Processes of Pollution Control and Resource Utilization

As environmental science and engineering technology continue to advance, pollution control technologies are constantly innovating and improving. These innovations and developments are essentially fine-tuned processes [1]. For instance, the evolution of environmental catalysis technologies is grounded in processes such as reaction pathways, intermediates, and energy conversion [2,3,4]. With the world facing increasingly complex and diverse environmental issues, the integration of multi-disciplinary and multi-technological approaches has become a prevailing trend. Exploring the chemical, biological, and interface processes behind these new avenues has important guiding significance for the optimization and development of similar technologies.

The rise of artificial intelligence (AI) is profoundly transforming the landscape of scientific research, offering powerful tools and new perspectives for the exploration of the unknown. AI plays a significant role in areas such as data mining, experimental design, process simulation, automated experiments, knowledge discovery, and interdisciplinary research [5,6]. It not only assists scientists in processing and analyzing vast amounts of data more efficiently and optimizing experimental processes but also fosters innovation and development in scientific theories. However, the application of AI still faces many challenges, such as how to obtain big data, how to understand the scientific principles and logic behind the data, and how to identify key parameters.

Process research serves as the cornerstone for the application of AI, especially in complex fields like environmental science. It aids in understanding the interactions and processes within complex systems, providing key information for constructing accurate AI models [7]. Process research not only helps us interpret the data from AI analysis more deeply but also optimizes algorithms and improves model accuracy and efficiency through insights into system dynamics. Moreover, it enhances the interpretability of AI models, which is crucial for ensuring the transparency and ethics of algorithms. Process research also promotes interdisciplinary integration, offering a scientific basis and innovative impetus for AI to tackle environmental issues [8]. It stimulates new technological innovations, advances the development of decision support systems, and assists decision-makers in making wiser decisions based on a comprehensive perspective. Therefore, before we embrace the age of AI, we still need to work hard to build a sounder theoretical foundation for the new era.

This Editorial refers to the Special Issue “Processes of Pollution Control and Resource Utilization”. This Special Issue highlights new discoveries in the chemical, biological, interface, and migration processes of pollution control and resource utilization.

Twelve manuscripts were submitted for consideration for this Special Issue, and all of them were subject to the rigorous Processes review process. In total, ten papers were finally accepted for publication and inclusion in this Special Issue (eight articles, one communication, and one review). The contributions are listed below:

• Qi, Y., Cao, X., Cao, R., Cao, M., Yan, A., Li, E., Xu, D., 2024. Research on the Analysis of and Countermeasures for the Eutrophication of Water Bodies: Waihu Reservoir as a Case Study. Processes, 12(4), 796. https://doi.org/10.3390/pr12040796
• Lv, B., Liu, X., Zhou, M., Jing, G., 2023. Catalytic Ozonation of Reactive Red 195 in Aqueous Solution over a Cobalt/Aluminum Oxide-Ceria Catalyst. Processes, 11(7), 2141. https://doi.org/10.3390/pr11072141
• Zhao, Y., Wang, Y., Sun, G., Lu, F., 2023. The Effects of Coexisting Elements (Zn and Ni) on Cd Accumulation and Rhizosphere Bacterial Community in the Soil-Tomato System. Processes, 11(5), 1523. https://doi.org/10.3390/pr11051523
• Chen, F., Zhu, L., Tang, J., Li, D., Yu, F., Bai, F., Zhou, Y., Cao, L., Geng, N., 2023. A Pilot-Scale Nanofiltration–Ultrafiltration Integrated System for Advanced Drinking Water Treatment: Process Performance and Economic Analysis. Processes, 11(5), 1300. https://doi.org/10.3390/pr11051300
• Cao, L., Chen, W., Wang, Y., Li, S., Jin, Z., Bian, J., Li, Q., Li, M., 2023. 11(4), 1136. https://doi.org/10.3390/pr11041136
• Shan, Y., Guo, Y., Jiao, W., Zeng, P., 2023. Single-Cell Techniques in Environmental Microbiology. Processes, 11(4), 1109. https://doi.org/10.3390/pr11041109
• Geng, N., Ren, B., Xu, B., Li, D., Xia, Y., Xu, C., Hua, E., 2022. Bamboo Chopstick Biochar Electrodes and Enhanced Nitrate Removal from Groundwater. Processes, 10(9), 1740. https://doi.org/10.3390/pr10091740
• Liu, N., Lv, J., Cai, Y., Yao, Y., Zhang, K., Ma, C., Li, J., Ren, X., Hu, J., Zhao, J., 2022. Study on Gaseous Chlorobenzene Treatment by a Bio-Trickling Filter: Degradation Mechanism and Microbial Community. Processes, 10(8), 1483. https://doi.org/10.3390/pr10081483
• Lu, D., Xia, Y., Geng, N., Wang, H., Qian, J., Xu, C., 2022. Estimation Parameters of Soil Solute Transport Processes by Using the Electric Resistivity Method. Processes, 10(5), 975. https://doi.org/10.3390/pr10050975
• Geng, C., Lu, D., Qian, J., Xu, C., Li, D., Ou, L., Ye, Z., 2023. A Review on Process-Based Groundwater Vulnerability Assessment Methods. Processes, 11(6), 1610. https://doi.org/10.3390/pr11061610

As shown in

Table 1. Analysis of the published contributions in this Special Issue.

N# of the Contribution	Research Area	Focus	Type of Research	Processes
1	Water environment	Eutrophication level, drinking water safety, control strategy	Field data collection and multi-dimensional analysis	Biological processes
2	Water treatment	Catalyzed ozonation, catalyst synthesis, pollutant degradation	Experimental research and mechanism analysis	Physicochemical processes
3	Soil environment	Heavy metal pollution, Cd accumulation, microbial community structure	Experimental research and metagenomic analysis	Biochemical processes
4	Water purification	Membrane treatment, process comparison, drinking water safety	Pilot experiment and data analysis	Interface processes
5	Water environment	Microplastic, distribution characteristics, seasonal variation	Field sampling and laboratory analysis	Migration processes
6	Environmental microbiology	Cell heterogeneity, microbial behavior, single-cell technology	Literature review	Biological processes
7	Water treatment	Biochar electrode, nitrate removal, adsorption	Experimental research and performance analysis	Electrochemical processes
8	Atmospheric environment	Volatile organic compounds, microbial degradation, reaction kinetics	Experimental research and performance analysis	Mass transfer processes
9	Soil environment	Priority solute transport, resistivity method, model fitting	Experimental research and data analysis	Migration processes
10	Water environment	Groundwater vulnerability assessment, groundwater resources, sustainable development	Systematic literature review	Physical processes

, the contributions covered major fields of environmental science and technology, including the water environment, atmospheric environment, and soil environment. Various processes were revealed underlying the environmental research. In detail, Contributions 1 and 6 pertain to biological processes; Contributions 5 and 9 attempt to reveal migration processes; Contribution 2 relates to physicochemical processes; Contribution 3 involves biochemical processes; Contribution 4 relates to interface processes; Contribution 7 discusses electrochemical processes; Contribution 8 relates to mass transfer processes; and Contribution 10 summarizes physical processes (Table 1).

It is worth mentioning that Contribution 5 attempted to establish a link between microplastic distribution and the Basin Development Index (BDI), providing a simple and rapid path for assessment of microplastic pollution. To improve the accuracy of such models, it is crucial to understand the mechanisms behind the BDI and microplastic distribution. Contribution 5 provided the underlying data for this purpose. Contribution 7 achieved a breakthrough in addressing a typical interdisciplinary complex process. By leveraging electrochemical action, the study successfully overcame the limitations of mass transfer rates, significantly enhancing the efficiency of nitrogen removal. Contribution 9 provided a new field scale parameter acquisition method for understanding the phenomenon of priority solute transport in soil.

Finally, most of the contributions were based on experimental research (5), with field sampling (2) and pilot experiment (1).

Contribution 1 conducted a comprehensive analysis of the pollution sources of the reservoir and suggested effective control strategies aimed at reducing the input of nutrients such as nitrogen and phosphorus to alleviate exogenous pollution. Additionally, it combined engineering interventions with ecological restoration strategies to remove nutrients from the aquatic environment, effectively managing endogenous pollution. It provided in-depth analysis of both endogenous and exogenous pollution and offers instructive guidance for the control and routine management of eutrophication in Waihu Reservoir, as well as for the management of similar issues in different reservoirs.

Contribution 2 investigated the degradation of Reactive Red 195 (RR195) in aqueous solution using a cobalt/aluminum oxide-ceria (Co/Al₂O₃-CeO₂) catalyst in catalytic ozonation. It found that while the Co/Al₂O₃-CeO₂ catalyst did not significantly improve the degradation efficiency of RR195 compared to ozonation alone, it was beneficial for the mineralization process of RR195. The optimal catalyst dosage was determined to be 3 g/L with the best pH value at 8, and the catalyst demonstrated good stability over four consecutive uses.

Contribution 3 investigated the impact of coexisting zinc (Zn) and nickel (Ni) on cadmium (Cd) accumulation in tomato plants and the rhizosphere soil bacterial community. The findings revealed that the presence of Zn and Ni at low levels can reduce the bioaccumulation of heavy metals in tomato roots, but this inhibitory effect lessens as pollutant concentrations increase. Moreover, the richness and diversity of the bacterial community in the rhizosphere soil were significantly altered under co-contamination with heavy metals, particularly when the concentrations of Zn or Ni in the soil are moderate, which positively affects the tolerance of tomato plant roots to heavy metals.

Contribution 4 investigated a pilot-scale water treatment system integrating ultrafiltration (UF) and nanofiltration (NF) technologies to enhance the quality of drinking water. It was found that UF, serving as a pretreatment process, effectively reduced the turbidity of raw water and improved water quality, providing stable and qualified influent for NF. The three commercial NF membranes (Dow Filmtec NF270-400, VONTRON TAPU-LS, and GE Osmonics-HL8040F 400) demonstrated different performance advantages in terms of desalination, organic matter retention, and softening capabilities. Additionally, from an economic perspective, the NF1 membrane had the lowest cost in terms of energy and chemical consumption, with a production cost of USD 0.165 per ton of water.

Contribution 5 investigated the spatiotemporal distribution characteristics of microplastics and their influencing factors in the Lincheng River of Zhoushan City, China. It was found that microplastics were widely distributed in the water and sediment of the Lincheng River, with significant variations in abundance across different seasons and sites. Human activities, particularly construction activities, were strongly correlated with the abundance of microplastics. Additionally, the study introduced the Basin Development Index (BDI) to quantify the impact of land use on microplastic pollution.

Contribution 6 reviewed the application of single-cell techniques in environmental microbiology, including microscopic observation, sequencing identification, flow cytometric identification and isolation, and Raman spectroscopy-based identification and isolation. It emphasized the role of single-cell technologies in revealing microbial community heterogeneity, understanding the impact of individual cell phenotypes and genotypes on functionality, and their use in environmental microbial ecology research. Additionally, this article discussed the advantages and limitations of different single-cell techniques and looked forward to the future development of multi-technique integration in environmental microbiology research.

Contribution 7 investigated the use of biochar electrodes prepared from disposable bamboo chopsticks to enhance the removal of nitrate from groundwater. It was found that the biochar derived from bamboo chopsticks pyrolyzed at 600 °C (BCBC-600-2) had an ideal specific surface area and electrical conductivity, making it an excellent material for biochar electrodes. By combining adsorption and electroreduction processes, these biochar electrodes achieved a nitrate removal efficiency of up to 75.8% under conditions of a 4-volt applied voltage and a 4 h hydraulic retention time.

Contribution 8 investigated the efficiency and mechanism of a bio-trickling filter (BTF) in treating exhaust gas containing chlorobenzene, finding that under specific operating conditions, the BTF achieved a removal rate of over 85% for chlorobenzene. The study also explored the effects of different initial concentrations of chlorobenzene, empty bed residence times (EBRT), and the presence of xylene as a mixed carbon source on the efficiency of chlorobenzene removal. Additionally, the diversity of the microbial community within the BTF was analyzed through high-throughput sequencing, identifying Thermomonas, Petrimona, Comana, and Ottowia as efficient bacteria for degrading chlorobenzene.

Contribution 9 introduced a novel method for estimating the parameters of solute transport processes in soil using electrical resistivity measurement. By establishing a quantitative relationship between soil resistivity and solute concentration, researchers can monitor changes in tracer concentration within the soil and derive solute breakthrough curves. The experimental results demonstrated that this method, compared to traditional indoor soil column tests, has an average error of less than 10% in obtaining breakthrough curve parameters, confirming the accuracy and applicability of the electrical resistivity method in describing field-scale solute transport in soil. Moreover, the study found that the method is more sensitive to concentration changes when the tracer concentration is within the range of 50–120 mg/L, providing data support for the selection of tracer concentration in solute transport experiments. This research offered a new technical approach for accurately describing the mechanism of pollutant transport in soil and water environmental governance, which was significant for promoting the simulation of regional-scale soil solute transport.

Contribution 10 provided an overview of the development of groundwater vulnerability assessment methods over the past 30 years, emphasizing the importance of physical processes in current evaluation methods, and suggested future research directions for the protection of groundwater resources. The authors believed that process-based evaluation methods, combined with modern technologies such as artificial intelligence, would become mainstream and contribute to sustainable development.

Several research gaps were bridged by the set of contributions gathered in this Special Issue.

Data-driven decision-making is a vital methodology in research and industry, emphasizing the importance of evidence-based actions. Data collection, analysis, and interpretation are integral steps that transform data into actionable insights, effectively bridging the gap between raw information and decision-making. These processes begin with meticulously gathering data from various sources, followed by thorough analysis to uncover patterns, trends, and relationships. The interpretation of these findings within a relevant context allows for the extraction of meaningful conclusions that can guide strategic and operational decisions. This cycle of continuous improvement not only refines the initial data collection methods but also enhances the overall decision-making process. Effective communication of these insights is crucial for engaging stakeholders and informing policy and strategy development. Moreover, data-driven approaches facilitate better resource allocation, risk management, and public engagement, ultimately contributing to more informed and impactful actions that address pressing issues such as environmental pollution.

In addition, interdisciplinarity is the key to promote the progress of environmental technology, which provides comprehensive solutions to solve complex environmental problems by integrating knowledge and skills in physics, chemistry, biology, geology, and other fields. This interdisciplinary collaboration has led to the development of innovative technologies, such as sensing groundwater contamination through resistivity methods, addressing the shortcomings of chemical methods that rely on sample collection [9]. Combining data science and statistics improves data-driven capabilities for environmental decision-making. Multidisciplinary approaches also help to more accurately assess environmental risks, protect public health, and consider social impacts in policy development to develop more equitable and effective environmental management strategies [10].

Moreover, a deep understanding of complex systems and simple processes is the cornerstone of building future AI-assisted research. The applications of AI in this area include pattern recognition, data-driven discovery, predictive modeling, automated experimental design, interdisciplinary knowledge integration, enhanced decision support, real-time monitoring and feedback, complex network analysis, simulations and simulations, knowledge discovery and management, and ethical and sustainable considerations [11]. These capabilities make AI a powerful tool for researchers, not only accelerating the process of scientific discovery, but also improving the efficiency and quality of research. With the continuous progress of AI technology, its application in scientific research will be more extensive, promoting knowledge innovation and breakthroughs, and providing support for solving global challenges.

Environmental challenges evolve with the progression of time, yet the foundational logic of addressing these issues is refined and perfected through ongoing study and process analysis. This Special Issue, “Processes of Pollution Control and Resource Utilization”, identifies the following directions:

• The development of advanced materials is critical to environmental issues by improving the efficiency of pollutant treatment, promoting resource recovery, enhancing energy conversion and storage, enabling green chemical production, developing environmentally friendly products, enhancing ecosystem restoration capabilities, enhancing environmental monitoring technologies, reducing governance costs, driving scientific and technological innovation, and addressing global environmental challenges. It provides innovative tools and solutions to solve and prevent environmental pollution and promote sustainable development.
• AI technology, with its powerful computational, learning, and decision-making capabilities, provides significant scientific and technological support for environmental research, especially in the areas of data feature analysis, exploration of mechanisms, prediction of complex environmental behaviors, and decision-making optimization. However, the application of AI in environmental science also faces a series of challenges, including ensuring data quality, improving model interpretability, meeting the increasing demand for computational power, and protecting data privacy and security. Overcoming these challenges is crucial for the in-depth application of AI technology in the field of environmental science and is key to advancing the development of environmental science and achieving sustainable development goals.
• The fine-tuning of processes is a pivotal technological approach to advancing environmental sustainability. It aids in mitigating pollutant emissions from industrial production, enhancing the conversion rates and selectivity of raw materials, and optimizing resource utilization. Moreover, such precise control significantly impacts the realization of green chemistry, which focuses on designing environmentally friendly and energy-efficient chemical synthesis pathways. It also enhances the sensitivity and accuracy of environmental monitoring techniques, which is crucial for the timely detection and assessment of environmental risks. Additionally, the fine-tuning of processes is instrumental in the development of new materials that play a vital role in environmental management and remediation. In essence, the meticulous regulation of processes is not only a means to reduce the environmental footprint of industrial activities but also a cornerstone for the sustainable development agenda.