Real Life Pollution Measurement of Cairo

Youssef Khalil

, Mariam Zaky, Mostafa ElHayani

and Hassan Soubra

Media Engineering and Technology, German University in Cairo, Egypt

Keywords:

ITS, AQI, Pollution, IoT, Machine Learning.

Abstract:

Today, the cost associated to the signiﬁcant growth in the transportation ﬁeld is air and noise pollution. Ac-

cording to the World Health Organization (WHO), an estimate of seven million people worldwide die every

year due to breathing bad quality air, in addition, morbidities such as high blood pressure, heart disease, sleep

disturbances and stress might be linked to noise pollution. In this context, many researchers have done efforts

in measuring air and noise pollution to be fully aware of the areas that have a negative impact on human health.

In this paper an intelligent transportation system is proposed which uses a low-cost sensor device and a mobile

application to monitor air pollution and noise pollution in Cairo, Egypt successively.

1 INTRODUCTION

The increase of vehicles with the existence of other

factors that cause a signiﬁcant increase in the pol-

lution level has become a serious issue. Due to

the deﬁcient urban planning of the city in the past,

homes, universities, schools, ofﬁces, hospitals, and

other community buildings were routinely built close

to the main roads to be easily accessed without giv-

ing attention to whether this zone is a safe or an un-

safe area for these sites. With the wide use of the

internet of things (IoT) technology nowadays, intelli-

gent transportation system (ITS) is developed to make

positive changes in the transportation system by pro-

viding the user a safer and healthier environment. In

our project, we are examining the potential of ITS

towards the pollution issue by pointing out the mea-

surement of noise and air pollution to inform the user

about the pollution levels in any chosen area. Our

aim is to develop a way to monitor a real-time air and

noise pollution measurement using low cost sensors

in order to detect the areas that have the most nega-

tive impact on human health, and to generate a data-

set documentation for each pollution type. An IoT

device is developed consisting of an MQ135 sensor,

which is responsible for measuring the air quality, and

a GP2Y1010AU0F dust sensor, to measure the PM10

in the air.

A mobile application is developed to record sound

levels using the built-in microphone sensor and calcu-

https://orcid.org/0000-0002-8151-7006

https://orcid.org/0000-0002-3679-2076

late noise pollution in decibels (dB). The application

accesses the location to spread awareness for the in-

dividuals. Based on the results that are collected and

using machine learning, a predictor model was used

to predict the level of noise and air pollution whether

it was in general or speciﬁc areas and chosen time-

frame. This project is a part of a bigger project which

aims to reduce trafﬁc air and noise pollution inside ur-

ban cities. To fulﬁll this, pollution produced by cars

as well as the desired city pollution levels are mea-

sured, and accordingly cars are being routed inside

the city; If either the city’s noise or air pollution level

is more that the set threshold, or if the car itself is pol-

luting enough to make the pollution level exceeds the

set threshold, so this particular car will be routed in a

way to minimize its traveling distance inside the city

(Zaky and Soubra, 2021).

2 LITERATURE REVIEW

Air and noise pollution measurement is considered a

serious concern covered by several studies, in this sec-

tion previous studies done in measuring air and noise

pollution will be discussed. (Gryech et al., 2020) in

Morocco have used an approach to identify areas with

poor air quality. PM10 concentrations exceeded the

limit only in the presence of some vehicles otherwise

PM2.5 remained low and stable. The results of this

paper show how efﬁcient their strategy to identify ar-

eas with low air quality. In (Chiang et al., 2020), They

carried a device for monitoring air quality on a mo-

torcycle and moved through a chosen route inside the

222

Khalil, Y., Zaky, M., ElHayani, M. and Soubra, H.

Real Life Pollution Measurement of Cairo.

DOI: 10.5220/0010896000003117

In Proceedings of the 11th International Conference on Operations Research and Enterprise Systems (ICORES 2022), pages 222-230

ISBN: 978-989-758-548-7; ISSN: 2184-4372

city on six main roads, the results shows that their pro-

posed system can effectively monitor PM2.5 concen-

trations while moving. In Zagreb, (Marjanovi

c et al.,

2017) conducted a study using the carry of air mon-

itor wearable devices following a predeﬁned route.

The predeﬁned route was chosen to include 2 major

roads with heavy trafﬁc, a park, a residential area, and

a business area. it was concluded that air pollution

is strongly depending on the trafﬁc exposure. Some

studies have done the experiment on one road to mea-

sure and evaluate the pollution produced by trafﬁc.

In (Moutinho et al., 2020), measurements were taken

along a highway in Atlanta.The results obtained were

compared to measurements that were taken in an ur-

ban area around Atlanta, observing that the concen-

trations of CO and NO2 in the highway were 35%

and 57% higher than the urban background concentra-

tions, respectively. In Pakistan, (Aamer et al., 2018)

conducted a data-set containing around 25000 sam-

ples during a month in a road segment between 2

cities. It was observed that humidity and temper-

ature are negatively correlated against NO2 and in-

herently affect the NO2 balance in air. Other stud-

ies have taken their measurements from ﬁxed loca-

tions either on long or short term. (Gunawan et al.,

2018) developed a portable device placed in three dif-

ferent locations, a hostel, a university, and a roadside.

The obtained results were compared to a dataset gen-

erated by air quality monitoring station at the same

time of the experiment, showing that different places

can have different AQI value even though they are

nearby to each other. (Spandana and Shanmughasun-

dram, 2018) took place in Amrita University, India, to

determine the pollution level inside the campus and

also in a metro city (Bengaluru). The results indi-

cated that the university atmosphere is less polluted

than Bengaluru. (Duangsuwan et al., 2018) measured

the AQI in just 2 points (Bangkok Yai district, and

Pathumwan district, Bangkok.) from Oct. 7 to Oct.

13, 2017. The results showed that the AQI level has

not exceeded 100, making it a safe zone for people.

An Android-based application was implemented by

(Ghosh et al., 2019) that uses the built-in microphone

sensor to capture ambient noise levels and the GPS

sensor for identifying the location, and for validation

they compared the data collected by the application

with sound level meter (Meco-970P 3). The outcome

of this test was illustrated, and the variation of the

application results compared to the sound meter was

in the range of ±3 dB, which clarify the efﬁcient of

the mobiles’ microphone sensors for detecting noise

pollution. (Marjanovi

c et al., 2017) proposed a real-

time system to monitor air and noise pollution. They

implemented a mobile application which uses mobile

phone’s microphone to collect noise data and con-

verts the recorded sound pressure to dB and shows

the equivalent sound pressure level for each second in

dB. The results showed that the average noise level

is higher 3dB at the rush hours due to the increas-

ing numbers of vehicles. AQI is the unit or the way

of communication between the institutes responsible

for calculating air pollution level and the public. AQI

is targeting many gasses and sources of air pollu-

tion like (Carbon Monoxide, Lead, Nitrogen Oxides,

Ozone, Particulate matter including PM2.5 and PM10

and Sulfur dioxide). Air quality standards were set

by the US Environmental Protection Agency (EPA)

and were divided into two parts (epa, ); Primary stan-

dards, which were set for the public health protection,

as protecting the health of “sensitive” groups such as

asthmatics, children, and the elderly people, and the

secondary standards, which provide protection to the

public welfare, this includes preventing reduced vis-

ibility and damage to animals, crops, vegetation, and

buildings. Parts per million by volume (PPM), parts

per billion by volume (PPB), and micro-grams per cu-

bic meter of air µg/m3 are the units of AQI measure-

ment. For noise levels, the WHO set guideline val-

ues based on speciﬁc environment and large health

impacts (Berglund et al., 2000). The guideline val-

ues are presented taking into consideration all harmful

health effects identiﬁed in a particular environment.

The negative effects of noise exposure refer to tempo-

rary or long-term impairment of physical, psycholog-

ical, or social functioning. Decibels (dB) is the unit of

measuring sound or noise level. No study was found

to monitor air or noise pollution in Cairo, Egypt. All

the devices located in Cairo are stationary with a high

cost and only one that is a real-time device, but it only

monitors and measures Pm2.5 concentration in the air

from Katameya Heights in New Cairo city(cit, ). Only

one study took place in Egypt for monitoring noise

pollution, particularly in Alexandria in 2009, (Ghat-

tas, 2009). The literature has proven that no data was

collected in a city-scale. It’s either collected indoors

or in speciﬁcally selected regions inside the cities. In

this paper, we aim to generate a dataset for both air

and noise pollution, as it will be the ﬁrst dataset doc-

umentation for these types of pollution inside Cairo.

Our approach will be as follows; regarding air pol-

lution measurement we are measuring the concentra-

tion of Carbon Dioxide and Nitrogen Oxide as they

are the common gasses that are emitted from vehicles

on the road. In addition of the concentration of PM10

in air, this will be done inside New Cairo City in 2

locations. For noise pollution measurement, a mobile

application was developed and distributed among the

public. The application will have access to the mo-

Real Life Pollution Measurement of Cairo

223

bile microphone sensor and the GPS sensor. It will

help the user to be informed by the amount of noise

pollution in their area by calculating a real-time noise

intensity value. A noise pollution dataset will be col-

lected from selected main roads and a comparison of

the values in peak-hours and off-hours as well as on

working days and weekends will be made.

3 METHODOLOGY

The data is received by NodeMCU which is pro-

grammed to transfer the data and send it to ThingS-

peak using its built-in Wiﬁ Chip. Every data received

consists of 7 ﬁelds; a unique ID to the record, the time

and the date of the reading, the location, current AQI

value, temperature, humidity, and the PM10 value.

Arduino MEGA is added to the device connecting

to it the 3 sensors, and serial communication is es-

tablished between the Arduino Mega and nodeMCU.

The user should press on button ”Start” to begin the

recording, once the user clicks it, the application mea-

sures the noise level in dB and starts recording for

10 seconds. Unless the user clicks the stop button,

the application will keep saving data in Firebase ev-

ery 1 minute. If the dB value exceeded 80dB the

record is saved in Firebase. The data collected con-

sists of sound level in decibels, the date of the read-

ing, and the phone’s location. Figure 1 shows screen-

shots from the application before and after the record-

ing starts.

(a) Before clicking Start (b) During recording

Figure 1: Screenshot of the Mobile Application.

Regarding the air pollution monitoring, complica-

tions were found with being able to station the device

for days, which made it difﬁcult to measure the air

quality all over Cairo. Hence, we down-scaled the

region of measuring air pollution to be done in New

Cairo city, It is one of the cities that were built in re-

cent years, it lies east of Cairo and has 3 main areas;

the ﬁrst, third, and ﬁfth settlements. The city has a

good transportation network of buses and micro-buses

that makes it easy to be reached from any other city in

Cairo. The most famous and most populated district

in New Cairo is the 5

district, 90

Road is consid-

ered as the main entrance to the 5

district. Heavy

trafﬁc can be observed on the 90

road and it is one

of its major issues due to the huge amount of services

located around it.

Figure 2: 90

Road on the map.

The device has been stationed in 2 locations

around the 90

St. in New Cairo as shown in Fig-

ure 3, the ﬁrst location is a building that is located ap-

proximately 250 meters far away from the 90

street.

The device is set to be 8 meters above the ground and

placed in a terrace of the building facing the street.

The other location is about 100 meters away from the

street. The device also placed in the terrace of the

building as well at height 4 meters above the street.

Figure 3: Both locations on the map.

3.1 Noise Pollution

The application is distributed among volunteers to

measure noise levels all over Cairo to help in obtain-

ing the most number of records and to be able to mea-

sure noise pollution in many areas. Besides that, 3

main streets were chosen in different regions in Cairo

that are popular of being crowded in the peak hours.

ICORES 2022 - 11th International Conference on Operations Research and Enterprise Systems

224

The 3 streets that have been chosen are 90

Street

in New Cairo, Abbas Al-Akkad in Nasr city, and 9

street in Al-Mokkatam. Al-Mokkatam district is a fa-

mous neighborhood in Cairo. 9

Street is the most

famous and exclusive street in Al-Mokkatam city, as

it is the main and the only entrance for this area and

goes through the neighborhood.

Figure 4: 9

street on Google maps.

Nasr City is another one of the famous districts in

Cairo. It’s also known for various landmarks and is

mostly crowded. Abbas Al Akkad street, shown in

Figure 5, is considered as the backbone of Nasr City,

it’s one of busiest shopping streets in Cairo.

Figure 5: Abbas Al Akkad street on Google maps.

4 RESULTS

Air Pollution. Air pollution measurement has been

done in 2 locations in exact locations ”Building 130,

St. No.54, First New Cairo, Cairo Governorate” and

”Building 241, St. No.55, First New Cairo, Cairo

Governorate” gathering around 3307 samples from

both locations. The data-set obtained contains 6

ﬁelds, AQI value, Pm10 concentration, Temperature,

humidity , the ID of the location (Latitude , Longi-

tude) that generated automatically by MongoDB, and

the date of the record. All the data represented in this

section is just a subset of the full dataset. The experi-

mental setup of the project is as follows.

Computing Unit. A NodeMCU ESP8266 module is

used along with its ESP-12E module containing an

ESP8266 chip having Tensilica Xtensa 32-bit LX106

microprocessor. The data and programs are stored

in 4MB of Flash memory. We also used an Arduino

Mega 2560 board.

Sensors Unit. The sensors used for data acquisition

are as follows:

1. MQ135: used in measuring AQI and to detect and

measure the concentrates of NH3, NOx, Alcohol,

Benzene, Smoke, CO2 in air.

2. DHT11: it is an ultra-low-cost digital temperature

and humidity sensor.

3. GP2Y1010AU0F: especially effective in detect-

ing very ﬁne particles like cigarette smoke and

dust, and is commonly used in air puriﬁer sys-

tems.

First Location. As this location is in front of a fa-

mous PlayStation & Cafe and a big mall, the week-

end days are the peak days in which the cafe receives

the most number of customers, and the peak hours

of these days would be at night; the time that the

cafe is in full capacity, and the off hours would be

at any other time in the day. However, in the work-

ing days peak hours would be in the morning and af-

ternoon and the off-hours would be at night as regu-

lar, as the cafe and the mall are almost empty in the

working days. At this location, samples were taken

within a period of 8 days from 09 − 07 − 2021 to

16 − 07 − 2021. A total of 1784 samples were taken

at different times in all the days monitoring the AQI

in PPM, temperature in Celsius, humidity as a per-

centage and the concentration of PM

in µg/m

. We

discuss the results for the following 3 scenarios:

• A scenario is represented within the peak hours

on the weekends which is consider the worst-case

scenarios.

• A scenario within an off-hour in a weekend.

• An exceptional case occurred during a working

day due to being the same day as the EURO 2020

ﬁnal game that played on 11 − 07 −2021.

Results

• Peak hour on a weekend.

Figure 6 shows 12 out of 56 total readings taken

for both PM

and AQI on a weekend. A max-

imum AQI value 214.096 PPM and minimum

value 33.00 PPM, and a maximum concentration

of PM

159.27 µg/m

and minimum 23.97 µg/m

were measured.

Real Life Pollution Measurement of Cairo

225

Table 1: First location Air pollution measurement Table.

Air Pollution measurements

Scenario Time Average

AQI

value

Average

PM10

Figure

Peak

hour

in the

week-

end

08:47PM

09:50PM

136.88

PPM

70.07

µg/m

Peak

hour

in the

week-

end

10:20PM

11:20PM

145.19

PPM

44.91

µg/m

Figure 6: Graph of Air pollution measurement on 09-07-

2021.

• Off-hour on a weekend.

Figure 7 shows 10 out of total 45 readings of PM

and AQI taken on an off-hour on a weekend. A

maximum AQI value 171.568 PPM and minimum

value 45 PPM, and a maximum concentration of

147.65 µg/m

and minimum 23.97 µg/m

were found.

Figure 8 shows 25 readings out of total 218 sam-

ples of both PM

and AQI taken within 4 hours

at the same time as the EURO 2020 ﬁnal game

between England and Italy and as stated above,

the device is located in front of a famous cafe,

which lead to a trafﬁc increase in front of the

building. The game started at 9:00 PM (GMT+2),

extra 45 minutes were added; so the whole game

took around 3 hours. So the 4-hour period of the

graph can be divided into 3 parts. First part dur-

ing the game (10:40 PM - 12:00 AM), and after

the game (12:00 AM - 1:00 AM) in which peo-

ple started leaving, last part after the ceremony

(01:00 AM - 2:40 AM) in which the rest of the

people left. During the game, 68 samples were

taken with an average AQI equals to 217.898 PPM

with maximum value 299.775 PPM and minimum

value 153.724 PPM, and an average concentra-

Figure 7: Graph of Air pollution measurement on 10-07-

2021.

Figure 8: Graph of Air pollution measurement on 11-07-

2021 / 12-07-2021.

tion of PM

equals to 85.888 µg/m

with max-

imum value 113.62 µg/m

and minimum value

62.16 µg/m

. In time right after the game ﬁn-

ished and during the ceremony, 54 samples were

taken with an average AQI equals to 153.91 PPM

with maximum value 229.328 PPM and mini-

mum value 53.86 PPM, and an average concentra-

tion of PM

equals to 83.1559 µg/m

with max-

imum value 115.28 µg/m

and minimum value

53.86 µg/m

, in the last part, 78 samples were

taken with an average AQI equals to 51.157 PPM

with maximum value 96.34 PPM and minimum

value 18.005 PPM, and an average concentration

of PM

equals to 82.143 µg/m

with maximum

value 126.07 µg/m

and minimum value 48.87

µg/m

Second Location. Being a regular location with no

popular places around, peak hours will be as usual in

which trafﬁc exists; from 7:00 am to 10:00 am and

from 3:00 PM to 6:00 PM as people going or coming

from their daily destinations (work,universities,etc.)

in the working days from Sunday to Thursday, as Fri-

days and Saturdays are the weekend in Egypt. Sam-

ples were taken within a period of 5 days from 25-

07-2021 to 29-07-2021, in the meanwhile a total of

1523 samples were taken at different times in all the

days monitoring the AQI in PPM, temperature in Cel-

sius, humidity as a percentage and the concentration

ICORES 2022 - 11th International Conference on Operations Research and Enterprise Systems

226

of PM

in µg/m

. Below we are representing 3 sce-

narios in a working day:

• After midnight of a working day.

• Within a peak hour of the same working day.

• Within an off hour of the same working day.

Air Pollution measurements

Scenario Time Average

AQI

value

Average

Pm10

Con-

centra-

tion

Graph

After

midnight

(working

day)

12:00AM

01:00AM

62.79

PPM

120.83

µg/m

Fig

Peak

hour

(same

working

day)

09:49AM

10:59AM

142.26

PPM

148.26

µg/m

Fig

Off hour

(same

working

day)

07:00PM

08:00PM

101.71

PPM

127.979

µg/m

Fig

• After midnight of a working day.

Figure 9: Graph of Air pollution measurement on 26-07-

2021.

In ﬁgure 9, 12 out of total 48 samples are shown of

both PM

and AQI during midnight of a working

day. A maximum AQI value 169.672 PPM and

minimum value 20.86 PPM were measured, and

a maximum PM

value 148.48 µg/m

and mini-

mum value 63.82 µg/m

were found.

• Peak hour of a working day.

Figure 10 shows 12 out of 57 total samples of

both PM

and AQI measured during a working

day. 231.728 PPM and 33.7 PPM were the maxi-

mum and minimum values found for the AQI. For

Figure 10: Graph of Air pollution measurement on 26-07-

2021.

the PM

concentration. 181.69 µg/m

and 87.89

µg/m

were its maximum and minimum values.

• Off hour of a working day.

Figure 11: Graph of Air pollution measurement on 26-07-

2021.

Figure 11 shows 13 out of 50 total samples taken

during a working day evening. A maximum AQI

value equals to 193.289 PPM and minimum value

equals to 26.731 PPM were found, and maximum

concentration 156.78 µg/m

and minimum

66.31 µg/m

were found.

4.1 Noise Pollution

Noise Pollution measurement has been done by 2

methods, getting readings from particular main streets

in different areas in Cairo, each street of them has its

own features that make it differs from the others as

stated in section 3.1 taking into consideration to take

readings in the peak and off-hours in each street, the

second method is done by distributing on the public to

get readings in random places with different time in-

side Cairo city. All the readings are combined in one

data-set consists of 5 ﬁelds; location address name,

location coordinates, DB value, the date and time of

the sample, Running on ( the device in which the ap-

plication is running on). Below we are introducing a

heat map for each street of the following streets (90

St, Abbas Al-Akkad St.) in addition to a heat map for

all the readings that are taken inside Cairo. All the

Real Life Pollution Measurement of Cairo

227

data represented in this section is just a sub data from

the full data set.

Figure 12: Heat map for 90

St.

As the 90

road is a very busy road almost all the

time of the day, so comparing the peak hours with the

off hours will be in this case not convenient enough

to show the difference between them, deﬁnitely, there

will be a difference between the average dBs taken

under a certain period (1 hour for example) in the

peak hours comparing with the average dBs taken un-

der the same period in the off-hours, however, it will

be a slight difference that will not show the huge ef-

fect of vehicles on raising the noise level, however, by

representing the heat map shown in Figure 12, we can

observe that as we got closer to the 90

street the plots

are getting more yellowish as the most yellowish ones

that indicate higher noise levels are located on the 90

street and as we go further the plots are heading to be

more blueish as the noise levels decrease.

Figure 13: Heat map for Abbas Al-Akkad St.

The heat map of Abbas Al-Akkad street as shown

in Figure 13 clarify more how main roads can raise the

noise level due to the trafﬁc on them, we can observe

that all the yellow plots are located on the street it-

self showing the increase in noise level there, and the

same as 90

street, as we go further from the street

the noise level decreases as the plots trends to be more

blueish.

Figure 14 represents all the readings were taken

either by taking it in particular streets or by distribut-

ing the app to take readings from random locations in

Cairo, a total of 558 samples were taken inside Cairo,

divided into 106 samples on the 90

street, 71 sam-

ples on Abbas Al-Akkad street, 77 samples on 90

street, and 304 samples were taken in random loca-

tions at a different time inside Cairo.

Figure 14: Noise Pollution readings in Cairo.

Predictor Model. The predictor model is a created

model to predict whether pollution levels are more

than the required threshold or not. For such, a ba-

sic model with two fully connected hidden layers is

implemented. For both networks a train-test-split of

0.2 is used, with Adam optimizer and trained for 100

epochs. The results of the models is as follows. The

air pollution network takes as input a feature vector of

Time, Date, Temperature, and Humidity, and classi-

ﬁes the pollution in three categories (good, moderate

or unhealthy). The model achieved 69% accuracy and

an crossentropy loss of 0.7058 on the test set.

Figure 15: Graph of model accuracy and the validation ac-

curacy against Epoch.

Figure 16: Graph of model loss and the validation loss

against Epoch.

The noise pollution network takes in a location

and time as input and predicts whether the db value

would exceed 80dB or not. The model achieved an

accuracy of 87% with a crossentropy loss of 0.2782.

ICORES 2022 - 11th International Conference on Operations Research and Enterprise Systems

228

Figure 17: Graph of model accuracy and the validation ac-

curacy against Epoch.

Figure 18: Graph of model loss and the validation loss

against Epoch.

5 CONCLUSION

In conclusion based on the above results, regarding

the air pollution measurement we could gather a total

samples of 3307 samples in 2 different locations in-

side New Cairo district. Air pollution monitoring was

done by measuring the concentration of PM

in the

air using GP2Y1010AU0F dust sensor and measuring

the AQI using MQ135 sensor. The results obtained by

the air pollution measurement shows how the AQI and

behave within the peak and off hours, as shown

in the EURO ﬁnal game example. Also, it shows how

the PM

concentration was relatively high in the sec-

ond location as it is located in front of an empty land

full of dust and smog. Regarding the noise pollution

measurement we have introduced a mobile applica-

tion that measures a real time noise level in dB, we

have concluded from the results that as we get closer

to main roads the noise level increases due to the ve-

hicles passing through the main roads as they are a

main source of noise pollution. The monitoring of ac-

cumulated data in the cloud storage helps to analyze

the various patterns in the environmental parameters

and accordingly implementing a predictor model us-

ing machine learning to be able to early notifying the

public about the changes in the pollution in different

areas.

Limitations: Setting up the air pollution measure-

ment device in main roads is not easy in Egypt, fac-

ing this limitation lead to setting the device up inside

balconies of buildings near the main road. For the

noise pollution measurement, the application doesn’t

get readings above 90.30dB which is a very high value

that we can barely reach.

Future Work: It is planned to locate the air pol-

lution measurement device in more locations inside

Cairo, with the addition of measuring PM

2.5

for more

efﬁciency. More data of noise levels is planned to

be taken as well, for more accuracy to the predictor

model.

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