Abstract: Methanotrophs represent a distinctive group of microorganisms characterized by their specialized metabolic pathways, unique morphological traits, and diverse taxonomic classifications. These bacteria play a crucial role in the global methane cycle by oxidizing methane, a potent greenhouse gas, thereby contributing to climate regulation. Over the past decade, our research group has made significant strides in exploring methanotroph diversity, particularly within the Indian subcontinent. We have successfully identified and described several novel species and two new genera of methanotrophs isolated from rice fields and wetland ecosystems in India. These discoveries mark the first and only documented methanotrophs from India, highlighting the untapped microbial diversity of these habitats and underscoring the importance of regional studies in global microbial taxonomy. To facilitate these findings, we developed a modified cultivation technique that has proven instrumental in isolating and characterising these elusive organisms. This innovative approach has not only expanded the known diversity of methanotrophs but also opened new avenues for their application in biotechnology. The unique physiological traits of these Indian methanotrophs hold promise for biotechnological innovations, including methane mitigation strategies, biofertilizer development, and bioconversion processes. As climate change continues to pose significant challenges, the valorization of methanotrophs through sustainable technologies presents a compelling opportunity. Our ongoing research aims to harness these microorganisms for environmental and industrial applications, positioning methanotrophs as key players in the pursuit of climate resilience and ecological sustainability.

Abstract: In Latin America, coffee is cultivated in distinct coffee agroecosystems (CAS), ranging from traditional agroforestry (“shade”) systems (CAFS) to intensive, unshaded (“sun”) monocultures (UCAS). While various socioenvironmental impacts of these systems have been studied, their implications have not yet been integrated within a Planetary Health perspective. This review of 146 studies applies the Planetary Boundaries and Nature’s Contributions to People frameworks and the DPSEEA (Drivers, Pressures, State, Exposure, Effects, Actions) model to map the relationships between socio-environmental drivers of change, different CAS, the state of natural systems at local and global scales, and human health and well-being. The analysis shows that conventional intensification, driven by low revenues for producers, climate change, and disease outbreaks, has accelerated deforestation, biodiversity loss, greenhouse gas emissions, agrochemical use and leakage, and water pressures. These changes create health risks for coffee-growing communities, such as pesticide exposure and increased vulnerability to external shocks. Conversely, agroecological practices can mitigate environmental pressures while reducing exposure to health hazards and improving resilience, food security, and income stability. However, mainstreaming these practices requires addressing structural inequities in the global coffee value chain to ensure fairer revenue distribution, stronger institutional support, and the protection of coffee-growing communities.

Abstract: Environmental awareness and sustainable development in the context of energy secu-rity, including reduced energy consumption, increasingly require the introduction of innovative solutions in urban areas. These will primarily be systems based on natural architecture, particularly green facades and green roofs. In the near future, they will undoubtedly constitute an integral element of most architectural solutions and are al-ready a standard within global development strategies. Society faces the priority of achieving carbon neutrality, reducing the consumption of natural resources, and minimising the carbon footprint. This paper analyses the temperature around the canopy and the rear surfaces of two experimental models of green facades located on the campus of the University of Life Sciences in the centre of Wroclaw and at the Re-search and Educational Station of the same institution located in the suburban area of Swojec, southern Poland. In both cases, the locations can be classified within the same region, a transitional temperate climate zone. The purpose of the study was to evaluate the thermal effect of two locations with different characteristics, determined in the first case by dense urban development, and in the second by open and sparsely devel-oped areas. Analysis of average temperature reductions for warm and sunny days re-vealed a significant cooling effect. This effect was observed for green surfaces, regard-less of the location and display of the model. For the variant at a distance of 5 cm from the plants, a higher data concentration and a lower variability were recorded. In the same group, on sunny days, the cooling effect, depending on the location, was 4-7° C. On cloudy days, average maximum cooling in this group did not exceed 4° C. It should be emphasised that future research should focus on other elements of these systems, employ a multidisciplinary approach, and additionally have a significant impact on the urban microclimate and the immediate surroundings of the facades.

Abstract: Plankton community respiration (PCR) plays a central role in aquatic ecosystems, driving the breakdown of organic matter and influencing global carbon cycling through its contribution to the production and consumption of carbon and oxygen. Coastal areas, which serve as critical interfaces between terrestrial and marine ecosystems, are regarded as metabolic hotspots in the oceans, due to their intense biological and biogeochemical activities. Additionally, they are particularly sensitive to the impacts of global climate change. In this regard, this review synthesizes experimental evidence to explore how environmental constraints and climate drivers affect PCR in European coastal waters. In total, 46 studies were found in which PCR was measured during experiments testing the effects of one or multiple global climate change drivers in European coastal waters. Among them, the majority of experiments focused on changes in temperature, nutrient concentrations and stoichiometry, and/or pH, while other stressors were less studied. Many experiments confirmed theoretical predictions, notably regarding the predicted positive effects of increased temperature and nutrient concentrations on metabolism, but more complex responses, often linked to trophic cascade mechanisms and thresholds between positive and negative feedbacks were also often reported. Overall, this review, the first comprehensive synthesis of experimental evidence on PCR in European coastal waters, highlights critical knowledge gaps, notably regarding non- and understudied areas and understudied interactions between stressors that occurs jointly in natural ecosystems. Future research should aim to integrate controlled experiments, long-term monitoring, and modeling approaches to deepen our understanding of PCR dynamics under changing environmental conditions and to predict potential feedbacks to global climate processes.

Abstract: Based on multi-batch filtration and cleaning experiments, this study systematically evaluated the fouling potential of pre-treated textile dyeing wastewater by membrane bioreactor on reverse osmosis (RO) membranes and the recovery performance of fouled RO membranes after different cleaning methods. After a permeate production of 625 L/m², continuous foulants accumulation resulted in a significant decline in RO membrane permeability. Protein-like substances and sol-uble microbial products were identified as the primary organic foulants via three-dimensional fluorescence excitation-emission matrix spectrometry (3D-FEEM). The single forward flushing with pure water, acid solution (pH 3.5), alkaline solution (pH 10.5), and sodium hypochlorite with low effective chlorine concentration (1-2 mg/L) showed very limited recovery of fouled RO membrane permeability. The combined forward flushing with acid solution (pH 2) followed by alkaline solution (pH 11.5) restored fouled membrane permeability up to 87% of new RO mem-brane. The addition of pure water backwashing at transmembrane pressure of 0.5 MPa after both acid and alkaline combined forward flushing restored fouled membrane permeability up to 97% of new RO membrane but deteriorated the rejection capacity of RO membrane. The backwashing parameters were further optimized as transmembrane pressure of 0.5 MPa and crossflow veloci-ty of 0.5 m/s, achieving fouled membrane permeability up to 96% of new RO membrane and no negative effects on the rejection capacity of RO membrane. Therefore, the combined cleaning of acid forward flushing → pure water backwashing → alkaline forward flushing → pure water backwashing was proposed for RO membrane cleaning from textile dyeing wastewater reuse application.

Abstract: The arsenic sandstone region constitutes one of the most severe soil erosion hotspots in the middle reaches of the Yellow River, China, where the soil and water conservation capacity is continuously deteriorating and landscape fragmentation is intensifying. Green infrastructure (GI), as a network system of green spaces, can effectively mitigate soil erosion and optimize regional landscape patterns. Based on land-use change data from 2003 to 2023, this study integrated Morphological Spatial Pattern Analysis (MSPA), landscape index method, and Minimum Cumulative Resistance (MCR) model to identify and analyze GI in the pisha sandstone region. The results revealed that: 1) The characteristics of land use type conversion exhibited distinct phased differences between 2003 and 2023. Prior to 2013, farmland was the primary outflow type, accompanied by a reduction in unused land and an expansion of forest land, water bodies, impervious surface, and grassland. After 2013, grassland became the dominant outflow type, with a decrease in water bodies and an increase in farmland, forest land, impervious surface, and unused land. 2) From 2003 to 2023, the total area of GI in the study region showed a trend of initial increase followed by decrease, maintaining a proportion between 84.66% and 87.70%. Spatially, it presented a pattern of aggregation in the northwest and sparseness in the southeast. 3) During the study period, the number of ecological source sites decreased from 20 to 14, the number of general ecological corridors reduced from 152 to 75, and the number of important ecological corridors declined from 38 to 16. 4) The network closure index (α index) decreased from 0.54 to 0.13, the line-point ratio (β index) dropped from 1.90 to 1.14, and the network connectivity index (γ index) fell from 0.70 to 0.44. The GI network structure exhibited a fragmented pattern characterized by local concentration and overall sparseness. This study focuses on the spatiotemporal evolution and pattern characteristics of GI in the special landform of pisha sandstone, providing a theoretical basis for territorial spatial planning, soil erosion control, and human habitat improvement in this region. It also offers new insights for research on ecological security and human habitat quality in special landform areas globally.

Abstract: Rapid urbanisation and global warming are intensifying the Urban Heat Island (UHI) effect, posing growing risks to human health and urban liveability. In Europe, cities face rising temperatures, ageing populations, and fragmented green infrastructure, which together amplify social and climatic vulnerability. This literature review synthesises current research on the relationships between UHI, health impacts, and the role of Nature-Based Solutions (NBS) in mitigation, with a focus on spatial and demographic analysis. A systematic search of recent peer-reviewed studies was conducted through Scopus, applying inclusion criteria centred on European urban contexts and the use of Geographic Information Systems (GIS) and remote sensing for analysis. The selected literature was categorised into four thematic areas: (1) NBS for UHI mitigation, (2) health impacts of heat exposure, (3) vulnerable populations and socioeconomic inequities, and (4) spatial and remote-sensing approaches. Findings show that NBS, including urban forests, green roofs, and blue-green infrastructure, effectively reduce surface temperatures through evapotranspiration and shading, while also supporting mental well-being and social resilience. However, unequal access to greenery and limited integration of health and spatial data prevent equitable adaptation. GIS-based frameworks emerge as critical tools for mapping heat exposure, identifying at-risk groups, and guiding targeted climate-resilient planning. The review highlights the need for inclusive, data-driven urban strategies that combine ecological and social perspectives to reduce heat-related vulnerabilities in European cities.

Abstract: High-resolution Digital Terrain Models (DTMs) are essential for precise terrain analy-sis, yet their production remains constrained by the high cost and limited coverage of LiDAR surveys. This study introduces a deep learning framework based on a modified Residual Channel Attention Network (RCAN) to super-resolve 10 m DTMs to 1 m res-olution. The model was trained and validated on a 568 km² LiDAR-derived dataset us-ing custom elevation-aware loss functions that integrate elevation accuracy (L1), slope gradients, and multi-scale structural components to preserve terrain realism and ver-tical precision. Performance was evaluated across 257 independent test tiles repre-senting flat, hilly, and mountainous terrains. A balanced loss configuration (α = 0.5, γ = 0.5) achieved the best results, yielding Mean Absolute Error (MAE) as low as 0.83 m and Root Mean Square Error (RMSE) of 1.14–1.15 m, with near-zero bias (–0.04 m). Er-rors increased moderately in mountainous areas (MAE = 1.29–1.41 m, RMSE = 1.84 m), confirming the greater difficulty of rugged terrain. Overall, the approach demonstrates strong potential for operational applications in geomorphology, hydrology, and land-scape monitoring, offering an effective solution for high-resolution DTM generation where LiDAR data are unavailable.

Abstract: The article analyses the direction and intensity of changes occurring in agriculture in mountain areas in Europe between 2000 and 2022. For the calculations, the ESA CCI Land Cover global land-use map set was used. This dataset was established by the European Space Agency (ESA) through the classification of satellite images obtained from the following data sources: MERIS, AVHRR, SPOT, PROBA, and Sentinel-3. In the next step, the organisational features and economic performance of farms located in mountain areas of the European Union were determined for the period 2004–2022. For this purpose, data from the European Farms Accountancy Data Network (FADN-FSDN) were used. Subsequently, using Poland as a case study, the capacity of mountain agriculture to implement key environmental interventions (eco-schemes, organic farming, and agri-environment-climate measures) under the Common Agricultural Policy (CAP) 2023-2027 was assessed. The results highlight the varying directions and intensity of organisational changes occurring in mountain agriculture across Europe. They also show that farms can operate successfully in these areas, although their economic situation varies between EU countries. The findings indicate the need for further adaptation of CAP instruments to better reflect the ecological and economic conditions of mountain areas. Strengthening support mechanisms for these regions within the current and future CAP is of crucial importance for protect biodiversity, promoting sustainable land use, and maintaining the socio-environmental functions of rural mountain landscapes.

Abstract:

Cocoa production in West Africa—dominated by Côte d’Ivoire, Ghana, Nigeria, Cameroon, and Togo—faces interconnected agronomic, environmental, and socio-economic challenges that limit productivity and threaten smallholder livelihoods. Integrating Regenerative Agriculture (RA), Unmanned Aerial Systems (UAS), and Artificial Intelligence (AI) present a transformative framework for achieving sustainable and climate-resilient cocoa farming. This review synthesizes evidence from 2000 to 2024 and establishes a tri-axial model that unites ecological regeneration, spatial diagnostics, and predictive intelligence. Regenerative practices such as composting, mulching, cover cropping, and agroforestry rebuild soil organic matter, enhance biodiversity, and strengthen ecosystem services. UAS-based multispectral, thermal, and LiDAR sensing provide high-resolution insights into canopy vigor, nutrient stress, and microclimatic variability across heterogeneous cocoa landscapes. When coupled with AI-driven analytics for crop classification, disease detection, yield forecasting, and decision support, these tools collectively enhance soil organic carbon by 15–25%, stabilize yields by 12–28%, and reduce fertilizer and water inputs by 10–20%. The integrated RA–UAS–AI framework also facilitates carbon-credit quantification, ecosystem-service valuation, and inclusive participation through cooperative drone networks. Overall, this convergence defines a precision-regenerative model tailored to West African cocoa systems, aligning productivity gains with ecological restoration, resilience, and regional sustainability.

Abstract: We want buildings to become energy producers. To this end, we need a new vision. The next generation of buildings, new or retrofit, must be both affordable and sustainable. To be affordable, they must have a uniform technology base, similar to traditional ones, and buildings must interact with the smart grid, taking energy during low-demand periods each night and returning energy during daily peak demand loads. To achieve this, buildings must integrate traditional fossil-based methods with new ecological technologies to reach zero emissions. This way, buildings would help the electrical grid obtain uniformity in its daily loads. Energy generation for return to the electrical grid is primarily achieved through renewable energy sources, including solar energy gains, photovoltaics, wind, biomass, and others. Yet, it is preferable to integrate renewable energy with traditional energy sources to enhance synergy with the electric grid. Nevertheless, in retrofitting existing buildings, solar engineering has limited use, and existing buildings were typically excluded from research on energy efficiency in construction. We have, therefore, added to the passive house thermo-active measures such as thermal mass, water-sourced heat pumps and storage, gray water management, and a district climatic network where preheated air and water are exchanged between buildings.

Abstract: This study estimated peanut (Arachis hypogaea L.) leaf area index (LAI), a critical vegetation parameter, using spectral bands and vegetation indices (VIs) derived from PlanetScope (~3m) imagery by comparing Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Partial Least Squares Regression (PLSR) algorithms. Most VIs exhibited strong relationships with LAI but showed saturation when LAI reached 3 m²/m². Thirteen VIs were individually evaluated for estimating LAI using the aforementioned machine learning and statistical algorithms, and the results showed that the best single predictors of LAI are: SR and RTVIcore (RF, R2 = 0.84, RMSE = 0.62 m2/m2); RTVIcore (XGBoost, R2 = 0.88, RMSE = 0.52 m2/m2); and RTVIcore and MSAVI (PLSR, R2 = 0.61, RMSE = 0.96 m2/m2). The top six ranked VIs were selected to calibrate the RF, XGBoost, and PLSR algorithms. The validation of the algorithms showed that the RF achieved the highest prediction accuracy (R2 = 0.844, RMSE = 0.858 m²/m², RRMSE = 25.17%), followed by XGBoost (R2 = 0.808, RMSE = 0.92 m²/m², RRMSE = 26.99%), while the PLSR showed relatively lower model accuracy (R2 = 0.76, RMSE = 0.983 m²/m², RRMSE = 28.85%). Further results demonstrate that VIs derived from spectral bands provide superior model accuracy in estimating peanut LAI compared to the use of spectral bands alone. Overall, the presented results are significant for future crop monitoring using RF to reduce overreliance on multiple models for peanut LAI.

Abstract: Accurate streamflow forecasting is vital for sustainable water resource management but remains challenging due to pronounced spatiotemporal variability. This study evaluates two process-based models SWAT (comprehensive) and GWLF (parsimonious)—and a data-driven Random Forest (RF) model for monthly streamflow simulation in two contrasting Chinese basins: the humid southern basin (SSB) and the semi-arid northern basin (SRB). Using four statistical metrics (NSE, R2, MAE, RMSE), we assess model accuracy, robustness in capturing extremes, and sensitivity to hydrological characteristics and data availability. Results reveal consistently superior performance in the SSB across all models, with SWAT demonstrating the highest overall accuracy—especially for peak flows—due to its physically based structure. GWLF provides acceptable simulations with minimal data requirements, offering a practical alternative in data-limited regions like the SRB. RF performs well in the SSB under zero-lag conditions but requires hydrologically informed lag structures in the SRB. However, it consistently underestimates high flows due to its lack of physical constraints. The findings underscore that model selection must therefore be guided not only by predictive performance, but by the underlying hydrological context, data availability, and the need for physical realism in decision-making.

Abstract: This study introduced a pre-training method for machine learning-based climate prediction models. The method leverages the advantage of climate events (some theoretical knowledge) to address their limitation (small sample size). It consists of the following steps: generating artificial samples via composite analysis of high and low anomaly events, pre-training predictive models with these samples, and selecting an optimal pre-trained model that most closely matches the observational training set from numerous repeated experiments with only the model’s random number seeds being varied. Sensitivity experiments demonstrate that this pre-training method not only substantially improves predictive skills but also significantly reduces prediction instability. This simple and practical pre-training method is applicable not only to the climate prediction events in this study but also to all climate events for which composite analysis is applicable.

Abstract: The TCGS-SEQUENTION framework, a timeless ontology constructed upon a 4D "timeless counterspace" (C) from which the observable 3D world (Σ) is projected, has historically faced the central empirical challenge of distinguishing true 4D "slice-invariants" from 3D "foliation-dependent artifacts." This report, an augmented version of our foundational synthesis, demonstrates that this challenge is now met by a robust, two-pillar empirical foundation from the geological sciences. Pillar I (The Slice) utilizes the geochemical analysis of Chicxulub impact spherules. This work provides a non-trivial anchor for the framework’s core *ontology*, by chemically separating, within a single co-genetic set of samples, a static, mass-independent *source invariant* (the 17-25% impactor contribution) from a dynamic, mass-dependent *process artifact* (the δ25Mg fractionation signature). Pillar II (The Foliation) utilizes the geophysical analysis of the Geological Time Scale. This work provides an anchor for the framework’s core *geometry*, demonstrating that the "timeline" of geological events is not a human convention but a "scaling (hence hierarchical) ’megaclimate’ regime" with a quantifiable "multifractal" structure. We demonstrate that the "Compound Multifractal-Poisson Process" (CMPP) proposed by Lovejoy et al. is a direct, testable empirical model of the TCGS projection mechanism (X : C → Σ). Together, these findings provide a powerful, multi-domain validation of the framework’s core axioms (A2, A3, A4) and its associated "Gravito-Capillary Foam" model.

Abstract: The article presents the results of research conducted on several rare metal deposits and ore occurrences within the North-Western Kalba region (Eastern Kazakhstan). The high demand for rare metals such as Li, Ta, Cs, Be, Sn, and the limited study of this region, are the driving factors behind the interest in this area. The article provides data on the geological structure of several ore occurrences, as well as the results of mineralogical and geochemical studies of granites, pegmatites, and greisen. Based on the analysis of the obtained results, it is concluded that all the rare metal deposits in North-Western Kalba formed through a unified process of differentiation of the parental magmas of the Kalba granite complex. It is suggested that the North-Western Kalba region could be considered promising for the discovery of new rare metal deposits.

Abstract: Large eddy simulation (LES) within a weather research and forecasting (WRF) model coupled with an active scalar transport equation was used to simulate Atmospheric Boundary Layer (ABL) conditions during the Mosquito wildland fire, the largest wildland fire in California during September of 2022. The simulations were conducted with realistic boundary conditions derived from the NOAA High Resolution Rapid Refresh (HRRR) model, with the aim of better understanding the two-way coupling between atmospheric boundary layer (ABL) and plume dynamics. The terrain was extremely inhomogeneous and the topography varied quite significantly within the numerical domain. Initially, LES of the smoke-free ABL were conducted on nested domains, and detailed ABL data were gathered from 08-09 September, 2022. LES simulations were validated using 4 ASOS stations and NOAA-MET Twin Otter measurements and the desired accuracy has been established. The smoke plume was then released into the ABL at noon on 09 September, 2022 and the plume simulations were conducted for a period of one hour from the release. During this period, the ABL transitioned from convective to buoyancy-shear-driven regimes. Late-night and early-morning conditions are influenced by the complex topography and low-level jet, whereas buoyancy and shear control the ABL dynamics during the morning and afternoon hours. The plume vertical transport is influenced by the ABL-depth and the size of the vertical turbulence structures during that time, whereas, the wind conditions and turbulent kinetic energy within the ABL dictate the horizontal transport scales of the plume. In addition, the results demonstrate that the plume modifies the microclimate along its path.

Abstract: Piggery farming is the largest source of livestock manure in South Korea, generating about 40% of total livestock waste annually. Yet greenhouse gas (GHG) emission data from piggery wastewater treatment systems remain limited, with most studies focused on farm slurry storage rather than process-level emissions. This study quantified methane (CH₄) and nitrous oxide (N₂O) fluxes from a full-scale piggery wastewater treatment facility in, to develop process, season, specific and diurnal specific emission fluxes. Continuous monitoring with a laser-based gas analyzer and customized PVC air-pool chamber was conducted across raw, anaerobic, and aerobic wastewater treatment stages. Mean CH₄ fluxes ranged 1.1-15.6 mg s⁻¹ m⁻², peaking in summer, while N₂O fluxes ranged 0.01-17971 mg s⁻¹ m⁻², with maxima in fall. Aeration tank II and Anaerobic tank I were the dominant emission stages, with night and intra-day peaks. Statistical analysis identified treatment stage and temperature as the main controls on CH₄ variability (p = 0.006 to 0.014), whereas N₂O showed weaker climatic sensitivity. The results provide refined emission factors and emphasize that aeration optimization and denitrification control are key to reducing GHG emissions from livestock wastewater systems in warm, humid regions.

Abstract: Botanical gardens in Ethiopia function as vital socio-ecological systems supporting biodiversity conservation, cultural heritage, environmental education, and climate resilience. This study conducts a multi-dimensional evaluation of three major botanical gardens Gullele (GUBG), Shashemene (SHBG), and Dilla University (DUBEG) using mixed methods involving 300 stakeholder surveys, 15 interviews, and field observations. Six performance domains were assessed: governance, research, education, infrastructure, health and well-being, and cultural integration. Quantitative results indicate that Gullele achieved the highest performance score (mean 4.08), attributed to effective governance and strong infrastructure. Shashemene performed best in cultural integration, while Dilla University excelled in research. Logistic regression highlighted governance and infrastructure as key predictors of institutional success. Qualitative analysis revealed persistent challenges, including fragmented mandates, unstable funding, low community participation, and infrastructural deficits limiting long-term sustainability. Despite these barriers, Ethiopian botanical gardens show substantial potential to advance the nation’s Climate-Resilient Green Economy and Sustainable Development Goals. Strengthening coordinated governance, diversifying funding sources, and promoting local knowledge systems are essential for improving institutional resilience. Enhancing these gardens’ capacities will reinforce their contributions to sustainable land management, biodiversity protection, climate adaptation, and public well-being within Ethiopia’s diverse ecological and cultural landscapes.

Abstract: Complex landslides have characteristics and parameters that are difficult to analyze. The landslide on June 16, 2024, in the rural community of Quilloturo (Ecuador) caused severe damage (14 deaths, 24 injuries, and hundreds of affected families) related to the area's geological, social, and anthropogenic conditions. Its location in the eastern foothills of Ecuador's Cordillera Real (Royal Mountain Range) exacerbated the effects of a landslide involving various processes (mud and debris flows, landslides, and rock falls). This event was preceded by intense rainfall lasting more than 10 hours, which accumulated and caused natural damming of the streams prior to the event. The lithology of the investi-gated area includes deformed metamorphic and intrusive rocks overlain by superficial clayey colluvial deposits. The relationship between the geological structures found, such as fractures, joints, schistosity, and shear, favored the formation of blocks within the flow, making mass movement more complex. Geomorphologically, the area features a relief with steep slopes, where ancient landslides or material movements, composed of these colluvial deposits, have already occurred. At the foot of these steep slopes, on plains less than 300 meters wide and bordered by the Pastaza River, there are human settlements with less than 60 years of emplacement and a complex history of territorial occupation, characterized by a lack of planning and organization. The memory of the inhabitants identified mass movements that occurred since the mid-20th century, with the highest frequency of occurrence in the last decade of the present century (2018, 2022, and 2024). Furthermore, it was possible to identify several factors within the knowledge of the in-habitants that can be considered premonitory of a mass movement, specifically a flood, and that must be incorporated as critical elements in the decision-making, both individual and collective, for the evacuation of the area.