Systematic bibliographic analysis of heavy metal remediation

Heavy metals, such as lead, mercury, cadmium, and arsenic, have long been recognized as severe environmental pollutants with significant implications for human health (Balali-Mood et al. 2021). These toxic elements are pervasive in various industrial processes and waste disposal activities, often contaminating water sources and soil (Singh et al. 2022). The presence of heavy metals in drinking water has been directly linked to severe health issues, including chronic conditions and cancers, such as gastric cancer (Parida & Patel 2023). The urgency to address this contamination is underscored by the substantial public health risks associated with exposure to these pollutants (Ali et al. 2021).

Effective heavy metal remediation is crucial for mitigating the adverse effects of these contaminants and ensuring the safety of environmental and human health (Rajendran et al. 2022). Remediation strategies are designed to remove or neutralize heavy metals in contaminated environments, including water and soil (Ayangbenro & Babalola 2017). However, the complexity and diversity of heavy metal pollution necessitate a multifaceted approach to remediation, involving various methods and technologies (Ayangbenro & Babalola 2017). The effectiveness of these remediation techniques depends on numerous factors, including the type and concentration of heavy metals, the characteristics of the contaminated medium, and the environmental conditions (Azhar et al. 2022).

This study adopts a bibliographic approach to systematically explore and analyze the current state of heavy metal remediation. Compared to traditional bibliographic methods like VOSviewer (Chen & Ding 2023a; Chen & Ding 2024), the study aims to provide a comprehensive summary of established practices via the platform R-4.4.1, allowing to provide more detailed information. This bibliographic analysis comprehensively reviews key remediation techniques, including physical, chemical, and biological methods. It aims to highlight the effectiveness and applicability of these approaches in addressing heavy metal contamination. Physical methods, including soil washing and filtration, are employed to physically separate or remove heavy metals from contaminated media (Dermont et al. 2008). Chemical methods, such as precipitation and redox reactions, transform heavy metals into less toxic forms or facilitate their removal (Shi et al. 2021). Biological methods, such as phytoremediation and bioremediation, utilize biological organisms to absorb, degrade, or stabilize heavy metals (Raklami et al. 2022).

This study aims to answer the critical question: What are the current trends in research on heavy metal remediation, and how can emerging technologies, such as big data and machine learning, shape future remediation strategies? By addressing this question, the study uses bibliometric analysis to map the global landscape of heavy metal remediation research, highlighting key methodologies, contributing organizations, and leading countries driving innovation in this field.

In addition to examining established remediation methods, this study explores the significant contributions of prominent organizations and research institutions at the forefront of heavy metal cleanup. It identifies the countries that are making the most notable advancements in remediation technologies, reflecting a global effort to combat heavy metal pollution. By mapping the contributions of these research entities, the study provides valuable insights into the interdisciplinary and collaborative nature of heavy metal remediation efforts worldwide.

The study further explores the prospects of heavy metal remediation, focusing on emerging technologies that could revolutionize the field. In particular, advances in big data analytics and machine learning are examined as transformative tools. Big data can provide comprehensive insights into pollution patterns, while machine-learning algorithms have the potential to optimize remediation strategies and predict outcomes based on large, complex datasets. These technologies offer promising opportunities to improve the efficiency and effectiveness of remediation processes, paving the way for innovative solutions to address persistent environmental challenges.

To ensure the selection of high-quality research papers, this study exclusively utilizes data downloaded from the Web of Science database (Raklami et al. 2022). The search was conducted for articles focusing on the topic of ‘heavy metal remediation,’ resulting in a dataset of 1,011 records, with the full record available for 1,000 articles as of August 26, 2024. The authors employed a comprehensive method to collect all available papers on heavy metal remediation.

To assess the contribution and productivity of different countries, organizations, and authors, the study employs the first-author rule. This approach allows for an accurate calculation of publication output and contributions by assigning a significant weight to primary authorship when evaluating the impact and productivity of the respective contributors.

By adhering to this method, the study aims to provide a rigorous analysis of the current research landscape in heavy metal remediation, identifying key contributors and trends within the field. The visualization is performed under platform R-4.4.1.

We compared our method with VOSviewer (Chen & Ding 2023b). VOSviewer is limited to analyzing fewer than 1,000 papers, while our method has no such restriction. VOSviewer highlights only key countries, regions, and institutions in heavy metal remediation, while our method includes a broader range, even those with fewer papers and citations. VOSviewer is less sensitive to annual trends, but our method effectively captures yearly changes. In short, VOSviewer offers a more visually appealing and direct representation of key information, while our method captures more detailed information.

Figure 1(d) provides a clear visual of the most productive affiliations ranked 15, with their total publications, highlighting the most productive affiliations. For instance, the Chinese Academy of Sciences, Tongji University, Hunan University, the Indian Institute of Technology System, Egyptian Knowledge Bank, the University of Illinois System, the Technical University of Denmark, Chongqing University, Human Agricultural University, Universiti Malaya, Osaka University, Oklahoma State University System, the Council of Scientific & Industrial Research (CSIR) – India, the China University of Mining & Technology, Central South University. The length of each bar indicates the total article contributions in the research field ‘heavy mental remediation,’ which corresponds to Figure 1(b) of country/region contributions. These results can be attributed to the heavy metal remediation problems in those countries/regions they have faced in the past 30 years. Technological issues and research problems are pending while they take action to address them. The most productive authors (top 10) and their accumulated citations are depicted in Figure 1(e) and 1(f). Gupta VK, Reddy KR, and Yang ZH ranked top 3 for the total citations of their articles. Author lists are the most productive when their articles range from 2 to 4.

Table 1 presents a comprehensive list of key heavy metal contaminants and their associated health impacts. This table highlights various heavy metals commonly released during industrial activities, such as chemical manufacturing and mining (Liu et al. 2022). These contaminants often enter the groundwater through soil contamination, posing significant health risks, particularly in many developing countries/regions (Zeng et al. 2023). In these regions, groundwater is frequently accessed through wells for drinking water, leading to widespread health issues among the population (Kapoor & Singh 2021). The table underscores how industrial practices contribute to environmental contamination and emphasizes the critical need for effective management and remediation strategies to mitigate the adverse effects of heavy metal exposure on human health.

Acute exposure to arsenic is known to induce a range of abnormalities in the gastrointestinal and dermal systems (Balakumar & Kaur 2009). The gastrointestinal effects can include symptoms such as nausea, vomiting, and abdominal pain, which are consistent with arsenic's ability to disrupt normal cellular functions and processes in the digestive tract. On the dermal side, arsenic can cause skin lesions, rashes, and other forms of dermatitis. These manifestations highlight the toxic nature of arsenic and underscore the importance of mitigating exposure to this hazardous substance. The variability in the severity of these symptoms can depend on the level and duration of exposure, as well as on individual susceptibility.

Cadmium exposure has been identified as a potential risk factor for osteoporosis (Genchi et al. 2020). This heavy metal can accumulate in the body, particularly in the kidneys, where it interferes with calcium metabolism and bone health. Studies suggest that cadmium may disrupt the balance of bone-forming and bone-resorbing cells, leading to decreased bone density and increased fracture risk. This association emphasizes the need for stringent control measures to limit cadmium exposure, especially in occupational settings and areas with high environmental contamination.

Hexavalent chromium, or Cr(VI), has been linked to an increased risk of stomach cancer based on limited epidemiological evidence (Zhitkovich 2011). Cr(VI) is a known carcinogen and can cause cellular damage through oxidative stress, which contributes to carcinogenesis. The ingestion of Cr(VI)-contaminated water or food poses a significant health risk, particularly in areas where industrial activities or waste disposal practices lead to chromium contamination. This connection between Cr(VI) and stomach cancer highlights the urgent need for regular monitoring and regulation of chromium levels in drinking water and food supplies.

Childhood lead poisoning remains a serious public health concern due to its profound impact on neurodevelopment and overall health (Tchounwou et al. 2012). Lead exposure in children is associated with decreased intelligence, delayed cognitive and behavioral development, and difficulties with hearing and speech. The neurotoxic effects of lead can manifest as lower IQ scores and behavioral issues, including increased impulsivity and reduced attention span. These outcomes underscore the critical need for preventive measures and effective interventions to reduce lead exposure, especially in vulnerable populations.

Mercury, once absorbed into the body, accumulates in the kidneys, neurological tissues, and liver, where it exhibits a slow excretion rate and induces various toxic effects (Tchounwou et al. 2003). Mercury exposure can lead to gastrointestinal disturbances, neurological symptoms such as tremors and cognitive deficits, and nephrotoxicity characterized by renal damage. The persistence of mercury in the body, coupled with its harmful effects, underscores the importance of reducing exposure sources and implementing effective management strategies to address mercury contamination in both environmental and occupational settings.

Heavy metal contamination in water bodies poses a significant threat to both environmental and human health (Peng et al. 2022). Consequently, effective remediation technologies are crucial for addressing this pressing issue (Rajendran et al. 2022). The primary methods for heavy metal pollution control include physical, chemical, and biological approaches, with some techniques integrating multiple methods (Azhar et al. 2022). Table 2 categorizes these technologies, providing an overview of their applications and effectiveness.

In physical approaches, natural zeolite powder, when incorporated into cementitious composites, demonstrates remarkable efficiency as a heavy metal absorbent. Zeolites, known for their high surface area and ion exchange capabilities, offer a sustainable solution for heavy metal removal (Rudžionis et al. 2021). Similarly, renewable P-type zeolite has shown exceptional performance in absorbing heavy metals, further emphasizing the role of zeolite-based materials in pollution mitigation (Chen et al. 2020). Functionalized layered double hydroxides (LDHs) are another advanced material used for heavy metal ion absorption (Tang et al. 2020). These materials can be engineered to enhance their affinity for specific metal ions, improving their effectiveness in various environmental settings.

In chemical approaches, carboxylic acids play a critical role in reducing Cr(VI) to the less toxic Cr(III), a process that occurs in natural and engineered environments. This reduction is essential for mitigating the risks associated with hexavalent chromium, a known carcinogen (Jiang et al. 2019). Iron metal has also proven effective in reducing Cr(VI) to a lower oxidation state, which is less harmful (Alowitz & Scherer 2002). The use of iron in remediation processes is well documented, highlighting its practical applications in treating contaminated sites. Similarly, carbonate green rust has been identified as a mediator for the reduction of Cr(VI), offering another method for addressing chromium pollution (Williams & Scherer 2001).

Biological methods provide additional avenues for heavy metal remediation. Disrupting putrescine biosynthesis in Shewanella oneidensis has been found to enhance biofilm cohesiveness and improve Cr(VI) immobilization (Ding et al. 2014). This approach underscores the potential of microbial and biochemical methods in managing heavy metal contamination. Furthermore, the detoxification mechanisms of Bacillus species offer valuable insights into biological strategies for heavy metal removal (Alotaibi et al. 2021). Lastly, biochar-enhanced bioretention cells represent a promising approach for heavy metal ion removal in rainwater runoff (Xiong et al. 2022).

In summary, the variety of methods available for heavy metal remediation, including physical, chemical, and biological approaches, provides a comprehensive toolkit for addressing environmental contamination. Each technique has its unique advantages and applications, and ongoing research continues to improve and refine these methods to achieve more effective and sustainable solutions for heavy metal pollution.

In the preceding sections, we have explored the primary types of heavy metals, their potential health risks, and the main remediation technologies currently in use. Moving forward, it is essential to consider the potential of emerging technologies to enhance our ability to address heavy metal pollution (Nti et al. 2023). Two such technologies – big data and artificial intelligence (AI) (Luan et al. 2020) – are gaining prominence in various fields such as facial recognition (Sharma et al. 2020; Coe & Atay 2021), autonomous driving (Dogan et al. 2011; Bachute & Subhedar 2021), and species distribution prediction (Chen & Ding 2022). These technologies also hold considerable promise for the prevention and management of heavy metal contamination.

For example, AI and big data have been used in water quality monitoring systems to predict and manage heavy metal contamination in real-time. By analyzing sensor data from various sources, AI algorithms can detect changes in water quality and predict when heavy metal contamination might occur, allowing for timely interventions (Zhao et al. 2023). This predictive capability can significantly improve the efficiency and effectiveness of remediation efforts, reducing the time and resources needed to address contamination issues.

By consolidating diverse data sources, including industrial emissions, population demographics, precipitation patterns, hydrological data, and heavy metal contamination monitoring, we can create comprehensive databases. These datasets can then be utilized to develop machine-learning models capable of analyzing complex patterns and predicting potential pollution events. Furthermore, machine-learning algorithms can analyze historical data to forecast the likelihood and location of future heavy metal contamination events (Zhang et al. 2023). This predictive capability allows for proactive measures, such as early warnings and strategic interventions, to be implemented. Policymakers and environmental managers can use these insights to prepare and respond more effectively, thereby mitigating the impact of heavy metal pollution and reducing associated health risks (Chen & Ding 2023b).

Moreover, the application of AI in modeling and simulation can lead to more accurate predictions of contamination hotspots and trends (Zhang et al. 2023). This could enhance our ability to target remediation efforts more precisely and allocate resources more efficiently. The synergy between big data, machine learning, and environmental science has the potential to revolutionize how we monitor, predict, and manage heavy metal pollution. The adoption of big data and machine-learning technologies in heavy metal pollution control offers a promising frontier for advancing our understanding and response capabilities. By leveraging these innovative tools, we can improve predictive accuracy, enhance preventive measures, and ultimately reduce the adverse effects of heavy metal contamination on public health and the environment.

Heavy metals often enter water systems due to industrial processes such as chemical production and metallurgy, leading to significant health risks, particularly in developing countries where people frequently rely on well water. This dependence results in the consumption of contaminated water, which poses serious health hazards. To address this pressing issue, our paper employs an innovative bibliographic analysis method that provides more detailed insights compared to traditional VOSviewer techniques, allowing us to identify key countries, regions, and organizations that are actively engaged in heavy metal remediation. Through an extensive literature review, we found that developing countries, especially China and India, are playing an increasingly important role in remediation efforts.

We examined the primary heavy metal pollutants and the current remediation technologies available, including physical, chemical, and biological methods. Each approach has its advantages and limitations, highlighting the complexity involved in mitigating heavy metal pollution. Additionally, we explored emerging trends and the potential of advanced technologies, such as big data and machine learning, to enhance remediation strategies. These technologies offer opportunities to improve predictive accuracy, optimize remediation efforts, and ultimately reduce the impact of contamination.

To ensure the effective management of heavy metal pollution, it is essential to continue promoting research and technological innovation. Based on our findings, we suggest that governments focus on developing predictive analytics and early warning systems to detect contamination events before they become widespread. Investment should also be directed toward advancing sustainable and cost-effective remediation methods, particularly biological approaches that show promise for long-term recovery. Furthermore, international collaboration between developing and developed nations should be encouraged to foster knowledge sharing and improve access to advanced remediation technologies. Finally, policymakers should establish frameworks for efficient resource allocation to ensure that financial and human resources are directed toward the most critical areas of remediation. By implementing these recommendations, we can better address the global challenge of heavy metal contamination and its associated health risks.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.