Pollution and contamination level of Cu, Cd, and Hg heavy metals in soil and food crop

Ramlan, _; Basir-Cyio, Muhammad; Napitupulu, Mery; Inoue, Takanobu; Anshary, Alam; Mahfudz, .; Isrun,; Rusydi, M.; Golar, Golar; Sulbadana,; Bakri, Ridla

doi:10.1007/s13762-021-03345-8

Cited by 19 publications

(6 citation statements)

References 85 publications

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“…5 ). Different crop species have different accumulation capacities for the same metal 42 . Overall, the average BAF values of Cu (0.22), Zn (0.37), and Mn (0.14) in wheat grains were significantly higher than those in rape and rice grains.…”

Section: Resultsmentioning

confidence: 99%

Effects of soil properties on heavy metal bioavailability and accumulation in crop grains under different farmland use patterns

Shen

Dou³

et al. 2022

Sci Rep

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Mining activities have increased the accumulation of heavy metals in farmland soil and in food crops. To identify the key soil properties influencing heavy metal bioavailability and accumulation in food crops, 81 crop samples and 81 corresponding agricultural soil samples were collected from rape, wheat, and paddy fields. Heavy metal (copper (Cu), zinc (Zn), lead (Pb), cadmium (Cd), iron (Fe), and manganese (Mn)) concentrations in soils and rape, wheat, rice grains were determined using inductively coupled plasma atomic emission spectroscopy, and soil physicochemical properties (pH, organic matter, total nitrogen, total phosphorus, available phosphorus, and available potassium (AK)) were analyzed. Soil extractable metals were extracted using various single extractants (DTPA, EDTA, NH4OAc, NH4NO3, and HCl). The average concentrations of Cu, Zn, Pb, Cd, and Mn in the soil samples all exceeded the local geochemical background value (background values of Cu, Zn, Pb, Cd, and Mn are 43.0, 81.0, 28.5, 0.196, and 616 mg/kg, respectively), and Cd over-standard rate was the highest, at 98%. Furthermore, soil total Cd concentrations (0.1–24.8 mg/kg) of more than 86% of the samples exceeded the soil pollution risk screening value (GB 15618-2018). The sources of Cu, Zn, Pb, Cd, and Mn in soils were mainly associated with mining activities. The key factors influencing heavy metal bioavailability were associated with the types of extractants (complexing agents or neutral salt extractants) and the metals. Cd and Pb concentrations in most wheat and rice grain samples exceeded the maximum allowable Cd and Pb levels in food, respectively, and Cd concentrations in approximately 10% of the rice grain samples exceeded 1.0 mg/kg. Furthermore, rice and wheat grains exhibited higher Cd accumulation capacity than rape grains, and despite the high soil Cd concentrations in the rape fields, the rape grains were safe for consumption. High soil pH and AK restricted Cd and Cu accumulation in wheat grains, respectively. Soil properties seemed to influence heavy metal accumulation in rice grains the most.

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Section: Resultsmentioning

confidence: 99%

Effects of soil properties on heavy metal bioavailability and accumulation in crop grains under different farmland use patterns

Shen

Dou³

et al. 2022

Sci Rep

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“…Figure 1 shows the operation process of deep Q network. ere is the problem of overestimation existing in the combination of deep Q network with neural network and reinforcement learning, which leads to the estimation error of prediction output value of the model and cannot truly reflect the actual value [11]. In addition, the convergence speed of deep Q network is slow, and the stability after model convergence is poor [12].…”

Section: Deep Q Networkmentioning

confidence: 99%

Prediction of Soil Heavy Metal Content Based on Deep Reinforcement Learning

Zhao

Wei

Wen³

2022

Scientific Programming

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Since the prediction accuracy of heavy metal content in soil by common spatial prediction algorithms is not ideal, a prediction model based on the improved deep Q network is proposed. The state value reuse is used to accelerate the learning speed of training samples for agents in deep Q network, and the convergence speed of model is improved. At the same time, adaptive fuzzy membership factor is introduced to change the sensitivity of agent to environmental feedback value in different training periods and improve the stability of the model after convergence. Finally, an adaptive inverse distance interpolation method is adopted to predict observed values of interpolation points, which improves the prediction accuracy of the model. The simulation results show that, compared with random forest regression model (RFR) and inverse distance weighted prediction model (IDW), the prediction accuracy of soil heavy metal content of proposed model is higher by 13.03% and 7.47%, respectively.

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“…The expansion of urban areas and their fringes has been accompanied by intensive dissemination and accumulation of metal elements in soil and water bodies (7). This has resulted in widespread concerns regarding water quality, soil contamination, and the safety of agricultural products supplied to metropolitan populations (8)(9)(10).…”

Section: Introductionmentioning

confidence: 99%

Heavy metals and trace elements contamination risks in peri-urban agricultural soils in Nairobi city catchment, Kenya

et al. 2023

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IntroductionWith urbanization and industrialization in the developing world, urban and peri-urban agriculture is increasingly contributing to urban food systems, employment, and income generation opportunities. However, urbanization and industrialization may release harmful pollutants, including heavy metals and trace elements into agricultural soils, posing ecological, environmental and public health concerns. This paper assessed the potential risks of soil contamination with heavy metals and trace elements in peri-urban farmlands in Nairobi city catchment in Kenya. MethodsA total of 60 soil samples were collected from ten vegetable farming zones (S1-S10) and processed following standard protocols. The concentrations of As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, V and Zn were analyzed in the samples using inductively coupled plasma-mass spectrometry (ICP-MS). Results and discussionResults revealed that the soil samples have elevated concentrations of As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, V and Zn, with some elements including As (1.7%), Cd (13.3%), Mn (80%), Pb (1.7%) and Zn (11.7%) exceeding the permissible thresholds for agricultural soils. The spatial distribution of the elements exhibited three similar distribution patterns with slight variations between the hotspot sites of the different elements: (i) Co and Mn, (ii) Cu and Cr and (iii) As, Cd, Fe, Hg, Pb and Zn, while Ni and V exhibited singular spatial distributions compared to other elements. Elements such as As, Cd, Co, Fe, Hg, Mn, Pb and Zn appeared to be of anthropogenic enrichment, while Cr, Cu, Ni and V appeared to originate mainly from lithogenic and natural sources. The elements As, Cd, Hg and Pb exhibited high ecological risks, with Cd contributing about 46-66% of the overall ecological risk. The sampling sites S1, S5, S7, S9 and S10 depicted the highest ecological risks of 145, 103, 146, 121 and 146, respectively. The findings call for proper zoning of suitable agricultural areas and sound waste management protocols in urban and peri-urban landscapes. Further, remediation of contaminated soils and farmers’ sensitization are recommended for ecological and public health risk alleviation.

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Pollution and contamination level of Cu, Cd, and Hg heavy metals in soil and food crop

Cited by 19 publications

References 85 publications

Effects of soil properties on heavy metal bioavailability and accumulation in crop grains under different farmland use patterns

Effects of soil properties on heavy metal bioavailability and accumulation in crop grains under different farmland use patterns

Prediction of Soil Heavy Metal Content Based on Deep Reinforcement Learning

Heavy metals and trace elements contamination risks in peri-urban agricultural soils in Nairobi city catchment, Kenya

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