Harnessing the Potential of Biological Recognition Elements for Water Pollution Monitoring

Sahu, Subhankar; Roy, Rohita; Anand, Ruchi

doi:10.1021/acssensors.1c02579

Cited by 21 publications

(11 citation statements)

References 88 publications

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“…Shukla, BishnuKant studied the groundwater quality parameters around the Punjab area and determined the pollution degree of these groundwater [1]. Sahu, Subhankar use the potential of biometric elements to monitor water pollution [2]. Ahmed, Shahid investigated the awareness level of Delhi youth on water pollution, its causes, health effects and solutions, and used the original data collected through the schedule of Delhi University/College students [3].…”

Section: Introductionmentioning

confidence: 99%

Engineering Method of Water Pollution Prevention and Control in Waterworks Integrated with Biological Therapy

2020

WPPCP

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Water pollution is easy to cause social concern and panic because of its harm to human body. As an important place for water treatment, the water pollution problem generated by the waterworks is a major issue of public concern. The water pollution prevention and control project has naturally become a topic of close attention by the public, and the biological treatment method has brought a reference path to water pollution prevention and control. Based on this, this paper uses the biological treatment method to manage the water pollution prevention project of the waterworks, and designs a method to improve it. This paper first introduces the causes of water pollution, then analyzes the problems in the process of water pollution prevention and control, and then puts forward the water pollution prevention and control measures for the waterworks. In this paper, the effect of water pollution prevention and control project in waterworks is analyzed by using biological therapy. Finally, it is found that biological therapy can greatly improve the effect of water pollution prevention and control in waterworks. Biotherapy has a relatively effective application in the water plant pollution prevention and control project, so as to achieve the prevention and control of water pollution.

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Section: Introductionmentioning

confidence: 99%

Engineering Method of Water Pollution Prevention and Control in Waterworks Integrated with Biological Therapy

2020

WPPCP

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“…Haghiabi Amir Hamzeh explored the effect of an algorithm model in AI technology in the analysis of water pollution, and determined the feasibility of AI technology in water pollution analysis [10]. Sahu Subhankar explored the role of an identification device in the analysis of water pollution and determined the reliability of this device [11]. Li Xiang explored the role of a metal frame in the treatment of water pollution, and determined the feasibility of this metal frame.…”

Section: Introductionmentioning

confidence: 99%

Construction of Comprehensive Prevention and Control Project of Water Works Based on Artificial Neural Network Algorithm and Artificial Intelligence

2020

WPPCP

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The further development of information technology has brought more convenience to people's lives. However, this process of social and economic development has also brought more burdens to the local ecological environment, making people face water pollution and water resource depletion and other problems. In order to improve or even solve these problems, it is necessary to control water pollution, and at the same time make people develop the concept of saving water. The safety of drinking water is not only an important guarantee for the maintenance of human health, but also can help the current ecological environment to carry out sustainable development and provide a certain auxiliary role for social and economic development. First of all, to solve the problem of water pollution, it is necessary to pay enough attention to the waterworks in the city, so as to further improve the performance of the water purification technology of the waterworks. At the same time, it is also necessary to optimize the water quality treatment mode of the waterworks to meet the needs of social development. However, the current artificial intelligence (AI) and artificial neural networks (ANN) algorithms have experienced a long period of development and have deep applications in many fields of real life. Among them, the ANN algorithm model can automatically analyze and process data. At the same time, it can transform problems that cannot be solved by existing solutions into mathematical problems for efficient solution. On the other hand, the ANN algorithm model can also be combined with the branch technology in AI to analyze all kinds of feature data of things. Based on these data analysis, fault detection and precise control in industry can be well completed. Through AI technology and ANN algorithm model, this paper proposed a comprehensive prevention and control project for water works, and compared the performance of this comprehensive prevention and control project with the existing comprehensive prevention and control of water works in many aspects. It is determined that the performance of this new comprehensive prevention and control project in many aspects has been improved by about 22% on average.

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“…Indeed, SERS signals from Au-decorated nanofiber probes inserted into breast cancer cells have been shown to detect toxic metal exposure at a LOD of 5 nM for mercury and 100 nM for silver ( 24 ). Obtaining reproducible responses in biosensors is a longstanding challenge ( 25 ). In particular, the reproducibility of SERS surfaces depends on nanoparticle (NP) morphology, nanogap distance, and surface chemistry ( 26 ).…”

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confidence: 99%

Decoding the metabolic response of Escherichia coli for sensing trace heavy metals in water

Hong

Huang

Santiago

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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Heavy metal contamination due to industrial and agricultural waste represents a growing threat to water supplies. Frequent and widespread monitoring for toxic metals in drinking and agricultural water sources is necessary to prevent their accumulation in humans, plants, and animals, which results in disease and environmental damage. Here, the metabolic stress response of bacteria is used to report the presence of heavy metal ions in water by transducing ions into chemical signals that can be fingerprinted using machine learning analysis of vibrational spectra. Surface-enhanced Raman scattering surfaces amplify chemical signals from bacterial lysate and rapidly generate large, reproducible datasets needed for machine learning algorithms to decode the complex spectral data. Classification and regression algorithms achieve limits of detection of 0.5 pM for As 3+ and 6.8 pM for Cr 6+ , 100,000 times lower than the World Health Organization recommended limits, and accurately quantify concentrations of analytes across six orders of magnitude, enabling early warning of rising contaminant levels. Trained algorithms are generalizable across water samples with different impurities; water quality of tap water and wastewater was evaluated with 92% accuracy.

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Harnessing the Potential of Biological Recognition Elements for Water Pollution Monitoring

Cited by 21 publications

References 88 publications

Engineering Method of Water Pollution Prevention and Control in Waterworks Integrated with Biological Therapy

Engineering Method of Water Pollution Prevention and Control in Waterworks Integrated with Biological Therapy

Construction of Comprehensive Prevention and Control Project of Water Works Based on Artificial Neural Network Algorithm and Artificial Intelligence

Decoding the metabolic response of Escherichia coli for sensing trace heavy metals in water

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