| Literature DB >> 35682145 |
Zhaolin Du1, Dasong Lin1, Haifeng Li2, Yang Li1, Hongan Chen1, Weiqiang Dou3, Li Qin1, Yi An1.
Abstract
The study of threshold levels of heavy metals in soil is essential for the assessment and management of soil environmental quality. This study reviewed the influencing factors, the derivation, and application aspects of heavy metals' threshold values comprehensively by a combination of bibliometric analysis and scientific knowledge mapping. A total of 1106 related studies were comprehensively extracted from the Web of Science database during the period from 2001 to 2020. The results showed that the publication output has been growing strongly. An analysis on the subject, journal, country, and institution was carried out to demonstrate the development and evolution of this research branch during the two decades. According to high-frequency keywords analysis, external factors (e.g., soil physicochemical properties) and internal factors (e.g., crop genotype) can affect heavy metal threshold values in the soil-crop system. The current methods mainly include the Point model (e.g., evaluation factor method), the Probability model (e.g., species sensitivity distribution method), and the Empirical model (e.g., ecological environment effect method). A threshold study can be applicable to the risk assessment for soil heavy metal contamination in order to determinate the soil pollution degree and its spatial and temporal distribution characteristics. Moreover, challenges and prospects of the study of heavy metal threshold values are proposed, indicating that research should focus on the relationships between human health risks and the established threshold values of heavy metals in the soil, long-term field trials and bioavailability of heavy metals for the derivation of the thresholds, and the establishment of more scientific and rational soil environmental benchmarks.Entities:
Keywords: crop; heavy metal; risk assessment; soil; threshold
Mesh:
Substances:
Year: 2022 PMID: 35682145 PMCID: PMC9180750 DOI: 10.3390/ijerph19116561
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Figure 1Evolution mapping of disciplines development during 2001–2020. Note: Each node represents a discipline, and the larger the node indicates the higher the number of articles related to this discipline. A pivotal point with high betweenness centrality is highlighted with a purple ring, showing this discipline has a great influence in the cooperation between disciplines.
Figure 2High-frequency keywords from 2001 to 2020. Note: the network is depicted with a series of tree rings in different colors, and every ring represents one keyword. The blue ring indicates the oldest keyword, and the orange ring indicates the newest. The links describe a co-occurrence of these keywords. Furthermore, pivotal points with high betweenness centrality are highlighted with a purple ring.
Figure 3Development and evolution of high-frequency keywords. Note: each node represents a keyword, and the size of the node indicates the number of times this keyword occurred. The colored links describe the co-occurrence of keywords.
Analysis on the advantages, disadvantages, and uncertainty of the methods for determining the threshold levels of heavy metals in agricultural land.
| Model | Representative Method | Advantages and Disadvantages | References |
|---|---|---|---|
| Point model | Evaluation | Advantages: simple and easy to operate, taking account of species sensitivity, independent of any theoretical model; quantitative result description | [ |
| Probability model | Species | Advantages: taking account of differences in species sensitivity, soil physical and chemical properties, bioavailability and sources of pollutants, and different pollution risk levels; quantitative result description | [ |
| Empirical model | Ecological environment effect method | Advantages: taking account of the effects of different soil physical and chemical properties; quantitative result description | [ |
Figure 4Top 13 keywords with the strongest citation bursts. Note: the duration of the citation burst of a keyword is represented by the red bar.