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NOA

National Observatory of Athens
Country: Greece
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121 Projects, page 1 of 25
  • Funder: EC Project Code: 316210
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  • Funder: EC Project Code: 247492
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  • Funder: EC Project Code: 772086
    Overall Budget: 1,128,750 EURFunder Contribution: 1,128,750 EUR

    Massive stars dominate their surroundings during their short lifetimes, while their explosive deaths impact the chemical evolution and spatial cohesion of their hosts. After birth, their evolution is largely dictated by their ability to remove layers of hydrogen from their envelopes. Multiple lines of evidence are pointing to violent, episodic mass-loss events being responsible for removing a large part of the massive stellar envelope, especially in low-metallicity galaxies. Episodic mass loss, however, is not understood theoretically, neither accounted for in state-of-the-art models of stellar evolution, which has far-reaching consequences for many areas of astronomy. We aim to determine whether episodic mass loss is a dominant process in the evolution of the most massive stars by conducting the first extensive, multi-wavelength survey of evolved massive stars in the nearby Universe. The project hinges on the fact that mass-losing stars form dust and are bright in the mid-infrared. We plan to (i) derive physical parameters of a large sample of dusty, evolved targets and estimate the amount of ejected mass, (ii) constrain evolutionary models, (iii) quantify the duration and frequency of episodic mass loss as a function of metallicity. The approach involves applying machine-learning algorithms to existing multi-band and time-series photometry of luminous sources in ~25 nearby galaxies. Dusty, luminous evolved massive stars will thus be automatically classified and follow-up spectroscopy will be obtained for selected targets. Atmospheric and SED modeling will yield parameters and estimates of time-dependent mass loss for ~1000 luminous stars. The emerging trend for the ubiquity of episodic mass loss, if confirmed, will be key to understanding the explosive early Universe and will have profound consequences for low-metallicity stars, reionization, and the chemical evolution of galaxies.

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  • Funder: EC Project Code: 749461
    Overall Budget: 164,653 EURFunder Contribution: 164,653 EUR

    DUST-GLASS aims at improving global dust prediction and monitoring by optimizing an advanced data assimilation system (LETKF scheme) coupled with a sophisticated atmospheric-dust model (NMMB/BSC-Dust). For the accomplishment of these core scientific goals, a fine resolution (0.1o x 0.1o) global dust optical depth (DOD) database, suitable for data assimilation, will be developed via a synergy of state-of-the-art Level 2 satellite retrievals acquired by MODIS, MISR and OMI sensors (2007-2016). The impacts of assimilating this novel dataset (DOD) on model’s predictive skills, both at global and regional scale, will be assessed objectively. Global forecasts (5 days) will be carried out for different periods aiming at studying dust aerosols’ mobilization and transport from the major dust sources of the planet, while a global reanalysis (0.5o x 0.7o) dataset will be generated for long-term dust monitoring. In addition, regional short-term (84 hours) forecasts will be conducted for 20 Mediterranean dust outbreaks identified by a satellite algorithm in the framework of the MDRAF project (fellow’s previous MC-IEF). In the evaluation analysis, the model’s dust outputs will be compared versus measurements derived by ground networks (AERONET, MAN, ACTRIS) as well as against columnar/vertical satellite retrievals (MODIS, MISR, CALIOP). Moreover, temperature and radiation will be also considered since “corrections” on dust fields, thanks to data assimilation, are expected to be evident on both parameters due to dust-radiation interactions. The aforementioned variables will be compared against observations obtained by ground networks (ISB, RAOB, BSRN) and reanalysis/analysis products (ERA-Interim, FNL). Considering the multifaceted role of dust, the scientific outcomes of DUST-GLASS are expected to contribute effectively to interdisciplinary studies regarding dust aerosols as well as their associated impacts on health, anthropogenic activities, environment, weather and climate.

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  • Funder: EC Project Code: 966837
    Funder Contribution: 150,000 EUR

    Poor air quality in cities around the world is a major societal and economic issue, causing more than 5.5 million premature deaths and resulting in €4.3 trillion in welfare losses. Preserving and improving the public health requires effective monitoring of air pollutants from their sources to their transport within the city web, however existing air quality monitoring networks have either very low spatial resolution or low accuracy to serve this purpose. Building on the technology solutions developed within the D-TECT ERC CoG, our project aims to cover this gap by commercializing the "PM-scanner", a remote sensing instrument which will monitor particulate matter (PM) concentration over large areas with unprecedented accuracy and high spatial and temporal resolution. Briefly, the instrument will emit laser pulses and observe the intensity and polarization state of the backscatter light as it travels in the atmosphere; these data will be analysed using dedicated Artificial Intelligence (AI) algorithms to provide PM2.5 and PM10 concentration at multiple locations above a city. We envision that the PM-scanner will be a powerful tool to regional governments, environmental protection agencies and polluting industries, allowing effective real-time monitoring of pollution agents and supporting data-driven air quality policies and actions. Within the proposed PoC project, we aim to verify the innovation potential of the PM-scanner idea by establishing a defensible IP position, building a realistic demonstrator of the PM data products based on the D-TECT Wall-E lidar prototype, and validating our approach by interacting with stakeholders in the envisaged value chain.

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