This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
This data set presents the reconstructed vegetation cover for 3083 sites based on harmonized pollen data from the data set LegacyPollen 2.0 (https://doi.pangaea.de/10.1594/PANGAEA.965907) and optimized RPP values. 1115 sites are located in North America, 1435 in Europe, and 533 in Asia. Sugita's REVEALS model (2007) was applied to all pollen records using REVEALSinR from the DISQOVER package (Theuerkauf et al. 2016). Pollen counts were translated into vegetation cover by taking into account taxon-specific pollen productivity and fall speed. Additionally, relevant source areas of pollen were also calculated using the aforementioned taxon-specific parameters and a gaussian plume model for deposition and dispersal. In this optimized reconstruction, relative pollen productivity estimates for the ten most common taxa were first optimized by using reconstructed tree cover from modern pollen samples and LANDSAT remotely sensed tree cover (Townshend 2016) for North America, Europe, and Asia. Values for non-optimized taxa for relative pollen productivity and fall speed were taken from the synthesis from Wiezcorek and Herzschuh (2020). The average values from all Northern Hemisphere values were used where taxon-specific continental values were not available. We present tables with optimized reconstructed vegetation cover for all Europe, North America and Asia. As further details we list a table with the taxon-specific parameters used and a list of parameters adjusted in the default version of REVEALSinR.
This data set presents the reconstructed vegetation cover for 1451 European sites based on harmonized pollen data from the data set LegacyPollen 2.0 and optimized RPP values. Sugita's REVEALS model (2007) was applied to all pollen records using REVEALSinR from the DISQOVER package (Theuerkauf et al. 2016). Pollen counts were translated into vegetation cover by taking into account taxon-specific pollen productivity and fall speed. Additionally, relevant source areas of pollen were also calculated using the aforementioned taxon-specific parameters and a gaussian plume model for deposition and dispersal and forest cover was reconstructed. In this optimized reconstruction, relative pollen productivity estimates for the ten most common taxa were first optimized by using reconstructed tree cover from modern pollen samples and LANDSAT remotely sensed tree cover (Sexton et al. 2013) for Europe. Values for non-optimized taxa for relative pollen productivity and fall speed were taken from the synthesis from Wiezcorek and Herzschuh (2020). The average values from all Northern Hemisphere values were used where taxon-specific continental values were not available. We present tables with optimized reconstructed vegetation cover for all records in Europe. As further details we list a table with the taxon-specific parameters used and a list of parameters adjusted in the default version of REVEALSinR.
This data set presents the reconstructed vegetation cover for 706 Asian sites based on harmonized pollen data from the data set LegacyPollen 2.0 and optimized RPP values. Sugita's REVEALS model (2007) was applied to all pollen records using REVEALSinR from the DISQOVER package (Theuerkauf et al. 2016). Pollen counts were translated into vegetation cover by taking into account taxon-specific pollen productivity and fall speed. Additionally, relevant source areas of pollen were also calculated using the aforementioned taxon-specific parameters and a gaussian plume model for deposition and dispersal and forest cover was reconstructed. In this optimized reconstruction, relative pollen productivity estimates for the ten most common taxa were first optimized by using reconstructed tree cover from modern pollen samples and LANDSAT remotely sensed tree cover (Sexton et al. 2013) for Asia. Values for non-optimized taxa for relative pollen productivity and fall speed were taken from the synthesis from Wiezcorek and Herzschuh (2020). The average values from all Northern Hemisphere values were used where taxon-specific continental values were not available. We present tables with optimized reconstructed vegetation cover for records in Asia. As further details we list a table with the taxon-specific parameters used and a list of parameters adjusted in the default version of REVEALSinR.
Zielsetzung: Dicht- und Klebstoffkartuschen finden in sehr vielen Bereichen zunehmende Anwendung. Kartuschen sind eine vom Endnutzer sehr gut akzeptierte Verpackung und Verarbeitungshilfe der Produkte. Sie zeichnen sich einerseits durch eine hohe Homogenität des Kartuschenmaterials, vorwiegend hochwertiges Polyethylen mit hoher Dichte (HDPE), und andererseits durch eine extrem variable chemische Zusammensetzung der Inhaltsstoffe aus. In ersten Voruntersuchungen wurde festgestellt, dass etwa 90 % der gesammelten Kartuschen MS (modifizierte Silan-)Polymer , Acryl- und Silikon-haltige Restinhaltstoffe aufwiesen. Die restlichen 10 % beinhalten eine Vielzahl anderer Inhaltsstoffe (u. a. Bitumen, Polyurethan, Zement). Die Menge und der Zustand der in den Kartuschen verbliebenen Restinhaltstoffe variiert stark. Dichtstoffkartuschen werden als „nicht recyclingfähig“ eingestuft. Dies liegt an der sehr variablen Zusammensetzung der Inhaltsstoffe und deren Rückstände in der Kartusche, die bei der Kreislaufführung des HDPEs zu massiven Problemen führen (z. B. Silikonrückstände). Deshalb werden Kartuschen in Deutschland derzeit thermisch verwertet, in anderen europäischen Ländern auch deponiert. Marktanalysen gehen davon aus, dass in Deutschland jährlich 60- 70 Mio. Stück Kartuschen in Verkehr gebracht werden. In Europa fallen pro Jahr rund 45.000 t Kartuschenabfälle an. Aufgrund der hohen Mengen und des ungelösten Entsorgungsproblems sollen die Hersteller verstärkt in die Pflicht genommen werden. Für die Verwendung von Kunststoffen werden von der EU zwischenzeitlich Aufschläge von 800 €/t erhoben. Es ist absehbar, dass diese Aufschläge früher oder später an die Hersteller weitergereicht werden. Auf EU-Ebene wurden und werden auch Diskussionen über ein Verbot nicht-recyclingfähiger Kunststoffverpackungen geführt. Im Rahmen des Forschungsvorhabens soll die Recyclingfähigkeit von Dicht- und Klebstoffkartuschen untersucht werden. Dies setzt zunächst ein effizientes Erfassungssystem voraus, das gleichermaßen beim Fachhandel, Handwerk und Sortieranlagen ansetzt und die gebrauchten Kartuschen als Monostrom separiert. Bei der Entwicklung des Recyclingprozesses sollen vorzugsweise mechanische und chemische, nachgeordnet thermische Verfahren betrachtet werden. Ziel ist die Kreislaufführung des hochwertigen HDPEs. Konkret: Aus gebrauchten Kartuschen neue Kartuschen produzieren. Wenn es gelingt HDPE in ausreichender Qualität zu gewinnen, existiert für das Rezyklat bereits ein Absatzmarkt.
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