Bedrock-soil layer slopes (BSLSs) are widely distributed in nature. The existence of the interface between bedrock and soil layer (IBSL) affects the failure modes of the BSLSs, and the seismic action makes the failure modes more complex. In order to accurately evaluate the safety and its corresponding main failure modes of BSLSs under seismic action, a system reliability method combined with the upper bound limit analysis method and Monte Carlo simulation (MCS) is proposed. Four types of failure modes and their corresponding factors of safety (Fs) were calculated by MATLAB program coding and validated with case in existing literature. The results show that overburden layer soil's strength, the IBSL's strength and geometric characteristic, and seismic action have significant effects on BSLSs' system reliability, failure modes and failure ranges. In addition, as the cohesion of the inclination angle of the IBSL and the horizontal seismic action increase, the failure range of the BSLS gradually approaches the IBSL, which means that the damage range becomes larger. However, with the increase of overburden layer soil's friction angle, IBSL's depth and strength, and vertical seismic actions, the failure range gradually approaches the surface of the BSLS, which means that the failure range becomes smaller.

The reasonable value of good gradation characteristic parameters is key in designing and optimising soil-rock mixed high fill embankment materials. Firstly, the DJSZ-150 dynamic-static large-scale triaxial testing instrument was used for triaxial compression shear tests on compacted skeleton structure soil-rock mixture standard specimens. The changes in strength and deformation indicators under different gradation parameters and confining pressure were analysed. Then, based on the Janbu empirical formula, relationships between parameters K, n, and (sigma 1-sigma 3)ult and the coefficient of uniformity Cu and coefficient of curvature Cc were explored. Empirical fitting formulas for Duncan-Chang model constants a and b were proposed, establishing an improved Duncan-Chang model for soil-rock mixtures considering gradation characteristics and stress states. Finally, based on significant differences in particle spatial distribution caused by gradation changes, three generalised models of matrix-block stone motion from different particle aggregation forms were proposed. Results indicate the standard specimen's strength and deformation indicators exhibit significant gradation effects and stress-state correlations. The improved Duncan-Chang model effectively simulates the stress-strain relationship curve under different gradations and confining pressure, with its characteristics explainable based on the matrix block stone motion generalised model.

Understanding soil organic carbon (SOC) distribution and its environmental controls in permafrost regions is essential for achieving carbon neutrality and mitigating climate change. This study examines the spatial pattern of SOC and its drivers in the Headwater Area of the Yellow River (HAYR), northeastern Qinghai-Xizang Plateau (QXP), a region highly susceptible to permafrost degradation. Field investigations at topsoils of 86 sites over three summers (2021-2023) provided data on SOC, vegetation structure, and soil properties. Moreover, the spatial distribution of key permafrost parameters was simulated: temperature at the top of permafrost (TTOP), active layer thickness (ALT), and maximum seasonal freezing depth (MSFD) using the TTOP model and Stefan Equation. Results reveal a distinct latitudinal SOC gradient (high south, low north), primarily mediated by vegetation structure, soil properties, and permafrost parameters. Vegetation coverage and above-ground biomass showed positive correlation with SOC, while soil bulk density (SBD) exhibited a negative correlation. Climate warming trends resulted in increased ALT and TTOP. Random Forest analysis identified SBD as the most important predictor of SOC variability, which explains 38.20% of the variance, followed by ALT and vegetation coverage. These findings likely enhance the understanding of carbon storage controls in vulnerable alpine permafrost ecosystems and provide insights to mitigate carbon release under climate change.

An analytical methodology was developed for the first time in this work enabling the simultaneous enantiomeric separation of the fungicide fenpropidin and its acid metabolite by Capillary Electrophoresis. A dual cyclodextrin system consisting of 4 % (w/v) captisol with 10 mM methyl-beta-cyclodextrin was employed in a 100 mM sodium acetate buffer at pH 4.0. Optimal experimental conditions (temperature 25 degrees C, separation voltage -25 kV, and hydrodynamic injection of 50 mbar x 10 s) allowed the simultaneous separation of the four enantiomers in <10.7 min with resolutions of 3.1 (fenpropidin) and 3.2 (its acid metabolite). Analytical characteristics of the method were evaluated and found adequate for the quantification of both chiral compounds with a linearity range from 0.75 to 70 mg L-1, good accuracy (trueness included 100 % recovery, precision with RSD<6 %), and limits of detection and quantification of 0.25 and 0.75 mg L-1, respectively, for the four enantiomers. No significant differences were found between the concentrations determined and labelled of fenpropidin in a commercial agrochemical formulation. The stability over time (0-42 days) of fenpropidin enantiomers using the commercial agrochemical formulation was evaluated in two sugar beet soils, revealing to be stable at any time in one sample, while in the other a decrease of 45 % was observed after 42 days. Individual and combined toxicity of fenpropidin and its metabolite was determined for the first time for marine organism Vibrio fischeri, demonstrating higher damage caused by parent compound. Synergistics and antagonists' interactions were observed at low and high effects levels of contaminants.

Earthquakes are common geological disasters, and slopes under seismic loading can trigger coseismic landslides, while also becoming unstable due to accumulated damage caused by the seismic activity. Reinforced soil slopes are widely used as seismic-resistant geotechnical systems. However, traditional geosynthetics cannot sense internal damage in reinforced soil systems, and existing in-situ distributed monitoring technologies are not suitable for seismic conditions, thus limiting accurate post-earthquake stability assessments of slopes. This study presents, for the first time, the use of a batch molding process to fabricate self-sensing piezoelectric geogrids (SPGG) for distributed monitoring of soil behavior under seismic conditions. The SPGG's reinforcement and damage sensing abilities were verified through model experiments. Results show that SPGG significantly enhances soil seismic resistance and can detect soil failure locations through voltage distortions. Additionally, the tensile deformation of the reinforcement material can be quantified with sub-centimeter precision by tracking impedance changes, enabling high-precision distributed monitoring of reinforced soil under seismic conditions. Notably, when integrated with wireless transmission technology, the SPGG-based monitoring system offers a promising solution for real-time monitoring and early warning in road infrastructure, where rapid detection and response to seismic hazards are critical for mitigating catastrophic outcomes.

The presence of frozen volatiles (especially H2O ice) has been proposed in the permanently shadowed regions (PSRs) near the poles of the Moon, based on various remote measurements including the visible and near-infrared (VNIR) spectroscopy. Compared with the middle- and low-latitude areas, the VNIR spectral signals in the PSRs are noisy due to poor solar illumination. Coupled with the lunar regolith coverage and mixing effects, the available VNIR spectral characteristics for the identification of H2O ice in the PSRs are limited. Deep learning models, as emerging techniques in lunar exploration, are able to learn spectral features and patterns, and discover complex spectral patterns and nonlinear relationships from large datasets, enabling them applicable on lunar hyperspectral remote sensing data and H2O-ice identification task. Here we present H2O ice identification results by a deep learning-based model named one-dimensional convolutional autoencoder. During the model application, there are intrinsic differences between the remote sensing spectra obtained by the orbital spectrometers and the laboratory spectra acquired by state-of-the-art instruments. To address the challenges of limited training data and the difficulty of matching laboratory and remote sensing spectra, we introduce self-supervised learning method to achieve pixel-level identification and mapping of H2O ice in the lunar south polar region. Our model is applied to the level 2 reflectance data of Moon Mineralogy Mapper. The spectra of the identified H2O ice-bearing pixels were extracted to perform dual validation using spectral angle mapping and peak clustering methods, further confirming the identification of most pixels containing H2O ice. The spectral characteristics of H2O ice in the lunar south polar region related to the crystal structure, grain size, and mixing effect of H2O ice are also discussed. H2O ice in the lunar south polar region tends to exist in the form of smaller particles (similar to 70 mu m in size), while the weak/absent 2-mu m absorption indicate the existence of unusually large particles. Crystalline ice is the main phase responsible for the identified spectra of ice-bearing surface however the possibility of amorphous H2O ice beneath optically sensed depth cannot be ruled out.

This study investigates the inter-annual variability of carbonaceous aerosols (CA) over Kolkata, a megacity in eastern India, using dual carbon isotopes (C-14 and C-13) alongside measurements of the optical properties of brown carbon (BrC). Sampling was conducted during the post-monsoon, winter, and spring seasons over two consecutive years (2020-21 and 2021-22). The analysis reveals that PM2.5 and CA concentrations were higher in 2020-21 (194 +/- 40 and 54 +/- 15 mu g m(-3), respectively) compared to 2021-22 (141 +/- 31 and 44 +/- 21 mu g m(-3)), likely due to higher precipitation in 2021-22. The contribution of biomass burning and biogenic sources to CA (f(bio_TC)) was slightly higher in 2020-21 (70 +/- 3 %) than in 2021-22 (68 +/- 3 %), with both years exhibiting a consistent decreasing trend from post-monsoon to spring. Observed lower values for oxidised CA proxies, such as the WSOC/OC ratio (0.41 +/- 0.08) and AMS-derived f(44) (0.13 +/- 0.02), throughout the study period suggest that surface CA over Kolkata primarily originates from local sources rather than long-range transport. The relative radiative forcing (RRF) also showed a clear reduction in the subsequent year; however, on average, the RRF of methanol-soluble BrC (16 +/- 6 %) was approximately three times higher than that of the water-soluble fraction (5.5 +/- 2.2 %), highlighting the substantial role of BrC in influencing regional radiative forcing. These findings underscore the substantial impact of local emissions over transported pollutants on Kolkata's ground-level air quality.

Root-knot nematodes (RKN) severely reduce watermelon yields worldwide, despite its nutraceutical value. This study investigated the effects of rock dust (RD) and poultry manure (PM) amendments, applied singly or in combination, on RKN suppression and watermelon fruit yield enhancement. A two-trial field experiment was conducted utilizing a randomized complete block design with three replicates. The treatments included RD and PM each applied at 0, 2.5, or 5 t/ha and combined applications of RD and PM at 2.5 or 5 t/ha each. At 60-66 days post-inoculation, root galling and RKN population density were assessed alongside root-shoot weight. The results indicated that root galling in watermelons was reduced by 60-85 % and 67-89 % in the combined RD- and PMtreated plots across the 1st and 2nd trials, respectively, in contrast to the control plots. Likewise, the RKN population was suppressed by 94-99 % in treated plots in both trials, differing from the control plots. Notably, watermelon fruit yield was significantly higher (p < 0.05) in combined RD and PM treated plots, ranging from 24.7 to 33.7 t/ha and 34.6-46.5 t/ha in the 1st and 2nd trials, respectively, compared to control plots with 13.5 t/ha in the 1st trial compared to and 20.9 t/ha yield in the 2nd trial. In conclusion, our study indicates that coapplication of RD and PM effectively reduced RKN damage and enhanced watermelon fruit yield, providing a sustainable strategy for watermelon production.

This study highlights the results of a palaeoecological analysis conducted on five permafrost peatlands in the northern tundra subzone along the Barents Sea coast in the European Arctic zone. The depth of the peat cores that were sampled was approximately 2 m. The analysis combined data on the main physical and chemical soil properties, radiocarbon dating, botanical composition, and mass fraction of polycyclic aromatic hydrocarbons (PAHs). The concentrations of 16 PAHs in peat organic layers ranged from 140 to 254 ng/g, with an average of 182 ng/g. The peatlands studied were dominated by PAHs with a low molecular weight: naphthalene, phenanthrene, fluoranthene, pyrene, chrysene. The vertical distribution patterns of PAHs along the peat profile in the active layer and permafrost were determined. PAHs migrating down the active layer profile encounter the permafrost barrier and accumulate at the boundary between active layer and permafrost layer. The deep permafrost layers accumulate large amounts of PAHs and PAH derivatives, which are products of lignin conversion during the decomposition of grassy and woody vegetation during the Holocene climate optima. The total toxic equivalency concentration (TEQ) was calculated. Peatlands from the Barents Sea coast have low toxicity for carcinogenic PAHs throughout the profile. TEQ ranged from a minimum of 0.1 ng/g to a maximum of 13.5 ng/g in all peatlands investigated. For further potential use in Arctic/sub-Arctic environmental studies, PAH indicator ratios were estimated. In all investigated sections and peatland horizons, the most characteristic ratios indicate the petrogenic (natural) origin of PAHs.

Char and soot represent distinct types of elemental carbon (EC) with varying sources and physicochemical properties. However, quantitative studies in sources, atmospheric processes and light-absorbing capabilities between them remain scarce, greatly limiting the understanding of EC's climatic and environmental impacts. For in-depth analysis, concentrations, mass absorption efficiency (MAE) and stable carbon isotope were analyzed based on hourly samples collected during winter 2021 in Nanjing, China. Combining measurements, atmospheric transport model and radiative transfer model were employed to quantify the discrepancies between char-EC and soot-EC. The mass concentration ratio of char-EC to soot-EC (R-C/S) was 1.4 +/- 0.6 (mean +/- standard deviation), showing significant dependence on both source types and atmospheric processes. Case studies revealed that lower R-C/S may indicate enhanced fossil fuel contributions, and/or considerable proportions from long-range transport. Char-EC exhibited a stronger light-absorbing capability than soot-EC, as MAE(char) (7.8 +/- 6.7 m(2)g(-1)) was significantly higher than MAE(soot) (5.4 +/- 3.4 m(2)g(-1))(p < 0.001). Notably, MAE(char) was three times higher than MAE(soot) in fossil fuel emissions, while both were comparable in biomass burning emissions. Furthermore, MAE(soot) increased with aging processes, whereas MAE(char) exhibited a more complex trend due to combined effects of changes in coatings and morphology. Simulations of direct radiative forcing (DRF) for five sites indicated that neglecting the char-EC/soot-EC differentiation could cause a 10 % underestimation of EC's DRF, which further limit accurate assessments of regional air pollution and climate effects. This study underscores the necessity for separate parameterization of two types of EC for pollution mitigation and climate change evaluation.