Visualization of Air Quality Conditions in Medan City

Azhari

1,2

, Lukman Hakim

1,2

and Fathurrahman

1,2

Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara, Medan, Indonesia

Integrated Research Laboratory, Universitas Sumatera Utara, Medan, Indonesia

Keywords: Visualization, Air Quality.

Abstract: Visualization of urban air pollution requires massive data processing because it has to create a map of air

pollution in either two dimensions or three dimensions and we have to deal with geographical data, that is,

GIS data. Weather data are multivariate and contain plane vectors formed by wind speed and direction.

Several tools are used to detect air quality. Data points are marked with location and time using on-board

GPS. Periodically, measurements are uploaded to the server, processed and then published on the Sensor

Map portal. With a sufficient number of nodes and diverse mobility patterns, a detailed picture of air quality

over a large area will be obtained at low temperatures. The purpose of this study is to determine the

visualization of air quality conditions in the city of Medan using pollutant sensors based on regional

mapping of Medan. With this research, it is expected that a tool that can read the data of pollutant conditions

in Medan will be obtained. This research method begins with hardware design using CO gas sensors namely

TGS 822, SHT31 temperature and humidity, GPS sensor, and SPS 30 sensor as particulate sensors. The

device is then connected to Bluetooth so that it is connected to a PC and can be read in realtime.The tool can

be used on cars with 9V power source. The tool is then used to detect pollution at several sample points that

represent the city of Medan as a whole. The results of the reading of pollutant values are then processed and

used to visualize the condition of air quality in the city of Medan. Based on the results of calibration testing

and measurement of ambient air quality in the city of Medan, obtained air quality data in the field city

during the test time has an AQI value ranging from 30 to 90 which shows that the air quality in Medan is

still relatively moderate.

1 INTRODUCTION

In 2012 WHO reported that around 7 million deaths

or 1/8 global deaths were caused by air pollution.

The highest fatalities are in low to middle income

countries namely in the Southeast Asian region with

a total of 1.69 million deaths due to air pollution

(WHO, 2014). Several countries and big cities have

implemented various policies to reduce the impact of

this air pollution (Smedley Team. 2017). Some of the

studies that have been conducted include the first

research that has designed a gas emission test device

in real time and was monitored via the web (Smedley

Team, 2017; Fikri, 2013; Rochmana et al., 2016).

Based on the Decree of the Minister of Health of

the Republic of Indonesia number 1407 of 2002

concerning Guidelines for Controlling the Impact of

Air Pollution, air pollution can be defined by the

entry or inclusion of substances, energy, and/or other

components into the air by human activities, so that

air quality drops to a certain level causing or affect

human health (Kadir, 2013). Increased industrial

development and increasing population will produce

a quantity of types of transportation that have an

influence on air quality in an urban setting (Mukono,

2011).

The air quality index is generally calculated based

on five main pollutants, namely surface

oxidants/ozone, particulate matter, carbon monoxide

(CO), sulfur dioxide (SO) and nitrogen dioxide (NO).

However, at present the calculation of the air quality

index uses two parameters namely NO and SO The

NO parameter represents emissions from motor

vehicles that use gasoline, and SO represents

emissions from industry and diesel vehicles that use

diesel fuel and other sulfur-containing fuels (Ministry

of Environment and Forestry, 2018). Carbon

monoxide (CO) is a pollutant. Based on estimates,

the amount of CO in Indonesia is estimated at close

to 60 million tons/year. One-eighth of this amount

comes from motorized vehicles that use gasoline and

a third come from stationary sources. Even though

112

Azhari, ., Hakim, L. and Fathurrahman, .

Visualization of Air Quality Conditions in the City of Medan.

DOI: 10.5220/0010137200002775

In Proceedings of the 1st International MIPAnet Conference on Science and Mathematics (IMC-SciMath 2019), pages 112-116

ISBN: 978-989-758-556-2

carbon monoxide is a flammable gas and very

poisonous to humans. In the World Health

Organization report, WHO is estimated that at least

one type of air pollution in large cities has exceeded

the tolerance limit of air pollution (The World Bank,

1994).

There is a very strong need we have to make

urban air pollution maps that can be efficiently and

easily used to help city officials to provide citizens

with a pleasant and safe urban environment: The

World Health Organization (WHO) states that 2.4

million people die every year from causes that are

directly linked to air pollution and we still remember

terrible memories of the Great Smog of 1952 in

London, England (Qu et al., 2007; Völgyesi et al.,

2008). Atmospheric particulate matter is a criterion

commonly used to evaluate air quality (Park et al.,

2011; Janssen et al., 2013). The level of adverse

health effects depends on the size and composition of

the particles (Zirui et al., 2015). PM2.5 and PM10 are

defined as particles with a diameter of 2.5 μm or less

and 10 μm or less, respectively; this parameter is

usually measured using the air quality index (AQI).

AQI is calculated from particle concentrations at the

monitoring station expressed as micrograms per

cubic meter (Popeet al., 2002). According to

technical regulations on the ambient air quality index

(on trial) (Air Quality Index, 2019), the air pollution

index for PM2.5 is divided into six levels, namely, 0-

50, 51-100, 101-150, 101-150, 151-200, 201-300,

and greater than 300. With this level, we can identify

the severity of air pollution.

2 RESEARCH METHOD

2.1 Tool Design and Manufacturing

2.1.1 Block Diagram

The hardware design of the air quality monitoring

system consists of Arduino Uno microcontroller,

TGS 822 sensor, MQ-135 sensor, SHT-31, GPS,

Ozone Sensor, and Arduino Uno. In general, system

hardware design is as follows:

1. The MQ-135 sensor is a sensor that will detect

carbon dioxide gas which is represented as a

gas sensor. This sensor output in the form

of analog voltage.

2. The TGS 822 sensor is a sensor that will detect

carbon monoxide gas which is represented as a

CO gas sensor. This sensor output in the form of

analog voltage.

3. The SHT-31 sensor is a sensor that will detect

temperature and humidity.

4. GPS sensor is a sensor that will detect the

location/position of the sensor against latitude

and longitude.

5. Ozone Sensor is a sensor that will detect ozone

gas. This sensor output in the form of analog

voltage.

6. Arduino Uno Microcontroller which functions

as a control center for all sensors.

Arduino microcontroller is the main component

that functions as a data processing center that will be

processed before sending to the viewer (PC) via

bluetooth. PC functions as a viewer of data obtained

from the sensor so that it can be directly seen

visually.

Figure 1. System work block diagram

2.1.2 Arduino Uno Microcontroller Circuit

Arduino is an electronic kit or open source

electronic circuit board in which there are main

components, namely a microcontroller chip with

AVR type from Atmel company. The Arduino

programming language is C language. But this

language has made it easier to use simple functions

so that beginners can learn it quite easily.

Visualization of Air Quality Conditions in the City of Medan

113

Figure 2. Arduino Uno Microcontroller Schematic

(Source: www.electroschematics.com)

The vehicle exhaust emission measurement tool

uses Arduino Uno which uses the ATmega 328

microcontroller as the central working controller of

all design tools, including reading sensor

measurement results and changing sensor

measurement results to digital because the sensor

output is analog. From figure 2, Arduino Uno

microcontroller is programmed using the Arduino

IDE software which is sent through a computer USB

port. The power supply used is 9V connected at 30

(VIN) and 29 (GND).

2.1.3

TGS Sensor Circuit 822

The sensing element of Figaro gas sensors is a tin

dioxide (SnO

) semiconductor which has low

conductivity in clean air. In the presence of a

detectable gas, the sensor's conductivity increases

depending on the gas concentration in the air. A

simple electrical circuit can convert the change in

conductivity to an output signal which corresponds

to the gas concentration. TGS 822 has high

sensitivity to the vapors of organic solvents as well

as other volatile vapors. It also has sensitivity to a

variety of combustible gases such as carbon

monoxide, making it a good general purpose sensor.

Figure 3. TGS Sensor Circuit 822 (Source:

www.researchgate.net/figure)

The structure of the sensor is shown in Figure 3.

TGS 822 is the main transducer used in this circuit,

which is a gas sensor. This sensor has a resistance

value of Rs that will change when exposed to gas

and also has a heater that is used to clean the sensor

room from outside air contamination.

2.2 Design and Manufacture of

Software

Software design is needed because in order to run

the Arduino Uno system the ATMega328P

microcontroller chip will be filled with the desired

command program.

3 RESULT AND DISCUSSION

a. Ambien CO Test Equipment Calibration

Results

Tool calibration is done using the CO meter shown

as shown in the following test:

Figure 4. Tool calibration process

IMC-SciMath 2019 - The International MIPAnet Conference on Science and Mathematics (IMC-SciMath)

114

The test is carried out in a closed container for 10

minutes. After that given the smoke coming from

burning paper then a fan will make the smoke spread

evenly in the container so that it triggers the sensor

to detect the smoke content in the air inside the

container. Standard tools and tools for CO sensor

design are Sensor TGS 822. After processing the

data, the graph is obtained as figure 5.

Figure 5. CO calibration regression graph with the TGS

822 sensor

In Figure 5 the test results using the TGS 822 sensor

compared with the CO meter tool obtained a

regression equation y = 0,0003x

- 0.3104 + 104

with a value of R

= 0.9997. A large R

value

indicates a strong correlation between the data from

the CO meter with the TGS 822 sensor.

Tool testing is done by attaching the tool to

the hood and connected to the 9V power source in

the car. Tests carried out in the afternoon during

rush hour (between 16:00 to 18:00) and carried out

observations along the road. The reading results can

be seen in realtime via a PC by connecting via a

Bluetooth network. The sensor voltage source is 9V

power that is owned by the car. The data collected

included temperature and humidity, latitude and

longitude, and CO content in ambient air. This can

be seen in Figure 6.

Figure 6. Reading of the ambient CO value via a PC

One of the sampling locations is the Medan Johor

area, Jl. Eka Rasmi and Jl. Karya Jaya. The results

of the reading can be seen in Figure 7.

Figure 7. Graph of CO Ambient

From the graph shown in Figure 7 it can be seen that

the air on Jl. Eka rasmi has a lower ambient CO

value compared to the CO value from Jl. Karya Jaya.

This is because Jl. Karya Jaya has a larger vehicle

volume because it connects several roads including

works of love, devoted work, eka rasmi, iridescent

and eka surya. While Jl. Eka Rasmi has a smaller

volume of vehicles because it is a branch road from

the victorious works and tourist works. Next is to do

an Air Quality Index (AQI) calculation which can be

determined using the following formula:

𝐼

𝐼



𝐼



𝑋



𝑋





𝑋𝑥  𝑋𝑏



𝐼𝑏

Real ambient concentration (ppm). Real AQI figures

I = Air Pollution Standards Index calculated

Ia = Air Pollution Standards Index upper limit

Ib = Air Pollution Standards Index lower limit

Xa = Ambient upper limit

Xb = Ambien lower limit

Xx = Real ambient level measurement results.

Figure 8. Graph of ambient air in some area in Medan

Visualization of Air Quality Conditions in the City of Medan

115

Measurement of the air quality index in the city

of Medan and surrounding areas in October or

during testing showed values between 30 to 90

shown in the graph in Figure 8. From the graph it

can be seen that the Medan city area, Medan

Sunggal, and Delitua have almost the same value in

various points and is an area that has the highest

AQI value (the lowest air quality) compared to the

other three. While Labuhandeli is the area with the

lowest AQI value so that the air quality is still better

compared to other places. But overall these values

indicate that air quality in the city of Medan and its

surroundings is still classified in the medium

category.

4 CONCLUSIONS

After testing and analyzing the data obtained,

conclusions can be drawn including:

a. The tool can measure ambient air quality with

sensor.

b. Based on the results of sensor testing and testing

of measuring devices, the measurement of

ambient quality air in Medan works well.

c. The Air Quality Index in the city of Medan and

its surroundings shows a value between 30 to

90.

ACKNOWLEDGEMENTS

The authors acknowledge the financial support of

this research by Universitas Sumatera Utara in

accordance with the USU Talent Research

implementation contract for fiscal year 2019

Number: 4167 / UN5.1.R / PPM / 2019 dated 1

April 2019.

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