Smart Air Quality Monitoring

1. Sensor Networks and MCU Technology
Modern air quality monitoring systems utilize extensive sensor networks that collect real-time data on various pollutants in the air, including PM2.5, PM10, sulfur dioxide (SO2), nitrogen oxides (NOx), and more. These sensors are often coupled with microcontrollers (MCUs), which are responsible for data acquisition, processing, and transmission. MCUs offer low power consumption and high efficiency, enabling optimized system performance while minimizing energy usage.

2. Implementation of AI Algorithms and TinyML
AI algorithms play a crucial role in air quality monitoring. Data collected by sensors can be analyzed using machine learning and deep learning algorithms, enabling accurate air quality predictions and alerts. This is where Tiny Machine Learning (TinyML) comes into play. TinyML is a form of machine learning specifically designed for low-power embedded devices. It allows for on-device data analysis and decision-making on MCUs without the need for extensive computational resources.

3. Real-Time Alerts and Decision Support
The combination of MCUs and TinyML-powered AI enables real-time air quality alerts. When monitoring data indicates that pollution levels exceed thresholds, the system can trigger immediate alerts to safeguard public health. These technologies also provide decision support, such as recommending actions for governments to take to improve air quality and mitigate pollution sources.

4. Urban Planning and Policy Making
AI applications in air quality monitoring further contribute to urban planning and policy-making. Through long-term data collection and analysis, governments can gain insights into air quality trends, formulate more effective environmental policies, and optimize urban development plans.

Applicable development board
NuMaker-HMI-M467 NuMaker-IoT-M467	Smart Home Air Quality Monitoring System Application Description: Install air quality sensors in the home environment to monitor indicators such as PM2.5, PM10, carbon monoxide, nitrogen dioxide, and volatile organic compounds (VOCs). The Cortex-M4 processes data from these sensors, continuously monitoring indoor air quality. When air quality indicators exceed safe limits or exhibit abnormal fluctuations, the system automatically raises the alarm and can be connected to devices such as air purifiers, automatically initiating the purification process.
NuMaker-M55M1	1. Anomaly Detection: The M55M1 development board's high-performance processor and machine-learning capabilities make it ideal for implementing air quality anomaly detection. This feature is particularly important as it can identify abnormal increases in harmful substances in the air in a timely manner, such as volatile organic compounds (VOCs), fine particulate matter (PM2.5), carbon monoxide, etc. Collect data using multiple air quality sensors connected to the M55M1. Data processing and analysis, including filtering, normalization, and anomaly detection. Apply machine learning algorithms to the collected data for pattern recognition, enabling precise identification of air quality anomalies. When an anomaly is detected, the system raises the alarm and provides corresponding suggestions or automatically adjusts connected air purification equipment.

Applicable development board

NuMaker-HMI-M467

NuMaker-IoT-M467

Smart Home Air Quality Monitoring System

Application Description:

Install air quality sensors in the home environment to monitor indicators such as PM2.5, PM10, carbon monoxide, nitrogen dioxide, and volatile organic compounds (VOCs).
The Cortex-M4 processes data from these sensors, continuously monitoring indoor air quality.
When air quality indicators exceed safe limits or exhibit abnormal fluctuations, the system automatically raises the alarm and can be connected to devices such as air purifiers, automatically initiating the purification process.

NuMaker-M55M1

1. Anomaly Detection:

The M55M1 development board's high-performance processor and machine-learning capabilities make it ideal for implementing air quality anomaly detection. This feature is particularly important as it can identify abnormal increases in harmful substances in the air in a timely manner, such as volatile organic compounds (VOCs), fine particulate matter (PM2.5), carbon monoxide, etc.

Collect data using multiple air quality sensors connected to the M55M1.
Data processing and analysis, including filtering, normalization, and anomaly detection.
Apply machine learning algorithms to the collected data for pattern recognition, enabling precise identification of air quality anomalies.
When an anomaly is detected, the system raises the alarm and provides corresponding suggestions or automatically adjusts connected air purification equipment.

More to explore:

Soil Condition Monitoring

Farmers can use technology to monitor soil conditions in order to use pesticides and fertilizers more efficiently and reduce negative impacts on the environment.

Animal Health Monitoring

Farmers can use technology tools to monitor the health of individual animals and adjust their nutrition as needed. This granular monitoring helps prevent disease and improves the herd's overall health.

Smart Lighting

Nuvoton Technology is committed to providing advanced technical support for smart cities.

Flow Monitoring

As many commercial spaces begin to reopen, the need for effective and practical ways to maintain social distancing in retail stores and other capacity-constrained spaces is growing.

Smart Home and City Automation

To alleviate the pressure of the ever-increasing population on infrastructure, cities, buildings, and homes must become smarter. That’s where TinyML (Tiny Machine Learning) and artificial intelligence (AI) technologies come into play.