Novel framework for modelling the cadmium balance and accumulation in farmland soil in Zhejiang Province, East China: Sensitivity analysis, parameter optimisation, and forecast for 2050

Fu, Tingting; Zhao, Ruihong; Hu, Bifeng; Jia, Xiaolin; Wang, Zhige; Zhou, Lu; Huang, Mingxiang; Li, Yan; Shi, Zhou

doi:10.1016/j.jclepro.2020.123674

Cited by 24 publications

(8 citation statements)

References 49 publications

(59 reference statements)

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“…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].…”

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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“…1 ) 13 , located in southeast China along the East Sea, with an area of around 105 km 2 , has a total land area of 104,141 km 2 and a population of 58.50 million. The elevation decreases from the southwest to the northeast and varies from 0 to 1800 m, and rivers cross the whole province 14 . Zhejiang experiences a typical northern subtropical monsoon climate, with a mean annual temperature of 15–18 °C and mean annual precipitation of 980–2000 mm 13 .…”

Section: Methodsmentioning

confidence: 99%

Grassland coverage change and its humanity effect factors quantitative assessment in Zhejiang province, China, 1980–2018

Lü

Yan

Chu

et al. 2022

Sci Rep

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This study aims to make clear of grassland coverage change and quantitative assessment its effect factors. We collected the data from the National Bureau of Statistics (http://www.stats.gov.cn) and "China 20th Century Land Use/Cover Change (LUCC) Spatio-temporal Platform". Grassland coverage area showed an upward trend from 1980 to 1990, and the grassland coverage area is gradually decreasing from 1990 to 2000, and the grassland coverage area has not changed much from 2000 to 2018. The medium-coverage grassland area has the highest correlation with the total population, and the high-coverage grassland area has the lowest correlation with the total population. Land use types and the composite of gross agricultural output have influence on grassland coverage area. It is hoped that relevant policies should consider land use types and ecological benefits while balancing economic development and urban development.

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“…As a data partitioning algorithm, it enables exploration of the nonlinear relationship in the observed data [49]. The predicted variable will be fitted by composed linear equations under rules generated by the cubist model, which is different from the CART regression tree model [50] in which its terminal nodes are not predictions [51]. The heterogeneity caused by predictor variables produces different data division conditions, which means "rules", and will be used to divide the total dataset [52].…”

Section: Cubistmentioning

confidence: 99%

Sea Surface Salinity Estimation and Spatial-Temporal Heterogeneity Analysis in the Gulf of Mexico

Zhang

et al. 2021

Remote Sensing

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As an important parameter to characterize physical and biogeochemical processes, sea surface salinity (SSS) has received extensive attention. Cubist is a data mining model, which can be well-suited to estimate and analyze SSS in the Gulf of Mexico (GOM) because it can reflect the SSS internal heterogeneity in the GOM—overall circular distribution, and the seasonality related to temperature and river discharge changes. Using remote sensing reflectance (Rrs) at 412, 443, 488 (490), 555, and 667 (670) nm and sea surface temperature (SST), a cubist model was developed to estimate SSS with high accuracy with the overall performance demonstrates a root mean square error (RMSE) of 0.27 psu and correlation coefficient of 0.97 of R2. The model divides the GOM area according to model rules into four sub-regions, which include estuary, nearshore, and open sea, reflecting the gradient distribution of SSS. The division of sub-regions and seasonal changes can be explained by the distribution of water bodies, river discharges, and local wind forces since it is obvious that the estuary region reaches the largest low-value area and spreads eastward with the monsoon in the spring when the river flow increases to the highest value. While the east to west wind in the non-summer monsoon period guides the plume westward, and the lowest river discharge in winter corresponds to the smallest low value area. After comparison with other statistical models, the cubist model showed satisfactory results in independent verification of cruise data, proving the estimation capability under different geographical conditions (such as estuaries and open seas) and seasons. Therefore, considering high accuracy and heterogeneity mining, the cubist-based model is an ideal method for coastal SSS estimation and spatial-temporal heterogeneity analysis, and can provide ideas for model construction for coastal areas with similar geographic environments.

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Novel framework for modelling the cadmium balance and accumulation in farmland soil in Zhejiang Province, East China: Sensitivity analysis, parameter optimisation, and forecast for 2050

Cited by 24 publications

References 49 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

Grassland coverage change and its humanity effect factors quantitative assessment in Zhejiang province, China, 1980–2018

Sea Surface Salinity Estimation and Spatial-Temporal Heterogeneity Analysis in the Gulf of Mexico

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