ABSTRACT:
Today technology has become an integral part of our day-to-day life. In this modern world, many technological advancements are taking place. It is important for us to exploit them as much as possible in order to gain more benefits. Technology provides many useful features for keeping our surroundings healthy. Here we come with an innovation which emphasizes on providing an IoT-Enabled Air Pollution Meter with Digital Dashboard on Smartphone that displays real-time air quality readings for the immediate surroundings. This is done to have a better understanding of our surroundings and ensure the quality of air is not compromised by constantly monitoring air quality on our smartphone. There is a wide range of scope for this product to be used in many situations. This system can be upgraded into a more advanced system with added features in the future.
INTRODUCTION:
A healthy environment is the first and foremost thing for our happiness. We need a pollution free surrounding for living a safe and secure life. The recent increase in pollution levels in metropolitan cities, especially in Delhi, is a worrying sign. In the world that is advancing rapidly with technology where cars could drive on their own and drones could capture your food, air pollution should not be of much concern but the above statistics just proves it wrong. Our application is one such thing which can provide the surrounding air quality index to the user. This is a basic level system which notifies the user of the various pollutants and their levels present in air. We have also additionally included a buzzer alarm which notifies the user when the pollutants level breaches a certain threshold mark. This will make the user understand that the place is not healthy and safe to live. So, the user can now take necessary steps to reduce air pollution, or move to a safer location. The concept of IoT allows us to store the data containing the types and number of pollutants present in air so that the user can analyze the changes that happens over a period of time. So, the user can decide whether the air quality is improving or reducing over a period of time. There is an urgent need for these kinds of systems which can be availed by any person especially in places which are heavily polluted. We can therefore think of curbing our daily human activities which leads to air pollution after making thorough analysis of the data. This gives us an opportunity to research on why pollution level is increasing or what good things need to be continued when the pollution level is decreasing. These kinds of decisions could be arrived through the data that the user gets through our system.
Objectives:
1. Real-time Monitoring – Deploy IoT sensors to measure air quality across multiple locations.
2. Website Integration – Send collected sensor data to a secure and scalable web server for centralized storage and visualization.
3. Data Analytics – Analyze trends and predict pollution patterns using AI/ML models where necessary.
4. Alerts & Notifications – Trigger automated alerts when pollution levels exceed defined safety thresholds.
5. User-Friendly Dashboard – Develop a responsive web interface to allow users and authorities to access, analyze, and act on pollution data in real time.
• Demo Video
• Complete project
• Full project report
• Source code
• Complete project support by online
• Lifetime access
• Execution Guidelines
• Immediate (Download)
Hardware Requirements (IoT Setup)
Microcontroller / IoT Device
ESP32 (recommended – has Wi-Fi + Bluetooth, low power)
Raspberry Pi (if more processing power needed, e.g. local ML)
Air Quality Sensors
Gas Sensors (for pollutants):
MQ-135 (CO₂, NH₃, benzene, alcohol, smoke)
MQ-7 (Carbon Monoxide CO)
MQ-2 (LPG, Smoke, Methane)
Particulate Matter Sensor (PM2.5/PM10):
PMS5003 or SDS011
Temperature & Humidity:
DHT22 or BME280 (humidity + pressure also)
Power Supply
USB power bank or Li-ion battery with solar panel for outdoor use
Connectivity
Wi-Fi (ESP32 built-in)
Optionally GSM/LoRa for remote areas
Software Requirements
IoT Device Code
Language: MicroPython / Arduino C++ (ESP32) or Python (Raspberry Pi)
Task: Read sensor data → send via MQTT/HTTP → server
Backend Server
Flask/ (Python) for API
Store data in MySQL
Machine Learning (Prediction)
Libraries: scikit-learn, TensorFlow, or PyTorch
Predict air pollution levels (AQI) from historical sensor data
Models: Random Forest, LSTM (time-series)
Web Dashboard
Frontend: HTML +
Backend: Flask/REST API
Real-time updates: WebSockets/MQTT integration
Cloud/Hosting
Immediate Download:
1. Synopsis
2. Rough Report
3. Software code
4. Technical support
Hardware Kit Delivery:
1. Hardware kit will deliver 4-10 working days (based on state and city)
2. Packing and shipping changes applicable (based on kit size, state, city)
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