Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils

Abstract

Sulfuric acid digestion analyses (SAD) provide useful information to environmental studies, in terms of the geochemical balance of nutrients, parent material uniformity, nutrient reserves for perennial crops, and mineralogical composition of the soil clay fraction. Yet, these analyses are costly, time consuming, and generate chemical waste. This work aimed at predicting SAD results from portable X-ray fluorescence (pXRF) spectrometry, which is proposed as a "green chemistry" alternative to the current SAD method. Soil samples developed from different parent materials were analyzed for soil texture and SAD, and scanned with pXRF. The SAD results were predicted from pXRF elemental analyses through simple linear regressions, stepwise multiple linear regressions, and random forest algorithm, with and without incorporation of soil texture data. The modeling was developed with 70 % of the data, while the remaining 30 % was used for validation through calculation of R 2 , adjusted R 2 , root mean square error, and mean error. Simple linear regression can accurately predict SAD results of Fe 2 O 3 (R 2 0.89), TiO 2 (R 2 0.96) , and P 2 O 5 (R 2 0.89). Stepwise regressions provided accurate predictions for Al 2 O 3 (R 2 0.87) and Ki-molar weathering index (SiO 2 /Al 2 O 3) (R 2 0.74) by incorporating soil texture data, as well as for SiO 2 (R 2 0.61). Random forest also provided adequate predictions, especially for Fe 2 O 3 (R 2 0.95), and improved results of Kr-molar weathering index (SiO 2 /(Al 2 O 3 + Fe 2 O 3)) (R 2 0.66), by incorporation of soil texture data. Our findings showed that the SAD results could be accurately predicted from pXRF data, decreasing costs, time and the production of laboratory waste.

Study of an abnormal occurrence of Oxisols in strongly undulated relief in the south of Minas Gerais, Brazil, with support of pXRF and geomorphology

“…P3 had a higher Si content compared with Al and Fe. Silva et al (2020) found lower contents of Fe 2 O 3 and TiO 2 and greater contents of SiO 2 in soils derived from gneiss compared to soils developed from itabirite, basalt, gabbro and tuffite. The Fe content varied less in the soils in upper and middle third of the landscape (P1=68,596; P2=75,160 mg kg -¹; values for Bo horizon), while in the lower third there was a drastic reduction (P3=53,297 mg kg -¹; Bo horizon).…”

Correcting field determination of elemental contents in soils via portable X-ray fluorescence spectrometry

“…Most prediction models were strongly influenced by texture. In tropical soils, the higher sand content tend to positively correlate with SiO 2 content, since this particle size fraction in most soils is dominated by quartz, composed by SiO 2 (Kämpf;Marques;Curi, 2012;Silva et al, 2019). Thus, the use of texture in SiO 2 (and in almost all the other) prediction models improved all statistical parameters evaluated in relation to models that did not use texture as an auxiliary variable.…”

“…Also, relationships between the content of certain chemical elements and the different textural fractions of the soil can be made Weindorf;Zhang, 2011). In tropical soils, for instance, greater contents of SiO 2 tend to correspond to soils rich in quartz (SiO 2 ), dominantly found in greater contents in soils rich in sand (Kämpf;Maques;Curi, 2012;Silva et al, 2019).…”