ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • MBL  (1)
  • biochar  (1)
  • Blackwell Publishing Ltd  (2)
  • American Chemical Society
  • 2020-2024  (2)
  • 1990-1994
  • 1
    Publication Date: 2023-01-21
    Description: Charcoal‐rich Technosols on century‐old relict charcoal hearths (RCHs) are the subject of ongoing research regarding potential legacy effects that result from historic charcoal production and subsequent charcoal amendments on forest soil properties and forest ecosystems today. RCHs consist mostly of Auh horizons that are substantially enriched in soil organic carbon (SOC), of which the largest part seems to be of pyrogenic origin (PyC). However, the reported range of SOC and PyC contents in RCH soil also suggests that they are enriched in nonpyrogenic SOC. RCH soils are discussed as potential benchmarks for the long‐term influence of biochar amendment and the post‐wildfire influences on soil properties. In this study, we utilised a large soil sample dataset (n = 1245) from 52 RCH sites in north‐western Connecticut, USA, to quantify SOC contents by total element analysis. The contents of condensed highly aromatic carbon as a proxy for black carbon (BC) were predicted by using a modified benzene polycarboxylated acid (BPCA) marker method in combination with diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy‐based partial least square regression (r2 = 0.89). A high vertical spatial sampling resolution allowed the identification of soil organic matter (SOM) enrichment and translocation processes. The results show an average 75% and 1862% increase in TOC and BPCA‐derived carbon, respectively, for technogenic Auh horizons compared to reference soils. In addition to an increase in aromatic properties, increased carboxylic properties of the RCH SOC suggest self‐humification effects of degrading charcoal and thereby the continuing formation of leachable aromatic carbon compounds, which could have effects on pedogenic processes in buried soils. Indeed, we show BPCA‐derived carbon concentrations in intermediate technogenic Cu horizons and buried top/subsoils that suggest vertical translocation of highly aromatic carbon originating in RCH Auh horizons. Topmost Auh horizons showed a gradual decrease in total organic carbon (TOC) contents with increasing depth, suggesting accumulation of recent, non‐pyrogenic SOM. Lower aliphatic absorptions in RCH soil spectra suggest different SOM turnover dynamics compared to reference soils. Furthermore, studied RCH soils featured additional TOC enrichment, which cannot be fully explained now. Highlights BC to TOC ratio and high resolution vertical SOC distribution in 52 RCH sites were studied. RCH soils non‐BC pool was potentially different to reference soils. RCH soils feature TOC accumulation in the topmost horizon. There is BC translocation into buried soils on RCH sites.
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Keywords: ddc:631.4 ; benzene polycarboxylated acid marker (BPCA) ; black carbon ; charcoal degradation ; charcoal kiln ; pyrogenic carbon ; relict charcoal hearth ; biochar
    Language: English
    Type: doc-type:article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 2
    Publication Date: 2023-12-12
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Infrared spectroscopy in the visible to near‐infrared (vis–NIR) and mid‐infrared (MIR) regions is a well‐established approach for the prediction of soil properties. Different data fusion and training approaches exist, and the optimal procedures are yet undefined and may depend on the heterogeneity present in the set and on the considered scale. The objectives were to test the usefulness of partial least squares regressions (PLSRs) for soil organic carbon (SOC), total carbon (C〈sub〉t〈/sub〉), total nitrogen (N〈sub〉t〈/sub〉) and pH using vis–NIR and MIR spectroscopy for an independent validation after standard calibration (use of a general PLSR model) or using memory‐based learning (MBL) with and without spiking for a national spectral database. Data fusion approaches were simple concatenation of spectra, outer product analysis (OPA) and model averaging. In total, 481 soils from an Austrian forest soil archive were measured in the vis–NIR and MIR regions, and regressions were calculated. Fivefold calibration‐validation approaches were carried out with a region‐related split of spectra to implement independent validations with n ranging from 47 to 99 soils in different folds. MIR predictions were generally superior over vis–NIR predictions. For all properties, optimal predictions were obtained with data fusion, with OPA and spectra concatenation outperforming model averaging. The greatest robustness of performance was found for OPA and MBL with spiking with 〈italic toggle="no"〉R〈/italic〉〈sup〉2〈/sup〉 ≥ 0.77 (N), 0.85 (SOC), 0.86 (pH) and 0.88 (C〈sub〉t〈/sub〉) in the validations of all folds. Overall, the results indicate that the combination of OPA for vis–NIR and MIR spectra with MBL and spiking has a high potential to accurately estimate properties when using large‐scale soil spectral libraries as reference data. However, the reduction of cost‐effectiveness using two spectrometers needs to be weighed against the potential increase in accuracy compared to a single MIR spectroscopy approach.〈/p〉
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Keywords: ddc:631.4 ; data fusion ; independent validation ; infrared spectroscopy ; MBL ; nitrogen ; outer product analysis ; pH ; soil organic carbon ; spiking ; total carbon
    Language: English
    Type: doc-type:article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...