Distribution and Influencing Factors of Metals in Surface Soil from the Yellow River Delta, China

Yu, Yang; Ling, Yue; Li, Yunzhao; Lv, Zhenbo; Du, Zhao-Hong; Guan, Bo; Wang, Zhikang; Wang, Xuehong; Yang, Jing; Yu, Junbao

doi:10.3390/land11040523

Cited by 5 publications

(5 citation statements)

References 57 publications

(88 reference statements)

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“…The Yellow River carries a large amount of sediment downstream from its source, and some of the sediments form piles along the banks. High concentrations on both sides of the river and around the lake may be due to compound industrial are similar to the spatial distribution maps of the metals obtained by Yu et al 41 . using the kriging interpolation method.…”

Section: Spatial Distribution Of Soil Heavy Metal Contentsupporting

confidence: 88%

Remote sensing inversion on heavy metal content in salinized soil of Yellow River Delta based on Random Forest Regression—a case study of Gudao Town

Fu,

Li,

Zhang

et al. 2024

Sci Rep

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To explore the potential of using the mineral alteration information extracted by remote sensing technology to indirectly estimate the heavy metal content of salinized soil, 23 sampling points were uniformly set up in the town of Gudao in the Yellow River Delta as the research area in 2022. The concentrations of seven heavy metals, Cr, Cu, Pb, Zn, As, Mn and Ni, at the sampling points were determined in laboratory tests. Spectral derivative indices, topographic factors, and mineral alteration information (iron staining, hydroxyl, and carbonate ions) were extracted and screened as modeling factors using Sentinel 2 imagery. An inverse model of heavy metal content was constructed using the random forest algorithm, and the model accuracy was evaluated using the cross-validation method. The results of the study show that: (1) Hydroxyl and carbonate ion alteration can be effectively used for the inversion of soil As and Ni content in this study area. Iron-stained alteration can be used as a modeling factor in the inversion of Cr, Cu, Pb, Zn, and Mn concentrations. (2) The inclusion of alteration information improves the accuracy of heavy metal content inversion. The Cu concentration was verified to be the best predictor, with an RMSE of 3.309, MAPE of 11.072%, and R2 of 0.904, followed by As, Ni, and Zn; the predictive value of Mn, Cr and Pb was average. (3) Based on the results of concentration inversion, the high concentration areas of As, Ni, and Mn are primarily distributed on both sides of the river and around lakes and ponds. The high-concentration areas of Zn were mainly distributed in the farmland areas on both sides of the river. Areas with high concentrations of Cu were mainly distributed in the eastern oil extraction area, both sides of the rivers, and around lakes.

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Section: Spatial Distribution Of Soil Heavy Metal Contentsupporting

confidence: 88%

Remote sensing inversion on heavy metal content in salinized soil of Yellow River Delta based on Random Forest Regression—a case study of Gudao Town

Fu,

Li,

Zhang

et al. 2024

Sci Rep

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“…A total of 516 soil samples are collected, and the latitude and longitude are located by GPS. In addition, we use the Nemerow index as the comprehensive pollution index, and its calculation method is shown in Equations ( 1) and (2) [37,38]. P is the Nemerow index.…”

Section: Study Areamentioning

confidence: 99%

Estimation of Heavy Metal Content in Soil Based on Machine Learning Models

Shi

Hou

et al. 2022

Land

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Heavy metal pollution in soil is threatening the ecological environment and human health. However, field measurement of heavy metal content in soil entails significant costs. Therefore, this study explores the estimation method of soil heavy metals based on remote sensing images and machine learning. To accurately estimate the heavy metal content, we propose a hybrid artificial intelligence model integrating least absolute shrinkage and selection operator (LASSO), genetic algorithm (GA) and error back propagation neural network (BPNN), namely the LASSO-GA-BPNN model. Meanwhile, this study compares the accuracy of the LASSO-GA-BPNN model, SVR (Support Vector Regression), RF (Random Forest) and spatial interpolation methods with Huanghua city as an example. Furthermore, the study uses the LASSO-GA-BPNN model to estimate the content of eight heavy metals (including Ni, Pb, Cr, Hg, Cd, As, Cu, and Zn) in Huanghua and visualize the results in high resolution. In addition, we calculate the Nemerow index based on the estimation results. The results denote that, the simultaneous optimization of BPNN by LASSO and GA can greatly improve the estimation accuracy and generalization ability. The LASSO-GA-BPNN model is a more accurate model for the estimate heavy metal content in soil compared to SVR, RF and spatial interpolation. Moreover, the comprehensive pollution level in Huanghua is mainly low pollution. The overall spatial distribution law of each heavy metal content is very similar, and the local spatial distribution of each heavy metal is different. The results are of great significance for soil pollution estimation.

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“…Thus, high levels of any given metal in an accumulating plant should be seen in relation to its levels in other plants grown in the same area. In the uptake of elements by plants, plant-specific differences are common [10][11][12][26][27][28][29][30][31][32][33][34][35]. In some of the heavily polluted areas, the pH value of the soil is in a constant state of flux.…”

Section: Introductionmentioning

confidence: 99%

Studies on the Accumulation, Translocation, and Enrichment Capacity of Soils and the Plant Species Phragmites Australis (Common Reed) with Heavy Metals

et al. 2023

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In the present study, heavy metal concentrations (Cu, Zn, Pb, As) from former industrial areas were analyzed in soil and the plant species Phragmites Australis (common reed), a plant species found in shallow river banks. The objectives of the study were to determine the accumulation, translocation, and enrichment capacity of soils and plants (root, stem, leaf, and flower) with heavy metals, and to evaluate the potential of the plant species Phragmites Australis (common reed) in the phytoremediation of heavy-metal-polluted soils. The sediments and plants investigated were particularly rich in heavy metals such as Cu, Zn, Pb, and As, and Pb and Zn concentrations were determined to be of phytotoxic level. The highest transfer coefficient of heavy metals from the soil to the root of the plant species Phragmites Australis (common reed) was for the metals Cu and Zn. The transfer of heavy metals from the root of the plant species Phragmites Australis (common reed) to the aerial part of the plant was lowest for Cu. A very low enrichment factor value was recorded for the metal As. The mobility of heavy metals was generally higher from the sediment to the plant roots, with the order of the average transfer coefficient values being Cu > Zn > Pb > As for all three areas analyzed. The translocation of heavy metals from the root to the upper aerial part of the plant was in the form of As > Pb > Zn > Cu in the plant species Phragmites australis (common reed). According to the average values obtained for the three coefficients, the accumulation, translocation, and enrichment capacity of heavy metals in soil and the plant species Phragmites Australis (common reed), it can be stated that the plant species Phragmites Australis (common reed) is very suitable for use in the process of soil remediation by phytoremediation.

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Distribution and Influencing Factors of Metals in Surface Soil from the Yellow River Delta, China

Cited by 5 publications

References 57 publications

Remote sensing inversion on heavy metal content in salinized soil of Yellow River Delta based on Random Forest Regression—a case study of Gudao Town

Remote sensing inversion on heavy metal content in salinized soil of Yellow River Delta based on Random Forest Regression—a case study of Gudao Town

Estimation of Heavy Metal Content in Soil Based on Machine Learning Models

Studies on the Accumulation, Translocation, and Enrichment Capacity of Soils and the Plant Species Phragmites Australis (Common Reed) with Heavy Metals

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