Analysis and Modeling of Environmental Monitoring Using Multicopters

Currently, environmental pollution is becoming a global problem, and there is an increasing need to introduce modern, fast, and effective methods of air quality control. The purpose of this study is to evaluate their effectiveness by analyzing and modeling methods of environmental monitoring using multicopters. To achieve this goal, the study monitored air quality in the industrial and urbanized region of Kazakhstan, particularly in Almaty, and examined the technical characteristics of multicopters, data processing methods, and their advantages compared to traditional stations. In the course of experimental studies, more than 500 GB of environmental data was collected using multicopters and processed using big data and machine learning algorithms, and the results showed that they allow for data collection 4 times faster than traditional methods and process it 30% more efficiently. The results showed that 65% of pollutants are concentrated at an altitude of 0-500 m, and changes in air quality can be predicted with an accuracy of 80-90%, and multicopters can measure concentrations of CO₂, NO₂, SO₂, PM2.5, and PM10 with an accuracy of ±5%. However, the limited flight time (50-90 minutes) of the UAV and the reduced stability in conditions of strong winds were identified as their main disadvantages. In general, this study proved the effectiveness of multicopters in environmental monitoring on a scientific basis and showed the need in the future to extend the time of their autonomous operation, improve their sensor systems, and enhance data processing methods based on artificial intelligence.