Abstract: The central Mediterranean and nearby regions were affected by extreme wildfires during summer 2021. During the crisis, Türkiye, Greece, Italy and other countries faced numerous challenges ranging from the near complete destruction of landscapes to human losses, and high outputs in emissions which compromised local air quality. In the Mediterranean basin, atmospheric monitoring stations perform continuous measurements of chemical and meteorological parameters meant to track and evaluate greenhouse gas and pollutant emissions in the area. In the case of wildfires, CO (carbon monoxide) and formaldehyde (HCHO) are effective tracers and the integration of satellite data on tropospheric column densities with surface measurements can provide additional insights on the transport of air masses originating from wildfires. At the Lamezia Terme (code: LMT) World Meteorological Organization – Global Atmosphere Watch (WMO/GAW) observation site in Calabria, Southern Italy, a new multiparameter approach combining different methodologies has been used to expand the knowledge the summer 2021 crisis. A previous study focused on wildfires affecting specifically the Aspromonte massif area in Calabria itself: in this study, the integration of surface concentrations, tropospheric columns, and backtrajectories has allowed to pinpoint additional contributions from other southern Italian regions, north Africa and Greece. CO data were available for both surface and column assessments, while HCHO data were only available through satellite. In order to correlate the observed peaks with wildfire phenomena, surface BC (black carbon) was also analyzed. The analysis, which focused on July and August 2021 data, pinpointed case studies highlighting distinct sources of combustion processes during the wildfire crisis of that year. Although wildfires have certain local effects in terms of biomass loss and direct damage to both the environment and human infrastructures, their broad impact on air quality also requires monitoring via the integration of multiple methodologies and the cross-analysis of satellite and surface data.

Abstract: CTD profilers are used as reference instruments to qualify temperature and salinity data. Their metrological specifications can be controlled in calibration bath and calibration coefficients can be applied to correct the linearity of sensors and the trueness of measured data with a given uncertainty. However, in ocean areas with thermal gradients, the uncertainty of the measured data is questionable due to the thermal inertia of sensors and the movements of the CTD, in relation with the roll or pitch of the boat. In order to evaluate these measurement uncertainties and in order to be able to use the upcast profiles, a double C-T sensors SBE 9 profiler was fixed under a carousel water sampler, the second C-T couple being at the top of the carousel frame. This configuration allows the evaluation of temperature measurement deviations of recorded profiles. In order to quantify the different sources of instrumental uncertainties, the temperature signal has been modelled accounting for the movements induced by the boat. The result allows to quantify what can be called the representativeness of CTD’s temperature measurements. This notion is very useful in data assimilation process. A table quantifying the various sources of uncertainty has been created from profiles obtained during four offshore campaigns. In the future, it could be used to find the representativeness of similar profiles obtained with a single pair of sensors.

Abstract: Heavy metal pollution in water resources represents a severe environmental and public health challenge, with cadmium (Cd) and lead (Pb) being some of the most toxic contaminants. Finding new sustainable, efficient, and cost-effective methods for water treatment is essential to address such problems. Therefore, this study investigates the biosorption capabilities of natural (SN) and surfactant-modified (SM) guava seed biosorbents to remove Cd and Pb from aqueous solutions. To enhance their adsorption efficiency, Guava seeds, an agricultural waste material, were treated with hexadecyltrimethylammonium bromide (HDTMA-Br). The biosorbents were characterized by FTIR, SEM-EDS, and zeta potential analysis to understand the surface modifications and their influence on the adsorption mechanisms. Batch experiments were performed to evaluate the effects of pH, contact time, temperature, biosorbent dosage, and concentration on Cd and Pb removal efficiencies. Adsorption isotherm and kinetic data were analyzed using mathematical models to obtain the basic parameters of the systems under study. The results showed that SM exhibited superior adsorption capacities of 328 mg/g for Cd and 594 mg/g for Pb at 25 °C, significantly outperforming SN. The study also analyzed the thermodynamic parameters of adsorption systems, revealing endothermic and exothermic properties for SN and SM. Functional groups, such as hydroxyl and carbonyl, were crucial in metal ion binding. HDTMA-Br introduced additional active sites and enhanced surface charge interactions. Regeneration tests showed the reusability of both biosorbents, maintaining an efficiency of over 85% after four cycles. Guava seeds, after surfactant modification, could be cost-effective and sustainable biosorbents for heavy metal removal in water treatment.

Abstract: The choice of efficient methods for immobilization of high-level waste (HLW) resulting from reprocessing of spent nuclear fuel (SNF) is an important scientific and practical task. The current policy of managing HLW within closed nuclear fuel cycle envisages its vitrification into borosilicate (B-Si) or alumina-phosphate (Al-P) glasses. These wasteforms have rather limited waste loading, and can potentially impair their retaining properties on devitrification. The optimal solution for HLW immobilisation could be by separating radionuclides into groups using dedicated capacious durable matrices. The phases of the Nd2O3 – ZrO2 – TiO2 system in this respect are promising hosts for REE – MA (MA = Am, Cm) fraction of HLW. We present herewith data on composition of samples analyzed, their durability in hot water, behavior under irradiation, and industrial manufacturing methods.

Abstract:

This paper presents findings from a longitudinal perception survey in the Italian Central Alps (N=1980) and introduces a conceptual model examining public support for flood risk management. The survey period coincided with a significant weather event in Central Europe, Storm “Adrian” (also known as “Vaia”), providing a unique pre- and post-event perspective. Results highlight the critical role of individual knowledge, trust in authorities, and social group dynamics in shaping risk perception processes. The study reveals how major weather events can alter perceptions, sense of security and institutional trust within local communities, and more interestingly, these changes can vary spatially. The findings are summarised using a systems thinking framework, which identifies different possible feedback loops between flood risk management interventions and long-term public support. The study underscores the importance of forward-looking, systems thinking approaches in designing, monitoring, and evaluating flood risk management plans to address often-overlooked dynamics, such as spatially diverse feedback loops and counter-intuitive effects, ultimately enhancing their medium- and long-term effectiveness.

Abstract: The phenomenon of landslide spatial aggregation is widespread in nature, which can affect the result of landslide susceptibility prediction (LSP). In order to eliminate the uncertainty caused by landslide spatial aggregation in LSP study, scholars have proposed some methods to quantify the degree of landslide spatial aggregation, such as class landslide aggregation index (LAI), which is widely used. However, due to the limitations of the existing LAI method, it is still uncertain when applied to the LSP study of the area with complex engineering geological conditions. Considering the landslide spatial aggregation, a new method, dual frequency ratio (DFR), was proposed in this paper to establish the relationship between the landslide occurrence and twelve predisposing factors (namely, slope, aspect, elevation, relief amplitude, engineering geological rock group, fault density, river density, annual average rainfall, NDVI, distance to road, quarry density and hy-dropower station density). And in the DFR method, an improved LAI was used to quantify the degree of landslide spatial aggregation in the form of frequency ratio. Taking the middle reaches of Tarim River Basin as the study area, the application of DFR method in LSP study was verified. Meanwhile, four models were adopted to calculate the landslide susceptibility indexes (LSIs) in this study, including frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR) and random forest (RF). Finally, the prediction performance of each LSP model was evalu-ated by the receiver operating characteristic curves (ROCs) and distribution patterns of LSIs. The results showed that DFR method could reduce the adverse effect of landslide spatial aggregation on LSP study and enhance the prediction performance of LSP model better. In addition, models of LR and RF had superior prediction performance, among which DFR-RF model had the highest prediction accuracy value and quite reliable result of LSIs.

Abstract: The protected areas (PA) of the Democratic Republic of the Congo serve as vital carbon reservoirs and are crucial for biodiversity conservation and climate regulation. Despite their significance, these areas face escalating rates of deforestation and degradation, often poorly understood at the local level. This study focuses on the dynamics of tropical moist forest (TMF) and the relative importance of the driving factors in the landscape of Kahuzi-Biega National Park (KBNP), one of the country's prominent PAs. Analyzing annual TMF dynamics from 1990 to 2022, using data classified by Vancutsem et al (2021) from Landsat imagery alongside spatial datasets of deforestation and degradation drivers, we employed a comprehensive analytical approach. This included meshing, multi-scale analysis, principal component analysis, zoning, multiple linear regression, and relative importance analysis through bootstrapping. Findings indicate that the grid size considered does not significantly influence TMF dynamics in the KBNP landscape (p-value=0.67). The edge and outer zones experienced substantial dynamics, with approximately 30% forest cover loss in both areas, contrasting with the relatively stable TMF cover (~100%) in the inner zone. Fire emerged as the most influential drivers, explaining TMF dynamics with relative importance of approximately 55%, 30%, and 23% in the inner, edge, and outer zones, respectively. The study underscores KBNP's efficacy in curbing TMF loss but highlights the need for enhanced protection around its periphery. Management efforts should prioritize sustainable land use practices, livelihood improvement, and establishment of an officially recognized buffer zone.

Abstract: The mechanism of slagging in municipal solid waste incinerators is complex, and the slagging process is simultaneously affected by the composition, temperature, and flue gas flow. In this study, slag samples on a water-cooled wall were first analysed, and the key components and fusion temperatures were measured. Second, a gas-phase combustion model of the incinerator was established, and the temperature and velocity distributions of the flue gas inside the incinerator were calculated. Based on the incineration process coupled with the Discrete Phase Model, a numerical simulation model of the slagging process on the water-cooled wall of the incinerator was constructed, considering the transport and adhesion processes of ash particles. The influence of parameters such as the ash particle size and concentration on the degree of slagging on the water-cooled wall was analysed. Smaller ash particles were less likely to adhere to water-cooled walls, with approximately 2.72% of ash particles with a particle size of 10 um adhering to water-cooled walls. The proportion of ash particles with a particle size of 50 um adhering to water-cooled walls was approximately 3.0 times that of those with a particle size of 10 um. As the concentration of ash particles increased, the number of ash particles adhering to the water-cooled wall increased, and the adhesion ratio decreased. These results are of great significance for optimising the operation of incinerators and reducing slagging rates.

Abstract: Walkability, defined as the degree to which urban environments encourage walking, is a vital component of sustainable urban development. It enhances public health, reduces environmental pollution, and fosters social cohesion. This review investigates the current state of walkability research, emphasizing tools such as Geographic Information Systems (GIS), Walk Score, and Agent-Based Modeling (ABM). While traditional methods offer valuable insights, they often neglect socio-economic and demographic factors influencing walking behaviors. ABM enables dynamic simulations that incorporate individual preferences, environmental conditions, and urban design features, offering a deeper understanding of pedestrian movement. Key components of walkability, including density, diversity, design, and safety, are examined alongside the application of multi-criteria decision-making frameworks (e.g., MCDM/MABAC). Insights from global case studies highlight the effectiveness of these tools in modeling pedestrian flows and assessing urban design interventions. This study identifies critical gaps, including the integration of emerging technologies, mental health outcomes, and inclusive design for diverse populations. The findings underscore the need for a global walkability assessment platform, integrating ABM, GIS, and real-time data to guide urban planners in creating healthier, more sustainable cities. Enhancing walkability can significantly improve public health, environmental quality, and quality of life in urban areas worldwide.

Abstract: This research study presents a comparison of an in-person and an online conference in terms of environmental impact and energy efficiency. The main goal of our research was to prepare a complete life cycle assessment of a two-day (15-hour), 200-participant in-person (with and without travel) and online conference for different functional units by comparing the carbon footprint values. Life cycle assessment methods focus on the numerical determination of the decarbonisation of conference consumption (lunch, dinner, food and beverage consumption during program breaks) and conference organisation (organisational discussions, correspondence, Abstract booklet, registration package). The meals were examined by connecting the stages of preparation, cooking, consumption and end-of-life cycle, i.e. we performed a cradle-to-grave LCA analysis. We paid particular attention to the calculation of energy consumption, which we covered in detail. In conclusion, there is no outstanding difference between the impact assessment methods for the carbon footprint investigation. The carbon footprint value is 57% of the total impact of an entire in-person conference. The environmental impact of meals is the second largest, with 8.41 kg CO2 equivalent/person/hour. Excluding meals and travel, the calculated carbon footprint is 0.362 kg CO2 equivalent/person/hour (only considering the effect of preparation, organisation, administration and registration package).