#How to Monitor Air Quality Using ESP32 | Air Quality Monitoring System | ESP32 | Blynk IOT Projects | IoT based Air Quality Monitoring using

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ESP32 Based Air Quality Monitoring using Blynk 2.0 App Notification with Gmail Alerts | IOT Projectshttps://www.youtube.com/watch?v=nKqDuVBCsgI

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DIY Air Quality Monitoring System

Introduction

In this DIY guide, we’ll walk you through building an Air Quality Monitoring System to keep tabs on the air quality in your home or surrounding environment. This project collects and tracks key air quality metrics — such as CO2 levels, PM2.5 particles, temperature, and humidity — and displays them in real time on a mobile app or web dashboard. Perfect for health-conscious individuals, this IoT project provides valuable insights into your environment and helps you make adjustments to improve indoor air quality.

Why Build an Air Quality Monitoring System?

The air we breathe has a direct impact on our health, yet indoor air quality often goes overlooked. By monitoring indoor CO2 and particulate matter levels, as well as temperature and humidity, you can create a healthier living space. This system helps you identify potential air quality issues, like high CO2 levels or excessive dust particles, that may require ventilation or filtration.

Benefits of Monitoring Air Quality:

Healthier Environment: Improve indoor air quality by tracking harmful pollutants.

Real-Time Alerts: Get notified when air quality drops below safe levels.

Data-Driven Decisions: Use data insights to decide when to ventilate or clean the air.

Long-Term Tracking: Log data over time to see how seasonal or activity changes impact air quality.

Key Components and Technologies

To build your Air Quality Monitoring System, you will need:

Microcontroller: Use a Raspberry Pi or ESP32 to serve as the main control unit. Both offer robust processing power and easy integration with sensors and cloud platforms.

Air Quality Sensors:

CO2 Sensor: The MQ135 sensor is an affordable option for measuring CO2 levels.

PM2.5 Sensor: The PMS5003 sensor detects fine particulate matter in the air, which can contribute to respiratory issues.

Temperature and Humidity Sensor: DHT11 or DHT22 sensors monitor environmental conditions that can affect indoor air quality.

Wi-Fi Module: ESP32 has built-in Wi-Fi, while Raspberry Pi can connect via a Wi-Fi dongle or built-in module, enabling remote data access.

Cloud Service for Data Storage and Visualization:

Use platforms like Cloudtopiaa, Blynk, or Thingspeak to store, visualize, and analyze your data. Cloudtopiaa provides reliable cloud services with secure data storage and custom visualization options.

Additional Components:

Jumper wires and breadboard

Power supply for the microcontroller

Basic tools like a soldering iron if needed

Step-by-Step Guide

Step 1: Set Up the Microcontroller

Choose and Prepare Your Microcontroller:

For this guide, we’ll use a Raspberry Pi. Set up the latest version of Raspbian OS on the Raspberry Pi.

Connect the Components:

Attach the MQ135 CO2 sensor to one of the GPIO pins.

Connect the PMS5003 PM2.5 sensor to another GPIO pin.

Connect the DHT11 or DHT22 sensor for temperature and humidity.

Step 2: Install Required Libraries

Open the terminal on your Raspberry Pi and install the libraries necessary for sensor data collection and Wi-Fi connectivity.sudo apt-get update sudo apt-get install python3-pip pip3 install adafruit-circuitpython-mq135 pip3 install adafruit-circuitpython-pm25 pip3 install Adafruit_DHT

Step 3: Program the Sensors

Write a Python script to read data from each sensor. Here’s a basic example for reading CO2, PM2.5, temperature, and humidity data.import time import Adafruit_DHT import board import adafruit_mq135 import adafruit_pm25

# Setup DHT_SENSOR = Adafruit_DHT.DHT22 DHT_PIN = 4 CO2_SENSOR = adafruit_mq135.MQ135(board.A0) PM25_SENSOR = adafruit_pm25.PM25_UART(board.UART1)

def read_sensors(): # Read temperature and humidity humidity, temperature = Adafruit_DHT.read_retry(DHT_SENSOR, DHT_PIN) # Read CO2 level co2_level = CO2_SENSOR.co2 # Read PM2.5 level pm25_data = PM25_SENSOR.read() pm25 = pm25_data['pm25'] if 'pm25' in pm25_data else None return temperature, humidity, co2_level, pm25

while True: temp, humidity, co2, pm25 = read_sensors() print(f"Temperature: {temp}°C, Humidity: {humidity}%, CO2 Level: {co2} ppm, PM2.5: {pm25} µg/m³") time.sleep(10)

Step 4: Cloud Integration with Cloudtopiaa

Set Up a Cloudtopiaa Account: Sign up for Cloudtopiaa to leverage their reliable data storage and visualization capabilities.

Create an IoT Project: Set up a new project and get an API key to send sensor data.

Connect Your Microcontroller: Update your script to send data to Cloudtopiaa.

Step 4: Cloud Integration with Cloudtopiaa

Set Up a Cloudtopiaa Account: Sign up for Cloudtopiaa to leverage their reliable data storage and visualization capabilities.

Create an IoT Project: Set up a new project and get an API key to send sensor data.

Connect Your Microcontroller: Update your script to send data to Cloudtopiaa.

import requests

CLOUDTOPIAA_API_KEY = "Your_Cloudtopiaa_API_Key"

def send_data_to_cloud(temp, humidity, co2, pm25): url = "https://api.cloudtopiaa.com/v1/data" # Example endpoint payload = { "temperature": temp, "humidity": humidity, "co2": co2, "pm25": pm25 } headers = { "Authorization": f"Bearer {CLOUDTOPIAA_API_KEY}" } requests.post(url, json=payload, headers=headers)

while True: temp, humidity, co2, pm25 = read_sensors() send_data_to_cloud(temp, humidity, co2, pm25) print(f"Sent data to Cloudtopiaa: Temperature: {temp}°C, Humidity: {humidity}%, CO2: {co2} ppm, PM2.5: {pm25} µg/m³") time.sleep(600) # Send data every 10 minutes

Step 5: Set Up Data Visualization

Log in to your Cloudtopiaa account to view your data in real-time. Use built-in visualization options to create charts and graphs that track air quality metrics over time.

Step 6: Build a Mobile App or Web Dashboard

Use a platform like Cloudtopiaa’s dashboard, Blynk, or a custom web interface to create a user-friendly dashboard that displays current air quality data and historical trends.

Conclusion

You’ve successfully built an Air Quality Monitoring System to help maintain a healthier environment in your home or workspace. With this system, you can track real-time air quality, make data-driven decisions about ventilation, and improve indoor air conditions for better health. Cloudtopiaa’s secure storage and powerful visualization tools make it easy to monitor your air quality from anywhere.

Additional Resources

Cloudtopiaa Documentation

Raspberry Pi Documentation

Blynk IoT Platform

This project demonstrates skills in IoT health applications, environmental monitoring, and data visualization — valuable tools for creating a smarter, safer living environment.

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