Earth, Volume 6, Issue 2 (June 2025) – 37 articles

This study presented a comprehensive physical–statistical analysis of atmospheric particulate matter (PM₁₀ and PM_2.5) and trace gases (SO₂ and O₃) over Faial Island in the Azores archipelago during 2024. We collected real-time data at the Espalhafatos rural background station, covering 35,137 observations per pollutant, with 15 min intervals. Descriptive statistics, probability distribution fitting (Normal, Lognormal, Weibull, Gamma), and correlation analyses were employed to characterize pollutant dynamics and identify extreme pollution episodes. The results revealed that PM_2.5 (fine particles) concentrations are best modeled by a Lognormal distribution, while PM₁₀ concentrations fit a Gamma distribution, highlighting the presence of heavy-tailed, positively skewed behavior in both cases. Seasonal and episodic variability was significant, with multiple Saharan dust transport events contributing to PM exceedances, particularly during winter and spring months. These events, confirmed by CAMS and SKIRON dust dispersion models, affected not only southern Europe but also the Northeast Atlantic, including the Azores region. Weak to moderate correlations were observed between PM concentrations and meteorological variables, indicating complex interactions influenced by atmospheric stability and long-range transport processes. Linear regression analyses between SO₂ and O₃, and between SO₂ and PM_2.5, showed statistically significant but low-explanatory relationships, suggesting that other meteorological and chemical factors play a dominant role. This result highlights the importance of developing air quality policies that address both local emissions and long-range transport phenomena. They support the implementation of early warning systems and health risk assessments based on probabilistic modeling of particulate matter concentrations, even in remote Atlantic locations such as the Azores. Full article

In conventional hydrology, a short-duration design rainstorm is typically used to estimate the design discharge in urban sewer systems. The reason for using a short duration is that engineers believe the time of concentration in urban watersheds is relatively small. The short-duration hyetograph is supposed to generate a flow hydrograph that accurately reflects the rainfall-runoff processes. In this study, we developed a street-sewer runoff model for an urban district of 2470 hectares. Detailed field flooding records were utilized to verify the stormwater model’s capability for inundation simulations. Subsequently, different rainfall series extracted from the recorded rainstorm data were used to investigate the causes of flooding corresponding to different durations of rainstorms. The results indicate that a 90 min main concentrated rainstorm causes small-scale flooding only; however, a 24 h rainfall series results in an extensive range of inundations. We further conducted similar short- and long-duration hyetograph tests in 16 urban drainage partitions (ranging from 2.3 to 193.5 hectares) to confirm the above findings. The results indicate that the maximum discharge in most partitions can only be found when the hyetograph duration exceeds 1080 min, which essentially contradicts previous engineering designs in urban watersheds in Taiwan. Full article

The Miombo woodlands are declining in both area and value, primarily due to over-harvesting of commonly preferred species. These forests, however, still contain several other species that are potentially of commercial importance. This study aimed to address the need for improved volume and biomass estimates for the sustainable management and utilization of two of the most abundant timber species in Mozambique’s Miombo woodlands: Brachystegia spiciformis (common name: Messassa) and Julbernardia globiflora (common name: red Messassa). Non-linear models were developed to estimate the merchantable wood volume under bark, heartwood volume, and biomass. The volume and biomass models for wood and heartwood volume, which included both diameter at breast height (DBH) and tree height as predictor variables, outperformed single-predictor models. However, the performance of some ratio models using DBH as the only predictor variable surpassed that of models using two predictor variables. The developed models are recommended for adoption by forest companies to increase economic and environmental benefits as they can refine harvest planning by improving the selection of trees for harvesting. Proper tree selection enhances the rate of recovery of high-quality timber from heartwood while observing sustainable forest management practices in Miombo and increasing the proportion of carbon removed from forests, which is subsequently stored in wood products outside the forest. Full article

Heavy metal pollution poses a significant threat to aquatic ecosystems and drinking water sources, necessitating comprehensive environmental assessments. This study investigated the spatial distribution, potential ecological risks, and sources of heavy metals in the surface sediments of Wanfeng Lake to inform effective pollution management strategies. Twelve sediment samples were collected and analyzed for eight heavy metals (Hg, As, Cu, Pb, Cd, Cr, Ni, Zn) using inductively coupled plasma mass spectrometry. The geo-accumulation index (I_geo) and potential ecological risk index (RI) were applied to assess contamination levels and ecological risks. Cluster analysis and Kriging interpolation were used to identify potential pollution sources and spatial patterns. Results revealed that heavy metal concentrations decreased in the order Zn > Pb > Cu > Cr > Ni > As > Cd > Hg, with Hg concentrations exceeding the national average for Chinese lake sediments. Ecological risk assessments identified Hg (0.06 μg/g) and Cd (0.10 μg/g) as the priority pollutants. The Hg posed a moderate ecological risk, particularly in upstream areas, while Cd pollution was concentrated downstream. Atmospheric deposition was identified as the primary source of Hg, whereas agricultural activities were determined to be the main driver of Cd contamination. These findings provide a scientific basis for developing targeted pollution control measures in Wanfeng Lake. Full article

Amidst global shifts in land use patterns due to urbanization, this study focuses on the rapid land use and land cover (LULC) changes in Midnapore City during the periods 2003–2014 and 2014–2024. The study employs Landsat 5 and 8 imagery with 30 m spatial resolution which were processed through Maximum Likelihood Classifier (MLC) algorithms. The results were attained through ArcGIS 10.2.2 and ERDAS IMAGINE 2014 software, with ground-truth validation using data from 117, 111, and 116 points for 2024, 2014, and 2003, respectively. For the validation, the kappa coefficient was calculated and achieved 87.3%, 88.1%, and 81.7% for 2024, 2014, and 2003, indicating substantial accuracy. Using statistical measures such as change matrix union, binary logistic regression, and correlation matrix analysis applied to classified LULC outputs and spatial drivers, the research highlights significant transformations in the region. The study reveals significant transformations, notably the conversion of 77% of forest areas and 5% of fallow land to built-up land. The increased rate of agricultural land conversion to built-up areas is evident after 2014, indicating rapid urban growth. These factors led to the reduction of LULC classes possessing substantial ecological value like forests and scrub lands which are becoming more accessible due to the increasing population. The results point out the drastic alteration of these developments and recommend a planning approach responsive to environmental needs for safeguarded ecological impacts. The research highlights the importance of reforestation, preservation of water bodies, and socio-economic surveillance in fostering urban management and sustainable development in Midnapore City. Full article

This study explores the spatiotemporal patterns and drivers of deforestation and forest degradation along the politically sensitive Iraq–Turkey border within the Duhok Governorate between 2015 and 2024. Utilizing paired remote sensing (RS) and high-end machine learning (ML) methods, forest dynamics were simulated from Sentinel-2 imagery, climate datasets, and topographic variables. Seven ML models were evaluated, and XGBoost consistently outperformed the others, yielding predictive accuracies (R²) of 0.903 (2015), 0.910 (2019), and 0.950 (2024), and a low RMSE (≤0.035). Model interpretability was further improved through the application of SHapley Additive exPlanations (SHAP) to estimate variable contributions and a Generalized Additive Model (GAM) to elucidate complex nonlinear interactions. The results showed distinct temporal shifts; climatic factors (rainfall and temperature) primarily influenced vegetation cover in 2015, whereas anthropogenic drivers such as forest fires (NBR), road construction (RI), and soil exposure (BSI) intensified by 2024, accounting for up to 12% of the observed forest loss. Forest canopy cover decreased significantly, from approximately 630 km² in 2015 to 577 km² in 2024, mainly due to illegal deforestation, road network expansion, and conflict-induced fires. This study highlights the effectiveness of an ML-driven RS analysis for geoinformation needs in geopolitically complex and data-scarce regions. These findings underscore the urgent need for robust, evidence-based conservation policies and demonstrate the utility of interpretable ML techniques for forest management policy optimization, providing a reproducible methodological blueprint for future ecological assessment. Full article

Climate change and variability are characterized by unpredictable and extreme weather events. They adversely impact the highly susceptible smallholder farmers in sub-Saharan Africa, who heavily rely on rain-fed agriculture. Climate smart agriculture (CSA) practices have been extensively promoted as offering long-term solutions to changing climate conditions, while enhancing the productivity and sustainability of African agricultural systems. Despite this, the adoption rate remains low among smallholder farmers. Understanding the factors that influence adoption of these practices among this key farming community is therefore necessary to increase their adoption. In this paper, we review and summarize findings from existing studies on the factors that influence the adoption of CSA practices by smallholder farmers in sub-Saharan Africa. Our review reveals that land tenure security, access to information and extension services, and affiliation to group membership positively influence adoption. On the other hand, gender, risk perception, and off-farm income had conflicting effects by reporting both positive and negative influences on CSA adoption. We conclude that CSA adoption options are local-specific, and their development and implementation should emphasize locally tailored knowledge, skills, and resources. Full article

The voluntary basis on which recycling and energy saving are performed at households brings forward the need to better understand the profile of recyclers and non-recyclers and to make meaningful comparisons between them. Hence, the aim of this study is to compare recyclers’ and non-recyclers’ profiles and practices in order to detect areas that require policy and educational interventions. To achieve this aim, this study collected a representative sample of 384 citizens in a fast-growing urban center and compared recyclers and non-recyclers in terms of their environmental practices. The results showed that both groups identified environmental protection as their leading motive to recycle, while plastic and paper were the most recycled materials. An interesting difference between the two groups was that recyclers were more engaged in energy-saving, suggesting that recycling engagement may be associated with the adoption of energy-saving practices. The Internet was the leading information source across both groups, emphasizing the role it can play in spreading accurate and motivating messages about recycling and energy-saving. This study provides a useful and nuanced picture of recyclers’ and non-recyclers’ profiles and their differences, and as such, it can introduce new angles for the design of strategies for encouraging pro-environmental behavior. Full article

The study presents data on the role of lithological factors in the divergence of soil formation in forest–steppe and steppe ecosystems in a region of the East European Plain characterized by extremely contrasting geogenic conditions. Soils from different lithologic–geomorphologic combinations in the Samara region were chosen as the study object. It was shown that, in some cases, bioclimatogenic conditions are less decisive in the formation of the morphological organization and basic chemical parameters of the solum than the lithological characteristics of a particular locality. These lithological factors can transform soil morphology and affect the taxonomic position of soils at the subtype level and below. In landscapes marked by spatial and lithological contrasts at meso- and macro-levels, the use of a bioclimatic classification approach becomes inadequate, because it fails to highlight individual soil features. Thus, the development of lithological taxonomic and diagnostic criteria is necessary for the protection, proper use, and mapping of soils in complex geogenic, particularly lithological, conditions. Within one soil climatic zone, there can exist a large number of lithological soil subtypes, genera, and varieties. In such cases, the lithological framework has a stronger influence on soil spatial distribution than climatic gradients and associated vegetation ecotones. Full article

Climate variability and land-use changes are critical challenges impacting agriculture globally, with Zimbabwe’s Gwayi catchment area experiencing noticeable effects. This study investigated how smallholder farmers in the region perceive these changes and their influence on agricultural productivity and livelihoods. The research addresses the gap in understanding local farmer experiences with climate change and land-use modifications in the context of food security. A cross-sectional survey (n = 483) was conducted using self-administered questionnaires to capture demographic information, perceptions of climate variability, land-use changes, and their impacts on agriculture. The results indicate a trend of increasing droughts, dry spells, and heatwaves, alongside altered rainfall patterns and rising temperatures, corroborating the observed climate data. Environmental degradation, including deforestation, gully formation, and land expansion, exacerbates these changes. Consequently, farmers reported substantial reductions in crop yields, with 84.05% experiencing significant-to-very negative impacts, alongside declining livestock health (32.51% reporting very negative impacts), increased water scarcity (43.3% reporting drying water sources), and more frequent disease outbreaks. These challenges collectively contributed to heightened food insecurity, with 74.12% of households reporting negative impacts on their food supply. The study underscores the synergistic impacts of climate variability and land-use changes, highlighting the urgent need for climate-smart agricultural practices and sustainable land management to enhance resilience and ensure long-term food security for smallholder farmers in the Gwayi catchment. Full article

Water-capped tailings technology (WCTT) is a strategy where oil sand tailings are sequestered within a mined-out pit and overlayed with a layer of water in order to sequester tailings with the aim that the resulting pit lake will support aquatic plants and organisms over time. The Base Mine Lake Demonstration (BML) is the first full-scale demonstration of a pit lake in the Athabasca Oil Sands Region (AOSR). In the BML, the release of methane from the fluid tailings influences several key processes, including the flux of greenhouse gases, microbial oxygen consumption in the water column, and ebullition-facilitated transport of organics from the fluid tailings to the lake surface. It is hypothesized that the residual low molecular weight hydrocarbons (LMWHCs) derived from diluent naphtha used during bitumen extraction processes are the carbon sources fueling ongoing microbial methanogenesis within the BML. The aims of this study were to identify the LMWHCs in the BML fluid tailings, to elucidate their sources, and to assess the extent of biogeochemical cycling affecting them. A headspace GC/MS analysis identified 84, 44, and 56 LMWHCs (C4–C10) present in naphtha, unprocessed bitumen ore, and fluid tailings, respectively. Equilibrium mass balance assessment indicated that the vast majority (>95%) of LMWHCs were absorbed within residual bitumen rather than dissolving into tailings pore water. Such absorbed compounds would not be readily available to in situ microbial communities but would represent a long-term source for methanogenesis. Chromatographic analysis revealed that most biodegradable compounds (n-alkanes and BTEX) were present in the naphtha but not in fluid tailings or bitumen ore, implying they are sourced from the naphtha and have been preferentially biodegraded after being deposited. Among the LMWHCs observed in bitumen ore, naphtha, and fluid tailings, C2-cyclohexanes had the highest relative abundance in tailings samples, implying their relatively high recalcitrance to in situ biodegradation. Full article

The sustainable development of the coastal watersheds can contribute to facing climate change. This research aimed to identify the climate change adaptation measures contributing to the sustainable development: environmental, social, and economic, applied to a local project that promoted an integrated environmental management of coastal watersheds, “The Coastal Watershed Conservation Project in the Context of Climate Change (C6)”. The study used a quantitative evaluation framework and analysis of variance that considered the Sustainable Development Goals (SDGs) of the Agenda 2030. The data was collected from project reports and key actors of the 29 interventions executed by 24 local civil society organizations in the Mexican Tabasco and Veracruz communities in the coastal Gulf of Mexico. The results indicate that the adaptation actions implemented through the C6 project contributed to sustainable development with the highest contribution in the social aspect (41%), followed by environmental (27%), economic (16%), peace (10%), and partnership (7%). Therefore, it can be concluded that local civil society organizations created strategies according to the local needs to face climate change and, at the same time, to improve sustainable development in their communities. Full article

The northern Yucatán Peninsula hosts a complex karstic environment shaped by carbonate platform development and the Chicxulub impact event, making subsurface characterization crucial for geological and hydrogeological studies. This work aimed to resolve the shallow crustal structure and identify major tectonic features that influence karst processes and groundwater dynamics. We applied a rapid 3D gravity inversion method, linear back projection (LBP), to Bouguer anomaly data, combined with Euler deconvolution to map shallow and deep fault systems. The inversion produced a high-resolution density model down to 12.8 km depth, revealing key geological structures. Multilevel thresholding delineated significant low-density basins, notably the Chicxulub crater, as well as buried sedimentary basins. Euler solutions identified fault networks that coincide with areas of intense karstification, particularly in the eastern peninsula. Results highlight the interplay between impact-related tectonics and karst evolution, influencing groundwater flow paths and recharge zones. This study demonstrates the effectiveness of gravity inversion and Euler deconvolution for regional crustal imaging in carbonate platforms and emphasizes the need for further local-scale surveys to investigate coastal aquifer vulnerability and saltwater intrusion processes. Full article

Trees play a vital role in urban landscapes, yet long-term dynamics in tree populations across different levels of urbanization remain poorly understood. We examined whether current spatial patterns of native tree populations predict future changes by monitoring two native birch species (Betula pendula and B. pubescens) in six residential areas of St. Petersburg, Russia. Birch density declined toward the city centre by 1.87 trees ha⁻¹ km⁻¹. From 2002 to 2022, birch populations in sparsely built-up areas (6–8 km from the centre) declined by 0.15 trees ha⁻¹ year⁻¹ due to ageing and urban development, while populations in densely built-up areas near the centre increased by 0.02 trees ha⁻¹ year⁻¹ due to limited tree cutting and greater planting efforts. These trends challenge the assumption that spatial patterns reliably predict temporal changes, emphasizing the complex interplay between ecological and societal factors in urban tree dynamics. We anticipate the continued decline in birch populations in sparsely built-up areas of St. Petersburg over the next 10–20 years until residents recognize the value of their declining greenery and either pressure the city government to intensify planting efforts or begin planting trees themselves. Full article

Comprehensive reviews of continuously vegetated areas to determine dispersed locations of invasive species require intensive use of computational resources. Furthermore, effective mechanisms aiding identification of locations of specific invasive species require approaches relying on geospatial indicators and ancillary images. This study develops a two-stage data workflow for the invasive species Kudzu vine (Pueraria montana) often found in small areas along roadsides. The INHABIT database from the United States Geological Survey (USGS) provided geospatial data of Kudzu vines and Google Street View (GSV) a set of images. Stage one built up a set of Kudzu images to be implemented in an object detection technique, You Only Look Once (YOLO v8s), for training, validating, and testing. Stage two defined a dataset of confirmed locations of Kudzu which was followed to retrieve images from GSV and analyzed with YOLO v8s. The effectiveness of the YOLO v8s model was assessed to determine the locations of Kudzu identified from georeferenced GSV images. This data workflow demonstrated that field observations can be virtually conducted by integrating geospatial data and GSV images; however, its potential is confined to the updated periodicity of GSV images or similar services. Full article

With global temperatures steadily rising, understanding the impacts of warming on regional climates has become crucial, particularly for countries like India, where climate sensitivity has significant socio-economic implications. This study assesses the trends and spatial distribution of summer days across India under different warming targets (1.5 °C, 2 °C, 2.5 °C, 3 °C, 3.5 °C, 4 °C, 4.5 °C, and 5 °C) and emission scenarios (RCP4.5 and RCP8.5). A Multi-Model Ensemble (MME) approach, combining five best-performing CORDEX-SA experiments, was utilized to analyze projected summer days in India. Non-parametric trend analysis techniques—such as the Mann–Kendall test, Modified Mann–Kendall, Sen’s Slope estimator, and Pettitt test—were used to investigate temporal patterns, and Reliability Ensemble Averaging (REA) was applied for uncertainty analysis to ensure robust projections. The results indicate that summer days are expected to increase significantly across India under both RCP scenarios, with the highest increases projected for northeastern regions and north-central regions of India. This study underscores the pressing need for region-specific adaptation strategies to manage extended periods of extreme temperatures and safeguard public health, agriculture, and socio-economic stability. Full article

Air pollution in cities is intensifying, inevitably affecting all living organisms, gincluding trees. Urban trees are vital for cities because they improve air quality and regulate the climate; however, like all living organisms, they are affected by the environment to which they are exposed. In cities, the primary atmospheric pollutants of inorganic origin include NO, SO_X, CO_X, O₃, and suspended particulate matter (PM_2.5 and PM₁₀). Each of these pollutants impacts population health, with urban trees undergoing a series of consequent alterations. In this study, we review the inorganic pollutants identified by the World Health Organization (WHO) as impacting air quality in cities in different regions of the world; discuss the regulations that govern NO₂, SO₂, CO, O₃, and PM_2.5 and PM₁₀ emissions and their impact they have on urban trees; analyze the processes involved in pollutant–tree interactions and the related tolerance and/or resistance mechanisms; and determine the tree species with the best tolerance, classified using an air pollution tolerance index (APTI). Full article

Land subsidence poses a significant risk in built-up environments, particularly in geologically complex and tectonically active regions. In this study, we integrated Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques—Persistent Scatterer Interferometry (PS-InSAR) and Small Baseline Subset (SBAS)—with Global Navigation Satellite System (GNSS) observations to assess ground deformation in the Metamorphosis (MET0) area of Attica, Greece. A Kalman filtering approach was applied to fuse displacement measurements from GNSS, PS-InSAR, and SBAS, reducing noise and improving temporal consistency. Additionally, the PS and SBAS vertical displacement data were fused using Kalman filtering to enhance spatial coverage and refine displacement estimates. The results reveal significant subsidence trends ranging between −10 mm and −24 mm in localized zones, particularly near hydrographic networks and active fault systems. Fault proximity, fluvial processes, and unconsolidated sediments were identified as key drivers of displacement. Random Forest regression analysis, coupled with Partial Dependence analysis, demonstrated that distance to faults, proximity to streams, and the presence of stream drops and debris zones were the most influential factors affecting displacement patterns. This study highlights the effectiveness of integrating multi-sensor remote sensing techniques with data-driven machine learning analysis (Kalman filtering) to improve land subsidence assessment. The findings highlight the necessity of continuous geospatial monitoring for infrastructure resilience and geohazard risk mitigation in the Attica region. Full article

Urban trees reduce particulate matter (PM) concentrations through dry deposition, interception, and modifying wind patterns, improving air quality and saving public health expenses in urban planning. The main objective of this article is to present an analysis of the influence of urban trees on PM₁₀ and PM_2.5 concentrations in a high-altitude Latin American megacity (Bogotá, Colombia) using UFORE-D modeling. Six PM monitoring stations distributed throughout the megacity were used. Hourly climatic and PM data were collected for seven years, along with dendrometric and cartographic analyses within 200 m of the monitoring stations. Land cover was quantified using satellite imagery (Landsat 8) in order to perform a spatial analysis. The results showed that the UFORE-D model effectively quantified urban forest canopy area (CA) impact on PM₁₀ and PM_2.5 removal, showing strong correlations (R² = 0.987 and 0.918). PM removal increased with both CA and ambient pollutant concentrations, with CA exhibiting greater influence. Sensitivity analysis highlighted enhanced air quality with increased leaf area index (LAI: 2–4 m²/m²), particularly at higher wind speeds. PM₁₀ removal (1.05 ± 0.01%) per unit CA exceeded PM_2.5 (0.71 ± 0.09%), potentially due to resuspension modeling. Model validation confirmed reliability across urban settings, emphasizing its utility in urban planning. Scenario analysis (E1–E4, CA: 8.30–95.4%) demonstrated a consistent positive correlation between CA and PM removal, with diminishing returns at extreme CA levels. Urban spatial constraints suggested integrated green infrastructure solutions. Although increased CA improved PM removal rates, the absolute reduction of pollutants remained limited, suggesting comprehensive emission monitoring. Full article

Soil erosion poses a significant global environmental challenge, causing land degradation, deforestation, river siltation, and reduced agricultural productivity. Although the Revised Universal Soil Loss Equation (RUSLE) has been widely applied in Brazil, its use in the tropical river basins of the Amazon remains limited. This study aimed to apply a GIS-integrated RUSLE model and compare its soil loss estimates with multiple linear regression (MLR) models based on terrain attributes, aiming to identify priority areas and key geomorphometric drivers of soil erosion in a tropical Amazonian river basin. A digital elevation model based on Shuttle Radar Topography Mission (SRTM) data, land use and land cover (LULC) maps, and rainfall and soil data were applied to the GIS-integrated RUSLE model; we then defined six risk classes—slight (0–2.5 t ha⁻¹ yr⁻¹), slight–moderate (2.5–5), moderate (5–10), moderate–high (10–15), high (15–25), and very high (>25)—and identified priority zones as those in the top two risk classes. The Caeté River Basin (CRB) was classified into six erosion risk categories: low (81.14%), low to moderate (2.97%), moderate (11.88%), moderate to high (0.93%), high (0.03%), and very high (3.05%). The CRB predominantly exhibited a low erosion risk, with higher erosion rates linked to intense rainfall, gentle slopes covered by Arenosols, and human activities. The average annual soil loss was estimated at 2.0 t ha⁻¹ yr⁻¹, with a total loss of 1005.44 t ha⁻¹ yr⁻¹. Additionally, geomorphological and multiple linear regression (MLR) analyses identified seven key variables influencing soil erosion: the convergence index, closed depressions, the topographic wetness index, the channel network distance, and the local curvature, upslope curvature, and local downslope curvature. These variables collectively explained 26% of the variability in soil loss (R² = 0.26), highlighting the significant role of terrain characteristics in erosion processes. These findings indicate that soil erosion control efforts should focus primarily on areas with Arenosols and regions experiencing increased anthropogenic activity, where the erosion risks are higher. The identification of priority erosion areas enables the development of targeted conservation strategies, particularly for Arenosols and regions under anthropogenic pressure, where the soil losses exceed the tolerance threshold of 10.48 t ha⁻¹ yr⁻¹. These findings directly support the formulation of local environmental policies aimed at mitigating soil degradation by stabilizing vulnerable soils, regulating high-impact land uses, and promoting sustainable practices in critical zones. The GIS-RUSLE framework is supported by consistent rainfall data, as verified by a double mass curve analysis (R² ranging from 0.64 to 0.77), and offers a replicable methodology for soil conservation planning in tropical basins with similar erosion drivers. This approach offers a science-based foundation to guide soil conservation planning in tropical basins. While effective in identifying erosion-prone areas, it should be complemented in future studies by dynamic models and temporal analyses to better capture the complex erosion processes and land use change impacts in the Amazon. Full article

This study assessed riverbank erosion and stability along the Mekong and Bassac Rivers to propose safe river corridors and mitigate erosion risks in the Mekong Delta. Using Landsat imagery (2000–2023), field surveys, and numerical simulations, we identified severe erosion hotspots, where erosion rates reach up to 40 m annually, in the meandering sections of the Mekong River,. In contrast, the Bassac River exhibited significant sedimentation, though this trend was diminishing due to upstream sediment deficits caused by hydropower dams. Stability assessments revealed optimal safety corridor distances ranging from 20 to 38 m, influenced by local geotechnical conditions and structural loads. A significant proportion of riverbanks in Dong Thap (88%) and An Giang (48%) do not comply with conservation standards, exacerbating erosion risks and threatening infrastructure. The results of this study highlight the urgent need for enforcing conservation regulations, implementing nature-based solutions like riparian buffers, and adopting sustainable land-use planning. By addressing the interplay between natural processes and anthropogenic pressures, these findings offer actionable insights to enhance riverbank stability, protect ecosystems, and sustain livelihoods in the Mekong Delta amidst growing environmental challenges. Full article

The occurrence of illegal waste activities is a worldwide problem, due to improper actions and inadequate services across many territories. Geographical Information Systems (GISs) software plays a crucial role in optimizing waste management and determining the shortest route paths for waste transportation. This work focuses on the development of a GIS-based workflow for the detection of Illegal Abandoned Waste Sites (IAWSs) and waste management planning. The integration of remote/ground sensing activities, geospatial data, and models within a GIS framework is a useful practice for conducting cost analysis and supporting the development of efficient waste management plans. Firstly, available satellite images are employed in a baseline assessment, combining ancillary and remote sensing data. As a result of satellite monitoring, a ground-piloted survey is carried out by checking the potential-IAWSs density map retrieved from the satellite pre-recognition phase. Hence, a total of 171 ground points are geo-localized and spatialized, according to qualitative on-site products and 2.5D volume analysis. Consequently, distances from illegal dumping sites to proper disposal plants are calculated, achieving the shortest route paths as geospatial information. From these data, a Functional Unit (FU) of 1 ton of mixed waste plus 381.6 kg of inert material is determined, a fundamental stage for comparing different cost analysis processes in similar contexts. By using a GIS-based workflow, a cost analysis assessment is provided, aiming to support principal activities such as waste transportation and disposal to the proper plant (e.g., landfill or incineration). In conclusion, spatial data analysis results are fundamental in managing illegal abandoned waste sites, helping to establish a cost analysis assessment. Full article

In recent years, large-scale wildfires have become a serious threat to terrestrial ecosystems and people in the Chiquitania region of Bolivia. Understanding public perceptions is fundamental to designing comprehensive and effective wildfire management strategies. The objectives of the study were to learn perception on the main causes of wildfires, to understand their perceptions of the impacts of these events, and to explore the most viable solutions to preventing future wildfires in the Chiquitania region of Bolivia. We developed a 15-questions online survey and disseminated it through social media platforms, mobile messaging service groups, and at two workshops held in two locations. A total of 597 people participated in the survey with a balanced sex distribution. The participants were mainly young people aged 18–24 (45.40%) and 25–34 (21.40%), representing university students (42.6%) and professionals (42.6%). The data came from seven departments, but Santa Cruz was more strongly represented (75.9%). In addition, although only 65% considered themselves part of the general population, the data shows that 76% had personal experience of wildfires. Respondents indicated that fires were caused by human activities (95.9%), mainly due to traditional agricultural practices. The most important perceived impacts included landscape and vegetation quality, fauna habitat and ecosystem regeneration. In addition, participants have prioritized the reinforcement of patrols and surveillance, the hiring of forest firefighters and the purchase of aerial firefighting units. For prevention, the most chosen was to change policies that promote fires, changing the vision for economic development and stricter penalties. The findings can be used to formulate public policies aimed at preventing wildfires, mitigating their impacts and promoting environmental conservation. Full article

With the increasing scarcity of global water resources, the exploitation of atmospheric water resources has emerged as a crucial strategy for mitigating water shortages. However, the development of regional atmospheric water resources remains constrained by the lack of precise atmospheric water resource assessments. Existing studies primarily focus on historical evaluations of atmospheric water resources in China, while future changes in cloud water resources across target regions have yet to be comprehensively investigated. In this study, projections of cloud water resources over China for the next 30 years are conducted based on CMIP6 global climate model simulations, in conjunction with observationally diagnosed cloud water resources datasets from 2000 to 2019. A random forest model, coupled with a fuzzy logic approach, is employed to estimate future cloud water resources, as well as their spatial distribution and temporal trends. The results indicate that the random forest model effectively captures the relationship between atmospheric physical variables and cloud water resources, demonstrating strong agreement with historical data. Over the next three decades, cloud water resources in China are projected to exhibit an overall increasing trend, with the most pronounced enhancement occurring under the high-emission scenario (Shared Socioeconomic Pathway 5–8.5). The spatial distribution pattern of cloud water resources is expected to remain largely consistent with that of the past two decades, while inter-model differences are primarily observed in southeastern China and the southern Tibetan Plateau. Further analysis using fuzzy logic inference reveals that the most significant increases in cloud water resources are anticipated in northwestern China, with the potential for an expansion of these increases toward the north under the high-emission scenario. This study provides a scientific framework for predicting future variations in cloud water resources across China, offering critical theoretical and data-driven support for the sustainable development and utilization of atmospheric water resources. Full article

Climate change is becoming more pronounced and affecting all environmental components, leading to river flow changes. This study aimed to investigate the annual and seasonal discharge trends for six rivers in Bosnia and Herzegovina in Europe in the period from 1961 to 2020. The trends were analysed using a linear regression (LR) analysis, the Mann–Kendal test (MK), and an innovative trend analysis (ITA). The fewest significant trends were obtained by the LR analysis, followed by the MK test, and the most were obtained by the ITA method. The ITA method identified 76.67% significant negative trends and 13.33% significant positive trends in all data groups. It can be concluded that the discharges in the second part of the observed period (1991–2020) were significantly lower compared to the first part (1961–1990). A more detailed ITA of the flow by data group (low, medium, and high) was also carried out. The results showed the occurrence of increasingly large extremes. Therefore, in the second subperiod, the minimum values were smaller and the maximum values were larger than in the first subperiod. The occurrence of high water levels increases the possibility of floods, and a long dry period can cause problems with the generation of electricity from hydropower plants. For this reason, analysing discharge trends in the future is certainly a justified recommendation. Full article

At the global level, numerous reservoirs exhibit a pronounced water degradation. Inadequate land use and climate change effects contribute to freshwater degradation and disrupt the ecosystem balances. This study aimed to evaluate the temporal and spatial effects of the surrounding area on two Portuguese reservoirs: Rabagão and Aguieira. For each reservoir sub-watershed scale, the evolution of land use and soil occupation and the pressures reported over the past decade were analyzed. Additionally, official records of water quality parameters were collected, and water quality was assessed according to the Water Framework Directive (WFD). Both reservoirs show anthropogenic pressure, reflected in the water quality. Rabagão has good water quality, associated with undeveloped lands (47%), agriculture (26%), and one pressure on the aquaculture sector. Aguieira is characterized by high nutrient concentrations, low transparency, and phytoplankton. This is linked to various land uses, including forestry (75%), and agriculture (19%), as well as multiple environmental pressures. Key contributors include urban discharge (27 sites) and water catchments allocated for agricultural purposes (89 sites) and others. The long-term data showed an increase in chlorophyll a concentration, water temperature, and pH values, and a decrease in the concentration of total phosphorus, but higher than the reference value. Additionally, the usage of the surrounding area of the hydrographic basin shows that it is extremely important for water quality and should be included in the WFD. Addressing the problems in the surrounding areas reservoirs is essential to adopting measures that improve water quality, therefore guaranteeing the health of the environment as expected under the One Health concept. Full article

The Cheremskyi Nature Reserve, situated in the Volyn region of Ukraine, constitutes a pivotal element of the European ecological network, distinguished by its distinctive mosaic of peatlands, bogs, and floodplain forests. This study utilizes Sentinel-2 satellite imagery and the Google Earth Engine (GEE) to assess the spatiotemporal patterns of various vegetation indices (NDVI, EVI, SAVI, MSAVI, GNDVI, NDRE, NDWI) from 2017 to 2024. The study aims to select the most suitable combination of vegetation spectral indices for future research. The analysis reveals significant negative trends in NDVI, SAVI, MSAVI, GNDVI, and NDRE, indicating a decline in vegetation health, while NDWI shows a positive trend, suggesting an increased vegetation water content. Correlation analysis underscores robust interrelationships among the indices, with NDVI and SAVI identified as the most significant through random forest feature importance analysis. Principal component analysis (PCA) further elucidates the primary axes of variability, emphasizing the complex interplay between vegetation greenness and moisture content. The findings underscore the utility of multi-index analyses in enhancing predictive capabilities for ecosystem monitoring and support targeted conservation strategies for the sustainable management of the Cheremskyi Nature Reserve. Full article

Considering the global competition to increase food productivity due to the increasing population growth, the use of chemical pesticides has become the quick solution, but by increasing awareness about the serious dangers of wasteful chemicals in various areas of life, it has become necessary to move immediately, albeit gradually, towards safe biological treatments. From this point of view, the use of biochar is one of the trends in reducing soil pollution with chemical pesticides. Therefore, the main objectives of this work are (i) to assess if the application of three pesticides based on imidacloprid, methyl thiophanate, and glyphosate has detectable adverse consequences on soil organisms’ activity and (ii) to evaluate if the addition of biochar modifies the effects of these chemicals. An agricultural soil was amended with different doses of biochar. The treated soil received realistic amounts of currently used pesticides. Samples were stored at 21 °C and 50% WHC (water holding capacity) for a period of 28 days under dark conditions. Oxygen consumption was measured for 12 consecutive hours after the addition of 2.5 g glucose kg⁻¹ as a stimulant for soil organisms. Biomass C was estimated from the difference between the amount of C in 0.5 M K₂SO₄ extracts of CHCl₃ fumigated soil and the extractable C in non-fumigated samples. Specific respiration was computed as the amount of O₂ consumed per unit of Biomass Carbon. The results of this work proved that the tested biochar could modulate the effects produced by the agrochemicals on soil biomass. Full article

This study investigates the estimation of the boundary layer height (PBLH) in Huelva, Spain, in November 2023, using different methods: Richardson number, humidity gradient and refractivity gradient. From the virtual potential profiles of temperature and specific humidity, in the case of daytime PBLH, which method works best in some situations when there are discrepancies between results is discussed. The results are then compared with the PBLH values obtained from the ERA-5 reanalysis. The synoptic analysis shows that the decrease in PBLH in the central weeks of the month is compatible with a thermal inversion by subsidence due to a persistent anticyclonic situation. Regarding air quality, the NO₂ concentrations in the air quality station of Matalascañas, which is a background station, show negative correlations with the PBLH. Full article

Today, the construction sector is largely responsible for climate change and global warming. The industry generates the largest carbon footprint and is also one of the least digitized industries in national economies. Faced with the challenge of reducing this carbon footprint, BIM is becoming an essential tool for building digital twins, which in turn makes it possible to calculate and track the carbon footprint over time for designed, constructed, and existing buildings. Semantically rich databases such as BIM make it possible to record the past, present, and future states of buildings and infrastructure facilities. To date, primary research using the free and popular UrbanBIM tool has been conducted on ready-made models, e.g., a previously prepared piece of space. In this secondary study, a specific pre-designed shell building in the BIM environment was examined, and the embedded carbon footprint was calculated for it. The calculated result of 76.35 tons of CO₂ provides an overview of the solutions used and an analysis of the various elements in terms of their environmental impact. The results of the study indicate a growing need to automate the modeling of building information for analysis and simulation, and then to further manage the information. The paper also identifies limitations and presents future research directions for carbon footprint calculation and tracking. Full article