Search Results (254)

Forests are considered one of the most valuable natural areas in metropolitan region landscapes. Considering the sensitivity and ecosystem services provided by trees, the definition of urban forest ecosystems is nowadays based on a comprehensive understanding of the entire urban ecosystem. The effective capturing of particulate matter is one of the ecosystem services provided by urban forests. These ecosystems function as efficient biological filters. Plants accumulate pollutants passively via their leaves. Therefore, another ecosystem service provided by city forests could be the use of tree organs as bioindicators of pollution. This paper aims to estimate differences in trace metal pollution between the wooded urban areas of Vienna and Krakow using leaves of evergreen and deciduous trees as biomonitors. An additional objective of the research was to assess the ability of the applied tree species to act as biomonitors. Plant samples of five species—Norway spruce, Scots pine, European larch, common white birch, and common beech—were collected within both areas, in seven locations: four in the “Wienerwald” Vienna forest (Austria) and three in the “Las Wolski” forest in Krakow (Poland). Concentrations of Cr, Cu, Cd, Pb, and Zn in plant material were determined. Biomonitoring studies with deciduous and coniferous tree leaves showed statistically higher heavy metal contamination in the “Las Wolski” forest compared to the “Wienerwald” forest. Based on the conducted analyses and the literature study, it can be concluded that among the analyzed tree species, only two: European beech and common white birch can be considered potential indicators in environmental studies. These species appear to be suitable bioindicators, as both are widespread in urban woodlands of Central Europe and have shown the highest accumulation levels of trace metals. Full article

The event scale method, employed for assessing changes in nitrogen (N) dynamics pre- and post-rain, provides insights into its transport to surface water systems. However, the relationships between N discharge in catchments dominated by different land uses and water quality remain unclear. This study quantified variations in key N components in ponds across forest, paddy field, and orchard catchments before and after six rainfall events. The results showed that nitrate (NO₃⁻-N) was the main N component in the ponds. Post-rainfall, N concentrations increased, with ammonium (NH₄⁺-N) and particulate nitrogen (PN) exhibiting significant elevations in agricultural ponds. Orchard catchments contributed the highest N load to the ponds, while forest catchments contributed the lowest. Following a heavy rainstorm event, total nitrogen (TN) loads in the ponds within forest, paddy field, and orchard catchments reached 6.68, 20.93, and 34.62 kg/ha, respectively. These loads were approximately three times higher than those observed after heavy rain events. The partial least squares structural equation model (PLS-SEM) identified that rainfall amount and changes in water volume were the dominant factors influencing N dynamics. Furthermore, the greater slopes of forest and orchard catchments promoted more N loss to the ponds post-rain. In paddy field catchments, larger catchment areas were associated with decreased N flux into the ponds, while larger pond surface areas minimized the variability in N concentration after rainfall events. In orchard catchment ponds, pond area was positively correlated with N concentrations and loads. This study elucidates the effects of rainfall characteristics and catchment heterogeneity on N dynamics in surface waters, offering valuable insights for developing pollution management strategies to mitigate rainfall-induced alterations. Full article

Investigating the effects of fire disturbance on soil microbial diversity and nitrogen cycling is crucial for understanding the mechanisms underlying soil nitrogen cycling. This study examined the fire burn site of the Larix gmelinii forest in the Greater Khingan Mountains, Inner Mongolia, to analyze the impact of varying fire intensities on soil nitrogen, microbial communities, and the abundance of nitrogen cycle-related functional genes after three years. The results indicated the following findings: (1) Soil bulk density increased significantly following severe fires (7.06%~10.84%, p < 0.05), whereas soil water content decreased with increasing fire intensity (6.62%~19.42%, p < 0.05). The soil total nitrogen and ammonium nitrogen levels declined after heavy fires but increased after mild fires; (2) Mild fire burning significantly increased soil bacterial diversity, while heavy fire had a lesser effect. Dominant bacterial groups included Xanthobacteraceae, norank_o_norank_c_AD3, and norank_o_Elsterales. Norank_o_norank_c_AD3 abundance decreased with burn intensity (7.90% unburned, 3.02% mild fire, 2.70% heavy fire). Conversely, norank_o_Elsterales increased with burning (1.23% unburned, 5.66% mild fire, 5.48% heavy fire); (3) The abundance of nitrogen-fixing nifH functional genes decreased with increasing fire intensity, whereas nitrification functional genes amoA-AOA and amoA-AOB exhibited the opposite trend. Light-intensity fires increased the abundance of denitrification functional genes nirK, nirS, and nosZ, while heavy fires reduced their abundance; (4) The correlation analysis demonstrated a strong association between soil bacteria and denitrification functional genes nifH and amoA-AOA, with soil total nitrogen being a key factor influencing the nitrogen cycle-related functional genes. The primary bacterial groups involved in soil nitrogen cycling were Proteobacteria, Actinobacteria, and Chloroflexi. These findings play a critical role in promoting vegetation regeneration and rapid ecosystem restoration in fire-affected areas. Full article

Rockfalls on railways are considered a natural disaster under the topic of landslides. It is an event that varies regionally due to landforms and climate. In addition to traffic density, the Karabük–Yenice railway line also passes through mountainous areas, river crossings, and experiences heavy seasonal rainfall. These conditions necessitate the implementation of proactive measures to mitigate risks such as rockfalls, tree collapses, landslides, and other geohazards that threaten the railway line. Undetected environmental events pose a significant threat to railway operational safety. The study aims to provide early detection of environmental phenomena using vibrations emitted through fiber optic cables. This study presents a real-time hazard detection system that integrates Distributed Acoustic Sensing (DAS) with a hybrid ensemble learning model. Using fiber optic cables and the Luna OBR-4600 interrogator, the system captures environmental vibrations along a 6 km railway corridor in Karabük, Türkiye. CatBoosting, Support Vector Machine (SVM), LightGBM, Decision Tree, XGBoost, Random Forest (RF), and Gradient Boosting Classifier (GBC) algorithms were used to detect the incoming signals. However, the Voting Classifier hybrid model was developed using SVM, RF, XGBoost, and GBC algorithms. The signaling system on the railway line provides critical information for safety by detecting environmental factors. Major natural disasters such as rockfalls, tree falls, and landslides cause high-intensity vibrations due to environmental factors, and these vibrations can be detected through fiber cables. In this study, a hybrid model was developed with the Voting Classifier method to accurately detect and classify vibrations. The model leverages an ensemble of classification algorithms to accurately categorize various environmental disturbances. The system has proven its effectiveness under real-world conditions by successfully detecting environmental events such as rockfalls, landslides, and falling trees with 98% success for Precision, Recall, F1 score, and accuracy. Full article

Accelerating the recovery of compacted soils caused by logging machinery using bioengineering techniques is a key goal of Sustainable Forest Management. This research was conducted on an abandoned skid trail with a uniform 15% slope and a history of heavy traffic, located in the Nav forest compartment of northern Iran. The main objectives were to assess (a) soil physical properties 35 years after skidding by a tracked bulldozer, (b) the impact of natural alder regeneration on soil recovery, and (c) the contribution of alder fine-root development to the restoration of compacted soils in beech stands. Soil physical properties and fine root biomass were analyzed across three depth classes (0–10 cm, 10–20 cm, 20–30 cm) and five locations (left wheel track (LT), between wheel tracks (BT), right wheel track (RT)) all with alder trees, and additionally control points inside the trail without alder trees (CPWA), as well as outside control points with alder trees (CPA). Sampling points near alder trees (RT, LT, BT) were compared to CPWA and CPA. CPA had the lowest soil bulk density, followed by LT, BT, RT, and CPWA. Bulk density was highest (1.35 ± 0.07 g cm⁻³) at the 0–10 cm depth and lowest (1.08 ± 0.4 g cm⁻³) at 20–30 cm. The fine root biomass at 0–10 cm depth (0.23 ± 0.21 g dm⁻³) was significantly higher than at deeper levels. Skid trail sampling points showed higher fine root biomass than CPWA but lower than CPA, by several orders of magnitude. Alder tree growth significantly reduced soil bulk density, aiding soil recovery in the study area. However, achieving optimal conditions will require additional time. Full article

As an important ecological barrier in Northwest China, the health of forest ecosystems in Shaanxi Province is crucial to regional ecological balance and sustainable development. However, forest degradation has become increasingly prominent in recent years due to both natural and anthropogenic pressures. This study aims to identify the spatio-temporal pattern of forest degradation in Shaanxi Province, construct an ecological network, and propose targeted restoration strategies. To this end, we first built a structural-functional forest degradation (SFD) assessment system and used the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm to identify degraded areas and types; subsequently, we used morphological spatial pattern analysis (MSPA) and the minimum cumulative resistance (MCR) model to construct a forest ecological network and identify key restoration nodes. Finally, we proposed a differentiated restoration strategy for near-natural forests based on the Miyawaki method as a conceptual framework to guide future ecological recovery efforts. The results showed that (1) in 1991–2020, the total area of forest degradation in Shaanxi Province was 1010.89 km², which was dominated by functional degradation (98%) and structural degradation (87.15%), with significant regional differences; (2) the constructed ecological network contained 189 ecological source sites, 189 ecological corridors, 89 key nodes, and 50 urgently restored; and (3) specific restoration measures were proposed for different degradation conditions (e.g., density regulation and forest window construction for functional light degradation and maintenance of the status quo or full reconstruction for structural heavy degradation). This study provides key data and systematic methods for the accurate monitoring of forest degradation, the optimization of ecological networks, and scientific restoration in Shaanxi Province, which holds great practical significance for establishing a robust ecological barrier in Northwest China. Full article

Acid rain alters soil chemistry significantly and is a key driver of heavy metal pollution. This study investigates the environmental impact of acid rain-induced heavy metal migration in the Siding Lead–Zinc mining area in south China. Tailings, surrounding soils, and riverbed sediments were examined through simulated acid rain soil column leaching experiments. Leachate parameters—including pH, redox potential (Eh), total dissolved solids (TDSs) and heavy metal concentrations—were used to develop machine learning models (Random Forest and Support Vector Machine) to quantify the influence of environmental factors on metal migration. The results showed that leachates were generally alkaline and reductive after leaching, with Cd, Pb, and Zn as the dominant migrating metals. Leachates from tailings and nearby soils exceeded safe drinking water standards, with significantly higher cumulative metal release than other samples. The RF model outperformed the SVM model in predicting heavy metal concentrations. Feature importance analysis revealed that, beyond sample characteristics, pH and Eh were critical factors driving metal migration. Zn and Cd showed strong sensitivity to these parameters, with pH and Eh contributing over 80% to their migration. The findings highlight that acid rain can enhance the solubility and migration of heavy metals, posing a serious threat to the quality of surrounding water and underscoring the requirement for effective mitigation strategies to protect the ecological environment in mining areas. Full article

Active and abandoned mining sites are significant sources of heavy metals and metalloid pollution, leading to serious environmental issues. This study assessed the environmental risks posed by potentially toxic elements (PTEs), specifically arsenic (As) and antimony (Sb), in the Technosols (mining residues) of the former Pejão coal mine complex in Northern Portugal, a site impacted by forest wildfires in October 2017 that triggered underground combustion within the waste heaps. Our methodology involved determining the “pseudo-total” concentrations of As and Sb in the collected heap samples using microwave digestion with aqua regia (ISO 12914), followed by analysis using hydride generation-atomic absorption spectroscopy (HG-AAS). The concentrations of As an Sb ranging from 31.0 to 68.6 mg kg⁻¹ and 4.8 to 8.3 mg kg⁻¹, respectively, were found to be above the European background values reported in project FOREGS (11.6 mg kg⁻¹ for As and 1.04 mg kg⁻¹ for Sb) and Portuguese Environment Agency (APA) reference values for agricultural soils (11 mg kg⁻¹ for As and 7.5 mg kg⁻¹ for Sb), indicating significant enrichment of these PTEs. Based on average I_geo values, As contamination overall was classified as “unpolluted to moderately polluted” while Sb contamination was classified as “moderately polluted” in the waste pile samples and “unpolluted to moderately polluted” in the downhill soil samples. However, total PTE content alone is insufficient for a comprehensive environmental risk assessment. Therefore, further studies on As and Sb fractionation and speciation were conducted using the Shiowatana sequential extraction procedure (SEP). The results showed that As and Sb levels in the more mobile fractions were not significant. This suggests that the enrichment in the burned (BCW) and unburned (UCW) coal waste areas of the mine is likely due to the stockpiling of lithic fragments, primarily coals hosting arsenian pyrites and stibnite which largely traps these elements within its crystalline structure. The observed enrichment in downhill soils (DS) is attributed to mechanical weathering, rock fragment erosion, and transport processes. Given the strong association of these elements with solid phases, the risk of leaching into surface waters and aquifers is considered low. This work underscores the importance of a holistic approach to environmental risk assessment at former mining sites, contributing to the development of sustainable remediation strategies for long-term environmental protection. Full article

Soil pollution is a global threat that seriously affects biodiversity in (agro)ecosystems and may compromise water and food quality. Therefore, the ability of tree species (Populus tremula, Salix caprea, and Betula pendula) to accumulate and phytoextract specific toxic heavy metals from soil was investigated. The study was conducted in and near relict mining areas of Freiberg (Germany) and sampling sites selected according to their spatial location relative to potential sources of metal(loid)s. The concentrations of geogenic (P, Fe, Mn, Ca) and pollutant (Pb, Cd, Zn, As) elements in soil and the present trees were measured using ICP-MS. The highest total soil concentrations of As (8978 µg g⁻¹) were found within the Davidschaft mining area, and for soil Pb, both in the Davidschaft vicinity (328 µg g⁻¹) and mining area (302 µg g⁻¹). Unexpectedly, the highest soil Zn (0.64 mg g⁻¹) and Cd (3.5 mg g⁻¹) concentrations were found in Freiberg city Forest. The lowest soil concentrations of pollutants (As, Cd, Pb, and Zn) were recorded for Seifersdorf. Total soil P was highest in Colmnitz, but Ca, Mn, and Fe concentrations were very similar across all sites. The available concentration of all measured toxic elements in the soil generally decreased in the order Davidschaft > Davidschaft vicinity, Colmnitz > Seifersdorf = Freiberg city forest. All studied tree species had higher concentrations of the essential elements in leaves than in branches. Generally, higher values of bioaccumulation coefficients (especially for Cd) were found for Salix caprea compared with Populus tremula and Betula pendula. Full article

Soil contamination with heavy metals poses a significant risk to human health and ecological systems through multiple exposure pathways: direct ingestion of crops, dermal contact with polluted soil, and bioaccumulation within the food chain. This study analyses eleven composite soils, each collected in triplicate from different sites in Iași County, four of which are designated Natura 2000 protected areas (Mârzești Forest, Plopi Lake—Belcești, Moldova Delta, and Valea lui David). The assessment includes measurements of soil humidity by the gravimetric method, pH, and organic matter content, examined in relation to heavy metal concentrations due to their well-established interdependencies. For heavy metal determination, energy-dispersive X-ray spectroscopy (EDS) using an EDAX system (AMETEK Inc., Berwyn, PA, USA) and X-ray fluorescence spectrometry (XRFS) with a Vanta 4 analyser (Olympus, Waltham, MA, USA) were employed. Additionally, scanning electron microscopy (SEM) with a Quanta 450 microscope (FEI, Thermo Scientific, Hillsboro, OR, USA) was used primarily for informational purposes and to provide a broader perspective. In the case of chromium, 45.45% of the samples exceeded the permissible levels, with concentrations ranging from 106 mg/kg to 186 mg/kg, the highest value being nearly twice the alert threshold. Notably, not all protected areas maintain contaminant levels within safe limits. The sample from the Mârzești Forest protected site revealed considerably raised concentrations of mercury, arsenic, and lead, exceeding the alert thresholds (1 mg/kg—mercury, 15 mg/kg—arsenic, and 50 mg/kg—lead) established through Order no. 756/1997 issued by the Minister of Water, Forests, and Environmental Protection from Romania. On the other hand, the sample from Podu Iloaiei, an area with intensive agricultural activity, shows contamination with mercury and cadmium, highlighting significant anthropogenic pollution. The findings of this study are expected to raise public awareness regarding soil pollution levels, particularly in densely populated regions and protected ecological zones. Moreover, the results provide a scientific basis for policymakers and relevant authorities to implement targeted measures to manage soil contamination and ensure long-term environmental sustainability. Full article

The growing global interest in mitigating climate change implies an increased importance of sustainable development to achieve greenhouse gas emission reductions. The paper analyses the impact of key economic and environmental factors, such as the share of renewable energy, gross domestic product (GDP), fossil fuel consumption, final energy consumption in households and industry, and forest area, on greenhouse gas (GHG) emissions in the European Union (consisting of 27 members) and Poland for comparison—for the period from 1990 to 2023. The study fills a gap in identifying the cross-sectoral determinants of greenhouse gas emissions in the EU, focusing specifically on Poland and the whole EU region since the beginning of the European Union. The research involved the implementation of statistical analyses, dynamic analyses, correlation analyses, and regression analyses. The results showed an increase in the share of renewable energy, GDP, and forest area, which was negatively correlated with the volume of GHG emissions. In contrast, final fossil fuel consumption and final energy consumption in industry and households (to a slightly lesser extent) were also significantly but positively correlated. It is worth noting that the strength of calculated relationships differed for the EU and Poland. The study revealed trends and correlations that affect GHG and are relevant to policy implications for EU climate goals. Considering the various determinants of GHG emissions and Poland’s unique situation (high dependence on coal and a large share of heavy industry), conclusions were formulated for Poland’s and the EU’s climate policies in the context of the European Green Deal. Full article

In recent decades, forests have experienced an increasing trend in the number of pest outbreaks worldwide, apparently driven by strong annual variability in precipitation, higher air temperatures, and strong winds. Pest outbreaks have negative ecological, economic, and environmental impacts on forest ecosystems, such as reduced biodiversity, carbon sequestration, and overall forest health. Traditional monitoring methods of these disturbances, while accurate, are time-consuming and limited in scope. Remote sensing, particularly UAV (Unmanned Aerial Vehicle)-based technologies, offers a precise and cost effective alternative for monitoring forest health. This study evaluates the temporal and spatial progression of bark beetle damage in a fir-dominated forest in the Zao Mountains, Japan, using UAV RGB imagery and DL (Deep Learning) models (YOLO - You Only Look Ones), over a four-year period (2021–2024). Trees were classified into six health categories: Healthy, Light Damage, Medium Damage, Heavy Damage, Dead, and Fallen. The results revealed a significant decline in healthy trees, from 67.4% in 2021 to 25.6% in 2024, with a corresponding increase in damaged and dead trees. Light damage emerged as a potential early indicator of forest health decline. The DL model achieved an accuracy of 74.9% to 82.8%. The results showed the effectiveness of DL in detecting severe damage but highlighted that challenges in distinguishing between healthy and lightly damaged trees still remain. The study highlights the potential of UAV-based remote sensing and DL for monitoring forest health, providing valuable insights for targeted management interventions. However, further refinement of the classification methods is needed to improve accuracy, particularly in the precise detection of tree health categories. This approach offers a scalable solution for monitoring forest health in similar ecosystems in other subalpine areas of Japan and the world. Full article

This study presents a multi-temporal analysis of vegetation cover changes in the Guayepo stream watershed (Cartagena de Indias, Colombia) for 2000, 2010, and 2020 and their impact on surface runoff generation. Hydrological data from 1974 to 2019 were processed to model intensity–duration–frequency (IDF) curves and simulate heavy rainfall events using six storms of nine-hour duration. Following the Soil Conservation Service guidelines, these were used to estimate runoff flows for return periods of 25, 50, and 100 years via the curve number method in HEC-HMS. Vegetation cover was assessed using the CORINE land cover methodology applied to official land use maps. The analysis revealed a significant loss of natural vegetation: dense forest cover declined dramatically from 14.38% in 2000 to 0% in 2020, and clean pastures were reduced by 46%. In contrast, weedy pastures and pasture mosaics with natural areas increased by 299% and 136%, respectively, reflecting a shift towards more degraded land cover types. As a result of these changes, total runoff flows of the model increased by 9.7% and 4.3% under antecedent moisture conditions I and II, respectively, for the 100-year return period. These findings reveal ongoing degradation of the watershed’s natural cover, linked to expanding agricultural uses and changes in vegetation structure. The decline in forested areas has increased surface runoff, elevating flood risk and compromising the watershed’s hydrological regulation. The study suggests that integrated land management and ecological restoration strategies could be key in preserving hydrological ecosystem services and reducing the negative impacts of land use change. Full article

RSSI-based proximity positioning is a well-established technique for indoor localization, featuring simplicity and cost-effectiveness, requiring low-price and off-the-shelf hardware. However, it suffers from low accuracy (in NLOS traffic), noise, and multipath fading issues. In large complex spaces, such as museums, where heavy visitor traffic is expected to seriously impact the ability to maintain LOS, RSSI coupled with Bluetooth Low Energy (BLE) seems ideal in terms of market availability, cost-/energy-efficiency and scalability that affect competing technologies, provided it achieves adequate accuracy. Our work reports and discusses findings of a BLE/RSSI-based pilot, implemented at the Museum of Modern Greek Culture in Athens, involving eight buildings with 47 halls with diverse areas, shapes, and showcase layouts. Wearable visitor BLE beacons provided cell-level location determined by a prototype tool (VTT), integrating in its architecture different functionalities: raw RSSI data smoothing with Kalman filters, hybrid positioning provision, temporal methods for visitor cell prediction, spatial filtering, and prediction based on popular machine learning classifiers. Visitor movement modeling, based on critical parameters influencing signal measurements, provided scenarios mapped to popular behavioral models. One such model, “ant”, corresponding to relatively slow nomadic cell roaming, was selected for basic experimentation. Pilot implementation decisions and methods adopted at all layers of the VTT architecture followed the overall concept of simplicity, availability, and cost-efficiency, providing a maximum infrastructure cost of 8 Euro per m² covered. A total 15 methods/algorithms were evaluated against prediction accuracy across 20 RSSI datasets, incorporating diverse hall cell allocations and visitor movement patterns. RSSI data, temporal and spatial management with simple low-processing methods adopted, achieved a maximum prediction accuracy average of 81.53% across all datasets, while ML algorithms (Random Forest) achieved a maximum prediction accuracy average of 87.24%. Full article

Global forest soil acidification has become a significant environmental concern, making it essential to gain a comprehensive understanding of research hotspots in this field. Acidic substances in forest soil originate from both external and internal factors. To investigate this issue, we conducted a visual analysis of 2325 papers published between 2004 and 2024 using the Web of Science Database, along with the visualization and analysis tools CiteSpace and VOSviewer. Over the past 20 years, the number of publications on global forest soil acidification has steadily increased. China and the United States have far more publications than any other country. Key research hotspots include soil acidification, atmospheric deposition, nitrogen deposition, heavy metals, soil pH, plant growth, impacts and governance, each displaying distinct characteristics at different stages. This review offers a comprehensive overview of recent advances in global forest soil acidification research and serves as a valuable reference for both research and practical applications. It examines the current state of this global environmental problem, the long-term effects of acidification and forest succession, and the eco-environmental effects associated with soil acidification. It also proposes sustainable solutions to mitigate forest soil acidification and outlines potential future research topics. These efforts aim to support the stable development of forest ecosystems and promote ongoing research in this critical area. Full article