“…Soil contamination arouse from potentially toxic element (PTE) such as chromium (Cr), lead (Pb), cadmium (Cd), mercury (Hg), arsenic (As), copper (Cu), zinc (Zn), nickel (Ni) in agricultural land has raised serious concerns worldwide [1][2][3][4][5][6][7], particularly in Land 2021, 10, 558 2 of 17 China, which has experienced rapid industrialization and urbanization in the past four decades [8][9][10][11][12][13]. Apart from natural weathering from parent soil materials, anthropogenic activities (including industrial waste production, sewage irrigation, agricultural inputs, mining, and smelting) are a major source of PTE accumulation in farmland soils [14][15][16]. Several previous studies have reported PTE pollution in farmland soils from different areas of China, particularly in regions like the Yangtze River Delta [17][18][19], Pearl River Delta [20], Beijing-Tianjin-Hebei [21,22], and Northeast China [23,24].…”
Potentially toxic element (PTE) pollution in farmland soils and crops is a serious cause of concern in China. To analyze the bioaccumulation characteristics of chromium (Cr), zinc (Zn), copper (Cu), and nickel (Ni) in soil-rice systems, 911 pairs of top soil (0–0.2 m) and rice samples were collected from an industrial city in Southeast China. Multiple linear regression (MLR), support vector machines (SVM), random forest (RF), and Cubist were employed to construct models to predict the bioaccumulation coefficient (BAC) of PTEs in soil–rice systems and determine the potential dominators for PTE transfer from soil to rice grains. Cr, Cu, Zn, and Ni contents in soil of the survey region were higher than corresponding background contents in China. The mean Ni content of rice grains exceeded the national permissible limit, whereas the concentrations of Cr, Cu, and Zn were lower than their thresholds. The BAC of PTEs kept the sequence of Zn (0.219) > Cu (0.093) > Ni (0.032) > Cr (0.018). Of the four algorithms employed to estimate the bioaccumulation of Cr, Cu, Zn, and Ni in soil–rice systems, RF exhibited the best performance, with coefficient of determination (R2) ranging from 0.58 to 0.79 and root mean square error (RMSE) ranging from 0.03 to 0.04 mg kg−1. Total PTE concentration in soil, cation exchange capacity (CEC), and annual average precipitation were identified as top 3 dominators influencing PTE transfer from soil to rice grains. This study confirmed the feasibility and advantages of machine learning methods especially RF for estimating PTE accumulation in soil–rice systems, when compared with traditional statistical methods, such as MLR. Our study provides new tools for analyzing the transfer of PTEs from soil to rice, and can help decision-makers in developing more efficient policies for regulating PTE pollution in soil and crops, and reducing the corresponding health risks.
“…Soil contamination arouse from potentially toxic element (PTE) such as chromium (Cr), lead (Pb), cadmium (Cd), mercury (Hg), arsenic (As), copper (Cu), zinc (Zn), nickel (Ni) in agricultural land has raised serious concerns worldwide [1][2][3][4][5][6][7], particularly in Land 2021, 10, 558 2 of 17 China, which has experienced rapid industrialization and urbanization in the past four decades [8][9][10][11][12][13]. Apart from natural weathering from parent soil materials, anthropogenic activities (including industrial waste production, sewage irrigation, agricultural inputs, mining, and smelting) are a major source of PTE accumulation in farmland soils [14][15][16]. Several previous studies have reported PTE pollution in farmland soils from different areas of China, particularly in regions like the Yangtze River Delta [17][18][19], Pearl River Delta [20], Beijing-Tianjin-Hebei [21,22], and Northeast China [23,24].…”
Potentially toxic element (PTE) pollution in farmland soils and crops is a serious cause of concern in China. To analyze the bioaccumulation characteristics of chromium (Cr), zinc (Zn), copper (Cu), and nickel (Ni) in soil-rice systems, 911 pairs of top soil (0–0.2 m) and rice samples were collected from an industrial city in Southeast China. Multiple linear regression (MLR), support vector machines (SVM), random forest (RF), and Cubist were employed to construct models to predict the bioaccumulation coefficient (BAC) of PTEs in soil–rice systems and determine the potential dominators for PTE transfer from soil to rice grains. Cr, Cu, Zn, and Ni contents in soil of the survey region were higher than corresponding background contents in China. The mean Ni content of rice grains exceeded the national permissible limit, whereas the concentrations of Cr, Cu, and Zn were lower than their thresholds. The BAC of PTEs kept the sequence of Zn (0.219) > Cu (0.093) > Ni (0.032) > Cr (0.018). Of the four algorithms employed to estimate the bioaccumulation of Cr, Cu, Zn, and Ni in soil–rice systems, RF exhibited the best performance, with coefficient of determination (R2) ranging from 0.58 to 0.79 and root mean square error (RMSE) ranging from 0.03 to 0.04 mg kg−1. Total PTE concentration in soil, cation exchange capacity (CEC), and annual average precipitation were identified as top 3 dominators influencing PTE transfer from soil to rice grains. This study confirmed the feasibility and advantages of machine learning methods especially RF for estimating PTE accumulation in soil–rice systems, when compared with traditional statistical methods, such as MLR. Our study provides new tools for analyzing the transfer of PTEs from soil to rice, and can help decision-makers in developing more efficient policies for regulating PTE pollution in soil and crops, and reducing the corresponding health risks.
“…It is well-established that smelting, fossil fuel mining and combustion are the most important anthropogenic sources of Hg in China. Fei et al reported that more than 90% of Hg in surface soil samples collected from Hangzhou City was discharged from coal mining and smelting industries [ 41 ]. In the western of the study area, five coal mines had operated for over twenty years, and over 15 million tons of coal and 0.75 million tons of coal gangue were produced annually.…”
To accurately assess the potential ecological risk posed by heavy metals in lime concretion black soil and quantify the risk contributions from different sources, an investigation of 217 surface soil samples and 56 subsoil samples was performed in the southern part of Suzhou City. Geochemical baseline values of soil heavy metals (Cr, Zn, Pb, Ni, Hg, Cu, Cd, As, Mn and Co) in the study area were calculated as 53.6, 61.5, 19.8, 27.6, 0.08, 18.4, 0.13, 12.9, 416.1 and 11.0 mg/kg, respectively, by using reference metal normalization and cumulative frequency curve methods. Subsequently, four potential sources of soil heavy metals were identified by the positive matrix factorization. Finally, the potential ecological risks arising from the identified sources were determined by the integrated model of positive matrix factorization and Hakanson potential ecological risk index. Results showed that the ecological risk posed by soil heavy metals in the study area ranged from low to moderate level. Hg and Cd were the two largest risk contributors, supplying 36.0% and 30.3% of total risk value. The origin of heavy metals in the soils is mostly related to four sources including agricultural activities, natural dispersion, coal consumption and traffic pollution. Source apportionment of the potential ecological risks revealed that the dominant risk source in the study area was natural dispersion (42.0%), followed by coal related industries (26.5%), agricultural activities (20.4%) and traffic pollution (11.1%). This work gives a clear baseline information of the heavy metal accumulations in lime concretion black soil and provides a successful case study for the source-oriented ecological risk assessment.
“…From the above analysis, the high-value areas of heavy metals are distributed in the rest of the TS mining area except the southeast, and I concentrate on the population of the TS mining area in the southeast. This shows that human life has relatively little impact on the local heavy metals, and the principal factors are mining and coal preparation coal, and heavy metal accumulation in coal mine soil was the main cause [ 27 , 29 , 30 ]. Fig.…”
Due to the special sensitivity of typical ecologically fragile areas, a series of human life, mining, and other activities have a greater impact on the environment. In this study, three coal mines in Ordos City on the Loess Plateau were selected as the study area, and the pollution levels of heavy metals in the area were studied by measuring As, Hg, Cr, Cd, Cu, Ni, and Pb in the soil of 131 sampling points. Combined with the concept of “co-occurrence network” in biology, the level of heavy metals in soil was studied using geostatistics and remote sensing databases. The results showed that the concentrations of Hg, Cr, Ni, Cu, and Pb in more than half of the sampling points were higher than the local environmental background value, but did not exceed the risk control value specified by China, indicating that human factors have a greater influence, while Cd and As elements are mainly affected Soil parent material and human factors influence. Heavy metal elements have nothing to do with clay and silt but have an obvious correlation with gravel. Cd, Pb, As and Ni, Cd, Cr are all positively correlated, and different heavy metals are in space The distribution also reflects the autocorrelation, mainly concentrated in the northeast of the TS mining area and the middle of the PS mining area.
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