Nexans (France)

Abstract. On-road transportation is a major contributor to CO2 emissions in cities, and high-resolution CO2 traffic emission maps are essential for analyzing emission patterns and characteristics. In this study, we developed new hourly on-road CO2 emission maps with a 100 × 100 m resolution for 20 major cities in France, Germany, and the Netherlands in 2023. We used commercial Floating Car Data (FCD) based on anonymized GPS signals periodically reported by individual vehicles, providing hourly information on mean speed and the number of GPS sample counts per street. Machine learning models were developed to fill FCD data gaps and convert sample counts into actual traffic volumes, and the COPERT model was used to estimate speed- and vehicle-type-dependent emission factors. These models were calibrated using independent traffic observations available for Paris and Berlin, and subsequently applied to the remaining 18 cities in an extrapolated manner due to data availability constraints. Hourly emissions, initially estimated at the street level, were aggregated to 100 × 100 m grid cells. Annual on-road CO2 emissions across the 20 European cities in 2023 ranged from 0.4 to 7.9 Mt CO2, with emissions strongly correlated with urban area (R2= 0.98) and, to a lesser extent, population size (R2= 0.74). Spatially, emissions are either highly concentrated along major highways in cities such as Paris and Amsterdam or more evenly distributed in cities such as Berlin and Bordeaux, highlighting the need for context-specific mitigation strategies. Temporally, this study shows the CO2 emission fluctuations due to holiday periods, weekly activity cycles, and distinct usage profiles of different vehicle types. Due to the low latency of FCD, this approach could support near-real-time traffic emission mapping in the future. Our approach enhances the spatial and temporal characterization of CO2 emissions in on-road transportation compared to the conventional method used in gridded inventories, indicating the potential of FCD data for near-real-time urban emission monitoring and timely policy-making. The datasets generated by this study are available on Zenodo https://doi.org/10.5281/zenodo.16600210 (Shi et al., 2025).

The SuperRail project is the world’s first installation of a high-temperature superconducting (HTS) cable system for commercial operation in a railway electric grid. The aim is to increase traffic capacity and achieve national low-carbon emission targets by strengthening the railway grid. The project involves the development, manufacture, installation, and operation of an HTS DC cable system at Paris’ Montparnasse railways station. Due to limited available rights-of-way, conventional cable technologies are not feasible, making HTS cable technology the only viable solution for supplying power to the railway tracks in such a dense urban area. Two HTS cables are installed in parallel. Each one can carry 2.25 MW and support fault conditions of 67 kA for 100 ms due to their direct connection to the transmissions grid. In order to validate the HTS cable system design and the accessories performances, a complete 35-m HTS cable type test loop was installed and tested at the SNCF Railway Test Agency. This paper shows the results, including dielectric performance, current carrying capacity and fault current tests. The HTS cable system successfully passed all the tests, and the design has been fully qualified.

<strong class="journal-contentHeaderColor">Abstract.</strong> On-road transportation is a major contributor to CO₂ emissions in cities, and high-resolution CO₂ traffic emission maps are essential for analyzing emission patterns and characteristics. In this study, we developed new hourly CO₂ emission maps at 100 &times; 100 m resolution for 20 major cities in France, Germany, and the Netherlands in 2023.&nbsp; We used commercial Floating Car Data (FCD) based on anonymized GPS signals periodically reported by individual vehicles, providing hourly information on mean speed and on the number of GPS sample counts per street. Machine learning models were developed to fill FCD data gaps and convert sample counts into actual traffic volumes, and the COPERT model was used to estimate speed- and vehicle type dependent emission factors. Hourly emissions, initially estimated at the street level, were aggregated to 100 &times; 100 m grid cells. Annual on-road CO₂ emissions across the 20 European cities in 2023 ranged from 0.4 to 7.6 Mt CO₂, with emissions strongly correlated with urban area (R&sup2; = 0.97) and, to a lesser extent, population size (R&sup2; = 0.72). Spatially, emissions are either highly concentrated along major highways in cities such as Paris and Amsterdam or more evenly distributed in cities such as Berlin and Bordeaux, highlighting the need for context-specific mitigation strategies. Temporally, this study shows the CO₂ emission fluctuations due to holiday periods, weekly activity cycles, and distinct usage profiles of different vehicle types. Due to the low latency of FCD, this approach could support near-real-time traffic emission mapping in the future. Our approach enhances the spatial and temporal characterization of CO₂ emissions in on-road transportation compared to the conventional method used in gridded inventories, indicating the potential of FCD data for near-real-time urban emission monitoring and timely policy making. The datasets generated by this study are available on Zenodo <a href="https://doi.org/10.5281/zenodo.16600210" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.16600210</a> (Shi et al., 2025).

Abstract. On-road transportation is a major contributor to CO2 emissions in cities, and high-resolution CO2 traffic emission maps are essential for analyzing emission patterns and characteristics. In this study, we developed new hourly CO2 emission maps at 100 × 100 m resolution for 20 major cities in France, Germany, and the Netherlands in 2023. We used commercial Floating Car Data (FCD) based on anonymized GPS signals periodically reported by individual vehicles, providing hourly information on mean speed and on the number of GPS sample counts per street. Machine learning models were developed to fill FCD data gaps and convert sample counts into actual traffic volumes, and the COPERT model was used to estimate speed- and vehicle type dependent emission factors. Hourly emissions, initially estimated at the street level, were aggregated to 100 × 100 m grid cells. Annual on-road CO2 emissions across the 20 European cities in 2023 ranged from 0.4 to 7.6 Mt CO2, with emissions strongly correlated with urban area (R² = 0.97) and, to a lesser extent, population size (R² = 0.72). Spatially, emissions are either highly concentrated along major highways in cities such as Paris and Amsterdam or more evenly distributed in cities such as Berlin and Bordeaux, highlighting the need for context-specific mitigation strategies. Temporally, this study shows the CO2 emission fluctuations due to holiday periods, weekly activity cycles, and distinct usage profiles of different vehicle types. Due to the low latency of FCD, this approach could support near-real-time traffic emission mapping in the future. Our approach enhances the spatial and temporal characterization of CO2 emissions in on-road transportation compared to the conventional method used in gridded inventories, indicating the potential of FCD data for near-real-time urban emission monitoring and timely policy making. The datasets generated by this study are available on Zenodo https://doi.org/10.5281/zenodo.16600210 (Shi et al., 2025).

Many paths can be designed to reach a successful ISO 55001 certification. Main Portuguese DSO, E-REDES, made recently that journey and details will be explained, such as involving all organization, top management commitment, adaptation of IT and training courses.

International audience

The perspective of increasing on board power on aircrafts leads to reconsider cable technologies, among which insulation processing. In this work, the thermal stability of three materials, Polyimide, Polytetrafluoroethylene and perfluoroalkoxy (PFA) is investigated. Based on a multi-step degradation model applied to thermogravimetric analysis curves, isothermal degradation curves are predicted. Outstanding thermal stability is anticipated for PFA on a chemical standpoint. With the advantage of being a extrusion-processable polymer, PFA constitutes therefore an excellent candidate for high performance cables with extruded insulation.

This study compares different polymer-nanofiller blends concerning their suitability for application as insulating thermoplastic composites for High Voltage Direct Current (HVDC) cable application. Two polymer blends, PP/EOC (polypropylene/ethylene-octene copolymer) and PP/PP-HI (polypropylene/ propylene - ethylene copolymer) and their nanocomposites filled with 2 wt.% of fumed silica modified with 3-aminopropyltriethoxysilane were studied. Morphology, thermal stability, crystallization behavior dynamic relaxation, conductivity, charge trap distribution and space charge behavior were studied respectively. The results showed that the comprehensive performance of the PP/PP-HI composite is better than the one of the PP/EOC composite due to better polymer miscibility and flexibility, as well as lower charging current density and space charge accumulation. Nanosilica addition improves the thermal stability and dielectric properties of both polymer blends. The filler acts as nucleating agent increasing the crystallization temperature, but decreasing the degree of crystallinity. Dynamic mechanical analysis results revealed three polymer relaxation transitions: PP glass transition ( $\beta$ ), weak crystal reorientation ( $\alpha 1$ ) and melting ( $\alpha 2$ ). The nanosilica introduced deep traps in the polymer blends and suppressed space charge accumulation, but slightly increased the conductivity. A hypothesis for the correlation of charge trap distribution and polymer chain transition peaks is developed: In unfilled PP/EOC and PP/PP-HI matrices, charges are mostly located at the crystalline-amorphous interface, whereas in the filled PP/EOC/silica and PP/PP-HI /silica composites, charges are mostly located at the nanosilica-polymer interface. Overall, the PP/PP-HI (55/45) nanocomposite with 2 wt.% modified silica and 0.3 wt.% of antioxidants making it $a$ promising material for PP based HVDC cable insulation application with $a$ reduced space charge accumulation and good mechanical properties.

Aluminum-Carbon nanoFibers (CNF) composites produce by stir casting process present a yield strengths (YS) and an ultimate tensile strength (UTS) improved up to 33%. The hardening of the Al-CNF composite was considered as the sum of elementary contributions of effects: natural hardness of pure Al; grain size; dislocation density; elements in solid solution; CNF. In order to quantify CNF effect, calculation was performed to quantify the contribution to yield strength of each other’s mechanisms. This theoretical calculation was compared to experimental results and the real effect of CNF on yield strength increase was estimated between 10 and 16%. Figure SEQ Figure \* ARABIC 1: Graphical Abstract (copper dots on CNF / stir casting process / contributions to hardening) Keywords: Aluminum matrix composite; copper-coated carbon nanofibers; liquid metallurgy elaboration; mechanical properties; hardening effect