Next Article in Journal
Financial Mechanism for Sustainable Development of the Marine Economy with Respect to Technology, Digitalization, and Low Carbonization
Previous Article in Journal
Evaluation of Groundwater Resources in the Middle and Lower Reaches of Songhua River Based on SWAT Model
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 16
Issue 19
10.3390/w16192840
water-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Research Hotspots and Trend Analysis in Modeling Groundwater Dense Nonaqueous Phase Liquid Contamination Based on Bibliometrics

by
Mengdie Ju
1,2,
Xiang Li
1,2,
Ruibin Wu
1,2,
Zuxin Xu
1,2 and
Hailong Yin
1,2,*
1
Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, Tongji University, Shanghai 200092, China
2
State Key Laboratory of Pollution Control and Resource Reuse Research, Tongji University, Shanghai 200092, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(19), 2840; https://doi.org/10.3390/w16192840
Submission received: 8 September 2024 / Revised: 2 October 2024 / Accepted: 4 October 2024 / Published: 6 October 2024
Browse Figures
Versions Notes

Abstract

:
Groundwater contamination by dense nonaqueous phase liquids (DNAPLs) poses a severe environmental threat due to their persistence and toxicity. Modeling DNAPL contamination is essential for understanding their distribution, predicting contaminant spread, and developing effective remediation strategies, but it is also challenging due to their complex multiphase behavior. Over the past few decades, researchers have developed various models, including multiphase flow, mass transfer, and solute transport models, to simulate the distribution of DNAPLs. To understand the research trends in DNAPL modeling in groundwater, a bibliometric analysis was conducted using CiteSpace based on 614 publications from the WoS Core Collection database (1993–2023). The publications were statistically analyzed, and the research hotspots and trends were summarized. The statistical analysis of the publications indicates that the United States is leading the international research on DNAPL models, followed by China and Canada; the collaboration between countries and disciplines in this field needs to be strengthened. Keyword clustering and burst detection reveal that the current research hotspots focus on multiphase flow models, mass transfer models, back diffusion, and practical applications of the models; the research trends are centered on back diffusion mechanisms, the characterization of contamination source zones, and prediction of the contaminant distribution at real-world sites, as well as optimization of the remediation strategies.

1. Introduction

Dense nonaqueous phase liquids (DNAPLs) refer to a class of organic contaminants that are denser than water and poorly soluble in water, existing in groundwater as a nonaqueous phase. Typical DNAPLs include chlorinated organic solvents [1], creosote [2], and polychlorinated biphenyls (PCBs) [3]. Once DNAPLs infiltrate the subsurface, they can become trapped in the soil and persist for extended durations due to their low water solubilities, mass transfer constraints, and resistance to biodegradation [4,5]. During this time, they slowly dissolve into the flowing groundwater and form an extensive contamination plume downstream. Meanwhile, the dissolved DNAPLs gradually seep into the low-permeability layers and become a potential secondary pollution source after the depletion of the original source, thereby keeping the DNAPL concentrations in the plume above the maximum contaminant level (MCL) [6]. Given the toxicity of DNAPLs and their potential health risks to humans [7], the remediation of groundwater DNAPL contamination has become an urgent issue that needs to be addressed.
Various remediation technologies have been employed to remove DNAPL contamination, such as surfactant-enhanced aquifer remediation (SEAR) [8,9], in situ chemical oxidation (ISCO) [10,11], and in situ thermal treatment [12,13]. While remediation technologies are the primary means of pollution control, modeling is also essential to ensure the effectiveness of remediation efforts because the DNAPL distribution in the subsurface cannot be observed directly. Therefore, modeling DNAPL contamination has consistently been a focal point of interest for scholars in the field of groundwater environmental studies. However, the research on DNAPL models varies in focus, and there is a lack of comprehensive statistical analyses of the research outcomes at the macro scale. Consequently, it is essential to systematically review the hotspots in the field of DNAPL models to accurately grasp the research directions and evolving trends.
Bibliometric analysis, as a method for systematically and quantitatively analyzing scientific literature, provides reliable data support and criteria for scientific research, making it an indispensable tool in the research field. Therefore, this study conducted a statistical analysis of nearly 30 years of literature related to DNAPL models following a bibliometric process [14]. This study also performed a visual presentation of the research status and trends, aiming to provide insights and references for future study.

2. Methodology

2.1. Database Selection

The WoS Core Collection database was selected as the primary data source for this study. In total, 695 publications were retrieved taking “DNAPL” and “model” as the search topics. Among these publications, 95% (i.e., 681 publications) were reported over the past 30 years, from 1 January 1993 to 31 December 2023. Therefore, the bibliometric analysis was performed based on the literature since 1993. Further, filtering the document type as “Article” and screening for deduplication, 614 publications finally remained for the following analysis.

2.2. Bibliometric Indicators and Tools Used

A general analysis of the annual publication trends was conducted based on the 614 articles, and the data were plotted using Origin software (version 2021) to gain an initial understanding of the overall trends in the DNAPL modeling research. The publishing countries, institutions, journals, and keywords over the past 30 years (1993–2023) were then statistically analyzed and mapped using CiteSpace 6.3.R1 [15] as the primary tool for bibliometric analysis.
In CiteSpace, the g-index is a metric used to measure the scientific productivity and impact of a set of articles or an author [16]. The k-value is a scaling factor that modifies the threshold for calculation of the g-index. In short, a larger k-value results in more nodes, while a smaller k-value results in fewer nodes in the map.
Based on the above, the CiteSpace parameters were set as follows. The time slice was set to 1 year. For the co-citation analysis of different countries, institutions, and journals, the node types were set to “Country”, “Institution”, and “Cited Journal”, respectively, with the k-value in the g-index parameter set to 25 for each. For the keyword clustering analysis, the node type was set to “Keyword”, with the g-index parameter k-value set to 20. This adjustment was made to reduce the number of nodes in the map, preventing it from becoming overly complex and difficult to interpret.

3. Findings of the Bibliometric Analysis

3.1. Analysis of the Publication Volume

The annual publication volume and its changes can reveal the development status of a research field and its current level of attention. From 1993 to 2023, the total number of publications related to DNAPL models accumulated each year (Figure 1). During the initial budding stage (1993–1999), the annual publication volume was generally below 10 papers. However, 1998 saw a notable increase, with 20 papers published. During the rapid development stage (2000–2010), the annual publication volume gradually increased and stabilized at 20–30 papers per year from 2002 onward, indicating sustained academic interest. During the maturity stage (2011–2023), the annual publication volume fluctuated significantly. The publication numbers were the lowest in 2011 and 2016, with only 12 papers, while 2023 saw a significant increase, with 40 papers published. From the above, it is clear that there is still room for further exploration in the research of DNAPL models.

3.2. Analysis of the Leading Countries

The national collaboration network reveals the importance of individual countries and the interconnections between multiple countries in a specific research field. The co-citation network of collaborative countries (Figure 2), based on the WoS Core Collection data, revealed that 48 countries have published research on groundwater DNAPL contamination modeling. The countries with 10 or more publications are listed in Table 1.
In terms of publication volume, the United States leads the field of groundwater DNAPL contamination modeling, with 307 publications, followed by Canada, with 112. This is related to the fact that most known DNAPL-contaminated sites are located in the United States and Canada [17]. China ranks third, with 82 publications, but started later than the United States and Canada, with relevant research beginning in 2003. Among the top ten countries by publication volume, the United States, Canada, Germany, and the Netherlands started their research early, while most of the other countries began their work after 2000.
Centrality is an indicator that reflects the importance of a country within a collaboration network. A centrality greater than 0.1 indicates that a country plays a crucial role in advancing the research field. In terms of centrality, the United States has a centrality of 0.95 in the national collaboration network, which is significantly higher than any other country. England follows, with a centrality of 0.23. Other countries with a centrality greater than 0.1 include China (0.18), France (0.16), Turkey (0.16), Greece (0.15), and Scotland (0.11). It is noted that the countries with high centrality are marked with a purple outer ring in Figure 2. Although Canada has a high publication volume, its influence is relatively weaker, with a centrality of 0.09.
Overall, the United States leads in DNAPL contamination modeling. This is due to its advanced chemical industry, which results in more contaminated sites [18] and draws significant scholarly attention. Furthermore, the establishment of laws and regulations, such as Superfund [19] and Underground Storage Tank (UST) regulations [20], provides essential legal, technical, and financial support for research on DNAPL contamination. Canada also plays a leading role in research in this field. Many major urban centers in Canada cover land contaminated by past industrial activities, and the resulting environmental threats have driven research on DNAPL contamination. In Canada, different provinces establish their own laws, regulations, and technical standards based on their circumstances to support pollution remediation research [21]. Then, China follows. Similarly, China has a large number of industrially contaminated sites, and the management policies vary across provinces [22]. However, unlike the other two countries, which focus on exploring the DNAPL contamination mechanisms based on models, China’s research on DNAPL models leans more towards methods for locating and identifying pollution sources.

3.3. Analysis of the Leading Institutions

A total of 365 institutions have published research related to DNAPL models according to the analysis of the selected publications. The top 25 institutions with more than 10 publications were selected to create a co-citation network map (Figure 3) for visual analysis.
In terms of timeline and publication volume, the United States Department of Energy (DOE), Queen’s University, and the University of Waterloo are the top three institutions with earlier research and higher publication numbers, with 38, 29, and 27 papers, respectively. Moreover, they have continued to produce relevant research in recent years. China and France started later but have recently focused more on DNAPL model research, achieving notable results. In DNAPL contamination modeling, China is represented by Nanjing University and Jilin University, with 24 and 22 publications, respectively.
Spatially, it is clear that most of the research institutions with a higher output are located in the United States, followed by Canada, which aligns with the earlier analysis of publication by country. In contrast, only Nanjing University and Jilin University stand out in terms of DNAPL model research in China, indicating that DNAPL models are not yet widely adopted. Additionally, there is significant collaboration between U.S. institutions, but international collaboration remains noticeably limited.
Therefore, increased collaboration between countries with substantial research capacities (like the United States) and developing nations could help alleviate the aforementioned imbalance and promote the development of the DNAPL modeling field. On the one hand, international cooperation can provide more site research material for both parties, enriching the real scenarios for model studies. On the other hand, the research directions may vary across countries, and collaboration allows for an exchange of research ideas that can complement different countries’ strengths and weaknesses, potentially even sparking innovative approaches. From this perspective, future research could potentially focus on the following two key areas: (1) increasing global awareness of groundwater DNAPL contamination and promoting the application of DNAPL models and (2) enhancing international collaboration to leverage research experience and drive innovation in DNAPL modeling technologies.

3.4. Analysis of the Dominant Journals

The top 10 journals ranked by citation count in DNAPL model research from 1993 to 2023 are listed in Table 2. The top three journals by citation count are Journal of Contaminant Hydrology, Water Resources Research, and Environmental Science & Technology, which also lead in terms of publication volume in this field. The publication journals indicate that DNAPL model research can be divided into two categories. One focuses on areas like “environment”, “chemistry”, and “materials”, with representative journals such as Environmental Science & Technology, Journal of Hazardous Materials, and Chemosphere emphasizing the exploration of DNAPL reaction mechanisms and generally having higher impact factors. The other category leans toward “groundwater”, “hydrogeology”, and “pollution distribution and remediation”, represented by journals like Journal of Contaminant Hydrology, Water Resources Research, and Groundwater, focusing on the simulation and prediction of the evolution of DNAPL pollution. In the top ten journals, most of their publications are related to the latter category, showing that DNAPL contamination modeling with a focus on hydrogeology is the primary area of research.
Notably, most of the journals publishing the DNAPL model literature have relatively low impact factors, likely due to the specialized and independent nature of the research. As shown in Figure 4, the DNAPL model research is mainly concentrated in fields such as “Ecology, Earth, Marine”, “Physics, Materials, Chemistry”, and “Mathematics, Systems, Mathematical” with limited interdisciplinary integration, resulting in restricted attention and impact. The proposed improvement ideas are as follows: (1) Further enhancing the collaboration between the above disciplines that significantly impact DNAPL contamination modeling to improve the model performance—for example, combining the experimental findings from physics, chemistry, and biology on DNAPLs to clarify their migration and transformation patterns, thereby allowing for adjustments to the model parameters or model validation [23,24,25]; coupling geophysical models and utilizing multi-source data to enhance the model accuracy [26,27]; and the flexible application of mathematical thinking and algorithms to improve the computational efficiency of the modeling [28,29]. (2) Developing human health risk models based on DNAPL contamination and expanding the field into areas such as health, society, economy, and policy, thereby increasing public awareness of DNAPL contamination and the influence of the DNAPL modeling field. For example, Pan et al. [7] developed a framework for human health risk assessment based on the DNAPL contamination distribution obtained from a DNAPL transport model.

3.5. Analysis of Research Hotspots and Trends Based on Keyword Clustering

3.5.1. Research Hotspots in Modeling DNAPL Contamination

The DNAPL model framework can be broadly divided into three parts: multiphase flow models, mass transfer models, and dissolved phase transport models. These models can be integrated with experimental findings to clarify the migration of DNAPLs in groundwater and predict the contamination distribution, aiding site remediation efforts. To understand the research status in DNAPL contamination modeling better, a keyword co-occurrence analysis was conducted, followed by clustering using the log-likelihood ratio (LLR) algorithm [30] to reveal current research hotspots. The keyword clustering yielded a Q value of 0.4169 and an S value of 0.7499 (Q > 0.3 indicates a significant structure; S ≥ 0.7 indicates full reliability). The 10 clusters related to the DNAPL models (Figure 5) highlight 5 prominent keywords each and exhibit the interconnections between clusters.
Cluster 0 (#0 multiphase flow): Multiphase flow models describe DNAPL multiphase migration and are the foundation of DNAPL modeling research. Due to their high density and low viscosity, DNAPLs infiltrate deeply into the subsurface, pass through the unsaturated zone, contaminate aquifers, and accumulate on the aquitard, forming pools [31]. In this process, DNAPLs remain as a NAPL phase and slowly dissolve into the groundwater, becoming a source of dissolved DNAPLs. Many researchers [32] use multiphase flow models to simulate this process and determine the DNAPL distribution in the source zone. Research indicates that the key factors to consider when setting up multiphase models include pollutant release scenarios [33,34,35], aquifer heterogeneity [36,37,38], constitutive relations representing the permeability–saturation–capillary pressure ( K r , N − S w − P c ) correlation [39,40,41], and groundwater flow velocity [31,42]. In complex hydrogeological scenarios, multiphase modeling often assumes field homogeneity, which can compromise the prediction accuracy in the presence of small-scale heterogeneities. To address this, some studies [43] use random permeability fields to capture spatial variability and improve the simulation accuracy. However, this approach requires challenging parameter acquisition and adds complexity to the multiphase models, significantly increasing the computational load. Thus, selecting the appropriate modeling method should be guided by the specific conditions of the site.
Cluster 1 (#1 reductive dichlorination): On the one hand, researchers focus on chlorinated organic solvents to enhance reductive dechlorination techniques. F. Fagerlund et al. [44] used experiments and modeling to study the coupled process of PCE dissolution and dechlorination by nanoscale zero-valent iron in DNAPL source zones. On the other hand, given the common occurrence of PCE and TCE contamination sites, most studies focus on simulating and predicting these pollutants [45,46,47].
Cluster 2 (#2 mass transfer): Understanding the mass transfer mechanism of DNAPLs to the dissolved phase and establishing an appropriate expression are crucial for source–sink terms in plume modeling. A typical empirical rate-limited expression [32] based on dissolution kinetics is
J = k ¯ a n w ( C s − C )
where J is the mass flux of dissolution from the NAPL phase to the aqueous phase, [ML−3T−1]; k ¯ is the average mass transfer coefficient at the NAPL–water interface, [LT−1]; a n w is the effective specific interfacial area between the NAPL phase and the aqueous phase, [L−1]; C s is the equilibrium aqueous phase concentration, also known as the effective solubility, [ML−3]; and C is the aqueous phase concentration, [ML−3].
Numerous studies have focused on optimizing the mass transfer coefficients to improve the analysis and mathematical representation of the DNAPL dissolution process [48,49,50,51]. These coefficients are then incorporated into solute transport models to achieve accurate estimates of the contaminant concentrations near source zones. To enable site-scale simulations, Parker and Park [52] developed an empirical expression for effective mass transfer coefficients under pseudo-steady-state conditions, providing a valuable reference for subsequent research [53,54,55,56,57].
In addition to methods with effective mass transfer coefficients, equilibrium streamtube methods and source strength functions are also used to simulate DNAPL dissolution. The equilibrium streamtube model [58] is an analytical model that predicts DNAPL dissolution by dividing the flow into independent streamtubes, assuming local equilibrium. The model parameters can be obtained through partitioning tracer tests, and the model has been applied to field-scale NAPL dissolution in a line-drive flow pattern [59]. A description of the source strength function can be found in the Cluster 4 section below.
However, the three mass transfer models mentioned above fail to capture the non-singular dissolution behavior caused by the complexity of the source zone structure. Therefore, Christ et al. [55] proposed a dual-domain model that divides the source zone into ganglia-dominated and pool-dominated regions, successfully simulating the two-stage NAPL dissolution process. Furthermore, considering that the dual-domain model struggles to simulate effectively when more than two regions are present, Kokkinaki et al. [56] proposed a process-based (PB) upscaled model that could describe the nonmonotonic, multistage average concentrations emanating from complex DNAPL source zones. In recent years, Guo et al. [60] proposed a simple one-dimensional, heterogeneous-source model to simulate the multi-stage dissolution behavior, which is more convenient for application in complex NAPL source zones. It is worth noting that most multi-stage dissolution mass transfer models are established based on methods with effective mass transfer coefficients.
Cluster 3 (#3 dense nonaqueous phase liquids): As a research subject, "DNAPL" appears as a name of a cluster. However, it is noteworthy that keywords such as “tomography”, “spectral induced polarization”, and “conductivity” appear under this cluster. This highlights that coupling geophysical multi-source data for DNAPL contamination modeling has become a key area of interest for scholars. Power et al. [61] developed a DNAPL-ERT numerical model by integrating Electrical Resistivity Tomography (ERT). This model calculates the resistivity response to key hydrogeological parameters (hydraulic permeability, porosity, clay content, groundwater salinity and temperature, and air, water, and DNAPL contents evolving with time), which enhances the sensitivity to heterogeneity in the DNAPL distribution and soil structure. Kang et al. [27,62,63] coupled geophysics with DNAPL models and integrated them into various inversion frameworks, which improved the source zone characterization.
Cluster 4 (#4 partial mass depletion): Mass depletion refers to the gradual dissolution and eventual depletion of the NAPL phase in the source zone over time. Similar to Cluster 2, this cluster describes the conversion of the NAPL phase into the dissolved phase. However, the difference is that the mass transfer model represented by the mass transfer coefficient links the multiphase flow model with the dissolved phase transport model, whereas the source strength function, which characterizes NAPL mass depletion, acts as a source term in the dissolved phase transport model. This simplifies the dissolution process in the source zone and reduces the model complexity. The source strength function is typically a power-law relationship between the effluent concentration and the remaining DNAPL mass. The source strength function proposed by Falta et al. [64] has been widely adopted:
C s ( t ) C 0 = ( M ( t ) M 0 ) Γ
where C s ( t ) and M ( t ) correspond to the DNAPL concentration in the source zone and the residual DNAPL mass at time t, respectively; C 0 and M 0 are the DNAPL concentrations in the source zone and the residual DNAPL mass initially; and Γ is a model parameter.
Cluster 5 (#5 source zone): This cluster focuses on the inversion and identification of DNAPL source zones in saturated aquifers, coupling models that simulate the fate of DNAPL contamination. China has conducted extensive research in this area, with most of its efforts focused on improving the accuracy of DNAPL source zone inversion. For example, Wang et al. [65] combined the ensemble Kalman filter with an improved butterfly optimization algorithm, improving the inversion accuracy and effectiveness. Furthermore, inversion modeling based on DNAPL models is gradually advancing towards deep learning and the integration of multi-source geophysical data. Kang [62] proposed a joint inversion framework (CVAE-ESMDA) combining a convolutional variational autoencoder (CVAE) with an ensemble smoother with multiple data assimilation (ESMDA). This approach integrates multiple data sources (Oscillatory Hydraulic Tomography, downstream DNAPL concentrations, and ERT) to estimate the DNAPL saturation in source zones more accurately. Based on hydraulic tomography, ERT, and partitioning interwell tracer test datasets, Guo et al. [66] proposed an iterative joint inversion framework coupling the multiphase flow model, which improves the identification of pool-dominated DNAPL source zone architectures.
Cluster 6 (#6 uncertainty analysis): Inversion of the DNAPL sources or optimization of the remediation strategies based on simulation-optimization methods often involves uncertainty, requiring repeated model runs and high computational costs. Therefore, many studies have developed surrogate models to reduce the computational load and conduct uncertainty analyses. Hou et al. [67] developed an integrated surrogate model based on support vector regression (SVR), kriging, and kernel extreme learning machine (KELM). A mixed homotopy–differential evolution (DE) algorithm was then combined with the surrogate for source inversion and uncertainty analysis, significantly improving the identification accuracy. Du et al. [68] developed a fast-running convolutional neural network (CNN) surrogate model to identify the optimal SEAR scheme under uncertainty, improving the optimization speed by 99.8% in 3D numerical experiments.
Cluster 7 (#7 immiscible displacement): In DNAPL contamination scenarios, immiscible displacement describes the relative movement between the NAPL and the water phases at the small-scale heterogeneous pore level, driven by differences in gravity and viscosity, leading to NAPLs displacing water and migrating downward. At the macro level, this corresponds to the multiphase flow described in Cluster 0. However, while multiphase flow simulation based on continuous models can statistically characterize the heterogeneity at the macro level, it is difficult for it to capture the displacement behavior between the NAPL and groundwater phases at the pore scale. Therefore, some researchers have developed models specifically for immiscible displacement at the pore scale. Trantham et al. [69] developed a Stochastic Aggregation Model (SAM) using an improved DLA algorithm to simulate the displacement of groundwater by DNAPLs with both higher and lower viscosities than groundwater. Nsir et al. [70] developed a numerical simulator based on a discrete network model, using pore body and throat size parameters from the particle size distribution in real porous media. The simulated NAPL–water immiscible two-phase flow results matched the experimental data.
Cluster 8 (#8 back diffusion): Back diffusion is the process of dissolved DNAPLs migrating into low-permeability zones, accumulating, and then diffusing back into the aquifer after a concentration reversal [71,72,73]. After the source zone DNAPLs are depleted or isolated, back diffusion can occur once the contaminant concentration in the aquifer drops to a certain level, becoming a secondary pollution source and keeping the plume concentrations above the MCL over time [6,74]. In recent years, back diffusion has gained attention due to its role in prolonging contamination persistence and making remediation more difficult [75]. Most studies have simulated and explored factors influencing its occurrence, such as DNAPL solubility [76,77], soil heterogeneity [71], adsorption–desorption [78,79], and biodegradation [72,80,81,82,83]. Notably, further exploration of the microscopic mechanisms of biodegradation in back diffusion is crucial for enhancing the detailed characterization of biodegradation and improving back diffusion simulations. Simulations of back diffusion are typically divided into two stages, marked by the removal or isolation of the source zone [29,71,74]. In the first stage, contaminants accumulate in low-permeability zones through forward diffusion. In the second stage, after source removal, the simulation continues to study back diffusion by observing plume tailing.
Cluster 9 (#9 contaminant mass discharge): The study of contaminant mass discharge is often closely linked to the mass transfer models in Cluster 2. Considering the challenges and costs of simulating DNAPL dissolution at the field scale, researchers have developed upscaled mass transfer models with domain-averaged coefficients to approximate real-site dissolution processes [53]. Simplified upscaled models, linked to mass discharge flux, can serve as an effective screening tool for evaluating source zone management strategies [55].
In summary, by reviewing and analyzing the current state of the research, the distribution of the key research hotspots is presented (Figure 6).

3.5.2. Assessment of Future Research Trends

The top 20 keywords with the highest burst strength from 1993 to 2023 are identified (Table 3). Based on analysis of the publication volume, the 30-year period can be divided into three stages: the initial budding stage (1993–1999), the rapid development stage (2000–2010), and the maturity stage (2011–2023). The main keywords during the initial budding stage were “two-phase flow”, “multiphase flow”, and “contaminant transport”, with the research focusing on the simulation of DNAPL multiphase migration. During the rapid development stage, more detailed descriptions were considered, including shifts in the contaminant types (from TCE to PCE), applications in heterogeneous sites, deeper exploration of the mass transfer processes, and increased emphasis on NAPL depletion in the source zone. In the maturity stage, the focus shifted toward contaminant removal and remediation, the impact of permeability, back diffusion in low-permeability zones, and the integration of geophysical techniques for source zone identification.
From a research trend perspective, DNAPL modeling has developed into a well-established system, with further studies primarily focused on optimizing the model details based on experimental findings. Future research directions include (1) investigating the back diffusion mechanisms and exploring methods for reducing plume persistence; (2) applying models to real sites for source zone characterization and pollution distribution; and (3) optimizing the remediation strategies to enhance their effectiveness and reduce costs.

4. Summary and Outlook

This study conducted a bibliometric analysis using CiteSpace on 614 DNAPL modeling-related publications from the WoS core database (1993–2023). Its findings are as follows:
(1)
DNAPL models remain a focus of scholarly attention, with research outputs continuing to grow steadily. The United States is leading the international research on DNAPL models, followed by China and Canada. However, the research priorities vary across countries, and international collaboration and exchange need to be strengthened.
(2)
The core of the DNAPL model research focuses on the simulation of DNAPL migration, transformation, and pollution distribution. In terms of published journals, the field is highly specialized, with a limited broader impact. To raise awareness and increase the research impact, developing health risk assessment models based on DNAPL contamination and strengthening cross-disciplinary connections could be beneficial.
(3)
Based on the keyword clustering analysis, the key research hotspots related to DNAPL models focus on multiphase flow models, mass transfer models, back diffusion, and practical applications of the models.
(4)
Based on the keyword burst analysis, the research trends in DNAPL modeling are centered on back diffusion mechanisms, characterization of the contamination source zones, and prediction of the contaminant distribution at real-world sites, as well as optimization of the remediation strategies.

Author Contributions

M.J.: Conceptualization, formal analysis, visualization, writing—original draft; X.L.: conceptualization, visualization, writing—original draft; R.W.: conceptualization and visualization; Z.X.: conceptualization, review and editing, supervision; H.Y.: conceptualization, methodology, review and editing, supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Project (Grant 2020YFC1808201).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ajo-Franklin, J.B.; Geller, J.T.; Harris, J.M. A survey of the geophysical properties of chlorinated DNAPLs. J. Appl. Geophys. 2006, 59, 177–189. [Google Scholar] [CrossRef]
  2. Wu, W.; Delshad, M.; Oolman, T.; Pope, G.A. Remedial Options for Creosote-Contaminated Sites. Groundw. Monit. Remediat. 2000, 20, 78–86. [Google Scholar] [CrossRef]
  3. Okuda, N.; Shimizu, T.; Muratani, M.; Terada, A.; Hosomi, M. Study of penetration behavior of PCB-DNAPL in a sand layer by a column experiment. Chemosphere 2014, 114, 59–68. [Google Scholar] [CrossRef] [PubMed]
  4. McCray, J.E.; Bai, G.; Maier, R.M.; Brusseau, M.L. Biosurfactant-enhanced solubilization of NAPL mixtures. J. Contam. Hydrol. 2001, 48, 45–68. [Google Scholar] [CrossRef]
  5. DiFilippo, E.L.; Carroll, K.C.; Brusseau, M.L. Impact of organic-liquid distribution and flow-field heterogeneity on reductions in mass flux. J. Contam. Hydrol. 2010, 115, 14–25. [Google Scholar] [CrossRef]
  6. Parker, B.L.; Chapman, S.W.; Guilbeault, M.A. Plume persistence caused by back diffusion from thin clay layers in a sand aquifer following TCE source-zone hydraulic isolation. J. Contam. Hydrol. 2008, 102, 86–104. [Google Scholar] [CrossRef]
  7. Pan, Y.; Zeng, X.; Xu, H.; Sun, Y.; Wang, D.; Wu, J. Assessing human health risk of groundwater DNAPL contamination by quantifying the model structure uncertainty. J. Hydrol. 2020, 584, 124690. [Google Scholar] [CrossRef]
  8. Schaerlaekens, J.; Carmeliet, J.; Feyen, J. Multi-objective optimization of the setup of a surfactant-enhanced DNAPL remediation. Environ. Sci. Technol. 2005, 39, 2327–2333. [Google Scholar] [CrossRef] [PubMed]
  9. Schaerlaekens, J.; Mertens, J.; Van Linden, J.; Vermeiren, G.; Carmeliet, J.; Feyen, J. A multi-objective optimization framework for surfactant-enhanced remediation of DNAPL contaminations. J. Contam. Hydrol. 2006, 86, 176–194. [Google Scholar] [CrossRef]
  10. West, M.R.; Kueper, B.H. Numerical simulation of DNAPL source zone remediation with in situ chemical oxidation (ISCO). Ad-Vances Water Resour. 2012, 44, 126–139. [Google Scholar] [CrossRef]
  11. Kim, U.; Parker, J.C.; Borden, R.C. Stochastic cost-optimization and risk assessment of in situ chemical oxidation for dense non-aqueous phase liquid (DNAPL) source remediation. Stoch. Environ. Res. Risk Assess. 2019, 33, 73–89. [Google Scholar] [CrossRef]
  12. Xie, Q.; Mumford, K.G.; Kueper, B.H. Simulating field-scale thermal conductive heating with the potential for the migration and condensation of vapors. J. Hazard. Mater. 2023, 453, 131439. [Google Scholar] [CrossRef] [PubMed]
  13. Xie, Q.; Mumford, K.G.; Kueper, B.H. Modelling gas-phase recovery of volatile organic compounds during in situ thermal treatment. J. Contam. Hydrol. 2020, 234, 103698. [Google Scholar] [CrossRef]
  14. Zare, F.; Elsawah, S.; Iwanaga, T.; Jakeman, A.J.; Pierce, S.A. Integrated water assessment and modelling: A bibliometric analysis of trends in the water resource sector. J. Hydrol. 2017, 552, 765–778. [Google Scholar] [CrossRef]
  15. Chen, C. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol. 2005, 57, 359–377. [Google Scholar] [CrossRef]
  16. Egghe, L. Theory and practise of the g-index. Scientometrics 2006, 69, 131–152. [Google Scholar] [CrossRef]
  17. Guleria, A.; Gupta, P.K.; Chakma, S.; Yadav, B.K. Unraveling the Fate and Transport of DNAPLs in Heterogeneous Aquifer Systems—A Critical Review and Bibliometric Analysis. Sustainability 2023, 15, 8214. [Google Scholar] [CrossRef]
  18. McGuire, T.M.; McDade, J.M.; Newell, C.J. Performance of DNAPL Source Depletion Technologies at 59 Chlorinated Solvent-Impacted Sites. Groundw. Monit. Remediat. 2006, 26, 73–84. [Google Scholar] [CrossRef]
  19. Kowalski, L.; Denne, J.; Dyer, R.; Garrahan, K.; Wentsel, R.S. Overview of EPA Superfund human health research program. Int. J. Hy-Giene Environ. Health 2002, 205, 143–148. [Google Scholar] [CrossRef]
  20. Yin, H. The Environmental and Economic Impacts of Environmental Regulations the Case of Underground Storage Tank Regulations; University of Pennsylvania: Philadelphia, PA, USA, 2006. [Google Scholar]
  21. Sousa, C. Contaminated sites: The Canadian situation in an international context. J. Environ. Manag. 2001, 62, 131–154. [Google Scholar] [CrossRef]
  22. Li, X.; Jiao, W.; Xiao, R.; Chen, W.; Liu, W. Contaminated sites in China: Countermeasures of provincial governments. J. Clean. Prod. 2017, 147, 485–496. [Google Scholar] [CrossRef]
  23. Yang, Z.; Niemi, A.; Fagerlund, F.; Illangasekare, T.; Detwiler, R.L. Dissolution of dense non-aqueous phase liquids in vertical fractures: Effect of finger residuals and dead-end pools. J. Contam. Hydrol. 2013, 149, 88–99. [Google Scholar] [CrossRef]
  24. Demiray, Z.; Akyol, N.H.; Akyol, G.; Copty, N.K. Surfactant-enhanced in-situ oxidation of DNAPL source zone: Experiments and numerical modeling. J. Contam. Hydrol. 2023, 258, 104233. [Google Scholar] [CrossRef]
  25. Kouznetsova, I.; Mao, X.; Robinson, C.; Barry, D.; Gerhard, J.I.; McCarty, P.L. Biological reduction of chlorinated solvents: Batch-scale geochemical modeling. Adv. Water Resour. 2010, 33, 969–986. [Google Scholar] [CrossRef]
  26. Knight, R. Ground Penetrating Radar for Environmental Applications. Annu. Rev. Earth Planet. Sci. 2001, 29, 229–255. [Google Scholar] [CrossRef]
  27. Kang, X.; Power, C.; Kokkinaki, A.; Revil, A.; Wu, J.; Shi, X.; Deng, Y. Characterization of DNAPL source zones in clay-sand media via joint inversion of DC resistivity, induced polarization and borehole data. J. Contam. Hydrol. 2023, 258, 104240. [Google Scholar] [CrossRef] [PubMed]
  28. Dokou, Z.; Pinder, G.F. Optimal search strategy for the definition of a DNAPL source. J. Hydrol. 2009, 376, 542–556. [Google Scholar] [CrossRef]
  29. Carey, G.R.; Chapman, S.W.; Parker, B.L.; McGregor, R. Application of an Adapted Version of MT3DMS for Modeling Back-Diffusion Re-mediation Timeframes. Remediat. J. 2015, 25, 55–79. [Google Scholar] [CrossRef]
  30. Tao, S.; Yang, D.; Zhang, L.; Yu, L.; Wang, Z.; Li, L.; Zhang, J.; Yao, R.; Huang, L.; Shao, M. Knowledge domain and emerging trends in diabetic cardiomyopathy: A scientometric review based on CiteSpace analysis. Front. Cardiovasc. Med. 2022, 9, 891428. [Google Scholar] [CrossRef]
  31. Erning, K.; Grandel, S.; Dahmke, A.; Schäfer, D. Simulation of DNAPL infiltration and spreading behaviour in the saturated zone at varying flow velocities and alternating subsurface geometries. Environ. Earth Sci. 2012, 65, 1119–1131. [Google Scholar] [CrossRef]
  32. Fetter, C.W.; Boving, T.; Kreamer, D. Contaminant Hydrogeology, 3rd ed.; Waveland Press: Long Grove, IL, USA, 2018. [Google Scholar]
  33. Dekker, T.J.; Abriola, L.M. The influence of field-scale heterogeneity on the infiltration and entrapment of dense nonaqueous phase liquids in saturated formations. J. Contam. Hydrol. 2000, 42, 187–218. [Google Scholar] [CrossRef]
  34. Lemke, L.D.; Abriola, L.M.; Goovaerts, P. Dense nonaqueous phase liquid (DNAPL) source zone characterization: Influence of hydraulic property correlation on predictions of DNAPL infiltration and entrapment. Water Resour. Res. 2004, 40, W01511. [Google Scholar] [CrossRef]
  35. Cheng, Z.; Lu, G.; Wu, M.; Hao, Y.; Mo, C.; Li, Q.; Wu, J.; Wu, J.; Hu, B.X. The Effects of Spill Pressure on the Migration and Remediation of Dense Non-Aqueous Phase Liquids in Homogeneous and Heterogeneous Aquifers. Sustainability 2023, 15, 13072. [Google Scholar] [CrossRef]
  36. Kueper, B.H.; Redman, D.; Starr, R.C.; Reitsma, S.; Mah, M. A Field Experiment to Study the Behavior of Tetrachloroethylene Below the Water Table: Spatial Distribution of Residual and Pooled DNAPL. Groundwater 1993, 31, 756–766. [Google Scholar] [CrossRef]
  37. Bradford, S.A.; Abriola, L.M.; Rathfelder, K.M. Flow and entrapment of dense nonaqueous phase liquids in physically and chemically heterogeneous aquifer formations. Adv. Water Resour. 1998, 22, 117–132. [Google Scholar] [CrossRef]
  38. Wu, M.; Yang, Y.; Lin, J.; Wu, J.; Wu, J.; Hu, B.X. The co-effect of heterogeneity and solute concentration on representative elementary volume of DNAPL in groundwater. J. Hydrol. 2020, 585, 124795. [Google Scholar] [CrossRef]
  39. Gerhard, J.I.; Kueper, B.H. Relative permeability characteristics necessary for simulating DNAPL infiltration, redistribution, and immobilization in saturated porous media. Water Resour. Res. 2003, 39, 1213. [Google Scholar] [CrossRef]
  40. Gerhard, J.I.; Kueper, B.H. Influence of constitutive model parameters on the predicted migration of DNAPL in heterogeneous porous media. Water Resour. Res. 2003, 39, 1279. [Google Scholar] [CrossRef]
  41. Gerhard, J.I.; Kueper, B.H. Capillary pressure characteristics necessary for simulating DNAPL infiltration, redistribution, and immobilization in saturated porous media. Water Resour. Res. 2003, 39, 1212. [Google Scholar] [CrossRef]
  42. Zheng, F.; Gao, Y.; Sun, Y.; Shi, X.; Xu, H.; Wu, J. Influence of flow velocity and spatial heterogeneity on DNAPL migration in porous media: Insights from laboratory experiments and numerical modelling. Hydrogeol. J. 2015, 23, 1703–1718. [Google Scholar] [CrossRef]
  43. Koch, J.; Nowak, W. Predicting DNAPL mass discharge and contaminated site longevity probabilities: Conceptual model and high-resolution stochastic simulation. Water Resour. Res. 2015, 51, 806–831. [Google Scholar] [CrossRef]
  44. Fagerlund, F.; Illangasekare, T.; Phenrat, T.; Kim, H.-J.; Lowry, G. PCE dissolution and simultaneous dechlorination by nanoscale zero-valent iron particles in a DNAPL source zone. J. Contam. Hydrol. 2012, 131, 9–28. [Google Scholar] [CrossRef] [PubMed]
  45. Pierce, A.; Chapman, S.W.; Zimmerman, L.; Hurley, J.; Aravena, R.; Cherry, J.A.; Parker, B.L. DFN-M field characterization of sandstone for a process-based site conceptual model and numerical simulations of TCE transport with degradation. J. Contam. Hydrol. 2018, 212, 96–114. [Google Scholar] [CrossRef] [PubMed]
  46. Yang, J.H.; Lee, K.K.; Clement, T.P. Impact of seasonal variations in hydrological stresses and spatial variations in geologic conditions on a TCE plume at an industrial complex in Wonju, Korea. Hydrol. Process. 2012, 26, 317–325. [Google Scholar] [CrossRef]
  47. Feo, A.; Pinardi, R.; Artoni, A.; Celico, F. Three-Dimensional High-Precision Numerical Simulations of Free-Product DNAPL Extraction in Potential Emergency Scenarios: A Test Study in a PCE-Contaminated Alluvial Aquifer (Parma, Northern Italy). Sustainability 2023, 15, 9166. [Google Scholar] [CrossRef]
  48. Zhu, J.; Sykes, J. The influence of NAPL dissolution characteristics on field-scale contaminant transport in subsurface. J. Contam. Hydrol. 2000, 41, 133–154. [Google Scholar] [CrossRef]
  49. Nambi, I.M.; Powers, S.E. Mass transfer correlations for nonaqueous phase liquid dissolution from regions with high initial sat-urations. Water Resour. Res. 2003, 39, 1030. [Google Scholar] [CrossRef]
  50. Maji, R.; Sudicky, E. Influence of mass transfer characteristics for DNAPL source depletion and contaminant flux in a highly characterized glaciofluvial aquifer. J. Contam. Hydrol. 2008, 102, 105–119. [Google Scholar] [CrossRef]
  51. Luciano, A.; Mancini, G.; Torretta, V.; Viotti, P. An empirical model for the evaluation of the dissolution rate from a DNAPL-contaminated area. Environ. Sci. Pollut. Res. 2018, 25, 33992–34004. [Google Scholar] [CrossRef]
  52. Parker, J.; Park, E. Modeling field-scale dense nonaqueous phase liquid dissolution kinetics in heterogeneous aquifers. Water Resour. Res. 2004, 40, W05109. [Google Scholar] [CrossRef]
  53. Christ, J.A.; Ramsburg, C.A.; Pennell, K.D.; Abriola, L.M. Estimating mass discharge from dense nonaqueous phase liquid source zones using upscaled mass transfer coefficients: An evaluation using multiphase numerical simulations. Water Resour. Res. 2006, 42, W11420. [Google Scholar] [CrossRef]
  54. Saenton, S.; Illangasekare, T.H. Upscaling of mass transfer rate coefficient for the numerical simulation of dense nonaqueous phase liquid dissolution in heterogeneous aquifers. Water Resour. Res. 2007, 43, W02428. [Google Scholar] [CrossRef]
  55. Christ, J.A.; Ramsburg, C.A.; Pennell, K.D.; Abriola, L.M. Predicting DNAPL mass discharge from pool-dominated source zones. J. Contam. Hydrol. 2010, 114, 18–34. [Google Scholar] [CrossRef] [PubMed]
  56. Kokkinaki, A.; Werth, C.J.; Sleep, B.E. Comparison of upscaled models for multistage mass discharge from DNAPL source zones. Water Resour. Res. 2014, 50, 3187–3205. [Google Scholar] [CrossRef]
  57. Boroumand, A.; Abriola, L.M. On the upscaling of mass transfer rate expressions for interpretation of source zone partitioning tracer tests. Water Resour. Res. 2015, 51, 832–847. [Google Scholar] [CrossRef]
  58. Wang, F.; Annable, M.D.; Jawitz, J.W. Field-scale prediction of enhanced DNAPL dissolution based on partitioning tracers. J. Contam. Hydrol. 2013, 152, 147–158. [Google Scholar] [CrossRef]
  59. Jawitz, J.W.; Fure, A.D.; Demmy, G.G.; Berglund, S.; Rao, P.S.C. Groundwater contaminant flux reduction resulting from nonaqueous phase liquid mass reduction. Water Resour. Res. 2005, 41, W10408. [Google Scholar] [CrossRef]
  60. Guo, Z.; Russo, A.E.; DiFilippo, E.L.; Zhang, Z.; Zheng, C.; Brusseau, M.L. Mathematical modeling of organic liquid dissolution in heterogeneous source zones. J. Contam. Hydrol. 2020, 235, 103716. [Google Scholar] [CrossRef]
  61. Power, C.; Gerhard, J.I.; Tsourlos, P.; Giannopoulos, A. A new coupled model for simulating the mapping of dense nonaqueous phase liquids using electrical resistivity tomography. Geophysics 2013, 78, EN1–EN15. [Google Scholar] [CrossRef]
  62. Kang, X.; Kokkinaki, A.; Kitanidis, P.K.; Shi, X.; Lee, J.; Mo, S.; Wu, J. Hydrogeophysical Characterization of Nonstationary DNAPL Source Zones by Integrating a Convolutional Variational Autoencoder and Ensemble Smoother. Water Resour. Res. 2021, 57, e2020WR028538. [Google Scholar] [CrossRef]
  63. Kang, X.; Kokkinaki, A.; Power, C.; Kitanidis, P.K.; Shi, X.; Duan, L.; Liu, T.; Wu, J. Integrating deep learning-based data assimilation and hydrogeophysical data for improved monitoring of DNAPL source zones during remediation. J. Hydrol. 2021, 601, 126655. [Google Scholar] [CrossRef]
  64. Falta, R.W.; Suresh Rao, P.; Basu, N. Assessing the impacts of partial mass depletion in DNAPL source zones I. Analytical modeling of source strength functions and plume response. J. Contam. Hydrol. 2005, 78, 259–280. [Google Scholar] [CrossRef] [PubMed]
  65. Wang, Z.; Lu, W.; Chang, Z.; Luo, J. A combined search method based on a deep learning combined surrogate model for groundwater DNAPL contamination source identification. J. Hydrol. 2023, 616, 128854. [Google Scholar] [CrossRef]
  66. Guo, Q.; Shi, X.; Kang, X.; Chang, Y.; Wang, P.; Wu, J. Integrating hydraulic tomography, electrical resistivity tomography, and partitioning interwell tracer test datasets to improve identification of pool-dominated DNAPL source zone architecture. J. Contam. Hydrol. 2021, 241, 103809. [Google Scholar] [CrossRef]
  67. Hou, Z.; Lao, W.; Wang, Y.; Lu, W. Cyclic Feedback Updating Approach and Uncertainty Analysis for the Source Identification of DNAPL-Contaminated Aquifers. J. Water Resour. Plan. Manag. 2021, 147, 04020103. [Google Scholar] [CrossRef]
  68. Du, J.; Shi, X.; Mo, S.; Kang, X.; Wu, J. Deep learning based optimization under uncertainty for surfactant-enhanced DNAPL remediation in highly heterogeneous aquifers. J. Hydrol. 2022, 608, 127639. [Google Scholar] [CrossRef]
  69. Trantham, H.; Durnford, D. Stochastic aggregation model (SAM) for DNAPL–water displacement in porous media. J. Contam. Hydrol. 1999, 36, 377–400. [Google Scholar] [CrossRef]
  70. Nsir, K.; Schäfer, G.; Roupert, R.d.C.; Mercury, L. Pore scale modelling of DNAPL migration in a water-saturated porous medium. J. Contam. Hydrol. 2018, 215, 39–50. [Google Scholar] [CrossRef]
  71. Yang, L.; Wang, X.; Mendoza-Sanchez, I.; Abriola, L.M. Modeling the influence of coupled mass transfer processes on mass flux downgradient of heterogeneous DNAPL source zones. J. Contam. Hydrol. 2018, 211, 1–14. [Google Scholar] [CrossRef]
  72. Ding, X.-H.; Feng, S.-J.; Zheng, Q.-T. Forward and back diffusion of reactive contaminants through multi-layer low permeability sediments. Water Res. 2022, 222, 118925. [Google Scholar] [CrossRef]
  73. Feng, C.; Liu, F.; Huang, F.; Chen, L.; Bi, E. Dense nonaqueous phase liquids back diffusion controlled by biodegradation and heterogeneous sorption-desorption. J. Clean. Prod. 2023, 382, 135370. [Google Scholar] [CrossRef]
  74. Chapman, S.W.; Parker, B.L. Plume persistence due to aquitard back diffusion following dense nonaqueous phase liquid source removal or isolation. Water Resour. Res. 2005, 41, W12411. [Google Scholar] [CrossRef]
  75. Blue, J.; Boving, T.; Tuccillo, M.E.; Koplos, J.; Rose, J.; Brooks, M.; Burden, D. Contaminant Back Diffusion from Low-Conductivity Matrices: Case Studies of Remedial Strategies. Water 2023, 15, 570. [Google Scholar] [CrossRef]
  76. Chapman, S.W.; Parker, B.L.; Sale, T.C.; Doner, L.A. Testing high resolution numerical models for analysis of contaminant storage and release from low permeability zones. J. Contam. Hydrol. 2012, 136–137, 106–116. [Google Scholar] [CrossRef]
  77. Adamson, D.T.; de Blanc, P.C.; Farhat, S.K.; Newell, C.J. Implications of matrix diffusion on 1,4-dioxane persistence at contaminated groundwater sites. Sci. Total Environ. 2016, 562, 98–107. [Google Scholar] [CrossRef]
  78. Maghrebi, M.; Jankovic, I.; Allen-King, R.M.; Rabideau, A.J.; Kalinovich, I.; Weissmann, G.S. Impacts of transport mechanisms and plume history on tailing of sorbing plumes in heterogeneous porous formations. Adv. Water Resour. 2014, 73, 123–133. [Google Scholar] [CrossRef]
  79. Maghrebi, M.; Jankovic, I.; Weissmann, G.S.; Matott, L.S.; Allen-King, R.M.; Rabideau, A.J. Contaminant tailing in highly heterogeneous porous formations: Sensitivity on model selection and material properties. J. Hydrol. 2015, 531, 149–160. [Google Scholar] [CrossRef]
  80. Adamson, D.T.; Chapman, S.W.; Farhat, S.K.; Parker, B.L.; Deblanc, P.; Newell, C.J. Characterization and Source History Modeling Using Low-k Zone Profiles at Two Source Areas. Groundw. Monit. Remediat. 2015, 35, 52–69. [Google Scholar] [CrossRef]
  81. Wanner, P.; Parker, B.L.; Hunkeler, D. Assessing the effect of chlorinated hydrocarbon degradation in aquitards on plume per-sistence due to back-diffusion. Sci. Total Environ. 2018, 633, 1602–1612. [Google Scholar] [CrossRef]
  82. Thouement, H.A.; Kuder, T.; Heimovaara, T.J.; van Breukelen, B.M. Do CSIA data from aquifers inform on natural degradation of chlorinated ethenes in aquitards? J. Contam. Hydrol. 2019, 226, 103520. [Google Scholar] [CrossRef]
  83. Halloran, L.J.S.; Hunkeler, D. Controls on the persistence of aqueous-phase groundwater contaminants in the presence of reactive back-diffusion. Sci. Total Environ. 2020, 722, 137749. [Google Scholar] [CrossRef] [PubMed]
Water 16 02840 g001
Figure 1. Variation in publications on DNAPL models from 1993 to 2023.
Figure 1. Variation in publications on DNAPL models from 1993 to 2023.
Water 16 02840 g001
Water 16 02840 g002
Figure 2. Co-citation network of collaborative countries in DNAPL models.
Figure 2. Co-citation network of collaborative countries in DNAPL models.
Water 16 02840 g002
Water 16 02840 g003
Figure 3. Co-citation network of collaborative institutions in DNAPL models.
Figure 3. Co-citation network of collaborative institutions in DNAPL models.
Water 16 02840 g003
Water 16 02840 g004
Figure 4. Knowledge map of periodical double graph superposition for WoS publications from 1993 to 2023 (The left area represents the citing journal clusters of knowledge frontiers, while the right area represents the cited journal clusters of knowledge foundations. The curves represent the citation paths, with their thickness indicating the frequency and intensity of the knowledge flow between journals. The size of the ellipses reflects each journal’s publication volume and author count. More papers lengthen the vertical axis, while more authors lengthen the horizontal axis).
Figure 4. Knowledge map of periodical double graph superposition for WoS publications from 1993 to 2023 (The left area represents the citing journal clusters of knowledge frontiers, while the right area represents the cited journal clusters of knowledge foundations. The curves represent the citation paths, with their thickness indicating the frequency and intensity of the knowledge flow between journals. The size of the ellipses reflects each journal’s publication volume and author count. More papers lengthen the vertical axis, while more authors lengthen the horizontal axis).
Water 16 02840 g004
Water 16 02840 g005
Figure 5. Co-occurrence network of keywords in DNAPL models from 1993 to 2023.
Figure 5. Co-occurrence network of keywords in DNAPL models from 1993 to 2023.
Water 16 02840 g005
Water 16 02840 g006
Figure 6. Research hotspots corresponding to the 10 clusters.
Figure 6. Research hotspots corresponding to the 10 clusters.
Water 16 02840 g006
Table 1. List of countries with 10 or more publications from 1993 to 2023.
Table 1. List of countries with 10 or more publications from 1993 to 2023.
CountryNumber of PapersCentrality
USA3070.95
Canada1120.09
China820.18
France330.16
Italy220.04
Republic of Korea210.02
England200.23
Germany200.05
Scotland160.11
Netherlands140.03
Australia130.01
Turkey110.16
Greece100.15
Table 2. List of the top 10 cited journals from 1993 to 2023.
Table 2. List of the top 10 cited journals from 1993 to 2023.
JournalNumber of Citations Number of
Papers		Proportion
/%		IF
(2024)
Journal of Contaminant Hydrology51416526.873.5
Water Resources Research512579.284.6
Environmental Science & Technology444386.1910.8
Groundwater349172.772
Advances in Water Resources312304.894
Groundwater Monitoring and Remediation232162.611.8
Journal of Hydrology186203.265.9
Journal of Hazardous Materials183132.1212.2
Transport in Porous Media158121.952.7
Chemosphere120101.638.1
Table 3. Top 20 keywords with the strongest citation bursts.
Table 3. Top 20 keywords with the strongest citation bursts.
KeywordsStrengthBeginEnd1993–2023
two phase flow6.0919982004
multiphase flow5.0519982005
contaminant transport4.9319982003
nonaqueous phase liquids4.3520002008
field3.6520022009
TCE5.8920032007
behavior4.6720042009
heterogeneous porous media4.5520042014
source strength functions5.9820082016
NAPL dissolution4.8720082013
partial mass depletion4.8220082012
reductive dechlorination7.0120092015
PCE3.5420092015
DNAPL4.2220142020
back diffusion5.2320162022
permeability4.1320172023
enhanced aquifer remediation3.6220172018
DNAPL migration3.9820182023
removal3.7320192023
tomography3.6920212023
Note: Red bars show keyword burst periods, dark blue bars indicate post-burst periods, and light blue bars represent pre-appearance periods.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ju, M.; Li, X.; Wu, R.; Xu, Z.; Yin, H. Research Hotspots and Trend Analysis in Modeling Groundwater Dense Nonaqueous Phase Liquid Contamination Based on Bibliometrics. Water 2024, 16, 2840. https://doi.org/10.3390/w16192840

AMA Style

Ju M, Li X, Wu R, Xu Z, Yin H. Research Hotspots and Trend Analysis in Modeling Groundwater Dense Nonaqueous Phase Liquid Contamination Based on Bibliometrics. Water. 2024; 16(19):2840. https://doi.org/10.3390/w16192840

Chicago/Turabian Style

Ju, Mengdie, Xiang Li, Ruibin Wu, Zuxin Xu, and Hailong Yin. 2024. "Research Hotspots and Trend Analysis in Modeling Groundwater Dense Nonaqueous Phase Liquid Contamination Based on Bibliometrics" Water 16, no. 19: 2840. https://doi.org/10.3390/w16192840

APA Style

Ju, M., Li, X., Wu, R., Xu, Z., & Yin, H. (2024). Research Hotspots and Trend Analysis in Modeling Groundwater Dense Nonaqueous Phase Liquid Contamination Based on Bibliometrics. Water, 16(19), 2840. https://doi.org/10.3390/w16192840

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop