Identification of the potential risk areas for soil heavy metal pollution based on the source-sink theory

Jia, Xiaolin; Fu, Tingting; Hu, Bifeng; Shi, Zhou; Zhou, Lu; Zhu, Yi

doi:10.1016/j.jhazmat.2020.122424

Cited by 177 publications

(40 citation statements)

References 29 publications

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“…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].…”

Section: Introductionmentioning

confidence: 99%

Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China

Xie

Zhu³

et al. 2021

Land

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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.

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Section: Introductionmentioning

confidence: 99%

Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China

Xie

Zhu³

et al. 2021

Land

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“…The mean concentrations of all five elements from these three sites followed the order YG > JLG > DZ ( Table 1), suggesting that both the operating and the abandoned Pb smelting facilities contributed to heavy metal(loid)s accumulation via atmospheric deposition. The concentrations of all elements at the DZ site (considered a control site in this study) were ∼3-2,500 times greater than the background soil elemental concentrations (Cheng et al, 2014) and were much greater than the heavy metal(loid)s concentrations from other studies (Jia et al, 2020;Liu et al, 2019). Given the distance of DZ from any industrial pollution source (∼25 km), element accumulation at this site was likely due to atmospheric resuspension and transportation/deposition of relatively small particles.…”

Section: Atmospheric Heavy Metal(loid)s Deposition Concentrationsmentioning

confidence: 51%

Atmospheric deposition of arsenic, cadmium, copper, lead, and zinc near an operating and an abandoned lead smelter

et al. 2020

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Atmospheric deposition samples were collected over 15 mo at several locations near an operating smelter and an abandoned Pb smelter to investigate the contribution of Pb smelting to depositional fluxes and potential local air quality degradation. Samples were analyzed for As, Cd, Cu, Pb, and Zn and subjected to scanning electron microscopy (SEM)-energy dispersive spectroscopy (EDS). Concentrations of Cd and Pb at both sites were greater than at the control site (p < .05), and significant correlations existed between Cd and Pb concentrations at both sites (p < .05). Monthly depositional flux variations at both sites were similar, with greater deposition during cold and dry periods. Heavy metal(loid)s deposition during these periods was correlated with wind speed. Greater Cd depositional flux differences were found between the smelter and control sites compared with other elements. The SEM images suggested that some particles at the operating smelter site were from Pb smelting material. However, most particles at both sites had no characteristics of smelting, suggesting reactions occurred between the smelter-emitted particles and soil components. The EDS results indicated that atmospheric deposition from both sites had lower Pb concentrations than smelting material or ash. The main atmospheric deposition source at the operating and abandoned sites was likely from the resuspension of heavy metal(loid)-enriched soil particles. Greater risk of air pollution from historical Pb smelting facilities exists years after closing down. Reducing soil wind erosional losses may help reduce heavy metal(loid)s dispersion across environments. 1 INTRODUCTION Nonferrous metal mining and smelting are main sources of environmental heavy metal(loid)s pollution (Ettler,

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“…IDW uses a simple reciprocal distance as weights to estimate the target variable at unmeasured sites. The IDW method includes a few parameters and is simple and easy to employ, which makes it one of the most widely used method [43,[47][48][49]. Therefore, in this study we used 3D-IDW [50] to map spatio-temporal variation of soil salinity at three dimensions in the soil profile.…”

Section: Model Performancementioning

confidence: 99%

Field-Scale Characterization of Spatio-Temporal Variability of Soil Salinity in Three Dimensions

Liu

et al. 2020

Remote Sensing

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Information on spatial, temporal, and depth variability of soil salinity at field and landscape scales is important for a variety of agronomic and environment concerns including irrigation in arid and semi-arid areas. However, challenges remain in characterizing and monitoring soil secondary salinity as it can largely be impacted by managements including irrigation and mulching in addition to natural factors. The objective of this study is to evaluate apparent electrical conductivity (ECa)-directed soil sampling as a basis for monitoring management-induced spatio-temporal change in soil salinity in three dimensions. A field experiment was conducted on an 18-ha saline-sodic field from Alar’s Agricultural Science and Technology Park, China between March, and November 2018. Soil ECa was measured using an electromagnetic induction (EMI) sensor for four times over the growing season and soil core samples were collected from 18 locations (each time) selected using EMI survey data as a-priori information. A multi-variate regression-based predictive relationship between ECa and laboratory-measured electrical conductivity (ECe) was used to predict EC with confidence (R2 between 0.82 and 0.99). A three-dimensional inverse distance weighing (3D-IDW) interpolation clearly showed a strong variability in space and time and with depths within the study field which were mainly attributed to the human management factors including irrigation, mulching, and uncovering of soils and natural factors including air temperature, evaporation, and groundwater level. This study lays a foundation of characterizing secondary salinity at a field scale for precision and sustainable management of agricultural lands in arid and semi-arid areas.

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Identification of the potential risk areas for soil heavy metal pollution based on the source-sink theory

Cited by 177 publications

References 29 publications

Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China

Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China

Atmospheric deposition of arsenic, cadmium, copper, lead, and zinc near an operating and an abandoned lead smelter

Field-Scale Characterization of Spatio-Temporal Variability of Soil Salinity in Three Dimensions

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