Land surface temperature (LST) plays an important role in Earth energy balance and water/carbon cycle processes and is recognized as an Essential Climate Variable (ECV) and an Essential Agricultural Variable (EAV). LST products that are issued from satellite observations mostly depict landscape-scale temperature due to their generally large footprint. This means that a pixel-based temperature integrates over various components, whereas temperature individual components are better suited for the purpose of evapotranspiration estimation, crop growth assessment, drought monitoring, etc. Thus, disentangling soil and vegetation temperatures is a real matter of concern. Moreover, most satellite-based LSTs are contaminated by directional effects due to the inherent anisotropy properties of most terrestrial targets. The characteristics of directional effects are closely linked to the properties of the target and controlled by the view and solar geometry. A singular angular signature is obtained in the hotspot geometry, i.e., when the sun, the satellite and the target are aligned. The hotspot phenomenon highlights the temperature differences between sunlit and shaded areas. However, due to the lack of adequate multi-angle observations and inaccurate portrayal or neglect of solar influence, the hotspot effect is often overlooked and has become a barrier for better inversion results at satellite scale. Therefore, hotspot effect needs to be better characterized, which here is achieved with a three-component model that distinguishes vegetation, sunlit and shaded soil temperature components and accounts for vegetation structure. Our work combines thermal infrared (TIR) observations from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the LEO (Low Earth Orbit) Sentinel-3, and two sensors onboard GEO (geostationary) satellites, i.e. the Advanced Himawari Imager (AHI) and Spinning Enhanced Visible and Infrared Imager (SEVIRI). Based on inversion with a Bayesian method and prior information associated with component temperature differences as constrained, the findings include: 1) Satellite observations throughout East Asia around noon indicate that for every 10 degrees change in angular distance from the sun, LST will on average vary by 0.6 K; 2) As a better constraint, the hotspot effect can benefit from multi-angle TIR observations to improve the retrieval of LST components, thereby reducing the root mean squared error (RMSE) from approximately 3.5 K, 5.8 K, and 4.1 K to 2.8 K, 3.5 K, and 3.1 K, at DM, EVO and KAL sites, respectively; 3) Based on a dataset simulated with a threedimensional radiative transfer model, a significant inversion error may result if the hotspot is ignored for an angular distance between the viewing and solar directions that is smaller than 30 degrees. Overall, considering the hotspot effect has the potential to reduce inversion noise and to separate the temperature difference between sunlit and shaded areas in a pixel, paving the way for producing stable temperature component products.

Despite the complexity of real earthquake motions, the incident wavefield excitation for soil-structure interaction (SSI) analysis is conventionally derived from one-dimensional site response analysis (1D SRA), resulting in idealized, decoupled vertically incident shear and compressional waves for the horizontal and vertical components of the wavefield, respectively. Recent studies have revealed potentially significant deviation of the 1D free-field predictions from the actual three-dimensional (3D) site response and obtained physical insights into the mechanistic deficiencies of this simplified approach. Particularly, when applied to vertical motion estimation, 1D SRA can lead to consistent overprediction due to the refraction of inclined S waves in the actual wavefield that is not correctly accounted for in the idealized vertical P wave propagation model. However, in addition to the free-field site response, seismic demands on structures and non-structural components are also influenced by the dynamic characteristics of the structure and SSI effects. The extent to which the utilization of vertically propagating waves influences the structural system response is currently not well understood. With the recent realization of high-performance broadband physics-based 3D ground motion simulations, this study evaluates the impact of incident wavefield modeling on SSI analysis of representative building structures based on two essential ingredients: (1) realistic spatially dense simulated ground motions in shallow sedimentary basins as the reference incident motions for the local SSI model and (2) high-fidelity direct modeling of the soil-structure system that fully honors the complexity of the incident seismic waves. Numerical models for a suite of archetypal two-dimensional (2D) multi-story building frames were developed to study their seismic response under the following incident wavefield modeling conditions: (1) SSI models with reference incident waves from the 3D earthquake simulation, (2) SSI models with idealized vertically incident waves based on 1D SRA, and (3) conventional fixed-base models with base translational motions from 1D SRA. The impact of these modeling choices on various structural and non-structural demands is investigated and contrasted. The results show that, for the horizontal direction, the free-field linear and nonlinear site amplification and subsequent dynamic filtering of the base motions within the structure can be reasonably captured by the assumed vertically propagating shear waves. This leads to generally fair agreements for structural demands controlled by horizontal motions, including peak inter-story drifts and yielding of structural components. In contrast, vertical seismic demands on structures are overpredicted in most cases when using the 1D wavefields and can result in exacerbated structural damage. Special attention should be given to the potentially severe vertical floor accelerations predicted by the 1D approach due to the combined effects of fictitious free-field site amplification and significant vertical dynamic amplification along the building height. This can pose unrealistic challenges to seismic certification of acceleration-sensitive secondary equipment necessary for structural and operational functionality and containment barrier design of critical infrastructures. It is also demonstrated that vertical SSI effects can be more significant than those in the horizontal direction due to the large vertical structural stiffness and should be considered in vertical floor acceleration assessments, especially for massive high-rise buildings.

Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multicomponent analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas. TRUST-DART utilizes the DART model and requires inputs including at-surface radiance imagery, downwelling sky irradiance, a 3D mock-up with component classification, and standard DART parameters (e.g., spatial resolution and skylight ratio). This method produces maps of component emissivity and temperature. The accuracy of TRUST-DART is evaluated using both vegetation and urban scenes, employing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images and DART-simulated pseudo-ASTER images. Results show a residual radiance error is approximately 0.05 W/(m2 & sdot;sr). In absence of the co-registration and sensor noise errors, the median residual error of emissivity is approximately 0.02, and the median residual error of temperature is within 1 K. This novel approach significantly advances our ability to analyze thermal properties of urban areas, offering potential breakthroughs in urban environmental monitoring and planning. The source code of TRUSTDART is distributed together with DART (https://dart.omp.eu).

The influence of surface Rayleigh waves (SRWs) on the seismic behavior of three archetype nonconforming reinforced concrete (RC) buildings including weak first story with four, six, and eight stories when subjected to earthquake ground motions (EQGMs) recorded during the strong September 19, 2017 Mw7.1 earthquake in Mexico City, is discussed in this paper. For this purpose, ground acceleration time histories corresponding to the retrograde and prograde components of SRWs were extracted from EQGMs collected at the accelerographic stations placed at the transition and soft soil sites. It was found that the SWRs contribute to about 50% of the median maximum IDR demand (IDRmax) triggered by the as-recorded earthquake ground motions at the ground level of the four- and six-story building models, while their contribution is about 30% of IDRmax for the eight-story building model. It should be noted that that SRWs induce median IDRmax demands to the four-story building model larger than about 11% and 49% than those to the six- and eight-story models, respectively, for soft soil sites. Moreover, the prograde component can trigger IDRmax demands in the four-story building model larger than 73% and 45% than those for the six- and eight-story models, respectively, for the transition sites. Particularly, it was shown that SRWs induce median IDRmax demands in excess of 0.35% at the first level of the archetype building models, which is associated to the light cracking damage state of nonductile RC columns, and even in excess of IDRmax of 0.71% associated to the severe cracking damage state when the record-to-record variability is considered in the IDRmax demand (i.e. the 84th percentile of IDRmax). Although the earthquake ground motion component of the surface Rayleigh waves was negligible in the median IDRmax, this study showed that the effect of the directionality of IDRmax is important for the CH84 station, where significant polarity of spectral ordinates was identified in previous studies.

This study aimed to evaluate the synergistic effects of zinc sulfate and Pseudomonas spp. in terms of mitigating drought stress in maize (Zea mays L.) by analyzing physiological, biochemical, and morphological responses under field conditions. A two-year (2018-2019) field experiment investigated two irrigation levels (optimal and moderate stress) and twelve treatment combinations of zinc sulfate application methods (without fertilizer, soil, foliar, and seed priming) with zinc-solubilizing bacteria (no bacteria, Pseudomonas fluorescens, and Pseudomonas aeruginosa). Drought stress significantly reduced chlorophyll content, increased oxidative damage, and impaired membrane stability, leading to a 42.4% increase in electrolyte leakage and a 10.9% reduction in leaf area index. However, the combined application of zinc sulfate and P. fluorescens, and P. aeruginosa mitigated these effects, with seed priming showing the most significant improvements. Specifically, seed priming with zinc sulfate and P. fluorescens increased catalase activity by 76% under non-stress conditions and 24% under drought stress. Principal component analysis revealed that treatments combining zinc sulfate and P. fluorescens, and P. aeruginosa were strongly associated with improved chlorophyll content, carotenoid content, and grain yield while also enhancing osmotic adjustment and antioxidant enzyme activity. These findings highlight the potential of the use of zinc sulfate and P. fluorescens as well as P. aeruginosa as sustainable strategies for enhancing maize drought tolerance, mainly through seed priming and soil application methods.

Hazardous waste from metal processing industries increases heavy metal contamination in ecosystems, threatening environmental health and regional sustainability. This study suggests a resilient and human-centered environmental monitoring approach that incorporates machine learning and decision analytics to address these challenges in line with Industry 5.0's goals. By utilising a PRINCIPAL COMPONENT REGRESSION (PCR)-based predictive model, the approach addresses variability in environmental data, predicting levels of heavy metals like lead, zinc, nickel, arsenic, and cadmium, frequently beyond regulatory thresholds. The suggested PCR-based model outperforms conventional models by lowering mean absolute error (MAE) to 2.9339, mean absolute percentage error (MAPE) to 0.0358, and nearly the same mean square error (MSE). This study introduces a more interpretable and computationally efficient alternative to existing predictive models by introducing a novel integration of PCR with machine learning for environmental monitoring. By predicting and optimising environmental outcomes, validation against test datasets confirmed its ability to optimise impurity control. After process adjustments, the average concentrations of lead, nickel, and cadmium were reduced from 13.23 to 11.26 mg/L, 2.83 to 2.70 mg/L, and 2.15 to 1.88 mg/L, respectively. This research supports sustainability, resilience, and decisionmaking aligned with Industry 5.0, offering scalable solutions and insights for global industries.HighlightsChemical plants' environmental risk is evaluated using a machine learning algorithmFor better monitoring, the PCR method forecasts process variables and interactionsIt identifies the key factors that affect the environmental risks in soil and waterAs a result, the local ecosystem's levels of toxic metals have notably decreasedInsights for managing environmental risks aligned with Industry 5.0 principles

The structures and the physical and mechanical properties of Ferrocalamus strictus culms were differently affected by the environment in different habitats. Correlation analysis, random forest and cluster analysis were used to investigate the effects of environmental factors in five habitats on the structure and physical and mechanical properties of bamboo poles or stalks of F. strictus. The air-dry density of F. strictus stalks ranged from 0.66 to 0.91 g/cm3. Data showed that the average annual temperature, soil water content and available potassium content were important factors affecting air-dry density the bamboo stalks. The compressive strength of F. strictus stalks varied from 60.62 to 126.16 MPa and was positively correlated with mean annual sunshine hour. The modulus of rupture (MOR) ranged from 57.95 to 252.09 MPa and the soil available phosphorus content limited the MOR of F. strictus. The modulus of elasticity (MOE) ranged from 6.04 to 12.89 GPa. The outer hardness ranged from 66.75 to 94.83 HD (Shore D hardness) and the inner hardness ranged from 28.42 to 58.42 HD. Soil silicon content affected the structures and mechanical tissue strength of F. strictus culms. The principal component analysis indicated that the Yuanyang was the optimal habitat of F. strictus with highest composite scores of 14.07, the F. strictus of Yuanyang had the highest bending strength in the world, suggested that selecting a habitat site as breeding materials be reasonable. The regulation of hydrothermal conditions, i.e. soil pH, silicon (Si), phosphorus (P) and potassium (K) elements, was essential for the growth rate and physical and mechanical properties of F. strictus stalks. Further research will work on regulating the growth conditions of F. strictus at Yuanyang according to the information found from this paper and evaluating the impact of regulation.

Carbonaceous aerosol components (CACs) significantly influence global radiative forcing and human health. We developed a simultaneous inversion algorithm for four CACs: black carbon (BC), brown carbon (BrC), watersoluble organic matter (WSOM), and water-insoluble organic matter (WIOM), considering their distinct optical, solubility, and hygroscopicity properties. Using AERONET data, we inverted the global concentrations of these components for 2022. We observed that the mass concentration of black carbon (BC) is highest in the South Asian region, with an annual average of 4.74 mg m(-2). High values of brown carbon (BrC) correspond well with regions and seasons of biomass burning, with the annual average reaching 9.03 mg m(-2) at sites in Central and West Africa. Water-insoluble organic matter (WIOM) is the most predominant component in carbonaceous aerosols, with an annual average concentration as high as 53.11 mg m(-2) at the Dhaka_University site in Eastern South Asia. Additionally, the study also points out a significant correlation between the dominant components of carbonaceous aerosols and their seasonal variations with local emissions. Furthermore, the validation of optical parameters against official AERONET products demonstrates a good correlation.

Thermal conductivity of frozen soil is a crucial property that influences heat transfer rate and freezing depth during the freezing process. However, accurately evaluating frozen soil's thermal conductivity is challenging due to its complex compositions and structures. To address this challenge, this study proposed the frozen soil quartet structure generation set (FSQSGS) to generate reasonable representative volume elements (RVEs) of frozen soil. The FSQSGS incorporates the ice phase and accounts for the freezing process, with clear physical meanings of input parameters. Then, the soil thermal conductivity of RVEs is calculated by the lattice Boltzmann method (LBM). This proposed calculation method is validated by experimental and analytical results of soil samples with various textures. The verification shows the broad applicability of the proposed model, especially for soils with fine grains or high saturation. Further, the influence of soil components and pore-scale geometry on the soil thermal conductivity is analyzed, with direct visualization of heat transfer. Results show that despite the soil skeleton geometry, i.e., the granular size and anisotropy, soil components have important effects on the soil thermal conductivity. Contents and thermal conductivity of soil particles are the main factors, while water and ice filling soil pores provide pathways for heat conduction, thereby improving thermal conductivity.

Overwintering frost damage is a major challenge for the wine grape industry in northern China. This study investigates overwintering treatments to improve survival rates and mitigate frost damage in the wine grape production area of the northern foothills of the Tianshan Mountains. Seven overwintering treatments were tested: soil-covered striped cloth, striped cloth, sandwiched striped cloth, thickened striped cloth, double-layered striped cloth, heat-insulating striped cloth, and heat-insulating sandwich striped cloth. Temperature and humidity were continuously monitored during the overwintering period, both aboveground and at depths of 20 and 40 cm underground. By analyzing temperature trends, the duration of low temperatures, and temperature fluctuations, comprehensive overwintering indices were derived through principal component analysis to assess heat retention, moisture preservation, and the impact on grapevine survival. The results showed that the sandwiched striped cloth treatment provided the best insulation, with a 4.4 degrees C higher minimum daily temperature and a 356% increase in overwintering indices compared to striped cloth alone. The double-layer striped cloth treatment also improved safety, with a 130% increase in overwintering indices. Other treatments, including the soil-covered and the heat-insulating striped cloth, showed reduced performance. The sandwiched striped cloth and double-layer striped cloth treatments are recommended for northern China's wine grape regions, with further research needed to evaluate their economic viability.