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Article

Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends

by
Javier De la Hoz-M
1,
Edwan Anderson Ariza-Echeverri
1 and
Diego Vergara
2,*
1
Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470004, Colombia
2
Technology, Instruction and Design in Engineering and Education Research Group (TiDEE.rg), Catholic University of Avila, C/Canteros s/n, 05005 Ávila, Spain
*
Author to whom correspondence should be addressed.
Resources 2024, 13(12), 171; https://doi.org/10.3390/resources13120171
Submission received: 31 October 2024 / Revised: 28 November 2024 / Accepted: 11 December 2024 / Published: 16 December 2024
(This article belongs to the Special Issue Advances in Wastewater Reuse)
Browse Figures
Figure 1
<p>PRISMA diagram illustrating the identification, screening, and selection of studies.</p> "> Figure 2
<p>Annual scientific publications and mean citations per article (1985–2024) related to AI in wastewater treatment.</p> "> Figure 3
<p>Geographical distribution of publications in AI-driven wastewater research (1985–2024).</p> "> Figure 4
<p>Top institutions contributing to AI-driven wastewater research (1985–2024).</p> "> Figure 5
<p>Collaboration network of countries in AI-driven wastewater research. This figure illustrates the global collaboration network, with node size representing the centrality and influence of each country. The connections depict collaborative ties between nations, with China serving as the dominant hub connecting various countries. This image can be best visualized in its HTML format, in <a href="#app1-resources-13-00171" class="html-app">Supplementary Material S1</a>.</p> "> Figure 6
<p>Collaboration network of institutions in AI-driven wastewater research. The figure illustrates the institutional collaboration network, with node size representing the influence and centrality of each institution. Colors correspond to different clusters, reflecting distinct communities within the global network. This image can be best visualized in its HTML format, in <a href="#app1-resources-13-00171" class="html-app">Supplementary Material S2</a>.</p> "> Figure 7
<p>Collaboration network of authors in AI-driven wastewater research. The figure illustrates the author collaboration network, with node size representing centrality and influence. Connections indicate collaborative ties, with prominent authors like Qiao J. and Wang Z. serving as major hubs in the global network. This image can be best visualized in its HTML format, in <a href="#app1-resources-13-00171" class="html-app">Supplementary Material S3</a>.</p> "> Figure 8
<p>Intertopic distance map of AI applications in wastewater management: LDA visualization using multidimensional scaling.</p> "> Figure 9
<p>Temporal evolution of research topics in AI-driven wastewater research (1985–2024).</p> "> Figure 10
<p>Heatmap of research topic distribution by country in AI-driven wastewater research.</p> "> Figure 11
<p>Heatmap of research topic distribution by journal in AI-driven wastewater research.</p> ">
Versions Notes

Abstract

:
Wastewater treatment is a critical process for ensuring water quality and public health, particularly in the context of increasing environmental challenges such as pollution and water scarcity. Artificial intelligence (AI) has emerged as a transformative technology capable of optimizing various wastewater treatment processes, such as contaminant removal, energy consumption, and cost-efficiency. This study presents a comprehensive bibliometric analysis of AI applications in wastewater treatment, utilizing data from Scopus and Web of Science covering 4335 publications from 1985 to 2024. Utilizing machine learning techniques such as neural networks, fuzzy logic, and genetic algorithms, the analysis reveals key trends in the role of the AI in optimizing wastewater treatment processes. The results show that AI has increasingly been applied to solve complex problems like membrane fouling, nutrient removal, and biofouling control. Regional contributions highlight a strong focus on advanced oxidation processes, microbial sludge treatment, and energy optimization. The Latent Dirichlet Allocation (LDA) model further identifies emerging topics such as real-time process monitoring and AI-driven effluent prediction as pivotal areas for future research. The findings provide valuable insights into the current state and future potential of AI technologies in wastewater management, offering a roadmap for researchers exploring the integration of AI to address sustainability challenges in the field.

1. Introduction

Water is an essential resource for life, and changes in its quality or availability can have profound implications for public health, ecosystems, and economic development [1]. Ensuring access to safe and high-quality water is critical for sustainable global development [2]. However, the increasing challenges of water pollution and scarcity pose significant threats to both human and environmental well-being [3]. Addressing these challenges requires innovative strategies in water resource management, where wastewater treatment plays a pivotal role [4].
Wastewater treatment is essential for purifying water before its release into the environment or reuse, helping to protect both public health and ecosystems. Increasingly stringent regulations on effluent quality, coupled with the need to optimize energy and chemical consumption, underscore the urgency for more efficient and sustainable treatment technologies [5]. In this context, artificial intelligence (AI) has emerged as a transformative interdisciplinary tool capable of replicating tasks traditionally requiring human intelligence, such as learning, decision-making, and problem-solving. AI techniques, including machine learning (ML), neural networks, fuzzy logic, and genetic algorithms, can drive significant advances in wastewater treatment. For instance, convolutional and recurrent neural networks have shown remarkable success in predictive modeling and real-time monitoring, while fuzzy logic addresses operational uncertainties, and genetic algorithms optimize multi-objective challenges like reducing energy use and maximizing pollutant removal [6,7].
The integration of AI into wastewater treatment can further revolutionize the field, enabling solutions to traditional challenges such as variability in effluent quality, energy-intensive operations, and dynamic environmental impacts. AI-driven approaches also support resource recovery, including the extraction of nutrients and bioplastics, aligning wastewater treatment practices with circular economy principles. Techniques such as reinforcement learning, model-based automatic control systems, training predictive models, clustering, and probabilistic modeling are techniques that could improve AI’s ability to navigate uncertainties and handle unstructured data, enabling the development of optimized adaptive wastewater management systems.
The literature reveals a growing focus on specific applications of AI in wastewater management. Early work by Fang et al. [8] demonstrated the potential of artificial neural networks (ANN) in modeling contaminant removal efficiency. However, this pioneering study did not explore the full spectrum of AI methodologies, such as fuzzy logic or genetic algorithms, nor did it address broader global trends. Subsequent studies, including those by Harrou et al. [9] and Ahmed [10], reviewed various AI algorithms for fault detection and energy optimization, but lacked comprehensive analyses of the evolution of AI applications in wastewater treatment. These technological advancements highlight AI’s critical role in achieving global sustainability goals, improving water quality, and combating pollution.
Recent reviews, such as those by Bhagat et al. [11] and Zhao et al. [12], explored AI applications for specific challenges, including heavy metal removal and economic performance. Niu et al. [13] expanded this scope by addressing membrane fouling control, a critical area for maintaining system efficiency. Despite their contributions, these studies often focus on isolated applications, leaving gaps in the broader understanding of AI’s role in addressing multidimensional wastewater treatment challenges. Studies like those by Baarimah et al. [14] and Zhang et al. [15] further illustrate this trend, highlighting AI’s potential in biological wastewater treatment and technological innovation while overlooking comprehensive global analyses. Yuan et al. [16] also conducted a bibliometric analysis on the use of hydrogels in water treatment, emphasizing their growing significance in tackling challenges such as heavy metal contamination and microplastics removal. Their study not only highlights the increasing interest in hydrogels but also underscores the importance of innovative materials in advancing sustainable water treatment technologies. This perspective complements the role of AI in optimizing water treatment processes, providing a broader view of how emerging technologies can enhance efficiency and sustainability.
To address these limitations, our study integrates bibliometrics with Latent Dirichlet Allocation (LDA), a powerful topic modeling technique, to identify emerging research trends [17,18,19,20,21,22,23] and provide actionable insights into AI-driven wastewater treatment. By analyzing data from Scopus and Web of Science, this study maps the dynamic evolution of AI applications, offering a broader perspective on global trends, collaborations, and research gaps. This approach builds on prior research challenges.
This investigation is structured around six key research questions (RQs) (Table 1), which guide the analysis presented in this article. These questions aim to identify patterns in AI research, technological advancements, and gaps in the literature, offering a roadmap for future studies on AI-driven wastewater treatment.

2. Materials and Methods

2.1. Data Collection and Search Strategy

During the data collection process, the “Preferred Reporting Items for Systematic Reviews and Meta-Analysis” (PRISMA) [24] protocol was followed to ensure a meticulous selection of articles. This methodology is widely recognized for enhancing the transparency, clarity, and quality of literature reviews.
This study combines the Scopus and Web of Science databases, which sets it apart from many literature reviews that typically rely on a single source. The integration of multiple databases is relatively uncommon in the existing literature [25]. Various studies have highlighted the unique strengths of each database, such as the extensive temporal coverage of Web of Science and the diverse range of publications in Scopus [26]. Although Scopus and Web of Science show a strong correlation, many researchers emphasize the value of analyzing them simultaneously, as their data complement each other optimally [27]. However, merging these datasets can be challenging due to variations in article information depending on the source, whether Scopus or Web of Science [25].
A method for merging the Scopus and Web of Science databases has been successfully replicated using the R ‘Bibliometrix’ package [28]. Following this approach, initial searches were conducted separately in Scopus and Web of Science. The search was carried out on 17 September 2024, using specific terms and Boolean operators (Table 2).
During the process of unifying the databases, 2038 duplicate documents, 17 documents without abstracts, and 31 documents without affiliation information were removed. As a result, the final dataset was consolidated into 4335 documents (Figure 1).

2.2. Bibliometric Analysis Methodology

To address the first three research questions, a comprehensive bibliometric analysis of publications on the use of artificial intelligence in wastewater treatment was conducted. This analysis was performed using the R package Bibliometrix [28], a specialized tool that facilitates the extraction, processing, and visualization of bibliographic data, providing an integrated view of the research structure in this field.
Formally, bibliometrics is a set of methods that allows for the quantitative analysis of academic literature and its evolution over time [29]. These methods evaluate and assess academic research across different countries, universities, research centers, research groups, and journals. Bibliometrics provides an objective criterion for measuring the work of scientists, making it an increasingly valued tool for assessing academic quality and productivity [30].
According to Aria and Cuccurullo [28], each bibliometric method is valuable for addressing specific research questions, and the most common queries can be effectively tackled using bibliometrics for science mapping. This study was developed considering three levels of analysis: sources, authors, and documents, in an objective and reliable manner. First, the focus was on identifying the relevance of topics at each level, understanding relevance as the most productive or cited item, depending on the unit of analysis. Second, the structures of knowledge were examined using various bibliometric techniques. Particularly, the social structure was highlighted, revealing collaboration between authors, institutions, and countries.
Table 3 outlines the key indicators and algorithms used in network analysis, focusing on their purpose and application in examining relationships within networks. It highlights how each tool, such as nodes and clusters, is utilized to identify and analyze connections, communities, and the importance of entities like authors, countries, and institutions. The table also specifies the bibliometric and statistical techniques applied at different levels of analysis, offering insights into the conceptual, intellectual, and social structures of the network being studied.

2.3. Latent Dirichlet Allocation Methodology

To address the final three research questions, the LDA technique was employed, allowing for the identification and analysis of latent topics within the corpus of scientific publications on the use of artificial intelligence in wastewater treatment. This part of the analysis was conducted using LDAShiny [36], an R package that provides a user-friendly web-based graphical interface for conducting literature reviews under the Bayesian approach of LDA and machine learning algorithms.
LDA operates on the bag-of-words assumption [17], where the order of words in a document is disregarded, and the focus is instead on the frequency of words, resulting in a Document-Term Matrix (DTM). This approach utilizes an unsupervised Bayesian learning algorithm, where the number of topics identified by the model is a free parameter, meaning no manual coding is incorporated into the learning process.
The language in this context is structured around latent proportions that the participants themselves may not consciously recognize [37]. A key challenge in the application of the LDA model is determining the optimal number of latent topics (K). Selecting the appropriate number of topics for a given collection of articles requires balancing the need for comprehensive coverage of the document collection with the need to maintain interpretability of the topics. In this study, simulations were conducted by varying the number of topics (K) from 5 to 40 in increments of one, using Gibbs sampling as the inference algorithm with 500 iterations [38]. The selection of the optimal K was based on a coherence score approach [39], which evaluates the model based on human interpretability rather than computational metrics like perplexity [40]. The coherence score provides a more reliable assessment of the meaningfulness of topics to human readers. Default Dirichlet parameters α and β were used from the topicmodel package, as these default values are generally well-suited for the moderate-sized dataset analyzed in this study. The use of Gibbs sampling, a commonly adopted inference algorithm, ensured effective topic discovery by iteratively sampling latent variables. The simulations identified an optimal number of topics that provided a meaningful balance between the granularity of insights and the interpretability of the resulting topics.
Since LDA does not automatically generate semantic labels for topics, and algorithmic methods have limitations in capturing the nuances of human language, manual labeling remains a common practice in topic modeling [41]. To provide accurate semantic interpretations, topics were manually labeled based on two primary sources: the most frequent words associated with each topic and a review of the titles and abstracts of the three most representative articles for each topic.
To explore and visualize the relationships between the identified topics, we employed the LDAvis package [42]. This tool produces an intertopic distance map using multidimensional scaling (MDS), providing an intuitive two-dimensional representation of how topics are related based on their shared terms. In this map, each topic is depicted as a circle, with the circle’s size indicating the prevalence of the topic within the corpus. The distance between circles represents the semantic similarity between the topics, where closer circles suggest higher overlap in their word usage.
Given the large volume of articles and the corresponding number of words, intuitively understanding the topics and their trends can be challenging. To address this, we used some quantitative indices proposed by Xiong et al. [43], which were obtained by aggregating document-topic and topic-word distributions to clarify the results and findings.
To characterize topic trends over time, simple regression analyses were conducted. The regression slope for each topic, with the year as the independent variable and the proportion of the topic in each year as the dependent variable, allowed for the identification of significant trends. Topics with positive or negative slopes, significant at the 0.01 level, were classified as upward or downward trends, respectively.

3. Results

3.1. Bibliometric Analysis

The bibliometric analysis demonstrates a marked growth in scientific output related to the application of artificial intelligence (AI) in wastewater treatment, with an annual increase rate of 15.92%. This trend reflects the expanding recognition of AI’s role in advancing sustainable resource management within water treatment systems. Collaboration among authors is notable, with an average of 4.52 co-authors per document and 16.29% of the publications involving international co-authorship, underscoring the field’s global and cooperative nature. Furthermore, the documents exhibit a strong impact in the scientific community, with an average of 19.3 citations per document, which highlights the relevance and influence of this research area in advancing both AI applications and environmental sustainability efforts (Table 4).
Figure 2 illustrates the progression of scientific output related to artificial intelligence (AI) applications in wastewater treatment from 1985 to 2024. Initially modest before 2000, the number of publications has grown significantly, with a particularly steep increase starting in 2010. This upward trend underscores the expanding role of AI in addressing critical challenges in sustainable water resource management. Research activity reached its highest point in 2024, with 636 publications, reflecting both increased interest and advancements in the field.
The blue line in Figure 2 represents the mean total citations per article, which fluctuates over time, with a peak in citation averages occurring in the mid-2000s. While citation averages have declined in recent years, the sustained increase in publication volume highlights the field’s growing relevance within the scientific community, as researchers increasingly recognize AI’s potential to drive sustainable innovations in wastewater treatment.

3.1.1. Key Factors in AI-Driven Wastewater Research: Countries, Institutions, and Authors (Q1)

The results presented in Table 5 highlight China’s prominent leadership in AI-driven wastewater research, with the highest number of published articles (1324) and total citations (18,455). This achievement is strongly supported by China’s significant investments in AI and environmental technologies, which are part of its strategic efforts to address pressing pollution challenges and establish itself as a global innovation leader [44]. A substantial portion of China’s publications arise from single-country collaborations (SCP), reflecting its robust domestic research ecosystem. India’s position, ranking second with 330 publications, reflects a growing emphasis on utilizing AI to address urgent water management challenges in resource-limited contexts. This focus highlights India’s efforts to leverage AI solutions tailored to local conditions and its increasing participation in international collaborations. Iran, in third place with 265 publications, showcases a similar trend, where regional priorities drive advancements in wastewater research, bolstered by strategic partnerships with global leaders such as China and the United States. Developed nations such as the United States, the United Kingdom, and Germany demonstrate relatively lower publication volumes compared to emerging economies, but exhibit significantly higher citation impacts. This disparity underscores their focus on high-quality, innovative research supported by advanced infrastructures, well-established academic institutions, and strong international collaborations. The prevalence of multiple-collaboration publications (MCP) in these countries emphasizes their role in fostering global partnerships that enhance research impact and visibility. Emerging economies such as Turkey, Malaysia, and Saudi Arabia are steadily increasing their contributions, driven by the growing availability of AI technologies and the need to address local environmental challenges. Regional factors such as water scarcity in Saudi Arabia and Algeria prioritize wastewater research, though their scientific output is constrained by funding and infrastructure limitations compared to leading nations. In addition, countries such as Canada and Australia showcase the importance of international collaboration, as evidenced by their high MCP ratios. These collaborations play a crucial role in amplifying research visibility and impact.
Collectively, the patterns in Table 5 illustrate the intricate interplay of economic resources, environmental priorities, and international partnerships in shaping the global landscape of AI-driven wastewater research. Emerging countries, like those in Latin America, can learn from global trends in AI-driven wastewater research by investing in AI and environmental technologies, focusing on local challenges, and fostering international collaborations. Strategic funding and policy alignment, as seen in China, can drive significant advancements, while tailored solutions, like those developed in India, address region-specific issues effectively. Building regional research networks and prioritizing high-impact areas, such as real-time monitoring and resource recovery, can enhance innovation and research visibility despite limited resources. Additionally, adopting policies that emphasize water sustainability, a desire of all countries, can improve water potability, optimize wastewater management, and contribute to regional and global environmental goals.
The data, focusing on corresponding authors, emphasize these nations’ leadership roles in coordinating and advancing research efforts. Among emerging economies, Brazil stands out with 78 articles, most of which are single-collaboration publications (68), reflecting its growing influence in AI-driven wastewater research. Other countries, such as Turkey, Malaysia, and South Africa, also show strong contributions, characterized by robust patterns of international collaboration.
Figure 3 illustrates the geographical distribution of publications, revealing that AI applications in wastewater treatment are being explored across all continents. However, while there are notable contributions from countries in Asia, Europe, the Americas, and Oceania, the figure also highlights significant research gaps in Africa, where several countries (shown in white) have yet to engage in this research area. This geographic overview underscores the global interest in leveraging AI for sustainable water management, while also revealing uneven research distribution that presents opportunities for expanded international collaboration in underserved regions.
As illustrated in Figure 4, Beijing University of Technology ranks as the leading institution in AI-driven wastewater research, contributing 431 publications. It is followed by South China University of Technology with 191 publications and the Egyptian Knowledge Bank (EKB) with 100 publications. Other prominent institutions include Harbin Institute of Technology and the Chinese Academy of Sciences, which have contributed 99 and 95 publications, respectively.
The presence of institutions from diverse countries, including China, Iran, Malaysia, and Spain, among the top contributors, underscores the global engagement in advancing this research area. This distribution highlights the significant contributions of both established research powerhouses and emerging academic institutions, demonstrating the widespread commitment to leveraging AI for sustainable advancements in wastewater treatment.
A total of 10,172 authors have contributed to the field of AI-driven wastewater research. Table 6 highlights the top 20 most influential authors, with Qiao J. leading as the most prolific and impactful researcher, boasting an h-index of 33 and 191 publications since 2004, amassing a total of 3563 citations (TC). This positions Qiao J. at the forefront of research in this domain.
Following closely, Han H. and Wang Y. have also made significant contributions, with 119 and 89 publications, respectively, further demonstrating their influence in the field. Other notable researchers, such as Liu Y. and Li X., display strong performance with h-indices above 16 and considerable citation counts, indicating the depth and impact of their work.
Several of these key contributors, including Wang X., Li Y., and Wang J., began publishing in the early 2000s and have sustained a consistent presence in the field. Their ongoing work has been instrumental in shaping the research landscape for AI applications in wastewater treatment over the past two decades.
The h-indices of the top 20 authors range from 9 to 33, underscoring the high level of impact and recognition their work has achieved, as each continues to advance this research area and contribute to the development of sustainable water management practices.

3.1.2. Leading Journals in AI-Driven Wastewater Research (RQ2)

Out of 1271 journals publishing research on AI-driven wastewater treatment, Table 7 identifies the top 30 journals, ranked based on impact metrics such as h-index, total citations (TC), and publication counts. These metrics provide a quantitative lens through which to evaluate the most influential platforms for disseminating research in this interdisciplinary field.
Water Research emerges as the preeminent journal in this domain, with an h-index of 39, 118 publications, and a remarkable 4000 total citations since its first publication in 1991. This highlights its fundamental role as a repository of high-impact studies, particularly in integrating AI technologies into water management systems. Similarly, the Journal of Environmental Management, ranking second, has an h-index of 30 and a strong citation record of 2927, emphasizing its critical contributions to environmental strategies leveraging AI tools. Other key contributors, such as the Chemical Engineering Journal (h-index: 29) and Science of the Total Environment (h-index: 28), underscore the interdisciplinary reach of AI applications in wastewater research. These journals serve as bridges between environmental engineering, chemical processes, and computational advancements, reflecting the diverse methodologies utilized in the field. Journals like the Journal of Cleaner Production and Water Science and Technology, each with an h-index of 27, further extend the scope by integrating sustainability and advanced water treatment technologies.
This array of influential journals demonstrates the broad disciplinary spread of AI-driven wastewater research, spanning environmental sciences, chemical engineering, and computational methodologies. Importantly, the varied focus of these journals highlights the integration of traditional environmental management practices with state-of-the-art computational solutions. For example, while some journals prioritize methodological innovations, others emphasize real-world applications of AI in improving sustainability and operational efficiency.
The inclusion of Table 7 also reveals valuable trends in publication patterns and the evolution of academic interest in this field. High-ranking journals, such as Environmental Modelling and Software and Expert Systems with Applications, point to the increasing relevance of computational approaches and AI tools in solving environmental challenges. Furthermore, emerging journals like Engineering Applications of Artificial Intelligence, despite their relatively lower h-indices, reflect the growing interest in applying AI specifically to address complex engineering problems within the water sector.
These insights not only guide researchers toward high-impact venues for publishing their work but also highlight the growing convergence of disciplines that define AI’s role in wastewater management. This diverse range of high-ranking journals encompasses multiple disciplines, including environmental sciences, chemical engineering, and artificial intelligence, highlighting the interdisciplinary nature of AI applications in wastewater treatment. The variety of fields represented emphasizes the broad relevance of this research, with publications appearing in journals focused on both traditional environmental technologies and state-of-the-art computational approaches. By identifying the leading journals and their respective areas of focus, this table underscores the importance of targeted dissemination strategies for fostering innovation and advancing global knowledge in AI applications for water sustainability.

3.1.3. Collaborative Social Network Structure Among Institutions, Countries, and Authors in AI-Driven Wastewater Research

The analysis of collaboration networks in AI-driven wastewater research reveals a complex, globally interconnected landscape, with several key countries emerging as central hubs. As shown in Figure 5, China is a dominant force within this network, serving as the primary bridge connecting numerous countries and facilitating international collaborations. With the highest betweenness centrality (435,869), China plays a critical role in linking research groups across regions, enabling efficient information flow and fostering collaboration throughout the global network. The United States follows closely with a betweenness centrality of 388,002, reinforcing its pivotal role in connecting research communities across North America and beyond.
This bridging function is further emphasized by the closeness centrality values of China and the United States (0.01 and 0.011, respectively), underscoring their strategic positions for rapid access and collaboration with other countries. These positions allow them to remain central to global knowledge dissemination in AI-driven wastewater research.
In addition to China and the United States, Saudi Arabia, India, and South Africa are also significant players in the network. Saudi Arabia, with a betweenness centrality of 309,992, serves as a key connector within the Middle East, facilitating both regional and international collaboration. India, with a score of 252,877, plays a similar role in South Asia, fostering strong connections within the region and with leading global researchers. South Africa is instrumental in bridging collaborations across Africa, connecting researchers within the continent and beyond.
Countries such as Brazil and Italy demonstrate growing influence within their respective regions. Brazil, with a betweenness centrality of 23,745, acts as a primary facilitator of research collaboration across Latin America, linking regional researchers with global counterparts. Italy similarly connects Southern Europe with both European and international research networks. Along with Spain (383,259) and the United Kingdom (68,705), these countries form a network of influential nations that drive cross-border collaboration in AI-driven wastewater research.
Despite the robust collaboration seen in many regions, gaps remain in certain areas. Several African and Latin American countries exhibit low or negligible betweenness centrality, indicating limited participation in global collaborations. For instance, Algeria, Botswana, and Argentina are on the periphery of the network, with minimal connections to central hubs. Strengthening collaborative efforts in these regions could enhance their integration into the global research community.
The influence of these countries is further reflected in the PageRank analysis, where China leads with a score of 0.114, followed by the United States at 0.07. These high scores highlight not only extensive collaboration but also the strategic importance of their connections with other influential countries. India and Saudi Arabia also rank highly, showcasing their growing impact in the field.
Overall, the global collaboration network is concentrated around a few key countries, particularly China and the United States, which dominate both in research output and as central connectors. However, limited integration in regions such as Africa and parts of Latin America presents an opportunity to expand collaborative efforts and strengthen global research initiatives in AI-driven wastewater treatment.
The institutional collaboration network in AI-driven wastewater research reveals a rich and interconnected landscape, with several key universities emerging as central hubs (Figure 6). Beijing University of Technology stands out as one of the most influential institutions, with a high betweenness centrality of 234,543, indicating its essential role in connecting other institutions and acting as a key node for information flow. Similarly, Tsinghua University demonstrates strong influence, holding the highest betweenness centrality of 552,159, which underscores its importance in bridging various research clusters and facilitating global collaborations.
Institutions such as Tsinghua University, the Chinese Academy of Sciences, and Beijing University of Technology exhibit high closeness centrality, reflecting their strategic positioning to rapidly reach and collaborate with other institutions within the network. This proximity allows these universities to play a pivotal role in influencing research dissemination and fostering efficient collaboration in AI applications for sustainable wastewater management.
The PageRank analysis highlights the central roles of Tsinghua University (0.039) and King Khalid University (0.039), both of which maintain significant connections with other influential institutions. These connections underscore their importance in research coordination and the development of collaborative projects. Additionally, the Chinese Academy of Sciences, with a strong PageRank score of 0.034, further affirms its role as a leading institution driving advancements in AI applications for wastewater research.
Beyond China, other global institutions, such as the University of Technology Sydney (Australia) and King Fahd University of Petroleum and Minerals (Saudi Arabia), play notable roles in the network. With betweenness centrality values of 183,953 and 13,087, respectively, these institutions act as crucial connectors within their regions, promoting cross-border collaboration and advancing regional research capabilities.
Interestingly, institutions such as the Universitat Politècnica de Catalunya and Universitat de Girona appear more isolated within the network, as shown in Figure 6. Despite making valuable contributions, these institutions are less integrated into the core of the global research network. This relatively isolated position could present opportunities for increased international collaboration to further integrate them into the broader research landscape.
Notably, institutions like K. N. Toosi University of Technology (Iran) and the University of Guilan exhibit high PageRank values of 0.022 and 0.015, respectively, despite their smaller size and lower overall centrality. This highlights their growing influence within specific subfields of AI-driven wastewater research.
Overall, the institutional collaboration network is dominated by a few key players—particularly in China—who lead the global landscape. However, there are numerous regional institutions that foster collaboration within specific clusters. Increasing connectivity among more isolated institutions, particularly those in Europe and the Middle East, could strengthen the global research network, promoting a more integrated approach to AI applications in sustainable wastewater treatment.
The author collaboration network in AI-driven wastewater research identifies several key researchers as central figures within the global landscape (Figure 7). Wang Z. and Wang X. emerge as pivotal nodes, with betweenness centrality values of 368,814 and 263,478, respectively. These positions highlight their role in connecting diverse research clusters and facilitating collaboration across the network, acting as bridges that enable information flow between otherwise disconnected research groups.
In terms of closeness centrality, Qiao J. stands out with the highest score of 0.062, indicating a well-positioned role for rapid collaboration and information exchange. Han H., another influential researcher, shows a closeness centrality of 0.043, underscoring their role in disseminating research and fostering collaborative efforts. PageRank values also emphasize the significance of these authors, with Qiao J. and Han H. achieving scores of 0.062 and 0.043, respectively, reflecting their extensive collaborations and influence within the research community.
Tsinghua University’s strong representation in the author network, with prominent authors such as Wang X. and Chen Y., reinforces its position as a leading institution in this field. These authors, with high betweenness centrality and PageRank values, are instrumental in driving collaboration and knowledge exchange in AI-driven wastewater research.
Other researchers, such as Zhang Y. and Li X., also play essential roles within their respective clusters. For instance, Zhang Y. holds a betweenness centrality of 144,134 and a closeness centrality of 0.018, making them an influential node in the network. Li X., with a betweenness centrality of 228,832 and a closeness score of 0.026, acts as a significant connector, facilitating collaborations between diverse research groups.
Certain authors, such as Poch M. and Comas J., are more isolated within the network, as indicated by their high closeness centrality values of 1.0. This suggests that while they may collaborate extensively within smaller research groups, they are less integrated into the broader network. Increasing collaborations with other prominent figures could enhance their influence across the global research landscape.

3.2. Latent Dirichlet Allocation

3.2.1. Research Topics in AI-Driven Wastewater Research (RQ4)

The Latent Dirichlet Allocation (LDA) analysis applied to AI-driven wastewater research publications reveals a wide range of research topics, showcasing the diversity and complexity of the field. Table 8 presents the 16 primary topics (t_1 to t_16) identified by LDA, along with representative terms and the number of publications (NP) associated with each topic.
The most prominent topic, Fuzzy Control in Treatment (t_9), includes 518 publications and features terms such as “control”, “fuzzy”, “process”, “strategy”, and “simulation”. This topic reflects the substantial focus on fuzzy logic-based control systems used to optimize wastewater treatment plant (WWTP) operations, enhancing both efficiency and adaptability. This emphasis illustrates the increasing role of AI in refining treatment processes and improving system responsiveness.
Another major research area is Pollutant Adsorption (t_4), with 497 publications. This topic centers on adsorption techniques for removing contaminants, with key terms like “adsorption”, “efficiency”, “dye”, and “kinetic”. The prominence of this topic underscores the significant research efforts dedicated to enhancing pollutant removal methods, often utilizing AI models to optimize adsorption conditions.
AI for Effluent Prediction (t_2), with 417 publications, highlights the application of machine learning techniques to develop predictive models for wastewater treatment. Terms such as “model”, “predict”, “machine learning”, and “performance” indicate the widespread use of AI to forecast effluent quality and optimize treatment based on historical data. These AI-driven models improve prediction accuracy, thus enhancing overall treatment outcomes.
Water Resource Management (t_14) and Real-Time Process Monitoring (t_16), with 320 and 339 publications, respectively, are also significant focal points. Research in these areas emphasizes AI-based decision-making in water resource management and the integration of real-time monitoring systems in wastewater treatment. These advancements allow WWTPs to utilize continuous sensor data for optimized operations, pushing plants toward greater automation and efficiency.
The application of Neural Networks (t_8) is another popular approach, featured in 413 publications. Terms like “artificial neural network” and “ANN model” signify the growing use of neural networks to enhance predictive accuracy and optimize treatment processes. Neural networks are proving to be powerful tools for addressing complex challenges in wastewater management.
Another noteworthy topic is Membrane Filtration (t_15), represented in 94 publications. This research focuses on improving separation and filtration processes, with AI playing a key role in optimizing membrane efficiency and addressing fouling issues, which are critical for operational performance in WWTPs.
Finally, topics such as Advanced Oxidation Processes (t_6) and Contaminant Detection and Risk (t_7) address the degradation of contaminants and the evaluation of risks associated with wastewater pollutants. Although these areas have fewer publications, they are essential for ensuring the safety and effectiveness of wastewater treatment technologies.
Figure 8 illustrates a distinct separation between traditional wastewater treatment methods and emerging AI-based technologies. Conventional approaches, such as pollutant adsorption and microbial treatments, appear more interconnected, while advanced AI techniques, although increasingly prominent, are developing somewhat independently. This division highlights a transitional phase in the field, where cutting-edge technologies like neural networks and advanced control systems are gaining traction but have not yet fully integrated with established methods.
The clustering of topics in the lower-left corner of the map suggests a strong interconnection among fundamental areas of wastewater treatment. For instance, topics such as Environmental Technologies for Wastewater Treatment (t_12), Water Resource Management (t_14), and Water Quality Monitoring (t_13) are positioned closely together, indicating shared concepts and frequent co-discussion in the literature. A similar proximity is observed in Microbial Sludge Treatment (t_5) and Advanced Oxidation Processes (t_6), highlighting their logical association within treatment and filtration technologies. These clusters represent traditional, well-established methodologies in wastewater management, particularly those focused on physicochemical treatment and ongoing contaminant monitoring.
In contrast, AI-driven topics such as Neural Networks for Wastewater Treatment (t_8) and AI for Effluent Prediction (t_2) are marked by large circles, reflecting their high prevalence in current research. However, their spatial distance from more conventional topics suggests that, while prominent, these AI-based methods have yet to fully integrate with traditional approaches. These topics are characterized by their potential to optimize and predict wastewater treatment processes, but their relative isolation on the map points to an ongoing development trajectory distinct from established methodologies.
The proximity between Neural Network Design (t_10) and Fuzzy Control in Treatment (t_9) is also logical, as both techniques relate to process optimization and real-time system control. Their position on the map suggests a shared application domain, specifically in enhancing treatment system performance through advanced control techniques.
Notably, certain topics, such as Real-Time Process Monitoring (t_16), Supercritical Fluid Modeling (t_3), and Membrane Filtration (t_15), though smaller in prevalence, occupy specific research niches. Despite their relatively smaller representation, these topics may signify emerging areas with potential for increased importance as more research explores and applies these specialized technologies.

3.2.2. Evolution of Research Topics in AI-Driven Wastewater Research (RQ5)

The analysis of research topic evolution in AI-driven wastewater research reveals distinct trends in certain areas, while others fluctuate without a consistent pattern over time (Figure 9). Topics such as AI for Effluent Prediction (t_2), Pollutant Adsorption (t_4), and Neural Networks for Wastewater (t_8) display positive trends, indicating growing interest in these areas. The steady increase in research related to predictive models for effluent quality, adsorption techniques for pollutant removal, and the application of neural networks reflects the expanding role of AI in enhancing wastewater treatment processes and addressing environmental challenges more effectively.
Conversely, topics like Supercritical Fluid Modeling (t_3), Fuzzy Control in Treatment (t_9), and Water Resource Management (t_14) exhibit declining trends. This decrease in focus may be due to a shift in research priorities toward more advanced and automated AI methodologies. For instance, fuzzy control systems, once central to process optimization, may be losing relevance as newer AI techniques gain traction, while water resource management might be transitioning to more technology-driven and integrated approaches.
Other topics, such as Energy Optimization (t_11), Membrane Filtration (t_15), and Real-Time Process Monitoring (t_16), show fluctuations in prominence over time without a consistent upward or downward trend. This variability suggests that while these areas remain significant for AI-driven wastewater research, their focus may shift in response to specific technological advancements or emerging industry challenges.
Overall, the field is witnessing a clear increase in AI-driven innovations like predictive modeling and neural networks, whereas more traditional approaches are receiving less emphasis. The fluctuating topics highlight ongoing relevance, albeit without the same consistent growth seen in more advanced AI applications.

3.2.3. Distribution of Research Topics Across Countries and Scientific Journals (RQ6)

The heatmap in Figure 10 provides an in-depth view of how different countries focus on various research topics within AI-driven wastewater treatment. The color intensity reflects the proportion of publications dedicated to each topic, with darker shades indicating a higher concentration of research. This visualization reveals natural groupings based on regional research priorities.
One prominent cluster includes developed countries such as the United States, the United Kingdom, Italy, Japan, and Germany, which show a strong focus on Water Resource Management (t_14) and Neural Networks for Wastewater (t_8). The United Kingdom, for instance, has a significant emphasis on t_14, indicating its leadership in applying AI to optimize water resources. Similarly, t_8—focused on the application of neural networks for wastewater treatment—is a key research area across these countries, suggesting that these nations prioritize advanced AI techniques for predictive modeling and process optimization in wastewater management.
A second notable cluster comprises countries like Canada, Spain, South Korea, and Portugal, where research is more balanced across several topics but with a distinct focus on Pollutant Adsorption (t_4) and Membrane Filtration (t_15). Spain and Portugal, in particular, show strong research output in t_4, indicating a concentrated effort to improve contaminant removal processes via adsorption techniques. These countries are likely investing in optimizing physical and chemical processes in wastewater treatment, specifically through filtration and adsorption technologies.
In contrast, a third cluster includes emerging economies such as India, Brazil, South Africa, and Malaysia, which demonstrate a clear focus on Advanced Oxidation Processes (t_6) and Fuzzy Control in Treatment (t_9). These nations dedicate substantial research to t_9, involving fuzzy logic systems to control and optimize wastewater treatment operations, and to t_6, which focuses on chemical oxidation techniques for contaminant removal. This emphasis suggests that these countries are prioritizing chemical treatment methods and control systems to enhance wastewater treatment efficiency.
China occupies an intermediary position among these clusters, with a diversified research portfolio that includes both Neural Networks (t_8) and Pollutant Adsorption (t_4). This distribution indicates China’s active role in advancing AI applications alongside traditional methods for wastewater treatment, underscoring its expanding influence in global research.
In summary, the heatmap reveals distinct regional patterns in AI-driven wastewater research. Developed nations lead in topics such as Water Resource Management (t_14) and Neural Networks (t_8), while emerging economies concentrate on Advanced Oxidation Processes (t_6) and Fuzzy Control (t_9). These clusters highlight how regional environmental challenges and technological capabilities shape research priorities, showcasing a globally diverse approach to addressing wastewater treatment through AI.
The heatmap in Figure 11 offers insights into the thematic strengths of different journals within AI-driven wastewater research. Some journals are key outlets for publishing on AI applications and control systems, while others focus more on conventional environmental and engineering methods, such as pollutant removal and resource management. This distribution underscores the interdisciplinary nature of the field, where advanced AI techniques are integrated with established environmental science and engineering practices to address wastewater management challenges.
The upper section of the heatmap (Cluster 2) includes journals like Applied Soft Computing, Journal of Process Control, and Expert Systems with Applications, which emphasize topics such as Neural Networks for Wastewater (t_8) and Fuzzy Control in Treatment (t_9). These journals are essential platforms for AI-based approaches, particularly neural networks and fuzzy control systems, which are used to optimize wastewater treatment processes. Their prominence in publishing this research reflects a strong focus on advanced computational methods and AI applications.
In Cluster 5, journals like Journal of Environmental Chemical Engineering, Chemosphere, and Journal of Hazardous Materials are closely associated with topics such as Pollutant Adsorption (t_4) and Membrane Filtration (t_15). These journals are heavily involved in publishing research focused on enhancing contaminant removal techniques through adsorption and filtration. For example, Chemosphere places particular emphasis on t_4, positioning itself as a leading outlet for studies on adsorption processes in wastewater treatment.
In the lower section of the heatmap (Cluster 3), journals like Water Research, Environmental Science and Technology, and Science of the Total Environment show a balanced distribution across multiple topics. However, they concentrate noticeably on areas such as Water Resource Management (t_14) and Water Quality Monitoring (t_13). Water Research, in particular, shows a strong focus on t_14, emphasizing its commitment to publishing research related to the management and optimization of water resources—an area of increasing importance given global water scarcity and quality concerns.
Specialized journals such as the Journal of Supercritical Fluids and Computers and Chemical Engineering exhibit narrower focuses. The Journal of Supercritical Fluids is closely associated with Supercritical Fluid Modeling (t_3), while Computers and Chemical Engineering strongly emphasizes Energy Optimization (t_11). These journals provide a platform for niche research areas that, although not as widespread, are crucial for advancing specific technical aspects of wastewater treatment.

4. Discussion

This study extends prior research on the role of artificial intelligence (AI) in optimizing wastewater treatment processes by offering a comprehensive bibliometric and thematic analysis. Zhang et al. [15] emphasized AI’s role in pollutant removal, predictive modeling, and process optimization, particularly focusing on patent-driven technological advances. This study builds on their findings, using Latent Dirichlet Allocation (LDA) within a bibliometric framework to capture emerging themes, such as AI-based Effluent Prediction (t_2) and Neural Networks (t_8). This approach not only expands the range of AI applications identified in wastewater treatment but also reveals trends that highlight AI’s transformative potential in enhancing operational efficiency and pollutant removal.
In parallel with Singh et al. [45], who concentrated on AI applications in biological systems for biological oxygen demand (BOD) and chemical oxygen demand (COD) removal, this study identifies Pollutant Adsorption (t_4) and Microbial Sludge Treatment (t_5) as key AI-driven areas. However, unlike the biological focus of Singh et al.’s research, this analysis offers a broader view, incorporating both biological and physicochemical AI applications, such as membrane filtration and adsorption. This breadth underscores AI’s flexibility and potential to optimize multiple wastewater treatment paradigms, including adsorption and filtration techniques traditionally used in physicochemical treatments.
Oruganti et al. [46] explored niche applications of AI in microalgae systems, focusing on sustainability and resource recovery, an area not extensively covered in this study. This gap points to potential research opportunities in using AI to enhance emerging technologies like nutrient recovery, biomass production, and circular economy approaches in wastewater management. Exploring these sustainable AI applications could significantly contribute to the broader environmental management landscape, as AI can optimize resource recovery processes, reduce waste, and support sustainable water treatment practices.
The integration of LDA with bibliometric analysis is a methodological advancement that enables dynamic tracking of evolving AI trends in wastewater treatment. Prior studies have often concentrated on specific AI applications or techniques, but this comprehensive approach provides a broader perspective on how AI is diversifying within wastewater treatment processes. For instance, Effluent Prediction (t_2) and Neural Networks (t_8) emerge as key areas with substantial growth potential, which speaks to the increasing need for predictive capabilities in wastewater management as facilities move toward more automated, data-driven operations.
A critical finding of this study is the identification of research gaps and declining interest in certain traditional approaches, such as Fuzzy Control (t_9) and Supercritical Fluid Modeling (t_3). These trends indicate a shift towards more sophisticated AI techniques, including deep learning and real-time monitoring, which offer greater adaptability and precision for handling complex operational variables. As wastewater treatment facilities strive to increase efficiency, these advanced AI methodologies are likely to become essential, facilitating predictive maintenance, adaptive control, and optimized resource utilization.
Additionally, the mapping of global collaboration networks reveals disparities in research contributions, with countries like China and the United States acting as central hubs in AI-driven wastewater research. These countries facilitate significant international collaboration, but regions such as parts of Africa and Latin America are notably underrepresented. This gap underscores the need for increased global partnerships and technology transfer initiatives, which could promote the adoption of AI solutions in regions facing critical water management challenges. Expanding international collaboration and capacity-building efforts could enable a more equitable distribution of AI-driven advancements, ensuring that emerging economies can access and benefit from innovations in wastewater treatment.
The increasing focus on AI-based Effluent Prediction (t_2), Pollutant Adsorption (t_4), and Neural Networks (t_8) reflects broader trends towards automation and predictive modeling in wastewater treatment. These technologies support global goals of operational efficiency, energy conservation, and enhanced effluent quality, aligning with heightened regulatory demands and sustainability objectives. By improving predictive capabilities and process optimization, these AI-driven methodologies can reduce resource consumption and support sustainable water management practices, positioning wastewater treatment as a key component in broader environmental strategies.
Conversely, the decline in topics such as Fuzzy Control (t_9) and Water Resource Management (t_14) suggests an industry shift towards more complex AI techniques, including hybrid AI systems and deep learning, which can better handle large datasets and adapt to dynamic environmental conditions. Future research should address how these advanced AI methods could improve treatment processes for emerging contaminants, including pharmaceuticals, microplastics, and endocrine disruptors, which are difficult to manage using conventional methods. Integrating Real-time Monitoring (t_16) with AI-driven control systems could further enable adaptive, self-optimizing processes that respond dynamically to environmental fluctuations, thereby enhancing resilience and treatment efficiency.
This study does, however, have limitations. The reliance on LDA and bibliometric analysis, while effective in capturing broad trends, may overlook the nuanced variations within specific AI applications and treatment methodologies. Additionally, the study’s dataset is limited to English-language publications, which may introduce a regional bias and underrepresent research published in other languages, particularly from non-English-speaking countries. Future research could address these limitations by incorporating multilingual datasets and exploring specific AI methodologies in greater detail to obtain a more comprehensive view of AI applications in wastewater treatment.
Ethical considerations surrounding AI applications, particularly in wastewater treatment, are crucial for ensuring responsible and equitable implementation. Key concerns include data privacy, the digital divide, and over-reliance on AI systems [47]. The digital divide is a significant ethical challenge, especially in developing countries, where disparities in access to technology and data quality reflect existing social inequalities [48]. Limited technological infrastructure and expertise in these regions restrict effective integration of AI, exacerbating inequalities [49]. To address these issues, a holistic approach is needed, emphasizing technology transfer, capacity building, and the development of AI tools tailored to resource-limited contexts, ensuring equitable benefits across regions.
Additionally, the integration of AI in wastewater treatment must consider the potential for over-reliance on AI systems, which could marginalize traditional expertise and fail to account for socio-environmental factors that are not captured in datasets [50]. Engaging local stakeholders and incorporating multidisciplinary perspectives in AI model development is essential to align these technologies with principles of equity, inclusiveness, and sustainability.
Environmental impacts associated with AI also require attention, particularly due to the high energy consumption involved in its development and use, which emphasizes the need for sustainable implementation practices [51]. Developing countries with evolving regulatory frameworks must prioritize policies that address data privacy, prevent algorithmic discrimination, and promote transparency and accountability. For instance, the European Union’s ethical guidelines for AI serve as a valuable model for establishing principles to manage these risks globally [52].
AI is increasingly recognized as a transformative tool in public policy and governance, with the potential to enhance decision-making and administrative efficiency in wastewater management. However, its adoption must be underpinned by robust data policies and ethical frameworks, particularly in developing countries where disparities in data availability and infrastructure often limit the impact of AI [49,53]. Thereby, this study provides critical insights that can shape policymaking in this area. By identifying global trends, technological advancements, and regional disparities in AI-driven wastewater management, it emphasizes the need for targeted infrastructure investments and capacity-building initiatives in resource-limited regions to bridge the digital divide. Furthermore, the findings support the development of policies promoting the sustainable and ethical integration of AI technologies. This includes implementing frameworks for data privacy, transparency, and accountability tailored to specific wastewater management challenges.
Policymakers can also leverage the study’s results to design decision-support tools aimed at mitigating risks associated with over-reliance on AI systems while encouraging responsible technology adoption. By addressing issues such as equitable access, environmental sustainability, and ethical considerations, these tools can optimize wastewater management systems and align them with broader sustainable development goals. The integration of AI in public policy has the potential not only to improve efficiency and effectiveness but also to reduce environmental impacts and ensure equitable benefits across diverse socio-economic contexts [53].
In summary, this study offers a robust foundation for understanding AI’s transformative impact on wastewater treatment. The findings highlight both the potential and the challenges of integrating AI into treatment processes, revealing clear growth trajectories in predictive modeling and neural network applications, while also identifying areas for further research and international collaboration. Addressing the identified research gaps and fostering equitable global access to AI-driven wastewater innovations will be crucial for advancing sustainable water management practices worldwide.

5. Conclusions

This study analyzed the application of artificial intelligence (AI) in wastewater treatment, emphasizing key trends, research gaps, and global collaboration patterns. Advanced AI methods, such as predictive modeling and neural networks, are central to optimizing treatment processes, improving pollutant removal, and reducing costs. Topics like effluent prediction and pollutant adsorption illustrate a shift towards data-driven, automated solutions aligned with global sustainability goals.
The findings highlight disparities in research contributions, with countries like China and the United States leading, while Africa and Latin America remain underrepresented. Expanding international collaboration is critical to addressing these imbalances and ensuring equitable access to AI-driven innovations. Additionally, traditional methods such as fuzzy control and supercritical fluid modeling are declining, marking a transition to sophisticated AI techniques that handle complex, real-time data.
While the study’s reliance on English-language publications is a limitation, future research could address this by incorporating multilingual databases and exploring underrepresented applications, such as resource recovery. These efforts will further enhance AI’s transformative role in wastewater treatment, supporting sustainable and inclusive global water management.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/resources13120171/s1, S1: Figure 5; S2: Figure 6; S3: Figure 7.

Author Contributions

Conceptualization, J.D.l.H.-M.; methodology, J.D.l.H.-M., E.A.A.-E. and D.V.; software, J.D.l.H.-M. and E.A.A.-E.; validation, J.D.l.H.-M., E.A.A.-E. and D.V.; formal analysis, J.D.l.H.-M., E.A.A.-E. and D.V.; investigation, J.D.l.H.-M. and E.A.A.-E.; resources, J.D.l.H.-M. and E.A.A.-E.; data curation, J.D.l.H.-M., E.A.A.-E. and D.V.; writing—original draft preparation, J.D.l.H.-M., E.A.A.-E. and D.V.; writing—review and editing, J.D.l.H.-M., E.A.A.-E. and D.V.; supervision, J.D.l.H.-M., E.A.A.-E. and D.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data available on request from the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. PRISMA diagram illustrating the identification, screening, and selection of studies.
Figure 1. PRISMA diagram illustrating the identification, screening, and selection of studies.
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Figure 2. Annual scientific publications and mean citations per article (1985–2024) related to AI in wastewater treatment.
Figure 2. Annual scientific publications and mean citations per article (1985–2024) related to AI in wastewater treatment.
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Figure 3. Geographical distribution of publications in AI-driven wastewater research (1985–2024).
Figure 3. Geographical distribution of publications in AI-driven wastewater research (1985–2024).
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Figure 4. Top institutions contributing to AI-driven wastewater research (1985–2024).
Figure 4. Top institutions contributing to AI-driven wastewater research (1985–2024).
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Figure 5. Collaboration network of countries in AI-driven wastewater research. This figure illustrates the global collaboration network, with node size representing the centrality and influence of each country. The connections depict collaborative ties between nations, with China serving as the dominant hub connecting various countries. This image can be best visualized in its HTML format, in Supplementary Material S1.
Figure 5. Collaboration network of countries in AI-driven wastewater research. This figure illustrates the global collaboration network, with node size representing the centrality and influence of each country. The connections depict collaborative ties between nations, with China serving as the dominant hub connecting various countries. This image can be best visualized in its HTML format, in Supplementary Material S1.
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Figure 6. Collaboration network of institutions in AI-driven wastewater research. The figure illustrates the institutional collaboration network, with node size representing the influence and centrality of each institution. Colors correspond to different clusters, reflecting distinct communities within the global network. This image can be best visualized in its HTML format, in Supplementary Material S2.
Figure 6. Collaboration network of institutions in AI-driven wastewater research. The figure illustrates the institutional collaboration network, with node size representing the influence and centrality of each institution. Colors correspond to different clusters, reflecting distinct communities within the global network. This image can be best visualized in its HTML format, in Supplementary Material S2.
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Figure 7. Collaboration network of authors in AI-driven wastewater research. The figure illustrates the author collaboration network, with node size representing centrality and influence. Connections indicate collaborative ties, with prominent authors like Qiao J. and Wang Z. serving as major hubs in the global network. This image can be best visualized in its HTML format, in Supplementary Material S3.
Figure 7. Collaboration network of authors in AI-driven wastewater research. The figure illustrates the author collaboration network, with node size representing centrality and influence. Connections indicate collaborative ties, with prominent authors like Qiao J. and Wang Z. serving as major hubs in the global network. This image can be best visualized in its HTML format, in Supplementary Material S3.
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Figure 8. Intertopic distance map of AI applications in wastewater management: LDA visualization using multidimensional scaling.
Figure 8. Intertopic distance map of AI applications in wastewater management: LDA visualization using multidimensional scaling.
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Figure 9. Temporal evolution of research topics in AI-driven wastewater research (1985–2024).
Figure 9. Temporal evolution of research topics in AI-driven wastewater research (1985–2024).
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Figure 10. Heatmap of research topic distribution by country in AI-driven wastewater research.
Figure 10. Heatmap of research topic distribution by country in AI-driven wastewater research.
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Figure 11. Heatmap of research topic distribution by journal in AI-driven wastewater research.
Figure 11. Heatmap of research topic distribution by journal in AI-driven wastewater research.
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Table 1. The research questions and supporting sections.
Table 1. The research questions and supporting sections.
IDResearch Question (RQ)Supporting Sections
RQ1What key factors (authors, institutions, and countries) are driving advances in research on the use of artificial intelligence in wastewater treatment?Section 3.1.1
RQ2Which journals and publications serve as epicenters for innovative studies on the use of artificial intelligence in wastewater treatment?Section 3.1.2
RQ3How is the social network of collaboration structured among countries, authors, and institutions in this field of research?Section 3.1.3
RQ4What are the main research topics in this field?Section 3.2.1
RQ5How do these research topics evolve?Section 3.2.2
RQ6What is the distribution of these topics across countries and scientific journals?Section 3.2.3
Table 2. Data collection and search strategy for systematic review of AI in wastewater treatment.
Table 2. Data collection and search strategy for systematic review of AI in wastewater treatment.
Bibliographic DatabaseSearch DateSearch StringResults
Scopus17 September 2024TITLE-ABS-KEY (“wastewater treatment” OR “wastewater management” OR “sewage treatment” OR “effluent treatment”) AND TITLE-ABS-KEY (“artificial intelligence” OR ai OR “machine learning” OR “deep learning” OR “neural network *” OR “fuzzy logic” OR “genetic algorithm *” OR “supervised learning” OR “unsupervised learning” OR “reinforcement learning” OR “support vector machines” OR “decision trees” OR “convolutional neural network *” OR “random forests” OR “predictive modeling” OR “data mining” OR “computer vision” OR “optimization algorithm *”) AND PUBYEAR > 1984 AND PUBYEAR < 2025 AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “re”)) AND (LIMIT-TO (SRCTYPE, “j”))n = 3693
Web of Science17 September 2024TS = (“wastewater treatment” OR “wastewater management” OR “sewage treatment” OR “effluent treatment”) AND TS = (“artificial intelligence” OR ai OR “machine learning” OR “deep learning” OR “neural network *” OR “fuzzy logic” OR “genetic algorithm *” OR “supervised learning” OR “unsupervised learning” OR “reinforcement learning” OR “support vector machines” OR “decision) trees” OR “convolutional neural network *” OR “random forests” OR “predictive modeling” OR “data mining” OR “computer vision” OR “optimization algorithm *”) n = 2728
Table 3. Indicators and algorithms used in the analysis of collaboration networks.
Table 3. Indicators and algorithms used in the analysis of collaboration networks.
Indicator/AlgorithmDescriptionPurposeApplication in
Analysis
NodeRepresents a single entity in the network, such as an author, country, or institution.To identify individual entities and their roles within the network.Used to map and analyze each entity’s position and connections.
ClusterRefers to a group of nodes that are more densely connected to each other than to other nodes.To determine communities or groups within the network.Helps in identifying collaborative groups or clusters within the network.
Betweenness Centrality [31]Measures the extent to which a node lies on the shortest paths between other nodes.To identify nodes that act as bridges within the network.Highlights nodes that facilitate information flow between other nodes.
Closeness Centrality [32]Measures how close a node is to all other nodes in the network.To determine nodes that can reach others more quickly.Assesses nodes’ accessibility and their potential influence.
PageRank [33]A ranking system that assigns scores to nodes based on their connectivity and importance.To assess the influence and importance of nodes within the network.Evaluates the significance of nodes based on their connections.
Walktrap Algorithm [34]Uses random walks to calculate distances between vertices, aiding in detailed information capture and community detection.To identify communities within the network.Facilitates the detection of network communities and clusters.
Kamada–Kawai Method [35]Arranges undirected graphs to reflect theoretical distances between vertices for better visualization.To visualize and interpret the structure of the network.Provides a visual layout of the network, reflecting the theoretical distances.
Table 4. Summary of bibliometric data.
Table 4. Summary of bibliometric data.
DescriptionResults
MAIN INFORMATION ABOUT DATA
Timespan1985–2024
Sources (Journals)1271
Documents4335
Annual Growth Rate (%)15.92
Document Average Age6.31
Average citations per doc19.3
DOCUMENT CONTENTS
Keywords Plus (ID)15,993
Author’s Keywords (DE)9848
AUTHORS
Authors10,172
Authors of single-authored docs111
AUTHOR COLLABORATION
Single-authored docs122
Co-Authors per Doc4.52
International co-authorships (%)16.29
DOCUMENT TYPES
Article4085
Review250
Table 5. Top 30 contributing countries in AI-driven wastewater research (1985–2024).
Table 5. Top 30 contributing countries in AI-driven wastewater research (1985–2024).
CountryArticlesTCSCPMCP
China132418,4551164160
India330478228248
Iran265659322441
USA223939119429
Spain204473417232
South Korea152314012230
Canada9918477128
Turkey982603926
Malaysia9225786725
Poland8713207314
Italy8023316812
United Kingdom8036395228
Brazil7811756810
Australia649114321
Saudi Arabia637284320
Germany531325467
Mexico524714111
France461131415
Portugal38515344
Romania384372810
Egypt3710772215
Japan37504307
Greece33728312
Algeria32328266
South Africa294721910
Denmark20899164
Iraq20140164
Morocco20164128
Netherlands19446154
Sweden19627136
Table 6. Top 20 authors in AI-driven wastewater research (1985–2024). h_Index: Hirsch index, TC: total citations, NP: number of publications, PY_Start: year of first publication.
Table 6. Top 20 authors in AI-driven wastewater research (1985–2024). h_Index: Hirsch index, TC: total citations, NP: number of publications, PY_Start: year of first publication.
Authorh_IndexTCNPPY_Start
Qiao J.3335631912004
Han H.2622031192007
Wang Y.161064892006
Liu Y.171138782006
Wang X.16963691999
Li X.181147672006
Li Y.16872651999
Wang J.15822582007
Li J.171005572002
Li W.16885542009
Zhang Y.13648542006
Zhang J.9301532005
Wang Z.14883502005
Liu H.16787502003
Zhang L.14880482005
Wang H.16761462008
Liu Z.15677462005
Yang C.10409442016
Zhang X.11517411998
Chen Z.13714402005
Table 7. Top 30 journals in AI-driven wastewater research (1985–2024). h-Index: Hirsch index, TC: total citations, NP: number of publications, PY_Start: Year of First Publication.
Table 7. Top 30 journals in AI-driven wastewater research (1985–2024). h-Index: Hirsch index, TC: total citations, NP: number of publications, PY_Start: Year of First Publication.
SourceIso Abbreviationh_IndexTCNPPY_Start
Water ResearchWater Res.3940001181991
Journal of Environmental ManagementJ. Environ. Manage.3029271182004
Chemical Engineering JournalChem. Eng. J.292049502004
Science of the Total EnvironmentSci. Total Environ.2827251042004
Journal of Cleaner ProductionJ. Clean. Prod.271976592008
Water Science and TechnologyWater Sci. Technol.2724121391991
ChemosphereChemosphere262082971998
Bioresource TechnologyBioresour. Technol.253007592008
Journal of Hazardous MaterialsJ. Hazard. Mater.242396472005
Environmental Modelling and SoftwareEnviron. Model. Softw.202038221999
Computers and Chemical EngineeringComput. Chem. Eng.19855242000
Journal of Water Process EngineeringJ. Water Process Eng.1911851082014
Environmental Science and Pollution ResearchEnviron. Sci. Pollut. Res.16979792012
Process Safety and Environmental ProtectionProcess Saf. Environ. Prot.161282352015
Expert Systems with ApplicationsExpert Syst. Appl.15885242002
Industrial and Engineering Chemistry ResearchInd. Eng. Chem. Res.15762251992
Engineering Applications of Artificial IntelligenceEng. Appl. Artif. Intell.14695291994
Environmental Science and TechnologyEnviron. Sci. Technol.141081221999
Journal of Environmental Chemical EngineeringJ. Environ. Chem. Eng.14580402014
Journal of Supercritical FluidsJ. Supercrit. Fluids14627222010
Water (Switzerland)Water14656342015
Desalination and Water TreatmentDesalination Water Treat.13523912012
Environmental Monitoring and AssessmentEnviron. Monit. Assess.13533292007
Environmental ResearchEnviron. Res.13823382009
Journal of HydrologyJ. Hydrol.13489142009
Applied Soft ComputingAppl. Soft Comput.12610182011
Environmental Science and TechnologyEnviron. Sci. Technol.12804242007
Journal of Process ControlJ. Process Control12352172004
Separation and Purification TechnologySep. Purif. Technol.12498222004
WaterWater12525492013
Table 8. Key research topics in AI-driven wastewater research.
Table 8. Key research topics in AI-driven wastewater research.
TTop_TermsLabelNP
t_1effluent, wastewat, cod, oxygen, plant, demand, chemic, concentr, oxygen_demand, paramet, bod, nitrogen, influent, solid, wastewat_plantWastewater Quality Parameters242
t_2model, predict, learn, data, machin, wwtp, machin_learn, perform, accuraci, approach, wastewat, support, vector, term, forecastAI for Effluent Prediction417
t_3flow, rate, temperatur, extract, flow_rate, transfer, condit, supercrit, heat, mathemat, pressur, design, experiment, fluid, estimSupercritical Fluid Modeling134
t_4remov, adsorpt, effici, concentr, dye, surfac, experiment, initi, time, adsorb, condit, respons, kinet, rsm, solutPollutant Adsorption497
t_5sludg, activ, product, reactor, anaerob, activ_sludg, microbi, rate, scale, oper, increas, commun, digest, load, bacteriaMicrobial Sludge Treatment234
t_6process, wastewat, industri, degrad, oxid, reaction, effici, remov, condit, paramet, fenton, effect, industri_wastewat, organ, textilAdvanced Oxidation Processes87
t_7wastewat, analysi, sampl, identifi, sourc, potenti, risk, contamin, health, human, type, level, resist, includ, environContaminant Detection and Risk176
t_8model, predict, ann, error, perform, artifici, input, artifici_neural, data, paramet, variabl, valu, develop, ann_model, squarNeural Networks for Wastewater413
t_9control, fuzzi, process, propos, base, simul, oper, wwtp, strategi, wastewat, design, perform, adapt, improv, paperFuzzy Control in Treatment518
t_10network, neural, neural_network, algorithm, function, method, model, structur, layer, propos, paramet, train, nonlinear, paper, network_modelNeural Network Design216
t_11optim, algorithm, energi, oper, consumpt, cost, genet, genet_algorithm, effici, multi, reduc, energi_consumpt, solut, improv, emissEnergy Optimization214
t_12applic, wastewat, technologi, environment, develop, challeng, techniqu, intellig, review, advanc, industri, wast, includ, enhanc, providEnvironmental Tech for Wastewater264
t_13water, qualiti, sewag, water_qualiti, pollut, plant, discharg, river, standard, urban, manag, increas, factor, level, sewag_plantWater Quality Monitoring170
t_14water, wastewat, decis, system, manag, develop, cost, resourc, approach, econom, evalu, requir, select, plant, supportWater Resource Management320
t_15membran, organ, foul, coagul, separ, mbr, turbid, flux, process, compound, membran_foul, perform, filtrat, oil, molecularMembrane Filtration94
t_16method, data, process, monitor, measur, base, propos, time, sensor, real, detect, soft, wastewat, real_time, variablReal-Time Process Monitoring339
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De la Hoz-M, J.; Ariza-Echeverri, E.A.; Vergara, D. Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends. Resources 2024, 13, 171. https://doi.org/10.3390/resources13120171

AMA Style

De la Hoz-M J, Ariza-Echeverri EA, Vergara D. Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends. Resources. 2024; 13(12):171. https://doi.org/10.3390/resources13120171

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De la Hoz-M, Javier, Edwan Anderson Ariza-Echeverri, and Diego Vergara. 2024. "Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends" Resources 13, no. 12: 171. https://doi.org/10.3390/resources13120171

APA Style

De la Hoz-M, J., Ariza-Echeverri, E. A., & Vergara, D. (2024). Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends. Resources, 13(12), 171. https://doi.org/10.3390/resources13120171

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