Порівняльний аналіз методів та засобів моніторингу стану атмосферного повітря

• Дата: 2025
• Автори: Kulykivskyi, Oleksandr, Ponomarenko, Oleksandr
• Формат: Стаття
• Мова: English
• Опубліковано: General Energy Institute of the National Academy of Sciences of Ukraine 2025
• Теми: моніторинг; інтелектуальні системи; сенсорні мережі; хмарні технології; машинне навчання; прогнозування.
• Онлайн доступ: https://systemre.org/index.php/journal/article/view/908
• Назва журналу: System Research in Energy

This paper presents contemporary approaches to ambient air quality monitoring in the vicinity of energy facilities, with a main focus on the use of intelligent systems and cutting-edge technologies. The primary objective of the study is to identify promising solutions for the development of intelligent information and measurement systems capable of tracking air parameter variations in real time, analyzing pollution levels, and supporting the decision-making process. This paper emphasizes key criteria for assessing air pollution, including the concentration of harmful substances (PM2.5, PM10, NO2, SO2, CO, O3), the air quality index, toxic emissions, and natural meteorological factors. Modern methods for data collection and processing are examined, including Internet of Things IoT- based systems, cloud platforms, optical image analysis methods, and artificial intelligence such as machine learning and deep neural networks. Special attention is given to the application of regression models and hybrid approaches (CNN+LSTM) for PM2.5 level forecasting, enabling high-accuracy estimations based on both meteorological data and visual inputs. The study also describes stationary monitoring systems, their architecture, operational principles, and implementation examples using LoRa, LPWA, sensor networks, and mobile platforms. The results demonstrate the high efficiency of integrating artificial intelligence, big data, and the Internet of Things into monitoring systems, revealing new opportunities for the modernization of air quality monitoring and environmental risk management in areas with significant anthropogenic impact.