Search Results (96)

Carbon dioxide (CO₂) emissions due to human activities are responsible for approximately 80% of the drivers of global warming, resulting in a 1.1 °C increase above pre-industrial temperatures. This study quantified the CO₂ assimilation and productivity of the brown macroalgae Lessonia spicata in the central Pacific coast of Chile, across seasonal and daily cycles, under different environmental stressors, such as temperature and solar irradiance. Measurements were performed using an infra-red gas analysis (IRGA) instrument which had a chamber allowing for precise quantification of CO₂ concentrations; additional photophysiological and biochemical responses were also measured. CO₂ assimilation, along with the productivity and biosynthesis of proteins and lipids, increased during the spring, coinciding with moderate temperatures (~14 °C) and high photosynthetically active radiation (PAR). Furthermore, the increased production of photoprotective and antioxidant compounds, including phenolic compounds, and carotenoids, along with the enhancement of non-photochemical quenching (NPQ), contribute to the effective photoacclimation strategies of L. spicata. Principal component analysis (PCA) revealed seasonal associations between productivity, reactive oxygen species (ROSs), and biochemical indicators, particularly during the spring and summer. These associations, further supported by Pearson correlation analyses, suggest a high but seasonally constrained photoacclimation capacity. In contrast, the reduced productivity and photoprotection observed in the summer suggest increased physiological vulnerability to heat and light stress. Overall, our findings position L. spicata as a promising nature-based solution for climate change mitigation. Full article

Mangrove forests are widely recognized for their effectiveness as carbon sinks and serve as critical ecosystems for mitigating the effects of climate change. Current research lacks comprehensive, large-scale carbon storage datasets for wetland ecosystems, particularly across Mexico and other understudied regions worldwide. Therefore, the objective of this study was to develop a high spatial resolution map of carbon stocks, encompassing both aboveground and belowground components, within the Marismas Nacionales system, which is the largest mangrove complex in northeastern Pacific Mexico. Our approach integrates primary field data collected during 2023–2024 and incorporates some historical plot measurements (2011–present) to enhance spatial coverage. These were combined with contemporary remote sensing data, including Sentinel-1, Sentinel-2, and LiDAR, analyzed using Random Forest algorithms. Our spatial models achieved strong predictive accuracy (R² = 0.94–0.95), effectively resolving fine-scale variations driven by canopy structure, hydrologic regime, and spectral heterogeneity. The application of Local Indicators of Spatial Association (LISA) revealed the presence of carbon “hotspots,” which encompass 33% of the total area but contribute to 46% of the overall carbon stocks, amounting to 21.5 Tg C. Notably, elevated concentrations of carbon stocks are observed in the central regions, including the Agua Brava Lagoon and at the southern portion of the study area, where pristine mangrove stands thrive. Also, our analysis reveals that 74.6% of these carbon hotspots fall within existing protected areas, demonstrating relatively effective—though incomplete—conservation coverage across the Marismas Nacionales wetlands. We further identified important cold spots and ecotones that represent priority areas for rehabilitation and adaptive management. These findings establish a transferable framework for enhancing national carbon accounting while advancing nature-based solutions that support both climate mitigation and adaptation goals. Full article

With the increasing frequency of marine activities, the significance of underwater target search and rescue has been highlighted, where precise and efficient path planning is critical for ensuring search effectiveness. This study proposes an underwater target search path planning method by incorporating the dynamic variations of marine acoustic environments driven by sea surface wind fields. First, wind-generated noise levels are calculated based on the sea surface wind field data of the mission area, and transmission loss is solved using an underwater acoustic propagation ray model. Then, a spatially variant search distance matrix is constructed by integrating the active sonar equation. Finally, a sixteen-azimuth path planning model is established, and a hybrid algorithm of quantum-behaved particle swarm optimization and tabu search (QPSO-TS) is introduced to optimize the search path for maximum coverage. Numerical simulations in three typical sea areas (the South China Sea, Atlantic Ocean, and Pacific Ocean) demonstrate that the optimized search coverage of the proposed method increases by 54.40–130.13% compared with the pre-optimization results, providing an efficient and feasible solution for underwater target search. Full article

The BeiDou-3 Navigation Satellite System (BDS-3) precise point positioning (PPP) service through the B2b signal (PPP-B2b) leverages precise correction data disseminated by satellites to eliminate or mitigate key error sources, including satellite orbit errors, clock biases, and ionospheric delays, thereby enabling high-precision timing and positioning. This paper investigates the disparities in time comparison and positioning capabilities associated with the PPP-B2b signals transmitted by the BDS-3 Geostationary Earth Orbit (GEO) satellites (C59 and C61). Three stations in the Asia–Pacific region were selected to establish two time comparison links. The study evaluated the time transfer accuracy of PPP-B2b signals by analyzing orbit and clock corrections from BDS-3 GEO satellites C59 and C61. Using multi-GNSS final products (GBM post-ephemeris) as a reference, the performance of PPP-B2b-based time comparison was assessed. The results indicate that while both satellites achieve comparable time transfer accuracy, C59 demonstrates superior stability and availability compared to C61. Additionally, five stations from the International GNSS Service (IGS) and the International GNSS Monitoring and Assessment System (iGMAS) were selected to assess the positioning accuracy of PPP-B2b corrections transmitted by BDS-3 GEO satellites C59 and C61. Using IGS/iGMAS weekly solution positioning results as a reference, the analysis demonstrates that PPP-B2b enables centimeter-level static positioning and decimeter-level simulated kinematic positioning. Furthermore, C59 achieves higher positioning accuracy than C61. Full article

This review explores the development of energy storage technologies and governance frameworks in the Asia-Pacific region, where rapid economic growth and urbanisation drive the demand for sustainable energy solutions. Energy storage systems (ESS) are integral to balancing renewable energy fluctuations, improving grid resilience, and reducing greenhouse gas emissions. This paper examines the role of international organisations, including the United Nations, International Energy Agency (IEA), and International Renewable Energy Agency (IRENA), in promoting energy storage advancements through strategic initiatives, policy frameworks, and funding mechanisms. Regionally, the Asia-Pacific Economic Cooperation (APEC), the Association of Southeast Asian Nations (ASEAN), and the Asian Development Bank (ADB) have launched programs fostering collaboration, technical support, and knowledge sharing. Detailed case studies of Japan, Thailand, and China highlight the diverse policy approaches, technological innovations, and international collaborations shaping energy storage advancements. While Japan emphasises cutting-edge innovation, Thailand focuses on regional integration, and China leads in large-scale deployment and manufacturing. This analysis identifies key lessons from these frameworks and case studies, providing insights into governance strategies, policy implications, and the challenges of scaling energy storage technologies. It offers a roadmap for advancing regional and global efforts toward achieving low-carbon, resilient energy systems aligned with sustainability and climate goals. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, with the Asia-Pacific (APAC) region bearing a disproportionate burden. This paper examines HCC challenges within seven APAC health systems, identifies key barriers at each stage of the patient journey, and proposes tailored, actionable solutions. To effectively address HCC challenges, a stepwise approach should prioritise high-impact solutions, focusing on prevention, early diagnosis, and expanding surveillance to maximise health outcomes and economic benefits, while tailoring strategies to each health system’s unique resources and constraints. Methods: A mixed-methods approach was used, including expert consultations from the 2024 HCC APAC Policy Forum, a literature review, and a review of Japan’s HCC management model. Data were collected through workshops and stakeholder feedback from healthcare professionals, policymakers, researchers and patient advocates across Australia, India, Malaysia, South Korea, Taiwan, Thailand, and Vietnam. Results: Key findings include significant disparities in HCC awareness, prevention, early detection, diagnosis, and access to treatment. Common challenges across APAC include limited public awareness, suboptimal surveillance infrastructure, and financial barriers to care. The integration of novel biomarkers and advanced surveillance modalities were identified as crucial priorities for improving early detection. Japan’s multi-faceted approach to HCC management serves as a successful model for the region. Conclusions: A customised and targeted approach is essential for reducing the HCC burden across APAC. The proposed recommendations, tailored to each health system’s needs, can significantly improve patient outcomes and reduce healthcare costs. Effective collaboration among stakeholders is necessary to drive these changes. Full article

In the era of the digital economy, where digitization permeates all sectors of society, digital transformation in agriculture stands as a crucial solution for addressing the growing challenges in agricultural production. Amid the competition to enhance the resilience of sustainable food systems, China sets an exemplary model with its achievements in digital agricultural transformation, providing a blueprint for developing countries in Asia and the Pacific. Primarily based on statistical data and typical case studies, this paper presents analytical findings on how digital transformation of agriculture enhances the adoption of green agricultural practices and promotes inclusive development in China. In light of the intricate challenges faced by China’s food system, the adoption of digitization emerges to facilitate the transformation from conventional agriculture to smart and sustainable practices. The pathways by which digital transformation of agriculture have the potential to address the over-application of chemical fertilizer and irrigation water, mitigation of carbon emissions, and the challenge of climate change and contribute to environmental sustainability of agriculture have been discussed. The implementation of digital transformation in sustainable agriculture—which enhances green practices and social inclusiveness by promoting digital literacy, reducing workload, creating job opportunities for low-skilled labor, and developing rural inclusive finance—has been completely explored. The challenges in digital transformation of agriculture are explained in this paper, which also provides evidence-based policy recommendations for its sustainable development applicable to developing countries. Full article

Sound speed profiles (SSPs) must be detected simultaneously to perform a multibeam depth survey. Accurate real-time sound speed profile (SSP) acquisition remains a critical challenge in deep-sea multibeam bathymetry due to the limitations regarding direct measurements under harsh operational conditions. To address the issue, we propose a joint inversion framework integrating World Ocean Atlas 2023 (WOA23) temperature–salinity model data, historical in situ SSPs, and surface sound speed measurements. By constructing a high-resolution regional sound speed field through WOA23 and historical SSP fusion, this method effectively mitigates spatiotemporal heterogeneity and seasonal variability. The artificial lemming algorithm (ALA) is introduced to optimize the inversion of empirical orthogonal function (EOF) coefficients, enhancing global search efficiency while avoiding local optimization. An experimental validation in the northwest Pacific Ocean demonstrated that the proposed method has a better performance than that of conventional substitution, interpolation, and WOA23-only approaches. The results indicate that the mean absolute error (MAE), root mean square error (RMSE), and maximum error (ME) of SSP reconstruction are reduced by 41.5%, 46.0%, and 49.4%, respectively. When the reconstructed SSPs are applied to multibeam bathymetric correction, depth errors are further reduced to 0.193 m (MAE), 0.213 m (RMSE), and 0.394 m (ME), effectively suppressing the “smiley face” distortion caused by sound speed gradient anomalies. The dynamic selection of the first six EOF modes balances computational efficiency and reconstruction fidelity. This study provides a robust solution for real-time SSP estimation in data-scarce deep-sea environments, particularly for underwater autonomous vehicles. This method effectively mitigates the seabed distortion caused by missing real-time SSPs, significantly enhancing the accuracy and efficiency of deep-sea multibeam surveys. Full article

Adsorption represents an effective strategy for water remediation applications, particularly when utilising eco-friendly materials in a circular economy framework. This approach offers significant advantages, including low cost, material availability, ease of operation, and high efficiency. Herein, the performance of cadmium ion adsorption onto hydroxyapatites, derived through a calcination-free process from shells of two mollusc species, Queen Scallop (Aequipecten opercularis) and Pacific Oyster (Magallana gigas), is examined. The phase and morphology of the synthesised adsorbents were investigated. The results showed that hydroxyapatites obtained from mollusc shells are characterised by high efficiency regarding cadmium removal from water, exhibiting rapid kinetics with equilibrium achieved within 5 min and high adsorption capacities up to 334.9 mg g⁻¹, much higher than many waste-based adsorbents reported in literature. Structural investigation revealed the presence of Cadmium Hydrogen Phosphate Hydrate in the hydroxyapatite derived from oyster shells loaded with Cd, indicating the formation of a solid solution. This finding suggests that the material not only has the capability to decontaminate but also to immobilise and store Cd. Overall, the results indicate that hydroxyapatites prepared via a synthetic route in mild conditions from waste shells are an economical and efficient sorbent for heavy metals encountered in wastewater. Full article

Traffic congestion and carbon emissions remain pressing challenges in urban mobility. This study explores the integration of UAV (drone)-based monitoring systems and IoT sensors, modeled as induction loops, with Large Language Models (LLMs) to optimize traffic flow. Using the SUMO simulator, we conducted experiments in three urban scenarios: Pacific Beach and Coronado in San Diego, and Argüelles in Madrid. A Gemini-2.0-Flash experimental LLM was interfaced with the simulation to dynamically adjust vehicle speeds based on real-time traffic conditions. Comparative results indicate that the AI-assisted approach significantly reduces congestion and CO₂ emissions compared to a baseline simulation without AI intervention. This research highlights the potential of UAV-enhanced IoT frameworks for adaptive, scalable traffic management, aligning with the future of drone-assisted urban mobility solutions. Full article

Management Strategy Evaluation (MSE) tools are inspired by the need for transparency, efficiency, and collaboration in harvest control rule (HCR) management. MSEs provide quantified metrics of the HCR performances and indicate the goodness in multiple dimensions, but providing HCR rankings based on such criteria is uncommon or use a simple Weight Sum Method (WSM). Acknowledging some theoretical limitations of the WSM, we propose using Technique for Order Preference by Similarity to Ideal Solution Method (TOPSIS) as an efficient alternative algorithm for recommending HCRs and conduct a sensitivity analysis of management objectives under the two frameworks, one based on simulated history and the other on the history of North Pacific Albacore (NPALB). Two conclusions are drawn based on the computation of the HCR ranking differences generated with the WSM and TOPSIS: (1) The alteration in the overall ranking of HCRs is visible, and its influence could vary substantially with user preference with theoretical merits. (2) It is common to notice shifts in the ranking for top HCRs, which potentially contributes valuable insights for practical decision making. Full article

Background: Indonesia is an archipelagic country with 17,508 islands spread over the Pacific and Indian Oceans, with thousands of inter-island routes requiring a large and engaged fleet. The vast expanse of the country also leads to challenges related to optimal fleet coverage, routing, and oil distribution while maintaining cost-effectiveness and reliable supply. Methods: This study combined a mixed-integer linear-programming (MILP) model with a response surface methodology (RSM) approach to optimize vessel assignment, vessel routes, and inventory control simultaneously and comprehensively across three regional clusters (i.e., Western, Central, and Eastern Indonesia). The model takes into account a fleet of 28 vessels (13 medium range [MR] and 15 general purpose [GP]) that can distribute three oil products: gasoline, diesel, and kerosene. Results: The optimized solution yields 100% service reliability at an operational cost of $ 2.83 million per month—far lower than currently operating services. The model is robust against variations in demand (±20%), port congestion (±50%), and changing fuel prices (±50%), which is confirmed by a sensibility analysis. The close correlation coefficient (0.987) between the MILP and RSM results confirms the framework’s accuracy. At the same time, the critical performance factors were found to be vessel speed (13.5 knots), fleet size, and port operation time. Conclusions: The study offers a cost-efficient and data-intensive model that could be implemented as a maritime logistics framework, as well as potential areas for future work and insight for relevant stakeholders. Future research will have to integrate real-time data fusion, mainly due to the need for environmental and stochastic modeling methods to foster operational resilience in dynamic maritime business ecosystems. Full article

Yu’nan County is located in the Pacific Rim geological disaster-prone area. Frequent landslides are an important cause of population, property, and infrastructure losses, which directly threaten the sustainable development of the regional social economy. Based on field survey data, this paper employs the coefficient of variation method (CV) and an improved TOPSIS model (Kullback-Leibler-Technique for Order Preference by Similarity to an Ideal Solution) to assess the social vulnerability to landslide disasters in 182 administrative villages of Yu’nan County. Also, it conducts a ranking and comprehensive analysis of their social vulnerability levels. Finally, the accuracy of the evaluation results is validated by applying the losses incurred from landslide disasters per unit area within the same year. The results indicate significant spatial variability in social vulnerability across Yu’nan County, with 68 out of 182 administrative villages exhibiting moderate vulnerability levels or higher. This suggests a high risk of widespread damage from potential disasters. Among these, Xincheng village has the highest social vulnerability score, while Chongtai village has the lowest, with a 0.979 difference in their vulnerabilities. By comparing the actual losses incurred per unit area from landslides, it is found that the social vulnerability results predicted by the CV-KL-TOPSIS model are more consistent with the actual survey results. Furthermore, among the ten sub-factors, population density, building value, and road value contribute most significantly to the overall weight with 0.269, 0.152, and 0.105, respectively, suggesting that in mountainous areas where the population is relatively concentrated, high social vulnerability to landslide hazards is a reflection of population characteristics and local economic level. The evaluation framework and evaluation indicators proposed in this paper can systematically and accurately evaluate the social vulnerability of landslide-prone areas, which provide a reference for urban planning and management in landslide-prone areas. Full article

The notion of resilience has been widely invoked as that essential resource by which sex workers may endure, cope, or thrive despite encountering adversities and stressors. A useful definition within the resilience discourse around sex work is the ability to connect, reconnect, and resist disconnection in response to hardships, adversities, and trauma. In this article, we will examine the history of ‘resilience’ and show how it has been ubiquitously applied to sex workers in some Pacific Island settings. The resounding message of resilience discourse is that sex workers must learn to cope, accommodate, and adapt themselves to conditions that oppress them, and in fact, presuppose a continued acceptance of a degraded place in the world. Rather than resistance as a political action aimed at changing the social, institutional, and economic structures that have placed sex workers there, resilience shifts the onus onto the individual sex worker or her community support to learn to adapt to those conditions. Resilience strategies may be pragmatic but, in the end, to present these as any kind of solution to sex worker struggles becomes little more than victim blaming. Full article

AmeriFlux is a network of hundreds of sites across the contiguous United States providing tower-based ecosystem-scale carbon dioxide flux measurements at 30 min temporal resolution. While geographically wide-ranging, over its existence the network has suffered from multiple issues including towers regularly ceasing operation for extended periods and a lack of standardization of measurements between sites. In this study, we use machine learning algorithms to predict CO₂ flux measurements at NEON sites (a subset of Ameriflux sites), creating a model to gap-fill measurements when sites are down or replace measurements when they are incorrect. Machine learning algorithms also have the ability to generalize to new sites, potentially even those without a flux tower. We compared the performance of seven machine learning algorithms using 35 environmental drivers and site-specific variables as predictors. We found that Extreme Gradient Boosting (XGBoost) consistently produced the most accurate predictions (Root Mean Squared Error of 1.81 μmolm⁻²s⁻¹, R² of 0.86). The model showed excellent performance testing on sites that are ecologically similar to other sites (the Mid Atlantic, New England, and the Rocky Mountains), but poorer performance at sites with fewer ecological similarities to other sites in the data (Pacific Northwest, Florida, and Puerto Rico). The results show strong potential for machine learning-based models to make more skillful predictions than state-of-the-art process-based models, being able to estimate the multi-year mean carbon balance to within an error ±50 gCm⁻²y⁻¹ for 29 of our 44 test sites. These results have significant implications for being able to accurately predict the carbon flux or gap-fill an extended outage at any AmeriFlux site, and for being able to quantify carbon flux in support of natural climate solutions. Full article