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In an era of climate change, supply chain issues and the necessary transitions, green chemistry, green engineering and inherent safety offer possibilities for a more safe and resilient industry. A literature study with application to a pilot Organosolv lignocellulosic feedstock bioreactor should show possibilities and ways to strengthen sustainable and safer production. It highlights challenges in practical implementation like solvent selection, solvent recovery, intrinsically safe equipment and process intensification like membrane processes for saving energy. Process safety techniques should guide the way to and should help to find possible restrictions and opportunities for more resilient processes and a more resilient future. Keywords: process safety; green chemistry; organosolv; biorefinery; sustainability; solvent selection;

SECURES-Energy Weather-dependent renewable electricity systems are vulnerable to climate change impacts. Electricity generation and demand profiles considering weather and climate impacts are needed in energy system modelling. We present a consistent and high-quality energy database in data formats useful for energy system modelling and keeping the high spatiotemporal complexity of climate data. The open-access dataset SECURES-Energy contains all relevant electricity demand and supply components for the EU and several additional European countries in hourly resolution covering the period 1981-2100. It is based on reanalysis data ERA5(-Land) for the historical period and two EURO-CORDEX emission scenarios (RCP 4.5 and RCP 8.5). On the generation side, impacts on onshore and offshore wind power generation, solar PV generation, and hydropower generation (run-of-river and reservoirs) – which is often missing in comparable datasets – are provided. On the demand side, all demand components relevant to future electricity systems including e-heating, e-cooling, e-mobility, and electricity demand in industry, are provided. The detailed methods are described in the final project report (see link below) in Chapter 2.2 and Chapter 4.3 and a related journal publication is currently in preparation. Further information: Project website SECURES: https://www.secures.at/ All project-related publications: https://www.secures.at/publications Final SECURES project report: https://www.secures.at/fileadmin/cmc/Final_Report_SECURES.pdf and https://www.klimafonds.gv.at/wp-content/uploads/sites/16/C061007-ACRP12-SECURES-KR19AC0K17532-EB.pdf The SECURES-Energy dataset provides variables visible in the table. Hourly profiles ERA5-Land 1981-2010 Hourly profiles RCP 4.5/RCP 8.5 2011-2100 Production profiles: Variable Short name Unit Temporal resolution Photovoltaics pv - hourly Wind onshore wind - hourly Wind offshore wind_offshore - hourly Hydro run-of-river hydro_ror - hourly Demand profiles: Variable Short name Unit Explanation Temperature temperature °C Population-weighted mean temperature (2 m) Rounded temperature rounded_temperature °C Temperature values rounded to zero decimal places Daytype day type - weekdays = typeday 0; Saturday or day before a holiday = typeday 1; Sunday or holiday = typeday 2 Month month - The column “month” refers to the month of the year. 1 = January, 2 = February etc. Season season - 0 = Summer (15/05 - 14/09) 1 = Winter (1/11 - 20/3) 2 = Transition (21/3 - 14/5 & 15/9 - 31/10) Load e-mobilty load_emobility - E-mobility electricity demand profile, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent) Non-metallic minerals non_metallic_minerals - Electricity demand profile of the industrial sector non-metallic minerals, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent) Paper paper - Electricity demand profile of the industrial sector paper, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent) Iron and steel iron_and_steel - Electricity demand profile of the industrial sector iron and steel, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent) Chemicals and petrochemicals chemicals_and_petrochemicals - Electricity demand profile of the industrial sector chemicals and petrochemicals, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent) Food and tobacco food_and_tobacco - Electricity demand profile of the industrial sector food and tobacco, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent) SHW residential shw_residential - Electricity demand profile for sanitary hot water in the residential sector, normalized to an annual demand of 1,000,000 (non-weather-dependent) SHW tertiary shw_tertiary Electricity demand profile for sanitary hot water in the tertiary sector, normalized to an annual demand of 1,000,000 (non-weather-dependent) Cooling residential cooling_residential - Electricity demand profile for cooling in the residential sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent) Heating residential heating_residential - Electricity demand profile for heating in the residential sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent) Cooling tertiary cooling_tertiary - Electricity demand profile for cooling in the tertiary sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent) Heating tertiary heating_tertiary - Electricity demand profile for heating in the tertiary sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent) Rest rest - Rest electricity demand profile, normalized to an annual demand of 1,000,000 (non-weather-dependent) Exogenous H2 exogenous_H2 - Electricity demand profile for electrolysis (flat profile), normalized to an annual demand of 1,000,000 (non-weather-dependent) Total total - Total electricity demand profile containing all components above (e-mobility, industry, residential heating, residential sanitary hot water, residential cooling, tertiary heating, tertiary sanitary hot water, tertiary cooling, rest, and exogenous H2 electricity demand), normalized to an annual demand of 10,000,000 in the reference year 2010 Electricity supply profiles for wind (onshore and offshore), hydro (run-of-river), and solar generation are provided for almost all European countries, namely: Andorra (AD), Albania (AL), Austria (AT), Bosnia and Herzegovina (BA), Belgium (BE), Bulgaria (BG), Switzerland (CH), Czech Republic (CZ), Germany (DE), Denmark (DK), Estonia (EE), Spain (ES), Finland (FI), France (FR), United Kingdom of Great Britain and Northern Ireland (GB), Greece (GR), Croatia (HR), Hungary (HU), Republic of Ireland (IE), Italy (IT), Liechtenstein (LI), Lithuania (LT), Luxembourg (LU), Latvia (LV), Montenegro (ME), North Macedonia (MK), Malta (MT), Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Sweden (SE), Slovenia (SI), Slovakia (SK), San Marino (SM), Ukraine (UA), Vatican (VA), and Kosovo (XK). The countries covered by the electricity demand profiles are the EU27 countries (except for Cyprus), CH, GB, and NO. Industrial, heating, and cooling demand profiles are based on regressions developed in the H2020 Hotmaps project [1] [2]. SECURES-Energy is available in a tabular csv format for the historical period (1981-2010) created from ERA5 and ERA5-Land and two future emission scenarios (RCP 4.5 and RCP 8.5, both 2011-2100) created from one CMIP5 EURO-CORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E) on the spatial aggregation level NUTS0 (country-wide). The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shapefiles of the different NUTS levels. Hydro reservoir profiles are also published and can be found in the related dataset SECURES-Met: https://zenodo.org/records/7907883. The project SECURES and corresponding publications are funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532. [1] Fallahnejad M. Hotmaps-data-repository-structure 2019. https://wiki.hotmaps.eu/en/Hotmaps-open-data-repositories. [2] Pezzutto S, Zambotti S, Croce S, Zambelli P, Garegnani G, Scaramuzzino C, et al. HOTMAPS - D2.3 WP2 Report – Open Data Set for the EU28. 2019.

For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling. The SECURES-Met dataset provides variables visible in the table. Variable Short name Unit Aggregation methods Temporal resolution Temperature (2m) T2M °C °C spatial mean population weighted mean (recommended) hourly Radiation GLO (mean global radiation) BNI (direct normal irradiation) Wm-2 Wm-2 spatial mean population weighted mean (recommended) hourly Potential Wind Power WP 1 normalized with potentially available area hourly Hydro Power Potential HYD-RES (reservoir) HYD-ROR (run-of-river) MW 1 summed power production summed power production normalized with average daily production daily SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (RCP 4.5 and RCP 8.5, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the spatial aggregation level NUTS0 (country-wide), NUTS2 (province-wide), NUTS3 (Austria only), and EEZ (Exclusive Economic Zones, offshore only). The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As population weighted temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only. The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.

This dataset was produced with funding from the European Space Agency (ESA) Climate Change Initiative (CCI) Plus Soil Moisture Project (CCN 3 to ESRIN Contract No: 4000126684/19/I-NB "ESA CCI+ Phase 1 New R&D on CCI ECVS Soil Moisture"). Project website: https://climate.esa.int/en/projects/soil-moisture/ This dataset contains information on the Surface Soil Moisture (SM) content derived from satellite observations in the microwave domain. Abstract The MODELFREE product of the ESA CCI SM v9.1 science data suite provides - similar to the COMBINED product - global, harmonized daily satellite soil moisture measurements from both radar and radiometer observations. This product contains soil moisture estimates at 0.25-degree spatial resolution, and covers the period from 2002-2023. Soil moisture is derived from observations of 13 different active and passive satellites operating across various frequency bands (K, C, X, and L-band). Unlike the COMBINED product, for which soil moisture fields from the GLDAS Noah model dataset are used to harmonize individual satellite sensor measurements, the MODELFREE product utilizes a satellite-only scaling reference dataset. This reference incorporates gap-filled soil moisture derived from AMSR-E (2002-2010) and from intercalibrated SMAP/SMOS brightness temperature data (2010-2023). The merging algorithm employed is consistent with that of the v9.1 COMBINED product. The new scaling reference leads to significantly different absolute soil moisture values, especially in latitudes above 60 °N. Data from the SMMR, SSMI and ERS missions are not included in this product. This product is in its early development stage and should be used with caution, as it may contain incomplete or unvalidated data. Summary First version of a model-independent version of the ESA CCI SM COMBINED product 2002-2023, global, 0.25 deg. resolution GLDAS Noah (model) is replaced with a purely satellite-based scaling reference Different absolute value range compared to the COMBINED product is expected due to the different scaling reference used Known issues A temporal inconsistency is observed between the AMSR-E and SMOS period (at 01-2010). This can affect long-term trends in the data In the period from 01-2002 to 06-2002 no data are available above 37 °N and below 37 °S respectively (all measurements in this period are from the TRMM Microwave Imager) Technical Details The dataset provides global daily estimates for the 2002-2023 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name has the following convention: ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED_MODELFREE-YYYYMMDD000000-fv09.1.nc Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables: sm: (float) The Soil Moisture variable reflects estimates of daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree). sm_uncertainty: (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of satellite observations. Derived using triple collocation analysis. dn_flag: (int) Indicator for satellite orbit(s) used in the retrieval (day/nighttime). 1=day, 2=night, 3=both flag: (int) Indicator for data quality / missing data indicator. For more details, see netcdf attributes. freqbandID: (int) Indicator for frequency band(s) used in the retrieval. For more details, see netcdf attributes. mode: (int) Indicator for satellite orbit(s) used in the retrieval (ascending, descending) sensor: (int) Indicator for satellite sensor(s) used in the retrieval. For more details, see netcdf attributes. t0: (float) Representative time stamp, based on overpass times of all merged satellites. Additional information for each variable is given in the netCDF attributes. Software to open netCDF files These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as: Xarray (python) netCDF4 (python) esa_cci_sm (python) Similar tools exists for other programming languages (Matlab, R, etc.) Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArCGIS You can also use the GUI software Panoply to view the contents of each file References R. Madelon et al., “Toward the Removal of Model Dependency in Soil Moisture Climate Data Records by Using an L-Band Scaling Reference," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 831-848, 2022, doi: 10.1109/JSTARS.2021.3137008. Related Records The following records are all part of the Soil Moisture Climate Data Records from satellites community 1 ESA CCI SM RZSM Root-Zone Soil Moisture Record 10.48436/v8cwj-jk556 2 ESA CCI SM GAPFILLED Surface Soil Moisture Record 10.48436/hcm6n-t4m35

Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. This dataset features a detailed map of material stocks for the whole of Germany on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Temporal extent The map is representative for ca. 2018. Data format Per federal state, the data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. The dataset features area and mass for different street types area and mass for different rail types area and mass for other infrastructure area, volume and mass for different building types Masses are reported as total values, and per material category. Units area in m² height in m volume in m³ mass in t for infrastructure and buildings Further information For further information, please see the publication or contact Helmut Haberl (helmut.haberl@boku.ac.at). A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Publication Haberl, H., Wiedenhofer, D., Schug, F., Frantz, D., Virág, D., Plutzar, C., Gruhler, K., Lederer, J., Schiller, G. , Fishman, T., Lanau, M., Gattringer, A., Kemper, T., Liu, G., Tanikawa, H., van der Linden, S., Hostert, P. (accepted): High-resolution maps of material stocks in buildings and infrastructures in Austria and Germany. Environmental Science & Technology Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). ML and GL acknowledge funding by the Independent Research Fund Denmark (CityWeight, 6111-00555B), ML thanks the Engineering and Physical Sciences Research Council (EPSRC; project Multi-Scale, Circular Economic Potential of Non-Residential Building Scale, EP/S029273/1), JL acknowledges funding by the Vienna Science and Technology Fund (WWTF), project ESR17-067, TF acknowledges the Israel Science Foundation grant no. 2706/19.

Setting up strategies for a sound management of plastic packaging waste (PPW) is becoming increasingly crucial at many levels of the value chain in Europe. After the very first implementation of an extended producer responsibility scheme in Germany in 1991, many EU Countries followed. This resulted in a complex network of schemes that differ from one member state to another. This paper brings together the three latest studies describing the current flows of PPW across the waste value chain from Austria (reference year 2013), Germany and the Netherlands (reference year 2017). With this aim, the models of the three single studies have been adapted to fit into a common model, allowing to perform a comparative analysis. Although with a relatively comparable product market, the three countries have different management systems (e.g., separate collection systems, target sorting products and treatment of residual waste), reflecting different national strategies to achieve the circular economy targets. Recycling rates (in terms of washed milled goods at the output of the recycling process) for the three countries resulted in 23%, 43% and 30% of the total mass of PPW generated in, respectively, Austria, Germany and the Netherlands. The fraction of mixed recycled plastics, relevant for Germany and the Netherlands only, was determined to be one of the major determinants of the differences in recycling rates. Furthermore, the discussion revolves around new political targets that have the potential to contribute to addressing the issue of tradeoff between quantity and quality of recycled plastics placed on the market, with measures such as design-for-recycling and eco-modulation of EPR fees playing a critical role, while also pointing out the aspects that inevitably hinder closed-loop recycling.

Ziel der Diplomarbeit war es, ein Comuputermodell in ANSYS Fluent zu erstellen, um die Tr��pfchenbildung der Eigenschaften des Thermo��ls D12 in einem Latentw��rmespeicher mit den Eigenschaften von Wasser und Wasser-TBAB w��hrend des Erstarrungsprozesses des Speichermaterials zu analysieren. Die Arbeit f��hrt den Leser in die Grundlagen der Strahl- und Tr��pfchenbildung ein und erkl��rt kurz dsas verwendete Modell, bevor die Ergebnisse der Parameterstudie vorgestellt werden. Da str��mungsdynamische Simulationen eine betr��chtliche Menge an PC-Resourcen ben��tigen, wurde das Modell auf die notwendige Gr����e f��r den eingehenden Strahl und den ersten Tropfen zugeschnitten Das Einstellen der kompletten oberen Wand des Modells als Auslass f��hrte zu R��ckstr��mungsbedingungen, die den W��rmestrom in gro��em Ma��stab beeinflussten. F��r die Parameterstuidie wurden der sogenannte Mushy-Parameter f��r die Erstarrung, die Eintrittstemperatur und die Eintrittsgeschwindigkeit variiert. 5 von 11 Berechnungen zeigten ein ��hnliches Verhalten und keine ausreichende Erstarrung, 2 Berechnungen zeigten ein Verhalten, das den Grundgesetzen der Physik widersprechen. Von den 4 Berechnungen mit auswertbaren Daten erfuhren nur 2 einen Erstarrungsprozess, der langsam genug war, um vollst��ndig ausgewertet zu werden. Die anderen 2 konnten konnten aufgrund eines Speicherintervalls von 0.5s, mit der Absicht, den erforderlichen Festplattenspeicher zu minimieren, nicht vollst��ndig aufgel��st werden. Die Ergebnisse zeigen, das CFD-Berechnungen zur Modellierung der Erstarrung in einem direkt Latentw��rmespeicher verwendet werden k��nnen. Allerdings m��ssen daf��r die Randbedingungen sorgf��lltig eingestellt werden. The aimk of this master thesis was to create a computer model in ANSYS Fluent with the intnetion of evaluating the droplet formation of the properties of tzhe thermal oil D12 in a direct contact latent heat storage with the properties of water and water-TBAB during the solification process of the storage aterial. The thesis will introduce the reader into the basics of the jet and droplet formation and briefly explain the used model before presenting and discussing the results of the parameter study. As compuitational fluid dynamic calculation need a significant amount of PC resources the model was created to fit just the incoming stream and the first droplet. Setting the complete top wall of the model geometry as outlet resulted in back-flow conditions effecting the heat flux on a great sccale. For the parameter study, the so-called mushy parameter for solification, the inlet temperature and the inlet velocity were varied. 5 of 11 calculations experienced similar behaviour and no sufficient solification, 2 calculations with exploitable data only 2 experienced a solification process slow enough to be evaluated fully. The other 2 could not be completly resolved due to a savinig interval of 0.5s caused by the intention of minimizing necessary disk space. The results show that CFD calculations can be used for modelling the solification in a direct contact latent heat storage, although the boundary conditions have set to be carefully.