Gunung Bromo Education Forest is a forest that functions as a buffer area to maintain the balance of the surrounding area. However, the undulating to hilly topography, the presence of rivers, and land management for annual crops can make the area vulnerable to erosion-induced degradation. This research aims to analyze and classify the erosion hazard level in Gunung Bromo Education Forest and analyze the relationship between research parameters and erosion in Gunung Bromo Education Forest. Erosion was predicted using the MUSLE method. This research used an explorative-descriptive method incorporating a survey and laboratory analysis. Furthermore, data analysis used was Analysis of Variance (ANOVA), Duncan's Multiple Range Test (DMRT) at a 5% significance level, and Pearson correlation test. The results showed that Gunung Bromo Education Forest erosion ranged from 0.025 to 78.36 t ha(-1)y(-1). The erosion hazard level in Gunung Bromo Education Forest is in the very light to heavy class and is dominated by the light class. The factors of erosivity (R), erodibility (K), slope (LS), and crop management (C) are positively correlated with erosion values. The conservation factor (P) is negatively correlated with erosion values. Making remedial efforts according to the erosion hazard level is important to avoid greater damage.
The cement-stabilization technique is employed on natural and recycled granular materials to improve their mechanical properties. The strength of these materials is assessed by the unconfined compressive strength on laboratory compacted specimens, typically after 7 days of curing. Standards and technical specifications specify different values of specimen height and diameter and different loading modes of testing. This makes the comparison between different materials and with the acceptance limits of technical specifications difficult. The research investigates the effect of specimen size and loading mode on the unconfined compressive strength of both natural and recycled cement-stabilized granular materials. The results revealed significant differences in strength due to variations in specimen size and loading mode. As expected, an increase in specimen slenderness resulted in a decrease in compressive strength. A linear regression model was developed to quantify the effect of the experimental variables on the compressive strength of the two cement-stabilized materials.
Micro- and nanoplastics (MNPs), pervasive environmental pollutants, contaminate water, soil, air, and the food chain and ultimately accumulate in living organisms. Macrophages are the main immune cells that gather around MNPs and engulf them through the process of phagocytosis. This internalization triggers M1 polarization and the secretion of inflammatory cytokines, including IL-1, IL-18, IL-12, TNF-alpha, and IFN-gamma. Furthermore, MNPs damage mitochondria and lysosomes, causing overactivation of iNOS and excessive production of ROS. This results in cellular stress and induce apoptosis, necroptosis, and, in some cases, metosis in macrophages. The internalization of MNPs also increases the expression of receptors, involving CD36, SR-A, LOX-1, and the macrophage receptor with a collagenous structure (MARCO) while decreasing ABCA-1 and ABCG-1. MNPs in adipose tissue macrophages trigger proinflammatory cytokine secretion, causing adipogenesis, lipid accumulation, insulin resistance, and the secretion of inflammatory cytokines in adipocytes. Various factors influence the rate of MNP internalization by macrophages, including size, charge, and concentration, which affect internalization through passive diffusion. Receptor-mediated phagocytosis of MNPs occurs directly via receptors like T-cell immunoglobulin and mucin domain containing 4 (TIM-4) and MARCO. The attachment of biomolecules, including proteins, antibodies, opsonins, or microbes to MNPs (forming corona structures) promotes indirect receptor-mediated endocytosis, as macrophages possess receptors like TLRs and Fc gamma RIII. MNPs also cause gut dysbiosis, a risk factor for proinflammatory microenvironment and M1 polarization. Here, we review the mechanisms and consequences of MNP macrophage exposure, which is linked to autoimmunity, inflammation, and cardiometabolic syndrome manifestations, including atherosclerosis and obesity, highlighting the immunotoxicity of MNPs.
Soil erosion has both on-farm and off-farm effects. On-farm, reduced soil depth can decrease land productivity, while off-farm, sediment transfer can damage streams, lakes, and estuaries. Therefore, optimal soil erosion modeling is a crucial first step in soil erosion research. One of the most important aspects of this modeling is the accuracy and applicability of the soil erosion factors used. Various methods for calculating these factors are discussed in the literature, but no single method is universally accurate. After an extensive review of the literature, we propose using the existing revised universal soil loss equation (RUSLE) factors for global application. Additionally, we conducted a grassroots-level experiment to demonstrate the effectiveness of the proposed methods. RUSLE is identified as the most suitable model for global-scale soil erosion modeling. We evaluated the potential impacts of climate and land use and land cover (LULC) by utilizing shared socio-economic pathways (SSPs) alongside projected LULC scenarios. A suitable general circulation model (GCM) was selected after comparing it with recorded data from a base period. This model was validated with experimental observations, confirming its effectiveness. This review article outlines the future direction of soil erosion modeling and provides recommendations.Graphical AbstractThe graphical abstract visually summarizes the comprehensive methodology and key findings associated with optimal soil erosion modeling and management. It highlights a structured approach, beginning with identifying optimal methods for assessing soil erosion factors: Rainfall and Runoff Erosivity (R), Soil Erodibility (K), Slope Length and Steepness (LS), Cover and Management (C), and Support Practice (P) integral components of the Revised Universal Soil Loss Equation (RUSLE). It illustrates the detailed methodological framework, emphasizing selecting suitable climate models for projecting future R factors, combined with projected land use and land cover (LULC) scenarios derived from Shared Socio-economic Pathways (SSPs). The scenarios shown range from lower emissions (SSP 126) to higher emissions (SSP 585), indicating progressive increases in future erosion risk. Moreover, it explicitly ties the research findings to policy recommendations, underscoring a holistic approach aligning soil conservation with Sustainable Development Goals (SDGs): specifically, Climate Action (SDG 13), Life on Land (SDG 15), and Zero Hunger (SDG 2). Suggested measures include integrating soil erosion control into broader policy frameworks, promoting sustainable land management practices such as agroforestry and contour plowing, and fostering policy integration and collaboration to enhance conservation effectiveness. Overall, the graphical abstract succinctly depicts how climate change, socio-economic dynamics, and LULC variations amplify future soil erosion risks, reinforcing the need for targeted, sustainable, and integrated soil conservation strategies globally.
Extreme weather events are increasing the frequency and intensity of forest fires, generating serious environmental and socio-economic impacts. These fires cause soil loss through erosion, organic matter depletion, increased surface runoff and the release of greenhouse gases, intensifying climate change. They also affect biodiversity, terrestrial and aquatic ecosystems, and soil quality. The assessment of forest fires by remote sensing, such as the use of the Normalised Difference Vegetation Index (NDVI), allows rapid analysis of damaged areas, monitoring of vegetation changes and the design of restoration strategies. On the other hand, models such as RUSLE are key tools for calculating soil erosion and planning conservation measures. A study of the impacts on soils and vegetation in the south of Salamanca, where one of the worst fires in the province took place in 2022, has been carried out using RUSLE and NDVI models, respectively. The study confirms that fires significantly affect soil properties, increase erosion and hinder vegetation recovery, highlighting the need for effective restoration strategies. It was observed that erosion intensifies after fires (the maximum rate of soil loss before is 1551.85 t/ha/year, while after it is 4899.42 t/ha/year) especially in areas with steeper slopes, which increases soil vulnerability, according to the RUSLE model. The NDVI showed a decrease in vegetation recovery in the most affected areas (with a maximum value of 0.3085 after the event and 0.4677 before), indicating a slow regeneration process. The generation of detailed cartographies is essential to identify critical areas and prioritise conservation actions. Furthermore, the study highlights the importance of implementing restoration measures, designing sustainable agricultural strategies and developing environmental policies focused on the mitigation of land degradation and the recovery of fire-affected ecosystems.
The damage caused by soil erosion to global ecosystems is undeniable. However, traditional research methods often do not consider the unique soil characteristics specific to China and rainfall intensity variability in different periods on vegetation, and relatively few research efforts have addressed the attribution analysis of soil erosion changes in tropical islands. Therefore, this study applied a modification of the Chinese Soil Loss Equation (CSLE) to evaluate the monthly mean soil erosion modulus in Hainan Island over the past two decades, aiming to assess the potential soil erosion risk. The model demonstrated a relatively high R2, with validation results for the three basins yielding R2 values of 0.77, 0.64, and 0.78, respectively. The results indicated that the annual average soil erosion modulus was 92.76 thm-2year-1, and the monthly average soil erosion modulus was 7.73 thm-2month-1. The key months for soil erosion were May to October, which coincided with the rainy season, having an average erosion modulus of 8.11, 9.41, 14.49, 17.05, 18.33, and 15.36 thm-2month-1, respectively. September marked the most critical period for soil erosion. High-erosion-risk zones are predominantly distributed in the central and eastern sections of the study area, gradually extending into the southwest. The monthly average soil erosion modulus increased with rising elevation and slope. The monthly variation trend in rainfall erosivity factor had a greater impact on soil water erosion than vegetation cover and biological practice factor. The identification of dynamic factors is crucial in areas prone to soil erosion, as it provides a scientific underpinning for monitoring soil erosion and implementing comprehensive water erosion management in these regions.
The Massarosa wildfire, which occurred in July 2022 in Northwestern Tuscany (Italy), burned over 800 hectares, leading to significant environmental and geomorphological issues, including an increase in soil erosion rates. This study applied the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates with a multi-temporal approach, investigating three main scenarios: before, immediately after, and one-year post-fire. All the analyses were carried out using the Google Earth Engine (GEE) platform with free-access geospatial data and satellite images in order to exploit the cloud computing potentialities. The results indicate a differentiated impact of the fire across the study area, whereby the central parts suffered the highest damages, both in terms of fire-related RUSLE factors and soil loss rates. A sharp increase in erosion rates immediately after the fire was detected, with an increase in maximum soil loss rate from 0.11 ton x ha-1 x yr-1 to 1.29 ton x ha-1 x yr-1, exceeding the precautionary threshold for sustainable soil erosion. In contrast, in the mid-term analysis, the maximum soil loss rate decreased to 0.74 ton x ha-1 x yr-1, although the behavior of the fire-related factors caused an increase in soil erosion variability. The results suggest the need to plan mitigation strategies towards reducing soil erodibility, directly and indirectly, with a continuous monitoring of erosion rates and the application of machine learning algorithms to thoroughly understand the relationships between variables.
Soil erosion by water is a serious problem in Ethiopia, contributing to diminishing crop yields and food shortages. Apart from understanding the magnitude, risk, and spatial distribution of the problem, identifying erosion hotspot areas is essential for effectively reversing the problem. This study aims to identify erosion hotspots in the Gotu watershed, in northeastern Ethiopia, using the revised universal soil loss equation (RUSLE) and incorporating local farmers' perspectives to prioritize conservation efforts. The RUSLE model reveals that 29,744.3 metric tons of soil is lost annually from the Gotu watershed, with an average loss of 65.3 to t ha(-)1 year(-)1. The main contributing factors to soil erosion in the watershed include undulating topography, loss of plant cover, and continuous cultivation. The highest soil loss rates (> 80 t ha(-)1 year(-)1) were found in the western, northern, and southern parts of the watershed, where cultivation occurs on moderate to steep slopes with sparse vegetation cover. These areas should be prioritized for conservation interventions. Farmers identified poor crop yields and damaged conservation structures as key indicators of soil erosion prevalence in the watershed. Increasing farmer's understanding of soil erosion and the importance of soil and water conservation is essential for effectively controlling soil erosion and improving food security in the area.
Controlling strata deformation during shield tunneling beneath unconsolidated soil layers poses a significant challenge in engineering construction. Limited research exists on optimizing pre-grouting mechanisms for shield tunnels in unconsolidated soil layers and controlling strata deformation. Therefore, conducting on-site optimization experiments for pre-grouting is crucial for controlling strata deformation. The paper employs crushed stone aggregate as a basis for modifying the shield jacket material. The primary method of verifying grout strength involves direct detection of foundation bearing capacity using a heavy-duty probe inside the tunnel. The feasibility of the comprehensive evaluation scheme is further confirmed through a combination of multi-point core sampling, five-point water pressure tests, and on-site shield monitoring data. The research results indicate that this technology effectively enhances the stability of deep-buried weak strata. By improving the physical and mechanical properties of backfill soil through a combination of crushed stone-cement slurry-soil skeleton, the self-stabilizing ability of surrounding rock is enhanced, and strata deformation is controlled. Additionally, a set of pre-grouting reinforcement and evaluation techniques suitable for deep-buried weak strata is proposed, providing valuable references for similar projects.
The particle breakage effect in sand exerts a significant influence on the design of underground space structure. However, the existing theories seldom consider the breakage effect and often lack accurate descriptions of void ratio changes, leading to substantial errors in the numerical calculations compared to the actual scenario. This study employs the simple critical state sand model (SIMSAND) to account for the particle breakage effect and transforms the drained cylindrical cavity expansion problem into a set of first-order ordinary differential equations described by the Lagrangian method. The analytical solution of the cylindrical cavity expansion problem is calculated using Matlab programming codes. Firstly, Fontainebleau sand is investigated to analyse the influence of initial stress, void ratio and specific volume around the cavity. The combined effects of initial stress and particle breakage on the soil around the cavities result in dilatancy characteristics and a reduction in void ratio. The stress path analysis reveals that the soil around the cavities only reaches a critical state under high initial stresses. Secondly, a plane strain numerical model is established for twice expansion to verify the calculation outcomes from the cylindrical cavity expansion theory. Finally, an axisymmetric cone penetration test (CPT) model is developed to analyse the theoretical and numerical solutions for expansion stress and sleeve friction. The research results indicate that the CPT in sand need to consider the particle breakage effect, especially under high stress conditions. Without considering particle breakage, the sleeve friction is overestimated. These research findings can offer guidance for geotechnical engineering applications, such as CPT, pressuremeter tests and predictions of bearing capacity for pile foundations in sand. Based on the cavity expansion theory and numerical simulations, particle breakage effect of sand is studied. The findings reveal that neglecting the particle breakage effect leads to a marked increase in the calculation results.image