Application of a Multi-sensor Network in Monitoring of Air Quality

Ade Silvia Handayani

, Nyayu Latifah Husni

, Ahmad Taqwa and Carlos R. S.

Department of Electrical Engineering, Politeknik Negeri Srwijaya, Jl. Srijaya Negara, Palembang, Indonesia

Keywords: Multi Sensor Network, Monitoring, Air Quality, Pollution.

Abstract: Air pollution is a condition when air quality becomes damaged and contaminated by harmful substances.

Several factors that caused air pollution are increasing infrastructure development, smoke factory, and vehicle

exhaust gases. Air quality in an environment needs to be determined with real-time air quality monitoring

with affordable and accurate sensors. One of the controls and monitoring systems currently being developed

is the Wireless Sensor Network (WSN) which consists of individual nodes that can interact with their

environment by sensing, controlling, and communicating physical parameters. In this research, an air quality

monitoring system will be designed using multi-sensor network technology, which will then be placed in

several locations. This research aimed to measure the parameter of Carbon Monoxide (CO), Carbon Dioxide

(CO2), Hydro Carbon (HC), temperature and humidity, and levels of particulates in the air (PM10). It will

then be collected and sent to the Raspberry Pi's database server via the internet network. Furthermore, the

data will be processed to become information that can be used by users or the general public.

INTRODUCTION

Air pollution is a condition when air quality becomes

damaged and contaminated by harmful substances.

Several factors that caused air pollution are increasing

infrastructure development, smoke factory, and

vehicle exhaust gases (Anjum et al, 2021); (Newbury

et al., 2019); (Sheng et al., 2020); (UNECE, 2021). It

can cause various diseases, including eye irritation,

upper respiratory tract infection, sore throat, even

death (WHO, 1992); (Peng et al., 2019); (Qin et al.,

2018); (Handayani et al, 2019).

Based on data from the World Health

Organization (WHO), about 4.2 million people died

from air pollution or about 5% of the 55 million

people who died every year in the world (Anjum et al,

2021); (WHO, 1992). 1500 million people who died

prematurely occurred in Asian cities. The morbidity

rate resulting from air pollution is much higher

(Tarmidi, 2019); (Prihatini et al., 2018); (Kelly and

Fussell., 2015); (Health Effects Institute, 2019).

Air quality in an environment needs to be

determined with real-time air quality monitoring. The

development of technology and information has

brought humans to a new generation of affordable and

https://orcid.org/0000-0002-4476-426X

https://orcid.org/0000-0003-0072-6664

accurate sensors (detection tools). One of the controls

and monitoring systems currently being developed is

the Wireless Sensor Network (WSN) (Deebak et al,

2020); (Idrees and Zheng, 2020); (Handayani et al,

2020). WSN consists of individual nodes that can

interact with their environment by sensing,

controlling, and communicating physical parameters

(Zervopoulos et al, 2020); (Handayani et al, 2021).

WSN is a network that carries a wireless network

as a link between nodes. It can be used for data

collection and monitoring a system or environment at

the location. WSN consists of several specialized

sensor nodes with sensing and computerized

capabilities (Sahfutri et al, 2018); (Zakaria et al,

2018). It makes WSN can sensing physical

parameters and transmitting the collected data to a

central area using wireless communication

technology (Handayani et al, 2020); (Yahya et al,

2020). The sensor obtains data in real-time, entered

into a database server via an internet network using

the Raspberry Pi to access the data in the monitoring

system application. It shows that the Raspberry Pi has

good complexity and low cost, so it is easy to develop

(Jadon et al, 2020). Multi-Sensor Network (MSN)

system is a new technology that utilizes multiple

sensors and Wireless Sensor Network (WSN) in one

130

Handayani, A., Husni, N., Taqwa, A. and S., C.

Application of a Multi-sensor Network in Monitoring of Air Quality.

DOI: 10.5220/0010941200003260

In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2021), pages 130-140

ISBN: 978-989-758-615-6; ISSN: 2975-8246

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

device. The signal collected from these sensors is

transmitted to the monitoring center using a smart

device to manage distributed resources and optimize

tasks in real-time automatically. This system can

produce object data that is detected by sensors

automatically (Marques et al, 2019); (Taştan and

Gökozan, 2019).

Some of the research was implemented the WSN.

In (Sahfutri et al, 2018), WSN was used to find out

the parking slots available in the parking area. The

monitor serves as a monitoring of the parking slot

area to be displayed so that visitors know whether the

parking slot is still available or even fully charged.

Furthermore, in the research by Xu and Liu (Xu and

Liu, 2017), monitoring water quality using

parameters of oxygen solubility, water pressure, PH,

and temperature by applying the Wireless Sensor

Network (WSN).

In this research, an air quality monitoring system

will be designed using multi-sensor network

technology, which will then be placed in several

locations. For example, in parking areas, on

roadsides, housing estates, industrial sites, etc. The

parameters measured include Carbon Monoxide

(CO), Carbon Dioxide (CO2), Hydro Carbon (HC),

temperature and humidity, and levels of particulates

in the air (PM10). The value of these parameters is

obtained from the sensor's sensing process

periodically. It will then be collected and sent to the

Raspberry Pi's database server via the internet

network. This air quality monitoring system has an

advantage in applying multi-sensor network

technology. More harmful pollutant gases can be

measured in real-time to monitor air quality in the

environment.

2 ENVIRONMENT MONITORING

2.1 Wireless Sensor Network

Wireless Sensor Network (WSN) is one type of

distributed wireless network that utilizes embedded

system technology and a collection of sensor nodes to

perform sensing processes, monitoring, sending data,

and presenting information to users via wireless

network communication (Handayani et al, 2019);

(Handayani et al, 2018). There are many sensors,

including air sensors, temperature sensors, motion

sensors, pressure sensors, radiation sensors, position

sensors, etc. (Zhou et al, 2021). Each sensor also has

software (application, operating system) and

hardware, respectively, which will run into a Wireless

Sensor Network system (Sahfutri et al, 2018).

Each point in the WSN is equipped with a radio

transceiver as a receiving or sending node also other

supporting devices. Therefore, WSN is also known as

a system consisting of several low-cost sensors small

in size and spread over a vast area with one container

node to collect the reading process results for other

sensor nodes (Zervopoulos et al, 2020).

According to the application, the number of nodes

used in a WSN can be in the thousands using low-cost

nodes placed in specific locations. Its small size does

not rule out weaknesses and limitations on sensor

nodes. WSN usually communicates using multi-hop

communication, which aims to save power usage. The

sensor node data will end at a select node called a sink

node. Another communication module or a gateway

to another network equipped is usually called a

gateway in charge of forwarding to cloud storage or

the internet. Sink node processes and computes more

complex data, which sensor nodes may not do. It

makes sink nodes have enough processors, memory,

and energy to properly carry out their tasks. The

architecture of the wireless sensor network can be

seen in Figure 1.

Figure 1: The architecture of the wireless sensor network.

2.2 WSN as Environment Monitoring

Industrial development and construction result in

increased pollution in the environment. Industrial

pollution consists of waste in water, gas, and solid. In

general, this waste is dangerous because most of its

components consist of additives and chemicals that

are difficult to degrade. These substances harm the

environment and threaten the survival of living things

(Yahya et al, 2020).

The environment is currently very polluted

because of the many factors that support pollution.

From industrial waste, environmental pollution also

comes from waste from vehicle emissions. Diesel

engines are considered the single most significant

contributor to environmental pollution caused by

exhaust gas emissions, and they are responsible for

several health problems (Jadon et al, 2020).

WSN is a distributed autonomous device that uses

sensors to monitor physical or environmental

Application of a Multi-sensor Network in Monitoring of Air Quality

131

conditions, such as temperature, sound, vibration,

pressure, and movement in different locations. WSN

cooperatively passes data through the network to the

Base Station. Data can be analyzed in this location

and acts as an interface between the user and the

network (Handayani et al, 2021).

WSN applications are used in commercial and

industrial applications to monitor complicated or

expensive data using wired sensors. WSN is spread

over an area intended to collect data via its sensor

nodes (Handayani et al, 2021).

Currently, the sensor node commonly used is

equipped with an onboard processor. The data

processing component is used to separate the required

data. It is due to many sensor nodes being used and

possible that the distance between nodes will be close

together. Therefore, multi-hop communication is

widely preferred to consume less power than single-

hop communication. Multi-hop communication is

effectively used to overcome some of the signal

propagation effects that often occur in long-distance

wireless communications.

2.3 Sensor TGS 2442 as a Carbon

Monoxide (CO) Detector

Carbon Monoxide (CO) is a gas that is colorless,

odorless, and tasteless. It consists of one carbon atom

covalently bonded to one oxygen atom. There are two

covalent bonds in this bond and one coordinating

covalent bond between the carbon and oxygen atoms.

Most of the CO gas comes from combustion and

emissions from motor vehicles. CO gas is hazardous

if inhaled by humans, especially respiratory problems

and can even cause death (Handayani et al, 2018).

Figure 2 shows the CO sensor used in this system

is the TGS 2442 sensor. This sensor is a carbon

monoxide (CO) gas detector with low power

consumption, minimalist size, and high sensitivity.

This sensor works at a reference voltage of 5 V

connected to the heater (Vh) and Rs. Rs itself is the

sensor's resistance connected to pin 2 and pin 3. Apart

from being a reference voltage, Rs' value is used for

input on the heating element (heater) on pin 1 and pin

2.4 Sensor MG-811 as Carbon Dioxide

(CO2) Detector

Carbon Dioxidant (CO2) is a colorless gas with a

density about 60% higher than air (1,225 g / L) that is

odorless at the concentrations typically encountered.

Carbon dioxide consists of a double covalent carbon

atom bonded to two oxygen atoms. It occurs naturally

Figure 2: Sensor TGS 2442 (Handayani et al, 2018).

in the Earth's atmosphere as a trace gas at a

concentration of about 0.04 percent (400 ppm) by

volume.

In general, the ventilation rate should keep the

carbon dioxide concentration below 1000 ppm to

create indoor air quality conditions that are acceptable

to most individuals (Zhou et al, 2021). In this system,

CO2 detection uses the MG-811 sensor to detect

carbon dioxide gas in the range of 350 - 10000 ppm

with a 5V DC circuit power supply. The MG-811

sensor is suitable for indoor air quality monitoring

systems, fermentation process control systems, etc.

2.5 Sensor GP2Y1010AU0F as PM10

Detector

Particulate matter (PM) is a term for solid or liquid

particles found in the air. Particles that are large or

dark enough can be seen as soot or smoke.

Simultaneously, tiny particles can be seen with an

electron microscope. The particles come from various

sources, both mobile and stationary (diesel trucks,

woodstoves, power plants, etc.) (Mufid et al, 2020).

The PM sensor used in this system is the Sharp

GP2Y1010AU0F sensor. The GP2Y1010AU0F

sensor is a dust sensor that utilizes light scattering or

the so-called optical sensing system. This sensor is

equipped with an LED, and a photodiode arranged

diagonally.

2.6 Sensor TGS 2611 as Hydro Carbon

(HC) Detector

Hydrocarbon (HC) is a gas that is not significantly

detrimental to humans, but it is the cause of the mixed

smog. The hydrocarbon emission in the exhaust gas

is in the form of unburned gasoline. Hydrocarbons are

found in fuel evaporation in the tank, carburetor, and

gas leaks through the gaps between the crank

cylinders commonly called the last gas. For

hydrocarbon gas emission limits in Indonesia, based

on the Decree of the State Minister for the

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environment, a maximum HC threshold of 2000 ppm

has been set for 2-wheeled and 3-wheeled vehicles.

For 4-wheeled vehicles or more than 4 wheels, the

maximum HC threshold is 200 ppm (part per million)

(Honeycutt et al, 2019). The HC sensor used in this

system is the TGS 2611 sensor from Figaro, which

has adequate sensitivity and selectivity to methane

gas (CH4). This sensor has a resistance value of Rs,

which changes when exposed to methane gas, and has

a heater that functions to clean the sensor room from

outside air contamination. The TGS 2611 sensor

requires a source voltage of 5 volts, which is well

regulated. This sensor requires two input voltages,

namely heating voltage (VH) and circuit voltage

(VC).

2.7 DHT-11 Sensor as Temperature

and Humidity Detector

Temperature is an energy that can move from a higher

to a lower temperature. Environmental temperature is

the level of hot air in a place expressed in degrees

Celsius (oC). The highest temperature is usually at

01.00 – 14.00 PM, and the lowest is at 04.00 - 05.00

AM.

Humidity is the water vapor content in the air,

measured with a hygrometer in units of %. Air

humidity changes inversely with air temperature

changes, i.e., when the air is cold, the humidity

increases, and when the air is hot, the humidity

decreases ( Honeycutt et al, 2019). The temperature

and humidity sensor used in this system is the DHT-

11 sensor because it has a wide measurement range,

0 to 100% for humidity and -40 degrees Celsius to

125 degrees Celsius for temperature. This sensor also

has a digital output (single-bus) with high accuracy.

3 RESEARCH METHODOLOGY

This study divided the design into two parts, namely

hardware design and software design.

3.1 Hardware Design

Figure 3: Block Diagram Hardware Systems.

Figure 3, the air quality monitoring system designed

using a Raspberry Pi microprocessor equipped with

Multisensor Network technology. It consists of

several sensors, namely, the Tgs2442 sensor as a CO

sensor, the MG811 sensor as a CO2 sensor, the

Tgs2611 sensor as a Hydrocarbon sensor, the Sharp

GP2Y1010 sensor as a dust sensor, the DHT11 sensor

as a temperature and humidity sensor, and the Neo-

6M GPS module to detect whereabouts. Each node.

The Raspberry Pi can only read the output value in

digital form, while each sensor's output value is

analogous. An ADC or Analog to Digital Converter

module is needed in getting the output value reading,

namely ADS1115 as a sensor reading value

converter. The Raspberry Pi can process it, which

functions as a gateway. The voltage source used in the

tool is a 12V battery.

Raspberry Pi acts as a gateway or intermediary to

forward data transmission before the database server,

which can also be used to store log data. In the

Raspberry pi, an interface called Raspbian is

installed, which functions as data visualization, and

data log storage. The data obtained at the gateway

will be forwarded to the server that has been created.

The communication process from the gateway to

the database server connects the Raspberry Pi to an

available Wi-Fi network, where the SSID and

Password have been predetermined. The Raspberry Pi

has better network capabilities with a dual-band

wireless connection that supports 802.11ac. In this

test, the Wi-Fi network uses a Wi-Fi modem as an

internet service provider. In this test, sensor nodes

will automatically detect air quality levels in a place

and send temperature, humidity, and gas content

information to the server in real-time while the device

is still on. Then the data obtained is entered into a

database table that has been prepared. The data is

used as a caller to display air quality monitoring data

and provide emergency messages about the state of

air quality. The air quality conditions are classified

into 3, namely, Normal, Moderate, and Hazardous.

The complete series of data reading hardware,

data transmission systems, and data storage consists

of 3 parts, namely Node 1, Node 2, and Node 3. In

general, the overall circuit scheme is shown in the

following figure 4.

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133

Figure 4: Overall Circuit Design Schematic.

In Figure 4, each Node 1, Node 2, Node 3 has the

same circuit scheme and components. The nodes

consist of several series, including the TGS-2442

sensor circuit, the TGS-2611 sensor circuit, the MG-

811 sensor circuit, the Sharp GP2Y1010 sensor

circuit, the DHT-11 sensor circuit, the NEO-6M GPS

module series, and the ADC-module series. 1115 as

an analog to digital converter.

The TGS-2442 sensor circuit functions to detect

and measure levels of CO gas. This sensor produces

output in the form of analog data, which is converted

into digital data, and calculations are carried out to

have output in the format of ppm (parts per million)

gas units. The primary sensor circuit consists of Vc

(legs 3 and 4) connected to a 5 Volt voltage source on

the Raspberry Pi GPIO pin, Vh (legs 1 and 2)

connected to the ground. In leg 1, before being

connected to the ground, it must first be connected to

a resistor (Rl 20K ohm). The sensor output is

measured at pin 1 connected to the Analog pin (pin

A1) of the ADC and used as input for CO sensor data

readings.

MG-811 sensor circuit functions to measure and

detect CO2 gas. This sensor produces output in

analog data that will later be converted into digital

data on the ADC, and calculations are made to

produce output in ppm (parts per million) gas units.

The MG-811 sensor has 5 pins, namely the VCC,

GND, DOUT (Digital Output), AOUT (Analog

Output), and TCM (Temp Compensation Output)

pins. The VCC pin is given a DC voltage of 5V then

the GND pin is connected to the ground pin on the

Raspberry Pi. In contrast, the AOUT (Analog Output)

pin is connected to the Analog pin (pin A0) ADC

(analog to digital converter), which will be used as

input for reading CO2 sensor data.

The TGS-2611 sensor circuit functions to measure

and detect HC gas. This sensor produces output in

analog data that will later be converted into digital

data by the ADC (Analog to Digital Converter). The

calculation is carried out so that the output is in the

form of ppm (parts per million) gas units. The TGS-

2611 sensor has 4 pins, including VCC, GND, AOUT

(analog output), DOUT (digital output) pins. The

VCC pin is given a DC voltage of 5V. Then the GND

pin is connected to the Raspberry Pi ground pin. At

the same time, the AOUT pin (analog output) is

connected to the analog pin (pin A2) of the ADC

(analog to digital converter), which is used as input

for reading the HC sensor data.

The SHARP GP2Y1010 sensors' series functions

to measure and detect dust particulates with an optical

sensing system. The LED emits an infrared (IRED)

diode, and the photo-transistor is diagonally arranged

in this device. This sensor produces output in analog

data, which will then be converted into digital data by

the ADC (analog to digital converter). The output is

PM10 data with the Dust unit (µg / m3). This sensor

has 6 pins, namely VCC, V-LED, LED-GND, LED,

S-GND, and Vo. The VCC and V-LED pins are

combined and given a DC voltage of 5V, then the

LED-GND and S-GND pins are also integrated to be

connected to the ground pin on the Raspberry Pi. The

LED pin is then connected to the GPIO pin 5

Raspberry Pi. The Vo pin is connected to the analog

pin (pin A3) of the ADC (analog to digital converter),

which will be used to read the PM10 dust particulate

sensor data.

DHT-11 sensor circuit functions to measure the

temperature and humidity of an environment. This

sensor's output data is in digital data where the values

of temperature and humidity measurements have

units of Celsius (˚C) for temperature and in percent

for humidity. This data can be read directly by the

Raspberry Pi by connecting it to the GPIO pin on the

Raspberry Pi. The DHT-11 sensor has 3 pins, namely

the VCC, DATA, and GND (ground) pins. The VCC

pin is given a DC voltage of 5V, and then the DATA

pin is connected to the GPIO 4 Raspberry Pi pin. The

GND pin is connected to the Raspberry Pi's ground

pin.

The GPS-NEO 6M sensor functions to provide

information in the form of latitude and longitude

when the device is on. The GPS-NEO 6M module has

4 pins, including VCC, GND, RX (Receiver), TX

(Transmitter). The VCC pin is given a DC voltage of

5V then the GND pin is connected to the ground pin

available on the Raspberry Pi. Furthermore, so that

the GPS-NEO6M module can communicate with the

Raspberry Pi, the RX pin on the module is connected

to the TX pin on the Raspberry Pi. The TX pin on the

module is connected to the RX pin on the Raspberry

Pi so that the GPS-NEO6M module can send

coordinate point data in the form of latitude and

longitude.

ADS1115 module is a type of ADC with a

resolution of 16 bits. It means that ADC has a high

level of accuracy in the conversion value than ADC

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with a little resolution. In this ADC, 4 channels can

convert values for 4 sensors at once with bipolar and

single differentials. The ADS1115 module has 10

pins, including VCC, GND, SCL, SDA, ADDR,

ALRT, and 4 analog pins A0-A3. The VCC pin is

given a DC voltage of 3.3V then the GND pin is

connected to the ground pin on the Raspberry Pi. The

sensor reading data received by the ADS115 will be

sent via I2C serial communication by connecting the

SDA and SCL pins on the ADS1115 with the SDA

and SCL pins on the Raspberry Pi to be processed by

Raspberry pi.

The tool in this final project is to put the sensor in

the right position so that each sensor can work

optimally. The hardware design that will be made is

as follows:

Figure 5: Hardware sensor block design.

(a)

(b)

(c)

Figure 6: (a) Top View Hardware Design (b) Side View

Hardware Design (c) Front View Hardware Design.

Figure 6 shows the sensor position in the planned

hardware design. This design uses a box made of

plastic. This plastic box aims to make the device

(hardware) more complex and more comfortable to

obtain. The Lippo battery will be arranged alongside

the Raspberry Pi in the box. Meanwhile, each sensor

will be positioned on the box's surface using a screw

to be more robust so that the sensor can read the air

quality around it without any obstruction.

3.2 Software Design

The software design will begin with the tool work

process starting from the sensors' initialization. Then

the sensors will start working on getting air quality

data. The data obtained from the sensor readings will

be sent to the server to be stored and displayed on the

web and Android applications.

3.2.1 Raspberry Pi Configuration

Raspberry Pi uses a Linux operating system called

Raspbian. Using the operating system must be flashed

first on the SD-card because the Raspberry Pi uses an

SD-card as a bootable operating system. After

booting for the first time, it is asked to enter ID: pi

and Password: raspberry, which is the system's

default ID and Password. It can be seen as below,

Figure 7: Login Raspbian.

After logging in, the Raspbian operating system is

ready for use, and the command line will appear as

follows:

Figure 8: Raspbian Command Line Display.

The Raspberry Pi will be connected to the

available internet network and use the SSH protocol

to simplify the operation process. It can remote this

operating system from other clients who are

connected to the same network. After the Raspberry

Pi is connected to the internet network, this mini-

computer will have a local IP.

It can be entering the command "ifconfig" to find

out the local IP, as shown below:

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135

Figure 9: Raspberry Pi’s Local IP.

Other clients can remote on this operating system

after knowing the Raspberry Pi's local IP using the

PuTTY application.

3.2.2 PuTTY Configuration

Ensure the client is connected to the same network as

the Raspberry Pi before running the PuTTY

application. Next, enter the previously known

Raspberry Pi IP address in the Hostname field, then

select the SSH type as follows:

Figure 10: PuTTY Configuration.

If the Host Name is entered and the SSH

connection is selected, then clicking the open button

and successful, the Raspberry Pi is ready to operate.

4 DESIGN RESULT

4.1 Air Quality Monitoring System

Design Results

The system design results are divided into hardware

design and monitoring system software. This system

is designed using Multisensor Network technology to

measure a lot of harmful pollutants. This test's overall

system performance will automatically work when

the sensor reads the pollutant gas levels and sends the

reading data to the server in real-time. Furthermore,

the data will be processed to become information that

users or the general public can use.

4.1.1 Hardware Design

This air quality monitoring system's hardware is

equipped with multi-sensor network technology. This

system used the TGS-2442 sensor as a CO (carbon

monoxide) gas meter, the TGS-2611 sensor as an HC

(hydrocarbon) gas meter, the SHARPGP2Y1010

sensor as a PM10 dust particulate gauge, MG sensor

-811 as a CO2 gas (carbon dioxide) meter, the DHT-

11 sensor as a temperature and humidity meter and

the Neo-6M GPS module as a provider of information

about the coordinates of the points of each node and

the ADS-1115 module as an analog to digital

converter from sensor readings. The system also

equipped with 3 cells 12 volt lithium battery.

Raspberry pi 3 module as a microprocessor, and

communication between hardware and the server as a

database. In the figure below are the results that have

been achieved in making hardware :

Figure 11: Inside of Hardware Display.

The hardware in this air quality monitoring system is

placed in a box to reduce the risk of damage. It

continues to work optimally in conditions of the data

collection process. It makes it easier to find the

location of the tool with location information in

latitude and longitude.

4.1.2 Software Design

Air quality monitoring data will be displayed in

results from readings of each sensor and information

in latitude and longitude. The data obtained from

sensor readings are the result of implementing source

coding on the hardware, which is displayed as

follows:

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Figure 12: Combined Source Coding Hardware.

Figure 12 is the source coding for each sensor.

The sensor reading data is sent to the database server

using the HTTP post request protocol, which will then

be displayed in the monitoring system application that

has been provided.

4.2 Test Results

In this tool's testing process, each node is distributed

to a predetermined location. The nodes are turned on

simultaneously to monitor air quality at the location.

It sends the read data to the server to be displayed on

the android and webserver interfaces provided in real-

time.

4.2.1 Node 1 Air Quality Monitoring Results

Testing for Node 1 was carried out in the KPA

parking lot of the State Polytechnic of Sriwijaya. This

test is carried out in the morning, afternoon, and

evening which is shown as follows:

Figure 13: Monitoring Node 1 Location.

The results of the sensor slowly show the decrease

in gas levels to normal again with a reading value of

57 ppm of CO, a value of 366-415 ppm of CO2, and

HC value of 275-276 ppm, a value of particulate dust

PM10 17-19 µg / m3, a temperature value of 32 - 33ºC

and humidity 60% - 64%. This is because the location

looks quiet, and there are not many vehicles passing.

4.2.2 Node 2 Air Quality Monitoring Results

Testing for Node 2 was carried out in the Electrical

Engineering State Polytechnic Sriwijaya parking lot.

This test is carried out in the morning, afternoon, and

evening which is shown as follows:

Figure 14: Monitoring Node 2 Location.

Gas levels at the location returned to normal with

a CO reading of 44 - 45 ppm, a CO2 value of 331 -

336 ppm, an HC value of 365 - 370 ppm, a dust

particulate value PM10 9 - 15 µg / m3, a temperature

value of 32 - 33 ºC and a humidity of 65 % - 66%.

This situation is due to the location that looks

deserted, and there are not many vehicles passing.

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137

4.2.3 Node 3 Air Quality Monitoring Results

Testing for Node 3 was carried out in the

Management Informatics State Polytechnic Sriwijaya

parking lot. This test is carried out in the morning,

afternoon, and evening which is shown as follows:

Figure 15: Monitoring Node 3 Location.

Gas levels at the Node 3 location returned to

normal with a reading value of CO 52 - 53 ppm, a

CO2 value of 298 - 304 ppm, an HC value of 295 -

298 ppm, a dust particulate value PM10 14-15 µg /

m3, a temperature value of 32 ºC and humidity 62 %-

63%. This condition is due to the location that looks

deserted, and there are not many vehicles passing.

4.3 Air Quality Monitoring System

Performance Analysis

Overall, the multi-sensor network technology works

when the sensor reads pollutant gases' levels in the

surrounding environment. In this study, the system

automatically detects pollutant gas levels and

provides information on each node's location

coordinates. The results will then be sent to the server

in real-time to be processed to provide air quality

information.

The air quality monitoring system has reached 4

parameters in the system testing, i.e., accuracy in

reading data, device durability, device integration,

and ease of use. This system is integrated with an

Android application and a webserver to facilitate the

monitoring process. Therefore, it makes public easier

to access information about air quality in an area.

The sensor testing results showed that the KPA

parking lot air condition at 09.00 AM was still quite

normal. This condition caused the lecture system is

carrying out online activities due to the Covid-19

pandemic. There was an increase in CO2 gas levels

up to 1673 ppm at 12.00 WIB. This condition is

caused by an increase of motorized vehicles so that

the gas from combustion is detected by the sensor,

considering that it is currently a rest time. In the

afternoon, which started at 14.00, the measured gas

levels looked back to normal because the location was

already deserted. There was a change in the value of

the gas during the testing process in other gas level

measurements, but the difference was not seen

significantly and still in normal conditions.

The sensor testing results showed that the air

condition at Electrical Engineering parking lot at

09.00 AM was still quite normal. This condition is

due to the lecture system is carrying out online

activities due to the Covid-19 pandemic. At 12.00

PM, there was an increase in CO2 gas levels up to 647

ppm, but the value of the increase was not as high as

KPA parking lot. At 14.00 , the measured gas levels

looked back to normal because the location

conditions were already deserted. There was a change

in the 0gas value during the testing process in other

gas level measurements, but the difference was not

seen significantly and still in normal conditions. The

results of sensor testing showed that the Management

Informatics parking lot air condition at

09.00 AM increased CO2 gas with a value of 670

ppm. This condition is caused by chemical

engineering students' burning activity while carrying

out practical lessons. Furthermore, at 12.00 PM, the

air had returned to normal because chemical

engineering students' activities had finished. At 14.00

, the measured gas levels still looked normal because

the location conditions were already deserted. There

was a change in the sensor reading value during the

testing process in other gas level measurements.

However, the difference was not seen significantly or

was still in normal conditions.

5 CONCLUSIONS

Based on the result and discussion from this paper,

the air quality monitoring system that has been

designed has a good level of accuracy performance in

determining the levels of gas measured. Measurement

against CO, CO2, HC, PM10, temperature, and

humidity is also good resistance. This monitoring

system successfully sends an “air quality status”

information to the server with an interval of 10-13

seconds. The Raspberry Pi also works well at

managing data and sending it to the server in real-

time.

Based on the conclusions above, authors

recommend developing other sensors such as NO2

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sensors, SO2 sensors, and NOx sensors in air quality

monitoring systems. More pollutant gas can be

measured to increase our awareness of air pollution in

the environment.

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