Citation: Duangsuwan, S.;
Prapruetdee, P.; Subongkod, M.;
Klubsuwan, K. 3D AQI Mapping
Data Assessment of Low-Altitude
Drone Real-Time Air Pollution
Monitoring. Drones 2022, 6, 191.
https://doi.org/10.3390/
drones6080191
Academic Editors: Efstratios
Stylianidis and Luis Javier
Sánchez-Aparicio
Received: 27 June 2022
Accepted: 26 July 2022
Published: 29 July 2022
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drones
Article
3D AQI Mapping Data Assessment of Low-Altitude Drone
Real-Time Air Pollution Monitoring
Sarun Duangsuwan
1,
* , Phoowadon Prapruetdee
1
, Mallika Subongkod
2
and Katanyoo Klubsuwan
3
1
Electrical Engineering, Department of Engineering, King Mongkut’s Institute of Technology Ladkrabang,
Prince of Chumphon Campus, Chumphon 86160, Thailand; phoowadon.pr@kmitl.ac.th
2
Business Administration, Department of Business Administration, King Mongkut’s Institute of Technology
Ladkrabang, Prince of Chumphon Campus, Chumphon 86160, Thailand; mallika.su@kmitl.ac.th
3
Smart City Thailand Association (SCTA), Bangkok 10230, Thailand; katanyoo@eideas.co.th
* Correspondence: sarun.du@kmitl.ac.th; Tel.: +66-99-1864411
Abstract: 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
2
) 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
3
), nitrogen dioxide (NO
2
),
particulate matter (PM
2.5,10
), and sulfur dioxide (SO
2
), 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
2.5,10
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%.
Keywords: 3D AQI mapping; BPNN–CNN model; smoke detection; open burning; Dr-TAPM;
data assessment
1. Introduction
Open burning is the burning of unwanted materials such as waste, tree branches,
leaves, grass, and plastics; through open burning, smoke and emissions are released
directly into the air. During open burning, air pollutants are the main considerations
for health, human life, society, and cities. Globally, 5.4 million deaths were attributed to
ambient air pollution in 2021. At 89.0%, low and middle-income countries had the highest
portion of deaths. In a regional breakdown, it was found that the southeast Asian regions
accounted for 943,000 deaths, while the highest number of deaths was 1,254,000 for the
western Pacific region [1].
In Thailand, open burning is a major cause of air pollution, resulting in numerous
public health problems and even death. It is a source of haze smoke in northern Thailand.
Most open burning is caused by activities such as agricultural management, forest foraging,
waste burning, and combustion. During the summer session from January to April, a high
air quality index (AQI) above 300 is typically reported every year, meaning it is dangerous
for human health. Smoke from the open burning of fields is a leading cause of the smog
Drones 2022, 6, 191. https://doi.org/10.3390/drones6080191 https://www.mdpi.com/journal/drones