Mixed chlorine/chloramines in disinfected water and drinking water distribution systems (DWDSs): A critical review

Pan, Renjie; Zhang, Tian-Yang; He, Huan; Zheng, Zheng-Xiong; Dong, Zheng-Yu; Zhao, Heng-Xuan; Xu, Meng-Yuan; Luo, Zhen-Ning; Hu, Chen-Yan; Tang, Yu-Lin; El-Din, Mohamed Gamal; Xu, Bin

doi:10.1016/j.watres.2023.120736

Cited by 5 publications

(1 citation statement)

References 136 publications

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“…We also analyzed the stability of the EPS removal and the viability of these genetically modified strains compared to those treated conventionally, thereby establishing a permanent EPS-deficient strain. With such a strain, we conducted real-time, in situ investigations into the roles of EPS in bacterial disinfection kinetics in response to three commonly used disinfectants, i.e., ozone, chloramine B, and free chlorine. , Lastly, we unraveled the underlying mechanisms by analyzing the changing patterns of cell integrity, ROS generation, and other metabolic activities upon exposure to disinfectants (Figure C). This work provides valuable insights into the central role of EPS in bacterial defenses and presents promising implications for developing more effective disinfection strategies.…”

Section: Introductionmentioning

confidence: 99%

Deciphering the Roles of Extracellular Polymeric Substances (EPS) in Shaping Disinfection Kinetics through Permanent Removal via Genetic Disruption

Sun,

Zhou,

Wen

et al. 2024

Environ. Sci. Technol.

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Extracellular polymeric substances (EPS) ubiquitously encapsulate microbes and play crucial roles in various environmental processes. However, understanding their complex interactions with dynamic bacterial behaviors, especially during the disinfection process, remains very limited. In this work, we investigated the impact of EPS on bacterial disinfection kinetics by developing a permanent EPS removal strategy. We genetically disrupted the synthesis of exopolysaccharides, the structural components of EPS, in Pseudomonas aeruginosa, a wellknown EPS-producing opportunistic pathogen found in diverse environments, creating an EPS-deficient strain. This method ensured a lasting absence of EPS while maintaining bacterial integrity and viability, allowing for real-time in situ investigations of the roles of EPS in disinfection. Our findings indicate that removing EPS from bacteria substantially lowered their susceptibility threshold to disinfectants such as ozone, chloramine B, and free chlorine. This removal also substantially accelerated disinfection kinetics, shortened the resistance time, and increased disinfection efficiency, thereby enhancing the overall bactericidal effect. The absence of EPS was found to enhance bacterial motility and increase bacterial cell vulnerability to disinfectants, resulting in greater membrane damage and intensified reactive oxygen species (ROS) production upon exposure to disinfectants. These insights highlight the central role of EPS in bacterial defenses and offer promising implications for developing more effective disinfection strategies.

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Section: Introductionmentioning

confidence: 99%

Deciphering the Roles of Extracellular Polymeric Substances (EPS) in Shaping Disinfection Kinetics through Permanent Removal via Genetic Disruption

Sun,

Zhou,

Wen

et al. 2024

Environ. Sci. Technol.

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DBPs in Drinking Water Treatment Plants

Goslan,

Chen

2024

The Handbook of Environmental Chemistry

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A survey on applications of reinforcement learning in spatial resource allocation

Zhang,

Wang,

Mango

et al. 2024

Comput.Urban Sci.

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The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Despite the progress, reinforcement learning still faces hurdles when it comes to spatial resource allocation. There remains a gap in its ability to fully grasp the diversity and intricacy of real-world resources. The environmental models used in reinforcement learning may not always capture the spatial dynamics accurately. Moreover, in situations laden with strict and numerous constraints, reinforcement learning can sometimes fall short in offering feasible strategies. Consequently, this paper is dedicated to summarizing and reviewing current theoretical approaches and practical research that utilize reinforcement learning to address issues pertaining to spatial resource allocation. In addition, the paper accentuates several unresolved challenges that urgently necessitate future focus and exploration within this realm and proposes viable approaches for these challenges. This research furnishes valuable insights that may assist scholars in gaining a more nuanced understanding of the problems, opportunities, and potential directions concerning the application of reinforcement learning in spatial resource allocation.

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Mixed chlorine/chloramines in disinfected water and drinking water distribution systems (DWDSs): A critical review

Cited by 5 publications

References 136 publications

Deciphering the Roles of Extracellular Polymeric Substances (EPS) in Shaping Disinfection Kinetics through Permanent Removal via Genetic Disruption

Deciphering the Roles of Extracellular Polymeric Substances (EPS) in Shaping Disinfection Kinetics through Permanent Removal via Genetic Disruption

DBPs in Drinking Water Treatment Plants

A survey on applications of reinforcement learning in spatial resource allocation

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