SET-THEORETICAL APPROACH TO AIR QUALITY MONITORING

Abstract

The article considers the application of set-theoretical methods for air quality monitoring. An approach based on the use of clear and fuzzy sets for data analysis from various sensors is proposed, which allows to increase the accuracy of pollution assessment. The advantages of the set-theoretical approach and the possibilities of its implementation in automated monitoring systems are analyzed. The modeling results are presented, which demonstrate the effectiveness of the considered approach in conditions of real environmental threats caused by air pollution. The problem of air pollution is one of the most important environmental threats of our time. Air quality monitoring is a key factor in preventing negative consequences for human health and the environment. The use of mathematical methods of data analysis allows to significantly improve the accuracy of measurements and forecasting changes in air quality. One of the promising areas is the application of the set-theoretical approach, in particular fuzzy sets. This approach allows processing data from various sources, taking into account the inaccuracy and uncertainty of measurements. The study of this topic is relevant given the need to monitor and predict air quality, which is a vital feature for both human society and ecosystems. The use of clear fuzzy sets to model the process of monitoring air composition will allow creating effective forecasting and management systems that can be applied both in scientific research and in practical environmental monitoring.

Air pollution has a great impact on the global and local levels; it is becoming a threat to all living things, especially humans. Many developed countries have tried to use modern emerging technologies to determine strategies and methods to improve air quality and, in the long term, mitigate the problem of air pollution. There is a great need to pay attention to air pollution problems, where urbanization and industrialization continue to grow with increasing population density