Abstract: Wildlife trafficking in Madagascar poses major threats to its globally unique biodiversity and the socio-economic well-being of its communities. This paper analyzes the effectiveness of national policies, institutional reforms, and community-based conservation mechanisms in response to this threat. Using spatial data, legal frameworks, and management tools such as IMET and METT, the study assesses improvements in governance and outcomes. This case also integrates recent data on 121 seizures from 2000–2021, including 80,112 logs and nearly 35,000 animals. The study incorporates spatial tools, socio-political analysis, and policy mechanisms that reflect advances in governance. It highlights the potential of moral, inclusive, and evidence-led conservation rooted in local realities.

Abstract: Mid–long term streamflow prediction (MLSP) plays a critical role in water resource planning amid growing hydroclimatic and anthropogenic uncertainties. Although AI-based models have demonstrated strong performance in MLSP, their capacity to quantify predictive uncertainty remains limited. To address this challenge, a DeepAR-based probabilistic modeling framework is developed, enabling direct estimation of streamflow distribution parameters and flexible selection of output distributions. The framework is applied to two case studies with distinct hydrological characteristics, where combinations of recurrent model structures (GRU and LSTM) and output distributions (Normal, Student’s t, and Gamma) are systematically evaluated. Results indicate that models employing the Gamma distribution consistently outperform those using Normal and Student’s t distributions. In the Upper Wudongde Reservoir area, the model using LSTM structure and Gamma distribution reduces RMSE from 1407.77 m³/s to 1016.54 m³/s. As the forecast horizon extends, the Gamma-based models demonstrate more reliable probabilistic predictions, reflected by sharper and better-calibrated prediction intervals. This is evidenced by substantially reduced CRPS values at the 18th forecast horizon (521.4 and 1746.6 m³/s), compared to Normal-based models (747.6 and 1877.5 m³/s). Although the improvements in predictive performance achieved by the proposed modeling framework vary depending on the RNN model architecture used and the specific application region, it generally delivers consistent enhancement in forecasting accuracy, thereby providing stronger support for practical applications.

Abstract: Cloud seeding is a targeted weather modification strategy aimed at enhancing precipitation, particularly in regions facing water scarcity. This study evaluates the impacts of wintertime cloud seeding events in the western United States, focusing on three key regions: the Lake Tahoe Area, the Santa Rosa Range, and the Ruby Mountains, using an integrated remote sensing approach. Ground-based AgI generators were deployed to initiate seeding, and the atmospheric responses were assessed using multispectral observations from the Advanced Baseline Imager (ABI) aboard the GOES-R series satellites and regional radar reflectivity mosaics derived from NEXRAD data. Satellite-derived cloud microphysical properties, including cloud-top brightness temperatures, optical thickness, and phase indicators, were analyzed in conjunction with radar reflectivity to evaluate microphysical changes associated with seeding. The analysis revealed significant regional variability: Tahoe events consistently exhibited strong seeding signatures, such as droplet-to-ice phase transitions, cloud-top cooling, and thickened cloud structures, often followed by increased radar reflectivity. These outcomes were linked to favorable atmospheric conditions, including colder temperatures, elevated mid-to-upper tropospheric moisture, and sufficient supercooled liquid water. In contrast, events in the Santa Rosa Range generally showed weaker responses due to warmer, drier conditions and limited cloud development, while the Ruby Mountains presented mixed outcomes. A key contribution of this study is the development of a reproducible satellite–radar integration framework that improves the detection and interpretation of cloud seeding impacts. This combined approach allows for a more comprehensive assessment of seeding outcomes, capturing the progression from initial cloud-phase transitions to potential hydrometeor development. The results highlight the importance of aligning seeding strategies with local atmospheric conditions and demonstrate the practical value of satellite-based tools for evaluating seeding effectiveness, particularly in data-sparse regions. Overall, this work contributes to advancing both the scientific insight and operational practices of weather modification through remote sensing.

Abstract: Coral reef ecosystems are home to diverse marine flora and fauna. However, these ecosystems are threatened by an array of environmental factors. Here, we investigated coral reef diversity, structure, and health status, and identified their key environmental drivers. Coral reef data was collected from Koh Seh Island located inside the Marine Fisheries Management Area in Kep archipelagos. We found that the reef covers largely comprised live corals (64%, mainly Porites and Tubinaria spp), followed by Zoanthids (15%) and sand/ rubble (15%). Based on Ward’s hierarchical cluster analysis, coral communities were grouped into three zones: East, South, and West zones. A higher coral diversity was found in zone East, although it was not significant. This could be due to fewer varied environmental factors. However, we found that Favites spp was the key indicator taxa for coral communities in zone East, which are mainly associated with oxygenated water, high pH levels and more sediments. Zone East is characterized by shallow water, active water currents and rather anthropogenic disturbances such as fishing and transportation. Interestingly, the coral reefs around Koh Seh were found with few dead corals and no disease encountered. This suggests that the protection effort co-enforced by the Marine Conservation Cambodia and the Marine Fisheries Department of the Ministry of Agriculture in Cambodia is effective. Taken as a whole, our study is a useful baseline for further study of benthic reefs and coral communities in the whole Kep Archipelago and surrounding areas.

Abstract: Pesticides play a significant role in agriculture, but their leaching into soil and water poses serious environmental risks. This study examines pesticide contamination in surface and groundwater in northern Tunisia, specifically in Kef governorate, involving a survey of 140 farmers to gather data on agricultural practices and pesticide use. Researchers monitored 24 pesticides and utilized the Pesticide Environmental Risk Indicator (PERI) model to evaluate environmental risk scores for each substance. Soil and water samples were collected and analyzed using a multi-residue method (QUECHERS) and liquid chromatography-tandem mass spectrometry. Results showed that 50% of the pesticides assessed had an Environmental Risk Score of 5 or higher. Contamination was identified in water and soil, with 18 and 15 pesticide residues, respectively. Notable concentrations included 7.8 µg/l of linuron and flupyradifurone in water, and 1718.4 µg/kg of linuron in soil. Commonly detected substances included the insecticide acetamiprid and fungicides like cyflufenamid and penconazole in water, while soil contamination was linked to fungicides metalaxyl and metalaxyl-m, as well as herbicides linuron and s-metolachlor. Factors such as proximity to treated water points and poor packaging management were discussed as risks. The findings emphasize the need for better monitoring and sustainable agricultural practices to mitigate contamination.

Abstract: Vertically resolved aerosol characterization is crucial for assessing the impact of aerosols on radiation, cloud formation, and climate. Atmospheric aerosol particles exhibit significant spatial and temporal variability, making long–term observations essential for understanding these variations and developing an aerosol climatology. We conducted, for the first time, long–term lidar measurements in Albania during 2022–2023 using a ground–based Raman lidar. In this study, we evaluate aerosol extinction profiles and aerosol optical depth (AOD) using 15 years of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data and one year of ground-based Raman lidar measurements. We present seasonal patterns of total, dust, and non-dust aerosol extinction coefficients along with air mass cluster backward trajectories. Additionally, we analyze monthly AOD variations over a 15–year period from CALIPSO and a one–year period from POLLY (POrtabLe Lidar sYstem). The maximum extinction coefficient retrieved by CALIPSO in the near-surface layer ranges from 80 to 100 Mm−1. In comparison, POLLY observations at 300m altitude indicate maximum extinction values between 75 and 125 Mm−1. The maximum height of the aerosol layer is detected between 8 and 10 km. Wildfire smoke layers were identified up to 10 km height during the summer season. The highest AOD values are observed during summer, specifically in August, reaching approximately 0.26 for CALIPSO and 0.19 for POLLY. In contrast, the lowest values occur in winter. For POLLY, the minimum AOD is recorded in December at 0.053, while for CALIOP, the lowest values are around 0.1, observed in December and January. HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) backward cluster analysis indicates regional aerosol transport primarily from the Balkans and Italy, along with long-range transport from Western Europe and North America.

Abstract: A correspondence between fracture damage mechanics and critical point models of seismic activation is outlined, and a method of testing the outlined correspondence against seismic measurements is presented.

Abstract: Climate change has resulted in declining water availability and increased water risk in various regions of Chile, a phenomenon also observed in other countries. A potential approach to address this situation is territorial planning, which could mitigate water vulnerability by managing water demand and protecting ecosystems that provide essential services such as drinking water supply and watershed regulation. The present study examines the role of Regional Land Use Plans (PROTs) in modifying water vulnerability levels through spatial management strategies. Four catchments are analyzed considering the current land use and zoning proposed by the PROTs. The results indicate that in three catchments the vulnerability indicator exhibits an increase under the proposed zoning, while in one catchment there is a slight decrease. The findings suggest a discrepancy between the development objectives articulated in the plans and the proposed land use patterns. Despite incorporating guidelines for water resource management within the PROTs, implementing these guidelines does not necessarily reduce vulnerability. This study posits the necessity of transitioning towards spatial planning methodologies that systematically incorporate hydrological criteria.

Abstract: This study aims to investigate the environmental distribution, trophic transfer, and public health risk associated with per- and polyfluoroalkyl substances (PFAS) in soil and biota near a legacy Aqueous Film-Forming Foam (AFFF) site, in the context of PFAS persistence, bioaccumulation, and the disproportionate burden faced by vulnerable ecosystems and communities. PFAS contamination represents a growing One Health concern, affecting soil organisms, plants, and humans through shared exposure pathways. Utilizing a field-based, multi-compartmental assessment, data were collected from twenty stratified sampling points, including surface and subsurface soils, earthworms (Lumbricus terrestris), and wild lettuce (Lactuca serriola). PFAS concentrations were quantified using liquid chromatography–high-resolution mass spectrometry (LC–HR-MS), and bioaccumulation factors (BAFs) were calculated by compound. The U.S. EPA’s Benchmark Dose Software (BMDS) was used to derive toxicity thresholds (BMDL₁₀) for perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA). Monte Carlo simulations were used to estimate probabilistic human hazard quotients (HQs). Two remediation approaches—granular activated carbon (GAC) and Brassica juncea phytoremediation—were evaluated for effectiveness and feasibility. The analysis revealed long-chain PFAS BAFs between 0.12 and 0.18, with 17% of simulated adult exposures exceeding HQ = 1. While GAC removed up to 98% of long-chain PFAS, it generated hazardous spent media. Phytoremediation removed only 25–35% and posed biomass disposal challenges. These findings underscore the need for adaptive, risk-informed remediation planning. The study contributes to One Health by linking contamination surveillance with health-protective decision frameworks that support environmental justice, community resilience, and the co-protection of ecosystems, food systems, and human health.

Abstract: This paper considers the possibility of using physics-informed neural networks (PINNs) to study hydrological processes of model river sections. The fully connected neural network is used for approximation of the Saint-Venant equations in both 1D and 2D formulations. The study addresses a forward problem to determine velocities and water level, discharge and area of water section in 1D case, as well as an inverse problem to calculate the roughness coefficient. To evaluate the applicability of PINN for modeling flows in channels, it seems reasonable to start with cases where exact reference solutions are available. For the 1D case the research examines a rectangular channel with given length, width, constant roughness coefficient. An analytical solution is obtained to calculate the discharge and area of water section. The 2D model examples were also examined. The synthetic data were generated in Delft3D code, which included velocity field and water level, for the purpose of PINN training. The influence of PINN hyperparameters on the prediction result was studied. Finally, the absolute error value was assessed. The prediction error of the roughness coefficient n value in 2D case for the inverse problem did not exceed 10%. The one typical training process took from 2.5 to 3.5 hours and the prediction process took 5-10 seconds using developed PINN model on server with Nvidia A100 40GB GPU.

Abstract: Background: Urban schoolyard greening is widely promoted for environmental, educational, and social benefits, yet its effect on residential property values remains insufficiently explored. The present study evaluates the association between schoolyard greening and nearby residential property values in three major U.S. cities.Methods: Housing sales data (2010–2022) from Denver, Los Angeles, and Boston were linked to georeferenced public schoolyard greening projects. A difference-in-differences (DiD) approach, employing matched controls and hedonic pricing models, was applied, with adjustments for socio-demographic and neighborhood factors. Robustness was assessed through event study analysis, placebo tests, and spatial spillover sensitivity analyses. Subgroup analyses considered effects by neighborhood income, race/ethnicity, and schoolyard quality. Full data processing code and synthetic data are provided for reproducibility.Results: Greening interventions were associated with a significant 4.1% (95% CI: 3.5–4.7%) average increase in residential property values within 400 meters, relative to matched controls. The largest effects occurred in high-quality, accessible schoolyards and in previously underserved neighborhoods. Subgroup analyses revealed the strongest gains in middle-income and racially mixed areas, with early indications of displacement in some low-income neighborhoods. Findings were robust across alternative specifications, with null results in placebo and spatial spillover tests. Event study analysis confirmed parallel trends.Conclusions: Schoolyard greening consistently predicts increased residential property values in adjacent areas. Policy integration with anti-displacement and housing affordability measures is necessary to ensure equitable distribution of benefits. Future research should leverage longitudinal resident data and administrative records to better track displacement and mobility over time.

Abstract: Australian agriculture managers deal with climates that are characterised by high var-iability and unpredictability. A simple framework for decision making is used to structure weather related enquiries using recent and long-term climate data to better inform de-cisions based on current conditions and future expectations. This paper describes the rationale, design philosophy, and development journey of Australian CliMate (CliMate), a contemporary climate analysis tool built to consolidate and modernise functionality of earlier DSTs. CliMate aimed to be simple, transparent, and user-driven, supporting tactical and strategic agricultural decisions. Ten core analyses were included from previous DSTs. With over 20,000 registered users and widespread adoption among farmers, consultants, and other professionals over a decade, CliMate demonstrates the enduring demand for accessible, mobile climate analysis tools. We reflect on lessons learned in the development process, advocating for minimalism, iteration with users, and integration of transparent data sources. This experience underscores the necessity for long-term support and evaluation to sustain the value of agricultural DSTs.

Abstract: Laser-induced breakdown spectroscopy (LIBS) has been used to explore the chemistry of three regions of Mars on respective missions by NASA and CNSA, with CNES contributions. All three LIBS instruments use ~100 mm diameter telescopes projecting pulsed infrared laser beams of 10-14 mJ to enable LIBS at 2-10 m distances, eliminating the need to position the rover and instrument directly onto targets. Over 1.3 million LIBS spectra have been used to provide routine compositions for eight major elements and several minor and trace elements on > 3,000 targets on Mars. Onboard calibration targets common to all three instruments allow careful intercomparison of results. Operating over thirteen years, ChemCam on Curiosity has explored lacustrine sediments and diagenetic features in Gale crater, which was a long-lasting (> 1 My) lake during Mars’ Hesperian period. SuperCam on Perseverance is exploring the ultramafic igneous floor, fluvial-deltaic features, and the rim of Jezero crater. MarSCoDe on the Zhurong rover investigated during one year the local blocks, soils, and transverse aeolian ridges of Utopia Planitia. The pioneering work of these three stand-off LIBS instruments paves the way for future space exploration with LIBS, where advantages of light-element (H, C, N, O) quantification can be used on icy regions.

Abstract: Over the past few decades, the concept of sustainable agriculture has gained popularity. However, the notion of sustainable agriculture is highly imprecise and unclear, making its application and execution exceedingly challenging. Moreover, disagreements about what sustainability means can lead to a deeper understanding of the intricate empirical procedures and possibly debatable principles involved in any effort to achieve sustainability in agriculture. Practices to increase crop resilience, lower chemical inputs, and boost efficiency are examples of future developments. In this context, this paper aims to investigate and understand the current state of a major subject of climate change and its impacts on the environment and particularly on agriculture. All these can be measured by the Life Cycle Analysis (LCA) method so that its environmental footprint can be reduced. For this purpose, a search of the bibliographic database was carried out and the results obtained were analyzed with the open-source tool bibliometrix for the total findings which numbered 2,328 with publication years from 1993 to 2025 which refers to a pre-publication. Then, a post-processing analysis of 1411 articles was conducted and a narrative review of around 100 publications was carried out where agricultural practices with life cycle analysis, current trends and research gaps were explored.

Abstract: Climate change affects One Health in a variety of ways, but for some locations, particularly in poor countries, there is sparse information about the projected changes in climate-related health risks. People respond to changes in climate-driven natural hazards—which affect health in ways that are often not captured by system-theory concepts. Community-level responses to repeated hazard impacts, such as shifts in social networks and economic strategy, can reduce welfare losses and raise collective risks elsewhere. This study is a scoping review, and numerous health risks associated with climate change are frequently ignored within a single One Health data set or inadequately integrated into broader climate adaptation frameworks, particularly at the global level. To effectively address these issues, methodologies for precisely assessing the single one health risks associated with climate change were developed and implemented. Provides a decision-support system for health that estimates a ‘heat-health watch/warning’ will require a set of decision thresholds to trigger dissemination of warnings and response plans by agencies with state-specific responsibilities. Concluded and acknowledged that numerous policies and training programs are necessary and that the insights gained from existing responses are more likely to be applicable in other industrialized contexts. Furthermore, the one health impact of heat waves and cold waves is relatively robust; there is a pressing need to address additional topics that are pertinent to public health on a continental scale moving forward. Concern over climate change’s range of one health impacts has contributed directly to the widespread use of ad hoc protective measures.

Abstract: This study investigates the transport of chloride—a conservative tracer and surrogate for contaminants—in the fractured Brunswick aquifer of northern New Jersey using a dual-porosity MODFLOW-MT3DMS model. Focusing on the First Watchung Mountain region—a microcosm of northern New Jersey’s hydrogeological environment encompassing Montclair State University and adjoining communities—the numerical model simulates groundwater flow and solute transport in a hydrogeologically complex, urbanized setting. Results indicate that chloride migrates through the fractured aquifer via both local flow systems (e.g., Third River) and regional flow systems (Passaic River) within decades. Chloride concentrations exceeded the EPA’s 250 mg/L threshold much faster in local discharge streams (5 years in the Third River) compared to regional base-level rivers (79 years in the Passaic River), demonstrating rapid fracture transport versus delayed matrix diffusion. Over 450 years, chlorides traveled approximately 7,000 meters, demonstrating potential for widespread salinization and contamination. The study also highlights "salting-out" effects, where elevated salinity enhances contaminant retention and complicates remediation efforts in fractured aquifers. These findings emphasize the need for integrated water management strategies—targeted deicing salt reduction, stormwater management, and recharge-zone protection—to mitigate long-term risks in fractured aquifers. By quantifying dual-domain dynamics previously unaddressed in the Brunswick aquifer, this work provides a framework for contaminant transport modeling and management in similar urbanized fractured systems.

Abstract: The combination of bioremediation and thermal remediation techniques, known as thermally enhanced bioremediation, aims to efficiently remove soil contaminants. However, increasing the temperature in remediation processes can have negative impacts on soil microorganisms. Therefore, we aimed to evaluate the effects of soil heating and naphthalene contamination on soil quality and phytotoxicity variables. To simulate thermally enhanced bioremediation, soil samples were artificially contaminated – with 5 naphthalene concentrations (0, 50, 100, 250 and 500 mg of naphthalene/kg of soil) and incubated at temperatures of 28, 38, 48 and 58 °C. The factors effects were determined using microbiological bioindicators – microbial activity and biomass, metabolic quotient and bacterial count on petri dishes, and phytotoxicity – germination and root length of lettuce seeds, assessed immediately and after 15 and 30 days of contamination. Based on the bioindicators applied, it was found that immediate contamination with naphthalene implied a reduction in biomass and microbial growth associated with greater environmental stress, due to increasing qCO2 values, at high concentrations (250 and 500 mg /kg). The temperature range between 28 and 38 °C stimulated the growth of microbial communities, regardless of concentration. These results indicate that temperature ranges below 40 °C are promising for application in thermally enhanced bioremediation strategies, as they benefit soil microorganisms and potentially favor contaminant degradation.

Abstract: Second-life electric vehicle (EV) batteries offer an opportunity to enhance grid flexibility while supporting circular economy goals in the energy sector. This study develops a PDCA-based management framework for the effective deployment of second-life EV batteries in grid applications. The methodology integrates KPI monitoring for lifecycle performance, degradation tracking, and economic assessment, combined with trigger-based dispatch strategies to ensure optimal operation under varying demand and renewable generation conditions. Scenario analysis is applied to evaluate the framework’s adaptability and scalability in emerging energy markets, including Ukraine, using typical load profiles and renewable variability. Results demonstrate the framework's potential to improve the utilization of second-life batteries by reducing degradation rates, enhancing economic viability through improved dispatch strategies, and supporting grid stability through responsive control. The proposed approach facilitates structured integration of second-life batteries into power systems, maximizing their value while minimizing environmental impacts. This work contributes a replicable methodology for system operators and stakeholders aiming to implement second-life battery projects within flexible and sustainable energy systems.

Abstract: Irish agriculture faces the challenge of balancing productivity with agri-environmental sustainability, where system models could play an important role in precise land use planning. This study focuses on enhancing the predictability of HOLOS-IE, an agricultural system model, by integrating geographically referenced soil data on detailed Land Parcel Identification System (LPIS) maps of Ireland. Soil data sourced from SoilGrids (https://soilgrids.org/) via Google Earth Engine (GEE) and the National Soil Database of Ireland were processed using ArcGIS tools and the Multiple Imputation by Chained Equations (MICE) method to ensure data completeness and accuracy. This refined soil information was integrated with LPIS maps to develop a robust soil database and thereby soil health indices using observed over referenced typical values. The findings identify the significant regional soil differences, with the western counties characterized by acidic, sandy soils with high organic carbon (pH 4.0-5.5, sand >40%, SOC >0.10 kg kg-1), com-pared to the counties in the east with neutral, clay soils (pH >6.0, clay >20%, bulk density >0.95 g cm-3 ), with clear northwest southeast trends in moisture retention and nutrient. The Soil Health Index (SHI) reveals that regions with extensive grasslands, mostly in central and southern areas, exhibit higher soil health indices compared to other areas. The data generated are overlaid on LPIS maps and integrated into the HOLOS-IE model to initiate and serve as driving variables for predicting growth, changes in soil organic carbon density, greenhouse gas emissions, and soil health, leading to inform precise land-use planning for climate change mitigation and adaptation.

Abstract: Methane (CH4) is known as the most potent greenhouse gas in the short term. With the growing urgency of mitigating climate change and monitoring methane emissions, many emerging satellite systems have been launched in the past decade to observe methane and other greenhouse gases from space. These satellites are either capable of pinpointing and quantifying super emitters or deriving regional emissions with a more frequent revisit time. This study aims to reconcile emissions estimated from point source satellites and those from regional mapping satellites, and to investigate the potential of integrating point-based quantification and regional-based quantification techniques. To do that, we quantified methane emissions from the Permian Basin separately by applying the divergence method to the TROPOMI Level-2 data product, as well as an event-based approach using CH₄ plumes quantified by Carbon Mapper systems. The resulted annual CH₄ emissions estimates from the Permian Basin in 2024 are 1.83 ± 0.96 Tg and 1.26 [0.78, 2.02] Tg for divergence and event-based methods, respectively. The divergence-based emissions estimate shows a more comprehensive spatial distribution of emissions across the Permian Basin, whereas the event-based approach highlights the grid cells with the short-duration super-emitters. The emissions from grids with detectable emissions under both methods show strong agreement (R²≈0.642). After substituting the overlap cells’ values from divergence-based emissions estimation with those from event-based estimation, the combined emissions estimate is 2.68 [1.88, 3.54] Tg, which is reconciled with Permian Basin emissions estimates from previous studies. We found that CH₄ emissions from the Permian Basin have been gradually reduced over the past five years. Furthermore, this case study indicates the potential for integrating estimations from both methods to generate a more comprehensive regional emissions estimate.