IMT Nord Europe

Abstract Plasma catalysis is gaining increasing interest for various gas conversion applications, such as CO2 conversion into value-added chemicals and fuels, CH4 activation into hydrogen, higher hydrocarbons or oxygenates, and NH3 synthesis. Other applications are already more established, such as for air pollution control, e.g. volatile organic compound remediation, particulate matter and NOx removal. In addition, plasma is also very promising for catalyst synthesis and treatment. Plasma catalysis clearly has benefits over ‘conventional’ catalysis, as outlined in the Introduction. However, a better insight into the underlying physical and chemical processes is crucial. This can be obtained by experiments applying diagnostics, studying both the chemical processes at the catalyst surface and the physicochemical mechanisms of plasma-catalyst interactions, as well as by computer modeling. The key challenge is to design cost-effective, highly active and stable catalysts tailored to the plasma environment. Therefore, insight from thermal catalysis as well as electro- and photocatalysis is crucial. All these aspects are covered in this Roadmap paper, written by specialists in their field, presenting the state-of-the-art, the current and future challenges, as well as the advances in science and technology needed to meet these challenges.

A growing number of companies have started commercializing low-cost sensors (LCS) that are said to be able to monitor air pollution in outdoor air. The benefit of the use of LCS is the increased spatial coverage when monitoring air quality in cities and remote locations. Today, there are hundreds of LCS commercially available on the market with costs ranging from several hundred to several thousand euro. At the same time, the scientific literature currently reports independent evaluation of the performance of LCS against reference measurements for about 110 LCS. These studies report that LCS are unstable and often affected by atmospheric conditions—cross-sensitivities from interfering compounds that may change LCS performance depending on site location. In this work, quantitative data regarding the performance of LCS against reference measurement are presented. This information was gathered from published reports and relevant testing laboratories. Other information was drawn from peer-reviewed journals that tested different types of LCS in research studies. Relevant metrics about the comparison of LCS systems against reference systems highlighted the most cost-effective LCS that could be used to monitor air quality pollutants with a good level of agreement represented by a coefficient of determination R2 > 0.75 and slope close to 1.0. This review highlights the possibility to have versatile LCS able to operate with multiple pollutants and preferably with transparent LCS data treatment.

The vehicular announcement network is one of the most promising utilities in the communications of smart vehicles and in the smart transportation systems. In general, there are two major issues in building an effective vehicular announcement network. First, it is difficult to forward reliable announcements without revealing users' identities. Second, users usually lack the motivation to forward announcements. In this paper, we endeavor to resolve these two issues through proposing an effective announcement network called CreditCoin, a novel privacy-preserving incentive announcement network based on Blockchain via an efficient anonymous vehicular announcement aggregation protocol. On the one hand, CreditCoin allows nondeterministic different signers (i.e., users) to generate the signatures and to send announcements anonymously in the nonfully trusted environment. On the other hand, with Blockchain, CreditCoin motivates users with incentives to share traffic information. In addition, transactions and account information in CreditCoin are tamper-resistant. CreditCoin also achieves conditional privacy since Trace manager in CreditCoin traces malicious users' identities in anonymous announcements with related transactions. CreditCoin thus is able to motivate users to forward announcements anonymously and reliably. Extensive experimental results show that CreditCoin is efficient and practical in simulations of smart transportation.

Magnetic-resonance-imaging rheometrical experiments show that concentrated suspensions or emulsions cannot flow steadily at a uniform rate smaller than a critical value ( ${\stackrel{\ifmmode \dot{}\else \.{}\fi{}}{\ensuremath{\gamma}}}_{c}$). As a result, a ``liquid'' region (sheared rapidly, i.e., at a rate larger than ${\stackrel{\ifmmode \dot{}\else \.{}\fi{}}{\ensuremath{\gamma}}}_{c}$) and a ``solid'' region (static) coexist. The behavior of the fluid in the liquid region follows a simple power-law model, while the extent of the solid region increases with the degree of jamming of the material.

One of the least understood aspects in atmospheric chemistry is how urban emissions influence the formation of natural organic aerosols, which affect Earth's energy budget. The Amazon rainforest, during its wet season, is one of the few remaining places on Earth where atmospheric chemistry transitions between preindustrial and urban-influenced conditions. Here, we integrate insights from several laboratory measurements and simulate the formation of secondary organic aerosols (SOA) in the Amazon using a high-resolution chemical transport model. Simulations show that emissions of nitrogen-oxides from Manaus, a city of ~2 million people, greatly enhance production of biogenic SOA by 60-200% on average with peak enhancements of 400%, through the increased oxidation of gas-phase organic carbon emitted by the forests. Simulated enhancements agree with aircraft measurements, and are much larger than those reported over other locations. The implication is that increasing anthropogenic emissions in the future might substantially enhance biogenic SOA in pristine locations like the Amazon.

A fundamental problem in signal processing is the estimation of unknown parameters or functions from noisy observations. Important examples include localization of objects in wireless sensor networks [1] and the Internet of Things [2]; multiple source reconstruction from electroencephalograms [3]; estimation of power spectral density for speech enhancement [4]; or inference in genomic signal processing [5]. Within the Bayesian signal processing framework, these problems are addressed by constructing posterior probability distributions of the unknowns. The posteriors combine optimally all of the information about the unknowns in the observations with the information that is present in their prior probability distributions. Given the posterior, one often wants to make inference about the unknowns, e.g., if we are estimating parameters, finding the values that maximize their posterior or the values that minimize some cost function given the uncertainty of the parameters. Unfortunately, obtaining closed-form solutions to these types of problems is infeasible in most practical applications, and therefore, developing approximate inference techniques is of utmost interest.

Abstract. Within the framework of air quality studies at the megacity scale, highly time-resolved volatile organic compound (C2–C8) measurements were performed in downtown Paris (urban background sites) from January to November 2010. This unique dataset included non-methane hydrocarbons (NMHCs) and aromatic/oxygenated species (OVOCs) measured by a GC-FID (gas chromatograph with a flame ionization detector) and a PTR-MS (proton transfer reaction – mass spectrometer), respectively. This study presents the seasonal variability of atmospheric VOCs being monitored in the French megacity and their various associated emission sources. Clear seasonal and diurnal patterns differed from one VOC to another as the result of their different origins and the influence of environmental parameters (solar radiation, temperature). Source apportionment (SA) was comprehensively conducted using a multivariate mathematical receptor modeling. The United States Environmental Protection Agency's positive matrix factorization tool (US EPA, PMF) was used to apportion and quantify ambient VOC concentrations into six different sources. The modeled source profiles were identified from near-field observations (measurements from three distinct emission sources: inside a highway tunnel, at a fireplace and from a domestic gas flue, hence with a specific focus on road traffic, wood-burning activities and natural gas emissions) and hydrocarbon profiles reported in the literature. The reconstructed VOC sources were cross validated using independent tracers such as inorganic gases (NO, NO2, CO), black carbon (BC) and meteorological data (temperature). The largest contributors to the predicted VOC concentrations were traffic-related activities (including motor vehicle exhaust, 15 % of the total mass on the annual average, and evaporative sources, 10 %), with the remaining emissions from natural gas and background (23 %), solvent use (20 %), wood-burning (18 %) and a biogenic source (15 %). An important finding of this work is the significant contribution from wood-burning, especially in winter, where it could represent up to ∼ 50 % of the total mass of VOCs. Biogenic emissions also surprisingly contributed up to ∼ 30 % in summer (due to the dominating weight of OVOCs in this source). Finally, the mixed natural gas and background source exhibited a high contribution in spring (35 %, when continental air influences were observed) and in autumn (23 %, for home heating consumption).

levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013-2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30-240 min) and long-term data coverage (9-36 months), providing essential information to improve/validate air quality, health impact, and climate models.

The fast development of intelligent transportation has paved the way for innovative techniques for highways, and an entirely new driving pattern of highway vehicular platooning might offer a solution to the persistent problem of road congestion, travel comfort, and road safety. In this vehicular platooning system, a platoon head vehicle provides platoon service to its user vehicles. However, some badly behaved platoon head vehicles may put the platoon in danger, which makes it crucial for user vehicles to distinguish and avoid them. In this paper, we propose a reliable trust-based platoon service recommendation scheme, which is called REPLACE, to help the user vehicles avoid choosing badly behaved platoon head vehicles. Specifically, at the core of REPLACE, a reputation system is designed for the platoon head vehicles by collecting and modeling their user vehicle's feedback. Then, an iterative filtering algorithm is designed to deal with the untruthful feedback from user vehicles. A detailed security analysis is given to show that our proposed REPLACE scheme is secure and robust against badmouth, ballot-stuffing, newcomers, and on-off attacks that exist in vehicular ad hoc networks (VANETs). In addition, we conduct extensive experiments to demonstrate the correctness, accuracy, and robustness of our proposed scheme.

Actual parallel-plate architecture of lithium-ion batteries consists of lithium-ion diffusion in one dimension between the electrodes. To achieve higher performances in terms of specific capacity and power, configurations enabling lithium-ion diffusion in two or three dimensions is considered. With a view to build these complex three-dimensional (3D) battery architectures avoiding the electrodes interpenetration issues, this work is focused on fused deposition modeling (FDM). In this study, the formulation and characterization of a 3D-printable graphite/polylactic acid (PLA) filament, specially designed to be used as negative electrode in a lithium-ion battery and to feed a conventional commercially available FDM 3D printer, is reported. The graphite active material loading in the produced filament is increased as high as possible to enhance the electrochemical performance, while the addition of various amounts of plasticizers such as propylene carbonate, poly(ethylene glycol) dimethyl ether average M n ∼ 2000, poly(ethylene glycol) dimethyl ether average M n ∼ 500, and acetyl tributyl citrate is investigated to provide the necessary flexibility to the filament to be printed. Considering the optimized plasticizer composition, an in-depth study is carried out to identify the electrical and electrochemical impact of carbon black and carbon nanofibers as conductive additives.

Abstract Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either by solving a multi-variate optimization problem, as in the maximum likelihood (ML) or maximum a posteriori (MAP) estimators, or by performing a multi-dimensional integration, as in the minimum mean squared error (MMSE) estimators. Unfortunately, analytical expressions for these estimators cannot be found in most real-world applications, and the Monte Carlo (MC) methodology is one feasible approach. MC methods proceed by drawing random samples, either from the desired distribution or from a simpler one, and using them to compute consistent estimators. The most important families of MC algorithms are the Markov chain MC (MCMC) and importance sampling (IS). On the one hand, MCMC methods draw samples from a proposal density, building then an ergodic Markov chain whose stationary distribution is the desired distribution by accepting or rejecting those candidate samples as the new state of the chain. On the other hand, IS techniques draw samples from a simple proposal density and then assign them suitable weights that measure their quality in some appropriate way. In this paper, we perform a thorough review of MC methods for the estimation of static parameters in signal processing applications. A historical note on the development of MC schemes is also provided, followed by the basic MC method and a brief description of the rejection sampling (RS) algorithm, as well as three sections describing many of the most relevant MCMC and IS algorithms, and their combined use. Finally, five numerical examples (including the estimation of the parameters of a chaotic system, a localization problem in wireless sensor networks and a spectral analysis application) are provided in order to demonstrate the performance of the described approaches.

In this work, three-dimensional (3D) printing system based on fused deposition modeling (FDM) is used for the fabrication of conductive polymer nanocomposites. This technology consists in the additive multilayer deposition of polymeric nanocomposite based on poly(lactic acid) (PLA) and graphene by means of a in house made low-cost commercial bench-top 3D printer. Further, 3D printed PLA/graphene nanocomposites containing 10 wt% graphene in PLA matrix were characterized for their mechanical, electrical and electromagnetic induction shielding properties of the nanocomposite. Furthermore X-ray computed micro-tomography analyses showed that printed samples have good dimensional accuracy and are significantly closer to the predefined design and the results of scanning electron microscopy (SEM) printed samples showed a uniform dispersion of graphene in PLA matrix The proposed material has uniquely advantageous when implemented in 3D printed structures, because incorporation of multifunctional graphene has been shown to substantially improve the properties of the resulting nanocomposite.

Abstract. Chemical Ionisation Mass Spectrometer measurements of hydroxyl radical (OH) and the sum of hydroperoxy and organic peroxy (HO2+RO2) radicals were conducted during the MEGAPOLI summer field campaign at the SIRTA observatory near Paris, France, in July 2009. OH and (HO2+RO2) showed a typical diurnal variation with averaged daytime maxima values around 5×106 and 1.2×108 molecule cm−3, respectively. Simultaneously, a large number of ancillary measurements, such as NOx, O3, HONO, HCHO and other VOCs were also conducted. These data provide an opportunity to assess our understanding of the radical chemistry in a suburban environment by comparing the radical observations to calculations. First, OH mixing ratios were estimated by a simple Photo Stationary State (PSS) calculation. PSS calculations overestimate the OH mixing ratio by 50%, especially at NOx mixing ratios lower than 10 ppb, suggesting that some loss processes were missing in the calculation at low NOx. Then, a photochemical box model simulation based on the Master Chemical Mechanism (MCM) and constrained by ancillary measurements was run to calculate radical concentrations. Three different modelling procedures were tested, varying the way the unconstrained secondary species were estimated, to cope with the unavoidable lack of their measurements. They led to significant differences in simulated radical concentrations. OH and (HO2+RO2) concentrations estimated by two selected model version were compared with measurements. These versions of the model were chosen because they lead, respectively, to the higher and lower simulated radical concentrations and are thus the two extremes versions. The box model showed better results than PSS calculations, with a slight overestimation of 12% and 5%, for OH and (HO2+RO2) respectively, in average for the reference model, and an overestimation of approximately 20% for OH and an underestimation for (HO2+RO2) for the other selected model version. Thus, we can conclude from our study that OH and (HO2+RO2) radical levels agree on average with observations within the uncertainty range. Finally, an analysis of the radical budget, on a daily basis (06:00–18:00 UTC), indicates that HONO photolysis (~35%), O3 photolysis (~23%), and aldehydes and ketones photolysis (~16% for formaldehyde and 18% for others) are the main radical initiation pathways. According to the MCM modelling, the reactions of RO2 with NO2 (~19%), leading mainly to PAN formation, is a significant termination pathway in addition to the main net loss via reaction of OH with NO2 (~50%).

The Internet of Things (IoT) is a computing paradigm whereby everyday life objects are augmented with computational and wireless communication capabilities, typically through the incorporation of resource-constrained devices including sensors and actuators, which enable their connection to the Internet.The IoT is seen as the key ingredient for the development of smart environments.Nevertheless, the current IoT ecosystem offers many alternative communication solutions with diverse performance characteristics.This situation presents a major challenge to identifying the most suitable IoT communication solution(s) for a particular smart environment.In this paper we consider the distinct requirements of key smart environments, namely the smart home, smart health, smart cities and smart factories, and relate them to current IoT communication solutions.Specifically, we describe the core characteristics of these smart environments and then proceed to provide a comprehensive survey of relevant IoT communication technologies and architectures.We conclude with our reflections on the crucial features of IoT solutions in this setting and a discussion of challenges that remain open for research.

Abstract Among the 3D-printing technologies, fused deposition modeling (FDM) represents a promising route to enable direct incorporation of the battery within the final 3D object. Here, the preparation and characterization of lithium iron phosphate/polylactic acid (LFP/PLA) and SiO 2 /PLA 3D-printable filaments, specifically conceived respectively as positive electrode and separator in a lithium-ion battery is reported. By means of plasticizer addition, the active material loading within the positive electrode is raised as high as possible (up to 52 wt.%) while still providing enough flexibility to the filament to be printed. A thorough analysis is performed to determine the thermal, electrical and electrochemical effect of carbon black as conductive additive in the positive electrode and the electrolyte uptake impact of ceramic additives in the separator. Considering both optimized filaments composition and using our previously reported graphite/PLA filament for the negative electrode, assembled and “printed in one-shot” complete LFP/Graphite battery cells are 3D-printed and characterized. Taking advantage of the new design capabilities conferred by 3D-printing, separator patterns and infill density are discussed with a view to enhance the liquid electrolyte impregnation and avoid short-circuits.

The electronic cigarettes (e-cigs) and more recently the heated tobacco products (HTP) provide alternatives for smokers as they are generally perceived to be less harmful than conventional cigarettes. However, it is crucial to compare the health risks of these different emergent devices, in order to determine which product should be preferred to substitute cigarette. The present study aimed to compare the composition of emissions from HTP, e-cigs and conventional cigarettes, regarding selected harmful or potentially harmful compounds, and their toxic impacts on the human bronchial epithelial BEAS-2B cells. The HTP emitted less polycyclic aromatic hydrocarbons and carbonyls than the conventional cigarette. However, amounts of these compounds in HTP aerosols were still higher than in e-cig vapours. Concordantly, HTP aerosol showed reduced cytotoxicity compared to cigarette smoke but higher than e-cig vapours. HTP and e-cig had the potential to increase oxidative stress and inflammatory response, in a manner similar to that of cigarette smoke, but after more intensive exposures. In addition, increasing e-cig power impacted levels of certain toxic compounds and related oxidative stress. This study provides important data necessary for risk assessment by demonstrating that HTP might be less harmful than tobacco cigarette but considerably more harmful than e-cig.

Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a lengthy exploration of this target, with a requirement on the number of simulations that grows with the dimension of the problem and with the complexity of the data behind it. Several techniques are available toward accelerating the convergence of these Monte Carlo algorithms, either at the exploration level (as in tempering, Hamiltonian Monte Carlo and partly deterministic methods) or at the exploitation level (with Rao-Blackwellization and scalable methods). This article is categorized under: Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC)Algorithms and Computational Methods > AlgorithmsStatistical and Graphical Methods of Data Analysis > Monte Carlo Methods.

Atrazine is one of the most widely-used chlorine herbicides in agriculture. In recent years, studies have shown a potential hazard of atrazine use in environmental health and human health. Due to its toxicity, widespread use, relatively high stability in water and soil, determining safe and efficient methods of its removal is crucial. The main aim of this review was to showcase the recent progress of atrazine degradation methods, along with their main advantages, disadvantages, potential efficiency, and degradation pathways. The overall goal was to create an information gateway for researchers, and stakeholders interested in choosing the best method for atrazine degradation. Thus, the current technologies for atrazine degradation are systematically reviewed and can be used for future improvements or the selection of the most appropriate strategy for a specific place.

In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs varied depending on the source: traffic/exhaust and industry were the source categories with the best results in the RMSEu tests while the most critical ones were soil dust and road dust. The differences between RMs and CTMs source reconstructions confirmed the importance of cross validating the results of these two families of models.

Importance sampling (IS) methods are broadly used to approximate posterior distributions or their moments. In the standard IS approach, samples are drawn from a single proposal distribution and weighted adequately. However, since the performance in IS depends on the mismatch between the targeted and the proposal distributions, several proposal densities are often employed for the generation of samples. Under this multiple importance sampling (MIS) scenario, extensive literature has addressed the selection and adaptation of the proposal distributions, interpreting the sampling and weighting steps in different ways. In this paper, we establish a novel general framework with sampling and weighting procedures when more than one proposal is available. The new framework encompasses most relevant MIS schemes in the literature, and novel valid schemes appear naturally. All the MIS schemes are compared and ranked in terms of the variance of the associated estimators. Finally, we provide illustrative examples revealing that, even with a good choice of the proposal densities, a careful interpretation of the sampling and weighting procedures can make a significant difference in the performance of the method.