IOT Based Environmental Pollution Monitoring System

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 06 | June-2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1823
IOT based environmental pollution monitoring system
A.N. Chaudhari1, Prof.G.A. Kulkarni2
1Student of M.E, Dept. of E&TC Engineering, S.S.G.B College of Engineering, Bhusawal, Maharashtra, India
2 Prof.G.A. Kulkarni, Dept. of E&TC Engineering, S.S.G.B College of Engineering, Bhusawal, Maharashtra, India
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Abstract - An extremely growth in an industrial and
infrastructural frameworks creating environmental affairs
like atmospheric changes, malfunctioning and pollution.
Pollution is becoming serious issue so there is need to build
such a flourishing system which overcomes the problems
and monitor the parameters that affecting the
environmental pollution. The solution includes the
technology Internet of Things (IOT) which is a hook up of
computer science and electronics. It can provide means to
monitor the quality of environmental parameters like Air,
Noise, Temperature, Humidity and light. To monitor
pollution levels in industrial environment or particular area
of interest, wireless embedded computing system is
proposed. The system is using a prototype implementation
consists of sensing devices, Arduino uno board, ESP8266 as
wi-fi module. These sensing devices are interfacing with
wireless embedded computing system to monitor the
fluctuations of parameters levels from their normal levels.
The aim is to build powerful system to monitor
environmental parameters.
Key Words: Internet of Things (IOT), Arduino Uno
board, wi-fi module ESP8266, MQ-7 gas sensor, M213
noise sensor, LM35 temperature sensor, SY-HS220
humidity sensor, LDR light sensor.
1. INTRODUCTION
As we know the industrial growth drastically
increasing, environmental pollution related issues rapidly
comes into existence [1]. To fulfil the need of flourishing
monitoring system, in our project we are establishing a
network called Internet of Things, in which sensing
devices are connected with wireless embedded computing
system. Internet of Things is a technology that hook up the
sensors with embedded system and allow the data from
these sensors to travel over an Internet. We are
implementing developing model which is able to monitors
the inconstancy of parameter like Air, Noise, Temperature,
Humidity and Light.
In the proposed model we use microcontroller
ATMEGA328 that is mounted on Arduino Uno board. We
are using 5 sensors, MQ-7 as a gas sensor. We are using 5
sensors, MQ-7 as a gas sensor. It detect the concentration
of carbon monoxide in air. To measure the fluctuations in
noise levels we use M213 high sensitivity microphone
sensor module. LM35 is used as a temperature sensor and
SY-HS220 as humidity sensor. To measure the intensity of
light LDR sensor is used. To transfer the data
Over an Internet we are using flexible wi-fi sensor
ESP8266. The data from these sensors is stored in the
cloud. After processing, through hotspot web browser will
ask about IP address, by putting IP address web page will
create that allows us to monitor the system [4]. We can
monitor the parameters on smartphones as well as pc or
laptop.
1.1 History of IOT Based Monitoring System
IOT is newly developed technology in which the
connectivity between physical objects along with
controllers, actuators and sensors synchronized over an
Internet. IOT able to provide means to monitor the quality
of parameters like Air, Noise, Temperature, Humidity and
Light [2]. It helps concern authorities to take action
against pollution crossing beyond defined level.
Objective of the Project
The main objective of the project is to provide a
platform that monitors the parameters and help to create
better and pollution free future life.
1.2 Literature Survey of Existing System
In ‘Smart Environment Monitoring System by
employing Wireless Sensor Networks on Vehicles for
Pollution Free Smart Cities’, Mr. Jamil invented ZigBee
based wireless sensor networks to monitor physical and
environmental conditions [3]. The sensor nodes directly
communicated with the moving nodes. Which avoided the
use of complex routing algorithm but local Computations
are very minimal.
Drawback of existing system
 Required more time and space to operate
 Complicated designing
 Maintenance is difficult

Fig -1: Model of Existing System
Problem Statement
Due to miscellaneous interactions, limited protocol
standardization, security of data storage and complex
identification systems to access data, problems arises in
field of monitoring hence to overcome these problems we
are designing, ‘ IOT based environmental pollution
monitoring system’, to gain pollution free future live.
Change to be implemented
 Devices must be easily integrated with IOT platform
 Uniform data format across multiple platforms
 Platform must be expandable and Fine-grained data
visibility model
2. PROPOSED SYSTEM SPECIFICATIONS
Proposed system specifications include hardware
requirements and software requirements.
2.1 Hardware Requirements
1. Arduino Uno board
Fig -2: Arduino Uno board
Arduino UNO is a microcontroller board based on
ATmega328. It has 14 digital input/output pins of which 6
can be used as PWM output, 6 analog inputs. Arduino uno
can be programmed with Arduino software Arduino IDE
(integrated development environment). The Atmel 8-
bitAVRRISC-based microcontroller combines 32 kB ISP
flash memory with read-while-write capabilities, 1 kB
EEPROM, 2 kB SRAM, 23 general purpose I/O lines, 32
general purpose working registers, three flexible
timer/counters with compare modes, internal and
external interrupts, serial programmable USART.
2. ESP8266 W-fi module
Fig -3: ESP8266 wi-fi module
ESP8266 is an UART to Wi-Fi module, a really cheap
and easy way to connect any small microcontroller
platform having network connectivity is good for any
computing system. And add to a system utility we can
fetch any data from www. We can push data to cloud for
storage, computation or monitoring. We need an external
hardware that convert wi-fi data into data format that
understood by common microcontroller like UAT, SPI, and
I2C
3. MQ-7 Gas Sensor
Fig -4: MQ-7 Gas Sensor
This is a simple-to-use Carbon Monoxide (CO) sensor,
suitable for sensing CO concentrations in the air. The MQ-7
can detect CO-gas concentrations anywhere from 20 to
2000ppm. This sensor has a high sensitivity having
heating time 60 seconds. The sensor’s output is an analog
resistance. The drive circuit is very simple operating at 5V.
Operates at temperature- -20℃-50℃.

4. M213 Noise Sensor
Fig -5: M213 Noise Sensor
The sound sensor module detects sound and its
intensity. It uses a microphone which supplies the input to
an amplifier, peak detector and buffer. When the sensor
detects a sound, it processes an output signal voltage to a
microcontroller. Normal voice sound level-19 to 60 dB.
Operating at 3.3V-5V.
5. LM35 Temperature Sensor
Fig -6: LM35 Temperature Sensor
The LM35 series are precision integrated-circuit
temperature sensors, whose output voltage is linearly
proportional to the Celsius (Centigrade) temperature. It
operates at 4Vto 30V. It has low impedance 0.1W for 1mA
Load. It has Linear + 10.0 mV/°C scale factor.
6. SY-HS220 Humidity Sensor
This sensor module converts relative humidity (30-
90%RH) to voltage and can be used in weather monitoring
application. Operates at DC 5V. Output humidity-1.98V (at
25 degree and 65RH).
Fig -7: SY-HS220 Humidity Sensor
7. LDR Light Sensor
LDR sensor module is used to detect the intensity of
light. It is associated with both analog output pin and
digital output pin labeled as AO and DO respectively on the
board. When there is light, the resistance of LDR will
become low according to the intensity of light. The greater
the intensity of light, the lower the resistance of LDR [5].
The sensor has a potentiometer knob that can be adjusted
to change the sensitivity of LDR towards light. LDR’s are
less sensitive than photo diodes. It operates at DC 3.3V to
5V.
Fig -8: LDR Light Sensor
2.2 Software Requirements
The microcontrollers are typically programmed using a
features from the programming languages C and C++. To
install the Arduino software on windows following steps
are useful [5].
Step 1- Download the Arduino software from google.
Step 2- Install the software. Plug in your board and wait
for Windows to begin its driver installation process.
Step 3- Open the control panel and open the system
device manager.
Step 4- Connect Arduino Uno board to system through
USB cable. After connecting select board and COM port in
Arduino IDE.
Step 5- Develop an Arduino Code for sensors to cloud
system in Arduino IDE, compile and upload the code in
Arduino Uno board.

Fig -9: Arduino Software
3. PROPOSED SYSTEM DESIGN
Fig -10: Model for Proposed System
From the above model, process is divided in 5 layers.
The environmental parameters which are to be measured
are introduced in layer 1. Study of the characteristics and
features of sensor devices is in layer 2. In layer 3, there is
decision making on sensing, measuring and fixing the
threshold value, periodicity of sensitivity, timing, space
and LED. Sensor data acquisition is done in layer 4. And
layer 5 as ambient intelligence environment. The sensors
can be operated by the microcontroller to retrieve the data
from them and it processes the analysis with the sensor
data and updates it to the Internet through Wi-Fi module
connected to it. User can monitor the parameters on their
smartphones as well as pc or laptop.
3.1 Block Diagram
Transmitter Section
In the transmitter section, to monitor the parameters,
we mount 5 sensors like MQ-7, M213, LM35, SY-HS220
and LDR to sense these parameters. The data from these
sensors integrated with microcontroller ATMEGA328
which is mounted on Arduino Uno board operates at 5V.
To allow the data to travel over an Internet we are
connecting flexible wi-fi module ESP8266. It works at
3.3V.
Receiver Section
In the receiver section, hotspot is to be activated on
user’s smartphone or pc to access web browser. An IP
address is to be entered in web browser to access related
web page which will show the monitoring results on user’s
smartphone screen.
Fig -11: Block Diagram for proposed System
3.2 Flow of the System
After starting the system, we have to connect all the
sensors to the microcontroller so that sensors get
interfaced with microcontroller. Then process the data
using microcontroller and embedded ‘c’ in Arduino uno.
Send AT commands to wi-fi module 8266. Microcontroller
starts processing data over an Internet. After processing,
the embedded ‘c’ is uploaded in Arduino.
Using hotspot, user can access internet browser on
their smartphones or laptops. Web browser needs specific
IP address. By putting IP address on browser, web page is
displayed. Web page shows the monitoring results of the
respected parameters.

Fig -12: Flow Chart of the Proposed System
4. COMPUTATIONAL ANALYSIS
1. MQ-7 Gas Sensor
The concentration level of carbon monoxide present
in environment is measured in units ‘parts per million
(ppm)’ and %. The conversion is shown below.
Table-1: Carbon Monoxide source concentration
Level of Carbon Monoxide Source
0.1 ppm Natural atmosphere
level
0.5 to 5 ppm Average level in homes
5 to 15 ppm Near properly adjusted
gas stove in homes
100 to 200 ppm Exhaust from
automobiles in the city
5000 ppm Exhaust from a home
wood fire
Conversion factors -
1 ppm = 1.145 mg/m3
1 mg/m3 = 0.873 ppm
1% = 1/100
1ppm = 1/1000000
1ppm = 0.0001%
Table-2: Conversion from ppm to percentage
ppm Percent (%)
0 ppm 0%
5 ppm 0.0005%
50 ppm 0.005%
500 ppm 0.05%
1000 ppm 0.1%
2. M213 Noise Sensor
Intensity of sound level is also known as sound
pressure level (SPL). It is measured in W/m2 as well as in
decibels (dB). Threshold intensity is the sound level at
threshold of hearing. Threshold of hearing is I0 = 10−12
W/m2. To calculate the intensity level in decibels, find the
ratio of the intensity of sound to the threshold intensity.
Multiply the logarithm of the ratio by 10. The resulting
equation is,
β= 10log (I/ I0)
Conversion factor -
Suppose the intensity of noise is 10−7 W/m2 then to
convert it in dB as follows,
10−7 W/m2/10−12 W/m2 =105
Log105 =5
Multiply the ratio by 10 i.e. 50 dB. Now in reverse manner,
to calculate noise intensity from decibel level. Suppose
100 dB,
Divide the decibel level by 10 i.e. 100/10= 10
Use that value as the exponent of the ratio= 1010
I/10−12 W/m2 =1010
We get result, intensity I= 10−2 W/m2
Table-3: Standard for Noise values
Night (10pm-
7am) Unit in
decibels
Day (7am-
10pm) Unit in
decibels
Type of region
45 55 Residential
40 60 Residential-
commercial

55 65 Commercial
60 70 Residential-
Industrial
65 75 Industrial
3. LM35 Temperature Sensor
The LM35 temperature sensor provides an output of
10mV per degree Celsius, with an accuracy of 0.5°C at25°C.
It can be powered by any DC voltage in the range 4V-30V.
The operating range is –55°C to +150°c.
Table-4: Conversion of Output in mV per degree Celsius
Temperature in °c Output voltage in mV
5 50
10 100
20 200
50 500
100 10000
Suppose at 20°C,
We will get 20 x 0.01 = 200 mV, or 0.2 volts.
4. SY-HS220 Humidity Sensor
Relative Humidity = (density of water vapor / density of
water vapor at saturation) x 100%
If actual vapor density =10 g/m3, at 20°C. Saturation vapor
density =17.3 g/m3, then the relative humidity is,
RH= (10 g/m3/17.3 g/m3) x 100% =57.8%
Put the sensor in water we get its maximum raw ADC
value, suppose we use 10bit ADC then raw ADC value is in
the range 0 to 1023.
If we get raw ADC value 1023 for RH 90 i.e., 2970 mV
then 1023 = 90
1023 * x = 90
x = 0.0879765395894428
%RH = (raw ADC value * 0.0879765395894428)
e.g. - if raw ADC value = 920 then 920 *
0.0879765395894428 = 80.93841642228739 % RH
Table -5: Relative Humidity in % per °c
Temperature in °c Relative Humidity in %
+40° 45%
+30° 40%
+20° 35%
+10° 30%
+0° 25%
-10° 20%
5. LDR Light Sensor
Lumens is the total amount of light output from a light
emitter. Lux is the amount of light cast on a given area.
The beam angle is the angle of radiation for a light source.
A change in the beam angle affects the luminous intensity
(lux) of a light source but not the luminous flux (Lumens).
Lux = lumens/ m2
I = L Cu LLF / A
Where,
I = illumination (lux, lumen/m2)
L = lumens per lamp (lumen)
Cu = coefficient of utilization
LLF = light loss factor
A= area per lamp (m2)
10 incandescent lamps of 500 W (10600 lumens per lamp)
are used in an area of 50 m2. With Cu =0.6, LLF = 0.8
I = 10 (10600 lumens) (0.6) (0.8) / (50 m2)
= 1018 lux
Table -6: Various light levels
Activity Illumination
(lux, lumen/m2)
Public areas with dark
surroundings
20 – 50
Simple orientation for
short visits
50 – 100
Warehouses, Homes,
Theaters, Archives
100 – 150
Easy Office Work, Classes 250
PC Work, Study Library,
Show Rooms, Laboratories
500
Supermarkets, Mechanical
Workshops
750

5. IMPLEMENTATION
Fig -13: Implementation view
From the implementation analysis, we can able to
build flourishing system that monitors the pollution
causing parameters and make reliable and pollution free
environment. This project is done keeping in mind the
small scale industries and hence it is affordable. Sensing
systems in the environment itself will considerably raise
the degree of environmental protection.
6. RESULT
Result will display on user’s smartphone screen or pc.
Fig -14: Monitoring Screen Display
7. CONCLUSION
The proposed system which is designed shows the
simulation output of sensing the carbon dioxide gas in air,
humidity, noise pollution and temperature pollution in
Environment. The sensor output is pushed to cloud and
can be viewed through internet. This is a flourishing
system which is very useful in industries because of the
increasing pollution due to increase in industries. This
system is user friendly and cost of the product is
affordable. The results of the project are accurate and
hence can be implemented in any industries for the safety
of workers and the environment.
ACKNOWLEDGEMENT
Inspiration and the guidance are valuable in every aspect
of life, especially in the field of academics, which we have
received from our respected project guide & Head of
Department Prof. G.A Kulkarni, who has put his careful
guidance and we will complete our project work. More
word won’t sufficient to our gradient to his untiring
deviation. He undoubtedly belongs to the members of the
artistic gallery who are master in all aspect.
REFERENCES
[1] Chandana, P. Sai, K. Sreelekha, A. Muni Likith Reddy,
M. Anil Kumar Reddy, and R. Senthamilselvan. "IOT
AIR AND SOUND POLLUTION MONITORING SYSTEM.”
International Journal on Applications in Engineering
and Technology, Volume 3, Issue 1-March 2017.
[2] Dr. A. Sumithra, PJ.Jane Ida, PK. Karthika, Dr. S.
Gavaskar, “A smart environmental monitoring system
using internet of things”, International Journal of
Scientific Engineering and Applied Science, Volume 2,
Issue 3- March 2016.
[3] Muhammad Saqib Jamil, Muhammad Atif Jamil, Anam
Mazhar, Ahsan Ikram, Abdullah Ahmed, and Usman
Munawar (2015). “Smart Environment Monitoring
System by employing Wireless Sensor Networks on
Vehicles for Pollution Free Smart Cities” -
Humanitarian Technology: Science, Systems and
Global Impact 2015, HumTech.
[4] Sushma Maithare, Dr. Vijaya Kumar B P, “Embedded
System for Noise Pollution Monitoring using IoT
Platform to create Smart Environment”, International
Journal of Advanced Research (2015), Volume 3, Issue
8, 658-666.
[5] Mr. Nerella Ome, Mr. G. Someswara Rao. “Internet of
Things (IoT) based Sensors to Cloud system using
ESP8266 and Arduino Due”, Vol. 5, Issue 10, October
2016.
BIOGRAPHIES
“ Student of M.E, Dept. of E&TC
Engineering, S.S.G.B College of
Engineering Bhusawal,
Maharashtra, India.”
“ Prof.G.A. Kulkarni, Dept. of
E&TC Engineering, S.S.G.B
College of Engineering,
Bhusawal, Maharashtra, India.”

IOT Based Environmental Pollution Monitoring System

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