How Has the Source Apportionment of Heavy Metals in Soil and Water Evolved over the Past 20 Years? A Bibliometric Perspective

Abstract

Exploring soil heavy metal sources is of great significance for ensuring the safety of ecological environments and agricultural product safety, as well as for guiding pollution control and management policies. This paper retrieved 452 research papers on soil heavy metal source analysis published over the 2004–2024 period from the Web of Science database. The collected literature was subjected to multidimensional bibliometric analysis using the CiteSpace 6.3.R1. The results showed significantly increasing trends in the scientific outputs and the number of papers on heavy metal source analysis in soils and water over the study period. In addition, related research topics have expanded from single to multiple heavy metal elements in environmental media and have increasingly recognized the impact of water pollution on soil contamination. Research methods have also evolved from basic statistical analysis to complex spatial analysis techniques, covering agricultural and urban soils. Previous related studies have focused on heavy metal pollution in different areas, and related research on heavy metal source analysis has now extended from ecological environments to associated human health risks. The present study provides directions for future related research and guidance for ensuring effective source control of heavy metal pollution and safe utilization of land and water resources.

1. Introduction

Soil heavy metal pollution is a global environmental challenge that has gained urgency due to the increasing impacts of urbanization, industrialization, and agricultural intensification. These processes have led to a significant escalation in the release of heavy metals into the environment, posing serious threats to ecosystem health, food security, and human health [1,2]. Therefore, the sources of soil heavy metals have attracted increasing attention from researchers worldwide [3,4,5,6,7,8]. Initially, methods were developed to determine the source of heavy metal elements in soils based on atmospheric particulate pollutants [9]. Source tracing methods from aquatic environments have also been adapted for use in soils, given the interconnectedness between water and soil pollution. However, the differences in these methodologies between soil, atmospheric, and aquatic environments has gradually become apparent [6,10]. In fact, the spatial distribution of heavy metals in soils is complex, with great regional and accumulation patterns, making the analysis of heavy metal sources from atmospheric environments (pollution source inventory/air quality dispersion model/receptor model) to soil environments inappropriate [9,11,12]. To overcome the limitations of single analysis methods and more accurately identify the sources and variation characteristics of soil heavy metals, numerous alternative methods have been applied in recent years. These methods include emission inventory assessment methods, chemical mass balance models (element/isotope ratios), multivariate statistical models (e.g., factor analysis, cluster analysis, principal component analysis, positive matrix factorization), machine learning algorithms (e.g., conditional inference trees and random forest), and spatial analysis methods [13,14,15,16,17,18,19,20,21,22]. The performance of models in source apportionment studies is heavily dependent on the quality and adequacy of the input data provided by users. On the basis of limited data, the combination of these methods can be used to quantitatively assess the contributions of different pollution sources to soil heavy metals contents.

With the continuous refinement of methodological techniques and the integration of multiple models, significant progress has been made in researching heavy metal source apportionment in soil. Natural factors are the main sources of soil heavy metal pollution, particularly in areas with less human disturbance, such as the Lalin River Basin (38%), Quanzhou City in Fujian Province (85.14%), and Lianyuan City in Hunan Province (33.6%) [22,23,24], and natural weathering of parent materials is the main source of soil heavy metals in the upper soil layers. However, in some regions, water pollution significantly influences soil pollution. For example, irrigation with contaminated water and deposition of pollutants from polluted rivers contribute to the accumulation of heavy metals in soils. In areas with intense human activities (e.g., industrial and agricultural activities), water bodies often receive untreated or partially treated effluents, contaminating soils through agricultural irrigation and flooding events. Therefore, the distributions of soil heavy metal contents and compositions are affected by direct anthropogenic inputs and water-mediated pollutant transfer [23,24,25,26]. Indeed, the contribution rate of electronic industrial activities in the high-tech development zone of Wuhan, Hubei, to soil cadmium (Cd) contents was estimated at 67%, making it the major source [27]. Soil heavy metals in the Hexi Corridor of Gansu were derived from various anthropogenic sources, such as traffic sources (Pb: 77%), industrial sources (Ni: 54%), and agricultural activities (Cu: 42%; Zn: 36%) [28]. In addition to the traditional approach of identifying the sources of heavy metals in soil, there is now a gradual evaluation of the contributions of basic geographical conditions, soil conditions, and socio-economic conditions to soil heavy metals. For instance, the distance from a city center contributed to soil heavy metal pollution levels in the northwestern part of Guangxi by 3 to 50% [29]. On the other hand, heavy metals in agricultural and surface soils in soils from Tianjin’s Wuqing District and the Pearl River Delta, respectively, were significantly controlled by soil types [30,31].

The past 2–3 years have witnessed a surge in studies that not only reaffirm the persistent nature of heavy metal pollution but also highlight the evolving challenges in source identification. In developed regions, where industrialization and urbanization have been prevalent for an extended period, the issue of heavy metal pollution in soil has not waned. For instance, a study by Xiao et al. (2022) utilized geostatistical analysis and machine learning to apportion sources of potentially toxic elements in arable land soil, emphasizing the complexity of identifying sources in urban and agriculturally intensive areas [18]. In developing regions, where industrial growth and agricultural expansion are more recent phenomena, the source apportionment of heavy metals is equally critical. A study by Qi et al. (2024) conducted a source analysis and contribution estimation of heavy metal contamination in agricultural soils in an industrial town in the Yangtze River Delta, China, highlighting the urgency of addressing pollution sources in these rapidly developing areas [13]. Similarly, Yao et al. (2024) assessed contamination levels and sources of heavy metals in agricultural soils surrounding low-emission industries, indicating that even low-level emissions can significantly impact soil quality [14].

Given this recent literature, a 20-year review of heavy metal source apportionment is not only timely but also essential. It provides an opportunity to assess the progress made in understanding and addressing soil heavy metal pollution. It also offers a dataset to synthesize the lessons learned over the past two decades, informing future research directions and policy strategies aimed at mitigating the impacts of heavy metal pollution on ecosystems and human health [4]. In this context, the present study aims to conduct data mining and a comprehensive scientific bibliometric analysis of the related literature using CiteSpace 6.3.R1 information visualization analysis software, a method that offers a distinctive vantage point into the historical evolution and current state of scholarly research within a specific domain. Bibliometric reviews are particularly important in the context of environmental sciences, such as soil heavy metal pollution and source apportionment, due to their ability to systematically map and assess the vast and dispersed body of literature that has accumulated over time. One of the most significant advantages of bibliometric analysis is its ability to uncover emerging trends and research frontiers. By identifying clusters of keywords and examining the temporal dynamics of these clusters, it can pinpoint topics that are gaining traction within the scientific community [32]. This may include new methodologies for source apportionment, novel findings on the impacts of heavy metal pollution, or an increased focus on specific heavy metals or environmental media. Bibliometric analyses are also effective at revealing research gaps by highlighting areas with limited scholarly activity. By identifying such gaps, our review can guide future research efforts toward areas that require more attention, thereby contributing to a more comprehensive understanding of soil heavy metal pollution on a global scale.

Specifically, this review aims to present an intuitive map that comprehensively demonstrates the evolution, development trends, research hotspots, and future directions in the field of soil heavy metal source analysis over the 2004–2024 period. In addition, previous related studies on soil heavy metal source analysis were summarized and comprehensively analyzed in this paper to identify major soil environmental quality issues and predict future related trends.

2. Materials and Methods

2.1. Data Sources

The Web of Science (WoS) database covers academic publications worldwide in various disciplines, integrating multiple authoritative academic databases. It includes the most important scientific search engines and the most relevant databases for retrieving academic literature. Therefore, we selected the WoS database to conduct a bibliometric analysis on the source of heavy metals in soil. The databases utilized in this study include the Science Citation Index-Expanded (SCI-E) and the Social Sciences Citation Index (SSCI). The most relevant published studies on soil heavy metal pollution over the 2004–2024 period were collected in this study using different keywords, including “soil”, “heavy metal”, and “source analysis”. Specifically, the selection approach of related studies was conducted on the same day to avoid bias that might result from daily updates of the WoS database. In addition, only research articles were considered in this study, excluding other papers, such as conference proceedings, conference abstracts, and review articles. A total of 452 relevant articles were selected in this study for subsequent bibliometric analysis. Given that the term “potentially toxic elements” gained widespread recognition and usage only after 2020, this paper did not incorporate this subject term during the literature selection process [33].

In this study, the term “soil” refers to the uppermost layer of the Earth’s surface that is capable of supporting plant life. Specifically, soil is operationally defined as the layer extending from the soil surface down to approximately 20 cm in depth [34]. This depth is chosen to align with standard agricultural practices and to capture the zone where the majority of root activity and nutrient cycling occurs [35]. The “source apportionment” refers to the process of identifying and quantifying the contributions of different sources to the total concentration of heavy metals in soil. This process is crucial for understanding the origins of soil contamination, which can inform targeted mitigation strategies and policies aimed at reducing heavy metal pollution [36].

2.2. Research Methods

In this study, CiteSpace 6.2 R4 software was employed to perform an in-depth bibliometric analysis and visualize the literature on soil heavy metal source analysis. CiteSpace is, in fact, a powerful scientific literature analysis tool that can extract and visualize key information from various published papers through advanced text mining and visualization techniques [37]. It has been widely applied in multiple fields, such as materials, tourism, and phytoremediation [32,38,39]. CiteSpace can not only reveal the development trends of research topics on soil heavy metal source analysis but also highlight current research hotspots and future related directions. For more information on CiteSpace, readers are directed to the official website (https://citespace.podia.com/, accessed on 1 June 2024). We used the free version of CiteSpace, which provided the necessary features for our bibliometric analysis.

The relevant literature published between 2004 and 2024 was first extracted from the WoS database before strictly selecting related research articles to ensure the reliability and accuracy of the bibliometric analysis results. The collected literature data from the WoS were converted into a format recognizable by the CiteSpace software. The time threshold (2004–2024), node types (e.g., keywords, authors, and institutions), and relationship strength parameters (Cosine) were subsequently set to control the analysis process before generating different visualization graphs, such as keyword co-occurrence networks, author collaboration networks, and institutional collaboration networks. These graphs were used to visualize the most important literature information, such as research hotspots, academic influences, and future directions in the field of soil heavy metal source analysis.

In conducting the bibliometric analysis with CiteSpace, several potential limitations inherent to this methodology were carefully avoided. Firstly, the literature references were thoroughly reviewed to ensure that the search terms were comprehensive and relevant. Secondly, the limitations posed by database restrictions were addressed by selecting the Web of Science, a database recognized for its extensive coverage. Lastly, review papers were excluded, and the focus was placed on original research articles to ensure the reliability of the findings.

2.3. Relevant Bibliometric Indicators

The parameters included in the scientometric analysis of the source identification for heavy metals are as follows:

1. Number of Network Nodes (N)

The number of nodes represents the count of different types of analytical indicators. As an independent entity, the number of nodes reflects the extent of research fields of heavy metal sources. Specifically, the more nodes there are, the more extensive the research scope.

2. Number of Links (E)

The number of links represents the total connections between nodes, indicating the co-occurrence relationships between the nodes. A higher number of links indicates closer connections between nodes, suggesting a strong correlation in research contents. In contrast, a lower number of links indicates a relative independence or low frequency of co-occurrence in research contents.

3. Node Density (Density)

Node density measures the compactness of network nodes. High node density indicates that there are many connections between nodes, suggesting frequent interactions or collaborations within the research field. This can reflect shared research interests, joint publications, or a common focus on specific topics or methodologies. A low-density network, on the other hand, indicates infrequent and dispersed academic exchanges. Therefore, keywords within high-density areas can effectively identify research hotspots and future directions.

4. Centrality

Centrality reflects the importance of nodes within a network. Nodes with high centrality are typically pivotal in connecting two or more distinct areas or subnetworks. For instance, high centrality in authorship analysis demonstrates the great academic influences and contributions of authors to a specific field.

3. Results and Discussion

3.1. Publication Volume Analysis

Annual publication volume is an important reference indicator that can be used to explore the development trend in research on soil heavy metal source analysis. In addition, publication volume can, to a certain extent, reveal the research interest, development process, trends, and potential change patterns of research fields. In this study, the extracted published studies from 2004 to 2024 were statistically analyzed to provide a more comprehensive understanding of soil heavy metal source analysis (Figure 1). The results showed temporal fluctuations in the annual volume of the published studies on soil heavy metal source analysis over the 2004–2024 period. Nevertheless, there was an increasing trend in the cumulative number of publications during this period. It is worth noting that there were very few publications on soil heavy metal source analysis from 2004 to 2011. Indeed, the cumulative number of publications was 42 in this period, with an average annual publication volume of only 5, indicating a lack of comprehensive insights into this field and thus requiring more exploration. From 2012 to 2017, the number of publications on soil heavy metal source analysis showed a gradually increasing trend, reaching a cumulative number of 107, with an average annual publication volume of about 17–18. From 2013, the annual publication volume on soil heavy metal source analysis remained in double figures, indicating this field has gradually attracted more attention and involvement from scholars. On the other hand, the 2018–2023 period exhibited a sharp increasing trend of publications on soil heavy metal source analysis, despite the slightly decreased number of publications in 2020. The number of publications in the field reached 65 articles, which was 13 times higher than the average annual volume observed in the 2004–2011 period. This change demonstrates the great attention devoted to this field by researchers, continuously increasing research outcomes. This finding demonstrates the broad development prospects and potential of research on soil heavy metal source analysis. The decrease in the number of publications in 2020 might be related to socio-economic factors. Indeed, soil heavy metal source analyses require mainly field measurements, which are often restricted by many factors, such as severe weather, natural disasters, and the spread of infectious diseases, thereby affecting related research outputs. It is also worth noting that there are other fluctuations in the data, which may reflect periodic changes in research priorities or availability of resources. The curve in Figure 1 highlights the general trend in publication volume, which could be influenced by a range of factors, including advancements in analytical methods, increased awareness of environmental issues, and evolving regulatory landscapes.

3.2. Keyword Analysis

3.2.1. Keyword Co-Occurrence Analysis

Keywords represent a summary of research contents, and keyword co-occurrence aims to identify and analyze pairs or combinations of keywords frequently appearing in the same document. Co-occurring keywords collectively designate specific topics, concepts, or issues of research. Therefore, keyword co-occurrence analysis can directly reflect research hotspots in a relevant field. In addition, trends in keywords correspond to the development trends in scientific research. N (number of nodes) represents the individual elements such as keywords, authors, or institutions discussed within the field, indicating its diversity. E (number of links) signifies the connections between these elements, reflecting relationships or collaborations. Density measures the ratio of existing to possible links, providing a sense of how interconnected the field is. These metrics, as depicted in Figure 2, offer insights into the complexity and interactions within the research community focusing on soil heavy metal source apportionment. The obtained results are shown in Figure 2, indicating N, E, and Density values of 496, 3469, and 0.0283, respectively. In addition to thematic keywords, such as Heavy metals (Centrality = 0.46)/Heavy metal (Centrality = 0.20), Urban soils (Centrality = 0.23)/agricultural soils (Centrality = 0.35), Source apportionment (Centrality = 0.38)/Source identification (Centrality = 0.31), the keywords focused mainly on high-frequency contents, such as Contamination (Centrality = 0.42)/pollution (Centrality = 0.37), Spatial distribution (Centrality = 0.40), ecological risk (Centrality = 0.16)/risk assessment (Centrality = 0.19), health risk assessment (Centrality = 0.28), and sediment (Centrality = 0.02)/surface sediment (Centrality = 0.04) over the 2004–2024 period. Cadmium (Cd) (Centrality = 0.13) and agricultural soils (Centrality = 0.1) were the central nodes, thereby playing a significant role in the overall research. This finding indicates that studies on soil heavy metal source analysis have focused mainly on Cd tracing in agricultural soils. Soil heavy metal contamination in agricultural areas is directly related to human health and safety. Numerous studies have explored the sources, migration, and transformations of Cd in soils, which is a common heavy metal pollutant in soils.

Numerous methods for exploring the sources of heavy metals in soils have been explored in recent years. In the early phase of research from 2004 to 2014, non-spatial mathematical statistical techniques were the main methods used to explore the source of soil heavy metals, such as correlation analysis, multivariate statistical analysis, principal component analysis, cluster analysis, and positive matrix factorization. However, the application of more advanced methods demonstrated the presence of frequent spatial distributions of soil heavy metal contents. Therefore, spatial analysis techniques, such as geostatistical analysis and spatial analysis, were gradually applied in the middle research phase (from 2015 to 2021). These methods can more accurately describe the spatial distribution patterns of heavy metals in soils and further reveal their sources and migration processes. In recent years, on the other hand, numerous machine learning algorithms (e.g., the random forest algorithm) have been used to identify the source of heavy metals. These methods exhibit strong predictive capabilities and handle complex nonlinear relationships, accurately and quantitatively exploring the contributions of different potential sources to heavy metal contents. By constructing and training machine learning algorithms, the sources of heavy metals can be predicted and identified more accurately, providing a scientific basis for environmental management and policy-making.

In addition to developing source tracing methods, the depth and breadth of research in this field have been continuously expanded. Previous related studies on soil heavy metal source analysis in the early phase have focused mainly on Cd. In recent years, on the other hand, researchers have devoted great attention to other heavy metal elements, such as copper (Cu), lead (Pb), and zinc (Zn). These heavy metal elements can exhibit different distribution and migration patterns in the environment, including significant mobility through water bodies. The scope of research areas has gradually expanded over the period considered. Specifically, initial related studies have focused mainly on agricultural soils before expanding to urban soils, mining area soils, delta regions, and water bodies. This shift recognizes that water pollution plays a critical role in the dispersion and accumulation of heavy metals in soils and sediments. These areas have different environmental characteristics and pollution sources and face different environmental management and policy challenges. Therefore, conducting heavy metal source tracing research in these areas can provide a more comprehensive understanding of heavy metal pollution and offer targeted recommendations for environmental management and policy-making in different regions.

3.2.2. Keyword Cluster Analysis

Figure 3 shows the clustering map of keywords related to heavy metal source analyses over the 2004–2024 period, revealing the research dynamics and evolution in this field. The top 10 network clusters highlight the main focus of the research field, including source analysis, source apportionment, heavy metal, principal component analysis, soil pollution, magnetic susceptibility, cluster analysis, trace metals, spatial variability, and material flow analysis. The issue of soil heavy metal source identification has attracted great attention from researchers due to two main reasons. The first is heavy metal pollution (trace metals/heavy metal), which demonstrates a significant concern for the soil environment. The second reason is the evaluation of the performance of related analytical methods (magnetic susceptibility, cluster analysis, trace metals, and spatial variability) in the identification of different sources of soil heavy metal elements. The concentration of high-frequency keywords between 2004 and 2010 reflects the extensive and in-depth exploration of soil heavy metal source analysis during this period. On the other hand, few new high-frequency keywords appeared after 2010, indicating that soil source analysis research has constructed a relatively mature and stable framework. On the other hand, research hotspots have become increasingly concentrated, with core keywords, such as soil pollution, heavy metals, source identification, and ecological risk assessment, becoming increasingly recognized in sectors. According to the keyword timeline graph, “source analysis” was a major keyword used in related research, particularly at the beginning of this field, focusing on methods, regions, environmental media such as soil and water, and objects of source identification. In addition to the core related keywords, such as “source apportionment”, other keywords were considered in the 2010–2014 period, including “speciation”, “pollution characteristic”, “ecological risk”, and “health risk assessment.” Over time, heavy metal source analysis has gradually interacted with the distribution, management, and risk assessment of pollution sources in both soil and water environments, showing a trend toward diversification and integration (Figure 4). These changes demonstrate the depth and expansion of research on soil heavy metal source analysis, as well as the contributions to associated practical applications and environmental protection. Future related studies on soil heavy metal source analysis can, therefore, provide further insights into associated environmental protection and human health.

The Keyword Cluster Analysis presented in this section offers direct feedback on the selection criteria used to identify the 452 citations included in our study. This study reveals the evolution of research topics, trends, and hot spots, thereby assisting us in assessing whether our search criteria effectively capture the breadth and depth of research on source apportionment of heavy metals in soil. Our selection criteria were designed to encompass a wide range of studies on soil heavy metal source apportionment, covering various methodologies, geographical locations, and environmental matrices. The clusters formed by keywords such as ‘soil pollution’, ‘heavy metals’, ‘source identification’, and ‘ecological risk assessment’ indicate the main areas of focus within the field and suggest that our search criteria were broadly aligned with the key themes and research priorities.

3.2.3. Keyword Emergence Analysis

The keyword emergence analysis of research on soil heavy metal source analysis revealed the dynamic development trends and cutting-edge research points over the 2004–2024 period. Emerging citation terms are keywords with sudden increases in academic publications over a short period, providing a valuable perspective to understand the research hotspots in a specific field. The emergence analysis results of keywords related to soil heavy metal source analysis are shown in Figure 5. In addition to the core keywords, such as “soil”, “heavy metals”, and “source apportionment”, burst terms over the 2004–2024 period mainly focused on statistical methods, such as “multivariate statistical analysis”, “principal component analysis”, “cluster analysis”, and “geostatistical analysis”. Among them, “principal component analysis” and “multivariate statistical analysis” had higher emergence intensities over a comparatively longer period, followed by “geostatistical analysis” and “cluster analysis”. Specifically, the applications of related research methods have attracted considerable attention from researchers in the field over the 2009–2019 period, highlighting the importance of continuous innovation and application of associated research methods in promoting the development of the field. Indeed, the core research hotspots in heavy metal source analysis have been mainly explored since 2018, including different related keywords such as “source analysis”, “source apportionment”, “soil”, “river”, and “heavy metal pollution.” Simultaneously, other important keywords have emerged in recent years, including “health”, “health risks”, and “health risk assessment”. This finding demonstrates that studies on heavy metals have not been limited to the determination of pollution sources in soils but have gradually extended into other issues, including water pollution and human health risks associated with heavy metal contamination in both soil and aquatic environments. Indeed, numerous researchers have focused on the potential human health risks associated with soil heavy metal pollution based on the theoretical foundation of heavy metal source analysis. Research on soil heavy metal source analysis has undergone a transformation in method innovation and research hotspots over the 2004–2024 period. The development and application of different related methods have driven in-depth development in the field of heavy metal source analysis, encompassing both soil and water environments. Moreover, health risk assessment studies have demonstrated the broad impacts and application prospects of this research topic, highlighting the need to address heavy metal pollution in an integrated manner across environmental media.

The keyword emergence analysis highlights the growing interest in statistical techniques such as principal component analysis and geostatistics for soil heavy metal source apportionment. However, it is also essential to recognize the emergence of innovative methods like machine learning and advanced GIS tools that are shaping the future of this field. Machine learning algorithms offer enhanced predictive capabilities and can handle complex, large-scale datasets, making them particularly suitable for identifying sources of heavy metal pollution. Similarly, advanced GIS tools are enabling more sophisticated spatial analyses, allowing researchers to better understand the distribution and transport of pollutants in the environment. The integration of these cutting-edge methods signals a significant advancement in the field and suggests that future breakthroughs may lie in their further development and application [40].

3.3. Analysis of Publishing Institutions

In this study, an in-depth analysis of the publishing institutions in the database was conducted to explore the academic trends in research on soil heavy metal source analysis. The results indicated 287 nodes of various research institutions, with E and Density values of 488 and 0.0119, respectively (Figure 6). The connection density among these 287 institutions was relatively low, suggesting low closeness of academic cooperation or citation among these institutions. The institutions with the highest publication volumes are reported in

Table 1. Publication volume statistics by institutions.

Institution Name	Number of Publications	Year
Chinese Academy of Sciences	86	2006
University of Chinese Academy of Sciences	33	2008
China University of Geosciences	24	2010
Ministry of Agriculture & Rural Affairs	20	2010
Zhejiang University	19	2009
Chinese Research Academy of Environment Sciences	18	2014
Institute of Geographic Science & Natural Resources Research	17	2008
Beijing Normal University	16	2011
China Geological Survey	15	2011
Lanzhou University	13	2014
China University of Mining & Technology	12	2005
Nanjing Institute of Soil Science	12	2008
Institute of Geochemistry	11	2010
Chinese Academy of Agricultural Sciences	10	2010
Nanjing University	10	2013

. The top five institutions were the Chinese Academy of Sciences, University of the Chinese Academy of Sciences, China University of Geosciences, Ministry of Agriculture & Rural Affairs, and Zhejiang University. This highlights the central role of universities and research institutes in soil heavy metal source analysis research. These institutions produced many related academic papers, providing useful directions for future related research. According to the obtained partial map, the different institutions formed a cluster centered on the Chinese Academy of Sciences. This institution exhibited a relatively high centrality value. The areas covered in this institution cluster are mainly geology, environmental sciences, water resources, hydrology, mining, agriculture, and policy-making institutions. Academic collaboration between these institutions can not only broaden the academic horizon but also promote international academic exchange and cooperation. It is worth noting that although there was some international cooperation, there was limited related research between institutions in different regions, showing fewer collaborative relationships. This suggests regional differences in directions and problems related to soil heavy metal source analysis. Therefore, it is crucial to further strengthen international cooperation and exchange between scholars to promote the development of research on soil heavy metal analysis.

3.4. Analysis of Publishing Authors

Table 2. Author publication statistics from 2004 to 2024 (Top 17).

Author Name	Institution	Number of Publications
Luo, Jie	Yangtze University	6
Bai, Junhong	Beijing Normal University	5
Ma, Jin	Chinese Research Academy of Environmental Sciences	4
Lei, Mei	Chinese Academy of Sciences	4
Chen, Tao	Northwest A&F University	4
Bi, Xiangyang	China University of Geosciences	4
Zhou, Jie	Yangzhou University	3
Zhang, Chaolan	Guangxi University	3
Xiao, Rui	Wuhan University	3
Ren, Zhouqiao	Zhejiang Academy of Agriculturalciences	3
Lv, Xiaonan	Zhejiang Academy of Agriculturalciences	3
Han, Peng	China University of Geosciences, Beijing	3
Feng, Ke	Yangzhou University	3
Fei, Xufeng	Zhejiang Academy of Agriculturalciences	3
Deng, Meihua	Zhejiang Academy of Agricultural Sciences	3
Christakos, George	San Diego State University	3
Chen, Tongbin	Chinese Academy of Sciences	3

presents the number of publications by authors (top 17). Among the 452 published papers, Luo Jie ranks first with a total of six publications, followed by five authors with four or five published papers. This finding indicates a large research community on the topic of soil heavy metal source analysis, with a substantial difference between the total number of publications and the individual publication counts. This indicates, on the one hand, that soil heavy metal source analysis has attracted the attention and involvement of many scholars. On the other hand, the presence of prolific authors also suggests an in-depth knowledge of soil heavy metal source analysis.

An author collaboration network map was generated in this study to gain an in-depth understanding of the collaboration network in this field (Figure 7). According to the obtained results, the collaborative relationships were mostly concentrated among a few prolific authors, forming multiple small collaborative groups. Although these groups cooperated closely with each other, the collaboration degree between them was relatively low. Specifically, the N, E, and Density values of the network were 510, 676, and 0.0052, respectively. These data indicate that although there was considerable collaboration within individual small researcher groups, the overall network connectivity and collaboration degrees between the authors were low. While this collaboration model promotes in-depth communication and cooperation within small researcher groups, it also limits knowledge exchange and academic innovation. The core authors in the network (e.g., Luo Jie, Chen Tao, Ma Jin, Bai Junhong, and Cai Limei) played significant roles in leading the field. Their research has not only had a profound academic impact but has also shaped the research direction and collaboration network of the field to a certain extent. However, the collaboration between these core authors and their teams has not formed an extensive network but existed relatively independently within the entire research ecosystem. Further analysis results revealed that the collaboration between individuals/teams from different countries and regions was relatively low. These geographical limitations may not only lead to relatively independent research findings and slow academic progress but also hinder the exchange and integration between researchers, thereby affecting the comprehensive development and advancement of research on soil heavy metal source analysis. It is, therefore, important to further promote academic exchanges and cooperation between different countries and regions to overcome this issue.

4. Future Research Directions

Soils are characterized by significant spatial heterogeneity due to the influences of various factors, such as climate, parent rocks, topography, and vegetation covers, on their formation processes. This spatial heterogeneity can substantially restrict the application of heavy metal tracing methods suitable for homogeneous environmental media to soil environments. Moreover, water environments often serve as pathways for the transport and redistribution of heavy metals, necessitating the integration of water and soil tracing methods to understand contamination sources and migration patterns fully. Therefore, when conducting source analysis exploration, it is essential to analyze the uncertainty of applied tracing methods and to reasonably divide tracing areas when exploring the sources of soil heavy metal elements.

The sources of soil heavy metal contents are variable and complex, making their spatial distribution characteristics significantly uneven. In fact, it is challenging to accurately identify the sources of soil heavy metal elements using single-source analysis methods. It is, therefore, crucial to adopt multi-method approaches in future related studies. Furthermore, it remains important to consider the impacts of natural conditions (e.g., soil type, topography, hydrological features, and water flow dynamics) in future research to enhance the accuracy of different methods and to bridge the gap between soil and water environment tracing methods. These suggestions can be useful for proposing a more comprehensive and precise analytical framework for soil heavy metal source analysis. The combined use of multiple analytical methods can also effectively reduce the errors and uncertainties of single-method applications, enhancing the accuracy of soil heavy metal source identification.

This study discerns a marked emphasis on soil heavy metal source apportionment in Asia. Nonetheless, it is imperative to acknowledge the research disparities in different regions. These disparities are presumably attributable to regional variations in regulatory frameworks, funding constraints, research capacities, and environmental priorities. The underrepresentation of certain regions may introduce bias into our findings and hinder the establishment of comprehensive global policies. With the implementation of the European Union’s Soil Monitoring Law, European countries are anticipated to amplify their investigative and monitoring efforts regarding soil heavy metal pollution [41]. This enhanced focus is projected to foster advancements in pollution source identification, pollutant migration mechanisms, and risk assessment. Such initiatives are expected to contribute to the formulation of more robust soil protection policies and the implementation of more effective pollution control strategies in Europe.

Our research underscores the interdependence of soil and water pollution, underscoring the need for policies that employ integrated management strategies to address both simultaneously. By pinpointing key heavy metals and their sources, policymakers can direct regulation and mitigation efforts towards specific industries or activities. Advanced methodologies, such as the enhanced use of machine learning algorithms and spatial analysis techniques, should be integrated into policy frameworks to boost the accuracy and efficiency of pollution monitoring and source apportionment. The existing gaps in the literature, including a dearth of research on certain regions or specific heavy metals, must guide policymakers in prioritizing areas for further research and investment. Policymakers can leverage these insights to develop targeted regulations for key industries, invest in state-of-the-art monitoring technologies, and support comprehensive land and water resource management strategies. Incorporating these recommendations into policy frameworks can enhance the effectiveness of heavy metal pollution management and ensure the protection of land and water resources.

5. Conclusions

In conclusion, our bibliometric analysis of soil heavy metal source apportionment research conducted over the past two decades indicates a rising trend in research output and the adoption of evolving methodologies. This expanding research scope underscores the escalating recognition of the intricacies surrounding heavy metal pollution. Nevertheless, notable regional disparities and specific research gaps have been identified, particularly the necessity for a global perspective and the integration of soil and water research. Our findings offer guidance for future research endeavors and enrich the existing literature by underscoring the need to address these gaps. By addressing these gaps, we can deepen our comprehension of heavy metal pollution and devise more efficacious strategies for environmental management and policy formulation.