Prediction models for evaluating the uptake of heavy metals by cucumbers (Cucumis sativus L.) grown in agricultural soils amended with sewage sludge

Abstract

Heavy metal (HM) concentrations in edible plants can develop many serious health risks to humans. The precise prediction of plant uptake of HMs is highly important. Thus, the present investigation was carried out to develop regression models for predicting the concentrations of HMs in cucumbers (Cucumis sativus L.) from their concentration in the soil and using the organic matter (OM) content and soil pH as co-factors. The results showed that cucumber roots had the highest significant concentrations of all HMs at P < 0.001, except Cd, Cu, and Zn were in fruits. The lowest concentrations of Cd, Co, Cr, Mn, Ni, and Pb were recorded in stems. HM concentrations in cucumbers were strongly correlated with soil HM, pH, and OM content. Soil pH and OM content had negative and positive correlations with all HMs in cucumber tissues, respectively. Regression analysis indicated that soil HM, pH, and OM contents were good predictors for HM concentrations in cucumbers. The regression models for root Co, Cr, Fe, and Zn were described by high model efficiency values that explain 48-58% variability. The best regression models for cucumber stem were for Cu, Mn, Ni, and Zn that are characterized by high R and model efficiency values. For cucumber fruits, R values were ranged from 54 to 82%, with best models for Cr, Pb, Cd, Cu, Ni, and Co in the fruit. We expect that these models will be beneficial for risk assessment studies on sewage sludge utilization in agriculture.

Uptake prediction of ten heavy metals by Corchorus olitorius L. cultivated in soil mixed with sewage sludge

“…Regression models are mathematical approaches that predict the concentrations of HMs in plants using some soil variables such as soil HMs, pH, and OM (Chaudri et al, 2007; Eid, Alrumman, Farahat, et al, 2018; Eid, Alrumman, Galal, et al, 2018, 2019; Eid, Shaltout, Abdallah, et al, 2019; Eid, Shaltout, Alamri, et al, 2019; Waegeneers et al, 2011). Data presented here suggested that the calculated prediction models are highly performed for all the estimated HMs in shoots and roots of C. olitorius , based on the parameters that evaluate the high performance of the models ( R 2 , ME, MNAE, and t ‐values).…”

“…Prediction models for the uptake of HMs by C. olitorius are comparable to those produced for some crop plants. For example, the determination coefficient ( R 2 ) for Cd in cucumber had a range of 49%–76% (Eid, Alrumman, Farahat, et al, 2018), in spinach had a range of 83%–88% (Eid, Alrumman, Galal, et al, 2018), in Eruca sativa had a range of 49%–81% (Eid, Shaltout, Abdallah, et al, 2019), in garden pea had a range of 25% – 87% (Eid, Shaltout, Alamri, et al, 2019), while it had a range of 69%–75% for C. olitorius in the current study. In this regard, Bešter et al (2013) found that R 2 values of the Cd models in some selected vegetables ranged from 41% for tomato to 90% for endive.…”

“…They are useful mathematical tool for predicting the levels of the HMs in crop plants depending on certain soil properties including soil HMs, pH, and OM as independent variables (Waegeneers, Ruttens, & Temmerman, 2011). Regression models are available for many crop plants (e.g., Bešter, Lobnik, Eržen, Kastelec, & Zupan, 2013; Chaudri et al, 2007; Eid, Alrumman, Farahat, & El‐Bebany, 2018; Eid, Alrumman, Galal, & El‐Bebany, 2018; Eid, Alrumman, Galal, & El‐Bebany, 2019; Eid, Shaltout, Abdallah, et al, 2019; Eid, Shaltout, Alamri, et al, 2019; Novotná, Mikeš, & Komprdová, 2015; Ramadan & Al‐Ashkar, 2007; Waegeneers et al, 2011; Zeng et al, 2011). For the safe crop production, it is useful to create multiple models of regression to investigate the correlation between HMs in plants and in soils supporting them to evaluate the dangerous levels of these HMs in soil.…”

Copper Toxicity and Prediction Models of Copper Content in Leafy Vegetables

“…Based on the soil pH (pH 5.84) in our study (Table 1), the EDTA solution is a suitable chelating reagent for model building. Although these extraction methods developed over more than two decades and the prediction models are usually not universal [24,[39][40][41], current studies still utilize these approaches to develop prediction models for toxic metals in maize, water lettuce, cucumber and pakchoi [42][43][44][45][46].…”

“…Nutrient solutions and hydroponic cultures with the addition of toxic metals are general approaches to study phytotoxicity in plants [4,5,7,8,43]. The mixtures of sewage sludge and cultivated field soil are alternative approaches to obtain various levels of metal contamination [45], or the sludge samples can be spiked with metal solutions to mimic the wastewater treatment [34]. In real cases of toxic metal pollution, high levels of copper in soil are often elevated with other metals.…”