IOT ENABLED AIR QUALITY MONITORING
SYSTEM
Parul University
Srinivas Dasathwar
March 2025
Abstract—Pollution poses one of the most significant threats to
our environment, with various types of pollution impacting the
Earth’s well-being. In the context of our project, we primarily
address air pollution, which is a major concern. Air pollution is
responsible for causing numerous health issues in both humans
and animals, including respiratory problems. Polluted air con-
tains a mixture of harmful gases such as CO2, CO, SO2, smoke,
and benzene. To mitigate the effects of these pollutants, we must
take measures like avoiding areas with high levels of polluted air.
To implement these measures effectively, we require instruments
to measure air quality. As a technical solution, we have chosen to
develop an Air Quality Index (AQI) system based on the Internet
of Things (IoT). This approach is cost-effective, decentralized,
efficient, and portable, providing a significant improvement over
the traditional method of using complex laboratory equipment,
which is both costly and lacks portability. Our project focuses
on implementing an AQI system using IoT technology, aiming to
transfer data from sensors to an application or web server via
the Internet. This technological advancement allows individuals
to check air quality in their surroundings easily, offering valuable
information for making decisions about their safety. For instance,
while travelling in a car, users can quickly assess the air quality
index, and if the particulate matter concentration exceeds 1000
ppm, they can identify the air as harmful. We plan to use
an Arduino Uno microcontroller for this purpose, as it offers
a suitable platform for programming in C and C++ and is
supported by a thriving community and libraries.
I.
INTRODUCTION
Air pollution is a pressing global issue that adversely affects
human health and the environment. With increasing industri-
alization and urbanization, monitoring air quality has become
essential for ensuring safe living conditions. Traditional air
quality monitoring systems are often expensive, stationary, and
inaccessible to the general public. To address these challenges,
we have developed an IoT-based Air Quality Monitoring
System, which provides real-time air quality data with remote
access capabilities.
Our system utilizes MQ135 and LM35 sensors to detect
harmful gases and measure temperature, respectively. These
sensors generate voltage outputs that correspond to the con-
centration levels of pollutants in the environment. The Arduino
UNO microcontroller serves as the central processing unit,
converting sensor data into PPM (Parts Per Million) values
for an accurate assessment of air quality. The processed
information is displayed on an LCD screen, providing users
with instant insights.
To enhance accessibility and usability, we have integrated
a Wi-Fi module, enabling wireless data transmission. This
feature allows users to remotely monitor air quality via a
dedicated web server, ensuring real-time data access from any
location with internet connectivity. Additionally, we plan to
develop an Android application, offering an intuitive interface
for users to visualize, analyze, and interpret air quality metrics
effortlessly.
By combining IoT technology, cloud-based data access, and
mobile app integration, our system provides a cost-effective,
scalable, and user-friendly solution for air pollution monitor-
ing. This project holds significant potential for deployment
in urban environments, industrial zones, and smart cities, con-
tributing to pollution awareness and proactive health measures.
II.
LITERATURE REVIEW
In this chapter, we present our comprehensive critical
assessment and summarize the research papers that have been
integral to our project. Our literature review encompasses a
wide array of reference papers, delving into topics closely
aligned with our project’s objectives. In addition to the
core features, we have also explored supplementary aspects
that promise to enhance the accuracy and efficiency of
our results. These additional facets encompass weather
prediction, integration with an Android application, utilization
of Raspberry Pi, incorporation of fuzzy logic, and adaptability
for smart cities. Our exploration of these extended dimensions
aligns with our pursuit of a more holistic and versatile
approach to address the complexities of our project
