SOFTWARE ANALYSIS OF RADIATION AIR POLLUTION STREAMING DATA
DOI:
https://doi.org/10.35546/kntu2078-4481.2023.2.26Keywords:
software methods, streaming data, Python, air pollution monitoring, radioactive particle sensor, Thing- Speak, Internet of Things, ESP12.OLEDAbstract
In today’s world, attention to environmental protection and sustainable development is increasing. Natural resources are limited, so it is important to take measures to control and preserve the ecological state of the environment. One of the effective tools for this is environmental monitoring systems that are able to collect and process streaming data. Streams come in real time from various sensors measuring environmental parameters such as air quality, noise level, water level, pollutant emissions and many others. Analysis of data from systems for monitoring environmental indicators is an important tool for monitoring and improving the state of the environment, allows for more effective management of natural resources, reduces the negative impact of human activity on the environment and promotes sustainable development. One of the main advantages of streaming data processing is the ability to analyze and respond to changes in real time. This allows to promptly detect dangers, deviations from standards, emergency situations or other problems arising in the environment and immediately take measures to eliminate them. The processing of streaming data allows analyzing trends and predicting possible risks and problems in the environment. This allows to take precautionary measures and develop strategies to preserve the ecological state. Data obtained from monitoring systems can serve as a basis for the development of policies and regulatory standards in the field of environmental protection. The article describes software methods for analyzing streaming data, in particular, for analyzing radiation air pollution. Open data for indicators of radiation air pollution in the Kyiv city were analyzed and data visualization was carried out using analytical tools of the Python programming language. A correlation was found between radiation indicators at different times of the day, the time periods of emergency shutdowns of the sensor were analyzed. The Google Colab cloud environment was used for data analysis. The technical characteristics of the monitoring system of radiation air pollution indicators in the Kyiv city from open data sources of the ThingSpeak platform are described.
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