Oxidative damage associated with exposure to heavy metals present in topsoils in central Chile

Madrid, Eva; Pino, José A.; Muñoz, Sergio; Cardemil, Felipe; Martinez, Felipe; Berasaluce, Maite; San Martín, Sebastián; Reyes, Juan; González-Miranda, Isabel

doi:10.1007/s10653-023-01771-w

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…Therefore, it is crucial to continue developing and improving analytical methods for trace element analysis in soil samples, as well as exploring the use of innovative techniques and approaches such as Machine Learning and X-ray fluorescence to overcome challenges and improve accuracy in quantification (Yang et al 2011) Several studies have examined the levels of heavy metals or potentially toxic elements in areas primarily used for agriculture and mining (Carvajal et al 2023;Corradini et al 2017;Madrid et al 2023;Medina-González et al 2023;Rubinos et al 2021;Tume et al 2019Tume et al , 2023. However, there is a lack of literature on the analysis of vineyard soils to evaluate potential contamination by arsenic and lead.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Arsenic and Lead Contamination in Itata Valley Agricultural Soils, Chile: Integration of Slurry-TXRF and Machine Learning Technique for Efficient Monitoring

Medina-González,

Medina,

Muñoz

et al. 2024

Preprint

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A machine learning method was applied to improve the accuracy of the determination of Arsenic and Lead by Slurry - Total Reflection X-ray fluorescence (Slurry-TXRF) with the idea of being applied to the ecological assessment of agricultural soils. Due to TXRF's relatively low resolution, a particular and well-known overlapping of arsenic signal Kα at 10.55 keV with Lαsignal at 10.54 keV of the lead can compromise its determination. Applying a multivariate calibration method based on a machine learning algorithm, for example, Partial Least Squares, could reduce variations due to interference and, consequently, improve the selectivity and accuracy in arsenic and lead determination. In this work the X-Ray fluorescence emission signals was evaluated for a set of 26 different synthetic calibration mixtures and a significant accuracy improvement for arsenic and lead determination was observed, overcoming the problems associated with spectral interferences. Furthermore, with these models, arsenic and lead were quantified from soils of a viticultural subregion of Chile, allowing the estimation of ecological indices in a fast and reliable way. The results report that the level of contamination of these soils concerning arsenic and lead is moderate to considerable.

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Section: Introductionmentioning

confidence: 99%

Evaluating Arsenic and Lead Contamination in Itata Valley Agricultural Soils, Chile: Integration of Slurry-TXRF and Machine Learning Technique for Efficient Monitoring

Medina-González,

Medina,

Muñoz

et al. 2024

Preprint

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“…In the latter study, arsenic ingestion through accidental ingestion of soil and house dust in localities close to the VIC was identified to pose a carcinogenic risk on infants (1-5 years), which contrasts with other contamination cases (e.g., Cao et al, 2022). In addition to environmental assessments, the studies by Madrid et al (2022Madrid et al ( , 2023 concluded that environmental pollution in Quintero and Puchuncaví localities has induced epigenetic changes in the genome and oxidative stress on DNA of its inhabitants, with arsenic being the primary contaminant.…”

mentioning

confidence: 98%

From risk assessment to land planning. The case of a trace element‐contaminated area in Chile

Mondaca,

Berasaluce,

Larraguibel‐González

et al. 2024

Land Degrad Dev

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While abundant scientific literature focuses on diagnosing contaminated areas, solutions with a scientific base are far from balanced. This is the case of the Quintero‐Puchuncaví Bay, a widely known contaminated area in the central coast of Chile. Here, arsenic in soils surrounding the industrial complex has been reported as a threat to human health. However, land planning based on As contamination becomes a challenge since the whole area is identified as contaminated. Such a lack of land‐planning constrains the occupation and remediation of contaminated soil leading to a brownfield‐like landscape. To face this challenge, we proposed using a geospatial decision support system (S‐DSS) to integrate the contamination assessment, health and ecosystem risks, and potential land uses. When characterizing soil arsenic concentration thresholds for different land uses in a S‐DSS, we could categorize the land in suitable, caution, and unsuitable areas (based on human health risks). This way, we unravel areas with potential use in the current condition while also discerning caution and unsuitable categories, that can undergo extensive and intensive remediation techniques. Similarly, we took a conservational approach to estimate emerging risk from the industrial complex associated to explosions. Altogether, it highlights the potential of S‐DSS to integrate different geographic information. We finally feature two APPs regarding current land‐use suitability and a modeled one considering future arsenic emissions.

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Elementomics of 32 elements in cord serum depicts the risk of orofacial clefts: A case-control study in Shanxi, China

Wei,

Fu,

Guo

et al. 2024

Environmental Pollution

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Oxidative damage associated with exposure to heavy metals present in topsoils in central Chile

Cited by 5 publications

References 39 publications

Evaluating Arsenic and Lead Contamination in Itata Valley Agricultural Soils, Chile: Integration of Slurry-TXRF and Machine Learning Technique for Efficient Monitoring

Evaluating Arsenic and Lead Contamination in Itata Valley Agricultural Soils, Chile: Integration of Slurry-TXRF and Machine Learning Technique for Efficient Monitoring

From risk assessment to land planning. The case of a trace element‐contaminated area in Chile

Elementomics of 32 elements in cord serum depicts the risk of orofacial clefts: A case-control study in Shanxi, China

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