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Author: search.filters.author.Duangsuwan, SarunSubject: search.filters.subject.Dr-TAPM

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    3D AQI Mapping Data Assessment of Low-Altitude Drone Real-Time Air Pollution Monitoring
    (2022-08-01)
    Duangsuwan, Sarun
    ;
    Prapruetdee, Phoowadon
    ;
    Subongkod, Mallika
    ;
    Klubsuwan, Katanyoo
    Air pollution primarily originates from substances that are directly emitted from natural or anthropogenic processes, such as carbon monoxide (CO) gas emitted in vehicle exhaust or sulfur dioxide (SO<inf>2</inf>) released from factories. However, a major air pollution problem is particulate matter (PM), which is an adverse effect of wildfires and open burning. Application tools for air pollution monitoring in risk areas using real-time monitoring with drones have emerged. A new air quality index (AQI) for monitoring and display, such as three-dimensional (3D) mapping based on data assessment, is essential for timely environmental surveying. The objective of this paper is to present a 3D AQI mapping data assessment using a hybrid model based on a machine-learning method for drone real-time air pollution monitoring (Dr-TAPM). Dr-TAPM was designed by equipping drones with multi-environmental sensors for carbon monoxide (CO), ozone (O<inf>3</inf>), nitrogen dioxide (NO<inf>2</inf>), particulate matter (PM<inf>2.5,10</inf>), and sulfur dioxide (SO<inf>2</inf>), with data pre- and post-processing with the hybrid model. The hybrid model for data assessment was proposed using backpropagation neural network (BPNN) and convolutional neural network (CNN) algorithms. Experimentally, we considered a case study detecting smoke emissions from an open burning scenario. As a result, PM<inf>2.5,10</inf> and CO were detected as air pollutants from open burning. 3D AQI map locations were shown and the validation learning rates were apparent, as the accuracy of predicted AQI data assessment was 98%.
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    Development of drone real-time air pollution monitoring for mobile smart sensing in areas with poor accessibility
    (2020-01-01)
    Duangsuwan, Sarun
    ;
    Jamjareekulgarn, Punyawi
    The topic of air pollution, especially in terms of particulate matter (PM), is a very serious problem in current society. This problem is caused by such factors as forest fires, construction, industrialization, and the ever-increasing number of motor vehicles. Thus, PM2.5 has become an important risk factor for citizens in Thailand as well as globally, not only in terms of the problems associated with health risks, but also the negative impact on the image of the country. Measuring pollution for air quality monitoring is a challenging task, especially when considering areas that have poor accessibility. The aim of this work is to develop a drone equipped with sensors to monitor and collect air quality data in real time from such areas of potential pollution. The proposed drone is called the drone for real-time air pollution monitoring (Dr-TAPM) and is equipped with the ability to measure the concentration of carbon monoxide (CO), ozone (O<inf>3</inf>), nitrogen dioxide (NO<inf>2</inf>), PM, and sulfur dioxide (SO<inf>2</inf>). Additionally, the collected data is transmitted to a cloud server every second over a wireless internet connection. In this study, the measurement was conducted in the experiment area, which is considered to be in the pollutant model scenario. The experimental results are shown as graphs of quantitative pollutant levels and air quality index (AQI) values obtained from realtime monitoring on a mobile application.