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Evaluation of health risk of glyphosate pesticide intake via surface and subsurface water consumption: A deterministic and probabilistic approach()

As the usage of pesticides for both agricultural and non-agricultural uses increases, it is more important than ever to employ probabilistic methods rather than deterministic ones to calculate the danger to human health. The current work demonstrates the application of deterministic and probabilisti...

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Detalles Bibliográficos
Autores principales: Pakzad, Parichehr, Taheri, Ensiyeh, Amin, Mohammad Mehdi, Fatehizadeh, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502399/
https://www.ncbi.nlm.nih.gov/pubmed/37719920
http://dx.doi.org/10.1016/j.mex.2023.102369
Descripción
Sumario:As the usage of pesticides for both agricultural and non-agricultural uses increases, it is more important than ever to employ probabilistic methods rather than deterministic ones to calculate the danger to human health. The current work demonstrates the application of deterministic and probabilistic approaches to assess the human health risk related to glyphosate during the consumption of surface and groundwater by different population groups. To that aim, the concentration of glyphosate pesticide in the surface and groundwater was measured and human health risk for three population groups including children, teens, and adults was evaluated. Overall, the probabilistic approach via Monte Carlo simulation showed a valid result for the estimation of human health risk and determination of dominant input parameters. • The health risk of glyphosate exposure during water consumption for various population groups were evaluated using deterministic and probabilistic methods. • The modeling is performed by Crystal Ball (11.1.2.4) software, as open access software, and requires a limited number of inputs. • The probabilistic method could reliably assess the risks of glyphosate by considering the variability and uncertainty in input variables.