Potato absorption and phytoavailability of Cd, Ni, Cu, Zn and Pb in sierozem soils amended with municipal sludge compost

Abstract

Effects of sludge utilization on the mobility and phytoavailability of heavy metals in soil-plant systems have attracted broad attention in recent years. In this study, we analyzed the effects of municipal sludge compost (MSC) on the solubility and plant uptake of Cd, Ni, Cu, Zn and Pb in a soil-potato system to explore the mobility, potato plant uptake and enrichment of these five heavy metals in sierozem soils amended with MSC through a potato cultivation trial in Lanzhou University of China in 2014. Ridge regression analysis was conducted to investigate the phytoavailability of heavy metals in amended soils. Furthermore, CaCl 2 , CH 3 COONH 4 , CH 3 COOH, diethylene triamine pentacetic acid (DTPA) and ethylene diamine tetraacetic acid (EDTA) were used to extract the labile fraction of heavy metals from the amended soils. The results show that the MSC could not only improve the fertility but also increase the dissolved organic carbon (DOC) content of sierozem soils. The total concentrations and labile fraction proportions of heavy metals increase with increasing MSC percentage in sierozem soils. In amended soils, Cd has the highest solubility and mobility while Ni has the lowest solubility and mobility among the five heavy metals. The MSC increases the concentrations of heavy metals in the root, stem, peel and tuber of the potato plant, with the concentrations being much higher in the stem and root than in the peel and tuber. Among the five heavy metals, the bioconcentration factor value of Cd is the highest, while that of Ni is the lowest. The complexing agent (DTPA and EDTA) extractable fractions of heavy metals are the highest in terms of phytoavailability. Soil properties (including organic matter, pH and DOC) have important impacts on the phytoavailability of heavy metals. Our results suggest that in soil-potato systems, although the MSC may improve soil fertility, it can also increase the risk of soils exposed to heavy metals.

Evaluating potential of municipal sewage sludge for agricultural use

“…As a result of application of sewage sludge, Cd content in potato tubers increased 4-fold, and an increase in the content of the remaining metals varied from 21% (Zn) to 96% (Pb). Studies by many authors evidenced the contribution of the application of sewage sludge to an increase in the content of heavy metals in plants, often in amounts exceeding acceptable values (Liu et al, 2018;Yang et al, 2018;Antonious, 2020;Buta et al, 2021;Eid et al, 2021;Kołodziej et al, 2023). Manure, in turn, is considered as a fertiliser that poses no threat for the environment in terms of content of heavy metals.…”

Effect of fertilization on the accumulation and health risk for heavy metals in native Andean potatoes in the highlands of Perú

“…Bioconcentration factor (BCF) or Bio‑accumulation factor is an indicator that evaluates the ability of vegetables to absorb heavy metals in plant tissues [22] , [23] , [24] . Several studies determined the BCF in vegetables such as Khan et al [25] ; Liu et al [26] ; Jalali et al [27] who reported BCF values greater than 1 for Cd, Cu, Zn, Ni and Pb in Solanum tuberosum ; Ju et al [28] , Jalali and Meyari [29] reported high values in edible leafy and root vegetables; Eliku and Leta [30] , Jolly et al [31] in leafy and non-leafy vegetables reported low BCF values. BCF values greater than 1 indicate that plants are enriched with heavy metals, and it is a risk to human health because these metals can enter the food chain based on their mobility and bioavailability in the soil [32] .…”

Regional Inversion of Soil Heavy Metal Cr Content in Agricultural Land Using Zhuhai-1 Hyperspectral Images

“…In light of these challenges, the second approach involves the integration of GMLS data. Many scholars have explored different spectral processing methods encompassing spectral pre-processing techniques such as Savitzky-Golay (SG) smoothing [15], SNV [16], multiplicative scatter correction (MSC) [17], first-order differential reflectance (FD) [18], and second-order differential reflectance (SD) [19]. Additionally, spectral selection methods such as principal component analysis (PCA) [20], competitive adaptive reweighted sampling (CARS) [21], UVE [22], and successive projections algorithm (SPA) [23] have been employed.…”