Climate Monitoring and Black Carbon Detection Using Raspberry Pi with Machine Learning

Abstract

The proposed climate monitoring system aims to address the substantial risks to human health, climate stability, and ecological balance posed by air pollution, utilizing Raspberry Pi as a central procession unit and integrating various sensors which also incorporate sensors to measure the concentrations of PM1, PM2.5, PM10, and black carbon. This method meets the need for effective and immediate air quality monitoring and offers useful information to communities, academics, and policy makers. Through IoT connectivity, the gathered data are sent to a cloud-based platform for analysis and visualization. The system offers a user-friendly interface that presents actionable insights for informed decision making. Its warning capabilities alert users when pollution levels exceed thresholds, and this system also contributes to a comprehensive understanding of air pollution. By measuring particulate matter and black carbon levels, it supports the development of effective air quality management strategies. The system helps to take proactive measures and create cleaner and healthier environments. In conclusion, the proposed climate monitoring system utilizing Raspberry Pi, sensors, IoT connectivity, and machine learning techniques offers an effective and real-time solution for monitoring air quality. The integration of IoT connectivity allows for remote access to air quality data, while machine learning algorithms analyze the data and initiate alerts.

Keywords

• climate
• Raspberry pi
• sensors
• IOT