Development of IoT Technologies for Air Pollution Prevention and Improvement

Zhao, Yu-Lin; Jiali, Tang; Huang, Han‐Pang; Wang, Ze; Chen, Tse-Lun; Chiang, Chih-Wei; Chiang, Pen‐Chi

doi:10.4209/aaqr.2020.05.0255

Cited by 22 publications

(5 citation statements)

References 16 publications

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“…also, tailwind patterns and dissipation mechanisms told by ventilation systems, doors, and windows can affect the rates of cross-ventilation and the rotation of adulterants within structures. thus, it's prudent to consider these dynamics when sticking sensors to ensure accurate monitoring of IAQ [16]. Figure 1 illustrates a Scenario of the system Placement of Sensors in Indoor Environments [17].…”

Section: B Placement Of Sensors In Indoor Environmentsmentioning

confidence: 99%

IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review

Qasim Flayyih,

Waleed,

M. Ibrahim

2024

IJCDS

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This exploratory disquisition delves into the world of Indoor Air Quality( IAQ) monitoring systems, using the solidarity of Artificial Intelligence( AI) and Internet of Effects ( IoT) technologies. Its overarching thing is to check the efficacity of these structures in regulating IAQ within structures, with a specific focus on mollifying pollutant degrees and their dangerous results on inhabitants. The study undertakes a comprehensive review of present literature and exploration trials, which depend upon AI and IoT algorithms for border monitoring, records analysis, and contrivance evaluation. also, it delves into the complications of machine armature, deployment ways, and functional efficiency. Furthermore, the exploration attracts different instructional budgets, including clever detectors and IoT bias stationed within the ambient surroundings. It elucidates the functionality of those instruments to accumulate real-time statistics, encompassing variables together with unpredictable natural composites, temperature oscillations, and moisture ranges. A vital aspect of this study is the disquisition of AI, contrivance getting to know Machine Learning ( ML), and Deep Learning ( DL) algorithms, showcasing their prophetic prowess within shadowing fabrics. also, they have a look at delving into the symbiotic dating among those algorithms, expounding their function in enhancing machine delicacy and optimizing energy intake. Moreover, the studies trials to delineate personalized health tips knitter-made to character inhabitants, decided from the wealth of records accrued through these structures. By integrating present-day technologies with empirical perceptivity, this takes a look at trials to pave the manner for better IAQ control strategies, fostering more healthy and lesser sustainable lodging surroundings.

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Section: B Placement Of Sensors In Indoor Environmentsmentioning

confidence: 99%

IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review

Qasim Flayyih,

Waleed,

M. Ibrahim

2024

IJCDS

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“…These models are capable of monitoring different compounds of pollutants present in air, sound, Water and Soil like various hazardous compounds in air, PH value in industrial effluents etc. A real time system to monitor and analyze air quality is designed that record the data remotely to the server, and update the data via the internet was proposed by Kennedy Okokpujie [ 17] The proposed system was developed using an Arduino microcontroller and it monitors the presence of gaseous, dust pollutants present in air & its level [18] [19] . Fulvio Corno et'al [20] introduces the IoT crowd-sensing platform using bicycle networks as IoT to provide various services to citizety.…”

Section: Related Workmentioning

confidence: 99%

Development of Internet of Things (IoT) Based Monitoring of Hazardous Exhaust Compounds in Air - A Review

Bagal¹,

Choudhari

Chaudhari

2022

ITM Web Conf.

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Presence of pollutants in Air is the most prominent problem globally confronted now a day. The extreme use of fueled vehicles and rapid urbanization has deteriorated the air quality. This deteriorated air contains hazardous compounds like Mono-oxides & Di-oxides of carbon, Sulphur, Nitrogen, Atmospheric Particulate Matter. Exposure to such hazardous compounds in the air for a long time can cause damage to the human health. Traditional air monitoring systems consists of monitoring stations. As far as traditional air quality monitoring methods are concerned, they are highly expensive and requires a regular maintenance. Due to these limitations, these stations are deployed in small numbers and also it provides indicative values of the sensed data. This monitored data has low resolution and precision. This paper proposes framework for the development of smart and portable system using Internet of Things (IoT) for monitoring of hazardous exhaust compounds in the air using real time. This framework consists of different types of sensors and a controller that are used for monitoring and assessment of the air quality. Also it is used to check the presence of hazardous compounds emitted by different industries and share this data through IOT. The outcome of the proposed research work can be utilized by industries as well as by other agencies to carry out an audit of hazardous exhaust components present in air, so as to take necessary precautions and to save human.

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“…Therefore, technology development and environmental impact reduction will become an issue worthy of in-depth discussion (Lin, 2018). Air pollution can cause stroke, lung cancer, chronic obstructive pulmonary disease, and cardiovascular disease (Yoon et al,2008;Yildiz et al,2013;Wang et al,2015;Kim et al, 2020;Zhao et al, 2020). In addition to its profound impact on human health, particulate pollutants can also affect the living environment, affecting atmospheric visibility (Lv, et al, 2019;Bardouki et al, 2003;Horton et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Particle Removal and Antibacterial of Nanofiber Doped with Metal and Ions Prepared by Electrospinning

Jiang

Cayron

Cheng

et al. 2023

Aerosol Air Qual. Res.

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Due to the increase in human activities and the application of nanotechnology, people's exposure to nano and submicron particles is increasing. Conventional particle separation technologies, such as electrostatic precipitators, cyclone, wet washing, and filtration method, cannot work well. In addition, the fiber diameter and pore size of the traditional fiber filter material is too large, and the bulk density is difficult to control. Nanofiber membranes have a large surface area, small pore size and high porosity. The nanofiber membranes prepared by electrospinning technology are easy to intercept submicron and nanoparticles. Furthermore, the electrospinning technology is simple to operate, makes fibers of various materials easy, and is convenient for assembly and replacement. The most common bacteria are Escherichia coli, which harms the ecological environment and human health. Therefore, this study used bacteria and particles as the target pollutants and controlled by prepared nanostructured materials doped with metals and ions. In order to improve the treatment efficiency of particles and bacteria, this study added various metals and ions to nylon 6 nanofibers and explored the antibacterial and filterability of artificial fibrils. Different operation parameters, such as types of metal, types of ion, metal and concentration, and surface velocity, were also investigated to prepare various fibers to make the best performance fiber. In addition, the best-operating conditions could be obtained through a filtration test. Different salts, metals and metal ions with different concentrations, three metal oxides (TiO2, CeO2 and ZnO) and three ions (Ag + , K + and Na + ) were used to test the filtration performance of various particle sizes for the best metal ion concentration, best filtration and bacterial removal performance. The experimental results show that the filtration efficiency of the composite fiber can reach 99%, and the composite fiber sprayed with a self-made antibacterial liquid has the best antibacterial ability.

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Development of IoT Technologies for Air Pollution Prevention and Improvement

Cited by 22 publications

References 16 publications

IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review

IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review

Development of Internet of Things (IoT) Based Monitoring of Hazardous Exhaust Compounds in Air - A Review

Particle Removal and Antibacterial of Nanofiber Doped with Metal and Ions Prepared by Electrospinning

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