Real Time Monitoring Approach for Underground Mine Air Quality Pollution Monitoring System Based on IoT Technology

Air Monitoring becomes a systematic approach for sensitivity and finding out the circumstances of the atmosphere. The major concern of air quality monitoring is to measure the concentration of pollution and other important parameter related to the contamination and provides information in real-time to make decisions at right time to cure lives and save the environment. This paper proposes an Architectural Framework for the air quality monitoring system based on Internet-of-Things (IoT) and via Fog computing techniques with novel methods to obtain real-time and accurate measurements of conventional air quality monitoring. IoT-based real-time air pollution monitoring system is projected to at any location and stores the measured value of various pollutants over a web server with the Internet. It can facilitate the process and filter data near the end of the IoT nodes in a concurrent manner and improving the Latency issue with the quality of services.

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Smart Sensor Network Based High Quality Air Pollution Monitoring System Using Labview

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v13i08.7161 ◽

2017 ◽

Vol 13 (08) ◽

pp. 79 ◽

Cited By ~ 2

Author(s):

Nagarjuna Telagam ◽

Nehru Kandasamy ◽

Nagendra Prasad G ◽

Menakadevi Nanjundan

Keyword(s):

Air Quality ◽

Real Time ◽

Sensor Network ◽

Monitoring System ◽

Smart Cities ◽

Ambient Air ◽

Pollution Monitoring ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Laptop Computer

A ZigBee based wireless sensor network is implemented in this paper which is of low-cost solar-powered air quality monitoring system. The main objective of the proposed architecture is to interfacing various sensors to measure the sensor analog data and displayed in LabVIEW on the monitor using the graphical user interface (GUI). The real time ambient air quality monitoring in smart cities is of greater significance for the health of people. The wireless network sensor nodes are placed at different traffic signals in the smart cities which collect and report real-time data on different gases which are present in the environment such as carbon monoxide (CO), nitrogen dioxide (NO2), methane (CH4) and humidity. The proposed system allows smart cities to monitor air quality conditions on a desktop/laptop computer through an application designed using graphical programming based LabVIEW software and provides an alert if the air quality characteristics exceed acceptable levels. The sensor network was successfully tested on the campus of the institute of aeronautical engineering, Hyderabad. The sensor data are indicated by different indicators on the front panel of LabVIEW and also different charts are plotted with respect to time and amplitude which explains the severity of polluted areas.

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Information Framework of Pervasive Real Time Monitoring System: Case of Peat Land Forest Fires and Air Quality in South Sumatera, Indonesia

Proceeding of the Electrical Engineering Computer Science and Informatics ◽

10.11591/eecsi.v3.1163 ◽

2016 ◽

Vol 3 (1) ◽

Author(s):

Siti Nurmaini ◽

Reza Firsandaya Malik ◽

Deris Stiawan ◽

Firdaus Firdaus ◽

Saparudin Saparudin

Keyword(s):

Air Quality ◽

Real Time ◽

Monitoring System ◽

Forest Fires ◽

Real Time Monitoring ◽

Information Framework

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Monitoring Air Quality and Estimation of Personal Exposure to Particulate Matter Using an Indoor Model and Artificial Neural Network

Sustainability ◽

10.3390/su12093794 ◽

2020 ◽

Vol 12 (9) ◽

pp. 3794 ◽

Cited By ~ 2

Author(s):

Hyeon-Ju Oh ◽

Jongbok Kim

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Air Quality ◽

Real Time ◽

Monitoring System ◽

Personal Exposure ◽

Operating Conditions ◽

Real Time Monitoring ◽

Potential Threat ◽

Artificial Neural

Exposure to particulate materials (PM) is known to cause respiratory and cardiovascular diseases. Respirable particles generated in closed spaces, such as underground parking garages (UPGs), have been reported to be a potential threat to respiratory health. This study reports the concentration of pollutants (PM, TVOC, CO) in UPGs under various operating conditions of heating, ventilation and air-conditioning (HVAC) systems using a real-time monitoring system with a prototype made up of integrated sensors. In addition, prediction of the PM concentration was implemented using modeling from vehicle traffic volumes and an artificial neural network (ANN), based on environmental factors. The predicted PM concentrations were compared with the level acquired from the real-time monitoring. The measured PM10 concentrations of UPGs were higher than the modeled PM10 due to short-term sources induced by vehicles. The average inhalable and respirable dosage for adult was calculated for the evaluation of health effects. The ANN predicted PM concentration showed a close correlation with measurements resulting in R2 ranging from 0.69 to 0.87. This study demonstrates the feasibility of the use of the air quality monitoring system for personal-exposure to vehicle-induced pollutant in UPGs and the potential application of modeling and ANN for the evaluation of the indoor air quality.

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