Atmospheric and Oceanic Sciences

Chile’s livestock industry faces growing demands for emissions reduction, animal welfare, and value creation, while continuing to play a key role in rural food security and pasture-based production systems. In light of Chile’s varied agroclimatic conditions, a diminishing national herd, and shifting market signals, such as alternative proteins and distinctive meat products, this narrative review explores four complementary transition pathways: sustainable intensification, organic and agroecological systems, heritage livestock, and regenerative practices. We map the structural challenges, including grazing dairy and beef herds, fragmented producer organization, and the absence of unified, farm-scale greenhouse-gas measurements. We assess the management strategies that have the strongest support; viz., efficiency gains at the animal/herd level, adaptive grazing and silvopastoral designs, nutrient cycling via manure management and local by-products, and welfare frameworks that are aligned with national law and World Organisation for Animal Health guidance. Heritage systems (e.g., Chilota sheep breed in the Chiloé archipelago) provide resilience, cultural identity, and low-input baselines for stepwise transitions. Regenerative procedures can improve soil function and drought buffering but require context-specific designs and credible outcome-based verification to avoid greenwashing. Key enabling policies include coordinated certification and labeling covering animal welfare and origin. Additional elements are cooperative and territorial governance, targeted R&D and extension services for smallholders, and a transparent, standardized greenhouse-gas measurement framework linking farm-level actions to national inventories. Chile’s pathway is not a single model but a practical combination shaped by regional conditions that can deliver long-term economic sustainability, ecosystem services, and nutrition.

Lost fishing gear (LFG), also known as “ghost gear,” is a global environmental problem that constitutes a substantial portion of marine plastic pollution, resulting in significant socio-economic and ecological impacts. This paper estimates the quantities and primary causes of gear loss along the Moroccan Mediterranean coast through surveys conducted with 138 artisanal fishermen, covering a total of twelve different types of fishing gear. In total, 20,115 kg of fishing gear was lost, averaging 138.29 ± 120.69 kg boat−1 year−1. This represents approximately 28.97% of all gear used across 26,568 annual fishing trips (averaging 192 trips boat−1 year−1). The study period spanned from January 2022 to February 2023. Net-gear emerged as the most frequently lost category (65.91%). Substantial percentages of gear loss were also recorded for trammel nets (36.93%), gillnets (24.65%), and octopus jigs (23.41%). According to the responses of the fishermen surveyed, adverse meteorological and oceanographic conditions were the main cause of loss (57%), followed by marine animal attacks (19%) and fishing gear conflicts (14%). These findings are crucial for addressing data gaps on quantities of lost fishing gear released from the artisanal fisheries sector, and to contribute to the development of strategies to reduce this environmental problem. These include the regular maintenance of fishing gear, improved gear marking, the adoption of tracking devices to ensure the traceability of lost gear, and the implementation of awareness-raising and incentive programs for fishermen. These measures help to limit gear loss while promoting the sustainability of fishing activities and the protection of marine ecosystems.

The year 2025 represented a landmark period for Sustainability, with the journal achieving an Impact Factor of 3 [...]

This paper examines the current state of Romanian seaports from the perspective of their transformation into smart ports, using the SWOT model to identify the most suitable strategies. It highlights the strengths and weaknesses of existing infrastructure, as well as the opportunities and risks, to outline coherent and sustainable courses of action for future development. A SWOT analysis was conducted based on information collected from a questionnaire sent to members of the maritime port authority, directors, and staff from various departments of the analyzed ports, as well as direct interviews with experts from the three ports. This analysis served as the foundation for developing strategies aimed at accelerating digitization, improving operational efficiency, and reducing environmental impact. The identified strategies were subsequently ranked using the AHP method. The weights assigned to the ten strategies emphasize the relative importance and systemic influence of each one on the process of ports transforming into smart entities. This study makes a significant contribution to the emerging literature on the transformation of Romanian seaports into “smart ports” by approaching this process through the lens of sustainable port development.

Understanding the structural drivers of global CO2 emissions is essential for designing effective climate-mitigation strategies and for supporting progress toward Sustainable Development Goal 13 (SDG 13—Climate Action). This study applies the Kaya Identity-based decomposition approach to quantify how population, gross domestic product per capita, energy intensity, and emission intensity jointly shape long-term emission dynamics. Using harmonized historical datasets for the period 1990–2020, the analysis compares global trends with country-level trajectories in major emitting regions, including China, India, the United States, the European Union, and Russia. Results indicate that although energy and emission intensity have improved in several regions, these gains remain insufficient to offset the combined effects of population growth and rising economic output, leading to continued increases in global emissions. Significant asymmetries emerge across countries in terms of development stages, historical responsibility, and capacity for decarbonization, raising important considerations for climate equity. Overall, the Kaya decomposition provides a transparent diagnostic framework for identifying policy-sensitive levers, particularly energy intensity and carbon intensity, and for highlighting where mitigation efforts are most urgently needed to advance progress toward SDG 13.

The present study evaluates the performance of hotel companies in Romania using Data Envelopment Analysis (DEA) integrated with a hybrid weighted TOPSIS model (Technique for Order Preference by Similarity to the Ideal Solution). This approach captures both technical efficiency and multidimensional competitiveness. The DEA included an output-oriented Variable Returns to Scale (VRS) model (with four inputs and one output). It was followed by TOPSIS aggregation with hybrid entropy weights to obtain a composite performance index. The research used cross-sectional financial data for 2023, specific to hotels in Romania, and allowed interpretation across five territorial categories based on predominant relief. The results show that the 852 analyzed hotels have a relatively homogeneous structure and moderate variations in performance scores. At the same time, top-performing units are strongly concentrated in economically or touristically dynamic counties. The integrated DEA–TOPSIS results indicate that high-performing hotels tend to cluster spatially, with plain counties hosting the largest number of hotels at the national level and also a substantial share of high-performance hotels relative to major urban centers; thus, their performance structure is not uniform but strongly polarized. In contrast, the other geographical areas show pronounced clustering, with top hotels concentrated around consolidated leisure destinations, such as Brașov, Sibiu, Constanța, and Prahova. Overall, research using the DEA–TOPSIS method highlights significant spatial disparities that influence both managerial decision-making and regional development policies, affecting the long-term sustainable performance and competitiveness of the Romanian hotel sector.

This study aims to quantitatively estimate the non-market value of Cheoram Coal-Mine History Town (CCM), a modern industrial heritage site. CCM is valued highly as a modern industrial relic and a tourist destination for Korean coal mines. However, its existence and preservation are at risk due to insufficient preservation efforts following the cessation of its mining operations. The preservation of CCM necessitates systematic verification, and its economic value could serve as a strong criterion for preservation. However, since the economic value of non-market goods cannot be determined through market mechanisms, it is estimated using the CVM, which assesses the economic value based on individuals’ willingness to pay. In addition to evaluating the economic value, the study explored how nostalgia influences the willingness to pay for preserving CCM. The findings reveal that the average willingness to pay for CCM was 18,756 KRW (=12.71 USD). Furthermore, nostalgia significantly impacts the willingness to pay. These results can assist the decision-making of the managing entity by providing a justification for the preservation of CCM.

Wet markets remain a cornerstone of fresh food retail in Chinese cities, continuously evolving alongside urbanization. However, the drivers and implications of their transformation at the city level remain underexplored. Drawing on government documents and survey data from Nanjing and Suzhou, this study reveals that China’s wet market evolution is characterized by incremental semi-formalization and upgrading, preserving their essential role in the food supply chain without displacing other retail formats. This transformation reflects shifting government attitudes, strategic urban planning for food security, and the effective integration of public and private interests. The hybrid governance model, which combines public oversight with private operation, has enhanced wet markets’ resilience, ensuring affordability, freshness, and social interaction. Their adaptability underscores a broader lesson: inclusive urban food systems require soft–hard infrastructure synergy, where physical upgrades coexist with social functions. In this paper, we argue that wet markets exemplify social infrastructure: they are not merely food hubs but spaces fostering civic life, cultural continuity, and equitable access. Their co-evolution with supermarkets and e-commerce challenges the “supermarketization” thesis, highlighting the importance of policy flexibility and localized governance. Our findings offer insights for Global South cities grappling with food system transitions, emphasizing the need to balance modernization with the preservation of informal economies’ social fabric.

This study explores, through the lens of rural revitalization, how local cultural heritage can be activated and turned into an endogenous driver for rural cultural and creative industries. Focusing on Jiande City in Zhejiang Province, China, it draws on longitudinal fieldwork conducted between 2021 and 2024. Methodologically, the research employed participatory observation, in-depth interviews, and focus group discussions with key stakeholders from local government bodies—such as the Organization Department and the Culture, Radio, Television, Tourism and Sports Bureau—as well as village communities, including Meicheng Town. Based on this empirical work, the study advances a theoretical framework centered on “cultural gene decoding,” structured around three core phases: cultural decoding, creative transformation, and multi-stakeholder collaboration. This process involves excavating fragmented local cultural memories and transforming them into culturally resonant narratives and creative products with contemporary appeal. Cases such as “Strawberry Town” and “Qianhe Women’s Culture” in Jiande illustrate the emergence of an integrated “culture + industry + technology + academia” ecosystem. Within this ecosystem, the international journal Agricultural & Rural Studies plays a pivotal role by translating local practices into academic discourse, thereby connecting grassroots experiences with global dialog and enhancing international visibility.

In the face of growing demand for marine nutrition and restrictions on wild fish capture, mariculture offers significant potential to enhance marine food production. The natural environment directly influences the growth of marine organisms, but socio-economic conditions are equally critical for sustainable and efficient development. This study investigated the multifaceted factors shaping mariculture in China, Vietnam, and India. We analyzed the natural environment and social economy qualitatively and quantitatively by adopting zonal statistics, chart analysis, and correlation analysis. Results show that all three countries generally possess suitable marine environments for the growth of cultured species. Meanwhile, China and Vietnam demonstrate how robust socio-economic systems and strategic policy support drive successful mariculture development, whereas India’s comparatively underdeveloped socio-economic foundation appears to constrain its sectoral advancement. These analyses suggest a principle: the natural environment is the necessary condition and the social economy serves as the sufficient condition, together determining the state of mariculture. Our study highlights the joint role of environmental suitability, socio-economic readiness, and policy frameworks, providing valuable insights for identifying potential mariculture sites and informing policy strategies to promote sustainable marine aquaculture globally.

Construction management plays a fundamental role in the sustainability of construction projects, as its primary objective is to enhance cost-effectiveness and efficient resource utilization. One of the main challenges encountered at the early stages of a project’s lifecycle, particularly during the planning phase, is the development and agreement of construction schedules among the stakeholders involved. The tools employed for time planning and scheduling during both the planning and construction phases should therefore be capable of modeling complex environments and supporting dynamic updates in response to resource constraints. Petri nets are known for their capability of modeling complex systems, such as resource management. Their use in project management is essential for resource constraint problems. This paper investigates the use of Petri Nets as a tool for the time scheduling of engineering and construction projects. A case study is presented and modeled using Timed Petri nets, enabling dynamic adaptation under time and resource constraints. Through simulation performed with the ROMEO (v3.10.6) software, the study identifies the critical paths and determines the total project duration under various scenarios of sensitivity by adjusting specific project parameters. The results demonstrate the effectiveness of Petri nets in project management and the benefits they offer when used in modeling complex systems, identifying critical activities and calculating resource constraints and time deadlines.

Conventional infrastructure appraisal in Africa prioritizes short-term economic performance while insufficiently accounting for the environmental conditions that govern long-term sustainability, spatial equity, and development resilience. To address this gap, this study develops an explicitly SDG-oriented spatial–ecological framework to examine how environmental quality conditions the economic returns of large-scale infrastructure investments under corridor-based development. The primary objective is to quantify infrastructure–environment complementarity and identify ecological thresholds regulating spatial spillovers and investment effectiveness along Tanzania’s Belt and Road Initiative (BRI) corridors. High-resolution remote sensing and spatially explicit socioeconomic data for 2012–2023 are integrated within a spatial econometric design. A Spatial Durbin Model (SDM) incorporating the Normalized Difference Vegetation Index (NDVI) is estimated to capture non-linear interaction effects, with economic activity proxied by Night-Time Light (NTL) intensity across 2680 corridor grid cells. The results identify a statistically robust ecological threshold at NDVI = −0.8σ, beyond which infrastructure investments shift from low to high economic effectiveness. A strong positive infrastructure–environment interaction (β = 6.44, p < 0.001) indicates that environmental quality functions as a productive modulating factor rather than a passive constraint. Spatial classification shows that 63% of corridor areas are investment-ready, while 15% require ecological restoration prior to effective infrastructure deployment. Although institutional quality and long-term post-construction dynamics are not explicitly modeled, the framework provides a replicable and policy-relevant decision-support tool, offering actionable guidance for aligning corridor development with SDGs 9, 11, and 13 and advancing sustainable infrastructure planning in the Global South.

Pastures represent one of the most significant ecological components of Mediterranean landscapes, occupying large surfaces and guaranteeing ecosystem functions of primary importance. In Mediterranean silvo-pastoral systems, the coexistence of trees, shrubs, and herbaceous layers creates a complex ecological mosaic in which grazing activity plays a decisive role. In this framework, understanding the ongoing transformations affecting Mediterranean pastures becomes essential for identifying the main degradation processes and their ecological implications. Remote sensing (RS) technologies are robust and cost-effective tools for quantifying vegetation dynamics, identifying degradation patterns, and supporting sustainable management decisions. This review aims to summarize the most recent scientific evidence on the role of Mediterranean pastures as elements of ecological regulation and fire risk mitigation, while highlighting the potential of RS as a monitoring and decision-support tool. The analysis was performed considering papers from January 2000 to October 2025, by querying the Scopus and Web of Science databases. The analysis allowed the selection of 83 pertinent papers. The selected papers were analyzed, allowing exploration of the literature on RS applied to Mediterranean pastures from multiple angles, highlighting the historical progression of publications, the main geographical locations of study areas, and the evolution and intertwining of recurring themes.

This study examines the relationship between board gender diversity, board size, and environmental, social, and governance (ESG) performance among Gulf Cooperation Council (GCC) listed firms. Drawing on Resource Dependence Theory (RDT), the analysis uses panel data from GCC-listed firms over the period 2018–2023. The findings show that board gender diversity is positively and consistently associated with aggregate ESG performance and its environmental and social dimensions, with results remaining robust across financial and non-financial firms, as well as energy and non-energy sectors. In contrast, board size is negatively associated with ESG performance, although sectoral heterogeneity is evident. Board size is negatively related to environmental performance in financial firms, while it is positively associated with ESG performance in energy firms, driven by the environmental dimension. Overall, the results highlight that the role of board characteristics varies across contexts and sectors in shaping ESG performance within the GCC.

This article presents a prospective analysis of the corn agro-industrial chain in Colombia up until 2035, using a mixed-methods approach that integrates technological surveillance, two rounds of the Delphi method, S-curve analysis, and patent–publication matrices and quadrants. Text-mining analysis was conducted using VantagePoint® v15.1 software, enabling the generation of multiple analytical outputs, including cluster maps, co-occurrence networks, and relational matrices. The study examines the dynamics of scientific and technological production related to the utilization of corn by-products and residues over the period 2003–2025. A total of 30 Delphi responses were collected from experts representing academia, industry, and government institutions in Argentina, Ecuador, Portugal, and Colombia. Based on expert consensus, the Delphi process identified 23 priority topics and 40 additional topics for discussion. Six priority themes were highlighted: (i) antioxidant and antimicrobial packaging derived from bioactive compounds extracted from corn by-products; (ii) bioethanol production; (iii) biodegradable straw manufactured from basket fibers; (iv) bioactive extracts for application in anti-aging cosmetic formulations; (v) modified biochar for the adsorption of ammonium and phosphate ions from aqueous systems; and (vi) the use of corn stover to enhance soil nitrogen content and grain yield. Finally, patent-based S-curve analysis and patent–publication matrices revealed notable asymmetries between scientific knowledge production and patenting activity, underscoring structural gaps in the translation of research into technological innovation within the corn agro-industrial sector.

Urbanization is intensifying congestion, emissions, and unequal mobility access in cities. This study aims to operationalize sustainability objectives—efficiency, environmental externalities, and service equity—in network-wide traffic system control. We propose SERL-H, a sustainability-aware hierarchical multi-agent reinforcement learning (MARL) controller. SERL-H separates fast intersection-level actuation from slower region-level coordination under a centralized-training decentralized-execution paradigm, and employs adaptive graph attention to capture time-varying interdependencies with bounded neighborhood communication. The learning reward explicitly balances delay/throughput, emissions/fuel, and an equity regularizer based on service dispersion across user groups. In a SUMO-based city-scale simulation with 100 signalized intersections, SERL-H reduces average delay from 45 s to 29 s and average travel time from 120 s to 88 s relative to fixed-time control, while increasing throughput and lowering total emissions (4800 kg to 3950 kg). A socio-economic assessment suggests higher annualized cost savings (e.g., $50.27 M/year to $65.91 M/year) and improved environmental quality indices. We also report, as supporting evidence, an optional sustainability-enhanced spatio-temporal graph predictor (SUT-GNN) that provides reliable short-horizon forecasts during peak-hour volatility.

A well-functioning talent ecosystem serves as a crucial foundation for promoting high-quality development of Hainan Free Trade Port (HFTP) in China, holding strategic significance for enhancing the competitiveness and sustainable development of its industrial parks. This study aims to evaluate the talent ecosystem within key industrial parks of HFTP and identify its key influencing factors. Data were collected through questionnaire surveys, with respondents who fully completed relevant measurement items selected as research subjects. Multiple linear stepwise regression analysis and robustness tests were comprehensively employed for data analysis. The findings reveal that: (1) gender, age, and political affiliation exert significant influences on talent ecosystem evaluations; (2) social capital demonstrates a significant positive impact on ecosystem assessments; (3) economic capital shows no statistically significant effect; and (4) cultural capital exhibits a significant negative influence. Based on these results, governors should embrace an ecological governance mindset. This approach involves establishing an innovative “Talent Ecosystem Health Index” monitoring system, with periodic evaluation and public reporting of its findings. A multi-stakeholder “Talent Ecosystem Governance Committee” should be formed to coordinate strategic planning and policy alignment. Additionally, “policy mix experiments” should be conducted to explore the optimal integrated conditions for talent policies. Ultimately, these initiatives aim to establish a self-adaptive regulatory mechanism based on dynamic monitoring and feedback, thereby enhancing the adaptability and long-term resilience of the talent ecosystem.

Microbiological contamination in public buildings is closely linked to human presence, such as airborne bacteria, fungi, and particulate matter, which strongly influence indoor air quality (IAQ). This study examined the distribution of microorganisms in a museum building in relation to time of day, air-handling unit (AHU) type, and ventilation operating mode. Exhibition rooms without natural light relied entirely on a central heating, ventilation and air conditioning (HVAC) system. Microbiological contamination was assessed using Koch’s passive sedimentation method over a 24 h cycle for two AHUs (I and III) and selected rooms, while CO2 levels were monitored as indicators of occupancy and ventilation demand in line with EN 16798-1:2019 and ASHRAE 62.1-2022. Although the demand-controlled ventilation system increased the outdoor air fraction from 40% to 70–100% during peak visitor density, localized increases in microbial contamination occurred. AHU I showed higher loads of Staphylococcus sp. and fungi, while AHU III exhibited pronounced fungal peaks influenced by elevated humidity from an open water reservoir. Psychrophilic bacteria reached 140–230 CFU·m−3, mesophilic bacteria 230–320 CFU·m−3, and fungi up to 740 CFU·m−3. Most CFU values remained below commonly referenced upper limits (<1000 CFU·m−3), but several peaks exceeded lower recommended thresholds, indicating a need for improvements. Enhanced filtration, humidity control, increased airflow during high occupancy, and reducing moisture sources in AHUs may mitigate microbial growth and improve IAQ in public buildings.

As global value chains integrate firms operating under varied institutional contexts and distinct technological capabilities, the uniform adoption of green standards becomes challenging. A “one-size-fits-all” sustainability approach often fails to account for the voids faced by firms in different contexts participating in one value chain, particularly in developing economies an area where academic research remains limited and fragmented. This research gap is the motivation for the present study. Through a systematic review of 56 articles, this paper examines how technological gaps and institutional voids in global value chains (GVCs) affect firms’ capacity to leverage environmental performance across different national and organizational contexts. Building on this synthesis, we develop an integrative conceptual framework that elucidates these dynamics and offers actionable insights for managers seeking to navigate environmental performance in heterogeneous institutional and technological settings. Our findings contribute to the literature on sustainable GVCs and guide practitioners aiming to foster effective cross-border collaborations that enhance environmental performance.

Smart microgrid is promising in providing a more affordable, efficient, and sustainable energy solution with increasing energy production from distributed renewable sources and diverse household electricity usage with large amounts of connected smart devices. Accurate prediction of the household electricity load and renewable energy production plays a significant role in achieving optimized efficiency of the microgrid. Meanwhile, the privacy and security of data sharing over the smart grid are crucial. This paper proposes a blockchain-enabled microgrid Internet of Things (MIoT) with accurate predictions of renewable energy production and household electricity load. The blockchain framework can guarantee the privacy and security of data sharing over the microgrid. An improved model by stacking long short-term memory (LSTM) and gated recurrent units (GRUs) is proposed for energy generation and electricity load predictions using historical data in the microgrid and the weather forecasting data. The sparrow search algorithm optimized by Levy flights (LevySSA) is used to optimize the hyperparameters of the stacked LSTM-GRU method. The experimental results verify the accuracy and robustness of the proposed method in the prediction of electricity load and renewable energy production for effective smart microgrid operation. For PV forecasting, the proposed LevySSA-LSTM-GRU achieves nRMSE = 0.0535, nMAE = 0.0455, and R2 = 0.9898, outperforming the strongest baseline. For load forecasting, averaged over four test intervals, it yields nRMSE = 0.1034, nMAE = 0.0836, with R2 = 0.9340, demonstrating consistent superiority compared with conventional baseline models. Overall, the proposed framework enables secure data sharing and high-accuracy forecasting, offering strong potential to support real-time energy management and operational optimization in smart microgrids.

Livestock and poultry manure is an important recyclable nutrient resource in Chinese agriculture, and heap composting, a low-input static method, is the most common treatment approach on farms. However, most studies have focused on aerobic composting, whereas systematic evaluations of physicochemical evolution and maturity/quality development during heap composting remain limited, hampering reliable assessment of compost performance and land-application readiness. Here, we compared heap and turned composting of chicken manure amended with rice bran under natural aeration. Five treatments were applied: manure alone (CM), manure with rice bran (CM+RB), covered heap compost (CM+RB+C), single-turned compost (CM+RB+ST), and multi-turned compost (CM+RB+MT), monitored for 66 days. Rice-bran addition rapidly induced the thermophilic phase and substantially enhanced organic decomposition, while turning further prolonged the thermophilic phase. Humic acid content increased in all rice-bran treatments, indicating clear humification, with only slight variation among aeration intensities. Nitrogen transformation also differed: turned piles showed faster nitrification, suggesting enhanced aerobic nitrogen conversion under stronger aeration. Compost maturity improved across treatments, and all rice-bran treatments except CM+RB+C achieved a germination index > 70%. Overall, heap composting largely achieved stability, humification, and maturity close to those of aerobic turning, while markedly reducing labor and energy inputs, supporting its suitability for small-scale manure recycling.

Megacities in the Yangtze River Economic Belt (YREB) are facing severe challenges and unbalanced development. Combating megacity syndrome is crucial for achieving modernization. This study developed a Development-Autonomy-Inclusiveness governance framework to characterize megacity governance capacity modernization (MGCM). The integrated model is employed to evaluate the MGCM for nine megacities along the YREB in China from 2013 to 2022. The key determinants are identified by Geo-detector. The configuration pathways and synergistic mechanisms for high MGCM are depicted using fsQCA. The empirical study indicates that: (1) The MGCM is at a low level but displays a slight upward trend, with development capacity > autonomy capacity > inclusiveness capacity. The MGCMs of three urban agglomerations manifest as being development-driven, autonomy-constrained, and autonomy-promoted. (2) The synergistic interaction among determinants is greater than any single factor. (3) Megacities exhibited distinct pathways to achieve high MGCM: tri-capacity synergy pattern in the Yangtze River Delta, development dominant pattern in Chengdu-Chongqing, autonomy weakness pattern in the Mid-Yangtze River, and adversity survival pattern during extraordinary periods. A novel theoretical framework and practical examples are proposed for boosting the modernization of governance capacity in China’s megacities, offering valuable insights for advancing the governance modernization of megacities worldwide.

The use of biosolids in agriculture enhances soil fertility and organic matter, yet concerns remain over the accumulation of contaminants of emerging concern in soils and food crops. Despite increased land application, long-term field-based evidence on the environmental fate and plant uptake of these compounds is limited. This study hypothesized that prolonged biosolid application improves soil carbon and nitrogen without promoting triclosan (TCS) or sulfamethoxazole (SMX) persistence or uptake under rainfed and rainfed + irrigation maize systems. Over a decade and half, a field trial was conducted with biosolids applied at rates of 0, 4, 8, and 16 t ha−1 yr−1. Soil samples were analyzed for organic carbon, total nitrogen, pH, electrical conductivity, TCS, and SMX. Maize stem, leaves, and grain were similarly analyzed for TCS and SMX. Results showed that biosolids significantly improved soil organic carbon and nitrogen (p ≤ 0.0001), but also increased soil acidification and salinity. SMX was not detected in either soil or plant tissues at any rate. Although TCS was absent in soils six months post-application, it was detected in maize shoots and grains at 8 and 16 t ha−1 yr−1, highest in stems (6.66–8.92 ng g−1) and lowest in grains (3.25–4.28 ng g−1). Estimated dietary intake was well below health risk thresholds. These findings support biosolid application ≤ 16 t ha−1 yr−1 as a safe and effective treatment for improving soil fertility in maize systems. Future research should explore transformation products, microplastics, and cumulative exposure under varied agroecosystems.

Aeolian sand hazards severely constrain highway safety and operation in arid regions. To support targeted mitigation along Highway S315 in the Gobi Desert of northern China, this study integrates meteorological observations with sand removal records to quantify wind regimes and classify sand hazard intensity. Event thresholds were objectively identified using change points in semi-logarithmic distributions of daily sand removal volumes, and spatial hazard severity was graded based on annual sand removal per unit road length. The results showed that (1) the study area was subject to intense aeolian activity, with a mean annual sand-driving wind frequency of 23.98%, an annual drift potential of 344.91 vector units (VU), and a resultant sand transport direction of 129.88° (east–southeast). (2) Based on inflection point characteristics, sand hazard events were classified into three intensity levels, namely, slight (<800 m3), moderate (800–3000 m3), and severe (>3000 m3), accounting for 13.0%, 76.1%, and 10.9% of all events along Highway S315, respectively. (3) Spatial grading criteria for sand hazard severity were defined as slight (<3 × 103 m3 km−1 yr−1), moderate (3 × 103–1.0 × 104 m3 km−1 yr−1), and severe (>1.0 × 104 m3 km−1 yr−1). Application of these criteria to a representative road section (K9+000–K30+600; 21.6 km) indicated that severe, moderate, and slight sand hazard segments extend over 6.0 km, 9.1 km, and 6.5 km, respectively, thereby delineating priority zones for targeted mitigation measures. This study proposes a quantitative framework that couples regional wind-driven sand dynamics with highway hazard severity, enabling targeted mitigation and offering a transferable reference for sand risk management in arid and desert regions.

Automatic recognition of endangered animal behavior is crucial for biodiversity conservation and improving animal welfare, yet traditional manual observation remains inefficient and invasive. This work contributes directly to sustainable wildlife management by enabling non-invasive, scalable, and efficient monitoring, which supports long-term ecological balance and aligns with several United Nations Sustainable Development Goals (SDGs), particularly SDG 15 (Life on Land) and SDG 12 (Responsible Consumption and Production). The current deep learning approaches often struggle with the scarcity of behavioral data and complex environments, leading to poor model generalization. To address these challenges, this study focuses on endangered animal behavior monitoring and proposes a multimodal learning framework termed ABCLIP. This model leverages multimodal contrastive learning between video-and-text pairs, utilizing natural language supervision to enhance representation ability. The framework integrates pre-training, prompt learning, and fine-tuning to optimize performance specifically for small-scale animal behavior datasets, with a focus on the specific social and ecological behaviors of giant pandas. The experimental results demonstrate that ABCLIP achieves remarkable accuracy and robustness in recognizing endangered animal behaviors, attaining Top-1 and Top-5 accuracy of 82.50% and 99.25%, respectively, on the LoTE-Animal dataset, which outperforms strong baseline methods such as SlowFast (78.54%/97.55%). Furthermore, in zero-shot recognition scenarios for unseen behaviors, ABCLIP achieves an accuracy of 58.00%. This study highlights the potential of multimodal contrastive learning in wildlife monitoring and provides efficient technical support for precise protection measures and scientific management of endangered species.