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

Aug 5, 2026

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Machine learning substantially improves arsenic and lead quantification using Slurry-Total Reflection X-ray Fluorescence (Slurry-TXRF) for Chilean agricultural soils, targeting ecological risk assessment. The approach addresses spectral overlap, where the arsenic Kα signal at 10.55 keV interferes with the lead Lα signal at 10.54 keV because of TXRF resolution limits. A Partial Least Squares (PLS) model mitigates interference variations and improves selectivity and accuracy. Average percentage error decreases from 15.6% to 9.4% for arsenic (lower RMSEP from 5.6 to 3.3 mg kg⁻¹) and from 18.9% to 6.8% for lead (RMSEP from 12.3 to 5.03 mg kg⁻¹) versus a previous univariable model. Validation with 26 synthetic calibration mixtures confirms improved performance, enabling fast, reliable quantification in an Itata Valley viticultural subregion. Results show arsenic and lead contamination from moderate to considerable levels.

LanguageEnglish

CategoryResearch & Report

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Pages17

Reading Time43 min

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Abstract

1. Introduction

Article # Integration of Slurry-Total Reflection X-ray Fluorescenceand Machine Learning for Monitoring Arsenic and LeadContamination:Case Study in Itata Valley AgriculturalSoils,Chile Guillermo Medina-González¹,*D,Yelena Medina²,Enrique Muňoz³,4D,Paola Andrade⁵,Jordi Cruz⁶D, Yakdiel Rodriguez-Gallo⁷Dand Alison Matus-Bello¹D 1 Department of Environmental Chemistry,Faculty of Sciences,Universidad Católica de la Santisima Concepción,Concepción 4090541,Chile;amatus@qaciencias.ucsc.cl 2 EMOingenieros Ltda.,Concepción 4090070,Chile;ymedina@emoingenieros.cl 3 Departament of Civil Engineering,Faculty of Engineering,Universidad Católica dela Santisima Concepción,Concepción 4090541,Chile;emunozo@ucsc.cl 4 Centro de Investigación en Biodiversidady Ambientes Sustentables CIBAS,Universidad Católicade laSantisima Concepción,Concepción 4090541,Chile 5 Departament of Ecology,Faculty of Sciences,Universidad Católica de la Santísima Concepción,Concepción 4090541,Chile;pandrade@ucsc.cl 6 Escola Universitaria Salesiana de Sarrià(EUSS School of Engineering,Barcelona),08017 Barcelona,Spain;jcruz@euss.cat 7 Faculty of Engineering,Don Bosco University,Calle a Plan del Pino Km 11/2,Soyapango 1874,EI Salvador;yakdiel.rodriguez@udb.edu.sv*Correspondence:guillermo.medina@ucsc.cl check for Abstract:The accuracy of determining arsenic and lead using the optical technique Slurry-TotalReflection X-ray Fluorescence (Slurry-TXRF)was significantly enhanced through the application of amachine 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 therelatively low resolution of TXRF could compromise the determination of lead.However,by applyinga Partial Least Squares(PLS)machine learning algorithm,we mitigated interference variations,resulting in improved selectivity and accuracy.Specifically,the average percentage error was reducedfrom 15.6%to 9.4%for arsenic(RMSEP improved from 5.6 mg kg-¹to 3.3mg kg-¹)and from 18.9%to 6.8%for lead(RMSEP improved from 12.3 mg kg-¹to 5.03mg kg-¹)compared to the previousunivariable 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 quantifyingthe presence of interferents.The evaluation of X-ray fluorescence emission signals for 26 differentsynthetic calibration mixtures confirmed these improvements,overcoming spectral interferences.Additionally,the application of these models enabled the quantification of arsenic and lead in soilsfrom a viticultural subregion of Chile,facilitating the estimation of ecological risk indices in a fastand reliable manner.The results indicate that the contamination level of these soils with arsenic andlead ranges from moderate to considerable. updates Citation:Medina-González,G.;Medina,Y.;Munoz,E.;Andrade,P.;Cruz,J.;Rodriguez-Gallo,Y.;Matus-Bello,A.Integration ofSlurry-Total Reflection X-rayFluorescence and Machine Learningfor Monitoring Arsenic and LeadContamination:Case Study in ItataValley Agricultural Soils,Chile. Processes 2024,12,1760. https://doi.org/10.3390/pr12081760 Academic Editors:Yang Chen,Qinzhong Feng and Liyuan Liu Received:10July 2024 Revised:16August 2024 Accepted:19 August 2024 Published:20 August 2024 Keywords:ecological indices;contamination;TXRF;machine learning;arsenic;lead Copyright:◎2024 by the authors.Licensee MDPI,Basel,Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution(CC BY)license(https://creativecommons.org/licenses/by/ ## 1.Introduction In environmental geochemistry,it is crucial to understand the concepts of geologicalbaselines and critical levels.This understanding is important for differentiating betweennaturally occurring substance levels and pollution caused by human activities [1].Severale

FAQs

Why does Slurry-TXRF face difficulty in simultaneously determining arsenic and lead?

Arsenic Kα at 10.55 keV overlaps with lead Lα at 10.54 keV due to the relatively low resolution of TXRF, creating spectral interference that can compromise lead determination.

How does the PLS machine learning approach improve quantification accuracy?

Partial Least Squares (PLS) modeling mitigates interference variations, increasing selectivity and accuracy and reducing prediction errors compared with a previous univariable model.

How were the proposed models validated before applying them to real soils?

X-ray fluorescence emission signals were evaluated using 26 different synthetic calibration mixtures to confirm improvements and overcome spectral interferences before quantifying arsenic and lead in Itata Valley soils.

Aug 5, 2026

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