the younger core GA, the clay content has a significant positive correlation with magnetic grain-size indicators (chi ARM/SIRM and chi fd%) and a negative correlation with demagnetization param-eters (S-100 mT and S-300 mT), reflecting the enrichment of fine-grained ferrimagnetic minerals and antiferro-magnetic minerals in finer sediments. On the contrary, the relationship between the above-mentioned magnetic properties and clay content is not significant in core VN, reflecting the impact of stronger post-depositional diagenesis. In addition, diagenesis in the shallow marine and delta front slope facies of two cores is more pro-nounced, which is due to the fact of higher input of marine-sourced organic carbon. A magnetic comparison of the upper parts of two cores indicates that there are sediment source changes within the last 2000 years, which is supported by DRS analysis. Sediments in delta front slope and delta front platform of two cores have similar goethite content, but core GA contains higher hematite content indicating a drier condition. Such a sediment source change can be interpreted by variations of monsoon climate and reworking of soil due to human disturbance. Surface soil erosion due to human activities over the last 0.5 ka is supported by the presence of ultra -fine grained magnetite. Our study demonstrates that magnetic properties together with DRS analysis can provide insights into paleoclimate and paleoenvironmental change in deltaic environments.