Air Quality Monitoring of Bangladesh (AQM): Data Driven Analysis

System

Noureen Islam

, Noor-E-Sadman

, Mahmudul Islam

and Mahady Hasan

Department of Computer Science & Engineering, Independent University, Bangladesh

Keywords:

Air Quality, Software System, Data Analysis, Machine Learning, IoT, Satellite Data, AQI, PM2.5.

Abstract:

Air pollution is a major concern for countries around the world. According to World Health Organization

(WHO), seven million people die worldwide every year caused by air pollution. Bangladesh has not only

serious pollution problems but also it is ranked ﬁrst among the world’s most polluted countries with a PM2.5

reading of 76.9 microgrammes per cubic meter (µg/m

) in the year 2021 (AQI Bangladesh, 2021). In this

paper, we propose to develop a data-driven software system for monitoring the air quality of Bangladesh. Our

proposed system will provide atmospheric maps and charts for monitoring the current and future Air Quality

Index (AQI) of any area. We conducted an experiment for 1-year time span to observe the concentration level

and data patterns of PM2.5 in our country focusing on the transportation routes and industrial zones. The

data is collected from the sensors and satellites of different stations covering multiple areas. The results are

analyzed in the context of divisions, transportation stations, industrial zones, and time. For a variety of air

quality indicators, the experimental results were compared to IQAir AirVisual Pro and showed good results,

with very small differences between our obtained result and IQAir AirVisual Pro. Our goal is to mainly

monitor the industrial zones, power plants, divisions, and transportation routes as most toxic compounds are

formed there.

1 INTRODUCTION

Air pollution is a worldwide crisis with limited so-

lutions because of the presence of compounds in the

atmosphere that are detrimental to the wellness of

habitats or pose a hazard to the ecosystem or objects.

Bangladesh is a densely populated country struggling

with serious air pollution. According to the most

recent World Health Organization data, Dhaka’s air

quality averages 90 µg/m3 of PM2.5 concentration

per year. Dhaka, having a yearly Air Quality Index

of 168, is considered to be unhealthy and is indicated

only as a warning since air pollution can reach signif-

icantly up to 300 plus dangerous higher levels.

The World Bank began a 7-year investment of

USD $62.20 million in the Clean Air and Sustainable

Environment (CASE) project in 2009. The goal of

this project was to install 11 Continuous Air Qual-

ity Monitoring Stations (CAMS) among 8 cities to

monitor air pollutants and create real-time air qual-

ity data including an air quality index for important

https://orcid.org/0000-0002-5193-4730

https://orcid.org/0000-0002-7531-8537

https://orcid.org/0000-0002-3270-8877

https://orcid.org/0000-0002-9037-0181

cities. Under this project, Air Quality Research and

Monitoring Center (AQRMC) has been established

at Dhaka University, Bangladesh. According to the

CASE project report, $10 millions USD have already

been used but the progress is not visible (Air Pollu-

tion, 2021).

We have collected data from different areas of

Bangladesh using sensors and compared the result

with the data obtained by an industrial-graded device

called AirVisual Pro by IQAir. This comparison is

performed to determine the data compatibility and it

has been observed that the data we yielded had good

results. We have also implemented multiple machine

learning algorithms for predicting the AQI on AOD

550 nm data. However, in this paper, we propose

to develop a data-driven solution to monitor the air

quality that will collect data from IoT-based devices,

Aqua satellite, and geospatial weather data bank. The

Aerosol Optical Depth (AOD) data is determined to

be collected from satellites for those areas which are

not covered by AQM devices. Our system will gen-

erate atmospheric maps and various charts with the

help of different MLAs which will display the AQI,

PM1.0, PM2.5, PM10, CO, CO

, NO

, VOCs of dif-

ferent areas of Bangladesh. The AQI forecast will

Islam, N., Noor-E-Sadman, ., Islam, M. and Hasan, M.

Air Quality Monitoring of Bangladesh (AQM): Data Driven Analysis System.

DOI: 10.5220/0011306300003266

In Proceedings of the 17th International Conference on Software Technologies (ICSOFT 2022), pages 205-212

ISBN: 978-989-758-588-3; ISSN: 2184-2833

205

help the government to make signiﬁcant decisions for

maintaining the air quality.

The rest of this paper is organized as follows. Lit-

erature reviews have been discussed in section 2 while

the research problem is stated in section 3. Our pro-

posed solution to solve the research problem is pre-

sented in section 4. The experimental results have

been presented in section 5 and ﬁnally, in section 6

we have drawn the conclusion along with future re-

search scope.

2 RELATED WORKS

Around the world, in different countries, many sys-

tems have been developed to monitor air quality. So

far in Bangladesh, very few works have been done

related to Air quality monitoring systems. In this

research work, A number of conference papers and

journal articles related to air quality monitoring sys-

tems have been studied for a deeper understanding.

An air quality monitoring system was designed by

(Gu and Jia, 2019). The system’s modular architec-

ture allows it to carry a variety of air pollution sensors

and integrate data from all of them with geo-location

information in real-time. The prototype’s preliminary

ﬁeld tests show that the onboard devices had no ef-

fect on the UAV’s power consumption or ﬂight time.

A similar kind of system is also developed by (Kan-

naki et al., 2020), the entire operation is controlled

by a microcontroller, which receives input signals and

sends output signals to components such as the DC

Brushless Fan and LED lamps. (Sung et al., 2019)

also developed a system where PM1, PM2.5, PM10,

CO, CO

, VOCs, temperature and humidity have been

monitored. They used short and long-distance com-

munication modules such as Bluetooth, Wi-Fi, and

Lora to communicate with a developed smartphone

application.

A big data driven urban healthcare system was

proposed by (Chen et al., 2018) where a method

was introduced for combining multi-source air quality

data which help to prepare data for AI based smart ur-

ban services. A testbed was also established by them,

including the deployment of air quality aware health-

care applications. Similar kind of system was pre-

sented by(Meli et al., 2020) where the system enables

the deployment of many low-cost nodes throughout a

building, yielding considerable location-based indoor

air pollution data. A new concept of a portable system

named GASDUINO which enables the users to detect

air quality via IoT was introduced by (Karar et al.,

2020). Their technology can warn users about harm-

ful levels of air quality index (AQI) values ranging

from 0 to 200 PPM. It detects the AQI with the MQ-

135 sensor and visualizes the data using the Remote

XY Arduino cloud. A low-cost air quality measure-

ment system was developed by (Arroyo et al., 2019)

where volatile organic substances such as benzene,

toluene, ethyl benzene, and xylene are detected by the

system.

A study describes the evaluation of a smart in-

door air quality and health system conducted by (Patil

et al., 2019) , which aims to allow the users to moni-

tor oxygen level and provide alerts when the environ-

mental atmosphere breaches the safe limit threshold.

A Wireless Sensor Network (WSN) based system has

been developed by (Purwanto et al., 2019) that can be

accessed through smartphone and internet. With the

capability to measure air pollution environmental fac-

tors like temperature, humidity, wind speed, S

, NO

Air quality monitoring via an array of sensors which

transmit their toxic gas readings through Bluetooth to

the nearest smartphone introduced by (Yang and Li,

2015). The readings get updated every time the ap-

plication is installed in the smartphone and clicked.

WSN based air pollution monitoring system was pro-

posed by (Suganya and Vijayashaarathi, 2016). This

system used a Mobile Ad Hoc Network routing algo-

rithm, where 28 moving vehicles cover a large area.

Each vehicle covers 300 meters and collects the data

using air pollutant detecting sensors.

A real-time air monitoring system has been de-

veloped by (Holovatyy et al., 2018) where real-time

toxic gas data has been extracted with an array of sen-

sors to determine the concentration of deadly gases

and vapours in air. Similarly, a system developed

by (Kiruthika and Umamakeswari, 2017) where a set

of data is extracted from multiple sensors which had

some threshold values set by the system. While the

collected value exceeds the threshold value, the mes-

sage alert has been sent by the communication mod-

ule to the client. An air pollution Geo-sensor net-

work has been modelled by (Al-Ali et al., 2010) to

obtain the AQI (Air Quality Index) where sensors

are taking 24/7 real-time readings of CO, NO

and

components and transmit the pollutant data to

a database through a server. (Dhingra et al., 2019)

developed a system to observe the concentration of

Carbon Monoxide (CO), Methane (CH

) and Carbon

dioxide (CO

) gases to measure the AQI. A platform

has been developed by (Zaldei et al., 2017) to moni-

tor air pollution and trafﬁc movements which captures

CO, NO

, and C

concentrations using open-source

Arduino technology.

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3 PROBLEM STATEMENT

The average annual PM 2.5 concentrations in

Bangladesh were 76.9 microgrammes per cubic meter

(µg/m3) which is seven times higher than the WHO

exposure guidelines. Dhaka the capital of Bangladesh

stands second among all cities around the world (AQI

Dhaka, 2021). To eliminate the climate change cri-

sis, we are proposing to develop an air quality mon-

itoring system which will forecast AQI using AOD

data from the satellite along with a device to detect

the concentration of pollutants and monitor the data

patterns in real-time. Our proposed AQM system, in

combination with the device, will help in forecast-

ing AQI in remote places where the devices cannot

be deployed. The forecast model of the AQM sys-

tem will also greatly reduce the cost of a large-scale

implementation of the air quality detection system by

reducing the quantity of device implementation. Our

collected data showed promising results with respect

to the industrial-approved device called AirVisual Pro

by IQAir. Therefore, we believe that our data-driven

air quality monitoring software system can be devel-

oped to monitor the air quality of Bangladesh and help

to take appropriate measures for decreasing air pollu-

tion.

4 PROPOSED SOLUTION

Inspired by the success of data-driven solutions, we

propose to develop a data-driven software system that

will be used to monitor and ensure the air quality of

Bangladesh. The main purpose of the proposed AQM

system is to improve air quality and to establish a low-

cost solution for improving AQI by monitoring the air

standard. A high-level diagram of the proposed AQM

system is presented in Figure 1.

Figure 1: High-level Diagram of AQM.

Our proposed AQM system will collect data

through different sensors which have been set in dif-

ferent regions of Bangladesh. Since it is not feasible

to set the sensors in every single place, therefore, the

system will also collect Aerosol Optical Depth (AOD)

550 nm data from Aqua Satellite for those areas which

are not covered under sensors and geospatial weather

data from Visual Crossing weather data bank. All the

data collected from the sensors, Aqua Satellite, and

the weather data banks will be stored in the cloud.

Data collected from different sensors are real-time

data whereas we get the previous day’s data from

aqua satellite and the weather data banks. Atmo-

spheric maps and charts will be generated based on

the stored real-time data collected from different sen-

sors. On the other hand, machine learning algorithms

and techniques will be applied on the data collected

from the aqua satellite, weather data banks and pre-

diction analysis will be performed. After prediction

analysis, atmospheric maps and charts will be gener-

ated and shown on the AQM system.

Figure 2 depicts the software system architecture

of our proposed AQM system where the air quality

data will be gathered from various sources i) Pollu-

tant data from IoT based air quality monitoring de-

vice ii) Aerosol Optical Depth (AOD) 550 nm data

from Aqua satellite iii) Geospatial weather data from

Visual Crossing weather data bank. Data obtained

from the IoT devices and the Geospatial Weather

Data along with the satellite data will be stored in a

Apache Cassandra NoSQL database. The real time

data from the AQM devices will be stored in the

database using MQTT protocol and Kafka connec-

tor whereas, the weather and satellite data in CSV

ﬁles will be imported directly using Apache Cassan-

dra. Real time data will be preprocessed using the

real time stream processing of the Apache Spark and

generate atmospheric maps and charts for different ar-

eas such as distinctive states, industrial zones, trans-

portation routes. Similarly, our proposed system will

operate a batch processing on the data collected form

AQM devices, weather, and satellite using the batch

processing tool of the Apache Spark. We will use

Apache Spark MLib tool to obtain prediction analy-

sis based on the preprocessed data and ﬁt the machine

learning models to forecast air quality index (AQI).

Finally, the system will generate AQI-based atmo-

spheric maps and charts for the betterment of pollut-

ing areas and route monitoring.

Figure 3 shows the communication overview of

the MQTT and Kafka Connector used in our proposed

AQM system. In the AQM system the AQM Devices

will use MQTT protocol to feed sensor data to the

MQTT Broker, then using Kafka MQTT connector

Air Quality Monitoring of Bangladesh (AQM): Data Driven Analysis System

207

Figure 2: Software System Architecture of AQM.

Figure 3: Overview of the MQTT and Kafka Connector in

our proposed AQM system.

the data will be sent to the Kafka Broker from the

MQTT Broker. Finally, the sensor data will be stored

in the Apache Cassandra Database from the Kafka

Broker.

4.1 Data Collection

For the detection of contaminating toxic gases, it

is necessary to outsource the sensors with greater

accuracy that is available and inexpensive, keeping

in mind the budget-friendly factor. After sourcing

the sensors, it is vital to narrow down the suitable

methodologies that will lead the proposed system to

its outcome. To determine the most-ﬁtted procedure,

it is better to do a literature review to execute the goal

and ﬁx the steps. The stated process above will be

executed according to the proposition.

According to the proposition design, the sensors

will be mounted to multiple cars as nodes. When the

car is in motion, the device takes readings from sen-

sors every minute and uploads the data to the cloud

storage with the location and time stamp. It will use

in-built GSM module to upload the sensor’s data to

the cloud database server, then all the data will be pro-

cessed and published on the AQM system portal along

with temperature, relative humidity data and GPS in-

formation. We will collect data also from Aqua Satel-

lite of Aerosol Optical Depth (AOD) 550 nm data for

those places which are not covered by sensors. We

will use the NASA Giovanni data visualization tool

to get the satellite AOD 550 nm data. The available

AOD 550 nm data which we will obtain are prepro-

cessed using the Aqua Satellite’s MODIS (Moderate

Resolution Imaging Spectroradiometer) instrument.

Aqua satellite travels from the South to the North

Poles. The AOD 550 nm data is essentially the level-3

atmosphere daily global product (MYD08 D3), which

is produced from four level-2 MODIS AQUA atmo-

sphere products (MYD04 L2, MYD05 L2, MYD06

L2, and MYD07 L2). And, the geospatial weather

data will be collected from Visual Crossing weather

data bank.

4.2 Data Preprocessing

In our experiment, we started with data cleaning dur-

ing the data preprocessing phase. Initially, we identi-

ﬁed all the missing data, noisy data, and global out-

liers caused due to equipment malfunction and have

inconsistencies with other recorded data using One-

Class Support Vector Machine (SVM). One-Class

SVM is used in one-class problems, in which all data

belongs to the same class. In One-Class SVM, the al-

gorithm knows the pattern of normal data therefore,

when new data comes it can identify whether the data

is normal or not. If not, the new data is classiﬁed as

anomalous. After identifying, we removed all missing

data, noisy data and global outliers. To accomplish

the data integration, the acquired AOD 550 nm data

from the Aqua Satellite, PM2.5 data from the ground

station, and geospatial weather data were merged into

a single coherent csv ﬁle. Data value conﬂicts such as

different scales were removed during the extraction of

the data as all the data were extracted in British Units.

Later on, the data were split into a 70:30 ratio respec-

tively for training and testing datasets.

Finally, data transformation was performed. Ex-

cept for the AOD 550 nm data, all the integrated data

was normalized by decimal scaling to two decimal

points. Decimal scaling the AOD 550 nm impacted

heavily in the prediction model thus it was discarded

from decimal scaling. Similarly, in our proposed sys-

tem we will be implementing the aforementioned data

preprocessing procedure.

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4.3 Reporting

One of the most important parts of our proposed data-

driven air quality monitoring software system (AQM)

is to generate atmospheric maps or graphical reports.

The overall air quality and other pollutant data will be

calculated and displayed in the form of atmospheric

maps and charts which will help the decision-makers

to analyze air quality and to take initiatives for im-

proving the air quality. Our proposed AQM system

will generate atmospheric maps for divisions, indus-

trial areas, and transportation routes for both land and

marine which are presented below. Using real data,

we have generated multiple atmospheric maps and

charts. Our generated atmospheric map for all the di-

visions of Bangladesh is presented in Figure 4.

In Figure 4, air quality index (AQI) is shown for

all the divisions of Bangladesh such as Dhaka, Sylhet,

Chittagong, Barisal, Khulna, Rajshahi, and Rangpur.

All the divisions were color coded according to their

individual mean AQI. We have also generated a demo

atmospheric map for Tongi industrial area of Dhaka

City which is presented in Figure 5.

Figure 4: Overall Monitoring of Bangladesh.

The AQM system will also monitor the industrial

areas which will greatly help to reduce the pollution

in the environment. Figure 5 represents the Industrial

area monitoring. In Figure 5, R1 represents the cen-

ter of the industrial area where the level of pollution

is highest, R2 represents the minimum distance from

the center where all the sensor nodes are inside the

industrial area, R3 represents the maximum distance

from center where most of the sensor nodes are out-

side the industrial area, and R4 represents the +δ dis-

tance away from the center where all the sensor nodes

will be outside the industrial area. We have divided

the industrial area into circular areas and plan to place

three sensor nodes in every single circle which will be

used to read the AQI.

Figure 5: Industrial Area Monitoring.

Figure 6 depicts the mean AQI per station radius

for the industrial area which is shown in Figure 5. The

AQI is highest in the center R1 which means the con-

centration of pollution is highest in R1 and the AQI is

falling as distance increases, indicating that air quality

is improving.

Figure 6: Bar Chart of Mean AQI Per Station Radius.

Figure 7: Change of Rate of AQI Per Station Radius.

Figure 7 shows the rate of change in the AQI per

station radius for the industrial area depicted in Figure

5. The rate of change in the AQI increases negatively

as distance increases.

5 RESULT AND DISCUSSION

The purpose of making the IoT-based device is to

present a cheaper option and easily accessible to the

citizens of the country, so that they become alert of the

alarming situation which is not being addressed yet.

Air Quality Monitoring of Bangladesh (AQM): Data Driven Analysis System

209

Our IoT-based device detects many of the pollutants

like PM1.0, PM2.5, PM10, CO, CO

, NO

, Volatile

Organic Compounds(VOCs), and helps us keep track

of temperature and humidity.

However, in this experiment, the PM2.5 data were

separately monitored to determine the pollutant’s con-

centration level in our country for one year time

span. To observe the data, we incorporated multiple

PM2.5 detecting sensors by mounting them over the

local transports which travel only through that spe-

ciﬁc route throughout the day. The recorded data is

later uploaded to the cloud for each transportation

stop, also known as stations. In the following parts

of the discussion, the results are examined and user

interfaces have been developed for analyzing station-

wise, hourly, monthly, and season-wise data patterns.

Like every other experimental result, some outliers

have been distinguished into the data patterns which

we will be covering in the following research below.

Figure 8: Data Distribution of PM2.5.

We have identiﬁed the data patterns in PM2.5 con-

centration readings. Figure 8 shows the data patterns

in PM2.5 concentration readings found in the busy

transportation route. The data was taken for one year

continually round the clock. We may deduce from

the distribution of PM2.5 data that the data is posi-

tively skewed. The majority of the PM2.5 concentra-

tion data falls on the lower bound in this positively

skewed distribution, although a rise in PM2.5 con-

centration during the daytime leads the distribution to

skew positively.

In our proposed system user interfaces will be

available so that users can observe the data with re-

spect to different times. Figure 9 presents the user

interface for the Box Plot diagrams portraying the

PM2.5 concentration behaviour in Bangladesh. The

drop-downs depend on the time chosen by the user on

the day, night, hourly, or monthly basis and the users

are independent to choose the season of their choices

Figure 9: Box Plot User Interface.

Figure 10: Division-Wise Time Based User Interface.

such as Winter, Spring, Summer, Autumn, or All Sea-

son.

There are different divisions in Bangladesh and

different divisions have a different levels of PM2.5

concentration since the population, the number of

transports, and factories are different. A division wise

time-based user interface for PM2.5 concentration be-

haviour in Bangladesh has been presented in Figure

10. This user interface displays PM2.5 for eight di-

visions such as Dhaka, Khulna, Barishal, Chittagong,

Rangpur, Rajshahi, Sylhet, and Mymensingh over the

year. Since the PM2.5 data are being updated to

the cloud according to the station, a station-wise box

plot diagram demonstrated in Figure 11 has the most

upper extreme and upper quartile in the most busy

routes.

Figure 12 presents the hourly box plot diagram

against PM2.5 and it has come to a surprise that the

most generation of PM2.5 occurs at the earliest time

of the day, evening and night-time. In the morn-

ing time, the routes are mostly used by citizens, dur-

ing the evening working citizens take the road and at

night-time, the paths are mostly occupied by trucks

and pickup vans for carrying goods.

The monthly box-plot diagram of PM2.5 data

in Figure 13 explains that the maximum production

of PM2.5 occurs during January, February, March,

November and December since the winter and au-

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210

Figure 11: Box Plot of Station-Wise PM2.5 Data.

Figure 12: Box Plot of Hourly PM2.5 Data.

Figure 13: Box Plot of Monthly PM2.5 Data.

tumn seasons last within those months. The rest of the

months has less concentration of this pollutant due to

having the most amount of humidity and rain.

Figure 14 validates our point placed in Figure 13

where we justiﬁed how humidity and higher precip-

itation are responsible for less generation of PM2.5.

The lower wind speed and shallower boundary layer

height causes less substantial amount of PM2.5 as

stated in (Dhaka et al., 2020).

Figure 14: Box Plot of Season-Wise PM2.5 Data.

6 FUTURE WORK AND

CONCLUSION

Bangladesh is a developing country, and it requires a

massive amount of power, electricity, industrial usage

with an uprising amount of infrastructure and techno-

logical advancement in the process of the country’s

establishment. With modernization, pollution comes

in all kinds. Air pollution in Bangladesh is so high

that a system is needed to monitor and control it. Our

target is mostly to monitor the polluting areas like in-

dustrial zones, power plants and busy transportation

routes. We are not against industrialization and mod-

ernization; our goal is to keep air pollution within a

favourable amount.

The key concept highlighted in this research paper

is air quality monitoring and assurance. We have per-

formed an experiment for a year and our experimen-

tal results showed promising results. Therefore, we

believe our proposed data-driven air quality monitor-

ing software system (AQM) in this research work will

help to monitor and ensure air quality. The proposed

AQM system will monitor the air quality of different

divisions, industrial areas, and transportation routes

by showing atmospheric maps. We have presented

different types of atmospheric maps in this paper that

will be implemented in our proposed system.

We have collected air quality data for different ar-

eas of Bangladesh for one year period time from vari-

ous sources and later analyzed them. Our experimen-

tal results have been compared to IQAir AirVisual Pro

for different number of air quality indicators, includ-

ing temperature, humidity, PM1.0, PM2.5, PM10.0,

and CO

showed good results. Differences between

our obtained result and IQAir AirVisual Pro are very

low. In future, we will implement our proposed AQM

software system in full ﬂedged based on our current

experiment. This AQM system will help to monitor

and ensure the air quality of Bangladesh as well as

Air Quality Monitoring of Bangladesh (AQM): Data Driven Analysis System

211

decision-makers to take effective initiatives for im-

proving and ensuring the air quality.

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