Prediction Models Based on Soil Characteristics for Evaluation of the Accumulation Capacity of Nine Metals by Forage Sorghum Grown in Agricultural Soils Treated with Varying Amounts of Poultry Manure.
Ebrahem M EidAhmed A HussainSaad A M AlamriSulaiman A AlrummanKamal H ShaltoutNasser SewelamSalma K ShaltoutAhmed F El-BebanyMohamed T AhmedDhafer A Al-BakreAhmed H AlfarhanYolanda PicóDamia BarceloPublished in: Bulletin of environmental contamination and toxicology (2023)
Predictive models were generated to evaluate the degree to which nine metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) were absorbed by the leaves, stems and roots of forage sorghum in growing media comprising soil admixed with poultry manure concentrations of 0, 10, 20, 30 and 40 g/kg. The data revealed that the greatest contents of the majority of the metals were evident in the roots rather than in the stems and leaves. A bioaccumulation factor (BAF) < 1 was calculated for Cr, Fe, Ni, Pb and Zn; BAF values for Co, Cu, Mn and Cd were 3.99, 2.33, 1.44 and 1.40, respectively, i.e., > 1. Translocation factor values were < 1 for all metals with the exception of Co, Cr and Ni, which displayed values of 1.20, 1.67 and 1.35 for the leaves, and 1.12, 1.23 and 1.24, respectively, for the stems. The soil pH had a negative association with metal tissues in plant parts. A positive relationship was observed with respect to plant metal contents, electrical conductivity and organic matter quantity. The designed models exhibited a high standard of data precision; any variations between the predicted and experimentally observed contents for the nine metals in the three plant tissue components were nonsignificant. Thus, it was concluded that the presented predictive models constitute a pragmatic tool to establish the safety from risk to human well-being with respect to growing forage sorghum when cultivating media fortified with poultry manure.
Keyphrases
- heavy metals
- health risk assessment
- health risk
- human health
- metal organic framework
- sewage sludge
- risk assessment
- plant growth
- antibiotic resistance genes
- aqueous solution
- organic matter
- anaerobic digestion
- climate change
- transition metal
- electronic health record
- big data
- antimicrobial resistance
- endothelial cells
- clinical trial
- single cell
- machine learning
- artificial intelligence
- room temperature
- ionic liquid
- visible light
- double blind