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AbstractRecreational fisheries contribute substantially to the sociocultural and economic well‐being of coastal and riparian regions worldwide, but climate change threatens their sustainability. Fishery managers require information on how climate change will impact key recreational species; however, the absence of a global assessment hinders both directed and widespread conservation efforts. In this study, we present the first global climate change vulnerability assessment of recreationally targeted fish species from marine and freshwater environments (including diadromous fishes). We use climate change projections and data on species’ physiological and ecological traits to quantify and map global climate vulnerability and analyze these patterns alongside the indices of socioeconomic value and conservation effort to determine where efforts are sufficient and where they might fall short. We found that over 20% of recreationally targeted fishes are vulnerable to climate change under a high emission scenario. Overall, marine fishes had the highest number of vulnerable species, concentrated in regions with sensitive habitat types (e.g., coral reefs). However, freshwater fishes had higher proportions of species at risk from climate change, with concentrations in northern Europe, Australia, and southern Africa. Mismatches in conservation effort and vulnerability were found within all regions and life‐history groups. A key pattern was that current conservation effort focused primarily on marine fishes of high socioeconomic value rather than on the freshwater and diadromous fishes that were predicted to be proportionately more vulnerable. While several marine regions were notably lacking in protection (e.g., Caribbean Sea, Banda Sea), only 19% of vulnerable marine species were without conservation effort. By contrast, 72% of freshwater fishes and 33% of diadromous fishes had no measures in place, despite their high vulnerability and cultural value. The spatial and taxonomic analyses presented here provide guidance for the future conservation and management of recreational fisheries as climate change progresses.

A sub-seabed release of carbon dioxide (CO2) was conducted to assess the potential impacts of leakage from sub-seabed geological CO2 Capture and Storage CCS) on benthic macrofauna. CO2 gas was released 12 m below the seabed for 37 days, causing significant disruption to sediment carbonate chemistry. Regular macrofauna samples were collected from within the area of active CO2 leakage (Zone 1) and in three additional reference areas, 25 m, 75 m and 450 m from the centre of the leakage (Zones 2, 3 and 4 respectively). Macrofaunal community structure changed significantly in all zones during the study period. However, only the changes in Zone 1 were driven by the CO2 leakage with the changes in reference zones appearing to reflect natural seasonal succession and stochastic weather events. The impacts in Zone 1 occurred rapidly (within a few days), increased in severity through the duration of the leak, and continued to worsen after the leak had stopped. Considerable macrofaunal recovery was seen 18 days after the CO2 gas injection had stopped. In summary, small short-term CCS leakage events are likely to cause highly localised impacts on macrofaunal communities and there is the potential for rapid recovery to occur, depending on the characteristics of the communities and habitats impacted.

AbstractGiven the accelerating rate of biodiversity loss, the need to prioritize marine areas for protection represents a major conservation challenge. The three‐dimensionality of marine life and ecosystems is an inherent element of complexity for setting spatial conservation plans. Yet, the confidence of any recommendation largely depends on shifting climate, which triggers a global redistribution of biodiversity, suggesting the inclusion of time as a fourth dimension. Here, we developed a depth‐specific prioritization analysis to inform the design of protected areas, further including metrics of climate‐driven changes in the ocean. Climate change was captured in this analysis by considering the projected future distribution of >2000 benthic and pelagic species inhabiting the Mediterranean Sea, combined with climatic stability and heterogeneity metrics of the seascape. We identified important areas based on both biological and climatic criteria, where conservation focus should be given in priority when designing a three‐dimensional, climate‐smart protected area network. We detected spatially concise, conservation priority areas, distributed around the basin, that protected marine areas almost equally across all depth zones. Our approach highlights the importance of deep sea zones as priority areas to meet conservation targets for future marine biodiversity, while suggesting that spatial prioritization schemes, that focus on a static two‐dimensional distribution of biodiversity data, might fail to englobe both the vertical properties of species distributions and the fine and larger‐scale impacts associated with climate change.

Global use of carbon-based fuels increased by 1.6 % in 2017 and continued increasing in 2018, after managing to maintain emissions flat between 2014–2017. This trend deviates from the emissions trajectory required to fulfill the climate change goals to maintain the earth's temperature below 2-degrees. The transport sector accounts for about a quarter of these emissions but its the sector with the highest dependence on fossil-fuels. In order to reduce emissions, several approaches have been taken, from increasing fuel efficiency to the use of alternative fuels altogether. The most recent trend leans towards electrifiying the transport sector. High penetration of mature renewable energy technologies such as wind and solar photovoltaics as well as energy storage improvements are leading the way. While mass adoption of electric-propulsion systems for boats are still years away, recent pilot projects suggest that electrifying boats for passenger transportation may be not only a sustainable transport solution but its lower operation costs could facilitate its penetration on densely populated coastal and river cities where conventional public transport systems are reaching their full capacity.

Current battery management systems (BMSs) in automotive applications monitor and control batteries in a relatively simple, conservative manner, with limited capabilities of sensing, estimation, proactive controls, and fault diagnosis. With ever-increasing computing power onboard and/or in the cloud, enhanced environmental perception and vehicular communications, emerging electrified vehicles and smart grids provide unprecedented opportunities for designing and developing next-generation smart BMSs. However, three entrenched technical challenges need to be addressed, including 1) limited knowledge of battery internal states and parameters; 2) poor adaptability to extreme operating conditions; and 3) lack of efficient predictive maintenance, resulting in great concern for battery safety and economy. This paper aims to present some critical insights into possible solutions to the three challenges. First, the multi-physics coupled battery modeling concept is introduced to emphasize that looking at mechanical-electrochemical-thermal-aging dynamics is critically important for devising revolutionary BMS algorithms. Second, electrothermal modeling, advanced optimization routines, and predictive control with vehicular autonomy and connectivity facilitate innovative designs in dynamically hysteresis-aware thermal management, heat transfer under extreme fast charging, and preheating in a cold climate. Third, battery models and machine learning are complementary and can be very useful for improving battery remaining useful life prediction and fault diagnosis, achieving high-efficiency predictive maintenance.

Abstract The growing market share of electric vehicles (EV) has increased the interest in charging strategies and their effects on the electricity system as well as their climatic soundness. However, the benefits of different charging strategies including Vehicle-to-Grid (V2G) on a large regional scale, e.g. in Europe, have not been analyzed sufficiently. This study examines the impact of different charging strategies on greenhouse gas (GHG) emissions from electricity generation and EV batteries in Europe in 2050. To consider indirect emissions and potentially additional battery degradation due to V2G, a model coupling concept is applied to link Life Cycle Assessment (LCA) with the electricity system model, PERSEUS-EU. Overall, EV could reduce the GHG emissions by 36% by simply replacing conventional cars. Controlled unidirectional charging and V2G add another 4 or 11 percentage points on the European level. However, for these gains an efficient implementation of V2G is required.

Abstract In this paper, a multi-objective framework is proposed for the daily operation of a Smart Grid (SG) with high penetration of sensitive loads. The Virtual Power Player (VPP) manages the day-ahead energy resource scheduling in the smart grid, considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G), while maintaining a highly reliable power for the sensitive loads. This work considers high penetration of sensitive loads, i.e. loads such as some industrial processes that require high power quality, high reliability and few interruptions. The weighted-sum approach is used with the distributed and parallel computing techniques to efficiently solve the multi-objective problem. A two-stage optimization method is proposed using a Particle Swarm Optimization (PSO) and a deterministic technique based on Mixed-Integer Linear Programming (MILP). A realistic mathematical formulation considering the electric network constraints for the day-ahead scheduling model is described. The execution time of the large-scale problem can be reduced by using a parallel and distributed computing platform. A Pareto front algorithm is applied to determine the set of non-dominated solutions. The maximization of the minimum available reserve is incorporated in the mathematical formulation in addition to the cost minimization, to take into account the reliability requirements of sensitive and vulnerable loads. A case study with a 180-bus distribution network and a fleet of 1000 gridable Electric Vehicles (EVs) is used to illustrate the performance of the proposed method. The execution time to solve the optimization problem is reduced by using distributed computing.

Abstract Within the framework of the STEMM-CCS project, a controlled CO2 release experiment was conducted under real-life conditions in the Goldeneye complex area, a depleted gas field located in the UK sector of the North Sea. Here, the viability of water column monitoring for the detection of the injected CO2 is evaluated. Real-time pH and pCO2 measurements were taken in the water column during the CO2 release experiment. Monitoring was carried out throughout the full water column, from the near-seafloor to the sea surface, in order to assess the spatial extent of the CO2 release. The dispersion of the CO2 plume was strongly influenced by tidal circulation in the area. The strongest signals were detected within 8 m of the bubble stream during low tide. The lowest pH and highest pCO2 values were 7.965 and 942.1 µatm, respectively, corresponding to variations of 16.4% [H+] and 125.6% from baseline values. The pCO2 baseline dynamics of Goldeneye area were assessed by the evaluation of the natural pCO2-O2 covariance. The estimation of seasonal thresholds for anomalous pCO2 (pCO2:O2 ratio May= 1.63 ± 0.04) allowed us to assess with confidence the non-biological origin of the detected CO2 during the release experiment.