article · International Journal of Phytoremediation
Regression models were developed to predict heavy metal uptake in spinach cultivated in soil treated with sewage sludge, incorporating soil pH and organic matter content as predictive co-factors. The amended soil was slightly alkaline with high organic matter. In both roots and leaves, iron showed the highest median concentration, whereas cadmium was the lowest. Heavy metal concentrations followed the order of iron, manganese, zinc, copper, chromium, nickel, cobalt, lead, and cadmium. Bio-concentration factors from soil to roots remained below one, and translocation factors were below one for all metals except zinc. Heavy metal uptake correlated positively with soil organic matter and negatively with soil pH. The resulting regression models demonstrated high predictive efficiency, strong coefficients of determination, and low mean normalised average errors, offering a mathematical basis to estimate metal accumulation in the crop.
Using sewage sludge in agriculture can introduce contaminants into the food chain. Accurate predictive models enable growers and safety assessors to estimate how much heavy metal crops like spinach will absorb based on basic soil characteristics. This helps evaluate contamination risks to human health without relying solely on continuous and costly laboratory testing of harvested crops.
These models could assist agricultural consultants, environmental risk assessors, and wastewater treatment bodies that recycle sewage sludge to predict crop safety. The mathematical tools are at an early, analytical stage of development based on experimental soil data. Moving toward real-world software applications or farm-level screening tools would require testing across broader soil types, varied sludge compositions, and different agricultural management systems.
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The risk evaluation of polluted soil requires the application of precise models to predict the heavy metal uptake by plants so possible human risks can be identified. Therefore, the present work was conducted to develop regression models for predicting the concentrations of heavy metals in spinach plants from their concentration in the soil by using the organic matter content and soil pH as co-factors. The soil improved with sewage sludge was slightly alkaline and had a relatively high organic matter content. Similar to the soil analysis, Fe had the highest median concentration, while Cd had the lowest concentration in the roots and leaves. Heavy metals accumulated in the roots and leaves in the order Fe > Mn > Zn > Cu > Cr > Ni > Co > Pb > Cd. The bio-concentration factor of the investigated heavy metals, from soil to roots, did not exceed one. The spinach was recognized by a translocation factor <1.0 for all of the heavy metals except Zn. Plant heavy metal concentrations were positively correlated with the soil organic matter content and negatively correlated with soil pH. The leaf Cr, Fe and Zn and the root Cr, Fe, Pb and Zn concentrations were positively correlated with the respective soil heavy metals. In addition, a linear correlation was found between the bio-concentration factor of heavy metals and soil pH and organic matter content. Regression models with high model efficiency and coefficients of determination and low mean normalized average errors, which indicate the efficiency of the models, were produced for predicting the plant heavy metal contents by using the soil pH and organic matter content as co-factors.
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DOI: 10.1080/15226514.2018.1488815
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