Earth and Environmental Sciences

Study region The Zayu River Basin in the southeastern Tibetan Plateau. Study focus Utilizing a dense network of 18 newly deployed rain gauges, this study characterizes spatiotemporal rainfall variability and evaluates six gridded precipitation products. We integrated multi-scale assessments (daily to hourly) with an event-based error decomposition framework to diagnose systematic biases in satellite and reanalysis datasets. New hydrological insights for the region: Observations reveal early-morning rainfall peaks and substantial spatial heterogeneity. At the daily scale, gauge-based CHM (China Hydro-Meteorology Precipitation) outperforms others. For all the hourly products, evaluations expose a distinct spatial disparity: western tributaries exhibit higher detection skill, while eastern tributaries suffer severe overestimation. Specifically, GMCP (Global Multisource Merging-and-Calibration Precipitation) proves most robust. Error decomposition reveals that ERA5-Land (the land component of the fifth-generation ECMWF atmospheric reanalysis) biases stem from "drizzle effects" (false-end errors), whereas IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement) biases arise from stochastic detection failures (miss-events). These diagnostics offer critical guidance for correcting precipitation data in data-scarce alpine regions.

The pock-marked surface of the Moon provides a stark reminder of the impact flux endured by the early Earth. Notwithstanding, the role of exogenic (impact-driven) processes in the generation and evolution of Earth’s continental crust has attracted relatively little attention compared to endogenic processes driven by loss of heat from the planet’s interior. Here we explore various isotope time series inferred to track crust production within the context of changing local mass density for the Solar System over the duration of its orbit through the Milky Way galaxy. Using a global dataset of zircon Hf isotopes during the Archean, we find an enhanced probability of a step change in composition during entry into the galactic spiral arms, on a periodicity of ∼190 Myr. Fluctuations in zircon oxygen isotopes between normal and non-normal distributions also reveal periods of less normality corresponding to spiral arm entry, implying the production of a greater volume of buoyant lithosphere due to an enhanced flux of energetic impacts. Additionally, the age distributions of post-Archean terrestrial hypervelocity impact craters and lunar impact-melt clasts show elevated probabilities during the predicted phases of spiral-arm crossing. For a Sun–spiral-arm recurrence interval of ∼190 Myr, the local Galactic rotation model predicts a radial epicyclic period of approximately ∼150 Myr, which is also resolved in the zircon Hf change-point record for the ancient Earth. Both frequencies have been related to periodic disturbance of the Oort cloud and modifications to the impact flux in the inner Solar System. Together, these correlations suggest that some episodes of production and reworking of continental crust during the Archean were triggered by large impacts, some of which were probably comets. That there seems to be a fundamental connection between events on Earth and the galactic tide supports a role for periods of catastrophism through Earth’s history.

Soil salinization severely restricts ecosystem stability and the sustainable development of agricultural productivity. However, current understanding of the spectral–salinity quantitative relationships under the influence of surface cracking still remains limited. To address this gap, this study collected hyperspectral reflectance data (350–2500 nm) from salt-affected soil in both cracked and uncracked surface conditions across the Songnen Plain, and applied fractional-order differentiation (FOD) processing with orders ranging from 0 to 2 and a step size of 0.1. Based on this, 14 types of FOD spectral indices were constructed, incorporating one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) structures. For each spectral index, the optimal fractional order and corresponding band combinations were first selected through Pearson correlation analysis for pH and EC under both surface conditions; subsequently, feature selection was performed using XGBoost-SHAP explainable analysis among the 14 optimal indices across different dimensions. Furthermore, the predictive performance of four modeling methods, including partial least squares regression (PLSR), Gaussian process regression (GPR), support vector regression (SVR), and random forest regression (RFR), was evaluated. The results showed that FOD transformations significantly enhanced correlations with EC and pH compared to raw reflectance. All prediction models demonstrated higher prediction accuracy under cracked surface conditions than uncracked surface conditions, indicating that desiccation cracks positively modulate spectral signals to enhance salinity information expression. Across different surface states, model performance generally followed the ranking: PLSR > GPR > SVR > RFR, with PLSR achieving the best predictions for EC and pH under cracked surfaces (R2 of 0.88 and 0.76, RMSE of 0.29 dS/m and 0.35). This study not only deepens the understanding of fractional-order spectral response mechanisms in saline–alkali soils but also provides methodological support for regional monitoring of soil salinization.

Abstract Concentration‐discharge (C‐Q) relations are used to infer water and solute storage and transport but are often informed by coarse‐temporal data. Here, we use high‐frequency and long‐term C‐Q datasets from two adjacent, but geologically and geomorphically distinct, watersheds in Colorado to evaluate how vertical and lateral flowpaths collectively control outlet water quality. We calculate C‐Q slopes at monthly, annual, and long‐term time scales and evaluate hysteresis behavior for three major solute classes—geogenics, metals, and biogenics. Geogenic C‐Q behavior is the most consistent across temporal scales and reflects vertical flowpath shifts from snowmelt to groundwater. In contrast, metals and biogenics have greater variability, particularly during high‐flow periods, as driven by laterally distributed landscape features, such as mine adits, an iron fen, floodplains, wetlands, and beaver ponds. Lateral heterogeneity in solute stores exerts major control on downstream chemistry and should be considered when predicting water quality changes.

Thermal shifts have accelerated ice-wedge degradation and reorganized polygonal trough networks along the Utqiaġvik, Alaska coastline. Thus, quantifying their structural variability and hydrologic connectivity across spatial scales remains challenging. This study applies high-resolution remote sensing and terrain analysis to evaluate hydrologic controls on ice-wedge polygon morphology. Using a 0.5 m lidar-derived DEM, polygon boundaries were manually digitized and compared with Thiessen (Voronoi) tessellations to quantify structural divergence, boundary misalignment, and differences in area. Hydrologic influences on polygon development were assessed through compound terrain analysis, drainage network extraction, and surface flow modeling. Spatial intersection analyses reveal geometric discrepancies underscoring the limitations of automated polygon proxies in permafrost terrain. Modeled flow paths exhibit spatial congruence with mapped trough networks, indicating that surface hydrology plays a role in trough evolution. Hydrologic and morphometric parameters demonstrate high runoff potential associated with low-relief topography. Elevated Topographic Wetness Index (TWI > 12 ) and Stream Power Index (SPI > 60) values delineate zones of saturation and flow accumulation that frequently coincide with trough depressions. Despite high predicted runoff, minimal gradients produce ponded flow regimes that promote wetland formation. This integrative framework enhances detection and interpretation of permafrost terrain features, providing a scalable methodology for monitoring Arctic landscape dynamics.

Abstract Inter-basin water transfers (IBWTs) are increasingly implemented to alleviate water scarcity; however, their environmental impacts remain heterogeneous and difficult to compare across projects. This study combined a bibliometric analysis of 643 unique articles and reviews indexed in Scopus and Web of Science (1971–2025) with a PRISMA-guided systematic review to examine the evolution of environmentally oriented IBWT research and to identify the environmental impacts quantified through remote sensing (RS) within the eligible evidence base. A total of 1,018 records were initially retrieved; 13 studies met the final inclusion criteria and were incorporated into the comparative synthesis. Results indicate substantial growth in the field over the last two decades, accompanied by increasing international collaboration, predominantly led by China and the United States, and growing thematic convergence around water management, sustainability, governance, water quality, and biodiversity. However, within the systematic review subset, the eligible RS evidence base was narrow and geographically concentrated, with 11 of the 13 included studies conducted in China and 2 in Iran. In this 13-study subset, RS applications focused mainly on terrestrial and landscape-scale impacts, particularly vegetation, land-use/land-cover change, riparian condition, and integrated eco-environmental assessment. In contrast, aquatic, hydraulic, and process-level impacts were less consistently captured within the eligible RS evidence base. Landsat-based optical workflows, typically operating at a 30 m spatial resolution and relying on discrete multi-year observations, were the most commonly reported configuration among the included RS studies. By linking bibliometric breadth from the full indexed corpus with systematically extracted evidence from a small and geographically concentrated RS subset, this study provides a more reproducible picture of where RS-based IBWT environmental monitoring is strongest within the available eligible evidence, where major gaps persist, and which methodological limitations still constrain cross-case comparability and long-term environmental decision making. These findings suggest that future research should prioritize standardized, explicitly reported, and methodologically comparable remote-sensing approaches to strengthen environmental monitoring and improve cross-case assessment in inter-basin water transfer systems.

Introduction Oil spills in the marginal seas surrounding the Korean Peninsula pose major ecological and socioeconomic threats, yet proactive spatial risk assessment remains limited because spill occurrence, transport-mediated exposure, and receptor sensitivity are often conflated. Methods We developed a high-resolution probabilistic Oil Spill Risk Assessment (OSRA) framework that explicitly separates spill likelihood from modeled oil exposure probability and integrates exposure with environmental and socioeconomic receptor sensitivity. Spill likelihood was mapped on a 300 m grid using 20 years of spill records, AIS-derived vessel traffic and collision likelihood, oil transport volume, and coastal oil storage capacity. A total of 310 representative spill locations were identified and 31,000 oil spill fate and trajectory simulations were conducted using 100 seasonally stratified release times. Results The resulting simulations were used to estimate oil exposure probability, which was subsequently combined with receptor sensitivity to generate an integrated spatial risk map. The Yellow Sea exhibited the highest oil exposure probabilities and overall risk, particularly in shallow semi-enclosed coastal areas where tidal flats, wetlands, and fisheries coincide with persistent oil retention. The South Sea showed moderate risk with localized hotspots near major industrial and port areas, whereas the East Sea exhibited generally low risk because receptor sensitivity was lower and open-coast circulation promoted rapid dilution and offshore advection. Discussion By distinguishing spill likelihood, transport-mediated exposure, and receptor sensitivity, the framework explains why areas with frequent spill sources do not necessarily correspond to the highest shoreline risk. The proposed OSRA framework provides an operationally relevant basis for prioritizing prevention, preparedness, and response planning and is adaptable to other heavily trafficked marginal seas under comparable data conditions.

) between sediment and bottom water in macroalgae-amended and unamended sediment cores showed a clear impact of macroalgae additions, with elevated carbon (C) turnover in these cores. The differences in total DIC release between unamended cores and macroalgae-amended cores were more than 100% and up to 298% of the added macroalgae C. The macroalgae addition, therefore accelerated the degradation of pre-existing organic matter in the sediment. This observation, known as a "priming effect," is well described in soils and freshwater systems but has only recently been considered in marine systems. Results from this experiment suggest that short term priming (<three months) did not depend on the macroalgae species added to the sediment, nor nutrient enrichment of the added tissue. The modification of organic matter in marine sediments, driven by secondary production from degrading macroalgal detritus, as well as the conditions that promote this priming, are poorly understood. These uncertain features of the C cycle are critical for understanding overall C turnover and assessing the C mitigation potential of both cultivated and natural macroalgae populations.

Sediment input is one of the most significant threats to coral reef ecosystems globally, yet its ecological impacts on biodiversity and community structure remain underappreciated. In this study, we utilized a multimarker environmental DNA (eDNA) metabarcoding technique to investigate how sediment load affects diverse taxa within algal turfs in coral reefs of the South China Sea. Using three assays (16S rRNA, nuclear 18S rRNA, and mitochondrial COI), we identified 1,143 genera, dominated by Proteobacteria, Cyanobacteria, Rhodophyta, Dinoflagellata, Mollusca, and Arthropoda. The diversity and species compositions of prokaryotes and eukaryotes varied across algal turf types subjected to different sediment load levels. Prokaryotic and eukaryotic diversity were higher in short algal turfs, whereas cyanobacteria dominated long, sediment-laden algal turfs (up to 62.30% of 16S sequences), revealing a clear shift in the dominance of primary producers from diverse eukaryotic algae to unique photosynthetic cyanobacteria. Sediment-related variables (algal canopy height, sediment mass, and grain size) were critical, explaining 25.42% and 28.00% of the variation in prokaryotes and eukaryotes, respectively─far more than other eco-environmental variables. These findings indicate that sediment load is strongly associated with the community structure of algal turfs and may be linked to reduced diversity of primary producers.

Poly(vinyl chloride) (PVC) poses a persistent environmental challenge due to its high chlorine content and additive-mediated recalcitrance. Herein, we report an N,O-dual-coordinated iron single-atom catalyst (Fe–N 2 O 2 -hCN) integrated with palladium nanoparticles (Pd NPs) for efficient hydrothermal Fenton-like upcycling of PVC into fuel-range hydrocarbons. The asymmetric Fe–N 2 O 2 configuration modulates the electronic structure of Fe centers, promoting H 2 O 2 activation and hydroxyl radical generation for efficient C–Cl and C–C bond cleavage under mild conditions, outperforming the conventional Fe–N 4 catalyst. The bifunctional Pd NPs/Fe–N 2 O 2 -hCN system achieves high PVC degradation efficiency (97.38%) and near-complete dechlorination, while selectively hydrogenating depolymerized intermediates into fuel-range alkanes (C 3 –C 20 ) with a high selectivity (86.01%). Mechanistic studies reveal enhanced electron transfer and a lowered energy barrier for H 2 O 2 dissociation, with Pd NPs generating reactive hydrogen species for olefin saturation. Life cycle assessment (LCA) demonstrates a 77% reduction in carbon emissions and significantly lower eco-costs than incineration. This work provides a coordination-engineered platform for converting hazardous plastic waste into valuable fuels, advancing a circular plastic economy.

Abstract Ongoing climate warming has intensified the influence of rising temperatures on terrestrial ecosystems. However, it remains unclear how the optimal land surface temperature (LST) for vegetation photosynthesis (LSTopt) has changed, and the frequency and magnitude of LST exceeding this optimum remains poorly understood. Here, we investigated the spatial distribution and temporal evolution of LSTopt across China using 8-day satellite-derived solar-induced chlorophyll fluorescence and LST from 2001 to 2024. Results show that LSTopt exhibits an overall increasing trend, whereas LST shows a decreasing tendency, indicating a divergence between optimal and observed temperature conditions. To further characterize vegetation thermal conditions, we introduced temperature exceedance metrics, including exceedance frequency (EF) and exceedance intensity (EI), to quantify the frequency and magnitude of LST exceeding LSTopt. Both EF and EI decrease over time, suggesting reduced exposure to supra-optimal temperatures. However, this does not imply improved thermal suitability, but rather indicates a structural shift in vegetation thermal regimes toward sub-optimal conditions. These findings highlight the importance of considering temperature relative to optimal conditions and provide a new perspective for understanding vegetation-climate interactions under ongoing climate change.

Achieving scalable monitoring of Alternate Wetting and Drying (AWD) for methane mitigation in rice cultivation depends on establishing field benchmarks for drainage behavior and demonstrating that satellite observations can reliably detect corresponding changes in water status. We analyzed about two million high-frequency in situ water-level observations from hundreds of sensors deployed in rice fields across the Philippines and Japan to quantify drainage duration from near-surface conditions to 15 cm below the soil surface and to test the sensitivity of open-access PALSAR-2 dual-polarization L-band SAR to vertical water-level variations. Across 564 drainage events, the median drainage duration was 19.0 h, and only 0.9% of events exceeded 240 h, indicating that drainage happens generally within a day. Seasonal differences were evident in Pangasinan, while small Chiba and Cagayan samples suggested exploratory longer-duration patterns; multiple drainage events occurred in 48.0% of Philippine dry-season fields but only 21.6% of wet-season fields. PALSAR-2 data showed a statistical significance in detecting inundation at Mid crop growth stage with cross-polarization band, but the significant overlap induces challenges in operational applications. These results provide empirical benchmarks for AWD-related drainage dynamics while showing that dual-polarization PALSAR-2 alone is unlikely to support robust field-scale monitoring of rice-field water status.

In recent years, there has been growing interest in applying vision–language models (VLMs) to quantitative remote sensing. This study evaluates whether three commercial VLMs (GPT-4o, GPT-5.5, and Claude Sonnet 4.6) can detect and classify the severity of harmful algal blooms (HABs) from Sentinel-2 satellite imagery of western Lake Erie and compares them against classical machine learning classifiers (Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost)) trained on both a three-band red, green, blue (RGB) composite representation of the imagery and a 10-band multi-spectral reflectance representation. Forty bloom events identified from the National Oceanic and Atmospheric Administration (NOAA) Harmful Algal Bloom Operational Forecast System (HAB-OFS) severity assessments were assembled into the evaluation dataset, spanning seven bloom seasons (2019–2025). For binary bloom detection, the VLMs did not match the classical RGB classifiers; their F1 scores (0.69–0.75) fell below the best RGB classifier (Random Forest, 0.76) and below a trivial always-present baseline (F1 = 0.77), and they carried false positive rates of 73–93% on bloom-absent images, against 27–40% for the RGB classifiers. The VLMs reached high recall by labeling most scenes as bloom-positive, which makes them operationally unreliable in this configuration. For severity classification, the VLMs assigned 60–70% of their predictions to the “moderate” category regardless of actual conditions and identified at most one of the two severe blooms, whereas the classical classifiers tracked the ground-truth distribution and delivered two to nearly three times the exact-match accuracy (0.44–0.59 vs. 0.20–0.225). The strongest method across all metrics was the multi-spectral SVM (F1 = 0.833, false positive rate 27%, accuracy 0.795). Switching the same SVM from RGB to multi-spectral features raised accuracy from 0.675 to 0.795, a 12-percentage-point gain that measures the spectral information carried by red-edge and shortwave infrared bands that are accessible through multi-spectral sensors but unavailable to standard VLM vision encoders. Feature-importance analysis showed that the multi-spectral classifiers ranked chlorophyll-specific indices, the Normalized Difference Chlorophyll Index (NDCI) and the Floating Algae Index (FAI), among their top predictors, the same signatures used in established operational algorithms, while the RGB classifiers relied on red-channel variability and green-dominant pixel fractions because RGB inputs cannot compute those indices. Two compounded limitations therefore constrain off-the-shelf VLMs for aquatic remote sensing: the limited spectral information available through standard RGB channels and a mismatch between the land-dominated training distributions of these models and aquatic optical conditions. Domain-specific classifiers operating on multi-spectral data remain the more suitable tools for continued development of HAB monitoring and water-quality retrieval.

Abstract Urban water scarcity is one of the most pressing governance challenges of our time, driven by climate change and rapid urbanization. Despite a growing body of research, scholarship remains fragmented across disciplines and regions, with the social and institutional dimensions of scarcity governance receiving far less attention than physical and economic drivers. This systematic literature review synthesizes social science research on acute urban water shortages involving rationing or restrictions published between 1995 and 2025, a period that captures the rise of urban water scarcity as a global research concern in connection with increased frequency and severity of major city-level water crises. Searches across three databases yielded 1,295 records, of which 64 articles met inclusion criteria for full-text review. Nearly half of the studies frame water crises as a supply-demand imbalance, reflecting a predominantly technocratic problem framing. Geographic attention clusters around several well-documented cases, including Cape Town’s “Day Zero”, Australia’s Millennium Drought, and the Brazilian Drought. Dimensions such as social equity and trust in leadership are well represented, whereas gender, informal water access, and water quality are thematically underrepresented. By reconceptualizing the diversity of urban water scarcity governance strategies in relation to research fields, water governance paradigms, temporality, and institutional pluralism, we highlight the need for increased reflexivity about underlying assumptions embedded in research. This review identifies theoretical, methodological, and empirical gaps and calls for greater engagement with sociological and human geographical perspectives on water-related risk, more comparative and longitudinal research, attention to underrepresented issues such as water quality, and future research avenues like flood-drought coupling and increased cooling demand. Reflexive and explicit engagement with underlying paradigmatic assumptions in research and practice, as well as a diverse and institutionally plural governance approach, will allow the field of urban water scarcity governance to be more future-proof.

Infrared Fourier transform spectrometers using interferometric spectroscopy are widely used in space remote sensing owing to their high spectral resolution and sensitivity. We investigated the distorted spectral characteristics introduced by nonlinear errors of different orders through simulation for infrared detectors with strong nonlinear effects. A high-order nonlinear correction scheme was proposed based on two iterative correction methods for in-band and out-of-band spectra. Further, the effects of second-order, third-order, in-band, and out-of-band correction methods were compared using prelaunch radiometric calibration experimental data from the DQ-2 satellite infrared hyperspectral atmospheric composition sounder. The results showed that the third-order in-band correction scheme performed the best, while various other correction schemes also effectively reduced nonlinear errors. The maximum average deviation was 0.18–0.25 K for the long-wave band and 0.11–0.19 K for the mid-wave band in the temperature range of 230–300 K. According to the correction evaluation and methods comparison, the proposed method is appropriate for nonlinearity detectors to improve radiometric calibration accuracy.

Vegetation net primary productivity (NPP) is a key indicator of terrestrial carbon sequestration and ecological restoration effectiveness. The karst mountainous region of Southwest China is characterized by fragmented terrain and high ecological vulnerability, making quantification of NPP dynamics and drivers essential for regional management. Using MOD17A3 NPP data (2000–2020), this study applied trend analysis, Hurst exponent analysis, partial correlation analysis, residual trend analysis, and Geodetector to investigate NPP spatiotemporal patterns and driving mechanisms in Guizhou Province. Results show a significant increasing trend in NPP (3.653 gC·m−2·a−1, p &lt; 0.01), with 78.61% of the area exhibiting growth and a spatial pattern of higher values in the south and lower values in the north. NPP shows persistence, indicating a continued increasing tendency. Along elevation gradients, NPP exhibits a unimodal pattern, peaking at 1000–1200 m, while growth rates increase with elevation and slope, with greater variability at higher altitudes. Temperature exerts a stronger and more extensive influence on NPP than precipitation, with significant correlations over 34.35% and 10.16% of the study area, respectively (p &lt; 0.05). Residual trend analysis indicates that non-climatic factors accounted for a larger share of NPP variation (64.49%) than climatic factors (35.51%), with ecological restoration likely the leading non-climatic driver. Geomorphological type is the primary driver of spatial heterogeneity (q = 0.220), followed by precipitation, temperature, and land use, with interaction effects mainly showing nonlinear enhancement. These findings provide insights for ecological restoration and vegetation management in karst regions.

Study region Nansi Lake Basin, a water-scarce region in northern China. Study focus This study conducted a data-driven vulnerability assessment of the Nansi Lake Basin water resource system. The Driver–Pressure–State–Impact–Response conceptual model was applied to define a three-tier vulnerability evaluation system comprising a target layer, 5 first-level indicators, and 26 second‐level indicators. Particle swarm optimization (PSO) was employed to determine the optimal hyperparameters of a long short-term memory (LSTM) network trained on data comprising randomly generated evaluation indicator values and their corresponding vulnerability grading standards. This hybrid PSO–LSTM model was used to assess the spatiotemporal vulnerability of the Nansi Lake Basin water resource system from 2010 to 2021, and its rationality was verified by comparison with the variable fuzzy assessment method. New hydrological insights for the region The hybrid PSO–LSTM model exhibited high accuracy ( R 2 > 0.999 for both training and validation samples) and stability, indicating its suitability for vulnerability assessments. Temporally, the vulnerabilities in the Nansi Lake Basin and its six subregions fluctuated downward over the study period, evolving from highly vulnerable in 2010 to slightly vulnerable by 2021. Spatially, vulnerability decreased faster in the western than eastern subregions. Finally, the obstacle degree model identified the causes of vulnerability to provide a scientific foundation for the utilization and management of water resources in the Nansi Lake Basin.

Study region Vilaine River basin, France. Study focus Phosphorus (P) concentrations have declined in Western countries since the 1970s due to reductions in point and diffuse sources, but recent concerns about a potential P return have emerged. We analyzed long-term trends in soluble reactive P (SRP) and total P (TP) across 18 subcatchments of a 10,137 km² temperate basin in western France. A discharge-normalization approach was used to separate the effects of hydrological variability from changes in P sources or forms. We also examined synchronies with chemical proxies and relationships with geographic variables to support attribution of observed trends. New hydrological insights From 2007–2024, 61% of subcatchments showed increasing SRP concentrations (0.3 – 7.7 μg P/L/year), whereas only 28% showed increasing TP (1.1 – 12.1 μg P/L/year). Because increases occurred mainly during baseflow conditions, trends in annual P loads were often not significant. Changes in discharge explained less than 10% of SRP trends. Instead, results point to remobilization of legacy P, associated with rising temperatures and decreasing dissolved oxygen, as the main driver. Remobilization occurs in both the surface water network (streams, lakes, ponds) and artificial systems such as riparian buffer strips and waste stabilization ponds, acting as sources of P to the stream network. Future work should identify where remobilization mainly occurs to improve targeted mitigation measures.

For small-satellite imagery, traditional image points derived from the direct back-projection of laser altimetry points onto the imagery often exhibit deviations of tens of pixels or more, owing to the relatively limited attitude and orbit determination accuracy of small-satellite platforms. Moreover, the diversity of imaging sensors, variations in image resolution, and inherently weak image geometric configurations further complicate the accurate acquisition of image-space coordinates for laser altimetry points. To facilitate the application of laser altimetry data for geometric positioning across multi-satellite, multi-sensor, and multi-resolution small-satellite imagery, this study proposes a measurement method for laser altimetry points tailored to small-satellite images and establishes a combined geometric positioning model that integrates virtual control points, laser altimetry points, and image-matching tie points. The framework comprises four key procedural components: (1) an image-point-measurement strategy for laser altimetry points; (2) the construction of a laser altimetry data-assisted geometric positioning model for small-satellite imagery; (3) the solution of the geometric positioning model using a total least squares approach based on the partial-EIV (errors-in-variables) models; and (4) a comprehensive accuracy assessment conducted under multiple image-combination scenarios, including single-satellite single-stereo, single-satellite multi-stereo, dual-satellite single-stereo, and multi-satellite multi-stereo imagery configurations. Experimental validation is carried out using Jilin-1 small-satellite panchromatic images (KF01A, GF02A, and GF02B) acquired over the Henan region of China. The experimental results demonstrate that, with the laser altimetry point-measurement method and the combined geometric positioning model, the vertical positioning accuracy is substantially improved across all tested image-combination scenarios. These findings further confirm the capability in enhancing the vertical geometric positioning performance of stereoscopic small-satellite imagery characterized by multi-satellite platforms, multi-sensors, and multi-resolutions over terrain conditions similar to those tested.

Abstract. The aroma compound ethyl butyrate (EB) and its methylated derivatives ethyl 2-methylbutyrate (EM), ethyl isovalerate (EI), and isopropyl butyrate (IB) are present in many consumer products. To evaluate the environmental and health impacts of these volatile organic compounds, a detailed understanding of their gas-phase photochemical reactivity is required. Here, we performed pulsed laser photolysis/laser-induced fluorescence (PLP-LIF) experiments to investigate the kinetics of their reactions with the hydroxyl radical (OH). Room temperature rate coefficients in units of 10-12molec.-1cm3s-1 with 2σ statistical errors were determined as: (5.5±0.2) for EB + OH, (7.0±0.3) for EM + OH, (11.2±0.4) for EI + OH, and (7.5±0.4) for IB + OH. All four reactions exhibited complex kinetics with distinct non-Arrhenius behaviour for temperatures up to about 400 K. This behaviour was attributed to pre-reaction complexes and is consistent with site-specific reactivities as predicted by an established structure-activity-relationship (SAR). In a second series of experiments, quasi-gas-phase UV-vis. spectroscopy and time-dependent density functional theory predictions were used to obtain absorption cross-sections. All four esters displayed an absorption band at around 213 nm (spin-forbidden π*←n transition), but did not absorb appreciably in the visible or UV-A part of the spectrum where light is abundant at ground level. Therefore, the reaction with OH was considered the main loss process, with lifetimes for tropospheric removal ranging from 22–45 h. Photochemical ozone creation potentials were estimated to be in a moderate range between 28 and 34.

The Kalamaili area in the Eastern Junggar, part of the Central Asian Orogenic Belt (CAOB), is a key locality for understanding the closure of the Paleo-Asian Ocean. This study focuses on the widely distributed Jiangbasitao Formation volcanic rocks in this region, which constitute a typical bimodal suite composed of alkali basalts and rhyolitic tuffs. Whole-rock geochemical analyses reveal that the alkali basalt exhibits a shoshonitic affinity and high TiO 2 contents, suggesting derivation from low-degree partial melting of an enriched lithospheric mantle source. In contrast, the rhyolitic tuff retains arc-like signatures, such as Nb-Ta depletions, indicating an origin primarily through crustal melting or fractional crystallization of mafic magmas. An integrated assessment of the geochemical characteristics demonstrates that this bimodal volcanic suite formed in a post-collisional extensional setting. Zircon U-Pb geochronological dating of a basaltic trachyandesite sample yields a weighted mean age of 322.8 ± 2 Ma (Serpukhovian, Early Carboniferous). This age provides a precise chronological constraint on the final closure of the Kalamaili oceanic basin. Our findings reveal a coherent tectonic evolutionary sequence: northward subduction of the Kalamaili oceanic basin beneath the Yemaquan block occurred from the Devonian to the Early Carboniferous, with oceanic closure culminating at approximately 321.9 Ma, followed by the onset of post-collisional extension. By integrating our new data with existing regional geological constraints (e.g., the ages of post-collisional granites and sedimentary sequences), this study refines the tectonic evolution of the Kalamaili region. The ca. 321.9 ± 2 Ma bimodal volcanism provides robust evidence for the initiation of post-collisional extension in the late Early Carboniferous, thereby supporting a model in which the Kalamaili Ocean had already closed prior to this time. This integrated framework offers new insights into the Paleozoic tectonic evolution of the Eastern Junggar and the CAOB.