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Abstract Mesopelagic fish constitute the most abundant vertebrate group in the marine environment. The current work reports on results of three seasonal acoustic cruises carried out in the Gulf of Corinth, a relatively small, deep, isolated basin located in the Central Mediterranean (Greece) that presents some unique geomorphological and ecological features. The aim of this study was to describe seasonal echo-types and the vertical distribution of the Deep Scattering Layers (DSLs) as well as to relate them with specific species or species groups. Mesopelagic fish dominated the pelagic ecosystem as confirmed by biological sampling with different gears during daytime and nighttime. In total, at least 15 species were caught, belonging to the families Myctophidae, Paralepididae, Sternoptychidae and Stomiidae, while the—elsewhere very abundant—families Gonostomatidae and Phosichthyidae were completely absent. Common echo-types included: (a) shoals and schools formed by the silvery lightfish Maurolicus muelleri, usually located along the shelf break (80–225 m), (b) a non-migrant thin DSL found at 150–280 m throughout the deep parts of the Gulf, dominated by juvenile half-naked hatchetfish Argyropelecus hemigymnus, and (c) one thick, partially migratory DSL at 250–600 m, mainly consisting of myctophids. The echo backscatter characteristics and species composition of the DSLs as well as the length distribution of the populations were found to differ seasonally. Species-specific and size related patterns in the vertical distribution of fish were detected both during daytime and nighttime. Overall, the Gulf of Corinth seems to sustain high densities of mesopelagic fish that constitute the basic food resource for the abundant dolphin populations that inhabit the area.

This paper presents the first version of the regional-scale personal exposure model EXPLUME (EXposure to atmospheric PolLUtion ModEling). The model uses simulated gridded data of outdoor O3 and PM2.5 concentrations and several population and building-related datasets to simulate (1) space–time activity event sequences, (2) the infiltration of atmospheric contaminants indoors, and (3) daily aggregated personal exposure. The model is applied over the greater Paris region at 2 km×2 km resolution for the entire year of 2017. Annual averaged population exposure is discussed. We show that population mobility within the region, disregarding pollutant concentrations indoors, has only a small effect on average daily exposure. By contrast, considering the infiltration of PM2.5 in buildings decreases annual average exposure by 11 % (population average). Moreover, accounting for PM2.5 exposure during transportation (in vehicle, while waiting on subway platforms, and while crossing on-road tunnels) increases average population exposure by 5 %. We show that the spatial distribution of PM2.5 and O3 exposure is similar to the concentration maps over the region, but the exposure scale is very different when accounting for indoor exposure. We model large intra-population variability in PM2.5 exposure as a function of the transportation mode, especially for the upper percentiles of the distribution. Overall, 20 % of the population using bicycles or motorcycles is exposed to annual average PM2.5 concentrations above the EU target value (25 µg m−3), compared to 0 % for people travelling by car. Finally, we develop a 2050 horizon projection of the building stock to study how changes in the buildings' characteristics to comply with the thermal regulations will affect personal exposure. We show that exposure to ozone will decrease by as much as 14 % as a result of this projection, whereas there is no significant impact on exposure to PM2.5.

CHIMERE is a chemistry-transport model designed for regional atmospheric composition. It can be used at a variety of scales from local to continental domains. However, due to the model design and its historical use as a regional model, major limitations had remained, hampering its use at hemispheric scale, due to the coordinate system used for transport as well as to missing processes that are important in regions outside Europe. Most of these limitations have been removed in the CHIMERE-2017 version, allowing its use in any region of the world and at any scale, from the scale of a single urban area up to hemispheric scale, with or without polar regions included. Other important improvements have been made in the treatment of the physical processes affecting aerosols and the emissions of mineral dust. From a computational point of view, the parallelization strategy of the model has also been updated in order to improve model numerical performance and reduce the code complexity. The present article describes all these changes. Statistical scores for a model simulation over continental Europe are presented, and a simulation of the circumpolar transport of volcanic ash plume from the Puyehue volcanic eruption in June 2011 in Chile provides a test case for the new model version at hemispheric scale.

Ozone, PM10 and PM2.5 concentrations over Paris, France and Stockholm, Sweden were modelled at 4 and 1 km horizontal resolutions respectively for the present and 2050 periods employing decade-long simulations. We account for large-scale global climate change (RCP-4.5) and fine-resolution bottom-up emission projections developed by local experts and quantify their impact on future pollutant concentrations. Moreover, we identify biases related to the implementation of regional-scale emission projections by comparing modelled pollutant concentrations between the fine- and coarse-scale simulations over the study areas. We show that over urban areas with major regional contribution (e.g. the city of Stockholm) the bias related to coarse-scale projections may be significant and lead to policy misclassification. Our results stress the need to better understand the mechanism of bias propagation across the modelling scales in order to design more successful local-scale strategies. We find that the impact of climate change is spatially homogeneous in both regions, implying strong regional influence. The climate benefit for ozone (daily mean and maximum) is up to −5 % for Paris and −2 % for Stockholm city. The climate benefit on PM2.5 and PM10 in Paris is between −5 and −10 %, while for Stockholm we estimate mixed trends of up to 3 % depending on season and size class. In Stockholm, emission mitigation leads to concentration reductions up to 15 % for daily mean and maximum ozone and 20 % for PM. Through a sensitivity analysis we show that this response is entirely due to changes in emissions at the regional scale. On the contrary, over the city of Paris (VOC-limited photochemical regime), local mitigation of NOx emissions increases future ozone concentrations due to ozone titration inhibition. This competing trend between the respective roles of emission and climate change, results in an increase in 2050 daily mean ozone by 2.5 % in Paris. Climate and not emission change appears to be the most influential factor for maximum ozone concentration over the city of Paris, which may be particularly interesting from a health impact perspective.

While previous research helped to identify and prioritize the sources of error in air-quality modeling due to anthropogenic emissions and spatial scale effects, our knowledge is limited on how these uncertainties affect climate-forced air-quality assessments. Using as reference a 10-year model simulation over the greater Paris (France) area at 4 km resolution and anthropogenic emissions from a 1 km resolution bottom-up inventory, through several tests we estimate the sensitivity of modeled ozone and PM2.5 concentrations to different potentially influential factors with a particular interest over the urban areas. These factors include the model horizontal and vertical resolution, the meteorological input from a climate model and its resolution, the use of a top-down emission inventory, the resolution of the emissions input and the post-processing coefficients used to derive the temporal, vertical and chemical split of emissions. We show that urban ozone displays moderate sensitivity to the resolution of emissions (~ 8 %), the post-processing method (6.5 %) and the horizontal resolution of the air-quality model (~ 5 %), while annual PM2.5 levels are particularly sensitive to changes in their primary emissions (~ 32 %) and the resolution of the emission inventory (~ 24 %). The air-quality model horizontal and vertical resolution have little effect on model predictions for the specific study domain. In the case of modeled ozone concentrations, the implementation of refined input data results in a consistent decrease (from 2.5 up to 8.3 %), mainly due to inhibition of the titration rate by nitrogen oxides. Such consistency is not observed for PM2.5. In contrast this consistency is not observed for PM2.5. In addition we use the results of these sensitivities to explain and quantify the discrepancy between a coarse (~ 50 km) and a fine (4 km) resolution simulation over the urban area. We show that the ozone bias of the coarse run (+9 ppb) is reduced by ~ 40 % by adopting a higher resolution emission inventory, by 25 % by using a post-processing technique based on the local inventory (same improvement is obtained by increasing model horizontal resolution) and by 10 % by adopting the annual emission totals of the local inventory. The bias of PM2.5 concentrations follows a more complex pattern, with the positive values associated with the coarse run (+3.6 μg m−3), increasing or decreasing depending on the type of the refinement. We conclude that in the case of fine particles, the coarse simulation cannot selectively incorporate local-scale features in order to reduce its error.

Ozone and PM2.5 concentrations over the city of Paris are modeled with the CHIMERE air-quality model at 4 km × 4 km horizontal resolution for two future emission scenarios. A high-resolution (1 km × 1 km) emission projection until 2020 for the greater Paris region is developed by local experts (AIRPARIF) and is further extended to year 2050 based on regional-scale emission projections developed by the Global Energy Assessment. Model evaluation is performed based on a 10-year control simulation. Ozone is in very good agreement with measurements while PM2.5 is underestimated by 20% over the urban area mainly due to a large wet bias in wintertime precipitation. A significant increase of maximum ozone relative to present-day levels over Paris is modeled under the "business-as-usual" scenario (+7 ppb) while a more optimistic "mitigation" scenario leads to a moderate ozone decrease (−3.5 ppb) in year 2050. These results are substantially different to previous regional-scale projections where 2050 ozone is found to decrease under both future scenarios. A sensitivity analysis showed that this difference is due to the fact that ozone formation over Paris at the current urban-scale study is driven by volatile organic compound (VOC)-limited chemistry, whereas at the regional-scale ozone formation occurs under NOx-sensitive conditions. This explains why the sharp NOx reductions implemented in the future scenarios have a different effect on ozone projections at different scales. In rural areas, projections at both scales yield similar results showing that the longer timescale processes of emission transport and ozone formation are less sensitive to model resolution. PM2.5 concentrations decrease by 78% and 89% under business-as-usual and mitigation scenarios, respectively, compared to the present-day period. The reduction is much more prominent over the urban part of the domain due to the effective reductions of road transport and residential emissions resulting in the smoothing of the large urban increment modeled in the control simulation.

Changes in temperature due to variability in meteorology and climate change are expected to significantly impact atmospheric composition. The Mediterranean is a climate sensitive region and includes megacities like Istanbul and large urban agglomerations such as Athens. The effect of temperature changes on gaseous air pollutant levels and the atmospheric processes that are controlling them in the Eastern Mediterranean are here investigated. The WRF/CMAQ mesoscale modeling system is used, coupled with the MEGAN model for the processing of biogenic volatile organic compound emissions. A set of temperature perturbations (spanning from 1 to 5 K) is applied on a base case simulation corresponding to July 2004. The results indicate that the Eastern Mediterranean basin acts as a reservoir of pollutants and their precursor emissions from large urban agglomerations. During summer, chemistry is a major sink at these urban areas near the surface, and a minor contributor at downwind areas. On average, the atmospheric processes are more effective within the first 1000 m above ground. Temperature increases lead to increases in biogenic emissions by 9±3% K−1. Ozone mixing ratios increase almost linearly with the increases in ambient temperatures by 1±0.1 ppb O3 K−1 for all studied urban and receptor stations except for Istanbul, where a 0.4±0.1 ppb O3 K−1 increase is calculated, which is about half of the domain-averaged increase of 0.9±0.1 ppb O3 K−1. The computed changes in atmospheric processes are also linearly related with temperature changes.

This study investigates the effects of the total solar eclipse of 29 March 2006 on surface air-quality levels over Greece based on observations at a number of sites in conjunction with chemical box modelling and 3-D air-quality modelling. Emphasis is given on surface ozone and other photooxidants at four Greek sites Kastelorizo, Finokalia (Crete), Pallini (Athens) and Thessaloniki, which are located at gradually increasing distances from the path of the eclipse totality and are characterized by different air pollution levels. The eclipse offered the opportunity to test our understanding of air pollution build-up and the response of the gas-phase chemistry of photo-oxidants during a photolytical perturbation using both a photochemical box model and a regional air-quality offline model based on the modeling system WRF/CAMx. At the relatively unpolluted sites of Kastelorizo and Finokalia no clear signal of the solar eclipse on surface O3, NO2 and NO concentrations can be deduced from the observations while there is no correlation of observed O3, NO2 and NO with observed global radiation. The box and regional model simulations for the two relatively unpolluted sites indicate that the calculated changes in net ozone production rates between eclipse and non eclipse conditions are rather small compared to the observed short-term ozone variability. Furthermore the simulated ozone lifetime is in the range of a few days at these sites and hence the solar eclipse effects on ozone can be easily masked by local and regional transport. At the polluted sites of Thessaloniki and Pallini, the solar eclipse effects on O3, NO2 and NO concentrations are revealed from both the measurements and modeling with the net effect being a decrease in O3 and NO and an increase in NO2 as NO2 formed from the reaction of O3 with NO while at the same time NO2 is not efficiently photolysed. This result is also supported by a positive correlation of observed global radiation with O3 and NO and a negative correlation with NO2. It is evident from the 3-D air quality modeling over Greece that the maximum effects of the eclipse on O3, NO2 and NO are reflected on the large urban agglomerations of Athens, and Thessaloniki where the maximum of the emissions occur.

In the era of Internet of Things (IoT) we have entered, the “Monitoring–Decision–Execution” cycle of typical autonomic and automation systems is extended, so it includes distributed developments that might scale from a smart home or greenhouse to a smart city and from autonomous driving to emergency management [...]

The evolution of smart cities depends on the effective integration of advanced technologies within urban environments. It necessitates a profound and nuanced analysis of urban residents’ aspirations and perceptions called User Preferences (UPs) concerning the Quality of Service (QoS) provision, both in its current state and in envisaged improvements across diverse sectors. This paper introduces a novel, hybrid methodology designed to interpret and evaluate residents’ feedback in the context of service provision that contributes to the smart city paradigm. The methodology integrates Intuitionistic Preference Relations (IPRs) with the Best-Worst Method (BWM) within the ambit of Multi-Criteria Decision Making (MCDM). IPRs play a pivotal role in this framework, providing a refined mechanism for capturing a spectrum of preferences, encompassing both affirmative and negational aspects through positive membership and non-membership functions. This aspect is particularly pertinent in group decision-making scenarios, where it facilitates aggregating diverse preference data by applying intuitive fuzzy arithmetic and weighted averaging operators. Additionally, the proposed methodology incorporates scoring and accuracy functions to prioritize and delineate the most feasible alternatives, adeptly navigating the intricate dynamics characteristic of urban settings. The BWM further augments this approach by providing a structured yet efficient, means of evaluating alternatives against multiple criteria. It begins with identifying the most and the least preferred criteria, followed by pairwise comparisons to establish their relative significance. It was applied in a hypothetical numerical scenario to appraise the efficacy of this proposed method. A comparative analysis was conducted utilizing the Analytic Hierarchy Process (AHP), another established pairwise comparison-based method. This comparison’s results underscore the proposed method’s superiority over AHP in several key metrics, including consistency ratio, minimum violation, total deviation, and overall conformity. In summary, the approach presented in this paper not only confronts the complexities inherent in urban environments but also lays the groundwork for more comprehensive and informed decision-making processes, thereby facilitating the evolutionary journey towards realising smart cities.

Drones, also known as unmanned aerial vehicles (UAVs), have become increasingly popular in the military and civil sectors. A swarm of UAVs (S-UAVs) is a group of UAVs that work together to complete a task. Because of the dynamic network topology of S-UAVs, routing schemes are complicated. Clustering is one of the most effective routing schemes for improving the performance of ad-hoc networks. The clustering scheme divides the network into groups known as clusters, each consisting of a cluster head (CH) and cluster members (CMs). The CH is a valuable element in the clustering scheme because it handles all inter- and intra-cluster communications. Proper selection of the CH is the key to enhancing the performance. Our study proposed a new clustering scheme for S-UAVs. Our scheme selects the CH and CMs based on a new weighted formula that consists of the following parameters: distance, speed, and reward index. The reward index is a newly calculated parameter based on latency. The weighted formula calculates the clustering index based on which CH and CMs are selected. This scheme was simulated using MATLAB to demonstrate its performance as a routing scheme. The simulation analyzed the latency due to the variation of the network’s parameters. In addition, the rewarding index and cluster index were analyzed. Finally, the proposed scheme was compared with an existing scheme known as the adaptive enhanced weighted clustering algorithm for UAV swarm. The comparison demonstrates that the proposed protocol is promising due to its lower-generated delays. The obtained results are displayed and analyzed towards the end of this paper, along with some ideas for future work.

LoRaWAN networks might be a technology that could facilitate extreme energy-efficient operation while offering great capacity for suburban and rural area deployment, but this can be a challenging task for a network administrator. Constraints that deform the trade-off triangle of coverage, scalability and energy efficiency need to be overcome. The scope of this study is to review the limitations of the LoRaWAN protocol in order to summarize and assess the crucial factors that affect communication performance, related to data rate allocation, bidirectional traffic and radio spectrum utilization. Based on the literature, these factors correspond mostly to configurable payload transmission parameters, including transmission interval, data rate allocation, requirement for acknowledgements and retransmission. In this work, with simulation experiments, we find that collision occurrences greatly affect channel occupancy. In particular, it was evaluated that collision occurrence is increasingly affected by transmission intervals, which have the most significant negative impact on packet delivery rate (PDR). We then validated that clustering of end nodes in the vicinity of a gateway, taking into account distance and transmission settings, can improve network scalability. This can assure distribution of the total transmission time to end nodes with respect to application-related QoS requirements. Following this clustering approach, we achieved a PDR greater than 0.90 in a simulation setting with 6000 end nodes in a 10 km coverage.

The fifth generation (5G) of mobile networks is designed to mark the beginning of the hyper-connected society through a broad set of novel features and disruptive characteristics, delivering massive connectivity, coverage and availability paired with unprecedented speed, throughput and capacity. Such a highly capable networking paradigm, facilitated by its integrated segments and available subsystems, will propel numerous cutting-edge, innovative and versatile services, spanning every possible business vertical. Augmented, response-capable healthcare services have already been identified as one of the prime objectives of both vendors and customers; therefore, addressing controversies and shortcomings related to the specific field is considered a priority for all stakeholders. The scope of this paper is to present the architectural elements of 5G which enable efficient, remote healthcare services along with emergency health monitoring and response capability. In addition, we propose a holistic scheme based on technical enablers such as Internet-of-Things (IoT) and Fog Computing, for mitigating common issues and current limitations which may compromise the proclaimed service delivery.

The ever-increasing number of internet-connected devices, along with the continuous evolution of cyber-attacks, in terms of volume and ingenuity, has led to a widened cyber-threat landscape, rendering infrastructures prone to malicious attacks. Towards addressing systems’ vulnerabilities and alleviating the impact of these threats, this paper presents a machine learning based situational awareness framework that detects existing and newly introduced network-enabled entities, utilizing the real-time awareness feature provided by the SDN paradigm, assesses them against known vulnerabilities, and assigns them to a connectivity-appropriate network slice. The assessed entities are continuously monitored by an ML-based IDS, which is trained with an enhanced dataset. Our endeavor aims to demonstrate that a neural network, trained with heterogeneous data stemming from the operational environment (common vulnerability enumeration IDs that correlate attacks with existing vulnerabilities), can achieve more accurate prediction rates than a conventional one, thus addressing some aspects of the situational awareness paradigm. The proposed framework was evaluated within a real-life environment and the results revealed an increase of more than 4% in the overall prediction accuracy.

This paper proposes a two-phase algorithm for multi-criteria selection of packet forwarding in unmanned aerial vehicles (UAV), which communicate with the control station through commercial mobile network. The selection of proper data forwarding in the two radio link: From UAV to the antenna and from the antenna to the control station, are independent but subject to constrains. The proposed approach is independent of the intra-domain forwarding, so it may be useful for a number of different scenarios of Unmanned Aerial Vehicles connectivity (e.g., a swarm of drones). In the implementation developed in this paper, the connection is served by three different mobile network operators in order to ensure reliable connectivity. The proposed algorithm makes use of Machine Learning tools that are properly trained for predicting the behavior of the link connectivity during the flight duration. The results presented in the last section validate the algorithm and the training process of the machines.