Instrumentation & Measurement Magazine 25-9 - 54

Pollutants Name
Table 1 - Safe level concentration and primary sources of various air particles
Safe level concentration
Primary sources
Carbon monoxide (CO)
Carbon dioxide (CO2
Nitrogen oxide (NOx)
Sulfur dioxide (SO2
)
)
Particulate Matter (PM2.5)
Particulate Matter (PM10)
Ozone (O3)
50 ppm
350 ppm
0.1 ppm
5 ppm
20 μg/m3
10 μg/m3
0.1 ppm
deluxe residential area. First, the drone was constructed from
a DJI F450 frame, composed of ultra-strength material. The incorporated
Wi-Fi module of the drone was connected to a local
server, from where the acquired data were collected continuously.
Next, several gas sensors, namely for MQ2, MQ7 and
MQ135, and particulate matter (PM) sensors were used for
monitoring toxic gases and dust pollutants in the air. The automatic
aircraft navigated to the designated locations with the
help of the GPS technology equipped to guide it to different
predetermined heights, and from there detected the pollution
density (the concentration of all the air pollutants present) only
when the values were above the recommended threshold density.
Finally, the drone returned back down to the ground from
where it initiated its flight.
The drone also provided live pictures from the aerial view
of the landscape in its operating area. The data collected
through live data plotting (streaming) by the sensors of the
proposed system were stored and graphically represent Dhaka's
air pollutants condition. We compared the data with safe
level concentrations, and comparative results were performed
to determine whether the Dhaka air was safe for the human
body. To the best of our knowledge, this study developed a
low-cost drone surveying system to detect air pollutants in
various places in Dhaka, Bangladesh for the first time.
Related Works
Many investigations have been performed to detect and analyze
air pollutants using UAV technologies and IoT sensors.
For instance, D. Gallacher [4] discussed different platforms,
applications, and risk factors of UAV technologies such as
drones. They used drones and various IoT sensors for environmental
management in urban areas, such as air and
water quality monitoring and agricultural purposes. In [5],
J. I. Hernández-Vega et al. presented a system that takes realtime
air pollution data, accumulates them into a UAV, and uses
them for monitoring purposes in a smart city. The authors detect
the pollutants from the air with a Tarot quadcopter and
consequently record and send the data to a ground station
using a radiofrequency communication protocol. They have
used the DAQ system for data acquisition, sensors, and GPS.
Finally, the authors recorded the amount of hydrogen and
ozone particles in real-time by utilizing MQ8 and MQ131 sensors,
respectively.
54
Fossil fuel burnings, unvented gas heaters
Fossil fuel burnings, deforestation
Vehicles' exhaust gases, emissions from power plants
Emissions from power plants and various industries
Vehicles' exhaust gases, emissions from power plants
Dust from construction sites
Chemical industries, gas stations
Some authors utilized various cost-effective IoT devices for
climate supervision. For instance, mobile participatory-based
air quality and urban heat island monitoring systems were
presented by M. A. Fekih et al. [6]. They used low-cost wireless
mobile sensing nodes to detect humidity, temperature, and
various air pollutants such as PM1, NO2
, PM2.5, and PM10.
Consequently, the authors made temperature and air quality
maps using the collected data. Using these low-cost IoT sensors,
they ended up with satisfactory solutions that can be used
in participatory environmental monitoring.
Dhingra et al. [7] conducted research on air quality monitoring
systems that used the internet of things (IoT) based smart
devices. They suggested a three-phase system that comprises
an IoT kit with multiple gas sensors (MQ7, MQ2, and MQ135),
an Arduino Uno microcontroller, and a Wi-Fi module. Gas
sensors collect data from the air and transmit them to the Arduino
IDE, sending to the server through a Wi-Fi module.
They also created an Android app called IoT-Mobair, allowing
users to access the server's air quality data from the Ubidots
cloud service platform. G. Rohi and his coauthors designed
an automatic flying drone to monitor and regulate various air
pollution data [9]. The proposed small-sized and lightweight
prototype employs an Xbee module for wireless communication.
In [10], R. D. Fazio and his team implemented an RC
drone to monitor air and sound pollution for smart cities. This
work utilized a Raspberry Pi Zero microcontroller and different
cost-effective sensing devices. Additionally, an IR camera
has been used to detect fires from hazardous materials and
monitor the traffic situation.
Few works performed indoor air pollution detection instead
of the outdoor environment. In [8], L. Gugliermetti and
D. A. Garcia developed a cost-efficient, easy-to-use indoor air
quality monitoring device for old persons and people with
cognitive problems. The proposed device, titled Home Pollution
Embedded System (HOPES), consists of an ATMega328
ARM processor microcontroller, multiple metal oxide gas
sensors, LED, and an IR particulate matter detector. The IoT
compatibility of the device connects to wireless networks to
transmit the air quality data obtained from different sensors.
Furthermore, the proposed system creates an alarm if any substance
concentrations reach the predefined threshold safety
limits. Finally, the authors performed some validation tests of
the device to evaluate the efficiency of the sensors' response.
IEEE Instrumentation & Measurement Magazine
December 2022
Instrumentation & Measurement Magazine 25-9

Table of Contents for the Digital Edition of Instrumentation & Measurement Magazine 25-9
