Abstract: This study presents an analysis of the Infrastructure Leakage Index (ILI) variability for two District Metered Areas (DMAs) in the Silesian Region (Poland), based on 2024 data. The objective was to assess the suitability of the ILI for short-term leakage evaluation and its potential to support operational decision-making. ILI values were calculated for daily, weekly, and monthly intervals using synchronized hourly data from an Advanced Me-tering Infrastructure (AMI) system and water network monitoring platforms. A key ad-vantage of the dataset was the temporal alignment of inflow, outflow, and consumption data at the DMA level. The study applied statistical measures of variability (standard deviation, variance, coeffi-cient of variation) and graphical methods (histograms, boxplots) to evaluate ILI behavior across time resolutions. The two DMAs were compared to explore the differences in ILI dynamics, range, and identify operational factors influencing leakage levels. The results confirm that ILI interpretation is highly dependent on temporal resolution. Daily data is more responsive to anomalies and operational events, while monthly data provides more stable values suitable for benchmarking. Despite the aggregation smooth-ing the variability, notable differences in system performance between zones persist. High-frequency AMI data enhances the precision and operational value of ILI analyses.

Abstract: Stereo cameras, also known as depth cameras or RGBD cameras, are increasingly employed in a large variety of machinery for obstacle detection purposes and navigation planning. This also represents an opportunity in agricultural machinery for safety purposes to detect the presence of workers on foot and avoid collisions. However, their outdoor performance at medium and long range under operational light conditions remains weakly quantified: authors then fit a field protocol and a model to characterize the pipeline of stereo cameras, taking the Intel RealSense D455 as benchmark, across various distances from 4 meters to 16 meters in realistic farm settings. Tests have been conducted using a 1 square meter planar target in outdoor environments, under diverse illumination conditions and with the panel being located at 0°, 10°, 20° and 35° from the center of the camera's field of view (FoV). Built-in presets were also adjusted during tests, to generate a total of 128 samples. Authors then fit disparity surfaces to predict and correct systematic bias as a function of distance and radial FoV position, allowing to compute mean depth and estimate a model of systematic error that takes depth bias as a function of distance, light conditions and FoV position. Results showed that the model can predict depth errors achieving a good degree of precision in every tested scenario (RMSE: 0.46 – 0.64 m, MAE: 0.40 – 0.51 m), enabling the possibility of replication and benchmarking on other sensors and field contexts while supporting safety-critical perception systems in agriculture.

Abstract: Sustainable development constitutes a global consensus achieved through humanity's response to environmental imperatives and critical reflections on prevailing development paradigms, serving as a fundamental framework for addressing pressing global challenges. Since the Industrial Revolution, successive technological revolutions have significantly enhanced humanity's capacity to generate material wealth, concurrently driving profound transformations in the global understanding of development—from early notions of "growth theory" to broader "development theory," and ultimately evolving into the contemporary "sustainable development theory." (1) As an emerging ideology and transformative development paradigm, sustainable development has gradually taken shape through sustained introspection and practical exploration in the modern era. Its emergence was prompted by widespread experiences of severe constraints on social progress, including adverse consequences associated with industrialization, rapid economic expansion, population growth, resource depletion, and ecological degradation. Seminal documents such as the Brundtland Report, Agenda 21, the United Nations Millennium Declaration, and the 2030 Agenda for Sustainable Development not only trace the formation and evolution of the sustainable development framework but also articulate its core guiding principles. (2) In the current phase of global development, achieving the United Nations Sustainable Development Goals (SDGs)—comprising 17 goals and 169 specific targets—has become a central priority for nations worldwide during the 2016–2030 period. Governments and regional entities are increasingly integrating the SDGs into national and sub-national development strategies and issuing comprehensive reports to document localized implementation efforts and measurable outcomes. (3) However, current assessments of progress toward the SDGs indicate that global advancement has significantly deviated from the intended trajectory. The resilience and well-being of the Earth system, human societies, ecosystems, and the broader environment are deteriorating, and the realization of the SDGs continues to confront numerous emerging challenges. There is a pressing global imperative to develop and implement more effective mechanisms and national-level measures through innovative approaches. (4) Over the years, China has systematically integrated SDG implementation with its medium- and long-term development strategies, including the 13th Five-Year Plan, the 14th Five-Year Plan, and the Outline of the 2035 Long-Range Objectives. While advancing the vision of a community with a shared future for humanity, China has prioritized the development of an ecological civilization model grounded in harmony between humans and nature, offering Chinese perspectives on addressing global challenges related to sustainable development. As one of the countries demonstrating the most consistent progress in implementing the SDGs, China has reinvigorated international confidence in achieving global sustainability targets. Initiatives such as the Green Belt and Road Initiative and the Global Development Initiative have provided practical frameworks for enhancing international cooperation in sustainable development. To accelerate the achievement of the SDGs, it is essential to: first, accurately understand and respond to the profound global transformations of the present era; second, position the SDGs as the central focus of international cooperation; third, strengthen data governance to support evidence-based decision-making for SDG implementation; fourth, expedite the localization of the SDGs within national and regional contexts; fifth, cultivate new drivers of global sustainable development; and sixth, reinforce institutional mechanisms for effective SDGs follow-up and review.

Abstract:

This paper presents an automated GIS-based procedure for the analysis and optimization of hiking trails. A preliminary analysis of the topological and environmental features of a trail network is performed by evaluating a set of connection metrics describing both the local and global connectivity of its graph. Subsequently, the evaluation of optimal hiking trails has been implemented in an automatic procedure, which can use walking time, distance or upward slope as costs to be minimized. The evaluation of the hiking times for trail sections has been implemented in a GIS as a function of terrain slope. A Python script has been used to automate this process in GRASS GIS. The process was tested on the network of mountain trails in Trentino, an alpine region of Italy, where a digital map of the routes is accessible online. Empirical times and estimated trip times agree fairly well. The optimal paths vary based on the cost choice, i.e., whether the distance, trip time, or total height difference is minimized. It is therefore possible to integrate the determination of optimal hiking paths in a GIS, allowing the integration of this tool with all the other spatial analysis available in this environment.

Abstract: Submerged macrophytes play a vital role in regulating carbon and nitrogen cycling in aquatic ecosystems; however, their effects on greenhouse gas (GHG) emissions in aquaculture sys-tems remain poorly understood. This study quantified methane (CH₄) and nitrous oxide (N₂O) fluxes after planting Hydrilla verticillata (HV), Elodea nuttallii (EN), and Vallisneria natans (VN) in Eriocheir sinensis tanks to elucidate emission dynamics and identify low carbon management strategies. Compared with unvegetated controls, vegetated treatments signifi-cantly enhanced CH₄ emissions, with CH₄ contributing over 97% of the global warming po-tential (GWP). The overall GWP values were in the order HV > VN > EN > CK (control), in-dicating that V. natans had the lowest GWP value among all macrophyte treatments. Mecha-nistically, nitrogen availability and redox conditions jointly regulated GHG fluxes, while differences in aerenchyma development, root exudation, and algal attachment explained the observed species-specific variation. These findings suggest that optimizing plant composition, particularly through the introduction of V. natans or species with similar ecological traits, can effectively mitigate GHG emissions in crab aquaculture. Moreover, the identified mechanisms provide new insights into methane regulation in vegetated wetlands, supporting broader de-carbonization strategies for aquatic ecosystems.

Abstract: As a pivotal market-based instrument for achieving sustainable development and carbon neutrality goals, China's Energy-Use Rights Trading Policy (EURT) was implemented to incentivize corporate energy efficiency and emission reduction. This study empirically examines the impact of China's 2016 pilot policy on energy-use rights trading on corporate earnings management, utilizing micro-level data from China's A-share listed companies. The results demonstrate that the policy significantly intensifies earnings management, finding that are corroborated by a series of robustness checks. Heterogeneity analysis reveals that the policy effects on corporate financial manipulation are more pronounced in: private enterprises, non-Big Four-audited firms, firms within industries characterized by high concentration, and firms located in regions characterized by lower environmental fiscal expenditure, and weaker waste gas treatment capacity. Extended analysis identifies two key moderating factors: heightened environmental governance pressure amplifies the effect, whereas a high proportion of environmental investment significantly mitigates it, suggesting a potential pathway for aligning environmental and financial sustainability. Further investigation reveals that the policy exacerbates earnings management not only by intensifying financing constraints but also by increasing corporate financial risk, thereby revealing the micro-level transmission channels through which the environmental mandate impacts economic decision-making. This study provides empirical evidence for policymakers to refine the design of market-based environmental instruments. Its findings highlight the necessity of integrated governance that harmonizes environmental sustainability with corporate financial integrity.

Abstract: The industrial city of Brno is the second largest city in the Czech Republic (402,739 permanent residents as of 31 December 2024) and the core of the country’s third largest metropolitan area, following Prague and the industrial region of Ostrava. Brno has had a metropolitan character since the High Middle Ages, and experienced extraordinary development during the Industrial Revolution in the second half of the 19th century. From a medieval core of just over 1 km², the city expanded in multiple stages to its current area of nearly 230 km². During this expansion, Brno absorbed smaller towns as well as numerous rural municipalities. As a result, large tracts of forest and agricultural land became part of the city. Land adjacent to the historic core was often converted, while the central parts of the incorporated villages retained a relatively rural appearance. Former agricultural plots in these areas were gradually transformed into residential quarters, consisting of villas or apartment buildings. Under state socialism, large housing estates of prefabricated blocks were constructed on the city’s inner periphery. At the same time, farmland was nationalised and consolidated into large-scale cooperative fields. This pattern persisted even after the political changes of 1989. Nevertheless, within the administrative boundaries of Brno, remnants of very old rural landscapes survived, often preserving the parcel structure of medieval small-scale agriculture. These areas are today largely maintained by gardening associations and individual gardeners. Between 2016 and 2020, these remnants were inventoried and classified. A total of 34 sites of varying size were identified. Based on their state of preservation, they were divided into two groups: (1) relatively well-preserved and (2) heavily degraded, through comparison with the situation around 1830, when detailed cadastral mapping was conducted. Well-preserved segments of the pre-industrial landscape were analysed and evaluated in GIS. Their survival has been influenced by natural factors (geological substrate, slope, exposure, elevation, topoclimatic conditions, soil quality), location (distance from the city centre, proximity to forests), land ownership (private, municipal, state), as well as the personal and recreational interests of residents. Interestingly, more remnants of the old landscape have been preserved inside Brno than in its rural surroundings, largely due to the role of urban gardening. Finally, the study assesses the prospects for the continued existence of these landscape relics. From the perspective of city administration and developers, they represent land reserves for other uses. Brno also hosts numerous modern allotment colonies, established either on former agricultural land or on abandoned and degraded sites (e.g. quarries, devastated or reclaimed areas) to meet the recreational needs of the urban population.

Abstract: This paper aims to give an analytical assessment of country’s level experience in moving towards agricultural decarbonization – from the idea of potential decarbonization measures to the assessment of their potential, the inclusion of the measure in political goals and practical implementation success. This paper is based on 10-year cycle that highlight main steps in building decarbonization awareness and an approach that can be used for monitoring, quantifying and evaluating the contribution of agriculture to climate change mitigation. This approach is based on the Marginal Abatement Cost Curve (MACC), which serves as a convenient and visual tool for evaluating the effectiveness of various agriculture greenhouse gas emission reduction measures and for climate policy planning. This study reveals the experience to date and the main directions in developing the MACC approach, which serves as a basis for analysing the potential of one specific European Union Member state, i.e., Latvia, agriculture for moving towards decarbonization. The results of the study are of practical use for the development of agricultural, environmental and climate policy or legal framework, policy analysis and impact assessment. As well findings of this study are useful for educating farmers and the public about measures to reduce GHG and ammonia emissions.

Abstract: As the climate continues to warm, the incidence of extreme weather events is increasing on a global scale. Presently, flooding remains a complex challenge for urban environments. Certain regions and countries are limited by geographic and historical urban planning factors, resulting in heightened flooding in some historic districts that are affected throughout the year. Since the mid-20th century, nations have initiated comprehensive research into flood protection measures, evolving from the initial construction of flood control dykes to the development of floodplains. However, with the escalation of climatic issues, traditional flood prevention strategies, such as floodplain management, are no longer sufficient to address current flooding phenomena. Consequently, this study will focus on the cities of Carlisle in northern England and New Orleans in the United States. These two cities have been selected due to their similar causes of flooding. The research aims to compare the underlying factors contributing to flooding in New Orleans and Carlisle in order to assess why conventional flood control strategies are becoming increasingly ineffective.

Abstract: The Fe/CoFe2O4 nanocomposite was synthesized in a single step via a hydrothermal method by treating coprecipitated iron and cobalt hydroxo complexes. The obtained samples were found to have a pronounced spinel crystalline structure with the presence of metallic iron. The crystallite size was determined by various methods. The Ms value determined from the hysteresis loop was 189.24 Em/g, and Hc was 602 Oe. The physical properties of the Fe/CoFe2O4 nanocomposite were studied using an Infrared spectrophotometer (IR), scanning electron microscopy (SEM), and ultraviolet-visible (UV-visible) spectroscopy. The photocatalytic degradation of ibuprofen, streptocide, furacilin, methylene blue, tetracycline was studied using a photocatalytic nanocomposite under UV light. The effects of various parameters, such as catalyst concentration, hydrogen peroxide concentration, and treatment time, on degradation were also examined. It was confirmed that the degradation rate of pollutants follows pseudo-first-order kinetics.

Abstract: The dominant paradigm of artificial intelligence (AI), concentrated in the Global North, operates through extractive models that concentrate wealth while externalizing social and environmental costs. This paper introduces RSquare AI (Regenerative & Responsible AI) as a sovereign alternative for the ASEAN region. Through a systematic review of literature (n=58) and qualitative analysis of 27 initiatives across six ASEAN nations, this research identifies a critical dual gap: a geographical bias in AI ethics scholarship (85% Western-focused) and a disconnect between regenerative economics and technological development. The findings inform a novel framework that transforms sectoral challenges into strategic assets. Central to this is the Regenerative AI Leadership Flywheel, a model for creating self-reinforcing innovation ecosystems grounded in polycentric governance, regenerative capital, and community-embedded living labs. The study concludes that ASEAN's cultural endowments, developmental agility, and sustainability imperative position it to not only adopt but to pioneer and export a form of AI that enhances, rather than extracts from, human and ecological systems.

Abstract:

This research examines the persistent shoreline erosion along the Damietta coast and the problem of sediment buildup within the navigation channel of Damietta Port, both of which provide major obstacles to navigation efficiency and coastal balance. To address these issues, the research uses the LITPACK numerical model to forecast shoreline evolution along the Damietta coast, which is divided into two sections of 3.3 km each, during the next 20 years. Considering both planned and existing coastal constructions, two realistic alternatives were proposed. The Coastal Modeling System (CMS) was then used to perform a two-dimensional simulation in order to examine sediment transport and hydrodynamic behavior in the port region. This phase concentrated on examining the effects of sedimentation rates following the most recent port development plan, which included building a massive 5560-meter-long western jetty and a new navigation channel with a depth of 9 m to service the dirty ballast terminal. In comparison to the benchmark case, the simulation results showed a 93% decrease in sedimentation rates within the navigation channel. The study's final phase evaluated the impact of changing the crest levels of the current detached breakwaters along the Ras El-Bar coastline on reducing coastal erosion. The study's conclusions promote the creation of effective and sustainable coastal protection plans in the Damietta area by providing insightful information for future coastal zone management and planning.

Abstract: This study explores the application of Artificial Intelligence (AI) algorithms for detecting hazardous substance discharges into water bodies and modeling their distribution. River pollution remains a major environmental concern, while traditional monitoring methods are labor-intensive, costly, and lack operational efficiency. The paper analyzes key water quality parameters and modern Internet of Things (IoT) systems that enable real-time data collection on indicators such as pH, dissolved oxygen, temperature, and turbidity. Emphasis is placed on Machine Learning (ML) algorithms for anomaly detection and pollution forecasting. Methods including linear regression, decision trees, neural networks, and clustering are compared, with particular attention to ensemble models such as Random Forest (RF) and XGBoost. Using open environmental data on Ukrainian surface waters for 2022, RF and XGBoost were found to effectively predict nitrogen and dissolved oxygen concentrations and identify anomalies linked to anthropogenic pollution sources, including municipal sewage and agricultural runoff. The integration of AI and IoT technologies significantly improves the accuracy, speed, and efficiency of water quality monitoring, supporting early warning systems and sustainable water management while highlighting ongoing challenges related to data quality and implementation costs.

Abstract:

Water is fundamental to the economy of Pakistan, and to many of the county’s 250 million people. Increasing groundwater use in Pakistan is occurring against a backdrop of climate change, dwindling surface water, and human population growth. Historically there has been little groundwater resource management in the country, and that little has mainly related to its use, that is to demand side management. There is some potential for Managed Aquifer Recharge (MAR) to contribute to groundwater supplies, that is, to supply side management. MAR is a complex process which to be effective requires consideration of social, institutional, environmental, technical, financial, and economic parameters. This paper introduces a methodology that has been developed to assess and sum the effect of multiple parameters into single numerical value, to enable the feasibility of proposed MAR projects; the “Managed Aquifer Recharge Feasibility Index (MARFI)”. MARFI was developed using a case study of an MAR project currently being implemented by the Punjab Irrigation Department in the bed of Old Mailsi Canal. MARFI is a suitable tool for examining the pre-project feasibility of any MAR scheme for contributing to wise investment decision making.

Abstract: Hospitals are water-intensive facilities that must simultaneously assure reliable potable supply and consistent compliance at their wastewater treatment plants (IPAL/WWTP). Using the RSJD Dr. Amino Gondohutomo (Semarang) as a case study, we translate daily consumption into wastewater loading under a conventional 80/20 grey–blackwater split. The data records 221.80 m³/day of water demand, which implies about 174.91 m³/day domestic wastewater to the IPAL; these values allow a data-driven assessment rather than assumptions. The operation is constrained by two interlocking limits: an installed IPAL capacity of 240 m³/day and a wastewater discharge permit (IPLC) capping the daily effluent to 175 m³/day to Kali Gemah, creating a narrow hydraulic margin during peak-load days. We develop two indicators, (i) daily effluent-quota compliance and (ii) process loading relative to capacity, and test operational scenarios with and without equalization and controlled outflow. On the supply side, a planned full switch to PDAM (municipal water) in 2025 is documented, potentially reducing dependence on groundwater and improving upstream quality control. Effluent-quality monitoring shows most months in 2024 met domestic standards, with an October episode (coliforms, ammonia, phosphate) resolved via draining the disinfection basin and chlorine-dose adjustment, underscoring the importance of SOPs and QA/QC. We find that, although process capacity is ample, the daily discharge quota is tight; therefore, right-sized equalization (6–12 h of average flow) and day-sum outflow control are pivotal to maintain compliance, complemented by disciplined QA/QC and integrated debit monitoring. The PDAM switch further supports aquifer conservation and operational reliability.

Abstract: Flood hazards in arid oil-producing regions are significantly influenced by both natural hydrological processes and terrain changes resulting from subsidence. This study develops a deformation-adjusted approach to flood risk assessment for the Yibal field in the northern part of the Sultanate of Oman, where long-term hydrocarbon production has led to measurable ground deformation. A high-resolution digital elevation model (DEM) from an aerial survey in 2013 was adjusted using cumulative surface deformation data (2013–2023) derived from PS-InSAR measurements. By subtracting the surface deformation from the baseline DEM, an updated DEM for 2023 was produced that explicitly incorporates subsidence into hydrological modeling. Weighted flood risk mapping was then performed using slope, precipitation, land use/land cover, and drainage parameters. Results show a marked increase of flood vulnerability, with high- to very high-risk zones (8.0-8.6, 8.7-9.6) expanding in areas affected by surface deformation. These findings demonstrate that conventional flood risk mapping, which neglects subsidence, can underestimate hazard levels in petroleum fields where ground deformation alters drainage pathways. Incorporating subsidence-adjusted terrain data into hydrological risk assessment provides more realistic flood susceptibility maps, improving early warning, infrastructure protection, and sustainable petroleum field management. Beyond the Yibal field, the method is transferable to other petroleum basins and arid regions where subsidence interacts with surface hydrology.

Abstract: This study proposes a sustainable framework for landscape renewal at the Alhambra by integrating rain-garden systems with hydrological restoration under the conceptual lens of Dialogue Between Palace and Land. Addressing challenges of seasonal drought, flash flooding, and soil degradation, the research identifies the site as an exemplary context for water-sensitive interventions that are both ecologically functional and culturally resonant. Topographic and hydrological analyses delineate priority zones for distributed rain-garden installations to regulate stormwater, enhance infiltration, and stabilize microclimates. In parallel, selective reactivation of historic water flows and passive irrigation channels reinforces the ecological memory embedded in the Alhambra’s historic fabric. The proposed strategies—vegetated swales, permeable surfaces, and native planting assemblages—are conceived as low-impact, reversible infrastructures compatible with heritage-conservation standards. Quantitative evaluations using GIS-based watershed modeling, soil-infiltration tests, and NDVI vegetation-health analysis demonstrate reduced peak runoff, increased infiltration capacity, and elevated biodiversity indices. By positioning hydrological function as both ecological infrastructure and a regenerative aesthetic medium, this research offers a transferable model for integrating nature-based systems into heritage landscapes, advancing broader discourses on climate adaptation, ecological resilience, and culturally sensitive landscape architecture.

Abstract: The widespread presence of estrogenic pollutants in aquatic environments poses a significant threat to ecosystems and human health, necessitating the development of efficient and sustainable removal technologies. This study aimed to develop a cost-effective biocatalyst for estrogen biodegradation using a fungal laccase. The enzyme was produced by the native strain Dichostereum sordulentum under semi-solid-state fermentation conditions optimized using a statistical Design of Experiments. The design evaluated carbon sources (glucose/glycerol), nitrogen sources (peptone/urea), inoculum size, and Eucalyptus dunnii bark as a solid support/substrate. The resulting laccase was entrapped within a hydrogel made of lignocellulosic biopolymers derived from a second-generation bioethanol by-product. Maximum laccase production was achieved with a high concentration of peptone (12 g/L), a low amount of bark (below 2.8 g), 8.5 g/L glucose and 300 mg/flask of inoculum. The subsequent immobilized laccase achieved 98.8 ± 0.5% removal of ethinylestradiol, outperforming the soluble enzyme. Furthermore, the treatment reduced the estrogenic biological activity by more than 160-fold. These findings demonstrate that the developed biocatalyst not only valorizes an industrial by-product but also represents an effective and sustainable platform for mitigating hazardous estrogenic pollution in water.

Abstract: This paper proposes a recurrent neural network (RNN) model of dead 10-h fuel moisture content (FMC) for real-time forecasting. Weather inputs to the RNN are forecasts from the High-Resolution Rapid Refresh (HRRR), a numerical weather model. Geographic predictors include longitude, latitude, and elevation. Forecast accuracy is estimated in a~study that utilizes a~spatiotemporal cross-validation scheme. The RNN is trained on HRRR forecasts and observed FMC from weather station sensors within the Rocky Mountain region in 2023, then used to forecast FMC at new locations for all of 2024. The forecasts are compared to observed data from FMC sensors that were not included in training. The accuracy of the RNN is compared to several common baseline methods, including a~physics-based ordinary differential equation, an XGBoost machine learning model, and an hourly climatology. The RNN shows substantial forecasting accuracy improvements over the baseline methods.

Abstract: Plastics are accumulating in the environment, and due to their extremely low biodegradability, this issue is expected to persist for centuries. Historically, oceans were used as dumping grounds for waste, leading to the accumulation of long-lasting materials that now cause severe pollution problems. Macro- and microplastic waste pose serious environmental, social, and economic threats, such as injuring or killing marine organisms and entering the food chain, resulting in potential health risks for humans. Microplastics have become one of the most critical global concerns, as they disrupt the balance of terrestrial and marine ecosystems. The growing presence of microplastics in the environment threatens biodiversity and endangers vulnerable marine species. Moreover, their ingestion by marine organisms can impact the entire food chain, affecting both wildlife and human health. Addressing this challenge requires the development of efficient and sustainable solutions for the control and mitigation of microplastics. This study focuses on the advancement of filtration processes and membrane technologies specifically designed to capture and remove microplastics based on their size, quantity, and origin. By evaluating the performance and suitability of various filtration methods, this research seeks to promote effective recovery, control, and final elimination of microplastics while increasing awareness of sustainable environmental management practices.