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

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 first calculate 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 SiO2 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 a sparser, 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 conterminous USA. 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 always validate results.