ArticleEnvironmental monitoring and assessment2018
Prediction models for evaluating the uptake of heavy metals by cucumbers (Cucumis sativus L.) grown in agricultural soils amended with sewage sludge.
Article in Environmental monitoring and assessment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 39 citations in OpenAlex.
- Article
- Pilot groundwater radon mapping and the assessment of health risk from heavy metals in drinking water of southwest, Nigeria.Heliyon · 2022Article
- Prediction models for monitoring selenium and its associated heavy-metal accumulation in four kinds of agro-foods in seleniferous area.Frontiers in nutrition · 2022Article
- Modeling of mineral elements uptake and localization in cabbage inflorescence (Brassica oleracea var. capitata) grown on sugar mill pressmud-amended soils.Environmental monitoring and assessment · 2021Article
- Evaluation of uptake of eight metals by Sorghum bicolor grown in arable soil combined with sewage sludge based on prediction models.Environmental monitoring and assessment · 2021Article
- Multivariate linear regression models for predicting metal content and sources in leafy vegetables and human health risk assessment in metal mining areas of Southern Jharkhand, India.Environmental science and pollution research international · 2021Article
- Bioaccumulation and risk assessment of heavy metals in soil-crop systems in Liujiang karst area, Southwestern China.Environmental science and pollution research international · 2021Article
- Heavy metal uptake by water lettuce (Pistia stratiotes L.) from paper mill effluent (PME): experimental and prediction modeling studies.Environmental science and pollution research international · 2019Article
- Regression models for monitoring trace metal accumulations by Faba sativa Bernh. plants grown in soils amended with different rates of sewage sludge.Scientific reports · 2019Article
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
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
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