Modeling the soil burn effect for temperature prediction by energy dispersive X ray Fluorescence in an haplic cambisol soil

Débora R Rocha; Fábio L Melquiades; Edivaldo L Thomaz

doi:10.1016/j.apradiso.2019.05.012

Modeling the soil burn effect for temperature prediction by energy dispersive X ray Fluorescence in an haplic cambisol soil

Appl Radiat Isot. 2019 Aug:150:26-30. doi: 10.1016/j.apradiso.2019.05.012. Epub 2019 May 11.

Authors

Débora R Rocha¹, Fábio L Melquiades², Edivaldo L Thomaz³

Affiliations

¹ Applied Nuclear Physics Group, Physics Dep., Universidade Estadual de Londrina, Londrina, Paraná, Brazil.
² Applied Nuclear Physics Group, Physics Dep., Universidade Estadual de Londrina, Londrina, Paraná, Brazil. Electronic address: fmelquiades@uel.br.
³ Geography Dep., Soil Erosion Laboratory, Universidade Estadual do Centro Oeste Guarapuava, Paraná, Brazil.

PMID: 31108337
DOI: 10.1016/j.apradiso.2019.05.012

Abstract

Considered an important hydrological and geomorphological agent, fire can cause physical, chemical and biological changes in the soil. Besides wildfire, the study of fire effects is also related to traditional agriculture. This study presents the characterization and analysis of soil samples submitted to burn simulation with the objective to build a temperature prediction model in order to determine the maximum temperature reached by real soil burn samples. For this purpose, surface soil samples (0-2.5 cm) classified as Haplic Cambisol were collected from a native forest area close to the studied field. The temperature of the simulation samples ranged from 50 to 750 °C. Moreover, a real burn set of samples were measured for temperature prediction using the proposed model. The characterization and quantification of the chemical elements present in the soil were done by Energy Dispersive X-ray Fluorescence (EDXRF) measurements. Plots with Fe concentration and with the Rayleigh and Compton scattering data versus temperature were constructed. The Fe/Rh and Fe/RhC ratios resulted in relative deviations ranging from 14% to 22% using univariate analysis. Multivariate analysis was also applied through partial least squares regression (PLSR) method in four different spectrum regions. The best result was obtained for the model using the spectrum scattering region with r² = 0.90 and relative deviation ranging from 8% to 25% for the predicted temperature. The use of local multivariate PLSR models improved the results when compared to the univariate regression results.

Keywords: Chemometrics; EDXRF; Prescribed fire; Soil; Wildfire.