Integration of Slurry–Total Reflection X-ray Fluorescence and Machine Learning for Monitoring Arsenic and Lead Contamination: Case Study in Itata Valley Agricultural Soils, Chile

Author:

Medina-González Guillermo1ORCID,Medina Yelena2,Muñoz Enrique34ORCID,Andrade Paola5,Cruz Jordi6ORCID,Rodriguez-Gallo Yakdiel7ORCID,Matus-Bello Alison1ORCID

Affiliation:

1. Department of Environmental Chemistry, Faculty of Sciences, Universidad Católica de la Santísima Concepción, Concepción 4090541, Chile

2. EMOingenieros Ltda., Concepción 4090070, Chile

3. Departament of Civil Engineering, Faculty of Engineering, Universidad Católica de la Santísima Concepción, Concepción 4090541, Chile

4. Centro de Investigación en Biodiversidad y Ambientes Sustentables CIBAS, Universidad Católica de la Santísima Concepción, Concepción 4090541, Chile

5. Departament of Ecology, Faculty of Sciences, Universidad Católica de la Santísima Concepción, Concepción 4090541, Chile

6. Escola Universitària Salesiana de Sarrià (EUSS School of Engineering, Barcelona), 08017 Barcelona, Spain

7. Faculty of Engineering, Don Bosco University, Calle a Plan del Pino Km 1 1/2, Soyapango 1874, El Salvador

Abstract

The accuracy of determining arsenic and lead using the optical technique Slurry–Total Reflection X-ray Fluorescence (Slurry-TXRF) was significantly enhanced through the application of a machine learning method, aimed at improving the ecological risk assessment of agricultural soils. The overlapping of the arsenic Kα signal at 10.55 keV with the lead Lα signal at 10.54 keV due to the relatively low resolution of TXRF could compromise the determination of lead. However, by applying a Partial Least Squares (PLS) machine learning algorithm, we mitigated interference variations, resulting in improved selectivity and accuracy. Specifically, the average percentage error was reduced from 15.6% to 9.4% for arsenic (RMSEP improved from 5.6 mg kg−1 to 3.3 mg kg−1) and from 18.9% to 6.8% for lead (RMSEP improved from 12.3 mg kg−1 to 5.03 mg kg−1) compared to the previous univariable model. This enhanced predictive accuracy, within the set of samples concentration range, is attributable to the efficiency of the multivariate calibration first-order advantage in quantifying the presence of interferents. The evaluation of X-ray fluorescence emission signals for 26 different synthetic calibration mixtures confirmed these improvements, overcoming spectral interferences. Additionally, the application of these models enabled the quantification of arsenic and lead in soils from a viticultural subregion of Chile, facilitating the estimation of ecological risk indices in a fast and reliable manner. The results indicate that the contamination level of these soils with arsenic and lead ranges from moderate to considerable.

Funder

Agencia Nacional de Investigación

Universidad Católica de la Santísima Concepción

InES Ciencia Abierta

Publisher

MDPI AG

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