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.

Abstract: Dry climate regions face heightened risks of flooding and infrastructure damage even with minimal rainfall. Climate change is intensifying this vulnerability by increasing the duration, frequency, and intensity of precipitation events in areas that have historically experienced arid conditions. As a result, accurate precipitation estimation in these regions is critical for effective planning, risk mitigation, and infrastructure resilience. This study evaluates the performance of five satellite- and model-based precipitation products by comparing them against in-situ rain gauge observations in a dry-climate region: The fifth generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) (analyzing maximum and minimum precipitation rates separately), the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), the Western Land Data Assimilation System (WLDAS), and the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The analysis focuses on both average daily rainfall and extreme precipitation events, with particular attention to precipitation magnitude and the accuracy of event detection, using a combination of statistical met-rics—including bias ratio, mean error, and correlation coefficient—as well as contingency statistics such as probability of detection, false alarm rate, missed precipitation fraction, and false precipitation fraction. The study area is Palm Desert, a mountainous, arid, and urban region in Southern California, which exemplifies the challenges faced by dry re-gions under changing climate conditions. Among the products assessed, WLDAS ranked highest in measuring total precipitation and extreme rainfall amounts but performed the worst in detecting the occurrence of both average and extreme rainfall events. In contrast, IMERG and ERA5-MIN demonstrated the strongest ability to detect the timing of pre-cipitation, though they were less accurate in estimating the magnitude of rainfall per event. Overall, this study provides valuable insights into the reliability and limitations of different precipitation estimation products in dry regions, where even small amounts of rainfall can have disproportionately large impacts on infrastructure and public safety.

Abstract: Precision agriculture technologies based on satellite remote sensing remain largely inaccessible to smallholder farmers in developing countries due to technical complexity, cost barriers, and infrastructure demands. This study presents the design and implementation of an open-source, web-based platform for processing Sentinel-2 Level-2A imagery tailored to the specific needs of family farming systems. The platform integrates a FastAPI backend for geospatial data processing with a Next.js frontend providing simplified tools for spectral index computation (NDVI, EVI, SAVI, NDWI, NDBI), crop classification using supervised and unsupervised machine learning, and interactive 2D/3D visualization. A laboratory module implements thirteen digital image processing techniques—including Gaussian filtering, edge detection, morphological operations, and thresholding—for educational and comparative analysis. The browser-based system eliminates installation requirements and automates key workflows such as coordinate reprojection, JP2 band extraction, and statistical evaluation. Validation using ground-truth data from coffee and soybean fields in the Brazilian Cerrado achieved classification accuracies above 85% and correlation coefficients exceeding 0.90 for biomass estimation based on NDVI-derived metrics. The platform contributes to the democratization of remote sensing technologies and enhances accessibility of precision agriculture tools for smallholder farmers.

Abstract: Accurate spatial estimation of rainfall is critical for hydrological modeling, water resource management, and agricultural planning—particularly in mountainous and semi-arid regions with sparse monitoring networks. This study presents an Enhanced Elevation-Weighted Local Regression (EWLR) model to generate a high-resolution (30 m) annual rainfall surface for Erbil Governorate, northern Iraq. The EWLR model integrates distance weighting, elevation similarity weighting, and orographic enhancement within a locally weighted regression framework. Average annual rainfall, derived from rainy seasons spanning 1997–1998 to 2024–2025 across 19 meteorological stations, along with a 30 m resolution digital elevation model (DEM), were used to construct and validate the model. Hyperparameters were optimized via Leave-One-Out Cross-Validation (LOOCV), and performance was benchmarked against conventional methods including Inverse Distance Weighting (IDW), Kriging, Thin-Plate Spline, and Radial Basis Function interpolation. Results indicate that EWLR outperforms all benchmarks, achieving R² = 0.797, RMSE = 120.9 mm, and MAE = 87.5 mm. Rainfall shows a strong positive correlation with elevation (r = 0.907, p < 0.001), increasing nearly fivefold from lowland plains (~270 mm) to mountainous areas (>1,350 mm). The final high-resolution rainfall map captures orographic effects accurately, providing a physically consistent, statistically robust dataset suitable for hydrological, climatic, and environmental modeling in data-sparse mountainous regions. The methodology offers a reproducible, elevation-centric framework adaptable to other elevation-driven variables (e.g., temperature lapse rates or snow accumulation) and complex terrains with limited observations.

Abstract: Heavy precipitation clustering is important for flood risk in Europe, but its description in reanalysis datasets is still uncertain. This study examined how well ERA5, ERA5-Land, and JRA-55 reproduce the size and timing of extreme precipitation from 1981 to 2022. Observations from the E-OBS dataset were used as reference, with heavy events defined as daily totals above the 95th percentile. Consecutive wet days were grouped into clusters, and measures such as mean cluster length (MCL) and mean gap between clusters (MGC) were used. Correlations between reanalysis and observed MCL were 0.58–0.63 across seasons, with mean absolute errors of 0.9–1.2 days. The largest bias was found in convective areas, where MGC was underestimated by up to 0.6 days. Sensitivity tests showed that thresholds and linking rules had stronger influence on clustering than the dataset used. The results show that reanalyses reproduce large-scale patterns but tend to underestimate storm duration and event order, which affects flood modeling. Better use of data, improved physical methods, and denser observation networks are needed to reduce these limits and support climate adaptation.

Abstract: The emergence of Artificial Neural Networks (ANN) and its deep learning form, called Artificial Intelligence (AI), opened a new path to improve energy efficiency and the indoor environment. A small collaborating network team is now extending the passive house approach in a book entitled "Retrofitting: The Energy and Environment of Buildings" (Gruyter Publishers [5]) and presenting generalized AI modeling in the following paper. This concept utilizes a long-term neural network with a short-term memory (LSTM) and three stages (training, validation, and testing) for the optimization of hourly data collected over one full year. The non-residential buildings are less affected by space occupants. This paper examines the feasibility of a uniform, climate-modified technology, as our objective is to create a universal and affordable approach to buildings, assisting in slowing the rate of climate change. Hence, the idea of creating a generalized neural network for predicting electricity consumption in relation to weather conditions was born. This network is designed to forecast electricity consumption for buildings linked to local weather conditions; however, different categories of buildings are grouped in one set. While this will lower the large set precision, our question is whether such a network would work. If so, in the future we will create multi-variant, local residential systems with the capability of predicting energy use.

Abstract:

Understanding how processes of magma genesis and magma differentiation control and modify the chemical composition of erupted lavas from the geochemical measurements of the latter is an under-constrained inverse problem as there is only one known parameter – the measured composition of the erupted lava – but two unknown parameters – the chemical composition and lithology of the source before melting and how melting, crystallization, and melt-rock interactions act to alter the lava en route to the surface. In this invited contribution, we review nearly seven decades of scientific research that demonstrate the potential of U and Th decay series measurements for unraveling the complexities of oceanic magmatism. We review the underlying nuclear theory, geochemical principles, and application of the ²³⁸U, ²³⁵U, and ²³²Th decay series for (i) defining the timescales of magma genesis during decompression mantle melting, (ii) establishing the timescales of magma recharge and magma degassing, and (iii) determining the eruption ages of oceanic Quaternary volcanism.

Abstract: Modern statistical techniques allow quantitative characterization of seismic activity. Analysis of the 2011 Tohoku megathrust earthquake revealed clear precursory signals: shortened inter-event intervals, increased magnitude scale (σ), and a pronounced precur-sory swarm immediately before the mainshock. While unique to this magnitude 9 event, here I present subtler anomalies may precede magnitude 7-class events, especially when swarms occur. In such cases, magnitude distributions often differ from background seis-micity, frequently showing elevated location (μ) and scale (σ). Conversely, σ was some-times reduced, particularly in volcanic regions, where large earthquakes may occur with-out discernible swarms. Detection of swarm activity and analysis of magnitude parame-ters thus remain central to seismic risk assessment. If swarm characteristics resemble background levels, the likelihood of a major event is presumably low. However, the dis-tinct, immediate precursory swarm observed before the Tohoku earthquake was not repli-cated elsewhere. These findings indicate that statistical anomalies may signal elevated risk but are unlikely to enable precise temporal prediction of seismic events.

Abstract: This work demonstrates that simple calibration procedures can reduce the error in de-termining relative humidity with a psychrometer by up to 75 times. The psychrometer uses two temperature transducers to measure relative humidity. The calibration consists of comparing both transducers before assembling the psychrometer. A psychrometer assembly is presented, built from readily available parts, which provides temperature data for comparison with a theoretical model. The results are compared against a refer-ence psychrometer to assess errors with and without calibration. A twenty-two-fold de-crease in error was observed for a relative humidity of 62%.

Abstract: Buried ferromagnetic targets, such as unexploded ordnance, generate an additional magnetic field to the main geomagnetic field, which manifests as a magnetic anomaly signal for localization. This paper presents an alternative scheme for the localization by using a rotating magnetic sensor array and a joint optimization algorithm. Multiple magnetic sensors are integrated into an automated rotating measurement platform to achieve efficient and convenient data acquisition. To solve the target's position coordinates, we combine the quantum particle swarm optimization (QPSO) with the genetic algorithm (GA) to develop a joint optimization algorithm, named QPSO-GA. It incorporates the advantages of rapid convergence and local refined search of QPSO with the advantages of global exploration and diversity preservation of GA. Field experiments demonstrate that the proposed measurement system and algorithm achieve an average localization error less than ten centimeters in a multi-sensors for multi-targets scenario within a 4 m × 4 m survey area, meeting general application requirements.

Abstract: In the northern part of the state of Espírito Santo, in the municipality of São Mateus, the physiological, biochemical, and anatomical responses and recovery capacity of cacao trees (Theobroma cacao L.) PS-1319 grafted onto rootstocks TSH-1188, Cepec-2002, Pará, Esfip-02, and SJ-02 under flooding conditions were evaluated. The plants were subjected to flooding for 60 days and their recovery capacity was evaluated after this period. Gas exchange, relative chlorophyll content, stem and leaf anatomy, photosynthetic pigments, and carbohydrates were evaluated. The time of exposure to flooding caused limitations in gas exchange. There was a reduction in net photosynthetic rate, stomatal conductance, and transpiration rate. During flooding, pigments were degraded and total soluble sugar was accumulated in the leaves. Lenticel formation was also observed on all rootstocks during the flooding period. After recovery, the rootstocks normalized their gas exchange, car-bohydrates and anatomy.

Abstract: Based on the author's self-developed Seismo-Geothermal Theory (SGT) system, this paper uses the M 4.0+ earthquake catalog of China and surrounding areas from the California Earthquake Center, USA, to determine the geographical and three-dimensional spatial distribution characteristics of earthquakes and volcanoes in the study area, the temporal progression of sub-crustal earthquakes, and the relationship between the stratified activity of Seismic Cone Tectonics (SCT) and strong intra-crustal earthquakes. It conducts detailed yet concise studies on 6 SCTs closely related to the study area, preliminarily depicting the surrounding environment of seismic activity in China, and introduces the concept of the upper mantle T-type tectonic belt, which spans the junction of the Pacific Ocean and the Eurasian continent. Operating in the mode of "T-type tectonic belt providing energy for the SCT driving layer → active layer conversion → energy storage layer accumulation → dissipation layer rupture leading to intra-crustal strong earthquakes and/or volcanic eruptions", it may become a powerful driver and source of power for seismic activity in China and surrounding areas. Sub-crustal earthquakes are an important geophysical parameter of the upper mantle that can currently be detected by human seismic instruments, and the stratified activity ε₀ values of sub-crustal earthquakes may also become a predictive indicator for strong intra-crustal earthquakes. On this basis, it may be possible to use multiple precursor indicators to explore the possibility of future moderate to strong seismic activity in the study area, contributing to mitigating disasters caused by intra-crustal strong earthquakes and volcanic eruptions.

Abstract: Food insecurity occurs due to the impact of climate change and intense global conditions. Thus, understanding the crop farming plans and monitoring crop yields have become major tasks for decision makers. Previous work has applied remote sensing techniques and empirical methods to predict the yields and analyse the relationships between spectral indices and historical crop yield data. However, a limitation of these studies is that they do not extract the values of spectral indices by crop types, which can cause inaccurate results when investigating the correlations between the yield and the spectral indices. This research develops a yield prediction framework with historical crop maps by unsupervised classification with zero ground truth using Sentinel-2 imagery to retrieve the values of spectral indices of winter barley. The extracted spectral indices, the meteorological and historical yield data in North Norfolk, UK are implemented in 1D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and CNN-LSTM for winter barley yield predictions. LSTM with Soil-Adjusted Vegetation Index (SAVI) has outstanding performance overall and the best result approaches a Mean Square Error (MSE) of 0.21 kg/hectare and a Mean Absolute Error (MAE) of 13.63 kg/hectare. SAVI is the best predictor due to the strong positive correlation with the yield of winter barley. The developed framework with unsupervised crop classification and LSTM-SAVI can be applied on multiple crop types and in different regions using opensource datasets, the historical yields, the spectral indices and the meteorological data. Correlations between these datasets indicate that higher maximum and minimum SAVI and temperature and sun hours at the germination and seedling growth stages increase the yields of winter barley but excess rainfall with higher NDMI at the tillering stage and sun hours at the stem elongation, flowering and grain filling stages lead to a decline in the yields of winter barley.

Abstract: Aiming at the problem of the scale difference between the measurement area model and the real model caused by low-quality images and low-precision control points in the vi-sion three-dimensional deformation monitoring by unmanned aerial vehicles (UAVs), which affects the accuracy of deformation monitoring, the paper develops a spatial 3D scale used to provide high-precision scale information, and puts forward a 3D scale-constrained UAV vision deformation monitoring method, which improves the ac-curacy of deformation monitoring in a wide range of measurement areas. The experi-mental results show that, compared with the monitoring method using only control points as constraints, the method in this paper has an improvement rate of 38.6% and 48.1% in monitoring accuracy in the horizontal and elevation directions in the four-phase UAV operation, which verifies the validity and reliability of the UAV-vision deformation monitoring method with three-dimensional scale constraints.

Abstract: This paper presents the results of assessing the influence of siltation factors in 23 ponds in one of the most agriculturally developed macro-regions of European Russia. Key natural and anthropogenic factors determining the intensity of pond siltation have been identified, and a typification of ponds has been developed to predict the rate of accumulation of bottom sediments in them. For the typification, statistical methods such as correlation analysis (Spearman's coefficient), cluster and factor analysis, as well as the Random Forest machine learning algorithm were used. Correlation analysis revealed that the percentage of catchment cultivation has a significant effect (0.55) on the volume of bottom sediments, while soil loss (0.47) and vertical terrain dissection (0.43) have a moderate effect. Forest cover (0.23) has a weak effect. The most important factors in the siltation process are the average slope of the catchment (24.5%), the percentage of soil cultivation (18.8%), and the average annual soil loss (14.1%). All factors were grouped into three clusters, which explained 77.8% of the variance. As a result, four pond types were identified, differing in their dominant limiting factors: pond hydrological characteristics, catchment morphometry, and the degree of anthropogenic transformation of the catchment. Verification of the typification was carried out based on the calculation of annual soil losses considering the sediment delivery coefficient; the discrepancies between the calculated and actual pond sediment volumes were 1.2–10.0%. The proposed approach allows, based on remote sensing and mapping data, to effectively identify the most degraded small water bodies and plan restoration measures without the need for costly field surveys at the initial stage.

Abstract: Long-term monitoring of radon (222Rn) and thoron (220Rn) radioactive gases has been used in earthquake forecasting. Seismic activity before earthquakes raise the levels of these gases, causing abnormalities in the baseline values of Radon and Thoron Time Series (RTTS) data. This study reports applications of Kernel Density Estimation (KDE) and Wavelet-Based Density Estimation (WBDE) to detect anomalies in radon, thoron, and meteorological time-series data. Anomalies appearing in the RTTS data have been assessed for their potential correlation with seismic events. Using KDE and WBDE, radon anomalies were observed on March 12, August 15, September 17, in the year 2017, and January 19, 2018. Thoron anomalies were recorded on March 12, August 15, September 17, 2017, and February 28, 2018. Irregularities in RTTS were observed several days before seismic events. Anomalies in RTTS, detected using KDE, successfully correlated five out of nine seismic events while WBDE identified four anomalies in RTTS which were successfully correlated with the corresponding seismic events. The wavelet transform has been used to reduce noise at higher decomposition levels in radon and thoron time series. Findings of the study reveal the potential of radon and thoron time series that can be used as precursors for earthquake forecasting.

Abstract: In January 2025, multiple wildfires erupted across the Los Angeles region, fueled by pro-longed dry conditions and intense Santa Ana winds. These events caused severe loss of life, extensive community damage, mass evacuations, and substantial air quality deterio-ration across Southern California and downwind regions through long-range smoke transport. This study integrates passive and active satellite observations to characterize the spatiotemporal and vertical distribution of wildfire emissions. TROPOMI (Sentinel-5P) and TEMPO provided high-resolution mapping of trace gases, including nitrogen dioxide (NO₂), carbon monoxide (CO), and formaldehyde (HCHO). Vertical column densities of NO₂ and HCHO reached 40 and 25 Pmolec/cm², respectively, representing more than a 250% increase in fire-affected zones. TEMPO observations revealed strong diurnal varia-bility and secondary photochemical production, offering insights into plume evolution on sub-daily scales. ATLID (EarthCARE) lidar profiling identified smoke layers concentrated between 1–3 km altitude, with optical properties characteristic of fresh biomass burning, and depolarization ratios indicating mixed particle morphology. The vertical profiling capability was critical for distinguishing transported smoke from boundary-layer pollu-tion and assessing radiative impacts. These findings demonstrate the value of synergistic passive–active satellite measurements in capturing the full extent of wildfire plumes and emphasize the need for integrated monitoring strategies as wildfire risk intensifies under climate change.

Abstract: Sustainable urban water governance in rapidly transforming cities requires integrative decision-making frameworks capable of balancing social equity, economic efficiency, and environmental resilience. This study develops a multi-criteria decision-making (MCDM) model designed to support policy optimization for sustainable water management in Yerevan City, Armenia. Building upon prior AI- and GIS-based diagnostics, the proposed framework integrates quantitative indicators of social participation, economic cost-efficiency, and ecological performance into a unified analytical structure. Using AHP–TOPSIS weighting and scenario analysis, the study evaluates alternative policy strategies such as leakage reduction, demand management, and decentralized reuse systems. Results reveal the trade-offs and synergies among sustainability dimensions, highlighting that equity-prioritized weighting schemes enhance social outcomes without significantly compromising economic performance. The Yerevan case demonstrates how adaptive, data-informed governance models can strengthen resilience, improve resource allocation, and guide policy under uncertainty. The framework contributes to advancing decision science in urban water management and offers transferable insights for mid-income cities facing institutional and environmental constraints.