Design and Implementation of Smart Air Pollution Monitoring System Based on Internet of Things

© 2021, AJCSE. All Rights Reserved 25
REVIEW ARTICLE
Design and Implementation of Smart Air Pollution Monitoring System Based on
Internet of Things
Raghad Hazim AlShekh, Rabee Hagem
Department of Computer Engineering, University of Mosul, Mosul, Iraq
Received on: 15-02-2021; Revised on: 01-04-2021; Accepted on: 15-05-2021
ABSTRACT
An internet of things (IoT)-based environmental air pollution monitoring (APM) system for monitoring
concentrations of major air pollutant gases in Mosul, Iraq, has been built. The system employs low-cost
air quality gas monitoring sensors with Particle Photon. This device detects gas concentrations such as
carbon monoxide CO, carbon dioxide CO2
, methane CH4
, dust, smoke, temperature, and humidity. The
sensors will gather data and send it to particle photons, which compare it with threshold for each sensor
then sends it to the IoT platform known as UBIDOTS. UBIDOTS sends data to cloud where they can be
realized and virtualized before displaying. The suggested system’s major goal is to provide a low-cost,
real-time monitoring system based on IoT that can be shown and displayed whenever and wherever
needed, and it has been tested in numerous sites throughout Mosul city.
Key words: Air pollution monitoring, Internet of things, MQTT, particle photon, Sensors, UBIDOTS
INTRODUCTION
The presence of particles, biological molecules,
or other harmful chemicals in the earth’s
atmosphere creates air pollution, which causes
illness, human casualties, and damage to other
living organisms such as animals and food crops,
as well as the natural or constructed environment.
Air pollution can be caused by either artificial or
natural sources.
According to different studies, about 225,000
people die from diseases, caused by care missions
in Europe every year.[1]
Mosul city which is known as the second biggest
governorate in Iraq, it is noted that there are a
large number of cases of increased polluted gasses
caused from combustion of fuel in cars, factories,
generators, burning fuel in homes for cooking,
and heating purposes.
Air pollution is an important determinant of health
and convincing evidence links air pollutants with
the risk of disease, including premature death even
at relatively low pollutant concentrations.[2]
Pregnancy exposure to high levels of air pollution
causes miscarriage, early delivery, autism, and
Address for correspondence:
Rabee Mwafaq Hagem,
E-mail: rabeehagem@uomosul.edu.iq
asthma in children.[3]
Figure 1 shows the effects of
air pollution on the human body.
The internet of things commonly abbreviated as
the word (IoT) is a more exemplary new path in
the field of IT, a revolution in the conversion of
data from things to things, from human to human
and from human to things. It was created in
1998 and Kevin Ashton first introduced the term
“Internet of Things” in 1999.[5]
The IoT is the most
widely used in various internet based mobiles,
vehicles, sensors, and other hardware devices.
This can be integrated with the various machine
learning and deep learning algorithms to improve
the performance of the system. Each factor is
unambiguously diagnosable through its installed
framework anyway is prepared to interoperate
inside the present web foundation. Specialists
gauge that the IoT can include essentially fifty
billion articles by 2020. The origination of the IoT
ended up a standard in 1999, through the Auto-
ID Center at college and associated showcase
investigation distributions.[6]
IoT architecture consist of five layers as shown
in Figure 2, perception layer network layer,
middleware layer application layer, and business
layer. The perception layer comprises physical
objects that are monitored/controlled by sensor
and actuator devices, with the primary goal of
sensor data collecting and command actuation.
Available Online at www.ajcse.info
Asian Journal of Computer Science Engineering 2021;6(2):25-34
ISSN 2581 – 3781

AJCSE/Apr-Jun-2021/Vol 6/Issue 2 26
AlShekh and Hagem: Design and Implementation of Smart Air Pollution Monitoring System based on IoT
Figure 1: Effects of air pollution on the human body[4]
The sensor data are subsequently communicated
to the Middleware layer through the Network
layer, which is distinguished by a variety of
network protocols. The Middleware layer is
where information is processed, where it is stored
and analyzed using advanced data analytics
approaches.
The primary goal is to establish an autonomous
decision-making process that sends actuation
orders back to physical objects to perform
activities that alter the overall conditions of the
physical environment. The information gathered/
analyzed can be displayed to an end user through
the application layer, which may also be used
to operate the total system. Finally, the business
layer enables system administrators to manage
and strategically govern the IoT platform’s overall
operation.[7]
When reviewing pastAPM systems, we discovered
that Souvik Manna and others introduced an air
pollution system with a wireless sensor network
(WSN), which is seen as an effective use of IoT
to deal with automobile pollution. Pollution may
be gathered throughout city streets and evaluated
with WSN, Arduino Uno, RFID, and MQ sensors
to monitor carbon monoxide, sulfur dioxide,
nitrogen dioxide, and methane. Using the quality
index(AQI),andanalgorithmwillbeimplemented
to collect data, remove duplicates, filter and
summarize useless readings in a simpler form, and
develop Mobile Discovery Net (MoDisNet) using
GUSTO sensor technology to monitor and analyze
pollution in real-time based on traffic conditions,
emissions, and ambient pollutant concentrations.[8]
Bedoui and others used WSN, and Zigbeeunder
LABWIEW environment to measure hydrogen,
sulfide gas, humidity and temperature. The results
demonstrate the efficacy of this technology in
terms of detection speed and real-time reaction
with minimal cost and power due to the utilization
of ZigBee with WSN. The system consists of three
steps: The initial step is data capture, followed
by wireless transmission, and ultimately by data
processing. Stations can be replaced with a tiny
portable metering system that contains several
sensors to improve it.[9]
GaganParmar and others proposed a system
that combined low-cost air quality monitoring
nodes and semiconductor gas sensors with Wi-Fi
modules. It monitors gas concentrations such as
CO, CO2
, SO2
, and NO2
. The system was low-
cost pollution control system, fast response, low
maintenance, and production capacity, continuous
measurements, small-sized wireless sensor
gateway node and database server, and a web
server all packed in one package.[10]
Landge and Harnebuilt a model begins with
sensors capable of sensing, calculating, and
connecting data in a network. It measures PM2.5,
temperature, and humidity, and then wirelessly
transmits the monitored data to the server over
Wi-Fi. Carbon monoxide (CO), carbon dioxide
(CO2
), nitrogen dioxide (NO2
), methane (CH4
),
hydrogen sulfide (H2
S), and ammonia sensors
were included in the functional circuit (NH3
), and
particulate matter (PM), ESP8266, ATCommand
Library.[11]
Figure 2: Internet of things architecture[7]

Kumar et al. created a portable air quality
monitoring system that allowed for real-time data
analysis. Three sensors are used: The MQ9 gas
sensor, which detects carbon monoxide, the MQ2
sensor, which detects smoke, and the PMS3003
G3 particle sensor, which detects PM2.5 and
sends this detection data to the Node MCU as a
processing unit, which stores and restores data
through Hypertext Transfer Protocol (HTTP) over
the Internet. The circuit includes the following
components: A bread board, a PMS3003G3
particle sensor, connecting wires, 180 and 330-
ohm resistors, MQ9 and MQ2 gas sensors, a 3.3–
5 v boost converter, an analog multiplexer 4051,
an OLED display, and a Battery Charger Board for
Li-ion batteries.[12]
Patil recommended employing an embedded
wireless computer system to monitor air and noise
pollution levels in an industrial environment.
This concept can be adapted and used in any
infrastructure environment. Circuit in use:
Raspberry Pi, humidity, temperature, and dust
level sensors, Arduino Nano which is linked to a
Wi-Fi module as well as temperature, humidity,
gas, and dust sensors.[13]
The literature analysis shows that most APM
systems differ in microcontroller type and sensors
type that is used for the practical circuit. Most
of the studies used Arduino or Raspberry pi or
ESP8266 as a microcontroller and used WSN
technology.
The main objective of this research is to design
and implement a smart air pollution monitoring
(APM) system and to test the system at different
locations in Mosul city, based on IoT using a
particle photon microcontroller then sending the
measured data over the internet to the UBIDOTS
cloud to store, visualize, and analyze information.
The system’s most essential properties are low cost
with a long life span, small size unit, low power
consumption, efficiency, eco-friendliness, and
the ability to achieve real-time results at different
locations, such as homes, industries, car parks,
and data gathered remotely by UBIDOTS which
is an appropriate IoT platform thus any connected
individual can access data at any time and from
any location where the internet is accessible.
Theremainderofthepaperisasfollows,withsection
2 explaining the proposed framework’s system
description. Section 3 discusses implementation
and working concepts. Section 4 discusses the
project’sresults.Section5containstheconclusions.
Section 6 contains references.
SYSTEM DESCRIPTION OF THE
FRAMEWORK
Components
The proposed system consists of the hardware
components as follows:
1. Particle photons as a microcontroller
2. MQ4 sensor for methane gas measurement
(CH4
)
3. MQ2 sensor for smoke detection.
4. Carbon monoxide (CO) gas sensor MQ7
5. MQ135 carbon dioxide sensor gas (CO2
)
6. Dust detector (DSM501)
7. Temperature and humidity sensor DHT11.
The system’s overall structure
The system is divided into two sections. The
first part is the previously described physical
components, and the second portion is the software
applications. The sensors are programmed
with the (Particle IDE) program and the (C ++)
programming language. The sensors are connected
to the photon, which collects data and delivers it
to UBIDOTS, where it is analyzed and virtualized
for display, the structure of the system is shown in
Figure 3.
Figure 3: The proposed system structure

WORK DESIGN AND THEORY
Particle photon
ParticleIndustriesInc.offersthePhotonIoTdevice,
which is a compact (37 × 20 mm) open source
module that combines an ARM microcontroller
and a Wi-Fi chip.[14]
It has 18GPIO peripherals that
enable photon to be linked with different devices
by coding it to special functions, Photon makes a
great IoT device, allowing remote controlling or
data gathering from possibly multiple connected
sensors etcetera.[14]
It is shown in Figure 4.
MQ4 gas sensor
Figure 5 shows that the MQ4 sensor (5) has high
Methane sensitivity over a wide range, is low-cost
and long-term, and has simple driving circuitry.[15]
MQ2 gas sensor
MQ2 gas sensor as shown in Figure 6 it can be
used as a digital or analog sensor, for smoke
detection it uses the analog pin of the sensor for
scope of 200–5000 ppm.[16]
MQ7 gas sensor
As illustrated in Figure 7, this sensor is ideal for
carbon monoxide (CO) gas. CO concentrations
ranging from 10 to 10,000 ppm can be computed.
This sensor’s performance is measured in terms
of analog resistance. It contains six pins, four for
signal extraction, and two for internal line heating.
Its advantages include extended life, low cost, and
a simple drive circuit.[17]
MQ135 gas sensor
MQ135 shown in Figure 8 is a device that
measures carbon dioxide CO2
emissions in the
atmosphere. It has a quick reaction time, excellent
sensitivity, and a long life and is stable due to its
wide detecting range.[18]
Figure 4: Particle photon pin diagram
Figure 6: MQ2 smoke
Figure 5: MQ4 methane Figure 7: MQ7 CO sensor.

DSM510 dust sensor
The diagram depicts it in Figure 9. The particle
counting hypothesis underpins the DSM501
theory.[6]
It can detect particles larger than 1 μ
in diameter. The built-in sensor warmer will
automatically inhale air. It is tiny in size, light in
weight, and simple to install and operate.[17]
DHT11 temperature and humidity sensor
The DHT11 is a humidity and temperature sensor,
it produces aligned advanced yield. DHT11 can
be associated with microcontrollers such as
Arduino, Raspberry Pi, and so on. DHT11 is a
minimal effort temperature and dampness sensor.
It gives high unwavering quality and long haul
dependability.[19]
The encapsulated form of DHT11is a single row of
4stitchesofwhichisusedforserialcommunication
of data. Single wire and serial interface make the
system integration simply and easily.DHT11 has
a 3–5.5V power supply voltage,[20]
it is shown in
Figure 10.
The IoT platform UBIDOTS
UBIDOTS is the IOT platform employed in this
research. It is an application builder for the IoT
that includes data analytics UBIDOTS Platform
(also known as the UBIDOTS IoT Application
Development Platform) Platform in IoT ONE)
debuted in January 2013, a year after its provider
business UBIDOTS was acquired originally
established.
The UBIDOTS Platform is also said to be able to
“scale from one device to 1,000s with assurance.”
Particles and UBIDOTS vary depending on
their application (devices and traffic) UBIDOTS
established its Education program in 2018 to
provide enthusiasts and students with easy access
to the IoT world. The obvious advantage of all
open source systems is the open source code and
the ability to modify it. Parts or entire components
can be edited. Some of these platforms have
additional features that make them stand out.[21]
Each UBIDOTS user has an API credential that
includes a specific authentication for each user to
be included in the code, and then the system is
connected and the data measurements obtained
by the hardware are sent to this IOT platform.[16]
Figure 11 depicts the UBIDOTS log in process.
The particle photon collects data from the sensor
and sends it to the UBIDOTS cloud using the
MQTTcommunication protocol; after the platform
has been changed, it gets hardware information
and displays a variable inside the platform; a
representation of the system in UBIDOTS is
displayed in Figure 12. This is accomplished
through the use of variable names in the code;
once the data is represented as a variable, it is
displayed as a widget in the dashboards. Data
can be displayed to users in the form of a table,
Figure 8: MQ135 CO2
sensor
Figure 10: DHT11 sensor
Figure 9: DSM501A

indication, or graph. Events can also be used to
send alert messages or e-mails to users.
MQTT: Message Queue Transport Remote
Service, is a protocol built specially for
“machine-to-machine” communication. MQTT
protocol runs over TCP/IP and has a data packet
size with a low overhead minimum ( 2 bytes)
so that power consumption is also kept to a
minimal architecture as shown in Figure 13. This
protocol is data-agnostic, capable of transmitting
data in a variety of formats such as binary data,
text, XML, or JSON, and it employs a publish/
subscribe architecture rather than a client-server
approach.[22]
IMPLEMENTATION OF THE SYSTEM
Hardware
Connecting of the electrical circuit of the system
is show in Figures 14 and 15.
Figure 11: UBIDOTS log in Kodali et al.[16]
Software
To generate the primary system code and download
therequiredlibraries,theparticleIDEplatformwas
utilized. C/C++ programming languages are used
in the IDE environment. The above-mentioned
sensors were used to acquire the data. Calibrated
sensors provided an analog output voltage that
was proportional to the pollutant content in the
gas, the obtained concentrations will be compared
to standard levels for each sensed gas, thus, if the
sensed concentration is above the threshold, the
event created by UBIDOTS sends smartphone
alerts to the relevant authorities, urging them to
take immediate action to prevent the air pollution
situation from increasing. The Wi-Fi module
Figure 13: MQTT protocol working principle[23]
Figure 12: System representation in UBIDOTS[16]
Figure 14: Real circuit of the system
Figure 15: The electrical circuit of the system

subsequently sends the measured data value to
the UBIDOTS platform’s server. UBIDOTS
allows data measurement to be accessed from any
computer with internet connectivity.
Code
void loop()
{if!clientMQTT.isConnected()) {clientMQTT.
connect(5); }
duration = pulseIn(pin, LOW);
lowpulseoccupancy+= duration;
endtime = millis();
int adc_mq2=analogRead(mq2);
int adc_mq4=analogRead(mq4);
int adc_mq7 = analogRead(mq7);
Steps for putting the system into action:
1. Connect sensors to particle photon.
Then they will sense all gases and give the
Pollution level in PPM (parts per million).
PPM: is an abbreviated form for “parts per
million” and can alternatively be written as
milligrams per liter (mg/L) and is frequently
used to identify pollution concentrations in the
air (as a volume fraction).
2. Data are gathered from sensors
3. Compare data with a predetermined threshold
4. Data are transmitted to UBIDOTS
5. The cloud handles data processing
6. The user can obtain data and alarm messages
through smartphone or online; the methods are
depicted in Figure 16.
RESULTS AND DISCUSSION
Monitoring results of different sensors on the
UBIDOTS platform are shown in the graphs
below. There are five variables used in Photon 1
and two variables used in Photon 2. The X-axis
on the dashboards represents the date and time,
and the Y-axis represents the gas concentrations in
(ppm), displaying the data of the sensors that each
sensor has its own variable and widget.
The air pollution concentrations shown are
measured in three study areas: the first is the
environment of generators in Mosul’s residential
neighborhoods, the second is the measurement of
air pollution in the natural outdoor setting, and the
third is the landfill (sanitary landfill) in the Kokjeli
area of the Nineveh Governorate Municipality.
THE ENVIRONMENT OF THE
GENERATORS IN THE RESIDENTIAL
NEIGHBORHOODS OF THE CITY OF
MOSUL
Figures 17 and 18 show APM and UBIDOTS
dashboards at a generator.
We evaluated our system in a generator setting for
5 days in the Zohour district, which is renowned as a
congested region with a significant number of houses
(400–450). The findings are shown in Figure 19.
Figure 16: Designed system flowchart
Figure 17: APM at thea generator
Figure 18: UBIDOTS dashboards for generator

The concentrations of some air pollutants for the
study area were measured weekly in conjunction
with work generated and for the period January
22, 2021, until January 28, 2021, by taking daily
readings over a 24 h period, the generator selected
for the study is working at a rate ranging between
12 and 20 h a day. It should be noted that the
pollutants were measured at temperatures ranging
from 2 to 22°C with humidity ranging 14 to 84%.
AIR POLLUTION IN NATURAL
OUTDOOR ENVIRONMENT
dashboards for outdoor pollution.
The equipment was utilized to monitor outdoor
pollution for 5 days away from the above-
mentioned generator (250 m), with the findings
shown in Figure 22:
The results reveal that when one moves away from
the generator’s emission source, the concentration
of all gasses decreases dramatically.
LANDFILL (SANITARY LANDFILL) IN
THE KOKJELI AREA OF THE NINEVEH
GOVERNORATE MUNICIPALITY
DEPARTMENT
dashboards for sanitary landfill in Kokjeli area.
Figure 23: APM at sanitary landfill in Kokjeli area
Figure 19: CO2
concentrations generator
Figure 21: UBIDOTS dashboards for outdoor pollution
Figure 20: APM at the garden
Figure 22: CO2
concentration for outdoor pollution
Figure 24: UBIDOTS dashboards for sanitary landfill in
Kokjeli area

Figure 25 shows CO2
concentrations for sanitary
landfill in Kokjeli area.
There is bacterial absorption of the organic elements
that make up the waste materials in the sanitary
landfill, which results in the release of biogas. It
is composed of 40–60% methane gas, with carbon
dioxide accounting for the remainder of the gasses.
Researchers see that the largest amount of carbon
dioxide gas is at the first site near the generator,
which was 11391 ppm, and that all polluting gas
concentrations have raised in the generator area.
Carbon monoxide gas CO had the lowest
concentration because it was not identified in any
of the three sites, which is a good thing because it
is a heavy, deadly, and inhaling large amount of it
can lead to death. The results are shown in Table 1.
The UBIDOTS will provide us with record
declarations that we can view at any time and
from any location, as an excel table or as other
types of widgets.
These reports indicate human readable date, like:
5/12/2020 08:56:25 and the readable value of
each measured. These reports are being used to
track air pollution in different spots as needed by
virtualizingandanalyzingdatathatcomeincontact
with the sensor, and to send alerts to the relating
concerned authority through Google account or
web applications chosen for this research, to avoid
any danger threatening life forms of all kinds.
For user interaction, the following signals will
be received through programmed UBIDOTS on
smartphones to guarantee that gas concentrations
do not exceed the specific thresholds for each of
them as in Figure 26.
CONCLUSION
In this paper, the APM system was designed in
varioussitesinMosulcityforthedetectionofharmful
concentrations of different air pollutant gases,
based on IoT technology, the system is essential
for sending warning messages to the appropriate
authorities to avoid hurting all types of life-giving
species. We chose three settings for this paper: the
environment of generators in Mosul’s residential
areas, the measurement of air pollution in the natural
outdoor environment, and finally the dump (sanitary
dump) in the Kokjeli area of Nineveh Governorate
Municipality. Carbon dioxide CO2 was monitored
using MQ135, Methane CH4 was measured using
MQ4, CO was monitored using MQ7, dust was
detected using a DSMA510 dust sensor, and smoke
was measured using MQ2. Data are continuously
transmittedanddisplayedontheUBIDOTSplatform
via particle photon in real time; we can obtain data
on a daily, weekly and monthly basis. The system is
already used in homes, car parks, gardens, factories,
landfills, and gas plants and can be used anywhere
Table 1: The main results
Location Dust
PPM
Min-Max
CO2
PPM
Min - Max
Methane
PPM
Min - Max
CO
PPM
Min - Max
Smoke
PPM
Min - Max
Temperature
°C
Humidity %
At the
generator
8330.0125–36102.032 90–11391.80 6.6–323.80 0–0 0–259 12–22 14–84
At a garden 1710.05176–148990.2188 96–9617 9.71–255.63 0–0 0–67 40–42 80–90
In the sanitary
landfill
5965.2098–61002.032 775–5540 32–223 0–0 0–8 23–27 32–42
Figure 26: UBIDOTS messages on smartphone
Figure 25: CO2
concentrations for sanitary landfill in
Kokjeli area

and anytime needed for real-time readings. These
data can be collected, analyzed and made early
warning by the responsible and related authorities.
FUTURE WORKS
1. System can be implemented in more locations
such as oil refineries, gas plants, and hospitals.
2. Using solar cell instead of the power supply to
store energy and ensure its continuity
3. Using more sensors to measure more different
pollutant gases such as ozone O3
and nitrogen
oxide NO2
.
ACKNOWLEDGMENT
AllthanksandappreciationtotheUniversityofMosul,
DepartmentofComputerEngineering,andtoProfessor
Dr. Rabee Muafaq, the supervisor of the work, and
thanks also to the Nineveh Municipal Department and
the Health Corridor Unit for facilitating them to carry
out measurements for the research.
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