Real-time air quality monitoring

Abstract

The article is dedicated to the development and implementation of a real-time air quality monitoring system based on modern information technologies. The study emphasizes the use of the MQTT protocol for efficient data transmission, the Django framework for building the server-side application, and Celery for asynchronous task processing. A key feature of the system is its modular architecture, which ensures high scalability and adaptability to growing requirements. The algorithms for calculating air quality indices, AQI and CAQI, enabling rapid assessment of atmospheric pollution levels, have been thoroughly analyzed. To process large volumes of environmental data, storage optimization methods have been used, including aggregation of average, minimum, and maximum values. These approaches reduce the volume of stored information, enhance processing speed, and improve system efficiency. The system gathers data from various sources, including IoT sensors, open APIs, and MQTT servers, enabling comprehensive air quality monitoring in urban and industrial areas. An interactive web interface with dashboards is provided to ensure user access to information, allowing real-time data visualization, analysis of historical trends, and alerts in case of threshold exceedances. The article highlights the importance of integrating innovative technologies and continuously improving monitoring methods. This includes the implementation of adaptive algorithms for automating data collection and analysis processes, integration with new types of sensors, and enhancement of big data processing capabilities. Such approaches aim to improve the accuracy and relevance of environmental data and establish a reliable foundation for effectively addressing ecological challenges arising from global atmospheric pollution.