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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Estimating the silica content and loss-on-ignition in the North American Soil Geochemical Landscapes datasets: a recursive inversion approach

Authors: Patrice de Caritat; Eric Grunsky; David Smith;

Estimating the silica content and loss-on-ignition in the North American Soil Geochemical Landscapes datasets: a recursive inversion approach

Abstract

Abstract: A novel method of estimating the silica (SiO2) and loss-on-ignition (LOI) concentrations for the North American Soil Geochemical Landscapes (NASGL) project datasets is proposed. Combining the precision of the geochemical determinations with the completeness of the mineralogical NASGL data, we suggest a ‘reverse normative’ or inversion approach to calculate first the minimum SiO2, water (H2O) and carbon dioxide (CO2) concentrations in weight percent (wt%) in these samples. These can be used in a first step to compute minimum and maximum estimates for SiO2. In a recursive step, a ‘consensus’ SiO2 is then established as the average between the two aforementioned estimates, trimmed as necessary to yield a total composition (major oxides converted from reported Al, Ca, Fe, K, Mg, Mn, Na, P, S, and Ti elemental concentrations + ‘consensus’ SiO2 + reported trace element concentrations converted to wt% + ‘normative’ H2O + ‘normative’ CO2) of no more than 100 wt%. Any remaining compositional gap between 100 wt% and this sum is considered ‘other’ LOI and likely includes H2O and CO2 from the reported ‘amorphous’ phase (of unknown geochemical or mineralogical composition) as well as other volatile components present in soil. We validate the technique against a separate dataset from Australia where geochemical (including all major oxides) and mineralogical data exist on the same samples. The correlation between predicted and observed SiO2 is linear, strong (R2 = 0.91) and homoscedastic. We also compare the estimated NASGL SiO2 concentrations with another publicly available continental-scale survey over the conterminous USA, the ‘Shacklette and Boerngen’ dataset. This comparison shows the new data to be a reasonable representation of SiO2 values measured on the ground over the same study area. We recommend the approach of combining geochemical and mineralogical information to estimate missing SiO2 and LOI by the recursive inversion approach in datasets elsewhere, with the caveat to validate results. Datasets: The original geochemical and mineralogical data for soils of the conterminous United States (A and C horizon datasets) were downloaded from https://mrdata.usgs.gov/ds-801/. The ‘Shacklette and Boerngen’ dataset was downloaded from https://mrdata.usgs.gov/ussoils/. A worked example for the five selected samples of Figure 5 is available as a Microsoft Excel spreadsheet (NALG_Ch_oxides_with_estimated_SiO2_LOI_worked example.xlsx) on Zenodo.org. The new datasets including sample identification, coordinates, converted major oxide concentrations, and the concentration estimates for SiO2 and LOI in wt% for the A and C horizon datasets from the North American Soil Geochemical Landscapes (NASGL) project are available as comma separated value files (NALG_Ah_oxides_with_estimated_SiO2_LOI.csv and NALG_Ch_oxides_with_estimated_SiO2_LOI.csv) on Zenodo.org.

An open-access peer-reviewed version of the parent article is now available in journal 'Geochemistry: Exploration, Environment, Analysis' Please cite it as: CARITAT, P. de, GRUNSKY, E. & SMITH, D., 2023. Estimating the silica content and loss-on-ignition in the North American Soil Geochemical Landscapes datasets: a recursive inversion approach. Geochemistry: Exploration, Environment, Analysis, 23, geochem2023-039. https://doi.org/10.1144/geochem2023-039

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Keywords

SiO2, LOI, geochemical survey, geochemistry, mineralogy, compositional data, normative analysis

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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