Sustainable development and climate change are related challenges for humanity because a good quality of life in the present and the future for human, natural, and economic systems must be achieved through the capacity to face climate change. In this context, this research aimed to identify the climate change adaptation measures contribution to the sustainable development aspects: environmental, social, and economic, applied to a case study, "The Coastal Watershed Conservation Project in the Context of Change Climate (C6)" through the Sustainable Development Goals (SDG) evaluation framework. The primary data collection was carried out directly from the project reports and the reports of 29 interventions in the communities. The results indicate that the adaptation actions implemented through the C6 project did contribute to sustainable development, to a greater extent, in the social aspect, followed by environmental aspects and later by economic ones; therefore, it can be concluded that one way to achieve sustainable development can be through the implementation of adaptation actions to face climate change.

This study aims to evaluate the impact of the Intervention Model on improving community knowledge, attitudes, and behavior related to ecotourism accessibility for people with disabilities in Ternate City, with a focus on addressing gaps in understanding and support through educational services and distribution of educational materials in the form of brochures with a participatory approach. The research design used a quasi-experiment involving 4,964 residents, where 287 participants were randomly selected through Cluster Random Sampling. Three tourist destinations were chosen as research locations, namely Sulamadaha Beach, Tolire Besar Lake, and Batu Angus Geotourism in Ternate City, North Maluku. The results showed that the intervention significantly improved community knowledge, attitudes, and behavior related to ecotourism accessibility, with a significance value (Sig. (2-tailed) of 0.000 <α = 0.05) indicating a significant difference between the experimental group and the control group after community-based interventions were carried out. In conclusion, this model has been proven effective in improving community knowledge, attitudes, and behavior towards ecotourism accessibility and can be used as a reference for similar programs in other locations to improve ecotourism accessibility globally.

The consumption of contaminated water contributes to the global burden of diarrhea and other water-borne diseases, especially among young children. While the decentralized solar water disinfection treatment system remains a viable option to have safe drinking water, our understanding of the effects of household water treatment before use on willingness to accept and adopt a decentralized solar water disinfection treatment system is limited. In this study, a complementary log-log regression analysis of the compositional and contextual factors that systematically vary with willingness to accept and adopt a decentralized solar water disinfection treatment system in the Sawla-Tuna-Kalba (STK) district of Ghana was carried out. Based on our findings, a greater proportion of households’ (97%) are willing to accept and adopt a decentralized solar water disinfection treatment system. Compositional and contextual factors such as age, marital status, education, religion, and geographical location significantly contribute to households’ willingness to accept and adopt a decentralized solar water disinfection treatment system in the STK district of Ghana. Households that treat their water before use (66%, P&lt;0.05) are more likely to accept and adopt a solar water disinfection treatment system compared to those that do not treat their water before use. The findings therefore suggest that scaling up solar water disinfection systems in the STK district is sacrosanct, and highlight the necessity to analyze compositional and contextual factors influencing willingness to accept and adopt solar water disinfection systems. Our findings will inform policies and programs aimed at implementing solar water disinfection systems in the study area to improve the health of communities that must rely on poor quality drinking water sources.

Urban Heat Islands (UHI) are a growing concern in rapidly urbanizing regions, leading to significant ecological and public health challenges. This study investigates the spatiotemporal dynamics of UHI intensity in Mbombela City, Mpumalanga Province, South Africa, from 2008 to 2023. Using Landsat satellite imagery and geospatial analysis techniques, the research quantifies changes in land cover and their impact on Land Surface Temperature (LST) and UHI intensity. The results show that built-up areas increased by 86.67% over the study period, while vegetation cover decreased by 22.22%. Mean LST rose from 28.3°C in 2008 to 30.1°C in 2023, indicating a significant intensification of the UHI effect. Statistical analyses reveal strong correlations between urbanization metrics and UHI intensity, highlighting the role of human activities in exacerbating this phenomenon. This research contributes valuable insights for urban planning and climate adaptation strategies, particularly in secondary cities like Mbombela, where rapid development is creating new environmental challenges. The findings underscore the need for green infrastructure and nature-based solutions to mitigate the negative impacts of urbanization.

In recent years, the "3-30-300" concept by Professor Cecil Konijnendijk has been gaining popularity, outlining what makes a city "green" and what we should strive for. This concept suggests that from every apartment, at least 3 trees should be visible, 30% of the city's surface should be covered with greenery, and the nearest park or forest should be no more than 300 meters away. However, the lack of detailed guidelines makes the implementation of this concept a significant challenge. The goal of the research presented here was to adapt this concept for Warsaw (Poland) and assess whether it can be considered a green city. I defined parameters such as the maximum distance for visible trees as 20 meters and determined what counts toward the 30% green areas. The results showed that 57.82% of apartments in Warsaw have a view of at least 3 trees, while only 5.44% do not meet this criterion, and the rest meet it only partially. Parks and forests cover 19.95% of the city, while all green areas combined cover 42.01%. Additionally, 45% of buildings are located within 300 meters from a park or forest. Ultimately, full compliance with the "3-30-300" concept applies to 22.19% of buildings, while only 12.66% meet the criteria when considering only parks and forests. This indicates that, while Warsaw is relatively green, not all of its areas fulfill these criteria.

Land use and cover change (LUCC) is a key factor influencing global environmental and socio-economic systems. Many long-term geospatial LUCC datasets have been developed at various scales during the recent decades owing to the availability of long-term satellite data, statistical data and computational techniques. However, most existing LUCC products can not accurately reflect the spatiotemporal change patterns of LUCC at regional scale in China. Based on these geospatial LUCC products, Normalized Difference Vegetation Index (NDVI), socioeconomic data, and statistical data, we developed multiple procedures to represent both spatial and temporal changes of the major LUC types by applying machine-learning, regular decision tree and hierarchical assignment methods using the northeastern China (NEC) as a case study. In this approach, each individual LUC type was developed in sequence under different schemes and methods. The accuracy evaluation using sampling plots indicated that our approach can accurately reflect the actual spatiotemporal patterns of LUC shares in the NEC, with an overall accuracy of 0.82, Kappa coefficient of 0.77 and regression coefficient of 0.82. Further comparisons with existing LUCC datasets and statistical data also indicated our approach and dataset can more accurately and comprehensively represent the spatiotemporal patterns of all LUC types at subpixel level. Our approach unfolded the mixed pixel issue and integrated the strengths of all LUCC products through the fusion process. The analysis based on our developed dataset indicated that forest, cropland and built-up land area increased by 17.11×104 km2, 15.19×104 km2 and 2.85×104 km2, respectively during 1980-2020, while grassland, wetland, shrubland and bareland decreased by 26.06×104 km2, 4.24×104 km2, 3.97×104 km2, and 0.92×104 km2, respectively. The temporal change patterns of all these LUC types were consistent with the provincial inventory data. Our developed approach can be widely applied in the entire China and worldwide, and our data products can provide accurate data supports for studying the LUCC consequences and making effective land use policies.

The mining industry is a fundamental pillar of economic development in Peru, but mine tailings can generate contamination by toxic heavy metals such as Hg, As, Pb, Cd, and Zn, affecting health, flora, and fauna. This research aimed to characterize the physical, chemical, mineralogical, and toxicological properties of active and passive tailings in the Arequipa region of Peru to identify and quantify the presence of these heavy metals. Analytical techniques, such as granulometry, ICP, TCLP, SEM, and EDS were used to obtain detailed information about the characteristics of the tailings. The results showed significant variability in particle size distribution, as well as differences in mineralogical composition, including the presence of valuable and potentially toxic minerals. This study will allow corrective actions to be taken to prevent, minimize, or remediate potential negative impacts in the region.

Starting from the objectives of the Nature Restoration Law and the concept of Forest Landscape Restoration, the objective of the following study is to present a methodological approach aimed at identifying areas that are best suited for the implementation of NbS for the improvement of landscape and habitat status in the study area identified by the Urban Atlas of the city of Campobasso (1028.64 km2). Using open data (ISPRA ecosystem services and Regional land use capability), an expert based approach (questionnaire) and a multi-criteria analysis (AHP), the Total Ecosystem Services Value (TESV) index was determined as a weighted additive sum of the criteria (Data) considered. The index was then classified into 8 clusters and the land use “Cropland” was extracted, matching Cluster 1 (740.09 Ha) with agricultural areas with less ES provision to be allocated to NbS and Cluster 8 (482.88 Ha) with valuable areas to be preserved, for a total of 2.34% of the study area. It was then possible to compare the “Forest” areas currently present in the study area with those of a possible future scenario, represented by the areas occupied today by forest with the addition of cluster 1 croplands. A landscape analysis was then conducted: it showed greater dispersion and fragmentation of forest patches in the future scenario but also greater connectivity and thus greater ecological functionality of the patches.

This research uses the DPSIR framework to analyse and anticipate Taiwan's marine ecosystems' ecological security. The goal is to understand how human-induced pressures including pollution, overfishing, habitat loss, and climate change affect Taiwan's marine habitats. For Taiwanese maritime ecological security assessments, the DPSIR framework, case studies, and assessment models are used. The DPSIR model classifies human activities, environmental pressures, marine ecosystem states, effects, and reactions. Assessment models complement the DPSIR framework for systematic and quantitative evaluation, while case studies are practical. This systematic literature review follows PRISMA guidelines for thoroughness and organisation. The research shows the DPSIR framework's adaptability and efficacy in analysing Taiwan's complex marine biological processes. Human-induced stressors on marine states are revealed via a comprehensive examination of causes, pressures, states, consequences, and responses, contributing to ecological security discourse. The DPSIR model helps Taiwan identify environmental change sources, analyse pressures, and provide sustainable marine environmental management solutions. The research concludes that the DPSIR framework is crucial to Taiwan's maritime environmental preservation. Despite its strengths, the DPSIR framework has to be refined and customised to Taiwan's unique maritime environments. Taiwan's maritime protection policies are shaped by the study's evidence-based findings. The report also recommends combining the DPSIR framework with SWOT analysis to improve evaluation and decision-making in Taiwan's marine environmental protection setting.

The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

This study examines the varying performance of low-cost sensors during seasonal smog events, particularly those resulting from solid fuel combustion linked to residential or industrial heating and other pollution sources. A prototype low-cost sensor node developed within this work was collocated for several months with reference equipment at an air quality monitoring station in the city of Ostrava (Moravian-Silesian region, Czech Republic). Deterioration of air quality during a pair of smog alert events, namely wintertime "Polish smog" episode and springtime "Saharan dust storm", which occurred through the extended period of co-location, was characterized quantitatively based on particulate matter and carbon monoxide concentrations measured by the low-cost sensors. These datasets were compared with reference data and model predictions provided by the Copernicus Atmospheric Monitoring Service. Selected low-cost sensors demonstrated linear response during Polish smog episode with highly correlated time series of carbon monoxide and particulate matter concentrations. However, when compared to reference data, the Sensirion SPS30 sensor is found to overestimate coarse particle (PM10) concentrations by the factor of two during winter evaluation period. On the other hand, oppositely biased PM10 response (underestimation by the factor of five) was determined from co-location measurement during Saharan dust storm event. This observation was attributed to combination of specific detection efficiency of the given low-cost sensor and shifts in particle size distribution due to seasonal change of ambient conditions.

Hydrogen is a clean-burning fuel with water as its only by-product, yet its widespread adoption is hampered by logistical challenges. Liquid organic hydrogen carriers, such as alcohols from sustainable sources, can be converted to hydrogen through aqueous phase reforming (APR), a promising technology that bypasses the energy-intensive vaporization of feedstocks. However, the hydrothermal conditions of APR pose significant challenges to catalyst stability, which is crucial for its industrial deployment. This review focuses on the stability of catalysts in APR, particularly in sustaining hydrogen production over extended durations or multiple reaction cycles. Additionally, we explore the potential of ultrasound-assisted APR, where sonolysis enables hydrogen production without external heating. Although the technological readiness of ultrasound-assisted APR currently trails behind thermal APR, the development of catalysts optimized for ultrasound use may unlock new possibilities in the efficient hydrogen production from alcohols.

The saguaro cactus (Carnegiea gigantea) plays a pivotal role in desert ecosystems, making its population monitoring essential. Traditional census methods used by the United States Forestry Service are resource-intensive, prompting a need for more cost-effective alternatives. Automated detection methods using advanced object detection models applied to drone imagery present a promising solution. In a proof-of-concept study, 244 drone images of saguaros were captured from a top-down perspective over an undeveloped hill adjacent to Sun Ray Park, Phoenix, Arizona (33.3188° N, 111.9980° W), from altitudes of 486, 507, and 519 meters above mean sea level (AMSL). We employed the Mask R-CNN model from the Detectron2 framework for model training. The images were divided into training, validation, and test sets in an approximate 8:1:1 ratio, with the sets separated by their physical locations within the park: training data was centralized, validation data was positioned to the east, and test data was located in the west. The Mask R-CNN achieved an average precision of 89.8% and an average F1 score of 90.3% in identifying saguaros across 27 test images from 486/507 m AMSL, demonstrating the model's effectiveness in accurately identifying saguaro cacti. Despite the limited sample size, the model's adaptability to diverse scenarios underscores its potential for practical applications in ecological conservation. This research contributes to the field of automated monitoring by offering a viable alternative to labor-intensive methods, thus supporting the sustainability of saguaro in their native habitat.

Accurate solar power forecasting is pivotal for the effective planning, management, and operation of power systems, ensuring a sustainable energy supply for consumers while optimizing the integration of renewable energy sources and the functioning of electricity markets. Recent advancements have focused on developing predictive models that offer precise daily forecasts for solar power generation, which are critical for power usage planning and production efficiency. These models typically leverage a diverse range of data inputs, including solar power metrics and meteorological variables such as temperature, humidity, precipitation, solar radiation, and wind speed—factors that significantly influence solar power generation due to their weather-dependent nature. This study introduces a novel hybrid forecasting model that synergizes the capabilities of the Prophet model and Gated Recurrent Units (GRU), leveraging their respective strengths to enhance predictive performance. The proposed model is rigorously evaluated across short-term (2 days, 7 days) and medium-term (15 days, 30 days) forecasting horizons. Experimental results demonstrate that the hybrid model significantly outperforms the existing models, delivering superior accuracy in solar power generation forecasts.

The purpose of this study is to examine how sales restrictions on gasoline-powered vehicles influence patent application behavior in automobile engine technology. This is achieved through a factorial analysis targeting structural changes in patent applications. We examine patterns in patent data from 1985 to 2019 using index decomposition based on the logarithmic mean Divisia index. The analysis utilizes patent data and focuses on one non-green (internal combustion engine vehicles) and three green automobile engine technologies (battery electric vehicle, hybrid electric vehicle, and full cell vehicle). Furthermore, the study focuses on four significant patent offices (China, Japan, the United States, and Germany) to examine global disparities. The analytical results reveal that a country’s green investment pattern changes depending on its growth stage. Intellectual property protection policies and encouragement of R&D will extend the scope of R&D activities in emerging nations where intellectual property markets are not well developed. Furthermore, different countries have distinct product development strategies when it comes to various forms of green automotive engine technologies. Green patents are likely to rise in industrialized nations for incentives such as tax breaks or subsidies for R&D spending on specific technological advancements and the extension of intellectual property protection.

Throughout history, natural stone has been a crucial building material due to its strength, durability, and aesthetic qualities. Today, it continues to be a valuable resource, representing both a cultural heritage asset and a significant economic material. However, the increasing frequency of heat waves and fires driven by climate change poses a growing threat to stone building materials. This paper presents a literature review aimed at assessing the effects of high temperatures on Global Heritage Stone Resources (GHSR). GHSR is an international classification designed to enhance the recognition and status of building stones. The study identifies a critical need for research to better understand how those stone materials behave when exposed to high temperatures in order to predict and mitigate the effects of external threats such as fires.

The cities of Lomé and Kara in Togo are undergoing rapid urbanisation due to strong demographic pressure, leading to a reduction in Urban Green Spaces (UGSs), which are essential for climate regulation and urban well-being. This situation highlights the climatic vulnerability of these areas, making it imperative to reconcile urban development and the preservation of UGSs for urban sustainability. This study contributes to the sustainable conservation of UGSs in Togo. More specifically, it uses Landsat images from 1988_2000_2012_2022 to analyse the spatiotemporal dynamics of UGSs in Lomé and Kara and develops vulnerability models in 2022 and for the 2060 and 2100 horizons based on SSP 2.45 and SSP 5.85 climate scenarios via the multifactorial method. The results revealed a reduction in UGSs in Lomé (from 85.98% in 1988 to 40.78% in 2022) and Kara (from 93.99% to 36.68%), mainly due to urban expansion. Low- and medium-vulnerability zones, often located in UGSs, will likely disappear by 2060 and 2100, accentuating urban climatic vul-nerability. Hence, there is an urgent need to implement conservation policies, create new UGSs, and increase awareness among city dwellers.

This study utilizes a combination of optical PlanetScope imagery and Synthetic Aperture Radar (SAR) data from Sentinel-1 to investigate land cover changes in Dili, Timor-Leste, following Tropical Cyclone Seroja. The primary objective is to evaluate how integrating these datasets enhances disaster monitoring and response in flood-affected areas. The Random Forest classifier, known for its robustness in handling high-dimensional and noisy data, was applied to classify land cover into six distinct classes: vegetation, water, built-up areas, bare soil, clouds, and shadows. The classification was performed across three phases—pre-disaster, post-disaster, and recovery—using Google Earth Engine (GEE). Binary segmentation using Otsu thresholding was applied to the SAR images to refine the classification by delineating water bodies from non-water areas. The study achieved high classification accuracy, with overall accuracy ranging from 97.2% to 98.7%, and the Kappa index, which measures agreement between classified and reference data, remained consistently strong, indicating reliable model performance. Key results include a significant increase in water bodies and extensive damage to vegetation and built-up areas during the post-disaster phase, followed by partial recovery in the subsequent period. Despite the high accuracy, urban areas posed classification challenges due to misclassification between built-up and bare soil categories. This research offers a methodological framework for integrating optical and SAR data with machine learning for land cover change detection in post-disaster scenarios. The societal benefits of this study include improved disaster preparedness, enhanced recovery planning, and valuable insights into flood impact mitigation in regions like Timor-Leste, where technical and data limitations persist.

This study aims to assess the impact of deforestation and carbon sequestration within the Gandoca-Manzanillo Wildlife Refuge, Costa Rica, using high-resolution satellite imagery from Planet NICFI and Sentinel-2. The research evaluates the impact of forest degradation on forest health and carbon absorption during two periods in 2024 (January and August). Advanced vegetation indices (NDVI, GNDVI, EVI, and SAVI) were applied to assess changes in forest cover and health. Planet NICFI data revealed a 0.41% loss in forest cover, identifying subtle degradation less detectable with Sentinel-2. In terms of carbon sequestration, Sentinel-2 showed a modest increase in CO₂ absorption from 180,231.88 tons to 186,211.33 tons, while Planet NICFI indicated a decrease from 3,984,470.82 tons to 3,820,917.83 tons, suggesting forest degradation. These findings highlight the importance of high-resolution satellite imagery in detecting fine-scale disturbances that lower-resolution data might miss. However, the study’s short timeframe, covering only two periods in 2024, limits long-term trend analysis. The research underscores the need for improved remote sensing technologies and stronger legal frameworks to protect areas like Gandoca-Manzanillo from illegal logging. Despite programs like Payment for Ecosystem Services (PES) in Costa Rica, significant challenges remain in protecting biodiversity-rich regions.

Analyzing the material metabolism of infrastructure provides insights into resource flow and accumulation, offering policymakers new perspectives on resource management and planning. This study comprehensively analyzed the material stock (MS) of infrastructure on Miao Island using a bottom-up approach and Pleiades stereo images. Key findings include: (1) in 2020, the total in-use MS was 249.8 kt, with stone, gravel, and sand accounting for 80%. Transportation infrastructure held the largest share (40.8%), followed by non-residential buildings (30.1%), residential buildings (26.7%), and pipeline infrastructure (2.4%); (2) Stone and wood are abundant resources on the island, whereas steel, ceramics, aluminum, and glass are entirely dependent on external supply; (3) Over the past 40 years, the MS has grown significantly, with a net addition stock of 179.6 kt (256% growth). Development periods from 1980-1990, 2000-2005, and 2015-2020 were closely linked to China's reform and opening-up, infrastructure stimulus plans, and local government policies; (4) Compared to urban areas, Miao Island has lower construction intensity, with a per capita MS growth rate of 10.12 t/yr versus the national average of 13 t/yr. The steel stock in residential buildings is 15.62 kg/m², only 37.82% of the urban average. However, the potential conversion of infrastructure stock into waste still poses a significant challenge to the island's environmental carrying capacity.

The detection of complex formations, initially suspected to be oil spills, is investigated using atmospherically corrected multi-spectral satellite images and deep learning techniques. Several formations have been detected in an inland lake in Northern Greece. Four atmospheric corrections (ACOLITE, iCOR, Polymer, and C2RCC) that are specifically designed for water applications are examined and implemented on Sentinel-2 multispectral satellite images to eliminate the influence of the atmosphere. Out of the four algorithms, iCOR and ACOLITE are able to depict the formations sufficiently; however, the latter is chosen for further processing due to fewer uncertainties in the depiction of these formations as anomalies across the multi-spectral range. Furthermore, a number of formations are annotated at a pixel level for the 10 m bands (Red, Green, Blue, and NIR), and a deep neural network (DNN) is trained and validated. Our results show that the 4-band configuration provides the best model for the detection of these complex formations. Despite not being necessarily related to oil spills, studying these formations is crucial for environmental monitoring, pollution detection, and the advancement of remote sensing techniques.

Water scarcity presents one of the greatest challenges of our time. Especially in naturally water scarce regions, the need of additional water resources is rising, requiring innovative and site-adapted technologies. In this review, selected non-conventional water technologies for sustainable water management are strategically evaluated. Focused on a matrix-based assessment incorporating key indicators, the study discusses the availability, applicability, environmental impact, scalability, and economic viability of the selected technologies. Based on a comprehensive literature review, the methodology involves a systematic comparison of technologies for desalination, water reuse, groundwater utilization, agricultural reuse, and unconventional approaches like cloud seeding, dew water, and fog water harvesting. The indicators covering the respective categories are presented in the results. On this basis, the different technologies are analyzed by matrix-based evaluation, highlighting various strengths and weaknesses and providing insights into technology selection based on regional needs. The discussion interprets the findings, deriving implications for dry environments, acknowledging limitations and suggesting pathways for future research. Through a rigorous analytical framework, this study contributes to the discourse on sustainable water solutions, offering valuable insights for policymakers, researchers and industries during a decision-making progress.

Sulfated denitrification process has attracted more and more attention because it does not depend. on the addition of organic matter. Nowadays, the tailwater from urban wastewater treatment plant has high nitrogen concentration. In this case, we propose a sodium thiosulphate siderite autotrophic denitrifying (STSAD) biofilter for the removal of nitrogen from the tailwater of the urban wastewater treatment plant. Under the optimal conditions, the NO3--N removal rate was 100%, TN removal rate was above 95%. Many microorganisms were enriched on the surface of the ceramsite and siderite filler in the reactor, and the main denitrification microorganisms in the filter were Thiobacillus and Ferritrophicum. The process has certain technical and economic feasibility for denitrification in the tailwater of urban wastewater treatment plant, the cost is only 0.042 CNY/t(water), and the effluent TN and COD meet the requirements of water quality standards, so it is a suitable method.

Demand for livestock products in East Africa is anticipated to triple by 2050. Therefore, sustainable intensification of livestock production systems for increased productivity is necessary in line with minimal negative environmental consequences. An agronomic field experiment was set up at the International Livestock Research Institute in Nairobi, Kenya, and the effects of organic and inorganic soil amendments on greenhouse gas emissions (particularly N2O) from a Humic Nitisol planted with Brachiaria brizantha cv. xaraes were evaluated between October 2018 and August 2019. The treatments comprised mineral NPK fertilizer, Lablab intercrop, FYM, FYM-BC, Bioslurry, and control. Fertilizer treatments were applied at a rate of 45 kg N ha-1 following each harvest. GHG emissions were measured using the static vented chamber technique. Treatment and season significantly influenced daily N2O emissions. The lowest (4.51±3.30 µg N m-2 h-1) and highest (27.16±3.61 µg N m-2 h-1) mean N2O emissions were recorded under NPK and Control treatments during the short rains and dry seasons, respectively. Cumulative N2O emissions and the corresponding yield-scaled emissions were similar across all the treatments but varied significantly (p < 0.001) between the wet and dry seasons. Cumulative N2O emissions were 0.31±1.49, 0.33±1.47, 0.33±1.74, and 0.37±1.74, 0.38±2.3 and 0.42±1.81 Kg N ha-1 under FYM-BC, Lablab, NPK fertilizer, and FYM, Bioslurry and control treatments respectively. The corresponding yield-scaled emissions were also higher during the wet (0.23±1.16 g N kg-1 DM) than in the dry seasons (0.16±0.50 g N kg-1 DM). Higher (-21.86±4.47 mg CH4-Ch-1) CH4 uptake was recorded under the control treatment whereas the lowest (-2.69±17.97 mg CH4-Ch-1) uptake was recorded under Bioslurry (P < 0.01). Treatment and season exhibited individual effects on daily CO2 emissions (P < 0.001), with a significant interaction effect (P < 0.001). The highest (157.5±28.76 mg CO2-C m-2h-1) and lowest (44.33±8.37 mg CO2-C m-2h-1) CO2 emissions were recorded under Control and FYM treatments during the October 2018-January 2019 and January-March 2019 HD. Since the experiment was newly established via ploughing a field which had been used as a permanent pasture during previous years, did not expect considerable yield differences between treatments. Yet, it is interesting to see first effects of fertilizer amendments, pointing to their potential as climate-smart forage intensification strategies. The study established that Manure + biochar is a better strategy for forage soil amendments in mitigating soil N2O emissions.

High-accuracy geolocation is crucial for high-resolution spaceborne SAR images. Most advanced SAR satellites have better theoretical geolocation accuracy than 1 meter but maybe unrealizable with less accurate external data, including the atmospheric parameters and the ground elevations. To investigate the actual SAR geolocation accuracy in common applications, we analyze the properties of different geolocation errors, propose a set of geolocation procedures, and conduct experiments on TerraSAR-X images and a pair of Tianhui-2 images. The results show that based on GNSS elevations, the geolocation accuracy is better than 1 meter for TerraSAR-X and 2 meters / 4 meters for the Tianhui-2 reference / secondary satellite. Based on the WorldDEM and the SRTM, an extra geolocation error of 2 and 4 meters will be introduced, respectively. By comparing the effectiveness of different tropospheric correction methods, we find that the GACOS mapping method has advantages in terms of resolution and computational efficiency. We conclude that the tropospheric error and the ground elevation error are the primary factors influencing the geolocation accuracy, and the key to improving accuracy is to use higher-accuracy DEMs. Additionally, we propose and validate a geolocation model for the Tianhui-2 secondary satellite.

In this study, we address key challenges in enhancing ruminant productivity across Ethiopia, Tanzania, Kenya, Uganda, Rwanda, and Burundi in eastern Africa. A critical focus is on improving access to forage seeds essential for cattle systems. Key factors such as ruminant population, feed deficits, and governmental priorities are examined. The primary challenge lies in inadequate access to affordable forage seeds, compounded by reluctance to invest in perceived expensive elite varieties. Many farmers rely on outdated planting materials, lacking purity and vigor. To overcome these barriers, our analysis stresses the urgency of transitioning to recently improved seed varieties through rigorous selection and breeding. Promoting domestic seed production within Africa is crucial to mitigate high import costs and logistical complexities like variety registration. A coordinated regional approach to seed production could enhance economic viability and foster widespread adoption of improved forages. Such initiatives align with governmental agendas for sector development, offering triple benefits: increased productivity, environmental gains through reduced methane emissions, and regional economic growth.

In the context of high frequency and intensity forest fires driven by future warming and drying climate, early detection and effective control of fires are extremely important to reduce losses. Satellite remote sensing, characterized by its broad observational scope and strong repeat observation capability, has become an important method for rapid monitoring of forest fires, replacing traditional means such as manual measurement and aerial photography. This paper addresses the misjudgments caused by solely relying on changes in infrared band brightness values and single-band separation in forest fire discrimination. It constructs the angle slope index ANIR based on the red band, near-infrared band and short-wave infrared band, Angle slope index AMIR based on the short-wave infrared band, mid-infrared band and far-infrared band, and angle slope index AMNIR based on the difference between the AMIR and ANIR, integrating the strong inter-band correlations and the reflectance characteristics of visible and short-wave infrared bands to simultaneously monitor smoke and fuel biomass changes in forest fires. To address the omissions caused by single-image fire discrimination algorithms, the paper constructs time-series Angle slope difference indices ∆ANIR and ∆AMIR. To address the different fire point discrimination thresholds caused by background difference in different time and space, the paper uses the decomposed three-dimensional OTSU(Maximum inter-class variance method) algorithm to calculate the segmentation threshold of the sub-regions constructed from the AMNIR data. The methods were validated using high temporal resolution meteorological satellite Himawari-8 and ground truth fire data from Hunan Province, China, from 2018-2019. The results show that the discrimination method based on the ASITR(Angle slope indices thresholds) method exhibits an accuracy of 0.88, a missed detection rate of 0.17, and an overall evaluation value of 0.85, which is 0.09 higher in accuracy and 0.03 higher in overall evaluation than the discrimination method based on infrared bands brightness values proposed by Feng et al. [1] (accuracy 0.79, missed detection rate 0.14, overall evaluation value 0.82); the method based on the FAMN_OTSU_ASITR (Fusion of Angle slope difference index (false) data, Decomposed three-dimensional OTSU adaptive threshold segmentation Algorithm and ASITR) exhibits an accuracy of 0.85, a missed detection rate of 0.08, and an overall evaluation value of 0.88, which is 0.09 lower in missed detection rate and 0.03 higher in overall evaluation as compared to the ASITR. The results demonstrate that the forest fire discrimination method based on Angle slope indices, Himawari-8 satellite data, and the decomposed OTSU adaptive threshold segmentation algorithm can improve the accuracy of forest fire discrimination and reduce errors in missed detections.

The application of machine learning (ML) and remote sensing (RS) in soil and water conserva-tion has become a powerful tool. As analytical tools continue to advance, the variety of ML al-gorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review on the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including biomass-vegetation, soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applica-tions in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the field of soil and water conservation.

The detection of microplastics (MPs) in drinking water has emerged as a critical environmental concern, with significant implications for public health. However, the variability in identification methods across studies has made it difficult to compare results, underscoring the urgent need for standardized protocols. This study presents a comprehensive meta-analysis and practical ap-proach to standardizing Fourier Transform Infrared Spectroscopy (FTIR)-based methodologies for detecting MPs in drinking water. By systematically reviewing studies from 2019 to 2023, this research identifies the most effective techniques for MP quantification, with an emphasis on the use of 0.45 μm cellulose nitrate filters and Nile red staining for fluorescence detection. The me-ta-analysis demonstrates consistent results across studies, supported by statistical analysis con-firming the reproducibility and reliability of the methodologies employed. Experimental findings highlight that the cellulose nitrate filter offers superior microplastic retention with low back-ground fluorescence, making it ideal for FTIR analysis. Moreover, the Nile red spray method is recommended for its efficiency and uniformity in staining, and random counting proves to be a rapid and reliable quantification technique. This study advocates for the adoption of standard-ized protocols, particularly in filter selection and staining methods, to ensure accurate and com-parable MP detection. The findings contribute valuable insights for optimizing water treatment processes and provide a solid foundation for future research and regulatory frameworks aimed at mitigating microplastic contamination in water resources.

It is challenging to apply the receiver function method to teleseisms recorded by ocean bottom seismographs (OBS) due to a specific working environment that differs from land stations. Tele-seismic incident waveforms reaching the vicinity beneath stations are contaminated by multiple reflections generated by seawater and sediments and noise resulting from currents. Furthermore, inadequate coupling between OBS and seabed reduces the signal-to-noise ratio (SNR) of seismograms, leading to poor quality of extracted receiver functions or even wrong deconvolution results. For instance, the poor results cause strong ambiguities regarding Moho depth. This study uses numerical modelling to analyze the influences of multiple reflections generated by seawater and sediments on H-kappa stacking and neighborhood algorithm. Numerical modellings show that seawater multiple reflections are mixed with coda waves of direct P-wave and slightly impact extracted receiver functions, thus can be ignored in subsequent inversion processing. However, synthetic seismograms have strong responses to the sediments. Compared to waveforms of horizontal and vertical components, sedimentary responses are too strong to identify the converted waves clearly. The extracted RFs correspond to the above influences, resulting in divergent results of H-kappa stacking (i.e., Moho depth and crustal average VP/VS ratio are unstable and have great uncertainties). Fortunately, waveform inversion approaches (e.g., neighborhood algorithm) is available and valid for obtaining S-wave velocity structure of the crust-upper mantle beneath the station with sediments varying in thickness and velocity.

Remote sensing (RS) and Geographic Information Systems (GIS) provide significant opportunities for monitoring and managing natural resources across various temporal, spectral, and spatial resolutions. There is a critical need for natural resource managers to understand the expanding capabilities of image sources, analysis techniques, and in-situ validation methods. This article reviews a range of image analysis tools applicable to natural resource management, including agriculture, water, forests, soil, and natural hazards, and compares their functionalities. Our study highlights that Google Earth Engine (GEE) is favored for wide-area analysis due to its extensive coverage and free access. Global Mapper excels in 3D and light detection and ranging (LIDAR) data, environment for visualizing images (ENVI) specializes in multi- and hyperspectral image processing, ERDAS IMAGINE is optimal for radar data, and eCognition is used for object-based image analysis. The article emphasizes the importance of in-situ validation data, which provides essential ground truth information to calibrate and validate RS models, thereby enhancing accuracy. Understanding these tools and integrating them with in-situ validation techniques enables natural resource managers to improve their monitoring and decision-making processes, facilitating effective collaboration with RS researchers.

Acidic soils limit plant nutrient availability, leading to deficiencies and reduced crop yields. Agricultural liming agents address these issues and are crucial for deploying silicate amendments used in enhanced rock weathering (ERW) for carbon sequestration and emission reduction. Grower recommendations for liming agents are based on the liming index (LI), which combines the neutralizing value (NV) and fineness rating (FR) to predict a mineral’s acidity neutralization relative to pure calcite. However, the LI was originally developed for carbonate minerals, and its applicability to silicates remains uncertain, with studies often yielding inconclusive results on soil carbon and liming efficiency. This study aims to evaluate the liming efficiency of silicates. We determined the LI of five candidate ERW minerals (basalt, olivine, wollastonite, kimberlite, and montmorillonite) and compared them to pure calcite. Post-NV acid digestion, we characterized the minerals and soils, applying nonparametric statistical tests (Wilcoxon, Kendall) to correlate liming results with LI, dosage, and amendment methods. We developed an empirical model incorporating mineralogy and kinetics to explain silicate behavior in liming, considering soil, climate, and crop factors.

Re-purposing wastewater is necessary to circumvent water shortage for vegetable farmers in urban areas and to reduce public health hazards associated with using contaminated water for irrigation. In this study, the efficiency of biochar filtration systems in removing contaminants from wastewater was investigated. The hydraulic retention time (HRT) and the cleaning mechanism of coconut biochar filtration systems were examined. The filtration systems were designed with biowastes, specifically coconut biochar, as an adsorbent to remove pollutants from wastewater. Two setups of bed, 40 cm and 30 cm thick, were examined under different HRTs. Fourier Transform Infrared Spectroscopy (FTIR) analysis and Scanning Electron Microscopy (SEM) imaging were used to ascertain the properties of the biochar that enhances the removal of pollutants in wastewater. For both setups, the removal of heavy metals (Lead, Pb and copper, Cu) as well as Total Coliform (TC) was judged satisfactory but appeared ineffective in removing E. coli.

The interplay between wildfires and drought stress has significantly limited forest recovery in the Mediterranean ecosystem of central Chile. Since 2010, the region has faced a prolonged 'Mega Drought,' which intensified into a 'Hyper Drought' in 2019, with record-low precipitation and high temperatures, further constraining forest recovery amidst increasing wildfire frequency. This study evaluates short-term vegetation recovery across drought gradients in three sclerophyllous evergreen forests, analyzing Landsat time series (1987–2022) for 42 wildfires. Using the LandTrendr algorithm, we assessed post-fire forest recovery based on NDVI changes between pre-fire values and subsequent years. The results revealed significant differences in recovery across drought gradients and fire severity interactions. After five years, the xeric forest dominated by Quillaja saponaria and Lithrea caustica showed 89% recovery during the Mega Drought and 34% during the Hyper Drought. In contrast, the mesic forest dominated by Cryptocarya alba and Peumus boldus exhibited 28% recovery during the Mega Drought and 43% during the Hyper Drought. Degraded thorny shrublands showed the lowest recovery, averaging 23% under Hyper Drought and medium fire severity. These findings underscore the critical role of prolonged, severe drought in shaping forest recovery dynamics and highlight the need to understand these patterns to improve future forest resilience under increasingly arid conditions.

Paroxysmal explosive activity at Etna volcano (Italy) became very common during the last three decades, rising concern on the civil protection authorities because of its tremendous impact on the local population, infrastructures, viability and air traffic. Between 4 July and 15 August 2024, during the peak of the touristic season when the local population doubles, Etna volcano gave rise to a sequence of six paroxysmal explosive events from the summit crater named Voragine. This is the oldest and largest of the four Etna’s summit craters and normally displays just degassing, with the previous explosive sequences occurred in December 2015 and May 2016. In this paper, we use thermal images recorded by the monitoring system maintained by the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (INGV–OE) and an automatic routine previously tested in order to automatically define the starting and ending time of each episode, the duration, total volume of erupted fluids (gas plus pyroclastic material), erupted volume of pyroclastic material, time-averaged discharge rate (TADR), and height (maximum and average) of the lava fountains. These data allowed us to infer the eruptive processes and gain some insights on the evolution of the explosive sequences useful for hazard assessment.

Copper, along with gold, was among the first metals that humans employed. Thus, copper pol-lution of the world's water resources is escalating, posing a significant threat to human health and aquatic ecosystems. It is crucial to develop a detection technology that is both low-cost and fea-sible, as well as ultra-selective and sensitive. This study explores the use of the NH2-Xxx-His motif-derived peptide from phage display technology for ultra-selective Cu2+ detection. Various Cu-binding M13 phage clones were isolated, and their affinity and cross-reactivity for different metal ions were determined. A detailed analysis of the amino acid sequence of the unique Cu-binding peptides was employed. For the development of an optical chemosensor, a peptide with an NH2-Xxx-His motif was selected. The dansyl group was incorporated during solid phase peptide synthesis, and fluorescence detection assays were employed. The efficacy of the Cu2+-binding peptide was verified through spectroscopic measurements. In summary, we de-veloped a highly selective and sensitive fluorescent chemosensor for Cu2+ detection based on a peptide sequence from phage display library that carries the N-terminal Xxx-His motif.

Soil carbon is newly refined as ~32,000 Gt C (gigatonnes) over ten times previous totals. Soil organic carbon (SOC) of ~22,000 Gt C plus plants at ~2,400 Gt C in both above- and below-ground stocks hold 99% of Earth’s biomass. Occupying a topographic surface area of 25 Gha with mean depth 21 m, soil has more organic carbon than all plants, oceans, fossil fuels, or the atmosphere combined. Soils are both the greatest biotic carbon store and the most active CO2 source. Basic inventory is required for proper understanding and utilization of Earth’s natural resources, especially with increasing soil degradation and species loss. Values are raised considerably, the disparity due to lack of full soil depth survey, neglect of terrain and of other omissions. Herein new totals are determined for mineral soils, permafrost, and peats (of all forms and ages), to full depth (easily doubling shallow values) and allowing for terrain that is ignored in all terrestrial models (doubling most values again), plus SOC in recalcitrant glomalins (~30%) and friable saprock (+26%). Additionally, soil inorganic carbon (SIC), aquatic sediments and dissolved fractions are factored in. Soil biota (e.g. fungi, bacteria and earthworms) are similarly upgraded. Primary productivity, also upped for terrain, is confirmed at 220 Gt C/yr on land supported by C/O isotope fluxes. Priority issues of species extinction, humic topsoil loss, and excess atmospheric CO2 are all remedied by SOC restoration and biomass recycling via (vermi-)compost for use in 100% organic husbandry and adoption of wider practices of Permaculture based upon scientific observation of Nature.

Considerable attention has recently been given to the contribution of greenhouse gas (GHG) emissions of the healthcare sector to climate change, which represents 4-6% of emissions worldwide. GHGs used in medical practice are regularly released into the atmosphere and contribute to elevation in global temperatures that produce detrimental effects on the environment and human health. Consequently, a comprehensive assessment of their global warming potential (GWP) characteristics and clinical uses, many of which have evaded scrutiny from policy makers due to their medical necessity, is needed. Of major interest are volatile anesthetics, analgesics, and inhalers, as well as fluorinated gases used as tamponades in retinal detachment surgery. In this review, we compare estimates of these gases’ GHG emissions in metric tons CO₂ equivalent (MTCO₂e) and their relative GWP. Notably, the anesthetics desflurane and nitrous oxide contribute the most emissions out of the major medical GHGs, equivalent to driving 12 million gasoline-powered cars annually in the US. Retinal tamponade gases have markedly high GWP up to 23,500 times compared to CO₂ and long atmospheric lifetimes up to 10,000 years, thus bearing the potential to contribute to climate change long-term. This review provides the basis for discussions on examining the environmental impacts of medical gases with high GWP, determining whether alternatives may be available, and reducing emissions while maintaining or even improving patient care.

The potential of both plasma and nanotechnology in the production of slow-release fertilizers is immense. These technologies, when combined, may offer green and cheap nitrogen fertilizers, from rich renewable resources available in local areas. Together these technologies may overcome some limitations of conventional synthetic fertilizers which currently are expensive and associated with low nitrogen use efficiency and significant environmental concerns. This review explores how recent advances in plasma and nanotechnology together might be leveraged to formulate new slow-release nitrogen fertilizers. While no attempt has been made so far to produce slow-release nano fertilizers from low-cost liquid nitrate generated by emission-free non-thermal plasma, plasma nitrate has already been reported as effective as a conventional fertilizer for crop production. We propose a more efficient electrocatalytic conversion of plasma nitrate to ammonium salt, followed by coating using plant-based cellulose nanoparticles into a slow-release form. This would synchronize nutrient release with the dynamic N requirements of plants. Formulations using agro-based low-cost cellulose nanomaterials might then replace high-cost carrier hydrogels associated with low mechanical strength. The review also highlights the isolation of nanocellulose from various plant materials and its characterization for use in different formulations of slow-release nano plasma N fertilizer. Additionally, we discuss mechanisms of N loss, slow-release, and retention in the soil. The knowledge from this review has potential to contribute to the production and use of efficient, sustainable fertilizers, to improving food security and, consequently the health of our planet.

Rural areas are not only faced with the problems of rural depopulation but also with the problem of water supply and water quality which is a basic necessity of life. In light of this, this paper assesses the rural water supply situation and the bacteriological quality of different water sources in the Obokun Local Government Area of Osun State, Nigeria. The safety of the water sources for human consumption and possible occurrence of the water borne diseases in the study area were determined. The study employed descriptive and analytical methods of data analysis to achieve the objectives of the inquiry. Findings showed that the study area suffered from inadequate water supply and poor locations of the sources of water: The study also revealed that the microbiological quality of the water sources used was unsafe, poor and not acceptable for human consumption. In addition, many man- days-hours were wasted in search of water. It is therefore recommended that the government should make the rural water supply and the maintenance of water quality a priority in its policy formulation.

Digital transformation, powered by technologies like AI, IoT, and big data, is reshaping industries and societies at an unprecedented pace. While these innovations promise smarter energy management, precision agriculture, and efficient resource utilization, they also introduce serious environmental challenges. This paper examines the dual impact of digital technologies, highlighting key threats such as rising energy consumption, growing e-waste, and increased extraction of raw materials. For instance, global e-waste reached 62 million metric tons in 2022, and data centers alone accounted for nearly 1% of the world's electricity demand in 2019. The review synthesizes findings from studies on topics like the energy use of blockchain technologies and the environmental costs of raw material extraction in the smartphone industry. Moreover, it identifies critical research gaps, particularly in understanding the environmental impact of digital usage at individual and household levels. Practical strategies such as integrating circular economy principles, promoting renewable energy, and green computing are proposed to balance technological advancement with sustainability goals. This study highlights the need for a holistic approach, suggesting future research directions to minimize digital transformation’s environmental footprint while maximizing its sustainability benefits.

Azolla is a substitute compost that has the potential to enhance nutrient cycling in agricultural systems for sustainable development. In this study, four experiments were conducted to compare the Department of Agriculture (DOA, Thailand)'s methodology for determining the suitable type and rate of animal manure and the optimal light intensity for the growth and yield of Azolla (Azolla microphylla). The results revealed that applying 100% pig manure gave the highest yield of Azolla compared to the other manure. However, there was no discernible difference in yield across the various doses (20.16, 30.16, and 40.16 gN m-2) of pig manure treatments, for which the minimal pig manure dosage of 20.16 gN m-2 was chosen. For further experimentation in the optimal light in-tensity, the 40% shading gave the highest yield of Azolla compared to no shading or 20 and 60% shading. When compared with the DOA Thailand methodology (1.27 kg m-2 of cow manure and covered with a size 32 mesh net), the findings indicated that the modified method (20.16 gN m-2 of pig manure + 40% shading) gave a 16% greater Azolla yield than that under the DOA Thailand methodology.

In a time of ecological and social crises, designing new institutions is crucial for meeting future challenges. Institutions emerge from the imaginations of individuals, moving into social movements and crystallizing in legal structures. As present movements seek to develop tomorrow’s governances, they turn to decentralized structures: distributed networks with little hierarchy, characterized by a diversity of actors and dynamics. While movements and technologies may seek to utilize decentralized ideas, they lack design principles: only recently has it been possible to decentralize at the national stage. Designs may come from biology and ecological networks; fungi, plants, and slime molds in soil to the neurons, glia, and blood vessels within our own bodies. This article blends these biological analogies with present technologies like blockchain and Web3 to posit future decentralized institutions, movements and legalities that give greater consideration to the more-than-human world.

Valles Marineris (VM), the most prominent example of a Martian Valley Network, has been extensively studied for over 50 years, yet no detailed examination of the principal azimuths of the system exists. To address this, two methods are presented to precisely calculate the principal azimuths, a Bezier Spline analysis, and a GIS technique. The medial axis of the main canyon of VM was determined analytically from cubic polynomial splines fitted to 93 coordinate points along both north and south edges of the canyon. These splines were optimized, and medial axis points were calculated through numerical techniques that ensured orthogonality between the tangents of each spline and their connecting normal lines. 1,000 medial axis points were extracted, and various regression models constructed, including fitting to sinusoidal and cubic polynomial curves, achieving accuracies with R² values of 0.98 and 0.99, respectively. Principal azimuths were obtained using the sinusoidal equation with the slope of the tangent at any point x simply determined by the derivative of the curve’s equation. This analytic approach was cross-validated by a GIS method (using QGIS software), where a vector medial axis was obtained which produced principal azimuths that agreed with values from the analytic study with a correlation coefficient of 1.00, and a p value of 6.43e-65. The findings demonstrate that an azimuthal framework can be rigorously constructed as a potential standard reference in VM geoscience, replacing less precise and ambiguous compass bearings with the accurate azimuths necessary for high-resolution spatial analysis for future investigations.

Crystallization of soluble salts represents a significant deterioration mechanism for porous building materials in cultural heritage, with substantial economic consequences. These salts can originate from geogenic sources (such as soil leachate, sea salt aerosols, and material incompatibilities in historical constructions) or anthropogenic sources (including environmental pollution and organic waste). This study investigates the capillary rise of retained and sustained water within soil, which is subsequently absorbed by calcareous rocks in monuments. This process leads to a range of damages, from superficial staining and efflorescence to more severe material degradation and mass loss. The paper examines the capillary rise phenomenon and analyzes the typology and origin of salts that predominantly impact the durability and mechanical resistance of calcareous rocks used in the construction of historic buildings in Spain.

Anaerobic digestion is a feasible solution for the treatment of organic wastes. The process can reduce the amount of biowaste by stabilizing the organic material and producing biogas susceptible to energetic valorization. However, the digestate needs further valorization when land application is considered unfeasible. Thermal treatments, such as gasification, pyrolysis, and hydrothermal carbonization, are alternatives capable of transforming this material into valuable syngas, obtaining, in many cases, a carbonized stream known as biochar. The feasibility of the process depends on the energy demand for the drying stage and the treatments available for removing contaminants from the syngas, attaining high-quality products, and treating the process-derived water. In the present manuscript, these critical aspects were reviewed considering the characteristics of digestates based on their origin, the modifications of this material during anaerobic digestion, and the way digestate structure affects the final thermal valorization outcome. Emphasis was placed on the energy demand of the global approach and by-product treatments.

Mercury, a toxic heavy metal produced through both natural and anthropogenic processes, is found in all major Earth’s systems. Mercury's bioaccumulation characteristics in the human body have a significant impact on the liver, kidneys, brain, and muscles. In order to detect Hg2+ ions, a highly sensitive and specific fluorescent biosensor has been developed using a novel, modified seven amino acid peptide, FY7. The tyrosine ring in the FY7 peptide sequence forms a 2:1 complex with Hg2+ ions that are present in the water-based sample. As a result, the peptide's fluorescence emission decreases with higher concentrations of Hg2+. The FY7 peptide's performance was tested in the presence of Hg2+ ions and other metal ions, revealing its sensitivity and stability despite high concentrations. Conformational changes to the FY7 structure were confirmed by FTIR studies. Simultaneously, we designed a miniaturized setup to support an in-house-developed micro-volume capillary container for volume fluorometry measurements. We compared and verified the results from the micro-volume system with those from the commercial setup. The micro-volume capillary system accommodated only 2.9 µL of sample volume, allowing for rapid, sensitive, and selective detection of toxic mercury (II) ions ranging from 0.02 µM.

After prolonged development, most domestic and international oilfields have entered a late stage of high water cut and high recovery, with a comprehensive water cut reaching up to 90%. Carbon-oxygen ratio logging can directly distinguish oil layers from water layers with low salinity, unknown salinity, or significantly varying salinity in cased wells. This makes it highly valuable for detecting oil and gas layers and locating and monitoring oil-water interfaces. It is currently one of the most effective methods for determining residual oil saturation in cased wells. This paper is based on two carbon-oxygen ratio spectrum interpretation models (the fan and ratio chart methods). It compares and analyzes these two methods and optimizes the interpretation parameters in the ratio chart model using an improved genetic algorithm. The results are effectively integrated with reservoir and conventional logging interpretation results, reducing the impact of lithology and physical properties on measurement values. This provides a theoretical foundation for improving the interpretation capability and accuracy of carbon-oxygen ratio logging, allowing for quicker and more accurate identification of water-flooded layers. This technical support aids in monitoring oil-water dynamics, determining water-flooded layers, and enhancing production potential during oilfield development.

Light pollution of the night sky is a phenomenon increasingly observed around the world and reported in the form of thematic maps. The major contributor to light pollution is ill-adjusted or inappropriately designed artificial lighting by outdoor light sources. The phenomenon occurs mainly over urban areas, but affects both the urban environment and the areas adjacent to it. The problem of excessive artificial light emission at night and the negative effects it causes has already become so serious and global in its impact that effective and comprehensible methods of cartographic presentation of this phenomenon have been explored. The literature on the subject provides examples of how to present the results of measurements of light pollution of the night sky, but these have been developed using different colour scales, often in discretionary ranges and units of measurement. This paper presents an overview of the measurement scales and units found in the literature to describe the phenomenon of light pollution that obscures the night sky. A proposal of the author’s color scale for the presentation of the phenomenon was proposed and shown by example. The differences between measurement scales and how they affect interpretation of results are discussed. Furthermore, a recommendation is made for a standardised method of cartographic visualisation of the spatial distribution and presentation of light smog measurement results.

A comprehensive study (monitoring, thermodynamic modeling) of natural and anthropogenically polluted waters of the Lovozero Massif has been carried out. A thermodynamic study of the weathering of the Lovozero Massif within the “water-rock-atmosphere” system at a temperature of 5°C showed that the elements contained in the rocks of the studied massif influence the formation of the chemical composition of natural waters. It has been established that an increase in the degree of “water-rock” interaction leads to an increase in the concentrations of F, Cl, SО42-, HCO3- in the solution. This affects the mobility of lanthanum, cerium and other elements due to the formation of complex compounds with them. The relatively high content of fluorine, phosphorus, HCO3- (weak and medium acids) in the solution promotes the dissolution of silicates while Si, Al, P are released into the solution. Monitoring of water from a flooded mine in which there is an increase in the degree of interaction of water with rock showed higher pH values for the concentrations of Na, HCO3-, F-, P, Al, Si, V, U, La, Ce. The conclusions are relevant in the context of groundwater extraction for drinking water supply purposes. The obtained information is useful to evaluated the health of the population of the region under study.

In this work, we present two datasets for specific areas located in South Tyrol (Italy) and in Tyrol (Austria) that can be exploited to monitor and understand water resource dynamics in mountain regions. The idea is to provide the reader with information about the different sources of water supply over five defined test areas. The Snow Cover Fraction (SCF) and Soil Moisture Content (SMC) datasets are derived from machine learning algorithms based on remote sensing data. Both SCF and SMC products are characterized by a spatial resolution of 20 m and are provided for the period from October 2020 to May 2023 (SCF) and from October 2019 to September 2022 (SMC) respectively, each covering 3 seasons of interest, winter for SCF and spring-summer for SMC. For SCF maps, the validation with very high-resolution images shows high correlation coefficients of around 0.9. The SMC products were originally produced with an algorithm validated at global scale, but here, to obtain more insight in the specific alpine mountain environment, the values estimated from the maps are compared with ground measurements of automatic stations located at different altitudes and characterized by different aspects in the Val Mazia catchment in South Tyrol (Italy). In this case a MAE between 0.05 and 0.08 and an unbiased RMSE between 0.05 and 0.09 m3·m-3 were achieved. The datasets presented can be used as input for hydrological models as well as to hydrologically characterize the study alpine area starting from different sources of information.

Reliable soil moisture information is essential for accurate irrigation scheduling. A wide range of soil moisture sensors are currently available on the market, but their performance needs to be evaluated as most sensors are calibrated under limited laboratory conditions. The aim of this study was to evaluate the performance of six commercially available moisture sensors (HydraProbe, Teros 10, Teros 11, EnviroPro, CS616 and Drill&Drop) and three tensiometers (Irrometer RSU-C-34, Teros 32 and Teros 21) and to establish calibration equations for a typical sandy soil of the Doñana National Park (Huelva, Spain). The calibration process for soil moisture sensors indicated dif-ferences between factory and corrected equations. All tested sensors improved with adjustments made to the factory calibration, except for the HydraProbe sensor which had a more accurate factory equation for a sandy soil. Among the various sensors tested, the Teros 10, Teros 11, and HydraProbe were found to be the easiest to install, typically positioned with an auger to prevent preferential pathways and ensure adequate sensor-soil contact. For tensiometers, the Teros 32 sensor requires specialized labor for its correct installation, as it must be positioned at a specific angle and maintained with distilled water. All tensiometers need a stabilization period after in-stallation.

Apart from headwind changes, crosswind changes may be hazardous to the aircraft operation. This paper presents two cases of recently observed crosswind changes from the range height indicator scans of ground-based remote-sensing meteorological equipment, namely, an X-band microwave radar and a short-range LIDAR. Rapidly evolving winds have been observed by the equipment in tropical cyclone situations, and they are found to have high level of turbulence and possibly to be associated with vertically propagating waves. In order to study the feasibility of predicting such disturbed airflow, mesoscale meteorological model and computational fluid dynamics model with high spatial resolution are used in this paper. It is found that the models could capture the rapidly evolving airflow features rather well, including the turbulence level, the waves, the rapidly changing wind speeds and the presence of reverse flow. This paper aims at providing a reference to the airports to consider the feasibility of performing high resolution numerical simulation of rapidly evolving airflow to alert the pilots in advance for airports in complex terrain and setup of buildings.

Traditional mining activities in Zaruma-Portovelo (SE Ecuador) have led to high concentrations of pollutants in the Puyango River due to acid mine drainage (AMD) from abandoned waste. Dispersed Alkaline Substrate (DAS) passive treatment systems have shown efficacy in neutralizing acidity and retaining metals and sulfates in acidic waters, achieving near 100% retention for Fe, Al, and Cu, over 70% for trace elements, and 25% for SO₄²⁻. However, significant solid residues are generated, requiring proper geochemical and mineralogical understanding for management. This study investigates the fractionation of elements in AMD precipitates. Results indicate that trivalent metals (Fe, Al) form low-crystallinity oxyhydroxysulfate minerals such as schwertmannite and jarosite, which retain elements like As, Cr, Cu, Pb, and Zn through adsorption-coprecipitation. Sulfate removal occurs via salts like coquimbite and gypsum. Divalent metals are primarily removed through carbonate and bicarbonate phases, with minerals such as azurite, malachite, rhodochrosite, and calcite. Despite the effectiveness of DAS, leachates from the precipitates exceed regulatory thresholds for aquatic life protection, classifying them as hazardous and posing environmental risks. However, these residues offer opportunities for the recovery of valuable metals.

By tonnage, corn (Zea mays L.) is the #1 crop produced globally, and recent research has suggested that no-till (NT) systems can lead to reduced yields of this important crop. Additionally, there is a lack of long-term data about the effects of tillage and N management on cropping systems. Corn is the most nitrogen (N)-fertilized crop in the USA, and N losses to the environment contribute to significant impacts on air and water quality. We conducted long-term studies on conventional tillage (CT), and conservation tillage systems such as strip tillage (ST) and NT, under different N rates. We found that immediately after conversion to NT, yields from NT were significantly lower than yields from CT (P<0.1), but after five years of NT, the NT yields were 1.5% higher than the CT yields (P<0.1). Initially, the NT yields were lower than the ST (P<0.01), but after seven years of NT, the NT yields were comparable to ST grain yields. Although the total aboveground N uptake with NT immediately after conversion to NT was lower than with CT and ST, these differences were not significant in the long run. The nitrogen use efficiency (NUE) with NT increased over time. The present work highlights the importance of long-term research for determining the cumulative impacts of best management practices such as NT. We found that NT becomes a more viable practice after five or seven years of implementation, demonstrating the high importance of long-term research.

There is a pressing need for tools that can rapidly predict PM2.5 concentrations and assess health impacts under various emission scenarios, aiding in the selection of optimal mitigation strategies. Traditional chemical transport models (CTMs) like CMAQ are accurate but computationally intensive, limiting practical scenario analysis. To address this, we propose a novel method integrating a conditional U-Net surrogate model with health impact assessments, enabling swift estimation of PM2.5 concentrations and related health effects. The U-Net model was trained with 2019 South Korean PM2.5 data, including precursor emissions and boundary conditions. Our model showed high accuracy and significant efficiency, reducing processing times while maintaining reliability. By combining this surrogate model with the EPA’s BenMAP-CE tool, we estimated potential premature deaths under various emission reduction scenarios in South Korea, extending projections to 2050 to account for demographic changes. Additionally, we assessed the required PM2.5 emission reductions needed to counteract the increase in premature deaths due to an aging population. This integrated framework offers an efficient, user-friendly tool that bridges complex air quality modeling with practical policy evaluation, supporting the development of effective strategies to reduce PM2.5-related health risks and estimate economic benefits.

Biobutanol is becoming more relevant as a promising alternative biofuel, primarily due to its advantageous characteristics. These include a higher energy content and density compared to traditional biofuels, as well as its ability to mix effectively with gasoline, further enhancing its viability as a potential replacement. A viable strategy for attaining carbon neutrality, reducing reliance on fossil fuels, and utilizing sustainable and renewable resources is the use of biomass to produce biobutanol. Lignocellulosic materials have gained widespread recognition as highly suitable feedstocks for the synthesis of butanol, together with various value-added byproducts. The successful generation of biobutanol hinges on three crucial factors: effective feedstock pretreatment, the choice of fermentation techniques, and the subsequent enhancement of the produced butanol. While biobutanol holds promise as an alternative biofuel, it is important to acknowledge certain drawbacks associated with its production and utilization. One significant limitation is the relatively high cost of production compared to other biofuels, additionally, the current reliance on lignocellulosic feedstocks necessitates significant advancements in pretreatment and bioconversion technologies to enhance overall process efficiency. Furthermore, the limited availability of biobutanol-compatible infrastructure, such as distribution and storage systems, poses a barrier to its widespread adoption. Addressing these drawbacks is crucial for maximizing the potential benefits of biobutanol as a sustainable fuel source. This document presents an extensive review encompassing the historical development of biobutanol production and explores emerging trends in the field.

Reiner Gamma is a potential target for low-orbiting spacecraft or even surface-landed missions in the near future. Unfortunately, thus far no comprehensive low altitude (below 20 km) or surface measurements of the magnetic field, magnetic source and plasma environment have been made post-Apollo to complement and complete our understanding of the solar wind interaction with lunar magnetic anomalies and swirl formation. Acquiring the detailed geological knowledge of the Reiner Gamma region is significant for the above scientific targets. In this study, the following research work in the lunar Reiner Gamma magnetic anomaly region was carried out for the regional geological investigation: (1) the topographic and geomorphologic analysis; (2) elements, minerals and sequence analysis; (3) 1:10,000 regional geological map analysis. Our work helps define measurement requirements for possible future low-orbiting or surface-landed missions to the Reiner Gamma area or similarly magnetized regions of the lunar surface.

Satellite navigation spoofing is a major challenge in the field of satellite/inertial integrated navigation security. To effectively enhance the anti-spoofing capability of low-cost GNSS/MEMS-SINS integrated navigation system, this paper proposes a method integrating a dual-antenna global navigation satellite system (GNSS) and a micro-inertial measurement unit (MIMU) to determine the two-dimensional (2D) directions of spoofing signal sources. The proposed method evaluates whether the single-difference carrier-phase measurements conform to the corresponding directions given in ephemeris files, and employs the micro-inertial navigation technology to determine the 2D directions of the signal source. Based on a set of short-baseline dual-station measurements, the accuracy of the proposed method in determining the 2D azimuths of satellites in synchronous orbits is verified, and the deviation from the real value is evaluated. The experimental results show that the proposed method can effectively identify the spoofed satellite signals while providing high-precision direction information at three different distances: 100m, 10km, and 36000km.The two-dimensional angles errors do not exceed 0.2 rad, 0.05 rad, and 0.01 rad, respectively.

Per- and polyfluoroalkyl substances (PFAS) are arguably the most common water contaminants in the world today. While several research experiments have been done to understand and remove PFAS from the environment, there is still a lot of unknown. Little has been known about the use of Machine learning (ML) to understand PFAS. This work hence reviews some leading ML approaches and applications in PFAS studies in the distribution, transport, removal, and occurrence predictions of PFAS. Several evaluation matrices were examined and used to perform this function. There are still a lot of areas whereby ML can be used to improve our PFAS knowledge base, some of these were briefly stated in this review.

Copper (Cu)-based fungicides are widely used in agriculture, but excessive Cu accumulation in soils threatens crop performance, particularly in perennial fruit crops like apples. This study investigated the effects of varying Cu concentrations on the physiological responses of three apple varieties—Braeburn, Fuji, and Cripps Pink—using a callus-media system. Callus cultures were exposed to Cu concentrations ranging from 0 to 20 mg Cu kg⁻¹ to assess traits such as growth, fresh and dry weight, copper accumulation, and bioconcentration factor (BCF). Results revealed that media Cu concentration had a stronger effect than variety on most traits, especially above 10 mg Cu kg⁻¹, where copper stress reduced genetic variability. At lower Cu concentrations, varietal differences were more apparent, with Cripps Pink showing greater copper tolerance. These findings suggest that high Cu levels suppress varietal differences, leading to a uniform stress response. This study provides valuable insights into copper toxicity in apple varieties and highlights the need to maintain soil Cu levels below critical thresholds to preserve varietal performance, with potential implications for soil-plant systems.

The aquatic vegetation of the Neva Bay is characterized by high biodiversity and play an important role. In recent years, hydrotechnical works associated with transport and industrial construction have been carried out in the Gulf of Finland. As a result of industrialisation and active anthropogenic impact these areas are a place of hyperaccumulation of nutrients and pollutants. This paper presents results of the ecotoxicological state assessment of subaquatic soils and investigation of the development of macrophyte thicket ecosystems of the Neva Bay.

Air pollution, particularly fine particulate matter (PM2.5), poses a significant threat to public health, especially in rapidly urbanizing areas like Lagos State. Despite the severity of the issue, there is a lack of comprehensive data on PM2.5 levels and the Air Quality Index (AQI) in Lagos. This study leverages data from AirQo, an innovative air quality monitoring network using low-cost sensors, to provide a detailed assessment of PM2.5 concentrations and AQI across eight locations in Lagos State, Nigeria. This approach offers real-time, continuous monitoring, filling a critical data gap in the region's air quality management efforts. This study uses AirQo data to assess the concentration levels and spatial distribution of PM2.5 and AQI across the selected eight locations. Data on PM2.5 concentrations were obtained from AirQo sensors deployed across the eight strategic locations in Lagos. The spatial distribution of PM2.5 levels and AQI were analyzed, and the results were compared with national and international air quality standards. The findings revealed that PM2.5 levels (minimum – 6.28 µg/m3 (Ikeja) and maximum - 204.68 µg/m3 (Banana Island) in many areas of Lagos exceed annual WHO and national air quality guidelines, indicating significant health risks. The spatial analysis identified pollution hotspots, particularly in densely populated and industrial regions. The data suggested vehicular emissions and industrial activities as major contributors to high PM2.5 levels. This study underscores the severe air quality issues in Lagos State, emphasizing the need for immediate and targeted interventions to mitigate PM2.5 pollution. The use of AirQo sensors proved effective in providing accurate and timely data for air quality assessment. It is recommended that Lagos State implements stricter emission controls, promotes cleaner technologies, and enhances public transportation systems to reduce traffic-related pollution. Additionally, expanding the air quality monitoring network and incorporating more environmental data will provide a more comprehensive understanding of air pollution impacts, thereby facilitating better policy and public health decisions.

Despite nearly a century of research on water related issues, water erosion remains one of the greatest threats to soil health and soil ecosystem services around the world. Yet to date, data on water erosion needed to develop mitigation strategies is scarce, especially in the Sahelian regions. The current study therefore sets out to estimate annual soil losses caused by water erosion and to analyze trends over the period of 1981-2020 in the Mékrou watershed, located in the Middle Niger river sub-basin in West Africa. The revised universal soil loss equation, remote sensing and the Geographic Information System (GIS) were deployed in this study. Several types of data were used, including rainfall, soil conditions, topography and satellite images. The estimated average annual amount of water erosion in the Mékrou watershed is 6.49 t/ha/yr over 1981-2020. The dynamics of the ten-year average are highly variable, with a minimum of 3.45 t/ha/yr between 1981 and 1990, and a maximum of 8.50 t/ha/yr between 1991 and 2000. Even though these average soil losses in the Mékrou basin are below the tolerable threshold of 10 t/ha/yr, mitigation actions are needed for prevention. In addition, the spatial dynamics of water erosion are noticeably heterogeneous. The study reveals that 72.7% of the surface area of the Mékrou watershed is subject to slight water erosion below the threshold, compared with 27.3%, particularly in the mountainous south-western part, which is subject to intense erosion above the threshold.

This work refers to sustainability (SDG - Sustainable Development Goals) highlighting the idea of ​​harmony of life along the lines of the biological model at various levels and of nature alike, thus defining the concept of bioharmonism. The purpose of this paradigm is to find a way of transition in the contemporary societal change, connecting the natural sciences, more precisely the life sciences, with the social sciences, more concretely along the lines of holistic approaches to the evolution of human society. The objective of this point of view is related to sustainability, resilience and planetary health, which indicates the need to analyse planetary bioharmony and transpose the model to the structure and functionality of human society through the complex, unitary and coherent approach of the bioharmonization process. The study analyses the issue, through: the definition and role of societal bioharmonism, then the argument related to the transition to another world using the proposed path, based on specific notions, principles and laws, and finally, the description of some lines of application of the pillars of bioharmonism. A series of solutions are highlighted that can contribute conceptually to the evolution of contemporary society, to the convergence of anthroposystems with ecosystems by interconnecting the aspects of the biological revolution with those specific to the information age. The solutions aim at societal bioharmonization supported by its specific pillars, which through integration and emergence lead to a dynamic balance. The state of equilibrium, sustainability can be highlighted at a systemic level by the directions of action in which the bioharmonization complementarities that indicate the Environment and biodiversity in the economic equation, the Citizen in the social equation and Science in the political equation will be taken into account.

Since engineering discipline is at the forefront of latest innovations and developments, there is an increasing emphasis on the critical responsibilities of engineers towards sustainability. There are several studies investigating the incorporation of sustainability principles into engineering curriculum but studies analyzing the knowledge, attitude, and behavior of engineering students are lacking. A deeper understanding of the current state of students’ knowledge, attitude, and behavior towards sustainability is crucial to determine the type of pro-sustainability trainings, strategies and pedagogical activities needed. Hence, the current study is aimed at assessing the level of sustainability cognizance of Mechanical Engineering students via an online Sustainability Consciousness Questionnaire. The final sample size was 134 students from a Higher Education Institute in India with 90% male responders and 52% from urban areas. Results indicate that 70% of the respondents have heard of the Sustainable Development Goals, only around 32% of the students demonstrate a positive behavior and attitude towards sustainability whereas 30% of the students do not hold any opinion towards sustainability principles. The current exploratory study contributes to the knowledge base about sustainability awareness and consciousness and advocates for measures to insert its principles in engineering courses.

As an important element of methane prevention in mining facilities, measurements of concentration serve to obtain information about methane hazards. Information about methane concentration must be linked to the place and time of its occurrence to create valuable data for ongoing hazard analysis. The article presents the results of a methane hazard analysis based on an airflow and methane concentration model, validated using measurement data. Subsequently, simulations of crew movement and the operation of individual methane detectors working according to the algorithms of the Methane Hazard Detection System were conducted based on this model.

The textile and clothing (T&amp;C) industry, despite its significant contribution to the European economy, is one of the most polluting industries globally. Therefore, the EU's Green Deal has identified the T&amp;C sector as a strategic area for allocating public funding and transitioning towards a circular economy (CE). In this context, the “Circular Design for Natural Fibers” project, financed by the PNRR MICS “Made in Italy Circolare e Sostenibile” programme, focuses on enhancing the use of natural fibrous material, related by-products and waste from local production chains. In the research, a crucial aspect is the evaluation of the effectiveness of circularity through life cycle approaches, such as LCA, LCC, S-LCA and LCSA, for environmental, economic and social sustainability evaluation, respectively. A systematic review was carried out, to provide a synthesis of the available scientific evidence regarding the life cycle approaches in the international/national context within the production, processing, and post-consumer supply chains of natural fibers. The study identifies gaps in the literature on life cycle evaluation approaches for natural fibers. Specifically, the LCA approach appears preponderant compared to the others. The paper offers insights for researchers and professionals interested in improving the sustainability of natural fiber production processes and the evaluation of their overall sustainability.

Aquatic environments are generally heterogeneous in space and time, and the marine organisms have their ways response to those environmental heterogeneities. Cutlassfish (Trichiurus spp.) once is one of the “Chinese Four Major Marine Fish Species” along the coast of northwest Pacific Ocean. There are some comments on the cutlassfish (T. spp.) name identification. In this study, three cutlassfish species, namely, the largehead hairtail (T. japonicas) in East China Sea (ECS), Chinese short-tailed hairtail (T. brevis), and South China Sea (SCS) cutlassfish (T. nanhaiensis), have been identified in the northwest Pacific Ocean. This work integrated 115 studies in cutlassfish population dynamics from 24 sites across 8 countries in the world. The biomass distribution of T. japonicas in the ECS and T. nanhaiensis in SCS, was found to be highly correlated with water temperature and salinity. Spatiotemporal heterogeneity of cutlassfish is found to correlate with both genetic and environmental characteristics in the northwest Pacific Ocean (e.g., density dependency, primary productivity and climate ocean oscillation). Closely related species coexisting in the same shelf region (e.g., T. japonicus and T. nanhaiensis in ESC) often differ in diet and microhabitat. The difference of skull phenology between T. nanhaiens and T. brevis is found to correlate with species separation and environmental heterogeneity. With the implications for the design of ecological research programs on cutlassfish, our study found that the effect of temporal changes outweighs (to some extent) the effect of spatial changes on cutlassfish in those shelf regions.

The objective of the study was to analyze the climate change experienced in Spain between 1971 and 2022 and to estimate the future climate (2050). The main objectives were: 1) to analyze the temporal evolution of temperature from 1971 to the present, to quantify the warming process experienced in the case study and to evaluate the increase in ex-treme heat events (heatwaves); 2) to study the evolution of the precipitation regime to determine whether there is a statistically representative trend towards a drier climate and an increase in extreme precipitation; 3) to investigate the interaction between annual precipitation and the continuous increase in temperature; and 4) to estimate the future climate scenario for mainland Spain and the Balearic Islands towards 2050, analyzing the trends in land aridity and predicting a possible change from a Mediterranean climate to a warm steppe climate, according to the Köppen classi-fication. In this study, we used the E-OBS dataset provided by the European Climate Assessment and Dataset (ECA&D) project. E-OBS is a high-resolution gridded daily climate dataset. Its advantage over other databases is that it is based on direct climate information from the weather stations included in the Copernicus ECA&D. Furthermore, a detailed interpolation and refinement process allows a complete, uniform view of the studied territory. This dataset has pro-vided daily weather information since 1950 and is presented as a complete dataset of the European region. It is availa-ble on a regular grid of 0.1 and 0.25 degrees for the elements of daily average temperature (TG), daily minimum tem-perature (TN), daily maximum temperature (TX) and the sum of daily precipitation (RR), among other climatic varia-bles. In this study, we obtained daily temperature data, including the average, maximum and minimum temperature, and precipitation from 1971 to 2022, using the 27.0e dataset. We used information from E-OBS from 1 January 1971 to 31 December 2022, with a resolution of 0.25 degrees. The main purpose of this study was to analyze whether there is a temporal evolution of the precipitation regime in Spain towards less precipitation, and to study the relationship between annual precipitation and the tendency to-wards progressive warming. Specifically, the aim was to test the hypothesis that the increase in temperature resulting from the global warming process implies a tendency towards progressive drought. The hypothesis is + T  - RR. Given the extreme annual variability of the climate, in addition to the ordinary least squares methodology, the techniques mainly used in this study were the Mann-Kendall test and the Kendall-Theil-Sen regression. The Mann-Kendall test confirmed the statistical significance of the relationship between RR and TX, with a statistical relia-bility close to 99.9%. The KTS regression predicted a reduction of 38.25 mm for each 1°C increase. If the warming trend experienced in recent years (1973– 2022) continues, it is foreseeable that by 2050 there will be a reduction in precipitation in Spain of between 14% and 23% with respect to the precipitation of the reference period (understood as the average between 1971 and 2000). Spain’s climate is likely to vary from Mediterranean to warm steppe in the Köppen classification system (from “C” to “B”).

The transition to renewable and sustainable energy sources is critical to solving the environmental and socioeconomic problems associated with the use of fossil fuel. This study uses an interdisciplinary approach to analyze the evolution, challenges and prospects of a sustainable energy transition in the context of recent discovery and exploitation of fossil resources. We study the case of Senegal from 2000 to 2027 and the role of natural gas in its energy transition. In 2000, Senegal's energy mix consisted of about 97% for fossil energy and only 3% for renewable energy. Nowadays, although fossil fuels are still dominant, but they account for about 70% of total electricity production. The country has indeed undertaken tremendous efforts to develop renewable energy sources including, solar, hydro and wind power, which currently account for about 30% of the total energy mix. Socio-economic indicators show that over the past two decades, Senegal's population and electricity production have grown significantly, leading to an increased energy demand/need. This growth has resulted in a five-fold increase in per capita energy consumption over the same period. Combining the strategic use of natural gas as a transition energy source with the continued development of renewable energy sources could make Senegal as a model for West Africa’s energy transition. The projections for 2027 suggest a structural shift in the energy mix, with natural gas, oil, and renewables at 47%, 32%, and 21%, respectively.

Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems, with a strong impact on their natural regeneration. This study presents a comprehensive analysis of post-fire forest recovery using Landsat time series data from 2000 to 2023 in the Middle Volga region of the Russian Federation. The analyses utilised the LandTrendr algorithm in Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To assess the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen’s slope estimator was applied. The results suggested that the post-fire spectral recovery is significantly influenced by the degree of the BS on affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belong to the BS classes of moderate and high severity. A regression model showed that land surface temperature (LST) was more significant to post-fire recovery than the variability in precipitation (Pr), explaining about 65% of the variance in post-fire recovery. This study provides new insights into the post-fire forest recovery dynamics and scientific bases for the cost-effective management strategies under changing climate conditions.

Evaluating aviation emissions and examining the dispersion properties of contaminants are crucial for understanding atmospheric pollution. To more properly assess the pollutant emissions and dispersion of aircraft during the landing and take-off (LTO) cycle, and to more effectively address air pollution surrounding the airport resulting from flight operations, this study assessed emissions throughout the LTO phase utilizing Quick Access Recorder (QAR) data in conjunction with the first-order approximation method. An improved Gaussian diffusion model for mobile point sources is employed to examine the diffusion characteristics of contaminants. Additionally, CFD calculation outcomes for various exhaust velocities and wind speeds were utilized to validate the trustworthiness of the improved Gaussian model. The discussion also encompasses the influence of diffusion time, wind direction, wind speed, temperature gradient, and particle deposition on the concentration distribution of contaminants. The findings indicated that the Gaussian diffusion model aligned with the results of the CFD calculations. The diffusion distribution of contaminants around airports varies over time and is significantly influenced by atmospheric environmental factors, including wind direction, wind speed, and atmospheric stability.

Human activities continuously alter the delivery of ecosystem services (ESs), which play a crucial role in human well-being. There is a pressing need for effective ecological management strategies that consider the spatial heterogeneity of ESs to support the transition from trade-offs to synergies. This study focuses on the Haba River Basin and examines characteristics of land use change and the shift from trade-offs to synergies. The results indicate that from 1990 to 2000, the initial phase of land development, 10.65% of the land experienced change. Subsequently, during the intensive period of land development from 2000 to 2010, 30.29% of the land underwent significant transformation, with approximately 78% of grassland, sparse grassland, forested land, and desert converted into arable land. However, between 2010 and 2020, as the focus shifted towards the establishment of native vegetation and the intensity of land development decreased, only a small percentage (3.65%) of the total area underwent changes. Based on an in-depth analysis of spatial heterogeneity from 1990 to 2020, it is believed there has been a shift from trade-offs to co-benefits between 2000-2010 and 2010-2020. The years 2010 and 2020 were pivotal time nodes for the transition from trade-offs to synergies and for reducing trade-offs, with NPP identified as a critical driving factor for Comprehensive ES （CES） functions. By considering the trade-offs/synergies relationship and hotspots of ecological service functions, combined with unified water resource management policies, comprehensive ecological management measures tailored to different regions are proposed. These measures have facilitated the implementation of robust ecological protection policies to shift ES development from trade-offs to synergies in arid areas, thereby enhancing overall ecosystem service functions in the Haba River Basin. The research findings offer crucial scientific support and guidance for ecosystem management in arid areas, particularly within Central Asia.

Contaminated soils are a challenge for implementing biotechnology in bioremediation, recovery of Critical and Strategic Raw Materials (CRMs and SRMs), and food security. European Union (EU) Governments have established strict limits on As, Pb, Cd, and Hg in foods (Document 32023R0915), and requested the recovery of 34 CRMs within a circular economy (CE) (5th CRMs list). This study proposed a biotechnological tool for the decontamination of soil with heavy metal(loids) by arbuscular mycorrhizal (AM)-assisted phytoextraction and subsequent recovery of CRMs, or by phytostabilization to prevent their entry into the food chain. It consisted of placing Baccharis salicifolia plants, inoculated or non-inoculated with AM fungi, into Bioreactors (BRs) containing mining soil with Cd, Ni, and Cu, according to the argentinian Patent (AR090183B1). The bioextractive potential (BP) was also estimated at the highest Technological Readiness Level (TRL) using a Vegetable Depuration Module (VDM, TRL 6). Inoculated plants showed significantly higher aerial bioaccumulation coefficients (Cd: 68.62; P: 2.99; Ni: 2.51; Cu: 0.18) in BRs, and the BP values reached 1.16 g, 9.75 g, 2.40 g, and 213.1 g for Ni, Cd, Cu, and P, respectively. Finally, those CRMs and SRMs could be recovered from biomass through hydrometallurgy within a CE framework.

Amphibians, which are essential components of ecosystems, are susceptible to pharmaceutical contamination, a phenomenon of increasing concern owing to the widespread consumption and detection of pharmaceutical compounds in environmental matrices. This review investigates oxidative stress (OS) as the primary mechanism of drug toxicity in these organisms. The evidence gathered reveals that various pharmaceuticals, from antibiotics to anesthetics, induce OS by altering biomarkers of oxidative damage and antioxidant defense. These findings underscore the deleterious effects of pharmaceuticals on amphibian health and development and emphasize the necessity of incorporating OS biomarkers into ecotoxicological risk assessments. Although further studies on diverse amphibian species, drug mixtures, and field studies are required, OS biomarkers offer valuable tools for identifying sublethal risks. Furthermore, the development of more refined OS biomarkers will facilitate early detection of adverse effects, which are crucial for protecting amphibians and their ecosystems. Ultimately, this review calls for continued research and mitigation strategies to safeguard biodiversity from pharmaceutical contamination.

The evaluation of landslide hazard in seismic areas is based on a deterministic analysis to define the stability of the sites. However, deterministic analysis is not able to consider various uncertainties in the analysis process. This paper focuses on the results established by the probabilistic seismic hazard analysis and extends the probabilistic landslide hazard analysis to consider the uncertainties of opposing ground deformations and strength. The work studies the areas between Nibbiano and Sant'Erasmo inhabited centres in the Camerino municipality located in central Italy. The current state of the areas shows dangerous conditions for the building structures. In fact, all constructions present evidence of damage caused by both the 2016 earthquake and slope instability. The studies, carried out through careful geological survey and detailed geophysical investigations, are performed to define the geological, geomorphological and hydrogeological characteristics of the areas and also to run a new analysis aimed to define the material stress-strain behavior. Both inhabited Centers are located on debris deposits with highly variable thicknesses. The key point regards the definition of the depth of unstable mass, its position and its extension. Therefore, this contribution aims to summarize the knowledge of the landslide phenomenon that affected the area, trying to interpret its evolution with new field investigations. Indeed, during the first step, related to the geological and geomorphological detailed surveys, a remarkable geological model was obtained to better interpret the slope dynamics in relation to the structures of this section of the ridge. This model was also confirmed by the geophysical investigations. The study involves a more detailed definition of the landslide perimeter. Indeed, to analyze the phenomenon, it was necessary to study both the local seismic amplification with finite element 2D simulations and the slope stability conditions with finite 3D difference simulation.

A web application – IMERG Precipitation Extractor (IPE) was developed that relies on the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) data available at a global coverage. The IPE allows users to query, visualize, and download time series satellite precipitation data for various locations, including points, watersheds, country extents, and digitized areas of interest. It supports different temporal resolutions ranging from 30 minutes to 1 week. Additionally, the IPE facilitates advanced analyses such as storm tracking and anomaly detection, which can be used to monitor climate change through variations in precipitation frequency and intensity. To validate the IMERG precipitation data for anomaly estimation over a 22-year period (2001 to 2022), the Rainfall Anomaly Index (RAI) was calculated and compared with RAI data from 2,360 NOAA stations across the conterminous United States (CONUS), considering both dry and wet climate regions. In the dry region (e.g., Nevada), the results showed an average correlation coefficient (CC) of 0.94, a percentage relative bias (PRB) of -22.32%, a root mean square error (RMSE) of 0.96, a mean bias ratio (MBR) of 0.74, a Nash-Sutcliffe Efficiency (NSE) of 0.80, and a Kling-Gupta Efficiency (KGE) of 0.52. In the wet region (e.g., Louisiana), the average CC was 0.93, the PRB was 24.82%, the RMSE was 0.96, the MBR was 0.79, the NSE was 0.80, and the KGE was 0.18. Median RAI indices from both IMERG and NOAA indicated an increase in rainfall intensity and frequency since 2010, highlighting growing concerns about climate change. The study suggests that IMERG data can serve as a valuable alternative for modeling extreme rainfall anomalies in data-scarce areas, noting its possibilities, limitations, and uncertainties. The IPE web application also offers a platform for extending research beyond CONUS, advocating for further global climate change studies.

Estimating Forest growing stock volume (GSV) is crucial for forest growth and resource management, as it reflects forest productivity. National measurements are laborious and costly, however integrating satellite data such as optical, Synthetic Aperture Radar (SAR), and Airborne Laser Scanning (ALS) with National Forest Inventory (NFI) data and machine learning (ML) methods has transformed forest management. In this study, Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point cloud data. Four variable combinations were tested: CO1 (vegetation indices and LiDAR), CO2 (vegetation indices and individual band reflectance), CO3 (LiDAR and individual band reflectance), and CO4 (a combination of vegetation indices, individual band reflectance, and LiDAR). Across Estonia's geographical regions, RF consistently delivered the best performance. In the northwest (NW), RF achieved an R² of 0.63 and an RMSE of 125.39 m³/plot. In the southwest (SW), it achieved an R² of 0.73 and an RMSE of 128.86 m³/plot. In the northeast (NE), RF achieved an R² of 0.64 and an RMSE of 133.77 m³/plot, and an R² of 0.70 and an RMSE of 120.72 m³/plot was achieved in the southeast (SE). These results underscore RF's precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy.

A novel method has been developed based on the amplitude data of the EM waves measured by Digisondes to calculate and investigate the relative ionospheric absorption changes. The effect of 13 solar flares (> C4.8) that occurred between 06:00 and 16:30 UT from 04 to 10 September 2017 have been studied at three European Digisonde stations (Juliusruh (54.63° N, 13.37° E), Průhonice (49.98° N, 14.55° E) and San Vito (40.6° N, 17.8° E)). Present study compares the results of the amplitude method with the absorption changes measured by the Finnish Riometer Network and determined by the NOAA D-RAP model during the same events. The X-class flares caused 1.5–2.5 dB attenuation at 30–32.5 MHz based on riometer data, while the absorption changes were between 10 and 15 dB in the 2.5–4.5 MHz frequency range according to the amplitude data. The impact caused by the energetic particles after the solar flares are clearly seen in the riometer data, while it can be observed only at Juliusruh (~55°) at some certain cases among the Digisonde stations. Comparing the results of the amplitude method with the D-RAP model it seems evident that the observed values exceed the values given by the model both at 2.5 MHz and at 4 MHz almost always during the investigated period. According to the comparison between the riometer data with the D-RAP, the model underestimates the values obtained from the riometers during the X-class solar flares, while it overestimates the caused impact during the particle events.

We assessed machine learning techniques (MLTs) for downscaling global climate model’s precipitation to local level in Bolivia. The skill, assumptions and uncertainty of the process are analyzed. Additionally, future projections are delivered. For that, an ensemble of 20 global climate models (GCMs) from CMIP6, with random forest (RF) and support vector machines (SVM), is used on four zones (highlands, Andean slopes, Amazon lowlands and Chaco lowlands). The downscaled series’ skill is evaluated in terms of relative errors. The uncertainty is analyzed through variance decomposition. MLTs’ skill is adequate in most cases, especially for the highlands and the Andean slopes. Moreover, RF tends to outperform SVM. Stationary assumptions are robust in the highlands and Andean slopes. Perfect prognosis assumption is poor in the highlands and Andean slopes; topographical complexity appears to be the reason. Regarding uncertainties, MLTs are the dominant source. In future projections, the highlands show shorter dry spell lengths with more frequent but less intense high rainfall events and higher annual rainfall. The Andean slopes exhibit an increased annual rainfall with a reduction in the high precipitation intensities but an increased frequency. The Amazon lowlands present a decrease in annual rainfall, and the Chaco lowlands an increase.

The emission of toxic gases from industrial production have intensified issues related to atmospheric pollution and human health. Consequently, the effective real-time monitoring and removal of these harmful industrial gases have emerged as significant challenges. In this work, the density functional theory (DFT) method was utilized to examine the adsorption behaviors and electrical characteristics of Ni-decorated InSe (Ni-InSe) monolayer when interacting with twelve gases (CO, NO, NO2, NH3, SO2, H2S, H2O, CO2, CH4, H2, O2 and N2). A comparative assessment of adsorption strength and sensing properties was performed through analyses of electronic structure, work function and recovery time. The results show that Ni doping enhances the electrical conductivity of the InSe monolayer and improves the adsorption capabilities for six toxic gases (CO, NO, NO2, NH3, SO2, H2S). Furthermore, the adsorption of these gases on the Ni-InSe surface is characterized as chemisorption, as indicated by the analysis of adsorption energy, density of states, and charge density difference. Additionally, the adsorption of CO, NO, NO2, and SO2 results in significant alterations to the bandgap of Ni-InSe, with changes of 18.65%, 11.37%, 10.62%, and –31.77%, respectively, underscoring its exceptional sensitivity. Moreover, the Ni-InSe monolayer exhibit moderate recovery times of 3.24 s and 22.72 s at 298 K for the SO2 and H2S, respectively. Consequently, the Ni-InSe is regarded as a promising gas sensor for detecting SO2 and H2S at room temperature. This research establishes a foundation for developing Ni-InSe based gas sensor in detecting and mitigating harmful gas emissions.

Studies on particulate air pollution indicate that a new type of pollutant should be considered from mainly fossil fuel combustion and automobile exhaust emissions, i.e. environmentally persistent free radicals. These radicals, ubiquitous in the environment, have a long life span and are capable of producing harmful reactive oxygen species. Samples of dandelion were collected in 2020 and 2021 in spring and late summer. Roots, leaves, flower stalks, and inflorescences of Taraxaum sp., were collected from 6 sites with 3 plants each, along with monitoring of PM air pollution. The free radical content in each part of the plant was measured by electron paramagnetic resonance. The leaf was selected as the most appropriate part of the plant for the measurement of carbon-derived free radicals. The geff value and the total number of spins were calculated. Relationships were found between location, season, and measurements. The electron paramagnetic resonance spectrum consists of at least two components, which can be attributed to C-type radicals, and mixed C+O radicals. Their increase in numbers in the fall seasons, compared to the spring seasons, is also noticeable. It has also been observed that leaves collected in autumn have a higher geff value, which is probably related to the higher amount of oxygen- and carbon-derived free radicals.

The United Nations Global Sustainability Agenda 2030 introduces a transformative framework through its Sustainable Development Goals (SDGs), which are designed to achieve equitable, prosperous, and sustainable development globally. Unlike the incremental Millennium Development Goals (MDGs), the SDGs aim for systemic changes across economic, social, and environmental dimensions, ensuring that no one is left behind. This research paper provides a comprehensive examination of Pakistan's progress towards achieving the SDGs under the UN Global Sustainability Agenda 2030. Specifically, it includes an analysis of the prioritization of SDGs in Pakistan, highlights federal and institutional initiatives aimed at promoting sustainable development, and explores the role of the China-Pakistan Economic Corridor (CPEC) in advancing these goals. Additionally, the paper critically analyzes the SDGs in Pakistan’s annual status report, identifies the key challenges faced by the nation in implementing these goals, and offers recommendations for enhancing the effectiveness of SDG implementation. Overall, the focus is primarily on the detailed analysis of the annual status report, the challenges encountered, and strategic recommendations for achieving excellence in SDGs in Pakistan.

Accurate assessment of crop vegetative state is crucial for agricultural management, particularly under varying environmental conditions. However, detailed field experiments face logistical and cost challenges. The Normalized Difference Vegetation Index (NDVI) and other vegetation indices offer non-invasive and large-scale solutions for assessing plant health. The integral of the NDVI curve over time aggregates NDVI values, providing a comprehensive view of vegetation dynamics, surpassing the insights from individual NDVI measurements. Developing robust methodologies to calculate the vegetation activity index (VAI) based on NDVI integral is essential for efficient vegetation evaluation, aiding sustainable agriculture. Biostimulants enhance plant growth and resilience against stressors. Sugarcane, vulnerable to various stresses, can benefit from biostimulants, especially under drought conditions. Studies on biostimulant effects on sugarcane's metabolism, yield, and quality have been limited. Analysis of NDVI data indicated significant variations between treated and untreated areas over time. Polynomial regression models (R² between 0.921 and 0.959) demonstrated a strong ability to explain vegetation variability. Normality tests confirmed that VAI and Tons of Sugar per Hectare (TSH) data follow a normal distribution. There was no significant difference in VAI between treated and untreated areas during the plant development cycle, but a significant increase in VAI was observed in treated areas post-treatment (p=0.021). No significant difference in TSH was found, indicating no impact on sugar productivity. L-alpha biostimulant increased vegetation activity during critical growth periods but did not affect sugar yield. Return on investment analysis suggested high profitability of the treatment.

Urbanization growth has intensified the challenge of managing and treating increasing amounts of municipal solid waste (MSW). Landfills are commonly utilized for MSW disposal because of their low construction and operation costs. However, this practice produces huge volumes of landfill leachate, a highly polluting liquid rich in ammoniacal nitrogen (NH3-N), organic compounds and various heavy metals, making it difficult to treat in conventional municipal wastewater treatment plants (WWTPs). In recent years, research has shown that microbial biofilms, developed on carriers of different materials and called “Moving Bed Biofilm Reactors” (MBBRs), may offer promising solutions for bioremediation. This study explored the biofilm development and the nitrification process of moving bed biofilms (MBBs) obtained from high am-monia-selected microbial communities. Using Crystal Violet Staining and Confocal Laser-Scanning Microscopy, we followed the biofilm formation stages correlating nitrogen removal to metagenomic analyses. Our results indicate that MBBs unveiled a 10-fold more enhanced nitrification rate than the dispersed microbial community pre-sent in the native sludge of the Porto Sant’Elpidio (Italy) WWTP. Four bacterial families, Chitinophagaceae, Comamonadaceae, Sphingomonadaceae, and Nitrosomona-daceae accumulate in structured biofilms and significantly contribute to the high ammonium removal rate of 80% in 24 hours as estimated in leachate-containing wastewaters.

This study aims at providing a climatological analysis of annual and long-term variability of dry periods in the Polish Carpathians in the period of 35 years between 1986 and 2020. Aside days with precipitation, dry periods, namely sequences of days with no precipitation, constitute an inherent characteristic of the climate. The input data for the research was formed by daily precipitation totals from 17 meteorological stations acquired from the Institute of Meteorology and Water Management - National Research Institute, and T. Niedźwiedź catalogue of circulation types over southern Poland. Frequency of dry periods of different duration has been presented. The incidence of long-term periods has been related to the atmospheric circulation. An analysis was performed regarding the change of the average quantity and average length of dry sequences in the 35-year period covered. At most stations, there were no statistically significant trends with respect to the analysed dry day characteristics. This is an obvious result of high variability year-to-year, which means that exclusively large changes may achieve statistical significance. Dry days have been evidenced to related to circulation types. A vast majority of dry days is related to anti-cyclonal systems, particularly the anti-cyclonal wedge (Ka) and western anti-cyclonal (Wa) situations.

The construction of an anaerobic digester coupled with post-digestion low level aeration for agricultural wastewater treatment is described. The digester employs underwater speakers to accelerate the anaerobic digestion process while retaining solids to reduce the strength of the effluent. The digester effluent is sent to a holding tank and fed at a low flow rate to an aeration tank to effect partial nitrification of the wastewater. The outlet of this tank is sent to a settling tank to retain biomass that developed in the aeration tank and the effluent is sent to a small, constructed wetland to further reduce wastewater nitrogen and phosphorus. The wetland was planted with the broadleaf cattail, Typha latifolia, and hence to a retention basin. The system has reduced energy consumption due to both the use of underwater sonic treatment and low-level aeration that is not designed to achieve full nitrification/denitrification but rather to achieve a mixture of ammonium, nitrite, and nitrate that might foster the development of a consortium of organisms (i.e. nitrifiers, Anammox bacteria) that can remediate wastewater ammonium at low cost. Biogas is sent to an inground condenser where different treatments may be tested to remove carbon dioxide and hydrogen sulfide. The system is meant to serve as a complex where various technologies and practices can be evaluated to improve the treatment of agricultural wastewater.

Coastal zones, although vital for human livelihoods and environmental services, are increasingly vulnerable to climate change, especially communities dependent on marine ecosystems. These areas face significant threats from urbanization, population growth, and climate impacts, leading to biodiversity loss and ecosystem degradation. Recognizing these challenges, the United Nations declared 2021--2030 the Decade for Ecosystem Restoration, led by the UNEP and FAO, with the aim of restoring degraded ecosystems to achieve sustainable development goals (SDGs). The link between healthy ecosystems and disaster risk reduction (DRR) has gained global prominence, especially through the Sendai Framework for Disaster Risk Reduction (SFDRR), which emphasizes the importance of ecosystems in mitigating risks. Despite this, many tools for assessing risks in different ecosystem scenarios remain inaccessible to key stakeholders in disaster management. Advances in spatial modelling have supported ecosystem management, policy-making, and ecosystem health assessment, particularly following the Millennium Ecosystem Assessment. These models have been applied in regions such as the Great Bahamas, Haiti, and the Republic of Congo, helping shape ecosystem-based approaches to development. Improving access to spatial tools and models is crucial to enabling effective ecosystem-based DRR and climate adaptation strategies.

Communication pathways are an underestimated source of synthetic particles in the environment. This study investigated the impact of traffic volume on microplastics (MPs) and tire wear particles (TWPs) pollution in road snow. An examination was conducted in a medium-sized city situated in northeastern Poland, known for being one of the cleanest regions in the country. MPs and TWPs were found at all 53 sites, regardless of the intensity of traffic. The average concentration for all samples was 354.72 pcs/L. Statistically significant differences were found between the average values of particles concentration on low, medium, and heavy traffic roads amounted 62.32 pcs/L, 335.97 pcs/L and 792.76 pcs/L respectively. Within all three studied groups of roads, MPs and TWPs with the smallest size, ranging from 50 to 200 μm, were prevalent. In all studied groups of roads, 4 analyzed shapes of particles were found, with irregular fragments being the most abundant form (89.23%). The most frequently recorded color among the collected samples was black (99.85%), and the least frequently recorded was blue, constituting only 0.01%. This study suggests that snow cover on the roads may act like temporary storage of pollutants during winter in the temperate climate zone and after thaw can significantly increase the concentration of MPs and TWPs in surface waters. Possible measures to decrease the release of MPs and TWPs into the environment in the city may include reducing traffic volume and speed, implementing street sweeping, utilizing filtration chambers, and installing stormwater bioretention systems or settling ponds.

Afghanistan is located on the Eurasian tectonic plate's edge, a highly seismically active region. It is bordered by the northern boundary of the Indian plate and influenced by the collisional Arabian plate to the south. The Hindu Kush and Pamir Mountains in Afghanistan are part of the western extension of the Himalayan orogeny and have been uplifted and sheared by the convergence of the Indian and Eurasian plates. These tectonic activities have generated numerous active deep faults across the Hindu Kush-Himalayan region, many of which intersect Afghanistan, resulting in frequent high-magnitude earthquakes. This tectonic interaction produces ground shaking of varying intensity, from high to moderate and low, with the epicenters often located in the northeast and extending southwest across the country. The reoccurrence and severity of earthquakes have been increasing since 1900. This study maps Afghanistan's tectonic structures, identifying the most active geological faults and regions with heightened seismicity. The increasing trend in recurrence and intensity of the earthquakes are drawn. Historical earthquake data were reviewed, and recent destructive events were incorporated into the national earthquake dataset to improve disaster management strategies. Additionally, the study addresses earthquake hazards related to building and infrastructure design, offering potential solutions and directions to mitigate risks to life and property.

The key towards a sustainable future is the reduction of mankind’s impact on natural systems via the development of new technologies and the improvement of source apportionment. Though days, years and seasons are arbitrarily set, their mechanisms are based on natural cycles driven by Earth’s orbital periods. This is not the case for weeks, which are a pure anthropic category and are known from literature to influence emission cycles. For the first time since it started data gathering operations, CO (carbon monoxide), CO₂ (carbon dioxide), CH₄ (methane) and eBC (equivalent black carbon) values detected by the Lamezia Terme WMO/GAW station in Calabria, Southern Italy have been evaluated via a two-pronged approach accounting for weekly variations in absolute concentrations, as well as the number of hourly averages exceeding select thresholds. The analyses were performed on seven continuous years of measurements, from 2016 to 2022. Moreso, the two results have been combined into a new parameter: the hereby defined WDWO (Weighed Distribution of Weekly Outbreaks) normalizes weekly trends of CO, CO₂, CH₄ and eBC on an absolute scale as percentages, with the scope of providing regulators and researchers alike with a new tool meant to better evaluate anthropogenic pollution and mitigate its effects.

This study provides a detailed numerical assessment of the productivity of single-slope solar stills across key cities in the Caspian region, including Aktau, Atyrau, Astrakhan, Makhachkala, Baku, Tehran, and Turkmenbashi. Employing a mathematical model based on heat balance equations and the 4th-order Runge-Kutta method, we simulated the distillation process under various climatic conditions. The results reveal that productivity is significantly influenced by geographic location and local meteorological conditions. Tehran demonstrated the highest productivity across all seasons, with values of 1.75 × 10³ kg/(m²·year) and an efficiency of 0.53, due to its optimal solar irradiation and ambient temperature. In contrast, Atyrau and Makhachkala exhibited lower productivity, particularly in colder months, highlighting the effect of ambient temperature on solar still efficiency. The analysis identified optimal water depth at 2 cm and insulation thickness between 4 and 9 cm for enhancing productivity in continental climates like Aktau. Additionally, the lowest cost of distilled water was $0.024 per kilogram in Baku. These findings align with existing literature, validating the numerical model's accuracy. Future research will explore integrating solar stills with other renewable and fossil fuel-based technologies.

Ash dieback (ADB), caused by the invasive fungal pathogen Hymenoscyphus fraxineus, poses a significant ecological and economic challenge across Europe. Fraxinus excelsior (European ash), a key component of forest ecosystems, has experienced mortality rates reaching up to 85% in affected regions, severely threatening its ecological role and economic value. This study examines the relationship between the phenological traits of ash clones, particularly the timing of spring bud burst, and their susceptibility to H. fraxineus infection. The study was conducted in a clonal seed orchard located in northeastern Poland, encompassing 31 ash clones from different bioclimatic regions. Phenological observations of bud burst were carried out from early April to late May during the years 2018-2020, and crown damage and de-foliation levels were assessed multiple times throughout the growing season. The results confirm that clones with earlier bud burst exhibit significantly higher survival rates and reduced crown damage. It was found that the timing of bud burst is strongly correlated with susceptibility to ash dieback, suggesting that phenological timing is a crucial factor in the selection of genotypes resistant to ash dieback. Statistical analyses, including ANOVA and mixed models, revealed significant differences in susceptibility to infection among clones from different bioclimatic regions. Furthermore, the study high-lights the potential of breeding strategies focused on these traits to enhance the resilience of ash popula-tions. These findings are critical for the development of effective forest management practices aimed at conserving ash resources and maintaining biodiversity in the face of climate change and the ongoing spread of the pathogen.

The increasing urbanization of cities worldwide has driven a parallel rise in resource consumption, waste generation, and environmental degradation. The transition to a circular economy (CE) offers a sustainable alternative, shifting from traditional linear systems of resource use and disposal to regenerative systems that prioritize material reuse, resource efficiency, and ecological restoration. Urban design and planning play an essential role in facilitating this shift by creating the physical and regulatory infrastructure necessary to implement CE principles in cities. This paper explores the role of urban design in promoting circular economy strategies, with a focus on adaptive reuse of buildings, green architecture, and the integration of nature-based solutions (NBS) into urban environments. Through detailed analysis of these strategies and case studies from cities around the world, this paper demonstrates that regenerative design is essential for closing material and resource loops and creating more sustainable, resilient cities.

According to EuroStat data recorded landings of fisheries products from European waters were estimated at about 6 million tons in 2001, down to 3.2 million tons in 2022. Gradual decline slowed after the entering into force of the reform of the European Common Fisheries Policy (CFP) end 2013, but was followed by a steeper decline after 2018 (1). This casts some doubt on claims that the resources are nowadays better managed, even if a warming ocean provokes measurable poleward migration of species. Within this general picture, the broad-brush landscape is influenced by policy applications more in favour of industrial exploitations and regulatory and market environments making it very hard for many small-scale fishers (SSF) to remain in business, let alone attract younger successors for generational transition. In crowded marine spaces it is a challenge to allocate access rights fairly between fisheries, exclusion zones for resource and habitat protection and much needed ecosystem recovery (2), platforms for fossil exploitation, wind farms, underwater cables and recreational uses. Two examples of local initiatives with recovery potential in the Mediterranean are briefly presented. They are spearheaded by artisanal fishers, who seek to restore spawning grounds and other coastal habitats as a way to procure enough fish and other complementary activities to secure their livelihoods in the future and supported by local scientists and nature conservation organisations. While promising, this is still rather the exception. Here it is argued that trust-building between artisanal fishers, conservationists and scientists, and greater systemic support to SSF by governments increase chances for the urgently needed structural shifts that can reverse the on-going decline in biodiversity and ocean productivity which all aspire to.

The Huaniushan lead-zinc-silver deposit is a hydrothermal sedimentary exhalative deposit (SEDEX). The mining area has complex geological conditions. The main tectonic structure in the area is the Huaheitan-Shuangfengshan Fault (F3), which controls the distribution of strata and magmatic rocks. Since the discovery of the Huaniushan Lead-zinc-silver polymetallic deposit, there are diverse understandings of the deposit's genesis and mineralization law, making it challenging to meet the geological interpretation requirements of the Huaniushan Lead-zinc-silver polymetallic deposit. Moreover, a single geophysical exploration method utilizes limited rock physical parameters, making it difficult to effectively characterize underground structures. The integrated exploration of multiple geophysical methods can effectively integrate the geophysical characteristics of different rock physical parameters, reducing the multiplicity and uncertainty of geophysical data inverse interpretation. The comprehensive interpretation of three-dimensional inversion based on various geophysical data, the construction of geological-geophysical models based on geological grounds, the establishment of hidden ore exploration and positioning, and the rapid evaluation of geophysical technology systems are the current development trends in mineral exploration. In light of this, this paper conducts research on the three-dimensional inversion interpretation of the heavy magnetoelectric exploration data of the Huaniushan sedimentary exhalative deposit lead-zinc-silver polymetallic deposit in the mining area and constructs a three-dimensional geological-geophysical model of the study area based on the obtained three-dimensional physical structure of the underground three-dimensional density, magnetization intensity, resistivity, and polarizability structure of the study area, in combination with the information of related geology and drilling holes. It comprehensively interprets the favorable mineralization parts in the study area.

Linear programming models have been used in forest management planning since the 1960s. These models have been formulated in three basic ways: Models I, II, and III, which are defined by the sequences of management unit states represented by the variables. In Model I, variables represent sequences of states from the beginning of the planning horizon to the end. In Model II, variables represent sequences of states from one intervention to the next. Finally, in Model III, variables represent a single arc in a management unit’s decision tree, i.e., two states. The objectives of this paper are to clarify the definitions of these model variations and evaluate the advantages and disadvantages of each model. This second objective was achieved by formulating a case study problem using each model type. The case study includes three increasingly complex scenarios, each incorporating additional ecosystem services. Results show that despite having more variables and constraints, Model III requires the least time to formulate due to its less dense parameter matrix. Model II has the shortest solution times, followed closely by Model III, while Model I requires the longest times for both formulation and solution. These results are increasingly apparent in more complex scenarios.

Ghana spends over $4000 (US dollars) annually managing municipal solid waste (MSW) with little improvement in regional sanitation. Energy demand in the country is also on the rise. Therefore, addressing waste problems via the waste-to-energy valorization approach is necessary for dual benefits. This paper thus seeks to comprehensively review Ghana's MSW's proximate and ultimate compositions for biological and thermochemical conversions. Studies between 2011 and 2024 were analyzed. The findings revealed that the organic fraction of Ghana’s MSW is currently about 10.47Kt per day. The proximate analysis reveals a moisture content from 13.55 – 61.9 %, volatile matter ranges from 6.24 - 80.25%, and the calorific value ranges from 14.96-21.46MJ/Kg. The ultimate analysis revealed that the carbon-to-nitrogen ratios range from 24.3-32.2, and the hydrogen content ranges from 5.53-22.8%. The sulphur and nitrogen contents were below 0.6 and 1.7% respectively. Approximately 2,534.65 GWh of electricity is estimated from the organic fraction. Ghana’s MSW is more suited for biochemical conversions.

We manufacture different Carbon Negative fuels at out manufacturing facilities. We manufacture fuel Butanol, fuel Isobutanol, fuel Diacetyl alcohol, and fuel Mesityl oxide. All said fuels are manufactured from the air CO2. Mixtures of our Carbon Negative fuels are great to fuel both Gasoline and Diesel fuel cars and trucks. The fuel mileage we can gather using said mixtures exceeds both either the Gasoline mileage aor the Diesel fuel mileage using respective cars / trucks. We publish herein the fuel mileage which always exceeds the gas and diesel fuel mileages for said fuels manufactured by the international petroleum corporations.