Determination of Heavy Metal Levels and Health Risk Assessment of Raw Cow Milk in Guelma Region, Algeria.
Ali BoudebbouzSofiane BoudaliaAissam BousbiaYassine GuerouiMeriem Imen BoussadiaMohamed Lyamine ChelaghmiaRabah ZebsaAbed Mohamed AffouneGeorge K SymeonPublished in: Biological trace element research (2022)
During the recent decades, adverse effects of unexpected contaminants, such as heavy metals on raw cow milk quality, have threatened human health. The objective of this study was to determine heavy metal levels in raw milk collected from autochthonous bovine breeds in the eastern region of Algeria. Eighty-eight pooled milk samples were analyzed using atomic absorption spectrometry for Pb, Cd, Cr, Cu, Ni, Fe, and Zn, and dietary risks were estimated for infants, children, and adults with minimum, average, and maximum milk consumption scenarios. Results revealed that Pb (0.94 ± 0.49 mg/kg), Cd (0.03 ± 0.01 mg/kg), and Cu (0.14 ± 0.08 mg/kg) levels in all analyzed samples were higher than their corresponding maximum residue levels (MRLs). The task hazard quotient (THQ) values suggest potential risk for infants in the three scenarios from Pb, Cd, and Cr; for children in the three scenarios from Pb and in the high scenario from Cr; and for adults in the medium and high scenarios from Pb. The hazard index (HI) values were higher than 1, and the contributions of each metal to the overall HI followed a descending order of Pb, Cr, Cd, Ni, Zn, Cu, and Fe with values of 68.19%, 15.39%, 6.91%, 4.94%, 3.42%, 0.88%, and 0.28%, respectively. Our results indicated that there may be a potential risk of heavy metals, especially Pb, for infants through raw cow milk consumption. Moreover, data actualization and continuous monitoring are necessary and recommended to evaluate heavy metal effects in future studies.
Keyphrases
- heavy metals
- risk assessment
- human health
- health risk assessment
- climate change
- health risk
- metal organic framework
- sewage sludge
- aqueous solution
- young adults
- nk cells
- clinical trial
- emergency department
- electronic health record
- high resolution
- machine learning
- quality improvement
- current status
- liquid chromatography
- single cell