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Exploring Heavy Metal and Metalloid Exposure in Children: A Pilot Biomonitoring Study near a Sugarcane Mill.

Mendoza-Cano OliverAgustin Lugo-RadilloMónica Riós-SilvaIrma Elizabeth Gonzalez-CurielJaime Alberto Bricio-BarriosArlette A Camacho-delaCruzMaría Fernanda Romo-GarcíaHerguin Benjamín Cuevas-ArellanoAna Luz Quintanilla-MontoyaRamón Solano-BarajasJuan Manuel Uribe-RamosLuis A García-SolórzanoÁngel Gabriel Hilerio-LópezAlma Alejandra Solano-MendozaRogelio Danis-RomeroEfren Murillo-Zamora
Published in: Toxics (2024)
Sugarcane production has been linked to the release of heavy metals and metalloids (HM/MTs) into the environment, raising concerns about potential health risks. This study aimed to assess the levels of 19 HM/MTs in children living near a sugarcane mill through a pilot biomonitoring investigation. We investigated sex-related differences in these element levels and their correlations. A cross-sectional study was conducted, analyzing data from 20 children in the latter part of 2023. Spearman correlation coefficients with 95% confidence intervals (CIs) were used to assess the relationships between urinary HM/MT levels. Detectable levels of 17 out of the 19 HM/MTs were found across the entire study sample, with arsenic and copper detectable in 95% of the children. Titanium exhibited higher levels in boys compared to girls (p = 0.017). We identified 56 statistically significant correlations, with 51 of them being positive, while the remaining coefficients indicated negative relationships. This study characterized HM/MT levels in school-aged children residing near a sugarcane mill through a pilot biomonitoring investigation. Further research employing larger sample sizes and longitudinal assessments would enhance our understanding of the dynamics and health impacts of HM/MT exposure in this vulnerable population.
Keyphrases
  • heavy metals
  • young adults
  • public health
  • healthcare
  • physical activity
  • study protocol
  • randomized controlled trial
  • mass spectrometry
  • health risk assessment
  • deep learning
  • big data
  • high speed