Comprehensive source identification and apportionment analysis of five heavy metals in soils in Wenzhou City, China

Shao, Shuai; Hu, Bifeng; Yunhan, Tao; You, Qihao; Huang, Mingxiang; Zhou, Lu; Chen, Qiuxiao; Shi, Zhou

doi:10.1007/s10653-021-00881-7

Cited by 16 publications

(5 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…China is the world's largest producer of rice, and rice is the staple food for majority of the Chinese population, particularly in Southern China. Therefore, PTEs pollution in soil and their subsequent accumulation in these crops, and related health risks of human exposure have been extensively studied around the world [30][31][32][33][34][35].…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…The rest of the HMs were presented at a moderate variability and followed the order of Ni (98.55%) > Cr (72.12%) > As (57.56%) > Cu (56.29%) > Cd (53.07%) > Zn (34.39%) > Pb (25.59%). Compared with agricultural soils in the other regions of the Yangtze River Delta [27][28][29][30][31], the overall concentrations of HMs in the study area are lower (Figure 2). It is shown that it pollutes the soil environment lightly in the process of economic development in the region.…”

Section: Descriptive Statistics Of Heavy Metals In Soilmentioning

confidence: 96%

“…The subtropical region of eastern China has a suitable climate and adequate water and heat conditions, and is considered one of the important food bases in the Yangtze River Delta. Over the past 20 years, unreasonable human activities have led to the accumulation of HMs in the agricultural soils, which have eventually grown to threaten the soil quality and safety of agricultural products [27][28][29][30][31]. Therefore, the identification of HM pollution sources and health risks in this region is important for the remediation of agricultural soils in subtropical regions.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach for Source Apportionment and Health Risk Assessment of Heavy Metals in Subtropical Agricultural Soils, Eastern China

Jiang

et al. 2021

Land

View full text Add to dashboard Cite

Unreasonable human activities may cause the accumulation of heavy metals (HMs) in the agricultural soil, which will ultimately threaten the quality of soil environment, the safety of agricultural products, and human health. Therefore, the accumulation characteristics, potential sources, and health risks of HMs in agricultural soils in China’s subtropical regions were investigated. The mean Hg, Cu, Zn, Pb, and Cd concentrations of agricultural soil in Jinhua City have exceeded the corresponding background values of Zhejiang Province, while the mean concentrations of determined 8 HMs were less than their corresponding risk-screening values for soil contamination of agricultural land in China. The spatial distribution of As, Cr, Ni, Cu, and Pb were generally distributed in large patches, and Hg, Zn, and Cd were generally sporadically distributed. A positive definite matrix factor analysis (PMF) model had better performance than an absolute principal component–multiple linear regression (APCS-MLR) model in the identification of major sources of soil HMs, as it revealed higher R2 value (0.81–0.99) and lower prediction error (−0.93–0.25%). The noncarcinogenic risks (HI) of the 8 HMs to adults and children were within the acceptable range, while the carcinogenic risk (RI) of children has exceeded the safety threshold, which needs to be addressed by relevant departments. The PMF based human health risk assessment model indicated that industrial sources contributed the highest risk to HI (32.92% and 30.47%) and RI (60.74% and 61.5%) for adults and children, followed by agricultural sources (21.34%, 29.31% and 32.94% 33.19%). Therefore, integrated environmental management should be implemented to control and reduce the accumulation of soil HMs from agricultural and industrial sources.

show abstract

“…The highest hot spot value, high hot spot value and high hot spot value of Sb in the study area were banded and distributed along yellow soil, yellow limestone soil and limestone soil. PCA is widely used to identify possible sources of interrelated heavy metals, but PCA does not consider the importance of each index on the impact of soil heavy metals, resulting in the failure to identify important indicators (Zhang and Huang et al, 2020;Shao and Hu et al, 2021). (Mama and Nnaji et al, 2021).…”

Section: Analysis Of Spatial Distribution Of Heavy Metals In Soilmentioning

confidence: 99%

Analysis and Source Identification of Heavy Metals in Soils of Central Urban Area of Chongqing, China

Chen

Zhu

Cai

et al. 2021

Preprint

View full text Add to dashboard Cite

In megacities, due to frequent human activities, large amounts of metals enter the soil indirectly or directly and eventually flow to people through the food chain. Therefore, the analysis and identification of soil heavy metal sources is an important part of revealing soil heavy metal pollution. The spatial content and potential sources of 11 heavy metals were analyzed from 342 surface soil samples collected from the central city of Chongqing in southwest China. The results showed that the main heavy metal elements under the first principal component loading were copper (Cu), nickel(Ni), zinc (Zn), manganese (Mn), cadmium (Cr),plumbum (Pb) and cadmium(Cd).The second principal component (F2) was mainly loaded with molybdenum (Mo), arsenic (As), mercury (Hg)and antimony (Sb), and the PCA-APCs receptor model of 11 heavy metals was constructed. The PCA-APCs receptor models of 11 heavy metals were constructed. The results of classification regression analysis confirmed the main sources of heavy metals. Population density mainly affected Cu (0.539), soil mainly affected Ni (0.411), Sb (0.493), Zn (0.472) and Mn (0.206), and water quality mainly affected As(0.453) and Mo (0.374). Air quality mainly affects Cd (0.332) and Cr (0.371), traffic activity mainly affects Hg (0.312), and slope mainly affects Pb (0.313). Hot spot analysis showed that heavy metals had a high degree of coincidence with environmental factors such as soil parent material, slope, soil type and traffic activities. The results of this study can be effectively used to make scientific decisions and strategies, and an effective strategy for prevention and control of soil heavy metal pollution should be formulated to protect the urban soil environmental quality.

show abstract

Comprehensive source identification and apportionment analysis of five heavy metals in soils in Wenzhou City, China

Cited by 16 publications

References 54 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

An Integrated Approach for Source Apportionment and Health Risk Assessment of Heavy Metals in Subtropical Agricultural Soils, Eastern China

Analysis and Source Identification of Heavy Metals in Soils of Central Urban Area of Chongqing, China

Contact Info

Product

Resources

About