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This data was collected as a part of a mesocosm study to investigate the ecosystem impacts of ocean alkalinity enhancement, within the EU H2020 OceanNETs project. Nine mesocosms were deployed in Taliarte Harbour (Gran Canaria, Spain) and were regularly sampled using integrated water samplers between 10th September-25th October 2021. A gradient design was used in this experiment with a total of nine different alkalinity concentrations. Seawater alkalinity ranged between ambient (0 µeq kg-1 added alkalinity, OAE0) and 2400 µeq kg-1 additional alkalinity (OAE2400). The alkalinity levels increased in equal intervals of 300 µeq kg-1 across nine mesocosms (OAE0, OAE300, OAE600, OAE900, OAE1200, OAE1500, OAE1800, OAE2100, OAE2400). This data set contains metazoan zooplankton biomass (µgC per L) from these nine mesocosms. Biomass was calculated based on zooplankton abundances transformed using carbon mass conversion factors. Metazoan zooplankton were sampled with apstein net (ø17cm, mesh size 55µm, 64.06285L) hauls taken every two days (except for days 5 and 9). Zooplankton were size fractioned and assessed in the correspondent size class (small: 55-200µm; medium: 200-500µm; large: 500µm-3mm). Within each size class, all organisms were counted and identified to the lowest possible taxonomic level, and developmental stages were differentiated where possible. Zooplankton abundances (individuals per L) converted to carbon biomass (µgC per L) using biomass conversion factors. Conversion factors are obtained from different sources (Sanchez et al. (in prep)). Briefly: i) metazoan zooplankton functional groups were sampled and measured for carbon biomass using an elemental analyser at specific points throughout the experiment, ii) individual zooplankton were photographed, measured, and their biovolumes and carbon masses derived using standard conversions cited in the literature, iii) zooplankton conversion factors from KOSMOS Gran Canaria 2019 (https://doi.pangaea.de/10.1594/PANGAEA.971765). The experiment, which lasted 33 days, was divided into four response phases (see Sánchez et al. (in prep)): i) pretreatment (days 1 to 4, treatment was implemented on day 4), ii) immediate (days 5-10), iii) shorter term (days 11-22), iv) longer term (days 23 to 33). This data set is associated to the submission by Paul et al. (in review) (https://doi.pangaea.de/10.1594/PANGAEA.966941), so we refer to this data set for basic parameters like water temperature, salinity, pH and carbonate chemistry, to avoid repetition.

Vehicle data consist of electric vehicle performance data collected directly from the vehicle during standard operations. Data were collected using onboard data loggers that were either installed by the project team or preinstalled by the original equipment manufacturer. Data recorded by the data loggers were made accessible via an online web portal or an application programming interface. Different data loggers were used (HEM, ViriCiti, and Geotab), and the method for each vehicle is defined in the vehicle attributes file. Some systems collected data on a “trip-level” basis, in which each row of a table represents a single trip (the period between a key-on and key-off event), whereas other data were collected on a per-day basis, in which each row represents a single day of operation. Data were collected over a range of data collection periods, depending on the project. Data have been anonymized by removing information or decreasing information resolution as necessary so that fleets are not identifiable. Due to the wide range of vehicle types represented and variation in data collection, data parameters and frequencies differ between vehicles and fleets The **Performance Data Daily/Trip Data Dictionaries** contain definitions for each available parameter associated with a vehicle’s operations, aggregated at either a daily or trip level. The parameters available will vary from vehicle to vehicle, but every possible parameter will be defined. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. The **Vehicle Data** tables contain the data from each vehicle’s operations, aggregated at either a daily or trip level, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

# Can niche plasticity mediate species persistence under ocean acidification? [https://doi.org/10.5061/dryad.x0k6djhtq](https://doi.org/10.5061/dryad.x0k6djhtq) This dataset originates from a study investigating the impact of ocean acidification on a temperate rocky reef fish assemblage using natural CO2 vents as analogues. The dataset covers various niche dimensions, including trophic, habitat, and behavioural niches. The study focused on how fish niches are modified in response to ocean acidification, assessing changes in breadth, shift, and overlap with other species between the acidified site and the control site. ## Description of the data and file structure #### Raw\_single\_niche\_data The “*Raw_single_niche_data*” dataset consists of seven spreadsheets, each sharing two essential columns: 'group' and 'community'. These columns are crucial for subsequent analysis using the SIBER framework. **group** = species * Common = common triplefin, *Forsterygion lapillum* * Yaldwyn = Yaldwyn’s triplefin, *Notoclinops yaldwyni* * Blue_eyed = blue-eyed triplefin, *Notoclinops segmentatus* * Blenny = crested blenny, *Parablennius laticlavius* **community** = treatment * C = control * V = CO2 vents **Description of the seven spreadsheets:** 1. **Isotopes -** the dataset includes ratios of 13C/12C and 15N/14N expressed in the conventional δ notation as parts per thousand deviation from international standards. Stable isotopes were derived from a total of 251 fishes collected across three years of sampling. iso1= δ13C iso2= δ15N 2. **Stomach volumetric** - The dataset includes estimated volumetric measures of stomach contents, where the volume contribution of each prey category relative to the total stomach content (100%) was visually estimated. Data were collected between 2018 and 2019. The stomach content was analysed with this method for common triplefin, Yaldwyn's triplefin, blue eyed triplefin and crested blenny. There are 19 prey categories. 3. **Stomach count** - All prey items were counted in 10 prey categories: copepods, ostracods, polychaetes, amphipods, gastropods, bivalves, tanaids, mites, isopods , and others. Digested items that were not identifiable were excluded from the analysis. The stomach content was analysed with this method for common triplefin, Yaldwyn's triplefin and blue eyed triplefin. 4. **Stomach biomass -** The dataset includes calculated biomass derived from the mass of prey subsamples within each category, multiplied by their count. 5. **Habitat** - The microhabitat occupied and habitat orientation (horizontal, angled and vertical) was recorded using free roaming visual surveys on SCUBA (February 2018). *Microhabitat types:* t. = turf algae <10 cm in height ca. = erect calcareous algae cca. = crustose coralline algae b. = bare rocky substratum sp. = encrusting fleshy green algae cobble. = cobbles (~0.5–2 cm in diameter) *Type of surface orientation:* hor = horizontal angle = angled vert = vertical 6. **Behaviour** - Behavioural variables quantified from underwater footage and expressed as rates per minute. The behaviours are: swimming, jumping, feeding, attacking and fleeing from an attack. 7. **Aquarium**: Data from an aquarium experiment involving *Forsterygion lapillum and Notoclinops yaldwyni*, showing the proportion of time spent in available habitat types to assess habitat preference in controlled conditions. Time in each habitat type and spent in activity was derived from video recordings of 10 minutes and expressed as a proportion of total observation time. Common = common triplefin, *Forsterygion lapillum* Yaldwyn = Yaldwyn’s triplefin, *Notoclinops yaldwyni* Common.c = common triplefin in presence of Yaldwyn’s triplefin Yaldwyn.c = Yaldwyn’s triplefin in presence of common triplefin turf.horizontal = time spent on horizontal turf substratum bare.horizontal = time spent on horizontal bare substratum turf.vertical = time spent on vertical turf substratum bottom = time spent on the bottom of the tank swimming = time spent swimming aquarium.wall = time spent on the walls of the tank switches = numbers of changes between habitats #### Unified\_overlap\_dataset The *“Unified_overlap_dataset”* consists of ten spreadsheets, each sharing “id”, “year”, “location” and “species “column (with few exceptions detailed). These first columns need to be factors for analysis using the Unified overlap framework. We used the R scripts provided in the original study ([Geange et al, 2011](https://doi.org/10.1111/j.2041-210X.2010.00070.x)), as detailed in the manuscript. Data for control and vents are in separate data sheets, with C = control and V = vent. **Id**: sample number **Year:** year the data were collected **Location:** North (n) or South (s), site location **Species**: fish species * Common = common triplefin, *Forsterygion lapillum* * Yaldwyn = Yaldwyn’s triplefin, *Notoclinops yaldwyni* * Blue_eyed = blue-eyed triplefin, *Notoclinops segmentatus* * Blenny = crested blenny, *Parablennius laticlavius* We used the same data as per previous section. **Isotopes C and Isotopes V:** * iso1= δ13C * iso2= δ15N **Diet V and Diet C:** For **stomach content**: we used only volumetric stomach content data as inclusive of all species of interest. It is not raw data, but we used the reduced dimension obtained from nonmetric multidimensional scaling (nMDS), thus the 2 columns resulting from this analysis are vol1 and vol2. Raw data are in the datasheet **Stomach volumetric** in the “*Raw_single_niche_data*” dataset. **Habitat association C and Habitat association V** / **Habitat - C and Habitat - V** For **Habitat association**, the columns are id, species, habitat and position. The habitat association for each species is categorical based on habitat occupied and position (e.g., turf - vertical). Information for Crested blenny were extracted from the behavioural video recordings (with each video being a replicate). The dataset is then linked to **Habitat cover** in both control (C) and vent (V) sites to determine the choice of the habitat based on habitat availability. Therefore, the habitat cover only presents the percentage cover of each habitat type at control and vent. *Habitat:* turf = turf algae <10 cm in height ca = erect calcareous algae cca = crustose coralline algae barren = bare rocky substratum sp = encrusting fleshy green algae cobble = cobbles (~0.5–2 cm in diameter) sand = sand *Position:* hor = horizontal angle = angled vert = vertical **Behaviour C and Behaviour V**: Behavioural variables quantified from underwater footage and expressed as rates per minute. The behaviours are: swimming, jumping, feeding, attacking and fleeing from an attack. Reference: Geange, S. W., Pledger, S., Burns, K. C., & Shima, J. S. (2011). A unified analysis of niche overlap incorporating data of different types. *Methods in Ecology and Evolution*, 2(2), 175-184. [https://doi.org/10.1111/j.2041-210X.2010.00070.x](https://doi.org/10.1111/j.2041-210X.2010.00070.x) We used a small hand net and a mixture of ethanol and clove oil to collect the four species of interest (Forsterygion lapillum, Notoclinops yaldwyni, Notoclinops segmentatus and Parablennius laticlavius) at both control and vent sites over four years. For stable isotope analysis, white muscle tissue was extracted from each fish and oven-dried at 60 °C. The dried tissue was subsequently ground using a ball mill. Powdered muscle tissue from each fish was individually weighed into tin capsules and analysed for stable δ 15N and δ13C isotopes. Samples were combusted in an elemental analyser (EuroVector, EuroEA) coupled to a mass spectrometer (Nu Instruments Horizon) at the University of Adelaide. We then analysed the isotopic niche in SIBER. For stomach content analysis the entire gut was extracted from each fish. Using a stereomicroscope, for count and biomass, all prey items in the stomach were counted first. For each prey category, well-preserved individuals were photographed and their mass was calculated based on length and width. The average mass per individual for each category was then multiplied by the count to determine total prey biomass. For the volumetric method, the volume contribution of each prey category relative to the total stomach content was visually estimated (algae were accounted for). Digested items that were not identifiable were excluded from the analysis. Each stomach content dataset was reduced to two dimensions with non-metric multidimensional scaling (nMDS) to be then analysed in SIBER. To assess habitat choice, visual surveys were conducted on SCUBA, to record the microhabitat type and orientation occupied by Forsterygion lapillum, Notoclinops yaldwyni and Notoclinops segmentatus. The resulting dataset comprised a total of 17 distinct combinations of habitat types and surface orientations. The dataset was simplified to two dimensions using correspondence analysis (CA) for subsequent SIBER analysis. Fish behaviour was assessed using GoPro cameras both in situ and during controlled aquarium experiments. In the field, recordings lasted 30 minutes across 4 days, with analysis conducted using VLC. Initial acclimation and periodic intervals (10 minutes every 5 minutes) were excluded from analysis. In controlled aquarium settings, individuals of Forsterygion lapillum and Notoclinops yaldwyni were observed both in isolation and paired. Their habitat preference, surface orientation, and activity levels were recorded for 10 minutes to assess behaviour independent of external influences. Both datasets were dimensionally reduced for analysis in SIBER: non-metric multidimensional scaling (nMDS) was applied to the in situ behavioral data, while principal component analysis (PCA) was used for the aquarium experiments. Unified analysis of niche overlap We quantified the local realised niche space for each fish species at control and vent along the four niche classes, adapting the data as follows: isotopes (continuous data): raw data. stomach content (continuous data): reduced dimension from the volumetric measure of the previous step. habitat association (elective score): habitat and orientation preference linked to Manly’s Alpha association matrix. behaviour (continuous data): raw data. Global change stressors can modify ecological niches of species, and hence alter ecological interactions within communities and food webs. Yet, some species might take advantage of a fast-changing environment, and allow species with high niche plasticity to thrive under climate change. We used natural CO2 vents to test the effects of ocean acidification on niche modifications of a temperate rocky reef fish assemblage. We quantified three ecological niche traits (overlap, shift, and breadth) across three key niche dimensions (trophic, habitat, and behavioural). Only one species increased its niche width along multiple niche dimensions (trophic and behavioural), shifted its niche in the remaining (habitat), and was the only species to experience a highly increased density (i.e. doubling) at vents. The other three species that showed slightly increased or declining densities at vents only displayed a niche width increase in one (habitat niche) out of seven niche metrics considered. This niche modification was likely in response to habitat simplification (transition to a system dominated by turf algae) under ocean acidification. We further show that at the vents, the less abundant fishes have a negligible competitive impact on the most abundant and common species. Hence, this species appears to expand its niche space overlapping with other species, consequently leading to lower abundances of the latter under elevated CO2. We conclude that niche plasticity across multiple dimensions could be a potential adaptation in fishes to benefit from a changing environment in a high-CO2 world. 

This data article describes a dataset consisting of 42 electric scooters available on the Polish market. There are many models of electric scooters that are easily customizable, but for consolidation purposes the dataset contains the most common and widespread variants of the model. Each observation represents a particular electric scooter model and have an identifiable variable (ID) which arose from the manufacturer’s name and its model. The data of the described scooters come from their manufacturers, which have been published on official websites, and if they do not exist, they come from confirmed information on Polish and foreign websites (mostly e-stores), where individual models are sold. Due to the small amount of data in this industry and interdependent branches that can be applied to scientific and educational elements, this dataset can play an important role in the development of science, further research and applications in scientific fields such as revenue management, machine learning, or data mining.

The EV Shuttle Bus Pilot dataset contains data and analysis from Hocking-Athens-Perry Community Action's demonstration of an electric bus on routes of their rural Athens Public Transit system. The vehicle used in the demonstration was a Ford E-450 cutaway equipped with an electric drivetrain, a 127-kWh battery system by Motiv Power Systems, and a cabin upfit by Turtle Top. Data gathered include route assignments, running time and distance, fuel economy, and charge cycles. A comparison of the vehicle's observed duty cycle with duty cycle modeling from other rural transit fleets in the National Transit Database is included to help better understand the rural adoption potential for this fleet technology.

This dataset is part of the thesis "Smart Charging Strategies for Electric Bus Fleets with Aggregator Support: A Robust Bi-level Optimisation Approach", submitted in accordance with the requirements for the degree of Doctor of Philosophy in Sustainable Energy Systems, supervised by Professor Carlos Henggeler Antunes and Professor João Pedro Fernandes Trovão, and presented to the Department of Electrical and Computer Engineering of the Faculty of Sciences and Technology of the University of Coimbra.

The Regulation (EC) No 510/2011 requires Member States to record information for each new van registered in its territory. Every year, each Member State shall submit to the Commission all the information related to their new registrations. In particular, the following details are required for each new van registered: manufacturer name, type approval number, type, variant, version, make and commercial name, specific emissions of CO2, mass of the vehicle, wheel base, track width, fuel type and fuel mode. Additional information, such as engine power and engine capacity were also submitted.

Liberia's 14-year civil war left much of the country's infrastructure shambles. The country's 170 megawatt power generation capacity and national grid were completely destroyed. In Monrovia, just 0.1 percent of households had access to electricity. According to the 2008 National Census, access to piped water fell from 15 percent of the population in 1986 to less than 3 percent in 2008. The national road network was left in severe disrepair. Peace brought many positive developments. The Freeport of Monrovia is now privately managed and has resumed normal operations. Essential rehabilitation work has been carried out, and the port's performance now matches that of neighboring ports along the West African coast. Liberia has also successfully liberalized its mobile telephone markets, with access surging to 40 percent in 2009, at some of the lowest prices in Africa. Despite the potential for private investment, Liberia will likely need more than a decade to reach the illustrative infrastructure targets outlined in this report. Under business-as-usual assumptions for spending and efficiency, it would take at least 40 years for Liberia to reach these goals. Yet with a combination of increased finance, improved efficiency, and cost-reducing innovations, it should be possible to significantly reduce that time.

Ir-Regolament (KE) Nru 443/2009 jirrikjedi li l-Istati Membri jirreġistraw l-informazzjoni għal kull karozza ġdida tal-passiġġieri rreġistrata fit-territorju tagħhom. Kull sena, kull Stat Membru għandu jippreżenta lill-Kummissjoni l-informazzjoni kollha relatata mar-reġistrazzjonijiet il-ġodda tiegħu. B’mod partikolari, id-dettalji li ġejjin huma meħtieġa għal kull karozza ġdida tal-passiġġieri rreġistrata: l-isem tal-manifattur, in-numru tal-approvazzjoni tat-tip, it-tip, il-varjant, il-verżjoni, l-għamla u l-isem kummerċjali, l-emissjonijiet speċifiċi tas-CO2, il-massa tal-vettura, il-bażi tar-roti, il-wisa’ tar-roti, il-kapaċità tal-magna, it-tip ta’ fjuwil u l-modalità tal-fjuwil. Tressqet ukoll informazzjoni addizzjonali, bħall-qawwa tal-magna. Id-data għall-EU-28 hija rrappurtata fil-bażi tad-data ewlenija. Le règlement (CE) no 443/2009 impose aux États membres d’enregistrer les informations relatives à chaque voiture particulière neuve immatriculée sur son territoire. Chaque année, chaque État membre communique à la Commission toutes les informations relatives à ses nouveaux enregistrements. En particulier, les informations suivantes sont requises pour chaque voiture particulière neuve immatriculée: nom du constructeur, numéro d’homologation de type, type, variante, version, marque et nom commercial, émissions spécifiques de CO2, masse du véhicule, base de roue, largeur de la voie, capacité du moteur, type de carburant et mode carburant. Des renseignements supplémentaires, tels que la puissance du moteur, ont également été fournis. Les données relatives à l’EU-28 figurent dans la base de données principale. O Regulamento (CE) n.º 443/2009 exige que os Estados-Membros registem as informações relativas a cada automóvel novo de passageiros matriculado no seu território. Todos os anos, cada Estado-Membro apresenta à Comissão todas as informações relativas aos seus novos registos. Em especial, são exigidas as seguintes informações para cada automóvel novo de passageiros matriculado: nome do fabricante, número de homologação, modelo, variante, versão, marca e designação comercial, emissões específicas de CO2, massa do veículo, base das rodas, largura da via, cilindrada do motor, tipo de combustível e modo combustível. Foram também apresentadas informações adicionais, como a potência do motor. Os dados relativos à UE-28 são comunicados na base de dados principal. Ο κανονισμός (ΕΚ) αριθ. 443/2009 απαιτεί από τα κράτη μέλη να καταγράφουν πληροφορίες για κάθε καινούργιο επιβατικό αυτοκίνητο που ταξινομείται στην επικράτειά του. Κάθε κράτος μέλος υποβάλλει κάθε χρόνο στην Επιτροπή όλες τις πληροφορίες που αφορούν τις νέες καταχωρίσεις του. Ειδικότερα, απαιτούνται τα ακόλουθα στοιχεία για κάθε καινούργιο επιβατικό αυτοκίνητο που ταξινομείται: όνομα του κατασκευαστή, αριθμός έγκρισης τύπου, τύπος, παραλλαγή, έκδοση, μάρκα και εμπορική ονομασία, ειδικές εκπομπές CO2, μάζα του οχήματος, μεταξόνιο, πλάτος μετατροχίου, κυβισμός κινητήρα, τύπος καυσίμου και τρόπος λειτουργίας καυσίμου. Υποβλήθηκαν επίσης πρόσθετες πληροφορίες, όπως η ισχύς του κινητήρα. Τα στοιχεία για την ΕΕ-28 αναφέρονται στην κύρια βάση δεδομένων. Reglamente (EB) Nr. 443/2009 reikalaujama, kad valstybės narės registruotų informaciją apie kiekvieną naują jos teritorijoje įregistruotą keleivinį automobilį. Kiekviena valstybė narė kasmet pateikia Komisijai visą informaciją, susijusią su jų naujomis registracijomis. Visų pirma apie kiekvieną užregistruotą naują lengvąjį automobilį reikalaujama pateikti šiuos duomenis: gamintojo pavadinimas, tipo patvirtinimo numeris, tipas, variantas, versija, markė ir komercinis pavadinimas, savitasis išmetamo CO2 kiekis, transporto priemonės masė, ratų bazė, tarpvėžės plotis, variklio darbinis tūris, degalų tipas ir degalų režimas. Taip pat buvo pateikta papildoma informacija, pavyzdžiui, variklio galia. ES 28 duomenys pateikiami pagrindinėje duomenų bazėje. Il regolamento (CE) n. 443/2009 impone agli Stati membri di registrare le informazioni relative a ciascuna autovettura nuova immatricolata nel suo territorio. Ogni anno ciascuno Stato membro presenta alla Commissione tutte le informazioni relative alle nuove registrazioni. In particolare, per ogni autovettura nuova immatricolata sono richiesti i seguenti dettagli: nome del costruttore, numero di omologazione, tipo, variante, versione, marca e denominazione commerciale, emissioni specifiche di CO2, massa del veicolo, interasse, carreggiata, cilindrata, tipo di carburante e modalità carburante. Sono state presentate anche informazioni supplementari, come la potenza del motore. I dati relativi all'UE-28 sono riportati nella banca dati principale. Regulamentul (CE) nr. 443/2009 impune statelor membre să înregistreze informații pentru fiecare autoturism nou înmatriculat pe teritoriul lor. În fiecare an, fiecare stat membru transmite Comisiei toate informațiile referitoare la noile lor înmatriculări. În special, sunt necesare următoarele detalii pentru fiecare autoturism nou înmatriculat: denumirea producătorului, numărul omologării de tip, tipul, varianta, versiunea, marca și denumirea comercială, emisiile specifice de CO2, masa vehiculului, ampatamentul, ecartamentul, capacitatea motorului, tipul de combustibil și modul de alimentare. De asemenea, au fost prezentate informații suplimentare, cum ar fi puterea motorului. Datele pentru UE-28 sunt raportate în baza de date principală. El Reglamento (CE) n.º 443/2009 exige a los Estados miembros que registren la información relativa a cada turismo nuevo matriculado en su territorio. Cada año, cada Estado miembro presentará a la Comisión toda la información relativa a sus nuevos registros. En particular, se requieren los siguientes datos para cada turismo nuevo matriculado: nombre del fabricante, número de homologación de tipo, tipo, variante, versión, marca y denominación comercial, emisiones específicas de CO2, masa del vehículo, base de la rueda, ancho de vía, capacidad del motor, tipo de combustible y modo de combustible. También se presentó información adicional, como la potencia del motor. Los datos relativos a la EU-28 se presentan en la base de datos principal. Регламент (ЕО) № 443/2009 изисква от държавите членки да записват информация за всеки нов лек пътнически автомобил, регистриран на тяхна територия. Всяка година всяка държава членка предоставя на Комисията цялата информация, свързана с новите си регистрации. По-специално за всеки нов регистриран лек пътнически автомобил се изискват следните данни: наименование на производителя, номер на одобрението на типа, тип, вариант, версия, марка и търговско наименование, специфични емисии на CO2, маса на превозното средство, междуосово разстояние, широчина на колеята, обем на двигателя, вид гориво и режим на гориво. Беше предоставена и допълнителна информация, като мощност на двигателя. Данните за ЕС-28 се докладват в основната база данни. Gemäß der Verordnung (EG) Nr. 443/2009 müssen die Mitgliedstaaten für jeden in seinem Hoheitsgebiet zugelassenen neuen Personenkraftwagen Informationen aufzeichnen. Jedes Jahr übermittelt jeder Mitgliedstaat der Kommission alle Informationen über ihre neuen Registrierungen. Insbesondere sind für jeden neuen Personenkraftwagen, der zugelassen ist, folgende Angaben erforderlich: Name des Herstellers, Typgenehmigungsnummer, Typ, Variante, Version, Fabrikmarke und Handelsname, spezifische CO2-Emissionen, Masse des Fahrzeugs, Radfuß, Gleisbreite, Hubraum, Kraftstoffart und Kraftstoffart. Darüber hinaus wurden zusätzliche Informationen wie die Motorleistung vorgelegt. Die Daten für die EU-28 werden in der Hauptdatenbank gemeldet.

Low-pressure loop exhaust gas recirculation (LP- EGR) combined with higher compression ratio, is a technology package that has been a focus of research to increase engine thermal efficiency of downsized, turbocharged gasoline direct injection (GDI) engines. Research shows that the addition of LP-EGR reduces the propensity to knock that is experienced at higher compression ratios. To investigate the interaction and compatibility between increased compression ratio and LP-EGR, a 1.6 L Turbocharged GDI engine was modified to run with LP-EGR at a higher compression ratio (12:1 versus 10.5:1) via a piston change. This work includes the results of baseline testing on an engine run with a prototype controller and initially tuned to mimic an original equipment manufacturer (OEM) baseline control strategy running on premium fuel (92.8 anti-knock index). This paper then presents test results after first adding LP-EGR to the baseline engine, and then also increasing the compression ratio (CR) using 12:1 pistons. As a last step, the 10.5 CR engine with LP-EGR was run on regular fuel (87.7 anti-knock index) to verify that this configuration could be calibrated to maintain performance like the baseline engine running on premium fuel. To understand the effect of each technology and operating strategy combination on vehicle fuel economy, the various engine maps were compared in EPA’s Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) tool over U.S. regulatory drive cycles. This work was done as part of the EPA's continuing assessment of advanced light-duty automotive technologies to support a Midterm Evaluation of Light-duty Vehicle GHG Standards.