Substituting peat moss with compost derived from organic waste in plant nurseries presents a promising solution for reducing environmental impact, improving waste management, and enhancing soil health while promoting sustainable agricultural practices. However, selecting the appropriate proportions of both materials is crucial for each plant species. This study investigates the effects of different ratios of compost and peat mixtures on the growth and development of pepper seedlings. The compost mixtures used in the study included the following combinations: sewage sludge with sawdust (A), sewage sludge with sawdust and biodegradable garden/park waste (B), and biodegradable garden/park waste with sawdust (C). The final substrates used for seedling production were composed of composts (A, B, C) and peat (O) as a structural additive, mixed in different proportions by mass: I-O 25%, II-O 50%, and III-O 75%. Seedlings grown in these substrates were assessed using biometric and physiological measurements. Nematode species present in substrates were identified by metabarcoding analysis. The results revealed that substrate productivity depended not only on nutrient content but also on structural properties, which were significantly influenced by the peat proportion. Among the tested compost mixtures, variant A I emerged as the most effective substrate, promoting optimal seedling growth. Molecular nematode analysis revealed significant nematode contamination in substrates with higher peat proportions (C II and C III), including Meloidogyne sp. Lichtenburg (26%), Meloidogyne hispanica (5%), Meloidogyne sp. Mi_c1 (3%), Meloidogyne ethiopica (2%), and Meloidogyne thailandica (1%). The findings underscore the critical importance of achieving an optimal balance between nutrient content and structural properties in substrates to support the healthy growth and development of pepper seedlings. To further enhance crop performance and reduce the risk of pest-related damage, it is essential to prioritize the improvement of substrate selection strategies. Monitoring for nematode contamination is crucial to prevent potential compromises in seedling quality and overall productivity.

Aviation emissions contribute to climate change and local air pollution, with important contributions from non-CO2 emissions. These exhibit diverse impacts on atmospheric chemistry and radiative forcing (RF), varying with location, altitude, and time. Assessments of local mitigation strategies with global emission metrics may overlook this variability, but detailed studies of aviation emissions in areas smaller than continents are scarce. Integrating the AviTeam emission model and OsloCTM3, we quantify CO2, NOx, BC, OC, and SOx emissions, tropospheric concentration changes, RF, region-specific metrics, and assess alternative fuels for Norwegian domestic aviation. Mitigation potentials fora fuel switch to LH2 differ by up to 3.1 x 108 kgCO2-equivalents (GWP20) when using region-specific compared to global metrics. These differences result from a lower, region- specific contribution of non-CO2 emissions, particularly related to NOx. This study underscores the importance of accounting for non-CO2 variability in regional assessments, whether through region-specific metrics or advanced atmospheric modelling techniques.

Desiccation cracking has a significant impact on the hydro-mechanical properties of soils, yet quantifying crack patterns remains challenging. This study presents a quantitative framework with a total of 26 parameters for characterizing the geometric and morphological characteristics of soil desiccation crack patterns, including soil clod parameters (soil clod area, soil clod perimeter, number of clods, and the probability density distribution of clod parameters, etc.) and crack network parameters (crack length, crack width, crack inter angle, number of crack segments, surface crack ratio, crack density, connectivity index, etc.). To implement this quantitative framework, the Crack Image Analysis System (CIAS) was developed to automatically identify and analyse complex crack patterns through image preprocessing, clod identification, crack network identification and batch processing. CIAS was then applied to quantify the crack images of soil with different thicknesses, validating its efficacy. To comprehensively describe the geometric and morphological characteristics of crack networks, it is recommended to use the number of soil clods per unit area, surface crack ratio, crack density, and connectivity index as key parameters. These metrics effectively capture information on crack spacing, area, length, width, and connectivity. This comprehensive framework for characterizing and quantifying crack images is of great significant for geological engineering. Moreover, it holds great potential for application in other different disciplines such as geotechnical, hydraulic, mineral engineering and material even planetary science.

California contains a broad geography over which climate conditions can be suitable for cultivating multiple varieties of winegrapes. However, climate change is projected to make winegrape cultivation more challenging across many of California's winegrowing regions. In order to understand the potential effects of climate change on winegrapes, this study models variety-specific phenology for six winegrape varieties and quantifies the change in phenology and viticulturally-important agroclimate metrics over 12 of California's American Viticultural Areas (AVAs) by the mid-21st century. Results show more rapid development for winegrapes with earlier budburst, flowering, veraison, and maturation across all varieties and AVAs. Cabernet Sauvignon shows the greatest change in phenology timing, while Chardonnay shows the least change. Likewise, the West Sonoma Coast AVA shows the greatest average change in phenology timing across varieties and development stages and Lodi AVA shows the least. Projected changes in agroclimatic metrics include an additional month of potentially damaging heat days (above 35 degrees C) in some AVAs, and decreases in frost days. These results have implications for numerous factors related to viticultural production, including water resources management and crop yield and quality, and underscore the need for California winegrape growers to improve their resilience to climate change by adopting strategies such as increasing soil health and water use efficiency and selecting cultivars suited for future climate conditions. By conducting climate effects analyses at the variety-specific and AVA scale, important information is provided to the winegrowing industry at a resolution that can support decision-making towards resilience.

Generating synthetic material microstructures is essential in the numerical modelling of geomaterials. The occurrence of permafrost and saline groundwater overlapping regions is crucial in a series of phenomena, such as carbon emissions and subgrade settlements. The microstructure of geomaterials in these regions is particular complexity because of the multiphase nature with salty water and ice crystals. This complexity renders existing generative models ineffective in synthesising their microstructures. Traditional generative methods are limited in the sense that require prior knowledge of material descriptors. Recently, machine learning generative models achieved unprecedented levels of performance and realism, but still lack the means to assess posterior error. This work aims to bridge the gap between traditional methods and deep learning generative models by assessing posterior image quality in the latter. A 3D Generative Adversarial Network (GAN) model is trained with image samples from an X-ray CT of a partially frozen salty sand. The metrics retained to assess posterior quality are particle fabric (shape parameter and anisotropy) and homogenised elastic coefficients obtained with Finite Element Method (FEM) simulations. A hyperparametric study on batch size and latent dimension serves to select the best configuration based on particle fabric. FEM simulations determine the deviation in the generated images elastic coefficients being 7.55% on average with respect real samples. With 803 3 voxels, generated images are the largest up-to-date in a three-phase material, allowing to reach REV criteria. Applications range from the generation of microscales in double-scale models to the calibration of image processing tools.

Landslides induced by freeze-thaw processes on grasslands are one of the major geohazards, and their scale and frequency are increasing as the global warms. Freeze-thaw induced landslides degrade surface vegetation and soil properties, reduce biodiversity, intensify landscape fragmentation, and lead to losses in economy, human and animal lives. Despite substantial progress in research on landslides, there has been little study focused on how ground freeze-thaw events affect landslides. By critically analyzing previous studies, this paper proposes a conceptual framework for the forms and types, development, dominant factors, monitoring techniques, and impact mechanisms of freeze-thaw induced landslides. Landslides are controlled by soil characteristics and topographic slope, which are major intrinsic determinants. Increased rainfall, rising temperatures, and thickening active layer due to climate change are all direct drivers of freeze-thaw induced landslides. Vegetation conditions, animal behavior interference, and wind erosion all affect the occurrence and development process of landslides by modifying vegetation cover, soil physical and chemical properties, and structure. Currently, landslide monitoring techniques have evolved rapidly with improved efficiency and accuracy, but with only few applications for freeze-thaw induced landslides. There are a variety of prediction models for landslides, but few consider freeze-thaw effects and lack field validation. The new perspective on the occurring types and dominant factors enhances theoretical understanding of the formation mechanisms, which helps further monitor and analysis of freeze-thaw induced landslides. Future studies should concentrate on the coupling mechanism of multiple factors and the development of an accurate prediction system, which will greatly benefit the understanding and early detection of freeze-thaw induced landslides.

Research on mountain ecosystem services (MES) under the influence of climate change and human activities has gradually become the focus of academic attention in recent years. Here, this study analyzes the research hotspots and frontiers of this field based on metrics including main research forces, core journals and papers, research hotspots and topics by using the methods of bibliometrics and text mining. The results revealed the following: (1) the number of papers is increasing rapidly in recent years. From 2015 to 2019, 929 papers were published, with an average of 185 papers per year. But the average cited times of those papers is declining, dropped from 6.01 in 2016 to 4.2 in 2019. The USA, UK, and China rank the top three of the number of papers. Univ Maryland, Univ Oxford and Univ Wisconsin have the greatest influence, with an average of more than 77 citations per paper; (2) The most cited journals are PNAS, WETLANDS, ECOLOGY, AND SOCIETY, which are cited 191.54, 53.91, and 40.00 respectively. Most papers were published in OA journals including SUSTAINABILITY, WATER, Forests since 2017. Ten core papers undertaking knowledge transfer in this field have been identified; (3) analysis of the keywords found a new trend of integration of natural science and humanities. In two development stages of 2000-2014 and 2015-2019, the research hotspots mainly focused on mountain water resources, forest resources, land resources and the impact of climate change and human activities, and there are obvious differences and characteristics in different stages. The hotspot worthy of attention in the near future is the assessment of mountain ecosystem services capacity and value. This is the first comprehensive visualization and analysis of the research hotspots and trends of mountain ecosystem services.

The knowledge graph based on research papers can accurately identify and present the latest developments in scientific and technological (S&T) innovation and is of great significance for supporting strategic decision-making relating to S&T innovation in undeveloped areas. Based on the international research papers produced in Gansu Province during the 13th Five-Year Plan period (2016-2020), five metrics, including the number and characteristics of papers, co-authors, main publications and their fields, major supporting institutions, and main research areas, are established herein. The results indicate that: (i) the total of 29,951 papers were published, which is about 2.89 times that in 2010-2015; (ii) Gansu Province collaborated with 149 countries/regions globally; (iii) the top 5 journals in terms of the number of papers were Medicine, Scientific Reports, RSC Advances, Science of the Total Environment, and Physical Reviews D; (iv) the funding sources were mainly from the national level; and (5) the top 5 research areas were chemistry, engineering, physics, material science, environmental science, and ecology, which accounted for 64.7% of all papers. Finally, the present study puts forward some recommendations for the decision-making process in the strategic layout of S&T innovation in Gansu Province.

Remote sensing, as a crucial method to obtain information on water environmental processes, has become a major source of data, particularly of water environment and water resources, which are sensitive to global climate change. The bibliometric analysis provided here shows the research characteristics and developments of remote sensing-based observations of water environmental processes under a changing climate from 2000 to 2018. Visualized knowledge mapping is introduced to investigate the development status, scientific collaboration, involved disciplines, research hotspots and emerging trends of this field. The breadth and depth of remote sensing application in water environmental process studies have improved significantly as the number of related publications rose at an average annual growth rate of 15.97% in the 21st century. The United States and China were the leading contributors with the largest number of publications and all of the top 15 most active institutions. In addition, this field is a highly interdisciplinary field that covers a wide range of interests, from water resources to environmental science, geology, engineering, ecology, and agriculture. The application of remote sensing technology has significantly promoted the estimation of evapotranspiration and soil moisture, thereby offering a more complete perspective to the understanding of the water cycle. Additionally, climate change and its complex interactions with water environmental processes, including the occurrence of drought events, are of great significance and require special attention.

High-latitude regions are experiencing rapid and extensive changes in ecosystem composition and function as the result of increases in average air temperature. Increasing air temperatures have led to widespread thawing and degradation of permafrost which in turn has affected ecosystems, socioeconomics, and the carbon cycle of high latitudes. Here we overcome complex interactions among surface and subsurface conditions to map near-surface permafrost through decision and regression tree approaches that statistically and spatially extend field observations using remotely sensed imagery, climatic data, and thematic maps of a wide range of surface and subsurface biophysical characteristics. The data fusion approach generated medium-resolution (30-m pixels) maps of near-surface (within 1 m) permafrost, active-layer thickness, and associated uncertainty estimates throughout mainland Alaska. Our calibrated models (overall test accuracy of similar to 85%) were used to quantify changes in permafrost distribution under varying future climate scenarios assuming no other changes in biophysical factors. Models indicate that near-surface permafrost underlies 38% of mainland Alaska and that near-surface permafrost will disappear on 16 to 24% of the landscape by the end of the 21st Century. Simulations suggest that near-surface permafrost degradation is more probable in central regions of Alaska than more northerly regions. Taken together, these results have obvious implications for potential remobilization of frozen soil carbon pools under warmer temperatures. Additionally, warmer and drier conditions may increase fire activity and severity, which may exacerbate rates of permafrost thaw and carbon remobilization relative to climate alone. The mapping of permafrost distribution across Alaska is important for land-use planning, environmental assessments, and a wide-array of geophysical studies. (C) 2015 Elsevier Inc. All rights reserved.