Search Results (30)

Fire is a key ecological driver in tropical savannas, yet its effects on plant biodiversity remain understudied in Amazonian savannas. This study investigates how fire recurrence influences taxonomic and functional diversity in savanna ecosystems in northeastern Amazonia. We conducted vegetation surveys across five phytophysiognomies in Amapá State, Brazil, and compiled trait data for 226 plant species. Generalized Additive Mixed Models (GAMMs) were used to evaluate the relationships between fire frequency and diversity metrics across five landscape scales. The results showed that taxonomic diversity—particularly Shannon diversity—exhibited a unimodal response to fire recurrence, with peak diversity occurring at intermediate fire frequencies. Abundance increased with fire frequency, indicating potential dominance by fire-tolerant species. Functional diversity responded more subtly: functional richness and dispersion showed weak, non-linear associations with fire, while functional evenness remained stable. These findings suggest that recurrent fire can reduce taxonomic diversity without strongly altering functional structure, possibly due to functional redundancy among species. The use of multiscale models revealed that biodiversity–fire relationships vary with spatial context. In conclusion, this study highlights the moderate resilience of Amazonian savannas to fire recurrence and emphasizes the need to incorporate these ecosystems into fire management plans in climate change scenarios. Full article

Cardanol is a derivative of cashew nut shell liquid (CNSL) and has the potential to be used when developing adhesives for wood boards. Adding nanostructures to adhesive can increase its bonding and reduce formaldehyde emission. Therefore, this study aimed to evaluate the different concentrations of nanolignin (1, 2, and 3%) added to the cardanol-formaldehyde adhesive for gluing plywood, in comparison to the cardanol-formaldehyde adhesive without nanolignin (0%). The plywood’s physical, mechanical, and formaldehyde emission properties were assessed. Plywoods with nanolignin showed shear strength increases of around 160% in the wet condition. With the addition of nanolignin, the modulus of rupture and of elasticity increased by approximately 150% and up to 400% in the parallel direction, respectively. The resistance to combustion also significantly improved. Physical properties did not show statistically significant differences with the percentages of nanolignin. Despite the increase in formaldehyde emission with nanolignin, all treatments met the marketing requirements (≤80 mg of formaldehyde/kg), demonstrating the adhesive potential for indoor use in plywood industries. Natural adhesives using cardanol and nanolignin are an innovative and ecological alternative, combining sustainability and high potential to reduce environmental impacts, which is aligned with at least four sustainable development goals (SDGs). Full article

The Amazonian forests located within the Guiana Shield store above-average levels of biomass per hectare. However, considerable uncertainty remains regarding carbon stocks in this region, mainly due to limited inventory data and the lack of spatial datasets that account for factors influencing variation among forest types. The present study investigates the spatial distribution of original total forest biomass in the state of Amapá, located in the northeastern Brazilian Amazon. Using data from forest inventory plots, we applied geostatistical interpolation techniques (kriging) combined with environmental variables to generate a high-resolution map of forest biomass distribution. The stocks of biomass were associated with different forest types and land uses. The average biomass was 536.5 ± 64.3 Mg ha⁻¹ across forest types, and non-flooding lowland forest had the highest average (619.1 ± 38.3), followed by the submontane (521.8 ± 49.8) and the floodplain (447.6 ± 45.5) forests. Protected areas represented 84.1% of Amapá’s total biomass stock, while 15.9% was in agriculture and ranching areas, but the average biomass is similar between land-use types. Sustainable-use reserves stock more biomass (40%) than integral-protection reserves (35%) due to the higher average biomass associated with well-structured forests and a greater density of large trees. The map generated in the present study contributes to a better understanding of carbon balance across multiple spatial scales and demonstrates that forests in this region contain the highest carbon stocks per hectare (260.2 ± 31.2 Mg ha⁻¹, assuming that 48.5% of biomass is carbon) in the Amazon. To conserve these stocks, it is necessary to go further than merely maintaining protected areas by strengthening the protection of reserves, restricting logging activities in sustainable-use areas, promoting strong enforcement against illegal deforestation, and supporting the implementation of REDD+ projects. These actions are critical for avoiding substantial carbon stock losses and for reducing greenhouse-gas emissions from this region. Full article

The antioxidant capacity and modulation of oxidative stress by industrially processed açaí pulp extract from the Amazon (APEA) and its major anthocyanins, cyanidin 3-glucoside (C3G) and cyanidin-3-O-rutinoside (C3R), were evaluated as potential strategies for preventing cardiovascular diseases. The APEA was chemically characterized using ultrafast liquid chromatography-mass spectrometry (UFLC-MS), which revealed six main phenolic compounds. Notably, 9-(2,3-dihydroxypropoxy)-9-oxononanoic acid, acanthoside B, roseoside, cinchonine, and nonanedioate were identified for the first time in açaí extracts. In vitro antioxidant assays demonstrated that APEA exhibited strong DPPH- and ABTS-radical-scavenging activities (up to 80% inhibition and 65 mmol TE/100g DW, respectively) and showed ferrous- and copper-ion-chelating activities comparable to those of EDTA-Na₂ at higher concentrations (up to 95% inhibition). Hydroxyl and superoxide radical scavenging activities reached 80% inhibition, similar to that of ascorbic acid. In H₂O₂-treated H9c2 cardiomyocytes, APEA significantly reduced the intracellular ROS levels by 46.9%, comparable to the effect of N-acetylcysteine. APEA also attenuated menadione-induced oxidative stress in H9c2 cells, as shown by a significant reduction in CellROX fluorescence (p < 0.05). In vivo, APEA (100 mg/kg) significantly reduced CCl-induced hepatic lipid peroxidation (MDA levels), restored glutathione (GSH), and increased the antioxidant enzymes CAT, GPx, and SOD, demonstrating superior effects to C3G and C3R, especially after 21 days of treatment (p < 0.001). These findings suggest that Amazonian açaí pulp (APEA) retains potent antioxidant activity after industrial processing, with protective effects against oxidative damage in cardiomyocytes and hepatic tissue, highlighting its potential as a functional food ingredient with cardioprotective and hepatoprotective properties. Full article

Attempts have been made to replace conventional plastics in food packaging with biodegradable materials as a promising alternative because they are natural, renewable, and low-cost. This study aimed to develop biodegradable and resistant films from cellulose nanofibrils (CNFs) from sugarcane bagasse when used as reinforcement in starch films. Sugarcane bagasse pulps were subjected to alkaline treatment, with the residual lignin remaining. Part of the material was subjected to a bleaching process with H₂O₂. The pulps were subjected to the mechanical fibrillation process, and unbleached and bleached CNFs were produced. Percentages of 10%, 20%, 30%, and 50% CNF were added to a solution containing 2.5% starch (m/m) solids to make the films. The addition of unbleached CNF promoted an average increase in the tensile strength and Young’s modulus values, especially for films with higher percentages of CNF (30% and 50%). The contact angle values increased with the CNF concentration, with all films being classified as hydrophobic (>90°), except for the films with 30% and 50% unbleached CNF. The 50% unbleached and bleached CNF samples showed low water vapor permeability (2.17 g.mm/Kpa⁻¹ day⁻¹ m²), indicating a good vapor barrier. Although the influence of residual lignin on the test results was not identified for the other samples, treatments with 50% CNF of sugarcane bagasse (unbleached or bleached) should be highlighted among the properties evaluated for reinforcing the structure and improving the barrier properties of cassava starch-based films. Furthermore, this study proposes using sugarcane bagasse, which is a waste widely available in Brazil, placing the study in line with three Sustainable Development Goals (SDGs). Full article

The assessment of urban ecological quality through remote sensing has gained prominence in recent years, due to the need for effective urban monitoring and improved territorial planning. This study presents a comprehensive review of the evolution of urban ecological-quality research from 1997 to 2023, focusing on trends, influential publications, and methodologies. From 1997 to 2023, research on urban ecological quality grew significantly, with annual publications increasing from 0.3 in the 1990s to six in the 2020s, driven by technological advancements, global collaboration, and alignment with policy goals like the UN Sustainable Development Goals (SDGs). Co-occurrence network analysis revealed six key research clusters, highlighting advancements in methodologies, spatial data integration, remote sensing, green sustainability, and multi-criteria frameworks, showcasing the field’s interdisciplinary evolution. China leads contributions, with 33.3% of research, followed by the United States and other countries, emphasizing robust international collaborations. Journals like Remote Sensing and Sustainability dominate, with highly cited publications from the 2010s and 2020s shaping the field’s direction. Prominent authors such as Xu H. and Zhang X. have played critical roles, though engagement in the field has surged more recently. Remote-sensing technologies, particularly in China, have been pivotal, with indices like the Remote-Sensing Ecological Index (RSEI) and its derivatives broadening analytical frameworks. These tools integrate ecological, socio-economic, and policy dimensions, aligning with global sustainability objectives and enhancing the field’s capacity to address urban ecological challenges and promote sustainable urban development. Urban ecological-quality research has evolved significantly, driven by advancements in remote sensing, interdisciplinary methods, and global collaboration. Future efforts should focus on expanding cross-regional studies, integrating comprehensive socio-economic and environmental indicators, and utilizing emerging technologies like machine learning, deep learning, and AI to address urbanization challenges and support sustainable development. Full article

Wildfires significantly impact ecosystems, economies, and biodiversity, particularly in fire-prone regions like the Caatinga biome in Northeastern Brazil. This study integrates machine learning with climate and land use data to model current and future fire dynamics in the Caatinga. Using MaxEnt, fire probability maps were generated based on historical fire scars from Landsat imagery and environmental predictors, including bioclimatic variables and human influences. Future projections under SSP1-2.6 (low-emission) and SSP5-8.5 (high-emission) scenarios were also analyzed. The baseline model achieved an AUC of 0.825, indicating a strong predictive performance. Key drivers of fire risk included the mean temperature of the driest quarter (with an importance of 14.1%) and isothermality (12.5%). Temperature-related factors were more influential than precipitation, which played a secondary role in shaping fire dynamics. Anthropogenic factors, such as proximity to farming and urban areas, also contributed to fire susceptibility. Under the optimistic scenario, low-fire-probability areas expanded to 29.129 Mha, suggesting a reduced fire risk with climate mitigation. However, high-risk zones persisted in the Western Caatinga. The pessimistic scenario projected an alarming expansion of very-high-risk areas to 12.448 Mha, emphasizing the vulnerability of the region under severe climate conditions. These findings underline the importance of temperature dynamics and human activities in shaping fire regimes. Future research should incorporate additional variables, such as vegetation recovery and socio-economic factors, to refine predictions. This study provides critical insights for targeted fire management and land use planning, promoting the sustainable conservation of the Caatinga under changing climatic conditions. Full article

Species distribution modeling (SDM) is a vital tool for ecological and biogeographical research, allowing precise predictions of species distributions based on environmental variables. This study reviews the evolution of SDM techniques from 1985 to 2023, focusing on model development and applications in conservation, climate change adaptation, and invasive species management. We employed a mixed review with bibliometric and systematic element approaches using the Scopus database, analyzing 982 documents from 275 sources. The MaxEnt model emerged as the most frequently used technique, applied in 85% of the studies due to its adaptability and accuracy. Our findings highlight the increasing trend in international collaboration, particularly between China, the United Kingdom, and Brazil. The study reveals a significant annual growth rate of 11.99%, driven by technological advancements and the urgency to address biodiversity loss. Our analysis also shows that while MaxEnt remains dominant, deep learning and other advanced computational techniques are gaining traction, reflecting a shift toward integrating AI in ecological modeling. The results emphasize the importance of global cooperation and the continued evolution of SDM methodologies, projecting further integration of real-time data sources like UAVs and satellite imagery to enhance model precision and applicability in future conservation efforts. Full article

The semi-arid Caatinga biome is particularly susceptible to fire dynamics. Periodic droughts amplify fire risks, while anthropogenic activities such as agriculture, pasture expansion, and land-clearing significantly contribute to the prevalence of fires. This research aims to evaluate the effectiveness of a fire detection model and analyze the spatial and temporal patterns of burned areas, providing essential insights for fire management and prevention strategies. Utilizing deep neural network (DNN) models, we mapped burned areas across the Caatinga biome from 1985 to 2023, based on Landsat-derived annual quality mosaics and minimum NBR values. Over the 38-year period, the model classified 10.9 Mha (12.7% of the Caatinga) as burned, with an average annual burned area of approximately 0.5 Mha (0.56%). The peak burned area reached 0.89 Mha in 2021. Fire scars varied significantly, ranging from 0.18 Mha in 1985 to substantial fluctuations in subsequent years. The most affected vegetation type was savanna, with 9.8 Mha burned, while forests experienced only 0.28 Mha of burning. October emerged as the month with the highest fire activity, accounting for 7266 hectares. These findings underscore the complex interplay of climatic and anthropogenic factors, highlighting the urgent need for effective fire management strategies. Full article

Coffee is one of the most consumed commodities globally, and its harvests generate large quantities of wood waste with low industrial value. This study aimed to explore the potential of residual Coffea arabica wood to produce lignocellulose microfibrils and lignocellulose nanofibrils (LCMF/LCNF) and biodegradable films with possible application in packaging. The fibers were treated with 5% NaOH and fibrillated in an ultrarefiner until they formed a gel. The resulting suspensions were used to create films whose physical, morphological, optical, and mechanical properties were analyzed. The NaOH treatment removed extractives and exposed hemicelluloses, allowing the gel point to be reached with just seven passes through the ultrarefiner, saving energy (~4700 kWh/t). More than 65% of the fibers had diameters of less than 100 nm and little sedimentation after 8 h. The films were flexible, thin (24.5 µm), with a high density (~1100 kg/m³) and good resistance to grease, as well as a water vapor permeability of ~1230 g/m²/day, suitable for packaging bread, fruit, and vegetables. However, the higher wettability of the surface may limit its use in humid environments. The films showed moderate tensile strength (~25 MPa) but low puncture resistance (~7 N mm), making them suitable for biodegradable packaging. Full article

Wildfires are a critical driver of landscape transformation on Earth, representing a dynamic and ephemeral process that poses challenges for accurate early detection. To address this challenge, researchers have increasingly turned to deep learning techniques, which have demonstrated remarkable potential in enhancing the performance of wildfire detection systems. This paper provides a comprehensive review of fire detection using deep learning, spanning from 1990 to 2023. This study employed a comprehensive approach, combining bibliometric analysis, qualitative and quantitative methods, and systematic review techniques to examine the advancements in fire detection using deep learning in remote sensing. It unveils key trends in publication patterns, author collaborations, and thematic focuses, emphasizing the remarkable growth in fire detection using deep learning in remote sensing (FDDL) research, especially from the 2010s onward, fueled by advancements in computational power and remote sensing technologies. The review identifies “Remote Sensing” as the primary platform for FDDL research dissemination and highlights the field’s collaborative nature, with an average of 5.02 authors per paper. The co-occurrence network analysis reveals diverse research themes, spanning technical approaches and practical applications, with significant contributions from China, the United States, South Korea, Brazil, and Australia. Highly cited papers are explored, revealing their substantial influence on the field’s research focus. The analysis underscores the practical implications of integrating high-quality input data and advanced deep-learning techniques with remote sensing for effective fire detection. It provides actionable recommendations for future research, emphasizing interdisciplinary and international collaboration to propel FDDL technologies and applications. The study’s conclusions highlight the growing significance of FDDL technologies and the necessity for ongoing advancements in computational and remote sensing methodologies. The practical takeaway is clear: future research should prioritize enhancing the synergy between deep learning techniques and remote sensing technologies to develop more efficient and accurate fire detection systems, ultimately fostering groundbreaking innovations. Full article

Service quality is crucial to consumer loyalty. However, it is challenging to understand and meet customer expectations effectively. Translating customer feedback into actionable insights in the service industry poses difficulties, particularly without a systematic approach that balances customer requirements with business constraints and strategic objectives. This study proposes an approach that integrates customer perspectives into multi-criteria decision models by utilizing the fuzzy Kano model to capture service perceptions and minimize response uncertainty. It also uses 5W2H and PROMETHEE II to formulate service improvement actions and establish prioritizations, providing a structured framework for managerial implementation. When implemented in the food truck sector, this framework proves effective in addressing unique challenges, enhancing service quality, boosting customer satisfaction, and fostering loyalty. This study offers a valuable contribution to management by presenting a replicable model that aids managers in making strategic decisions, aligning customer perspectives with management efforts, and providing insights for continuously improving initiatives within the food service industry. Full article

Understanding current fire dynamics in the Amazon is vital for designing effective fire management strategies and setting a baseline for climate change projections. This study aimed to analyze recent fire probabilities and project future “fire niches” under global warming scenarios across the Legal Amazon, a scale chosen for its relevance in social and economic planning. Utilizing the maximum entropy method, this study combined a complex set of predictors with fire occurrences detected during 1985–2022. It allowed for the estimation of current fire patterns and projecting changes for the near future (2020–2040) under two contrasting socioeconomic pathways. The results showed strong model performance, with AUC values consistently above 0.85. Key predictors included “Distance to Farming” (53.4%), “Distance to Non-Vegetated Areas” (11.2%), and “Temperature Seasonality” (9.3%), revealing significant influences from human activities alongside climatic predictors. The baseline model indicated that 26.5% of the Amazon has “moderate” to “very high” fire propensity, especially in the southern and southeastern regions, notably the “Arc of Deforestation”. Future projections suggest that fire-prone areas may expand, particularly in the southern border regions and near the Amazon riverbanks. The findings underscore the importance of incorporating both ecological and human factors into fire management strategies to effectively address future risks. Full article

The rise in the global mean sea level (MSL) is a significant consequence of climate change, attributed to both natural and anthropogenic forces. This phenomenon directly affects the dynamic equilibrium of Earth’s oceanic and estuarine ecosystems, particularly impacting the Amazon estuary. In this study, a numerical model was employed to investigate the long-term impacts of MSL fluctuations on key hydrodynamic parameters crucial to regional environmental dynamics. Our investigation was based on scenarios derived from Representative Concentration Pathways (RCPs) and Coupled Model Intercomparison Project Phase 5 (CMIP5) projections, incorporating MSL variations ranging from 30 to 150 cm above the current mean level. Following careful calibration and validation procedures, which utilized observational and in situ data, notably from field expeditions conducted in 2019, our simulations unveiled significant impacts on certain hydrodynamic parameters. Specifically, we observed a pronounced increase in diurnal tidal amplitude (p < 0.05) within the upstream sections of the North and South channels. Additionally, discernible alterations in water renewal rates throughout the estuary were noted, persisting for approximately 2 days during the dry season (p < 0.05). These findings provide valuable insights into the vulnerability of key parameters to hydrologic instability within the Amazonian coastal region. In conclusion, this study represents a pivotal scientific endeavor aimed at enhancing the preservation of aquatic ecosystems and advancing the environmental knowledge of the Lower Amazon River, with the goal of proactively informing measures to safeguard the current and future sustainability of these vital ecosystems. Full article

The diversity of species in Amazonia is exceptionally vast and unique, and it is of great interest for industry sectors to explore the potential of derivatives with functional properties for packaging applications. This study proposes the functionalization of cellulose micro/nanofibril (MFC/NFC) suspensions from Heteropsis flexuosa with andiroba oil to produce films with packaging potential. MFC/NFC was produced by using mechanical fibrillation from suspensions of H. flexuosa fibers. Proportions of 1, 3, and 5% of andiroba oil were added to make films with concentrations of 1% (m/m). Suspensions with andiroba oil provided greater viscosity, with changes in the physical properties of the films. Functionalization with andiroba oil provided films with lower degradation in water, greater contact angle, and lower wettability despite high permeability to water vapor. The films with 1% andiroba oil showed a hydrophobic characteristic (contact angle > 90°) and greater puncture resistance (6.70 N mm⁻¹). Films with 3% oil showed a more transparent appearance and high biodegradation, while 1% oil generated more opaque films with a higher thermal degradation temperature and high antioxidant activity. It was concluded that films produced from H. flexuosa fibers functionalized with andiroba oil showed packaging potential for light, low-moisture products due to their adequate thermal and barrier characteristics. Full article