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

Li, Ping; Wu, Tao; Jiang, Guangyao; Liu, Pu; Li, Yán; Zhang, Jianzhen; Xu, Fei; Xie, Xuefeng

doi:10.3390/land10101016

Cited by 14 publications

(5 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 3: The retrieved literature was counted, and the evaluation indices used in the selected literature were divided into four categories: physics [27,28], chemistry [29,30], biological [31,32] and environment [33,34]. Finally, the frequency of index selection was analysed.…”

Section: Methods Of Indices Selectionmentioning

confidence: 99%

Research on the Evaluation Index System of the Soil Remediation Effect Based on Blockchain

Wang

Liang

2021

Land

View full text Add to dashboard Cite

Soil remediation technology has achieved good results at present, but evaluation research on the soil remediation effect is relatively lacking. In the field of soil remediation effect evaluation, there are problems such as unscientific and imperfect indices and the inability to guarantee the authenticity of data. Based on the research results of domestic and foreign scholars and the soil remediation standards of some developed countries, a set of recommended indices for soil remediation evaluation has been formed through the analysis and screening of a large number of documents and the results of expert consultation. However, there are phenomena such as lack of data authenticity, information asymmetry, and low supervision efficiency. Based on the technical characteristics of the blockchain and the current soil remediation effect evaluation requirements, combined with the research results of domestic and foreign scholars and the soil remediation standards of some developed countries, the core technology and corresponding application scenarios were analysed for functional requirements in this paper. The introduction of blockchain technology will improve the authenticity of data and form a decentralized, trustworthy, and traceable supervision model. Finally, an evaluation index system of the soil remediation effect based on blockchain was constructed from the following five aspects: collection of soil remediation indices, the addition of a new blockchain-based soil remediation effect evaluation and supervision model, the determination of index weights, a list of soil remediation effect evaluation indices, and providing feedback and suggestions. Through the application of blockchain technology and the innovative addition of the “regional policy” evaluation index, changes and innovations were made in all aspects of the soil remediation effect evaluation process.

show abstract

Section: Methods Of Indices Selectionmentioning

confidence: 99%

Research on the Evaluation Index System of the Soil Remediation Effect Based on Blockchain

Wang

Liang

2021

Land

View full text Add to dashboard Cite

show abstract

“…In particular, industrial emissions from heavy metal smelting are a significant source of heavy metal pollution in soil, as several metal smelters are distributed throughout the study area [61]. The spatial distribution of heavy metals in soil is characterized by diffusion around the center of metal smelters, likely resulting from atmospheric diffusion deposition and rainfall [62]. The target areas with high heavy metal content are located in the south of Kejing Town and the north of Chengliu Town, both of which are near metal smelters.…”

Section: Heavy Metal Concentration Distributionmentioning

confidence: 99%

Estimation of Pb and Cd Content in Soil Using Sentinel-2A Multispectral Images Based on Ensemble Learning

Xie

Liu

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

With the increasing economic growth in developing nations, soil heavy metal pollution has become a growing concern. Monitoring the heavy metal concentration in soil through remote sensing is crucial for safeguarding the ecological environment. However, the current indoor spectral measurement method has limitations, such as the discrete soil sampling space and weak spectral characteristics of soil heavy metals, leading to a poor robustness of remote sensing inversion models. This study presents a novel approach to address these challenges by incorporating a spatial feature of pollution sources and sinks to evaluate the spatial factors affecting pollutant diffusion and concentration. An integrated learning model, combining spatial and spectral information, is developed to estimate heavy metal content in soil using Sentinel-2A satellite data. A total of 235 soil samples were collected in Jiyuan, China, and the effective spectral transformation characteristics of Sentinel-2A data were screened. The impact of spectral characteristics, topographic characteristics, and spatial characteristics on retrieving soil heavy metal lead (Pb) and cadmium (Cd) content were analyzed. The optimal inversion method was determined through various integrated learning models, and the spatial distribution of heavy metals Pb and Cd was mapped. The results indicate that the accuracy of the inversion model was significantly improved by incorporating terrain features and spatial features of pollution sources. The Blending integrated learning method showed a 65.9% and 73.2% reduction in the RMSE of Pb and Cd, respectively, compared to other regression models. With R2 values of 0.9486 and 0.9489 for Pb and Cd, respectively, and a MAPE less than 0.2, the Blending model demonstrated high prediction accuracy.

show abstract

“…The optimal model varies depending on the specific subjects of study and geographic regions. For example, LiPing [28]et al employed two models to pinpoint the sources of heavy metals in the subtropical agricultural soils of eastern China. Upon comparison, the PMF model was determined to be more suitable.…”

Section: Introductionmentioning

confidence: 99%

Spatial variability and source analysis of soil heavy metals: A case study of the key planting area of special agricultural products in Cangxi County, China

Feng,

Chen,

Meng

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Heavy metal pollution in farmland soil represents a considerable risk to ecosystems and human health, constituting a global concern. Focusing on a key area for the cultivation of special agricultural products in Cangxi County, we collected 228 surface soil samples. We analyzed the concentration, spatial distribution, and pollution levels of six heavy metals (Cr, Cu, Pb, Ni, Zn, and Hg) in the soil. Moreover, we investigated the sources and contribution rates of these heavy metals using Principal Component Analysis/Absolute Principal Component Scores (PCA/APCS) and Positive Matrix Factorization (PMF) models. Our findings indicate that none of the six metals exceeded the pollution thresholds for farmland soils. However, the mean concentrations of Cr and Ni surpassed the background levels of Sichuan Province. A moderate spatial correlation existed between Pb and Ni, attributable to both natural and anthropogenic factors, whereas Zn, Cu, Hg, and Cr displayed a strong spatial correlation, mainly due to natural factors. The spatial patterns of Cr, Cu, Zn, Pb, and Ni were similar, with higher concentrations in the northern and eastern regions and lower concentrations centrally. Hg’s spatial distribution differed, exhibiting a broader range of lower values. The single pollution index evaluation showed that Cr and Ni were low pollution, and the other elements were no pollution. The average value of comprehensive pollution index is 0.994, and the degree of pollution is close to light pollution. Predominantly, higher pollution levels in the northern and eastern regions, lower around reservoirs. The PCA/APCS model identified two main pollution sources: agricultural traffic mixed source (65.2%) and natural parent source (17.2%). The PMF model delineated three sources: agricultural activities (32.59%), transportation (30.64%), and natural parent sources (36.77%). Comparatively, the PMF model proved more accurate and reliable, yielding findings more aligned with the study area’s actual conditions.

show abstract

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

Cited by 14 publications

References 59 publications

Research on the Evaluation Index System of the Soil Remediation Effect Based on Blockchain

Research on the Evaluation Index System of the Soil Remediation Effect Based on Blockchain

Estimation of Pb and Cd Content in Soil Using Sentinel-2A Multispectral Images Based on Ensemble Learning

Spatial variability and source analysis of soil heavy metals: A case study of the key planting area of special agricultural products in Cangxi County, China

Contact Info

Product

Resources

About