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An IoT-based PM2.5 and PM10 air quality monitoring system is designed to measure and monitor the levels of particulate matter with diameters of 2.5 micrometers (PM2.5) and 10 micrometers (PM10) in the air. These particulate matters are tiny particles suspended in the air that can have negative effects on human health and the environment. Building a monitoring system using Internet of Things (IoT) technology allows for real-time data collection, analysis, and remote accessibility. Here's an overview of how such a system might work:
Components of the System:
1. Sensor Modules: These are the devices that directly measure the PM2.5 and PM10 levels in the air. There are various types of sensors available for this purpose, such as laser-based sensors, optical sensors, and gravimetric samplers. These sensors detect particles and convert their measurements into electrical signals.
2. Microcontroller or Microprocessor: A microcontroller (e.g., Arduino, Raspberry Pi) receives the signals from the sensor modules and processes the data. It can also perform other tasks like data storage, communication, and control of other system components.
3. Communication Module: This module enables the transfer of data from the microcontroller to the cloud or a centralized database. Common communication protocols include Wi-Fi, cellular networks, Bluetooth, or LoRaWAN.
4. Cloud or Database: The collected data is sent to a cloud-based platform or a centralized database for storage, analysis, and visualization. Cloud platforms like AWS, Google Cloud, or Microsoft Azure can be used for this purpose.
5. Web or Mobile Interface: Users can access real-time air quality data through a web application or a mobile app. This interface allows them to view current and historical data, set alerts, and visualize trends.
6. Alerting System: The system can be programmed to send alerts to users or authorities when PM2.5 and PM10 levels exceed predefined thresholds. This is especially important for ensuring public health and safety.
7. Power Supply: Depending on the deployment location, power could be supplied through mains electricity, batteries, solar panels, or a combination of these.
System Workflow:
1. Data Collection: The sensor modules continuously measure PM2.5 and PM10 levels in the air. The microcontroller processes the sensor data and converts it into meaningful values.
2. Data Transmission: The microcontroller sends the processed data to the cloud or database through the communication module. This can be done in real-time or at regular intervals.
3. Data Storage and Analysis: The cloud/database stores the received data and performs analysis to generate insights and trends. Machine learning algorithms can be applied to predict air quality patterns.
