Instrumentation & Measurement Magazine 25-9 - 56

The working sequences of the proposed air quality monitoring
system are illustrated in Fig. 3.
Fig. 2. Complete designed model of the proposed drone with external set-up.
output pin and four sensors exist in this work, a multiplexor
with four inputs and one output pin is used before connecting
to the microcontroller. Finally, the output is connected directly
to the nodeMCU.
The open-source IoT platform nodeMCU is employed to acquire
data from various sensors and transmit them to the local
server via the Wi-Fi module accordingly. Several components
other than sensors and camera are used to make the entire system,
such as nodeMCU, capacitors, resistors, a multiplexer,
and an LM2596 dc-dc buck converter. The buck converter is
used to control the power supply at the desired level.
Fig. 2 illustrates the designed model of the proposed drone
with all of the sensors used in this research. An 8-megapixel
Raspberry Pi camera module v2 is utilized to capture live photos,
which are the structural aerial views, to supervise why the
pollutants vary over different places. A specially created addon
module for Raspberry Pi 4 model B hardware works as the
Raspberry Pi camera board. Through a unique CSI interface, it
connects to the Raspberry
Pi microcontroller. Finally,
it is linked to the nodeMCU
framework through a Wi-Fi
connection.
After continuously receiving
air concentration
information from various
locations, the data are automatically
plotted using the
IoT platform Blynk. This
application allows fast connections
for controlling and
monitoring data remotely
and creates graphs on the
screen of a smartphone.
56
Data Analysis and Discussion
In this section, real-time air quality data collected from various
sensors of the proposed device are presented with appropriate
figures and tables. The data were collected from two different
places in Dhaka, Bangladesh: Uttara and Bashundhara. The inputs
were taken as strings from the sensors, transmitted to the
local server via the attached Wi-Fi module, and consequently
accessed through an IoT application for analyzing data. Finally,
statistical analysis was performed, and the results were
compared with the safe level of air pollutants from the collected
data. It is worth mentioning that real-time data from
five consecutive days were considered for data analysis. The
air quality data were acquired from the mentioned specified
places several instances of the day, i.e., morning, noon, and afternoon,
for two times each. The outdoor air contamination
data were also taken from different places, such as crowded
and uncrowded areas. Therefore, our proposed unmanned air
quality monitoring device analyzed the variation of air density
pollutants in the same place. The proposed drone can back
up for 12-13 min for one trial, and within five days, approximately
120 sets of air quality data of different air pollutants
were acquired. The outdoor air pollutant data were acquired
during the flight with significant movement along with longitudinal
directions. For the measurement in the indoor
environments, the drone was placed on the floor of the living
room of a 1,600-square feet apartment, and usual kitchen activities
were suspended. The apartment was located on the third
floor of a residential neighborhood of Uttara, Dhaka. Finally,
we analyzed the project outcomes, where all the measured
data were compared with the default safe level concentration
of pollutants.
Collection of Sampling Data from Different Places
In this work, air quality data were collected from both indoor
and outdoor places. In indoor data, as expected, we found
fewer pollutants than in outdoor data. The concentration of air
contaminants was higher in a crowded place, as many vehicles
Fig. 3. Working overview of the proposed system.
IEEE Instrumentation & Measurement Magazine
December 2022
Instrumentation & Measurement Magazine 25-9

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